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The Effects of Sovereign Credit Rating on Foreign Direct Investment

Peilin Cai , Suk-Joong Kim and Quan Gan

Abstract:

This paper examines the relationship between sovereign credit ratings and FDI flows from 31

OECD donor countries to 72 recipient (OECD and non-OECD) countries over the period of 1985

- 2012. There are three main findings in the paper. First, sovereign credit ratings of donor and

recipient are important drivers of bilateral FDI flows. FDI in general flows from low-rated donor

countries to high-rated recipient countries. Second, an OECD recipient receives high FDI inflow

when its credit rating is high, whereas a non-OECD recipient receives high FDI inflow when its

credit rating is low. Third, countries have more FDI inflows when their geographic region has

higher average credit rating compared to other regions.

Keywords: Foreign Direct Investment, Sovereign credit ratings

JEL Code: G15, F34

Corresponding author: Miss Peilin Cai, Discipline of Finance, The University of Sydney Business School, The University of Sydney, NSW 2006, Australia Phone no: +61 426626538. Fax no: +61 2 9351 6461. E-mail address: P.Cai@econ.usyd.edu.au

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1. Introduction

Foreign Direct investment (FDI) is a direct cross-border investment that investors have control or

a significant degree of influence over the management of a company in a foreign recipient country.

FDI is beneficial to the recipient country as it represents a direct, relatively stable and long-lasting

investment linkage among economies. An OECD's report (2008) suggests that, under appropriate

policy environments, FDI is an important vehicle for recipient country’s enterprise development

and help improve competitive positions of both recipient and donor countries. In particular, FDI

encourages the transfer of technology and know-how between countries, and provides

opportunities for the recipient country to promote its products more widely in the international

markets.

An important question for both researchers and policymakers is - what factors drive FDI

flows? Compared to other cross-border capital flows (e.g. bank flows and portfolio flows), FDI is

a long-term investment. FDI cannot be easily withdrawn when the recipient country’s financial

conditions decline. Thus sovereign credit rating should be one important factor which FDI

investors will consider when they make their decision.

Our paper aims at identifying sovereign credit rating’s impact on FDI through three

research questions. First, do sovereign credit ratings of both donor and recipient countries impact

bilateral FDI flows? Second, is there any difference between OECD and non-OECD recipient

countries regarding their sovereign credit ratings’ impact on FDI inflows? Third, are FDI inflows

impacted by a geographic region’s average credit rating?

We use a dataset on bilateral FDI flows from 31 OECD countries to 72 recipient (OECD

and non-OECD) countries over the period from 1985 to 2012. The OECD data in 2013 and years

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after are incomplete1. Our empirical results suggest that, donor and recipient countries’ sovereign

ratings are important drivers of FDIs. Recipient countries with high sovereign credit ratings receive

high FDI inflows. Donor countries with high sovereign credit ratings have low FDI outflow. In

general, FDI flows from low credit rating countries to high credit rating countries. We also find

that OECD countries with high ratings tend to receive high FDI, while non-OECD countries with

low ratings receive high FDI. This finding suggests that investors have different credit risk attitude

toward investment in developed and emerging economies. Last, we find that a recipient country’s

FDI inflow responses positively to its geographic region’s average credit rating and negatively to

other regions’ average ratings. FDI investors prefer high average credit rating region compared to

low average rating regions.

The structure of the remaining parts of this paper is as follows. Section 2 reviews the related

literature. Section 3 describes the data and empirical methods. Section 4 presents empirical results

and Section 5 concludes.

2. Literature Review

Prior literature identifies three drivers of FDI flows: institutional quality, market size and trade

barrier. A group of study (Borensztein et al., 1998; Greenwood and Jovanovic, 1990; Levine, 1991;

Levine 1997; Saint-Paul, 1992), each from different angles, argues that the quality of a country’s

financial system reflects its ability to mobilize savings, improve the efficiency of capital

allocation, and diversify risk, therefore positively impacts the FDI. Wheeler & Mody (1992)

distinguish between developed and developing recipients and argue that the institutional quality is

1 See https://stats.oecd.org is the detail of bilateral FDI data

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more important to developing countries than developed countries as developed countries already

have high quality institutional infrastructures. Romita (2002) uses a sample of FDI flows from

U.S. to 44 countries in 1983 - 1990 and finds that infrastructure quality, regime type, regime

duration and property and contractual right are crucial determinants of FDI.

Market size for goods and services is also an important factor identified by empirical

studies. Barrell and Pain (1996) study the stock FDI from U.S. companies to 7 OCED countries,

and find market size (measured by GNP) is one important factor impacting FDI. Lael Brainard

(1997) develop a theoretical model of proximity-concentration and ague that, when choosing

between export and FDI to serve the recipient countries’ economies, investors tend to choose FDI

to enter larger recipient markets since larger scale of economy has lower productivity cost.

Trade barrier directly affect cross-border transaction cost. Countries with higher degree of

trade openness have lower transaction cost and encourage FDI (e.g. Asiedu, 2002). In contrast,

When FDI is market-seeking, trade restriction positively impacts on FDI, because investors

consider setting up plants in the recipient market if they find it difficult or costly to import their

products to the recipient countries (e.g. Barrell and Pain, 1999; Belderbos, 1997; Blonigen, 2002;

Motta, 1992).

Information asymmetry between foreign and domestic investors is one of the challenging

problems in cross-border investment (e.g. Barron and Ni, 2008; Hatchondo, 2005; Van

Nieuwerburgh and Veldkamp, 2009). Information asymmetry increases the difficulty on analysing

and predicting a country’s investment environment. The sovereign credit rating, given by a third

party, is therefore important to cross-border investors.

Sovereign credit rating is regarded as a measure of a country’s investment environment.

The rating incorporates all of the risk factors that are perceived to be relevant by rating agencies.

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Cantor and Packer (1996) find that a country’s sovereign credit rating is determined by its per

capita income, GDP growth, inflation, external debt, level of economic development, and default

history. Sovereign credit rating also represents a rating ceiling for most private issuers, in particular

banks, within the country (e.g. Bannier and Hirsch, 2010; Boot et al., 2006; Williams et al., 2013).

Gande and Parsley (2004) examine the response of equity mutual fund flows to sovereign rating

changes in 85 countries from 1996 to 2002. Their results indicate that the sovereign downgrades

news is strongly associated with outflow capital from the downgraded countries. Kim and Wu

(2011) find sovereign credit ratings have positive and significant influence on the international

bank flows from developed markets in a sample of G7 countries bank flows to 33 emerging

markets in 1995 - 2008.

Previous studies also investigate the impact of sovereign credit rating on financial markets.

Negative sovereign credit news are usually more informative than positive ones, given the stronger

negative reputational effects. Downgrade news impacts a country’s equity, bond, credit default

swap and currency exchange markets, and also significantly spillovers to other countries’ financial

market. While, upgrade news has limited or insignificant impact. (e.g Afonso et al., 2012; Alsakka

and ap Gwilym, 2012; Brooks et al., 2004; Dichev and Piotroski, 2001; Ferreira and Gama, 2007;

Hill et al., 2010).

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3. Data Description

3.1 FDI Bilateral Flow Data

We collect bilateral FDI data from OECD International Direct Investment Statistics database2. The

OECD database covers bilateral FDI flow data related to 34 OECD member countries to 277

countries and region groups from 1985 to 2013. We select countries that have a sufficient data

coverage (more than 50% coverage). In total we have 31 OECD donor countries and 72 OECD

and non-OECD recipient countries.

Figure 1 shows the aggregate FDI flows from 31 OECD donor countries to 72 OECD and

non-OECD recipient countries between 1985 and 2012. From 1985 to the mid-1990s, FDI flows

increased gradually. In the late 1990s, FDIs increased significantly because of the sharp equity-

price increase in the late 1990s. In 2001, FDI flows fell largely reflecting by the Asia crisis. After

2002, FDI increases significantly and reaches its highest in 2007. FDI fell again affected by the

global financial crisis in 2007-2008 and sovereign debt crisis in 2012. Figure 1 also show that, the

OECD recipient countries have long dominated on receiving of FDI. From 1985 to early 2000s,

FDI activities are mainly within OECD member countries. From the mid-2000s, the proportion of

FDIs flow to the non-OECD countries were increasing significantly.

