National Research University: Higher school of Economics

International college of Economics and Finance

Final qualification work

Do the usual FDI-promoting factors help or hinder investment during global financial crisis in developing countries?

Student

Granovskaya Anastasia Aleksandrovna

4th course

3d group

Research supervisor

Kozlov Konstantin Konstantinovich

Moscow, 2013

Introduction

Importance of cross-border investment flows have increased distinctly in the past three decades.

Particularly, developing countries have seen unprecedented levels of FDI inflows. Whereas in

the 1970s FDI flows aggregated to 1 percent of GDP, pre-crises share of GDP in developing

countries have increased up to 3 percent. Inflows of foreign investments to developing countries

have been rising during the period reaching an unprecedented level of $1979 billion in 2007,

however just like other types of economic activities FDI flows experienced dramatic changes

beginning from the end of 2008, when sharp economic crises burst out and made investors feel

lots of doubt the stability economic system as a whole and credibility of developing economies

in particular. Unprecedented magnitude of current crises has questioned whether transnational

corporations will ever enjoy such levels of propensity and capability to invest and expand in

other countries as we have seen before. However, it turned out that a period of downfall hasn’t

been far too long. It was in 2010 when investment flows started to recover and in 2011 they have

exceeded the pre-crises level of $1,5 trillions. However they are still far from the highest point

which has been recorded in 2007 and was close to $2 trillion.

It is important to note that FDI inflows are one of the major sources of growth for developing

countries. Comparing to portfolio investments FDI flows provide more persistent source of

external financing for developing economies. While portfolio investments can be easily reversed,

investments in productive assets are far more stable. It is generally accepted that foreign direct

investments not only contribute to the economic growth of the host country but also enhance

institutions, infrastructure and lead to the improvement of host country human capital. All these

reasons show the importance of examining FDI more formally and finding out which factors

attract FDI.

Beginning from the 60th of previous century, starting from Hymer and Dunning, researchers have

been questioning the factors influencing inflow of FDI. While no theory provides clear guidance

which exact bundle of factors affect foreign direct investments flows, many scientists considere

such factors as market size, past values of growth, infrastructure, and economy’s openness to be

important. However while it is more or less clear how these factors behave under normal

conditions of economic development, this is still an opened question how these factors behave in

the periods of economic turbulence. In our paper we will try to examine which factors are more

important for stimulating a growth of FDI and which are more important for preventing its

decrease, because we suspect that those factors will not necessarily coincide. A recent financial

crisis of 2008 has given a basement for such researches. Those large and sharp changes in FDI

flows have never occurred before and this gives a good field for the deeper investigation of the

problem which we highlighted above. To be exact that factors that stimulate growth of FDI can

generally not be the same as those factors that impede FDI downfalls. This question can be of

interests to policymakers as it sheds light how to counteract sharp drops of FDI.

Main Part

Foreign Direct Investment is an investment which is taken by a firm . Investing company can

make foreign direct investment abroad by several ways such as founding a company in another

country purchasing its shares or taking mergering activities as well as by giving subsidies. There

exist three main types of foreign direct investment such as Horizontal FDI, Platform FDI,

Vertical FDI. In the Horizontal FDI firms take the same activities as in the other countries. The

models of Horizontal FDI are concentrated around the trade-off between costs of trade and fixed

costs. An interesting finding from here is that companies are not able to get into the FDI and

exports at the same time. This is in contrast to the Vertical FDI when they can do it. Vertical FDI

associates with an activity of the firm when it works stage by stage in the host country. Models

of the Vertical FDI are trying to solve the problem of the location to minimize its costs. A

Platform FDI is associated with prevailing of exporting activities rather than serving the local

market.

There is quite strong empirical evidence which shows that short-term capital flows are relatively

volatile and easily reversed, as being attracted by interest rate differentials, or exchange rate

fluctuations in contrast to FDI flows (long-term flows) which are relatively stable. Therefore

there exists widespread opinion that opposed to other types of cash flows, FDI contribute to

economic growth through series of positive effects it generates, such as transfer of technology,

enhancement of human capital as well as creating more competitive business environment.

However, empirical evidence related to contribution of FDI to growth is mixed, but given some

conditions, FDI can generate a higher economic growth (Borensztein, de Gregorio and Lee

(1995)). That is why factors that affect the inflow of FDI are of great interest for policymakers.

