Master Thesis

Home-country determinants of outward FDI: Evidence from BRICS economies and five

developed countries

Msc International Financial Management Msc Business and Economics

Faculty of Economics and Business Faculty of Social Sciences

University of Groningen Uppsala University

Student number: S2980991 Student Number: hawa0038

Haiyan Wang

Supervisor: Dr. Y.R. Kruse

13 January 2017

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Abstract

This paper studies the home-country determinants of outward FDI with a focus on nine

empirically recognized host-country determinants of inward FDI, namely market size, labor

cost, exchange rate, inflation, interest rate, political risks, corruption, openness, and

technology. Based on a panel with 183 observations from BRICS and five developed

countries (Australia, Germany, Japan, UK, US), evidence is found that market size, inflation,

interest rate, political risks, and openness have significant influence on FDI outflows.

Moreover, the results of this study show that there are striking differences between

developing and developed countries regarding to the drivers for outward FDI.

Key words: market size; labor cost; exchange rate; inflation; interest rate; political risks;

corruption; openness; technology; outward FDI; BRICS

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1. Introduction

In order to expand business overseas, a firm or individual could conduct Foreign Direct

Investment (FDI) to gain effective control of a foreign business. For a long time, developed

countries have been the main role of outward FDI, which contributed to the economic growth

for both investing and recipient countries. However, in the past decades, rise in investment

flows from emerging and transition economies are noticeable. While developed countries

conduct large amount of FDI in developing countries, overseas investments from developing

countries to developed ones also experience an upward trend. According to the World

Investment Report 2016, China is the third largest investor in the world after the United State

and Japan with $128 billion FDI outflows. Further, BRICS, a league consists of five major

emerging countries: Brazil, Russia, India, China and South Africa, represented approximately

a third of FDI flows to developing and transition economies. Moreover, China, Brazil and

India are on the top 20 FDI inflows host countries list and Russia and China are on the list of

top 20 FDI outflows in 2015. Such phenomena raise a question: what are the drivers for

countries to make outward FDI? Is there any differences for developed and developing

economies?

Previous studies mainly focus on host-country determinants of inward FDI from developed

countries (Culem, 1988; Bevan and Estrin, 2004; Buckley et al., 2007). However, the

motivations of outward FDI from both developed and developing countries have received few

attentions. Therefore, it is open to be questioned that whether economic development is

systematically related to outward FDI in emerging countries. This is also supported by

institution theory that changes in institutional environment should be considered when doing

study in developing economics. Thus, more empirical researches about outward FDI when

taking developing economies as home countries are needed.

The recognized determinants of inward FDI from prior studies include market size, labor cost,

political risk, etc., which are considered significantly influence the location choice of foreign

investments (Culem, 1988; Bevan and Estrin, 2004; Buckley et al., 2007). Conversely, do

these factors also influence the outward FDI? What are the determinants of outward FDI? Do

they vary from country to country? With these research questions, this paper focus on the role

of home-country factors on outward FDI from BRICS countries and five developed countries:

Australia, Germany, Japan, United Kingdom and the United States. The inclusions of 9

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determinants (market size, labor costs, exchange rate, inflation, interest rate, political risks,

corruption, openness, and technology) are based on empirical work on FDI and other relevant

research. This study is undertaken by a panel data model with country fixed effects based on

the full sample which contains 183 observations. Also, I run the regressions with two sub

samples. One consists of the BRICS economies, the other one contains the five developed

countries. The final results suggest that determinants of FDI outflows may base on home

country characteristics. With this research, the literature on home-country factors that relate to

outward FDI would be enhanced.

The remainder of this paper is structured as follows. Section 2 holds an overview of empirical

research as well as the determinants have been researched and hypotheses, after which section

3 introduce data collection and the methodology employed. Section 4 presents the regression

results and corresponding analysis. Finally, a conclusion with contributions and suggestions

for further research is given in section 5.

2. Literature review and Hypotheses

In this part, I will first introduce what have been studied on FDI and on the recognized

determinants. Besides, the motivations behind the selected determinants would also be

discussed using Dunning's eclectic paradigm (1980). Next, hypotheses are built on certain

analysis on existing literature.

2.1 Inward and outward FDI

FDI is a popular topic on academic literature. It is natural to argue that FDI has positive

influences on economic growth in the host countries. For developing countries, FDI could

contribute to capital accumulation which is suggested by the Solow growth model and future

industrialization. While in developed economies, FDI is a way to absorb new technologies

and business practices (Cipollina, et al., 2012). Therefore, policymakers tend to attract foreign

investors by providing certain incentives.

On the other hand, multinational enterprises engage in outward FDI for many different

reasons. According to location advantages proposed in Dunning's eclectic paradigm, three

elementary motives are recognized: getting access to foreign market, achieving cost efficiency

or lacking supply of natural resources in home country (Dunning, 1979). However, this seems

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to be the case for most developed countries, especially for the last two reasons. For instance,

Singaporean government encourage offshore investments by reducing tax on foreign income

in order to encounter the domestic limitations of human capitals and natural resources (Lee et

al., 2016). Nonetheless, developing countries like China, with abundant natural resources and

labor force, has also become a major source of outward FDI.

A large amount of previous studies focus on the determinants of FDI inflows to host countries.

The main determinants of inward FDI from prior studies include market size, economic

growth, labor cost, inflation, corruption, etc. (Kobrin, 1976; Bevan and Estrin, 2004; Benáček,

et al., 2014). However, studies on the motivations of outward FDI from home countries are

incomplete. Therefore, this paper seeks to examine the home-country determinants of FDI

outflows and test whether the inward determinants are also applicable for explaining the

outflows.