Figure 2 shows the aggregate FDI flow to OECD and Non-OECD recipient countries. The

aggregate FDI flows to OECD recipient countries account for nearly 86 per cent. While the

aggregate FDI flow to non-OECD recipient countries account for 14 per cent, which is about 6

times less than the OECD recipients.

2 See https://stats.oecd.org is the OECD International Direct Investment Statistics database. Outward FDI includes

the net assets of resident enterprises exerting control or influence on non-resident enterprises (net assets of resident

direct investors and net assets in fellow enterprise abroad when the ultimate controlling parent is resident)

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3.2 Sovereign and Regional Credit Rating Data

Our sovereign credit rating data are from Standard & Poor’s, Fitch and Moody’s. Credit rating

agencies report both short-term and long-term credit ratings and outlooks for sovereigns in their

local and foreign currency debts. For each rating agency, we collect four types of ratings, which

are long-term foreign currency rating, long-term local currency rating, foreign currency rating

outlook and local currency rating outlook. The rating data are then processed in three steps.

First, ratings are transformed into numerical scores. Table 1 lists the mapping of ratings to

scores. The sovereign rating grades are from the highest AAA to the lowest D and the outlook

grades are from positive to negative. We assign numerical values on a linear scale for each of the

rating grades, from 20 for AAA to 0 for D. For outlook grades, we assign +0.5 to outlook positive,

+0.25 to watch positive watch, 0 to stable, -0.25 to watch negative and -0.5 to negative outlook,

respectively. For grades from each agency for each country, an aggregate score is calculated by

summing the rating score and the outlook score for foreign (local) currency debts. The average

rating score is the average of the aggregate scores from all three rating agencies. We use foreign

currency debt scores in our main empirical study. We use local currency debt scores to conduct

our robustness check, the results do not depend on foreign or local currency debt scores we use.

Second, we identify the rating announcement days for each country and then recalculate

the average rating score on each announcement day. The average rating score does not change

between two announcement days. In this way, we generate daily average rating scores for each

country in our sample. Third, we calculate an annual rating score by averaging the daily average

rating scores.

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3.3 Control Variables

We consider three groups of control variables that can potentially impact FDI: bilateral linkage

(common language and distance); economic and financial development measures (population,

bank credit extended and interest rate spread); and degree of openness (foreign exchange regime,

investment barriers and total trade).

We first control the bilateral linkages between pair of countries. Language and distance

barriers has been commonly tested in the literature proxy for the resistance of trade, such as culture

different and information frictions (e.g. Bevan and Estrin, 2004; Ferreira and Gama, 2007; Kim

and Wu, 2008; Lael Brainard, 1997; Van Nieuwerburgh and Veldkamp, 2009; Portes et al., 2001).

Countries are sharing the same language and with closer geographic distance tend to have better

integration leading to lower trade barriers.

Both language and distance data are collected from CIA’s World Factbook3 . Language is

a dummy variable indicating whether a pair of recipient and donor countries sharing the same

official or business language. Distance is the geographic distance between two capital cities of

donor and recipient countries, which is calculated based on capital city’s longitude and latitude.

Second, we control the economic and financial developments of a country by measuring

population, bank credit and interest rate spread. The population, bank credit data and interest rate

spreads data are taken from World Bank’s World Development Index report. Population measures

the market size of a country. Bank credit refers to the financial resources to the private sector

provided by financial institutions. Interest rate spread (difference between lending and deposit

rates) reflects banking sector efficiency and stability.

3 See https://www.cia.gov is the Central Intelligence Agency (CIA)’s World Factbook

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Third, we control economic and financial openness between a pair of countries by

measuring exchange rate regime, trade barriers and direction of trade. The exchange rate regime

and trade barriers data are from the “Annual Report on Exchange Arrangements and Exchange

Restrictions” by IMF (2012). FDI generally involves higher sunk cost, then FDI investors prefer

less volatile exchange rate environments in the recipient countries. There are ten levels of exchange

regime and IMF classify them in four categories: floating arrangement (free floating and floating),

other managed arrangement (other managed arrangement), soft peg (pegged exchange rate within

horizontal bands, crawling peg, stabilized arrangement, conventional peg and currency board) and

hard peg (no separate legal tender). Following previous papers (e.g. Cho, 2014; Kim & Wu, 2008

and Yusoff & Nuh, 2015), we map the recipient country’s exchange regime into a range between

0 to 3 (0 = floating arrangements, 1 = other managed arrangement, 2 = soft pegs, 3 = hard pegs).

Trade barrier measure a country’s financial and economic openness, it is the sum of direct

investment restriction dummy4 and financial credit restriction dummy5 of a pair of countries.

Direction of trade is the sum of exports and imports as a percentage of GDP from IMF.

Table 2 lists the definition of all (dependent and independent) variables in our empirical

study. Table 3 provides the correlation matrix of the independent variables described in this

section. There is no significant collinearity issue as the correlations are relative small.

4 It refers to investments for the purpose of establishing lasting economic relations both abroad by residents and

domestically by non-residents. These investments are essentially for the purpose of producing goods and services,

and, in particular, in order to allow investor participation in the management of an enterprise. The category includes

the creation or extension of a wholly owned enterprise, subsidiary, or branch and the acquisition of full or partial

ownership of a new or existing enterprise that results in effective influence over the operations of the enterprise.

5 It includes credits other than commercial credits granted by all residents, including banks, to non-residents, or vice

versa.

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4. Methodology

We use panel regression models to conduct our empirical studies. Random effect estimation is

applied for two reasons. First, the Hausman specification test is adopted random effects to detect

an appropriate model specification. Second, some of our independent variables (e.g. distance,

language, exchange regime) are time invariant. Table XXX presents means and standard

deviations for all the independent variables.

4.1 Sovereign Credit Rating’s Impact on FDIs

We begin our estimation by examining whether sovereign credit rating influences FDI flow. In

this specification, we include both donor and recipient countries’ sovereign credit ratings. We also

include other control variables, our model is as follows,

𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝛽2𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃

′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (𝟏𝐚)

where 𝐹𝐷𝐼𝑡 represents annual bilateral FDI flow from a donor country to a recipient

country in year t, denominated in million US Dollars. In total we have 31 × 72 donor-recipient

pairs in our panel data.

𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 is the annual foreign debt credit score of the donor country in year t;

𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 is the annual foreign debt credit score of the recipient country in year t. 𝑳𝒊𝒏𝒌𝒕 =

(𝐿𝐴𝑁𝑡, 𝐷𝐼𝑆𝑇𝑡)′ is a vector which includes two bilateral linkage variables between donor and

recipient countries. 𝑫𝒆𝒗𝒎𝒕𝒕 = (𝑃𝑂𝑃𝑈𝑡, 𝐵𝐴𝑁𝐾𝐶𝑅𝐸𝐷𝑡, 𝐼𝑁𝑇𝑆𝑃𝑅𝐸𝐴𝐷𝑡)′ is a vector which

consists of three control variables measuring the economic and financial development. 𝑶𝒑𝒆𝒏𝒕 =

(𝐹𝑋𝑅𝐸𝐺𝑀𝑡, 𝐵𝐴𝑅𝑡, 𝐷𝑂𝑇𝑡)′ is vector which includes three control variables on the economic

openness. Details of control variables are listed in Table 2.

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We also investigate how rating difference affects the direction of FDI flows by estimating

the panel regression below,

𝐹𝐷𝐼𝑡 = 𝛽1𝑑𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃

′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (𝟏𝐛)

where 𝑑𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 = 𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 − 𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡. All control variables remain the same.