The studies of FDI began in 60s and 70s with Hymer (1960), Vernon (1966), Kindleberger

(1969) Caves (1971) and Dunning (1981). Hymer (1960) who deviated from the most popular

neoclassical financial theory during that times, according to which capital flows were due to

interest rate differentials, focused on activities of multinational enterprises. Neoclassical

financial theory ignored existence of MNE and intra-industry trade, the great contribution of

Hymer was to focus attention on FDI as a mean by which MNE firms extend their territorial

horizons abroad. The great contribution was made by an outstanding scientists such as

Vernon(1966), Caves (1971), Kindleberger (1969), Dunning (1981) and Basi (1961) who studied

whether political instability affects FDI.

Dunning’s (1981, 1988) ‘electric theory’ which is widely known as OLI paradigm suggests

that FDI is determined by three measures of advantages which FDI should have for supplying its

products both at home and abroad over other types of investments (e.g licensing or joint

venture). First type of advantage is ownership specific advantage: advantages of this type are

usually intangible and easily transferrable into other countries, they may include names as we

know as brands, patents, technology, marketing or economies of scale. Those advantages enable

the firm to have either greater revenues or smaller costs comparing to the home producers of the

host country and to offset the costs that arise from operating in the host location (lack of

knowledge of host country market conditions, cultural, language barriers, etc). The second type

of advantages is location advantages, which are related to the foreign factors of production the

firm will use, and the choice of country which will become the host country of MNE. Location

advantages can be of three types: economic superiors (qualities and quantities of factors of

production, communication and transportation costs, size of the market), political advantages

(clarity of countries laws, level of corporate tax rates, efficiency of bureaucracy, level of

corruption, political stability etc) social and cultural advantages (language and cultural

diversities, understanding of foreign market preferences, etc). And finally internalization

advantages: the firm can choose various entry tools. The possibility for a company’s preference

to enter into the foreign production itself compared to the process of getting the license to do it ,

increases with the benefits it can get from the internalization of cross-border markets. FDI

inflows are dependent on the home economy’s features such as its placement, development of

economy and the size of the market. All the mentioned factors make it extremely more attractive

to investors from abroad. Adding the fact that FDI is a highly studied process we can mention

that lots of literature is produced on that topic.

Market size and growth potential

It is reasonable to suppose that larger host country market size should be associated with larger

FDI the reason behind is larger demand and potential economies of scale. Empirical studies

generally provide support to that supposition, for instance, Resmini(2000) who observed FDI in

manufactures, finds that countries with larger population in Central and Eastern Europe attract

more FDI comparing to those with smaller population. Jonhson (2006), Wijeweera (2010),

Bevan and Eastrin (2000) also found positive correlation between population and FDI, transition

economies with larger market size tend to attract greater FDI. As a proxy to market size

researcher also use GDP and economic growth Krugell and Naude (2007), Maniam (2007) found

a positive impact between real GDP and GDP growth and FDI inflow.

Edwards (1990) and Jaspersen (2000) used the GDP per capita as proxy for the return on capital.

They came to the conclusion that GDP per capita is inversely related to FDI/GDP. Opposing

their results were Schneider and Frey (1985) who found a positive relationship between the two

variables.

Trade and financial openness

Trade openness is regarded as other important determinant of FDI. It is reasonable to expect

greater trade openness increase amount of vertical FDI in those sectors where trade flows of

intermediate goods and capital goods are important. Lots of empirical studies confirm that

hypothesis. For example Resmini (2000) finds that vertical FDI increase in manufacturing sector

with increase in openness. Skabic and Orlic (2007)

Financial openness is another important factor helping to determine FDI. Aizenman and Noy in

2004 studied measures of financial openness. Their studies have proved that total financial and

commercial openness factors are related to each other. They divided causality and have reached

the results proving significant effects in both directions. Portes and Rey (2005), showed that

trades in goods and in assets can be explained by the same regressions. Recent significant

empirical works use financial and trade openness as important determinants of FDI flows

Jeon and Rhee (2008), Maniam (2007) find positive correlation between teconomy openness and

FDI.

Infrastructure

Physical infrastructure is an important determinant of FDI growth. Existence of infrastructure

can attract foreign direct investment by subsidizing the expenses of the investment in total.