2.2 Market size

According to Kobrin (1976), market size and economic growth of host countries are

significantly related to inward FDI. Bevan and Estrin (2004) find that the disparity of market

size between home and host countries is a powerful explanation for FDI flows from

developed western to central and eastern Europe. They argue that market size of home

country is a surrogate for product demand and production capacity, therefore market size of

the home country is likely to positively alter FDI flows. Moreover, Egger and Pfaffermayr

(2004) suggest that market size could serve as an indicator of capital abundance. Countries

with abundant capital are expected to engage in more outward FDI activities. This is

supported by Kimino et al. (2007) who argue that countries with larger market size have

greater capacity to conduct foreign production, with greater amount of capital reserve and

intangible assets such as marketing experience and technologies. On the other hand, it is

argued that firms from a large home country could raise capital for foreign investment more

easily. Because there are larger amount of firms seek to expand into global market on larger

scale (Pan, 2003). Besides, some prior studies suggest that home country size is positively

related to outward FDI (Tallman, 1988; Grosse and Trevino, 1996). On the contrary, based

Dunning's eclectic paradigm, countries with relatively small market size tend to conduct

market-seeking FDI. Therefore, the hypotheses with regard to market are formed as:

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H1a: The market size of home country and FDI outflows are positively related.

H1b: The market size of home country and FDI outflows are negative related.

2.3 Labor cost

As labor cost accounts for a considerable portion of production costs, it is very important for

investment location decision. Most of prior studies conclude that labor cost in host country is

negatively related to FDI from developed to developing countries. For instance, with data of

FDI flows from EU to CEEC economics, Janicki and Wunnava (2004) find that countries with

relatively low labor cost are more attractive for foreign investors as international firms tend to

relocate production to where human capitals are available at lower wages level, especially for

firms that engage in labor-intensive production. Likewise, Bevan and Estrin (2004) also state

that high labor cost of host country has negative impacts on its inward FDI using FDI data

from western Europe to central and eastern Europe. On the other hand, based on data from

Germany and United Kingdom, Hatzius (2000) find that high unit labor cost of home country

drives FDI outflows to grow. Similarly, Dunning’s paradigm suggests that firms engage in

outward FDI for cost-reduction purpose.

Because of the economic and educational development, labor cost in China has experienced a

continuous increase. BRICS countries may share the same experience in this case. Moreover,

foreign outsourcing is associated with increase in the wages paid to skilled employees in the

United States, Japan, Hong Kong (China) and Mexico (Feenstra and Hanson, 2001).

Therefore, the changes of labor cost of home country could be driver for outward FDI. Thus,

H2: The labor cost of home country and FDI outflows are positively related.

2.4 Exchange rate

Exchange rate volatility could generate both challenges and chances for multinational

enterprises, forcing them to manage the potential risks. From previous research, results about

effect of exchange rate uncertainty on FDI flows are mixed. Cushman (1985) examine the

impacts of exchange rate risk on FDI and find that devaluation of home currency encourage

inward FDI in order to lower capital cost, but such effect may be offset by varied costs of

other inputs. Thus, no significant impacts on FDI as results. On the other hand, appreciation

of home currency lead to reduced capital costs which enable multinational enterprises

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associate with foreign investments more easily (Benassy-Quere et al., 2001). Moreover, There

are also some theoretical models declare that exchange rate volatility will inhibit foreign

investments. Because erratic variation of exchange rate will cause fluctuations in production

costs and revenues of foreign affairs (Russ, 2007). Campa (1993) also argue that volatility of

home currency will deter inward FDI.

Meanwhile, some argue that there is positive relationship between exchange rate volatility and

FDI flows. Itagaki (1981) posits that the profits of a foreign affiliate will increase when home

currency depreciates and therefore international firms tend to invest abroad under the

exposure of exchange rate risk. Additionally, Sung and Lapan (2000) suggest that

multinational firm tend to strengthen its production flexibility by transferring production to

the countries where costs of inputs are lowest resulted from reduced value of local currency.

Thus, exchange rate volatility stimulate FDI flows. Moreover, short-term volatility will drive

risk-averse investors to conduct outward FDI to reduce risks (Goldberg and Kolstad, 1995).

Overall, empirical studies suggest that FDI activities response to the exchange rate volatility.

As results from previous are mixed, three hypotheses are made:

H3a: The exchange rate volatility of home country and FDI outflows are positively related.

H3b: The exchange rate volatility of home country and FDI outflows are negatively related.

H3c: There is no significant impacts of exchange rate volatility of home country on FDI

outflows.

2.5 Inflation rate

Uncertainty of the economic environment may discourage FDI flows to developing countries.

Empirical literature employ plenty of variables to explain such effect, e.g., volatility of

inflation and exchange rate (Jun and Singh,1996; Resmini, 2000). High inflation would lead

to price volatility which could reduce expected return on investment, and reflect host

government failures to conduct proper expansionary macroeconomic policies (Okafor, 2015).

Likewise, Udoh and Egwaikhide (2008) and suggest that inflation and inward FDI are

negatively related and put such relationship down to poor-functioned macroeconomic policies

in host country, which boost the inflation rate. Hence, conversely, if inflation is high at home,

domestic investors are likely to avoid potential loss. In sum, there are limited research on the

relationship between inflation and FDI, especially with outward FDI. Thus, the two

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hypotheses are proposed as followed:

H4a: The inflation rate of home country and FDI outflows are positively related.

H4b: The inflation rate of home country and FDI outflows are negatively related.