4.2 Sovereign Credit Rating Effect on OECD vs non-OECD Recipients’ FDI

We separate recipient countries into OECD and non-OECD countries to capture the potential

difference in the power of attracting FDI between the two groups of countries. We estimate the

following two panel regressions,

𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡+𝛽2𝑂𝐸𝐶𝐷_𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃

′ 𝑫𝒆𝒗𝒎𝒕𝒕

+ 𝜷𝒄′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (𝟐𝐚)

𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝛽2𝑁𝑜𝑛𝑂𝐸𝐶𝐷_𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃

′ 𝑫𝒆𝒗𝒎𝒕𝒕

+ 𝜷𝒄′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (𝟐𝐛)

where 𝑂𝐸𝐶𝐷_𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 is the annual foreign debt credit score of OECD recipient

country at year t. 𝑁𝑜𝑛𝑂𝐸𝐶𝐷_𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 is the annual foreign debt credit score of non-OECD

recipient country at time t. All control variables remain the same.

4.3 Regional Credit Rating’s impact on FDI

We use two models to examine the impact of a geographic country group’s average sovereign

credit rating on FDI inflows of recipient countries,

𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝛽2𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝛽3𝑂𝑤𝑛𝑅𝑒𝑔_𝑆𝐶𝑅𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕

+𝜷𝒃′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄

′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (𝟑𝐚)

𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝛽2𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝛽3𝑂𝑡ℎ𝑒𝑟𝑅𝑒𝑔_𝑆𝐶𝑅𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕

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+𝜷𝒃′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄

′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (𝟑𝐛)

where 𝑂𝑤𝑛𝑅𝑒𝑔_𝑆𝐶𝑅t is the average of annual foreign debt credit scores of the countries

in the same region as the recipient country. 𝑂𝑡ℎ𝑒𝑟𝑅𝑒𝑔_𝑆𝐶𝑅t is the average of annual foreign debt

credit scores of countries in other regions. Our regional classification on each recipient country is

based on the United Nations Regional Groups of Member States6.

In addition, we examine the impact of regional rating difference affect the FDI flow. Our

model is:

𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝛽2𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝛽3𝑑𝑅𝑒𝑔_𝑆𝐶𝑅𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕

+𝜷𝒃′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄

′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (𝟑𝒄)

where 𝑑𝑅𝑒_𝑆𝐶𝑅𝑡 = 𝑂𝑤𝑛𝑅𝑒𝑔_𝑆𝐶𝑅𝑡 − 𝑂𝑡ℎ𝑒𝑟𝑅𝑒𝑔_𝑆𝐶𝑅𝑡. All control variables remain the

same.

5. Empirical Results

5.1 The impact of Sovereign Credit Rating on FDI flows

Table 4 summarizes Granger causality test results on panel regressions (1a) and (1b). Except for

6 out of 31 countries (Czech, Denmark, Finland, Greece and Luxemburg and New Zealand), we

find strong evidence of both donor and recipient country’s ratings cause FDI flows. On the other

hand, except for 6 countries (Australia, island, Korea, Slovakia and Slovenia and United State)

FDI flows do not cause the credit ratings. In general, there is a unidirectional causal flow running

from the credit ratings to FDI. A country’s rating is determined by many factors (Cantor and Packer

6 See http://www.un.org/depts/DGACM/RegionalGroups.shtml is the United Nations Regional Groups of Member

States.

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1996). FDI alone cannot have causal effect on the ratings. However, sovereign rating is the most

important indicator of a country’s investment environment thus it has causal effect on the FDI

flows.

Table 5 (a) reports the estimation results of panel regressions (1a) and (1b). Table 5 (b)

summarises the signs of the significant coefficients. From regression (1a), we find evidence (17

negative signs vs. 4 positive signs) of a negative impact of donor countries’ ratings on FDI. Higher

donor countries’ credit rating leads to lower FDI flows to the recipient countries. We interpret this

result as following. High-rating countries have good investment environment. Investors in high-

rating countries are less likely to invest directly abroad, instead they tend to keep their investment

in domestic market. We also find an evidence (23 positive signs vs. 1 negative signs) of a positive

impact of recipient countries’ ratings on FDI. High rating of a recipient country associate with

high FDI flow. This finding is consistent with the international trade literature that, countries with

better macroeconomic condition tend to attract more oversea funds (Asiedu, 2002; Blanchard and

Fischer, 1990; Gordon and Bovenberg, 1996; Lucas, 1990; Reinhart and Rogoff, 2004).

From regression (1b), we find strong evidence that higher credit difference between the

donor and the recipient countries, lower is the FDI flows (16 negative signs vs. 4 positive signs).

That is, low rating donor countries are more likely to engage in FDI in high rating recipient

countries. This significant effect of rating difference result is consistent with our finding on donor

and recipient ratings impact considered separately in (1a), that FDIs flow to high ratings recipient

countries for their good investment environment.

Turning to control variables, the explanatory variables are in general statistically

significant and with expected signs.

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Strong bilateral linkage between a pair of donor and recipient countries increases FDI. The

countries sharing the same language have more FDI flows than countries that do not. Shorter

geographic distance between countries increases FDI flows between them. The finding is

consistent with the fact that shared language and geographical proximity reduce transaction cost

between countries. These results are consistent with the previous literature (Bevan and Estrin,

2004; Ferri et al., 2001; Kim and Wu, 2008; Lael Brainard, 1997; Van Nieuwerburgh and

Veldkamp, 2009; Wheeler and Mody, 1992).

We find economic and financial development such as population and bank credit positively

and significantly influence FDI. This indicates that countries with bigger market size and more

financial resources facilitate FDI. Similar results are found by international trade studies without

credit rating (e.g. Greenwood, 1990; Hermes and Lensink, 2003; Levine, 1991; Saint-Paul, 1992).

Moreover, we find that there is a negative and significant relationship between FDI flows and

interest rate spread, indicating that FDI flows are more likely to be directed to countries with more

developed banking sector and financial stability. This part of results are consistent with previous

studies (Barrell and Pain, 1996; Romita, 2002).

We find that countries with higher degree of economic openness (i.e. more stable foreign

exchange regimes, lower investment barriers and larger trade flows) are more likely to receive FDI

flows. FDIs tend to go into more open economies because higher transaction costs associate with

more trade protections (Asiedu, 2002; Klasra, 2011).

In summary, our findings suggest that FDI flows are higher when donor countries have

lower sovereign ratings combined with the following recipient countries’ characteristics: higher

sovereign rating, shared language and close proximity with donors, large population, and

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developed financial system with stable exchange rate regime, lower trade barriers and high

economic openness.

5.2 The impact of sovereign rating on FDI flows to OECD and non-OECD countries

We investigate credit rating impacts by separating the recipient countries into two groups – OECD

and non-OECD countries. The estimation results and a coefficient sign summary on statistically

significant results of (2a) and (2b) are reported in Table 6 (a), (b).

Sovereign credit rating coefficients are statistically significant for the majority of countries,

however the sign of its impact is opposite in OECD and non-OECD countries. For OECD recipient

countries, the recipient country’s sovereign rating (21 positive signs vs. 1 negative sign) positively

impact FDI flows. This finding is consistent to our previous finding from (1a) regression.

In contrast, for non-OECD recipient countries, the recipient country’s sovereign rating (21

negative signs vs. 4 positive signs) negatively impact FDI flows. This finding is interesting and

deserves more discussion. One explanation of our finding is that investors are prepared to take risk

when investing in less-developed countries. They chase high return rate from such investments.

While in developed countries where the potential large return is rare, investors focus more on the

quality of investment environment. Frenkel et al. (2004) examine the determinants of FDI flows

to emerging markets by using bilateral FDI flows from the G5 countries to 22 emerging markets,

they find the capital return (measured by GDP growth rate) is important factor for emerging

markets on attracting FDI flows. Another reason is that less-developed countries create a variety

of favourable terms (e.g. bilateral free-trade agreement and tax-rate system) for attracting FDI

flows to compensate for the lower sovereign ratings. Büthe and Milner (2008) examine the impact

of trade agreement on FDI by analysing a sample of FDI flow to 122 developing countries from

1970 – 2000, they show that the trade agreements as commitments to foreign investors, reassuring

16

investors and increasing investments. Control variables’ impact on the FDI do not change from

the ones reported in Table 5 (a), (b).