2.6 Interest rate

If rise in exchange rate or interest rate volatility derive from home country, such changes will

attract inward FDI. Vice versa, if changes of interest rate originate in the host country, foreign

investment would be discouraged (Russ, 2007). Empirical research suggest that business cycle

has significant effect on FDI outflows. Cavallari and d'Addona (2013) view interest rate as

one of the drivers of outward FDI based on two mechanisms: imperfect capital market and

sunk cost of entry. As for the former one, there is an positive income effect during cyclical

expansion period. Increasing prices raise the net value of the company and therefore promote

the financing for new investments. Sunk cost of entry, on the other hand, influence the entry

decision at the first place. Both models advocate that interest rate do matter for the FDI

decisions. As results, Cavallari and d'Addona (2013) find that interest rate mainly affects the

amount of FDI. Moreover, Yeyati et al. (2007) also suggest that interest rate cycle of home

country is a crucial determinant of FDI flows. To be specific, interest rate and outward FDI

are negatively related in US and Europe. Cuts in interest rate of home country reduce

financial costs when financing mainly at home, which also contribute to FDI outflows. This is

also supported by Kyrkilis and Pantelidis (2003) that domestic investors can gather capital for

overseas investments more easily with decreased opportunity cost of capital. In a similar vein,

Cushman (1985) also find that international firms from countries have cost advantage over

local competitors with competitively low interest rates. Thus, the hypotheses are developed

as :

H5a: The interest rate of home country and FDI outflows are negatively related.

H5b: The interest rate of home country and FDI outflows are positively related.

2.7 Political risk

Political risk is a popular topic in international business literature. It could be viewed as the

probability of occurrence of political events that will influence expected profitability of

investment activities or governmental interference with business operations (Kobrin, 1979). In

other words, political risk could alter investors investment decisions in order to reduce risks.

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According to Hatzius (2000), the average level of FDI barriers, which relate to the costs of

producing overseas, is determined by policy. By studying FDI flows to central and eastern

Europe, Benacek et al. (2000) state that political risks may have power on the distribution of

investment across countries and on the investment location decisions. FDI is affected by

political risk along with volatility of future cash flow.

For host countries, previous studies generally suggest that a less risky investment environment

could contribute to the ability to attract inward FDI and political instability reduce inward FDI

significantly (Schneider and Frey, 1985; Janicki and Wunnava, 2004). However, Pan (2003)

find the reversed result in the case of China. The explanation is that foreign investors have to

acquire more equity in order to take control of the venture during risky periods. Interestingly,

Méon and Sekkat (2012) find that inward FDI are negatively related to political risks, but

become less sensitive to political instability when the global amount of FDI flows are larger.

Therefore, the effects of political risks on FDI could be neglected or even come along with

greater volume of inward FDI. For home country, the extent to which political risks influence

FDI flows rests primarily on the attitude towards risk and the political characteristics of home

country (Kimino et al., 2007). Further, Tallman (1988) states that a poor investment climate

with higher political risks of the home country would encourage outward FDI to the relatively

stable countries. As there is no mainstream suggestions, two hypotheses are formed:

H6a: The political risk of home country and FDI outflows are positively related.

H6b: The political risk of home country and FDI outflows are negatively related.

2.8 Corruption

A corrupt government does not provide impartial market competition opportunities to all the

players. Payers engage in bribery for purposes like reducing transaction costs or getting

priorities and advantages for competition. And political interference could change competition

on illiberal market with power of local bureaucracy (Benáček, et al., 2014). Nonetheless,

according to Bardhan (1997) and Aidt (2003), corruption could serve as either a grabbing

hand or a helping hand for inward FDI.

On one hand, bribery is costly and therefore firms are less willing to conduct business in

countries with high level of corruption. Egger and Pfaffermayr (2004) find a negative

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relationship between corruption of host countries and inward FDI. Further, they argue that

corruption significantly affects OECD FDI but not non-OECD FDI. For extra-OECD

countries, the growth of FDI is mainly induced by economic growth rather than corruption.

On the other hand, corruption can offset negative effects of government failures under certain

circumstances. For instance, Lui (1985) argues that firms placing high value on time are more

likely to pay bribes to accelerate business process. In this way, corruption could further

allocation efficiency. Moreover, Beck and Maker (1986) find that the most productive

companies are the ones offer most brides in bidding competition.

As it is well-known, corruption is a serious issue for BRICS countries. According to

Transparency International’s Corruption Perception Index 2016, Brazil and India rank 76th,

Russia ranks 119th, China ranks 83th, south Africa ranks 61th, among 168 countries.

Corruption would make room for flexibility and improve competitiveness of the market and

limit opportunities for productivity (Ngunjiri, 2010). Therefore, corruption problems of home

country may force domestic investors choose overseas investment. However, as mentioned

above, corruption could also serve as a helping hand. Therefore,

H7a: Corruption in home country and FDI outflows are positively related.

H7b: Corruption in home country and FDI outflows are negatively related.

2.9 Openness

In general, empirical studies show a positive correlation between the level of openness and

FDI inflows. International orientation as a measure of openness is an crucial determinant to

FDI and could contribute to national competitiveness (Habib and Zurawicki, 2002). The more

open a country is to international trade, the more appealing it is for inward FDI (Chakrabarti,

2001). Rodriguez and Pallas (2008) also argue that liberal trade regime of host countries can

encourage inward FDI with given internalization advantages for investing firms. In a similar

vein, the ease of entry plays an important role and trade barriers would inhibit FDI (Culem,

1988). Higher level of openness could help domestic firms conduct foreign investments more

easily. As openness regarding to both import and export, it is expected to be positive related

to FDI outflows as inflows. Thus, a hypothesis is formed as:

H8: Trade openness of home country and FDI outflows are positively related.

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2.10 Technology

While FDI inflows can bring benefits like advanced technology and innovative management

to the host country (Kimino et al., 2007), international firms may also undertake outward FDI

for improving their competitiveness in technology and innovation, especially for firms in

developing countries. For example, there is evidence that Chinese multinational firms

internationalized through M&As in order to acquire foreign technology (Buckley, et al., 2007).

Because Chinese firms that following an mimic strategy and producing low-end products are

stuck in a standstill period. And such situation at home will drive firms to conduct FDI

activities abroad (Deng, 2009). Furthermore, as Deng (2004) argued, Chinese firms prefer to

acquire an existing foreign company than start a new business abroad. In this way, they may

transfer superior technologies back to home country and enhance their competitiveness in a

relatively short time. Therefore, it is reasonable to predict that countries with relatively low

level of technological development are more motivated to undertake outward FDI. Hence,

H9: There is a negative relationship between technology development in home country and

FDI outflows.