Korea, New Zealand and Slovenia have positive coefficients on both OECD and non-

OECD recipient sovereign ratings. Traditionally, Korea’ FDI have been directed towards

developed countries, and perceived as channels of technological transfer and market access. Since

the mid-1990s, with an aim to enhance the competitiveness of Korean firms in the global market,

Korea increased FDIs into developing countries, such as Indonesia, Vietnam and China have begun

to attract a greater portion of Korean FDIs.

In summary, we find that recipient countries’ development status play an important role in

attracting FDI flows. FDI flows go to higher rated OECD countries and lower rates non-OECD

countries when we distinguish the recipient countries according to their development status.

5.3 The impact of regional sovereign rating on FDI flows

There is potential competition of FDI funds among regions. We examine whether FDI is sensitive

to regional stability of the recipient countries. Moreover, we check whether the ratings of other

regions impact the FDI flow. A deterioration of ratings in one region may encourage international

investors to move to other regions where ratings may have improved or deteriorated less. The

estimation results of (3a) - (3c) are reported in Table 7 (a), (b).

We find that the recipient country’s own regional rating tend to positively impact FDI

inflow (13 positive signs vs. 5 negative signs). A country in a high rating region tends to receive

more FDI flows because neighbouring countries’ investment environment also impacts the

17

recipient country’s domestic investment environment. Our result shows a “neighbourhood effect”

which neighbour country’s credit status impacts FDI flow.

We also find that the credit rating of other regions tend to negatively impact FDI inflow

(16 negative signs vs. 5 positive signs). This negative relationship suggests that high average credit

ratings in other regions reduce FDI flows to recipient countries. Competition for FDI funds

happens not only at country level but also at regional level.

Regression result of (3c) shows that FDI tends to flow to countries in a region that has a

higher relative regional ratings (14 positive signs vs. 5 negative signs). This result confirmed our

previous finding that FDIs tend to flow to countries which have a relative high regional ratings.

Overall, results show that the higher (lower) rating of a recipient country’s region (other

regions) is associated with more FDI flows. The “neighbourhood effect” we find has important

policy implications. Countries in the same geographic region not only compete for attracting FDI

but also indirectly help each other to attract FDI by maintaining high credit ratings. Constant

monitoring member countries’ credit status should be on the agenda of regional development

organisations such as APEC, EU.

6. Conclusion

This study analyses the effect of sovereign credit ratings on FDI flows between 31 donor and 72

recipient countries during the period 1985 - 2012. We find that sovereign ratings of recipient

countries have a positive impact on FDI inflow they receive and donor countries’ ratings tend to

negatively impact FDI flows from them. FDI flow pattern and its revealed risk attitude depends on

the national income groups of the recipient countries. We find that higher rating OECD recipient

countries attract more FDI flows, whereas lower rating non-OECD recipient countries receive

18

more FDIs. This finding suggests that investors prefer high risk investment environment in low

national income countries. This risk-taking behaviour reverses to risk-averse behaviour when

investors invest into high national income countries. We find that more FDI flows to countries

whose own regional rating is high. However, when other regions’ ratings are higher, then there is

a FDI crowding out effect – FDI flows to the higher rated regions. We find that higher FDI flows

typically associate with closer bilateral linkages in terms of common language, geographical

proximity, and the amount of trade between donor and recipient countries. In addition, higher level

of financial and economic development and openness in recipient countries also tend to foster FDI

inflows.

19

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23

Appendix

Figure 1. Aggregate FDI Outflow, 1985-2012

The figure shows the aggregate FDI outflow from 31 OECD donor countries to 72 OECD and non-OECD

recipient countries from 1985 to 2012, in millions of US dollars.

Figure 2. Aggregate FDI flow to OECD and Non-OECD Recipient Countries

FDI Outflow

Variable OECD Non-OECD Total

Volume 16234178.24 2534859.36 18,769,038

% 86.49% 13.51%

0

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24

Table 1. Rating Scores for Sovereign Rating Grades

This table describes the construction of the sovereign credit rating scores. The sovereign rating grades are

from the highest AAA to the lowest D and the outlook grades are from positive to negative. We assign

numerical values for each of the rating grades, which vary from 20 for AAA to 0 for D. For outlook grades,

we assign +0.5 to outlook positive, +0.25 to watch positive watch, 0 to stable, -0.25 to watch negative and

-0.5 to negative outlook, respectively.

Interpretation Moody's Standard

and Poor's Fitch

Numerical

Value

Investment-grade ratings

Highest credit quality Aaa AAA AAA 20

High credit quality

Aa1 AA+ AA+ 19

Aa2 AA AA 18

Aa3 AA- AA- 17

Strong payment capacity

A1 A+ A+ 16

A2 A A 15

A3 A- A- 14

Adequate payment capacity

Baa1 BBB+ BBB+ 13

Baa2 BBB BBB 12

Baa3 BBB- BBB- 11

Speculative-grade ratings

Speculative Ba1 BB+ BB+ 10

Credit risk developing, Ba2 BB BB 9

due to economic changes Ba3 BB- BB- 8

Highly speculative,

credit risk present,

with limited margin safety

B1 B+ B+ 7

B2 B B 6

B3 B- B- 5

High default risk

Caa1 CCC+ CC+ 4

Caa2 CCC CCC 3

Caa3 CCC- CCC- 2

Default-grade ratings

Near or in bankruptcy

or default

Ca CC CC 1

C/D SD D 0

Rating Outlook Numerical Value

Positive 0.5

Watch Positive 0.25

Stable 0

Watch Negative -0.25

Negative -0.5

25

Table 2. Description of Variables

Variable Descriptions

FDI Bilateral annual FDI flow from a donor country to a receipt country

SCR_Don Annual foreign (local) debt credit score of the donor country

SCR_Recpt Annual foreign (local) debt credit score of the recipient country

dSCR = SCR_Don- SCR_Recpt

Annual foreign (local) debt credit score difference

𝑂𝐸𝐶𝐷 Dummy variable equal to one if the country is member of OECD, and zero

otherwise

Non_OECD Dummy variable equal to one if the country is not a member of OECD, and

zero otherwise

OECD_SCR_Recpt

= 𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡 × 𝑂𝐸𝐶𝐷

Annual foreign (local) debt credit score of the OECD recipient country

NonOECD_SCR_Recpt = 𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡 × 𝑁𝑜𝑛𝑂𝐸𝐶𝐷

Annual foreign (local) debt credit score of the non-OECD recipient country

OwnReg_SCR Average of annual foreign debt credit scores of the countries in the same

region as the recipient country

OtherReg_SCR Average of annual foreign debt credit scores of countries in other regions

DIST Natural-log of distance between pair of country capital cities in kilometres

LAN Dummy variable, equal to one if a pair of recipient and donor countries

sharing the same official or business language, and zero otherwise

POPU_Recpt Population of the recipient country, in 10,000

POPU_Don Population for the donor country, in 10,000

POPU = 𝑃𝑂𝑃𝑈_𝑅𝑒𝑐𝑝𝑡 × 𝑃𝑂𝑃𝑈_𝐷𝑜𝑛

Overall effect of population

26

Table 2. Description of Variables (continued)

Variable Descriptions

BANKCRED_Recpt Annual domestic credit provided by banking sector of the recipient country

in natural logs, US$

BANKCRED_Don Annual domestic credit provided by banking sector of the donor country in

natural logs, US$

BANKCRED = BANKCRED_Recpt × BANKCRED_Don

Overall effect of bank credit

INTSPREAD_Recpt Annual interest rate spread of the recipient country in percentage points

INTSPREAD_Don Annual interest rate spread of the donor country in percentage points