3. Data and Methodology

3.1 Dependent and independent variables

FDI outflows is measured by capital received, either directly or indirectly, by a foreign direct

investor from a FDI enterprise. The interpretation of the results is then discussed in relation to

the independent variables as followed.

With respect to market size, GDP per capita is used to capture its effects. To determined labor

costs, a Relative Unit Labor Costs Index is used, which takes the year of 2008 as standard.

Unit labor costs, calculated as the ratio of total labour costs to real output, measure the

average cost of labour per unit of output. Annual interest rate of government securities is

indicator of interest rate in this paper. As for political risk, a indicator for government

effectiveness which comprises the quality of public and civil service, and the degree of its

independence from political stress, the quality of policy formation and application, and the

integrity of the government's commitment to such policies. The higher score means lower

risks. The perceived levels of public sector corruption is ruled by Corruption Perceptions

Index (CPI) based on expert opinions. In particular, CPI, published by Transparency

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International annually, currently ranks 168 countries on a scale from 100 (very clean) to 0

(highly corrupt). Regarding to openness, it is measured by the ratio of merchandise trade

(imports and exports) of GDP. The sum of patent applications from both nonresidents and

residents is employed as indicator for technology development.

3.2 Data collection

The data comprise a panel of 10 investing countries with 183 observations for the period 1996

to 2014. The sample home countries and time period are mainly decided by the extent to

which collected information is capable to develop persistent measures of the selected

variables over time. For the research, 10 countries are selected, including 5 developing

countries (Brazil, Russia, India, China and South Africa) and 5 developed countries (Australia,

Germany, Japan, United Kingdom and United States). All these sample countries play an

important role for global FDI flows. As mentioned above, most empirical studies focus on

host-country determinants of FDI from developed to developing countries. Therefore, analysis

based on these sample countries may provide a preliminary understanding of the relationship

between home-country determinants and outward FDI. Further, it is worth to study whether

the drivers of FDI outflows are the same between developed and emerging economies.

DataStream is used to collect data. Details and sources of data are described in table 1.

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Table 1. List of variables and sources of data

Variables Proxy for Source

OFDI Outward foreign direct investment, flows United Nation Conference on Trade and Development (UNCTAD)

MS GDP per capita (US dollar) World Bank Development Indicator

LABOR Relative unit labor cost Oxford Economics

ER Exchange rate (home currency per US dollar) WM/Reuters

INR Interest rate Oxford Economics

INF Inflation rate International Monetary Fund (IMF)

POR Political risk index Oxford Economics

CORR Corruption Perceptions Index Transparency International

OPEN Openness World Trade Organization

TE Technology World Intellectual Property Organization (WIPO)

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3.3 Panel data regression model with country fixed effect

As Kimino et al. (2007) argued, the application of panel estimation with fixed effect could

control the heterogeneity of individual country and therefore reduce the probability of

misspecification and yield more precise delivery of end results. For double confirm, a

Redundant Fixed Effects Tests is undertaken. The results showed in Appendix A suggest that

for this model, cross-section fixed effects are statistically significant and needed to be

controlled, while period fixed effects are insignificant. Moreover, a Hausman Test is

employed to determine whether a random effects model is more appropriate. As shown in

Appendix B, the p-value of the test is 0.051 implying that the fixed effects model is to be

preferred.

Hence, based on Kimino et al. (2007) and Buckley, et al. (2007), a model is created for the

research:

OFDIit = α + β1MSit + β2LABORit + β3ERit + β4INRit + β5INFit + β6PORit + β7CORRit +

β8OPENit + β9TEit + uit

where i = subscript for each individual country, t = subscript for time, α is a constant, β1,2,…,10

are the coefficients for the independent variables. The error term uit accounts for any

unobserved individual home-country effect that is implicitly included in the regression. The

regressions analyses are conducted with the use of EVIEWS 9.5.

In many cases, time series appear to be non-stationary, that means, the values of time series

do not fluctuate with a constant variance or with a constant mean. Such data may lead to

spurious regressions, which could be identified by high R2 and high residual autocorrelation.

In order to avoid spurious regressions and results, I transform data for OFDI, MS, LABOR,

TE into growth rates. In this way, the data are “smoothed” and scaled down, thereby,

accountability of the regression results would be improved.

4. Results and analysis

This part consist of four sections. First, summary statistics would be introduced, followed by

the correlations test results as the second section. In the third section, regressions results of a

panel data model with fixed effect would be presented. Except for regression based on the

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whole data set, for comparison, results of two different groups: BRICS and developed

countries would also be presented. Fourth, a number of robustness tests would be conducted

by testing the model one by one country. Discussions are along with the corresponding results.

4.1 Descriptive statistics

Table 2 presents the summary statistics based on 10 countries within the period 1996-2014.

Regarding to the dependent variable OFDI, the mean of 0.747 is comparatively close to the

median -2.145, which means the data is resilient against extreme values and the distribution is

normal. With respect to the independent variables, it is reasonable that difference between the

mean and median of ER is large as exchange rate are based on local currency per US dollar.

Besides, except for CORR, all factors has similar mean and median respectively.

To be more specific, descriptive statistics of dependent variable OFDI are also showed on

Table 3. Generally, all countries have experienced considerable fluctuations of FDI outflows.

Among all countries, Brazil has largest volatility of FDI outflows with a minimum of -

190.674 and a maximum of 189.487, while China, India and Russia have positive noticeably

positive mean and median. Similar to Brazil, outward FDI of South Africa has fluctuated a lot

with negative mean (-5.867) and median (-31.093). Japan is the only country with both

positive mean and median among the five developed country. Meanwhile, Australia have

fluctuated from -155.910 to 155.510. UK also has negative mean and median growth rate as

Australia, but at a smaller scale. Both Germany and the US have positive mean but negative

median. Taking the mean of every country as reference, FDI outflows from Russia increased

at most while the ones from Brazil decreased at best. Furthermore, the countries listed on top

20 FDI outflows in 2015, namely the US, Japan, China, Germany, Russia, all have positive

mean growth.

4.2 Correlations

According to Brooks (2014), high correlations between independent variable could lead to

multicollinearity issue. Therefore, the correlation matrix showed on table 3 is used to present

a rudimentary check for multicollinearity. All correlations of 0.5 or higher would be

considered whether they should be dropped out of the model. There are four correlations are

higher than 0.5, namely between inflation rate and interest rate (0.576), between inflation and

political risks (-0.613), between inflation and corruption (-0.618), and between political risks

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and corruption (0.679). Determination on keeping which variables would be based on results

of a Variance Inflation Factor (VIF) test. As could be seen in the Appendix C, the VIF of all

variables are far below a threshold of 10 with a highest VIF score of 1.630 for political risks.

Therefore, no independent variables would be excluded in the regression.

Table 2. Descriptive statistics of the full sample for the period 1996 to 2014

Mean Minimum Median Maximum Std. Dev. Observations

OFDI 0.747 -190.674 -2.145 189.487 71.958 183

MS 2.759 -7.849 2.081 13.600 3.265 183

LABOR 0.576 -38.624 0.766 28.133 8.405 183

ER 19.952 0.503 2.422 133.775 33.344 183

INR 7.031 0.550 5.370 36.400 5.924 183

INF 3.863 -1.408 2.826 15.757 3.462 183

POR 5.032 3.449 5.288 6.447 1.006 183

CORR 58.032 21.000 65.700 88.600 22.611 183

OPEN 36.061 12.293 35.953 72.625 14.656 183

TE 4.998 -85.590 3.462 70.063 14.966 183

Table 3. Descriptive statistics of OFDI of individual country for the period 1996 to 2014

Country Mean Minimum Median Maximum Std. Dev. Observations

Brazil -41.396 -149.295 -74.338 189.487 90.581 19

China 18.274 -63.424 14.165 123.012 51.043 19

India 15.446 -80.217 15.971 171.639 65.361 19

Russia 40.884 -41.559 25.979 174.126 66.044 12

South Africa -5.867 -136.744 -31.093 139.429 93.358 19

Australia -25.344 -155.910 -46.018 155.510 88.066 19

Germany 6.778 -70.602 -17.728 163.233 64.974 19

Japan 13.370 -41.651 9.796 91.242 33.723 19

UK -7.841 -190.674 -8.424 99.421 77.440 19

US 7.958 -94.788 -3.516 127.986 46.641 19

Note: Data of OFDI are transformed into annual growth rate.

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Table 4. Correlations among variables

OFDI MS LABOR ER INR INF POR CORR OPEN TE

OFDI 1.000

MS 0.209*** 1

LABOR 0.125* 0.330*** 1

ER 0.108 -0.057 -0.144* 1

INR -0.082 -0.098 -0.039 -0.254*** 1

INF -0.069 0.139* 0.019 -0.124* 0.576*** 1

POR -0.035 -0.442*** -0.193*** -0.094 -0.483*** -0.613*** 1

CORR -0.081 -0.461*** -0.195*** -0.096 -0.452*** -0.618*** 0.679 1

OPEN 0.106 0.205*** 0.081 -0.299*** -0.292*** 0.023 0.049 0.038 1

TE 0.095 0.350*** 0.098 -0.079 0.061 -0.011 -0.239*** -0.251*** -0.013 1

*** Indicates significance at the 1% level; **Indicates significance at the 5% level; * Indicates significance at the 10% level

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4.3 Regression results and analysis

Table 4 shows that there are four significant variables for FDI outflows, namely market size,

interest rate, inflation rate and trade openness based on regressions on the full sample. The

same estimations are also undertaken for two country group: BRICS and 5 developed

countries. Corresponding results are included in table 4. The R2 of these three regressions are

0.218, 0.331, and 0.172, respectively. The two country groups show striking different results.

4.3.1 Market size

Hypothesis 1a predicts a positive relationship between market size and outward FDI. It is

proved by the results that the variable market size is statistically significant at the 10% level

with a positive coefficient of 4.638. This indicates that home countries with larger market size

are more likely to conduct outward FDI. Further, it also implies that economic growth could

be a driver force for increasing outward FDI. The results are corresponding with the findings

of previous studies (Bevan and Estrin, 2004). The underlying reason could be that countries

with larger market size have greater capital abundance to engage in overseas investment

activities (Egger and Pfaffermayr, 2004).

As the market size is measured by GDP per capita, which is also an indicator of income level.

It is common that developed countries have relatively mature domestic market with less

opportunities for business expansion. Hence, domestic investors in these countries may have

incentives to expand business in foreign market by FDI. As aforementioned, BRICS countries

have become major investors among the world in recent with relatively low GDP per capita

level. However, the overall results derived from 10 sample countries do not provide

explanations for this situation. Surprisingly, results from two country groups show that market

size is significant neither for BRICS and the five developed countries. Therefore, the overall

results may be driven by several individual sample countries. Additionally, according to

Dunning's eclectic paradigm (1980), countries with relatively small market size have more

intentions to engage in outward FDI for market-seeking purpose, which is contrary to results.

Thus, the insignificant results of two country groups may due to offset effects of combined

reasons.

4.3.2 Interest rate

The results with respect to hypothesis 5b confirm that interest rate serve as a driver of

18

outward FDI, with a coefficient of 5.735 significant at 1% level. Interestingly, interest rate

significantly influence BRICS countries significant at 1% level with a beta of 5.710, but is

insignificant for the five developed countries. This is could be explained by the findings of

Cavallari and d'Addona (2013) that competitively high interest rate has positive income effect

on the net value of the firm and therefore financing for new investments becomes more easier.