𝐼𝑁𝑇𝑆𝑃𝑅𝐸𝐴𝐷𝑖,𝑡𝑗

= 𝐼𝑁𝑇𝑆𝑃𝑅𝐸𝐴𝐷_𝑅𝑒𝑐𝑝𝑡 × 𝐼𝑁𝑇𝑆𝑃𝑅𝐸𝐴𝐷_𝐷𝑜𝑛

Overall effect of interest rate spread

FXREGM_Recpt Recipient country i’s reign exchange regime indicator range from 0 to 3 (0 =

floating arrangements, 1 = other managed arrangement, 2 = soft pegs, 3 =

hard pegs

FXREGM_Don Donor country j’s reign exchange regime indicator range from 0 to 3 (0 =

floating arrangements, 1 = other managed arrangement, 2 = soft pegs, 3 =

hard pegs

𝐹𝑋𝑅𝐸𝐺𝑀 = FXREGM_Recpt + FXREGM_Don

Overall effect from foreign exchange regime

BAR_Recpt Trade barrier of the recipient country range from 0 to 2 (0 = no trade

restriction, 1 = investment restriction on either direct investment or financial

credit, 2 = investment restriction on both direct investment and financial

credit

BAR_Don Trade barrier of the donor country range from 0 to 2 (0 = no trade

restriction, 1 = investment restriction on either direct investment or financial

credit, 2 = investment restriction on both direct investment and financial

credit

BAR_Don = BAR_Recpt + BAR_Don

Overall effect from total trade restriction

DOT_Recpt Ratio of total trade and GDP for recipient country

DOT_Don Ratio of total trade and GDP for donor country

DOT = DOT_Recpt + DOT_Don

Overall effect from total trade

27

Table 3. Correlation Matrix of variables

This table presents the correlation matrix of independent variables.

Country 1 SCR_Recpt SCR_Don OECD_SCR_Recpt NonOECD_SCE_Recpt OwnReg_Recpt OtherRe_SCR_Recpt LAN DIST FPOPU BANKCRED INTSPREAD EXREGIM DOT BAR

SCR_Recpt 1.000 0.030 0.789 -0.316 0.630 -0.599 0.078 0.332 -0.183 0.484 -0.127 0.230 0.227 0.106

SCR_Don 1.000 0.017 0.002 0.054 0.117 0.005 -0.003 0.070 0.401 -0.086 0.011 0.252 0.007

OECD_SCR_Recpt 1.000 -0.832 0.699 -0.676 0.014 0.385 -0.098 0.401 -0.128 0.466 -0.033 0.097

NonOECD_SCE_Recpt 1.000 -0.510 0.503 0.050 -0.294 -0.014 -0.182 0.083 -0.513 0.257 -0.054

OwnReg_Recpt 1.000 -0.950 0.331 0.137 -0.120 0.473 -0.154 0.392 0.040 0.075

OtherRe_SCR_Recpt 1.000 -0.322 -0.135 0.131 -0.367 0.138 -0.379 0.013 -0.072

LAN 1.000 -0.396 -0.085 0.149 -0.085 0.070 0.193 0.034

DIST 1.000 -0.141 0.041 0.055 0.025 -0.157 0.004

FPOPU 1.000 0.027 -0.007 0.147 -0.303 0.119

BANKCRED 1.000 -0.097 0.163 0.200 -0.006

INTSPREAD 1.000 -0.060 -0.041 -0.067

EXREGIM 1.000 -0.105 0.129

DOT 1.000 0.002

BAR 1.000

Country 2 SCR_Recpt SCR_Don OECD_SCR_Recpt NonOECD_SCE_Recpt OwnReg_Recpt OtherRe_SCR_Recpt LAN DIST FPOPU BANKCRED INTSPREAD EXREGIM DOT BAR

SCR_Recpt 1.000 0.014 0.788 -0.314 0.629 -0.597 0.161 -0.149 -0.157 0.518 -0.111 0.229 0.182 -0.055

SCR_Don 1.000 0.007 0.002 0.016 0.029 -0.001 -0.002 -0.021 -0.108 0.029 -0.004 -0.132 0.003

OECD_SCR_Recpt 1.000 -0.832 0.698 -0.675 0.275 -0.236 -0.082 0.437 -0.120 0.466 -0.050 0.066

NonOECD_SCE_Recpt 1.000 -0.510 0.503 -0.279 0.231 -0.015 -0.207 0.086 -0.513 0.241 -0.151

OwnReg_Recpt 1.000 -0.950 0.386 -0.234 -0.121 0.517 -0.149 0.392 0.040 0.042

OtherRe_SCR_Recpt 1.000 -0.376 0.230 0.129 -0.458 0.130 -0.379 0.030 -0.043

LAN 1.000 -0.249 -0.224 0.231 -0.045 0.135 0.103 0.278

DIST 1.000 0.146 -0.072 -0.079 -0.076 -0.017 0.000

FPOPU 1.000 -0.003 -0.020 0.148 -0.281 0.267

BANKCRED 1.000 -0.071 0.182 0.143 0.024

INTSPREAD 1.000 -0.059 -0.074 -0.115

EXREGIM 1.000 -0.099 0.192

DOT 1.000 -0.141

BAR 1.000

28

Table 4. Granger Causality Test

This table provide Ganger Causality F test results. We use 2 lags of all variables on the Granger causality test. The

statistically significance at 10 % level is denoted by *, significance at 5 % level is denoted by ** and significance at

1% level is denoted by ***.

Series Granger Causality test

Sovereign Credit Rating cause FDI FDI cause Sovereign Credit Rating

AUS 9.181*** 0.393

AUT 3.316** 0.45

BEL 3.26** 1.084

CHL 2.985** 1.824

CZE 1.872 1.612

DNK 1.131 0.581

EST 3.428*** 0.401

FIN 1.473 1.8

FRA 12.521*** 0.633

DEU 6.081*** 2.115

GRC 0.881 0.853

HUN 6.549*** 1.467

ISL 6.158*** 3.92**

IRL 2.853** 0.39

ITA 7.503*** 0.01

KOR 4.754*** 5.267***

LUX 2.049 1.936

NLD 8.806*** 0.595

NZL 1.248 0.071

NOR 4.472*** 0.056

POL 4.522*** 2.374

PRT 3.24** 1.856

SVK 3.324*** 4.559**

SVN 2.636** 5.036***

ESP 12.31*** 1.133

SWE 10.212*** 0.475

CHE 8.071*** 0.004

GBR 9.094*** 2.424

USA 5.357*** 3.649**

JPN 3.411*** 2.116

TUR 2.904** 0.705

29

Table 5 (a). The Impact of Sovereign Credit Rating on FDI flows

We report estimation results from the following panel regressions: 𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝛽2𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝜷𝒂

′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄

′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (1a) 𝐹𝐷𝐼𝑡 = 𝛽1𝑑𝑆𝐶𝑅𝑡 + 𝜷𝒂

′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄

′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (1b)

Panel model (1a) examines the impact of sovereign credit rating from donor and recipient countries on FDI flow. Model (1b) examines the impact of sovereign credit rating difference

between donor and recipient countries on FDI flow. The dependent variable 𝐹𝐷𝐼𝑡 represents annual bilateral FDI flow from a donor country to a recipient country in year t, denominated

in million US Dollars. 𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 is the annual foreign debt credit score of the donor country in year t. 𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 is the annual foreign debt credit score of the recipient country in

year t. 𝑑𝑆𝐶𝑅_𝐷𝑜𝑛𝑡(= 𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 − 𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡) is the rating difference between donor and recipient countries. 𝑳𝒊𝒏𝒌𝒕 = (𝐿𝐴𝑁𝑡 , 𝐷𝐼𝑆𝑇𝑡)′ is a vector which includes two bilateral

linkage variables between donor and recipient countries.𝑫𝒆𝒗𝒎𝒕𝒕 = (𝑃𝑂𝑃𝑈𝑡 , 𝐵𝐴𝑁𝐾𝐶𝑅𝐸𝐷𝑡 , 𝐼𝑁𝑇𝑆𝑃𝑅𝐸𝐴𝐷𝑡)′ is a vector which consists of three control variables measuring the

economic and financial development. 𝑶𝒑𝒆𝒏𝒕 = (𝐹𝑋𝑅𝐸𝐺𝑀𝑡 , 𝐵𝐴𝑅𝑡 , 𝐷𝑂𝑇𝑡)′ is vector which includes three control variables on the economic openness.The statistically significance at

10 % level is denoted by *, significance at 5 % level is denoted by ** and significance at 1% level is denoted by ***. P-values are shown in braces.