However, such arguments are based on the cyclical expansion period. In the past decade,

BRICS countries have experienced great economic progress, while developed countries have

relatively lower growth rate as shown. Thus, interest rate has more explanatory power for the

changes of FDI outflows from BRICS. Another reason could be volatility of interest rate

rather than interest rate itself affects the decisions of engaging in overseas investment

activities (Sung and Lapan, 2000). As shown on Appendix D, exchange rate volatilities of

BRICS are larger than the ones of the developed countries. Besides, the absolute interest rates

of BRCIS are also higher. An imperfect capital market could create uncertainty which leads to

interest rate volatility. When comparing to BRICS, the five developed countries have

relatively complete and integrated capital market. Therefore, it is expected that there are

higher uncertainty of interest rate changes in BRICS markets.

4.3.3 Inflation rate

The results regarding to hypothesis 4b suggest a negative relationship between inflation rate

of home country and FDI outflows. Many previous studies argue that high inflation rate of

host country would discourage inward FDI as price uncertainty may reduce expected return

on investments (Udoh and Egwaikhide, 2008). For home country, high inflation rate implies

failure of macroeconomic policies and would reduce the rate of investment as the market

becomes unstable. However, results from two different country groups are similar to interest

rate that inflation significantly affects BRICS, but not the developed countries. As shown on

Appendix E, BRICS have experienced more fluctuations of inflation rate compared to the five

developed countries. In a similar vein, volatility of inflation rate derived from an imperfect

capital market would deter domestic investors. Furthermore, the value of home currency may

depreciate due to high inflation, which reduces capital abundance and makes financing for

investments more costly.

4.3.4 Political risks

As higher rating means lower political risks in this paper, the results of the full sample

19

confirm that lower political risks relate to more FDI outflows as hypothesis 6b proposed. This

finding add new evidence to current studies as most of them state that political risks of host

countries would either attract or deter foreign investors (Janicki and Wunnava, 2004; Pan,

2003). However, such negative relation between political risks and outward FDI is contrary to

the argument of Tallman (1988) that high political risks would push domestic investors to

invest overseas. Therefore, the underlying reasons may lie with other aspects related to

political risks. One possible explanation as Kimino et al. ( 2007) argued, the extent to which

political risks affect FDI flows primarily determined by the investors’ attitude towards risk

and the political characteristics of home country. On the other hand, political risks are not

significant for the two sub-sample groups.

4.3.5 Openness

Hypothesis 8 suggests a positive relationship between openness of home country and outward

FDI. With a coefficient of 1.878 at 5% level, the independent variable openness significantly

influence FDI outflows. Basically, this is matched with the previous literature findings

(Rodriguez and Pallas, 2008). As openness is measured by the ratio of merchandise trade

(imports and exports) of GDP, higher level of openness provides greater opportunities for

both inward and outward FDI. Besides, opening of an economy could contribute to economic

growth, which is positively related to FDI outflows as mentioned above. Likewise, trade

openness could narrow the income differences between poor and rich countries by diffusion

of knowledge and technology, as results, contribute to greater national productivity and

economy (Apergis and Cooray, 2015). Hence, high level of openness also has indirect

influence on the dependent variable. Apergis and Cooray (2015) also find that countries at

similar development stage tend to have same patterns for both inward and outward FDI

changes. Nonetheless, regression results from two country groups show distinct outcomes

again. While openness is one of the significant drivers for outward FDI from BRICS (β =

3.191, p-value = 0.018), FDI outflows from developed countries do not seem to be affected by

openness.

4.3.5 Further discussions

From the full sample results, except for the significant results discussed above, the other

variables are not statistically significant for FDI outflows. However, it is interesting to notice

that for BRICS countries, exchange rate and corruption significantly influence outward FDI,

20

with a coefficient of -5.129 and -5.097 significant at a 10% level respectively.

Consistent with Itagaki (1981) who posits that devaluation of home currency would be

beneficial for foreign investment returns. In addition, exchange rate volatility of home

countries would drive domestic investor to conduct foreign investments in order to hedge

exchange rate risks. On the other hand, developed countries have experienced less exchange

rate volatility in the past decades as Appendix F shows. Therefore, exchange rate has limited

explanatory power for FDI outflows from developed economies. Furthermore, Cushman

(1985) suggests that appreciation of home currency may lead to lower capital cost of foreign

investment, but increase costs of other input. As results, exchange rate has no significant

impacts on outward FDI. Such argument may help to explain the overall results with regard to

the hypothesis 3c.

As for corruption, higher CPI score indicates less corruption in this study. Bardhan (1997) and

Aidt (2003) suggest that corruption could serve as a grabbing hand or a helping hand for

inward FDI. In the case of BRISC, the result indicate that higher corruption would drive

outward FDI increase. Hypothesis 7a is therefore confirmed with respect to the findings of

Ngunjiri (2010). As it is well known, BRICS have serious problems with regard to corruption

issue. Although their government made great efforts on improving this situation. Corruption

level of these countries is still rather high compared to developed countries, which could be

seen on Appendix G.

The overall results show that labor costs and technology are insignificant for the selected

sample countries. Buckley et al. (2007) argue that bilateral FDI are associated with the

cultural and geographic proximity, which are not taken under consideration in this paper.

Therefore, the regression results could be biased using data of the 10 selected countries,

which have large cultural or geographical differences with each other. For example, most

current literature about labor cost based on studies within European countries (Hatzius, 2000;

Janicki and Wunnava , 2004; Bevan and Estrin, 2004). Moreover, determinants of inward FDI

are not necessary the ones for outward FDI. These could explain why the results show

unexpected outcomes.

21

Table 5. Regression results

Note: The full sample consists of 183 observations from ten selected countries for the period

1996-2014. Sub-sample 1 contains data 88 observations from Brazil, China, India, Russia,

and South Africa. Sub-sample 1 contains 95 observations from Australia, Germany, Japan,

United Kingdom, and United States.

OFDI

Full sample Sub-sample 1 Sub-sample 2

Constant -262.030 53.230 -416.086

(172.005) (242.117) (265.957)

MS 4.638* 2.619 4.868

(2.494) (3.790) (4.303)

LABOR 0.972 0.879 1.406

(0.678) (1.028) (1.056)

ER -0.230 -5.129* 0.437

(0.978) (2.676) (1.090)

INR 5.735*** 5.710*** 8.253

(1.842) (2.073) (7.642)

INF -7.835*** -9.789*** 2.754

(2.763) (3.166) (8.124)

POR 34.787* -22.520 53.646

(32.399) (56.944) (42.759)

CORR -2.727 -5.097* 1.437

(1.710) (2.632) (2.546)

OPEN 1.878** 3.191*** 1.256

(0.873) (1.316) (1.614)

TE 0.236 0.119 -0.288

(0.374) (0.410) (1.522)

R-Squared 0.218 0.331 0.172

Adjusted R-Squared 0.132 0.213 0.051

Observations 183 88 95

Country fixed effects YES YES YES

Time fixed effects NO NO NO

*** Indicates significance at the 1% level

22

** Indicates significance at the 5% level

* Indicates significance at the 10% level

4.4 Robustness test

In order to study whether there is a common pattern for both developing and developed

countries to conduct outward FDI, this paper examines the selected variables based on data

from 10 countries, which are the main actors of global FDI activities. For further analysis, I

separate the countries into two groups. And the results of developed country group are

strikingly different from the primary results of all sample countries. Therefore, in this part, I

would do robustness check by using the same estimation for every sample countries

individually. By this way, I try to find out whether the overall results stay robust for

individual country.

Table 5 shows the combined regression results for the 10 sample countries. With respect to

Brazil, inflation rate is negatively related to outward FDI with a beta of -14.291 significant at

a 10% level. Besides, the variable openness has a coefficient of 23.360 significant at a 10%

level. Both results are consistent with the results of the whole data set and the BRICS country

group. Additionally, exchange rate have significantly negative impacts on FDI outflows from

Brazil with a beta of -137.518 significant 10% level. When it comes to India, labor cost has

significant influence with a coefficient of -6.020 significant at 10% level. Besides, inflation

rate has a negative relation with outward FDI with a beta of -13.903 significant at 5% level.

Similar to India, labor cost are the only significant variable with a coefficient of -6.601

significant at 10% level in China. Conversely, labor cost is positively related to FDI outflows

from UK, consistent with the empirical findings of Hatzius (2000). Inflation and political risks

are also positively related to FDI flows from UK. As for South Africa, only corruption has

significant influence on FDI. While interest rate is the only significant determinant for

Australia, market size is the only one for Germany. Regarding to Japan, there are three

significant variables, namely inflation rate (β = -21.152, p-value = 0.022), openness (β =

10.698, p-value = 0.001), and technology (β = 8.789, p-value = 0.004). Lastly, political risks,

corruption and technology have significant impacts on outward FDI from the US. In sum,

every sample country have different drivers for outward FDI. This provides evidences that

home-country determinants of outward FDI may based on region or country characteristics.

Table 6. Robustness check for individual sample country.

23

OFDI

Brazil China India Russia South Africa

Constant -1392.472 -207.903 -1051.006 -734.616 -1684.797

(832.829) (456.013) (930.750) (1328.642) (1893.230)

MS -16.891 3.502 4.188 25.379 -15.265

(14.560) (9.984) (7.208) (19.864) (20.490)

LABOR 3.005 -6.601* -6.020* 0.510 2.645

(2.081) (4.293) (3.112) (18.187) (2.997)

ER -137.518* -0.463 1.838 48.722 -15.599

(64.651) (19.287) (3.558) (23.244) (15.725)

INR 5.073 14.494 12.237 -108.028 11.187

(3.446) (15.003) (8.462) (95.095) (21.211)

INF -14.291* -3.100 -13.903** 56.645 -29.398

(7.174) (6.526) (5.292) (29.494) (17.194)

POR 309.522 7.915 255.575 -276.047 234.420

(232.723) (93.119) (207.163) (457.161) (369.701)

CORR -1.021 7.499 -6.870 24.779 -21.558*

(10.237) (5.850) (8.376) (9.534) (11.596)

OPEN 23.360* 3.319 -1.854 -40.028 15.984

(12.529) (2.019) (1.996) (36.472) (9.215)

TE -0.177 0.376 0.545 -1.823 -0.155

(1.445) (0.709) (0.554) (4.437) (1.065)

R-Squared 0.555 0.699 0.668 0.698 0.371

Adjusted R-

Squared 0.110 0.459 0.402 0.106 0.132

Observations 19 19 19 12 19

24

Table 6 (continued)

Note: As exchange rate is measured by home currency per US dollar and data were collected

from the same database, data for US are not available.

OFDI

Australia Germany Japan UK US

Constant 730.545 -668.431 141.609 -2854.215** -898.3109

(1357.439) (764.870) (288.874) (1247.026) (1033.377)

MS -11.603 14.566* -6.879 -6.242 -8.337

(24.392) (8.024) (3.798) (12.522) (62.474)

LABOR 4.117 1.937 0.632 5.162* -0.184

(3.984) (4.946) (0.696) (2.419) (21.153)

ER -38.109 -259.561 0.372 133.378 NA

(86.059) (167.137) (0.448) (309.197) NA

INR 47.281* -7.921 17.702 -25.429 160.585

(25.318) (24.809) (12.434) (17.011) (155.492)

INF 36.006 -51.186 -21.152** 102.507*** 35.625

(27.767) (36.171) (7.827) (26.941) (159.239)

POR -74.838 167.105 -77.152 450.819* 448.492***

(204.456) (108.771) (51.792) (196.184) (117.930)

CORR 6.271 -1.737 0.026 8.525 -21.641**

(14.847) (13.212) (1.815) (6.458) (8.884)

OPEN -17.085 -0.267 10.698*** -14.658 10.093

(15.889) (4.280) (2.231) (8.502) (71.118)

TE -5.541 -3.098 8.789*** 3.364 -4.462*

(3.650) (6.202) (2.366) (3.621) (2.051)

R-Squared 0.507 0.651 0.768 0.765 0.717

Adjusted R-

Squared 0.113 0.371 0.583 0.577 0.491

Observations 19 19 19 19 19

*** Indicates significance at the 1% level

** Indicates significance at the 5% level

* Indicates significance at the 10% level

25

5. Conclusion

Due to globalization, the world economy have become more integrated. Developing countries

gain more and more attentions as the main actors. Whether the rules are applicable all the

players is needed to be studied. Although a lot of research have been done on the FDI topic,

especially on inward FDI, it is still unclear what are motivations for conducting outward FDI.