Donor country j = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

AUS AUT BEL CHL CZE DNK EST FIN FRA DEU GRC HUN ISL IRL ITA KOR

(1a)

SCR_Don -38.115 15.353*** -65.005*** 1.360 -0.131 -45.638*** 1.876*** -14.691* -162.113*** -105.383*** 0.697 -0.421 0.912 -10.801* -60.428*** -25.249***

{0.108} {0.003} {0.002} {0.743} {0.882} {0.000} {0.002} {0.063} {0.000} {0.000} {0.461} {0.868} {0.315} {0.094} {0.003} {0.000}

SCR_Recpt 15.765*** 3.926** 51.777*** 1.110 -0.042 29.518*** -0.172 18.141*** 133.953*** 113.749*** -2.854*** -0.189 0.646 10.890*** 51.163*** 11.761***

{0.000} {0.045} {0.000} {0.117} {0.884} {0.000} {0.563} {0.007} {0.000} {0.000} {0.004} {0.659} {0.343} {0.000} {0.000} {0.000}

(1b) dSCR 1.334 -260.927*** -25.463*** -53.390*** -50.969*** 15.350*** 35.865 -0.131 -45.033*** 1.875*** -14.691* -162.045*** -105.383*** 0.704 -1.625 0.876

{0.749} {0.000} {0.000} {0.000} {0.000} {0.003} {1.000} {0.882} {0.000} {0.002} {0.063} {0.000} {0.000} {0.458} {1.000} {0.334}

Control Variables

(1a)

LAN 364.880** 174.365*** 1216.379*** 36.243 -20.320*** -159.024*** -7.848 689.696** 1146.865** 560.358* -182.599*** -34.790** 4.029 96.497 -78.618 -35.256

{0.011} {0.006} {0.001} {0.441} {0.008} {0.008} {0.163} {0.040} {0.028} {0.063} {0.002} {0.022} {0.749} {0.159} {0.567} {0.164}

DIST 3.006 -12.234*** -8.276 -10.478*** -1.240*** 7.135** -1.006*** -5.462* -68.402*** -41.755*** -2.246*** -2.187*** -2.220*** -17.216*** 12.488 2.110**

{0.764} {0.000} {0.309} {0.003} {0.002} {0.021} {0.001} {0.086} {0.000} {0.001} {0.001} {0.000} {0.002} {0.000} {0.351} {0.033}

POPU 17.750** 13.245*** 20.209 3.231*** 1.335*** 22.796*** -4.524** 21.035*** 96.074*** 78.263*** 2.332*** 0.116 -2.774 16.127 23.748** 21.867***

{0.035} {0.000} {0.525} {0.004} {0.005} {0.000} {0.019} {0.000} {0.000} {0.000} {0.000} {0.893} {0.263} {0.183} {0.026} {0.000}

BANKCRED 12.263 -7.604** 16.273 1.968 0.694 -6.260 0.579 -7.586 64.589*** 62.622*** 3.299*** 0.808 2.935*** 8.603 13.010 12.441***

{0.125} {0.018} {0.283} {0.208} {0.265} {0.237} {0.193} {0.379} {0.000} {0.001} {0.000} {0.183} {0.004} {0.126} {0.422} {0.000}

INTSPREAD 0.095** -0.046*** 0.080* 0.006** -0.007*** 0.019 -0.001* 0.023 0.015 -0.106** -0.010*** -0.002 -0.005** -0.012 -0.024 -0.202

{0.015} {0.003} {0.088} {0.018} {0.001} {0.284} {0.057} {0.333} {0.841} {0.010} {0.004} {0.159} {0.019} {0.268} {0.105} {0.174}

EXREGIM -8.095* 1.789 33.787*** 1.334 0.778 48.670*** -2.437*** 9.388 27.429** 15.277 1.241* 0.631 -0.845 3.443 16.546** -12.902***

{0.059} {0.556} {0.001} {0.292} {0.138} {0.006} {0.003} {0.114} {0.033} {0.235} {0.070} {0.443} {0.437} {0.251} {0.015} {0.000}

DOT -174.180 16.876 39.591 34.645*** 13.078** 744.264*** -4.758*** 230.638* 978.806** 97.115 0.840 15.558 -14.449 -15.807 38.303 89.153***

{0.206} {0.556} {0.491} {0.000} {0.040} {0.001} {0.007} {0.055} {0.048} {0.733} {0.931} {0.227} {0.213} {0.548} {0.816} {0.003}

BARR 0.070 -47.554** 16.030 13.702** -8.245*** 108.635*** 1.085 9.720 -127.464 -263.376*** -9.720*** 0.369 -15.603*** -16.964 -60.314 -47.531***

{0.157} {0.012} {0.882} {0.016} {0.001} {0.002} {0.165} {0.705} {0.264} {0.003} {0.000} {0.950} {0.003} {0.523} {0.391} {0.000}

30

Table 5 (a). The Impact of Sovereign Credit Rating on FDI flows (continued)

We report estimation results from the following panel regressions: 𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝛽2𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝜷𝒂

′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄

′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (1a) 𝐹𝐷𝐼𝑡 = 𝛽1𝑑𝑆𝐶𝑅𝑡 + 𝜷𝒂

′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄

′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (1b)

Panel model (1a) examines the impact of sovereign credit rating from donor and recipient countries on FDI flow. Model (1b) examines the impact of sovereign credit rating difference

between donor and recipient countries on FDI flow. The dependent variable 𝐹𝐷𝐼𝑡 represents annual bilateral FDI flow from a donor country to a recipient country in year t, denominated

in million US Dollars. 𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 is the annual foreign debt credit score of the donor country in year t. 𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 is the annual foreign debt credit score of the recipient country in

year t. 𝑑𝑆𝐶𝑅_𝐷𝑜𝑛𝑡(= 𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 − 𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡) is the rating difference between donor and recipient countries. 𝑳𝒊𝒏𝒌𝒕 = (𝐿𝐴𝑁𝑡 , 𝐷𝐼𝑆𝑇𝑡)′ is a vector which includes two bilateral

linkage variables between donor and recipient countries.𝑫𝒆𝒗𝒎𝒕𝒕 = (𝑃𝑂𝑃𝑈𝑡 , 𝐵𝐴𝑁𝐾𝐶𝑅𝐸𝐷𝑡 , 𝐼𝑁𝑇𝑆𝑃𝑅𝐸𝐴𝐷𝑡)′ is a vector which consists of three control variables measuring the

economic and financial development. 𝑶𝒑𝒆𝒏𝒕 = (𝐹𝑋𝑅𝐸𝐺𝑀𝑡 , 𝐵𝐴𝑅𝑡 , 𝐷𝑂𝑇𝑡)′ is vector which includes three control variables on the economic openness.The statistically significance at

10 % level is denoted by *, significance at 5 % level is denoted by ** and significance at 1% level is denoted by ***. P-values are shown in braces.