The goal of this paper is therefore provide insights into the home-country determinants of FDI

outflows with a focus on comparing emerging and developed countries. An empirical analysis

of outward FDI from 10 countries for the period 1996 to 2014 is undertaken, where five are

developing countries and the other five are developed for comparison. Besides, a panel data

model with country fixed effects is applied.

When the entire sample is concerned, the market size of home country have significant and

positive impacts on FDI outflows, thereby confirming prior studies on the subject. However,

when the same estimators were used for the two country groups. Market size is insignificant

for neither of them. With respect to interest rate, it is significantly and positively related to

outward FDI. Particularly, it is persuasive that both absolute level and the volatility of interest

rate affect the outward FDI, as different regression results were gained from the two country

group. Regarding to inflation rate, it follows similar pattern as interest rate but with

significantly negative effects on FDI outflows. Both interest rate and inflation rate are related

to cost-reduction purpose for engaging in FDI with regard to Dunning's eclectic paradigm.

Unexpectedly, lower political risks relate to more FDI outflows. Finally, trade openness of

home country is beneficial to both inward and outward FDI combining the findings of

previous research. Moreover, exchange rate and corruption are only significant for BRICS,

but not for the full sample. Unexpectedly, labor cost and technology do not have significant

influences on the dependent variable. As most of existing literature focus on single country or

at least countries within the same region, how relevant the findings of this paper to other

countries is needed to be further examined. Last but not least, robustness checks are

conducted for every sample country individually and the results intimate that there is no

common home-country determinants for all countries within the scope of selected

independent variables and limited data.

Though for policy makers, determinants of inward FDI are more noteworthy. Studies on

outward FDI provide guidance that is useful for domestic investors to make foreign

26

investments decisions. This paper contributes to the existing literature on determinants of FDI.

The conflicting evidence found in the prior studies and in this paper might be caused by

different sample countries or different approach. Besides, significant determinants of inward

FDI are not necessary the ones for outward FDI. Even though the results are not robust to

different samples, this study could provide preliminary evidence for what are the important

drivers for FDI within the home country and how could they differ from country to country.

As for limitations, first, this paper only use data of 10 sample countries, which are rather

small. In order to find out whether there are common home-country determinants of outward

FDI for developing and developed countries, larger sample including more countries is

necessary. Second, although BRICS represent the largest emerging economies, there are

enormous differences within these countries in various aspects. Thus, empirical results from

study based on BRICS are applicable for other developing countries are open to be questioned.

In a similar vein, the five developed countries also share the same issue as BRICS. All these

sample countries have remarkable differences with regard to, for instance, political, cultural

and geographical aspects. Third, this paper suffered from data availability with a relatively

short time period from 1996 to 2014. The reversed effects of FDI on the independent

variables such as market size may also lead to biased results. Hence, for future studies, larger

sample with considerations of these omitted factors or single country study on examination of

bilateral FDI with host countries are needed. Furthermore, sector/industry studies on FDI

outflows are also incomplete and could be interesting topics for future research.

27

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32

Appendices

Appendix A. Redundant Fixed Effects Tests

Effects Test Statistic Prob.

Cross-section F 2.299 0.019

Cross-section Chi-square 24.253 0.004

Period F 0.927 0.547

Period Chi-square 19.811 0.344

Appendix B. Correlated Random Effects – Hausman Test

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.

Cross-section random 16.864 9 0.051

Appendix C. VIF statistics

Variable Coefficient VIF

MS 6.162 1.184

LABOR 0.463 1.115

ER 0.953 1.117

INR 3.365 1.360

INF 7.919 1.306

POR 1429.857 1.630

CORR 2.925 1.565

OPEN 0.800 1.450

TE 0.140 1.048

33

Appendix D. Interest rate of 10 sample countries from 1996 to 2014

Note: The data is retrieved from Oxford Economics database.

Appendix E. Inflation rate of 10 sample countries from 1996 to 2014

Note: The data is retrieved from International Monetary Fund (IMF) database.

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

19961997

19981999

20002001

20022003

20042005

20062007

20082009

20102011

20122013

2014

Interest Rate (%)

Brazil China India Russia South Africa

Australia Germany Japan UK US

-10.0

0.0

10.0

20.0

30.0

40.0

50.0

60.0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Inflation rate (%)

Brazil China India Russia South Africa

Australia Germany Japan UK US

34

Appendix F. Exchange rate fluctuations of 10 sample countries from 1996 to 2014

Note: Home currency: Brazilian real (Brazil); Chinese Yuan (China); Indian Rupee (India);

Russian Ruble (Russia); Rand (South Africa); Australian Dollar (Australia); Euro (Germany);

Japanese Yen (Japan); British Pound (UK), US Dollar (the US). The data is retrieved from

WM/Reuters database.

35

Appendix G. Corruption ratings of 10 sample countries from 1996 to 2014

Note: Corruption is indexed by Corruption Perceptions Index (CPI) established by

Transparency International annually, currently ranks 168 countries on a scale from 100 (very

clean) to 0 (highly corrupt).