Donor country j = 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

LUX NLD NZL NOR POL PRT SVK SVN ESP SWE CHE GBR USA JPN TUR

(1a)

SCR_Don -264.996*** -85.135*** 1.851 -24.005** -3.708* -5.799 0.724*** 1.573*** -59.715*** -4.353 -63.753*** -180.698*** -256.331*** -116.648*** 0.115

{0.000} 0.000 0.143 0.019 0.083 {0.267} {0.000} {0.000} {0.000} {0.504} {0.000} {0.000} {0.000} {0.000} {0.845}

SCR_Recpt 358.873*** 108.531*** 4.730*** 13.102*** 1.085 4.207*** -0.005 0.230** 56.096*** 32.942*** 54.644*** 206.206*** 282.175*** 136.904*** 1.501***

{0.000} {0.000} {0.001} {0.000} {0.159} {0.001} {0.962} {0.014} {0.000} {0.000} {0.000} {0.000} {0.000} {0.000} {0.000}

(1b) dSCR -10.982 -60.449*** -319.590 -85.135*** -24.005** -4.303*** -2.535 0.725*** 1.570*** -87.728*** -4.311 -63.753*** -212.700*** -37.850 -0.110***

{1.000} {0.003} {1.000} {0.000} {0.019} {0.000} {1.000} {0.000} {0.000} {0.000} {0.504} {0.000} {0.000} {0.109} {0.000}

Control Variables

(1a)

LAN -265.237 2480.050*** -6.106 119.571** -16.206 102.839* -7.422*** -5.062* 464.102*** 436.034 125.428 1576.405*** 1801.826*** -59.065 3.654

{0.646} {0.000} {0.572} {0.011} {0.142} {0.096} {0.005} {0.081} {0.000} {0.139} {0.337} {0.001} {0.000} {0.776} {0.366}

DIST -17.469 -22.614* -3.958** -7.860* -3.208*** -4.264** -0.477*** -0.674*** -37.128*** -13.918*** -0.491 -68.492*** -285.274*** 25.425** -0.865***

{0.668} {0.096} {0.019} {0.073} {0.000} {0.038} {0.000} {0.000} {0.000} {0.000} {0.941} {0.001} {0.000} {0.035} {0.005}

POPU -469.182*** 61.437*** 6.862* 31.887*** -1.478 5.186** -0.224 1.692*** 28.859*** 27.871*** 91.518*** 166.045*** 115.902*** 115.065*** 1.200***

{0.000} {0.003} {0.056} {0.000} {0.237} {0.032} {0.361} {0.000} {0.002} {0.000} {0.000} {0.000} {0.000} {0.000} {0.000}

BANKCRED 141.105** 17.517 -1.577** 0.807 4.329** 3.853 -0.313* -0.287 17.672* -0.828 44.177*** 78.602*** 120.712*** 68.325*** 1.180**

{0.017} {0.370} {0.015} {0.876} {0.012} {0.230} {0.092} {0.117} {0.097} {0.905} {0.000} {0.000} {0.000} {0.000} {0.024}

INTSPREAD -0.788*** -0.020 0.008 0.058** 0.000 -0.003 0.000 0.001*** 0.015 -0.039*** -0.033 0.165 -0.322***

{0.005} {0.594} {0.110} {0.024} {0.135} {0.590} {0.965} {0.000} {0.685} {0.006} {0.629} {0.529} {0.000}

EXREGIM 207.297*** -11.957 -0.831 8.207* -0.619 0.420 0.417*** -0.178 33.410*** 0.288 3.032 -26.774 89.166*** -39.481*** 0.363

{0.000} {0.388} {0.594} {0.064} {0.724} {0.879} {0.006} {0.125} {0.000} {0.960} {0.706} {0.164} {0.006} {0.000} {0.402}

DOT 144.477 65.125 -23.457 512.288*** 12.309 31.820 1.275 -1.062* -121.025 -56.598 -254.974 -1377.161** 4873.588** -714.237** 13.451

{0.523} {0.469} {0.219} {0.007} {0.331} {0.604} {0.366} {0.088} {0.609} {0.462} {0.217} {0.018} {0.031} {0.047} {0.400}

BARR -857.014*** 5.578 -3.214 34.025 6.432 -13.805 -0.580 -5.363*** 25.096 -77.077*** -140.670*** -460.594*** -246.272* -560.181*** -12.488***

{0.000} {0.959} {0.363} {0.341} {0.323} {0.129} {0.378} {0.000} {0.691} {0.005} {0.005} {0.001} {0.084} {0.000} {0.000}

31

Table 5 (b). The Impact of Sovereign Credit Rating on FDI flows – Signs of significant coefficients

This table summarises the signs of significant coefficients from the results in Table 5 (a).

No. of

Countries with

significant

coefficient

Positive

coefficient

Negative

coefficient

(1a) SCR_Don 21 4 17

SCR_Recpt 24 23 1

(1b) dSCR 20 4 16

Control Variables

(1a)

LAN 18 12 6

DIST 26 3 23

POPU 25 23 2

BANKCRED 16 13 3

INTSPREAD 14 5 9

EXREGIM 14 10 4

DOT 12 8 4

BARR 16 2 14

32

Table 6 (a). The Impact of Sovereign Rating on FDI Flows to OECD and non-OECD Countries

We report estimation results from the following panel regressions:

𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡+𝛽2𝑂𝐸𝐶𝐷_𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃

′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (2a)

𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝛽2𝑁𝑜𝑛𝑂𝐸𝐶𝐷_𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃

′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (2b)

Panel model (2a) examines the impact of sovereign rating on FDI flow to OECD recipient country. Model (2b) examines the impact of sovereign rating on FDI flow to non-OECD

recipient country. The dependent variable 𝐹𝐷𝐼𝑡 represents annual bilateral FDI flow from a donor country to a recipient country in year t, denominated in million US Dollars.

𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 is the annual foreign debt credit score of the donor country in year t. 𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 is the annual foreign debt credit score of the recipient country in year t.

𝑂𝐸𝐶𝐷_𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 is the annual foreign debt credit score of OECD recipient country at year t. 𝑁𝑜𝑛𝑂𝐸𝐶𝐷_𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 is the annual foreign debt credit score of non-OECD

recipient country at time t. 𝑳𝒊𝒏𝒌𝒕 = (𝐿𝐴𝑁𝑡 , 𝐷𝐼𝑆𝑇𝑡)′ is a vector which includes two bilateral linkage variables between donor and recipient countries. 𝑫𝒆𝒗𝒎𝒕𝒕 = (𝑃𝑂𝑃𝑈𝑡 ,𝐵𝐴𝑁𝐾𝐶𝑅𝐸𝐷𝑡 , 𝐼𝑁𝑇𝑆𝑃𝑅𝐸𝐴𝐷𝑡)′ is a vector which consists of three control variables measuring the economic and financial development. 𝑶𝒑𝒆𝒏𝒕 = (𝐹𝑋𝑅𝐸𝐺𝑀𝑡 , 𝐵𝐴𝑅𝑡 , 𝐷𝑂𝑇𝑡)′ is

vector which includes three control variables on the economic openness.The statistically significance at 10 % level is denoted by *, significance at 5 % level is denoted by ** and

significance at 1% level is denoted by ***. P-values are shown in braces.

Donor country j = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

AUS AUT BEL CHL CZE DNK EST FIN FRA DEU GRC HUN ISL IRL ITA KOR

(2a) OECD_SCR_Recpt 8.285*** -0.460 27.362*** -0.267 0.461 26.249*** -0.473** 10.134*** 81.292*** 62.025*** 0.390 0.030 0.885** 7.525*** 32.134*** 1.605

{0.000} {0.759} {0.000} {0.666} {0.125} {0.000} {0.030} {0.001} {0.000} {0.000} {0.139} {0.937} {0.047} {0.000} {0.000} {0.116}

(2b) NonOECD_SCR_Recpt -5.135** 4.464* -13.823 1.412 -0.971** -30.193*** 0.926*** -7.929* -70.906*** -43.920*** -2.715*** -0.183 -1.474*** -7.389*** -33.779*** 4.203*

{0.018} {0.099} {0.115} {0.250} {0.049} {0.000} {0.001} {0.057} {0.000} {0.001} {0.000} {0.787} {0.005} {0.000} {0.000} {0.075}

(2a) - (2b) Control Variable Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

SCR_Don Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Donor country j = 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

LUX NLD NZL NOR POL PRT SVK SVN ESP SWE CHE GBR USA JPN TUR

(2a) OECD_SCR_Recpt 235.937*** 57.794*** 1.354* 12.477*** 0.854 0.854 3.046** 0.084 -0.031 34.300*** 18.684*** 30.942*** 121.163*** 156.317*** 55.496***

{0.000} 0.000 0.070 0.000 0.179 {0.447} {0.001} {0.000} {0.004} {0.372} {0.000} {0.000} {0.000} {0.000} {0.136}

(2b) NonOECD_SCR_Recpt -224.024*** -37.405*** 0.866 -17.338*** -1.094 -3.435* -0.169 0.233* -32.556*** -15.282*** -20.838*** -95.122*** -119.964*** -32.526*** -1.496**

{0.000} {0.000} {0.478} {0.001} {0.227} {0.068} {0.348} {0.088} {0.000} {0.000} {0.003} {0.000} {0.000} {0.001} {0.016}

(2a) - (2b) Control Variable Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

SCR_Don Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Table 6 (b) The impact of sovereign rating on FDI flows to OECD and non-OECD countries – Signs of significant coefficients

This table summarises the signs of significant coefficients from the results in Table 6 (a).

No. of Countries

with significant

coefficient

Positive

coefficient

Negative

coefficient

(2a) OECD_SCR_Recptt 22 21 1

(2b) NonOECD_SCR_Recptt 25 4 21

33

Table 7 (a). The Impact of Regional Sovereign Rating on FDI Flows

We report estimation results from the following panel regressions:

𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝛽2𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝛽3𝑂𝑤𝑛𝑅𝑒𝑔_𝑆𝐶𝑅𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃

′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (3a)

𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝛽2𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝛽3𝑂𝑡ℎ𝑒𝑟𝑅𝑒𝑔_𝑆𝐶𝑅𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃

′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (3b)

𝐹𝐷𝐼𝑡 = 𝛽1𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 + 𝛽2𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 + 𝛽3𝑑𝑅𝑒𝑔_𝑆𝐶𝑅𝑡 + 𝜷𝒂′ 𝑳𝒊𝒏𝒌𝒕 + 𝜷𝒃

′ 𝑫𝒆𝒗𝒎𝒕𝒕 + 𝜷𝒄′ 𝑶𝒑𝒆𝒏𝒕 + 𝜀𝑡; (3c)

Panel model (3a) examines the impact of recipient country’s own regional rating on FDI flows. Model (3b) examines the impact of other regional rating on FDI flows. Model (3c)

examines the impact of regional rating difference between recipient country’s own region and other region on FDI flow. The dependent variable 𝐹𝐷𝐼𝑡 represents annual bilateral

FDI flow from a donor country to a recipient country in year t, denominated in million US Dollars. 𝑆𝐶𝑅_𝐷𝑜𝑛𝑡 is the annual foreign debt credit score of the donor country in year

t; 𝑆𝐶𝑅_𝑅𝑒𝑐𝑝𝑡𝑡 is the annual foreign debt credit score of the recipient country in year t. 𝑂𝑤𝑛𝑅𝑒𝑔_𝑆𝐶𝑅t is the average of annual foreign debt credit scores of the countries in the

same region as the recipient country. 𝑂𝑡ℎ𝑒𝑟𝑅𝑒𝑔_𝑆𝐶𝑅t is the average of annual foreign debt credit scores of countries in other regions. 𝑑𝑅𝑒𝑔_𝑆𝐶𝑅𝑡 (= 𝑂𝑤𝑛𝑅𝑒𝑔_𝑆𝐶𝑅t −𝑂𝑡ℎ𝑒𝑟𝑅𝑒𝑔_𝑆𝐶𝑅t) is the regional rating difference. 𝑳𝒊𝒏𝒌𝒕 = (𝐿𝐴𝑁𝑡 , 𝐷𝐼𝑆𝑇𝑡)′ is a vector which includes two bilateral linkage variables between donor and recipient

countries.𝑫𝒆𝒗𝒎𝒕𝒕 = (𝑃𝑂𝑃𝑈𝑡 , 𝐵𝐴𝑁𝐾𝐶𝑅𝐸𝐷𝑡 , 𝐼𝑁𝑇𝑆𝑃𝑅𝐸𝐴𝐷𝑡)′ is a vector which consists of three control variables measuring the economic and financial development. 𝑶𝒑𝒆𝒏𝒕 =(𝐹𝑋𝑅𝐸𝐺𝑀𝑡 , 𝐵𝐴𝑅𝑡 , 𝐷𝑂𝑇𝑡)′ is vector which includes three control variables on the economic openness.The statistically significance at 10 % level is denoted by *, significance at 5

% level is denoted by ** and significance at 1% level is denoted by ***. P-values are shown in braces.

Donor country j = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

AUS AUT BEL CHL CZE DNK EST FIN FRA DEU GRC HUN ISL IRL ITA KOR

(3a) OwnReg_SCR -16.850*** -6.653 -19.678 -0.236 1.454** 68.976*** -0.676*** 4.318 76.409*** 40.248** 8.017*** 0.092 1.764 0.489 44.169*** -6.375***

{0.008} {0.103} {0.323} {0.914} {0.021} {0.000} {0.000} {0.550} {0.000} {0.036} {0.001} {0.907} {0.294} {0.926} {0.001} {0.006}

(3b) OtherReg_SCR 50.352*** 41.223** 141.236 -14.843 -4.631*** -226.483*** 3.372** -21.144 -209.159*** -46.923 -7.503*** 4.452** -8.663** -7.339 -84.150** -23.580***

{0.004} {0.011} {0.143} {0.159} {0.004} {0.000} {0.013} {0.388} {0.006} {0.558} {0.002} {0.014} {0.012} {0.673} {0.021} {0.000}

(3c) dReg_SCR -13.132*** -5.923* -18.737 0.877 1.222*** 54.820*** -0.637*** 4.075 57.608*** 27.607* 6.267*** -0.457 2.437* 1.658 34.115*** -2.098

{0.005} {0.071} {0.262} {0.668} {0.010} {0.000} {0.000} {0.483} {0.000} {0.072} {0.001} {0.403} {0.090} {0.723} {0.000} {0.191}

(3a)-3(c)

Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

SCR_Don Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

SCR_Recpt Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Donor country j = 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

LUX NLD NZL NOR POL PRT SVK SVN ESP SWE CHE GBR USA JPN TUR

(3a) OwnReg_SCR 365.638*** 28.610* 2.227** 29.313*** -0.194 0.785 -0.995*** -0.193 9.663 15.807** 30.195*** 10.719 -70.664* 7.385 1.057**

{0.000} 0.097 0.022 0.001 0.849 {0.714} {0.006} {0.216} {0.466} {0.016} {0.005} {0.787} {0.091} {0.560} {0.033}

(3b) OtherReg_SCR -1308.988*** -40.715 -6.836* -63.820*** -11.999*** -7.487 -0.732** 0.704 -160.205*** -87.095*** -55.679 -2.902 471.496*** -215.319*** -4.637***

{0.000} {0.540} {0.091} {0.000} {0.000} {0.339} {0.042} {0.219} {0.000} {0.000} {0.162} {0.988} {0.004} {0.000} {0.007}

(3c) dReg_SCR 294.051*** 20.222 1.740** 21.804*** 1.148 1.736 -0.402* -0.174 16.834 14.478*** 21.595*** 6.976 -66.803** 28.927** 1.914***

{0.000} {0.124} {0.027} {0.000} {0.119} {0.328} {0.068} {0.190} {0.117} {0.005} {0.008} {0.832} {0.049} {0.017} {0.000}

(3a)-3(c)

Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

SCR_Don Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

SCR_Recpt Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

34

Table 7 (b). The Impact of Regional Sovereign Rating on FDI Flows – Signs of significant coefficients

This table summarises the signs of significant coefficients from the results in Table 7 (a).

No. of Countries

with significant

coefficient

Positive

coefficient

Negative

coefficient

(3a) OwnReg_ SCR 18 13 5

(3b) OtherReg_ SCR 21 5 16

(3c) Reg_SCR 19 14 5