Hence there will be a raised rate of return. The topic of physical infrastructure influencing FDI

has been covered in many studies of Loree and Guisinger (1995), and Mody and Srinivasan

(1996). Here are provided the examples of studies which consider infrastructure as a factor

attracting FDI inflows. Hoang(2008) states that GDP growth, the size of market, financial

openness and infrastructure development are the factors influencing FDI. Wheeler and Moody

proved that infrastructure is one of the most important factors among others which influences

investment. Cheng and Kwan mentioned infrastructure to be one of the important factor using

density of roads as a proxy for infrastructure measure. Coughlin(1991) found a significant

correlation between FDI in America and infrastructure. Head and Ries(1996) have found the

same dependence but for China. Cheng and Kwan(2000) just supported this statement by their

own research. Proxies such as internet and electricity costs are one of the most important proxies

for infrastructure. Lots of works have supported this view. Botric and Skuflic(2005) have used

internet as one of the proxies of infrastructure as well as Pazienza and Vecchionee in 2009 who

used the same proxies and have found correlation between internet users and FDI. Rural

electricity consumption was used as a proxy in the studies of Easterly and Rebelo. Number of

telephones is taken as a proxy of infrastructure as stated by Fan. In contrast to education and

roads electricity is the investment of stated-owned enterprises and it is not free charged.

Political stability

Political stability is one of the most important indicators that potential investors look at

according to surveys of investors conducted. This is non-surprising, as long as sunk costs of FDI

are high, investors are expected to be really sensitive to uncertainties of different type, including

political uncertainty. However, empirical results on this topic are mixed. According to Wheeler

and Mody (1992) political risk and efficiency of administration are not significant when US

firms choose production location. On the contrary, Root and Ahmed (1979), as well as Shneider

and Frey( 1985) who looked at FDI into developing countries found that political instability

really affects FDI inflows.

Fielding (2003) also showed that during the Intifada time, political instability was associated

with significant decrease in FDI inflows. Robin, Liew and Stevens (1966) showed that

importance of political risk is greater for developing markets, compared to developed ones.

Kogut and Chang (1996) also found that political instability lead to greater exchange rate

volatility and resulting decline of FDI

Institutions

Quality of institutions is likely to be a determinant of FDI, especially for less developed

countries for various reasons. First, quality of governance plays crucial role for economic

growth, which in turn attracts FDI. Moreover, poor institutions are associated with high

corruption, which increase in investment costs and leads to decline of profits. However, in reality

it is not as simple to measure how good institutions perform, therefore empirical results are

unclear. It their analysis of US firms, Wheeler and Mood (1992) did not find transparency of

judicial system excessive regulation, bureaucratic hurdles, to be significant. Whereas Wei(2000)

found that presence if corruption raises firms cost put obstacles of the way of FDI inflows. Those

two differences in results can be explained by the fact that those papers take different measure of

qualities of institutions in their analysis, as well as use different types of data (firm-level data

versus FDI aggregate flows).

Bénassy-Quéré, Coupet and Mayer (2007) further examined the role of institutions and found

that independently of level of development measured by GDP per capita, institutions, such as

corruption, bureaucracy, banking sector institutions, and legal institutions really matter for FDI

inflows, independently of level of development of the countries. Interestingly convergence of

institutions in original country and host country also enhance FDI inflows according to this

study. Onieiwu and Shrestha (2004) found opposite results concerning the role of institutions in

their study of African countries. According to their paper, in spice of institutional reforms

performed in Africa in last decades, FDI inflows are still episodical and uneven. They do not

find quality of infrastructure or legal institutions to be important for FDI inflows into Africa

Various papers differ across the countries they examine: some look at various developing

countries (for example, Shneider and Frey (1985)), other papers concentrate on some small

regional samples (Resmini). Those papers that performs their analysis on the basis of firm-level

data are almost always about single country, usually US. And in this connection question about

applicability of those results to other especially emerging markets arise. Wheeler and Mody

(1992) in their study find that FDI decisions between developed and developing countries are

different.

However literature on FDI case so far examined mainly the level of FDI flows, whereas the

volatility of FDI flows seems important as well. Why is volatility important? Recent studies

show that past volatility tends to reduce investments in future Aizenman and Marion (1996).

Serven (1998) also indicate the existence of negative effect of uncertainty. According to them

any type of uncertainty that comes from high level of volatility, adversely affects economic

variables of any type. Recent study of Lensink and Morrissey (2006) shows that rapid

fluctuations in FDI flows have negative effect on economic growth. Guillaumont and Chauvet

(2001), Choong and Liew (2009) also find volatility of FDI flows is strongly negatively

correlated with growth level. Similar results are shown by Mobarak (2005), according to him

volatility of any sort leads to lower level of growth for least developed countries. Pallage and

Robe (2003) that welfare costs that comes from volatility are crucially large for LDCs. Therefore

it is control of volatility is in interest of policy makers and they should be interested in looking

for tools to reduce volatility to stabilize level of economic growth. Especially this should be the

case for least developed countries where volatility of investments is a big problem.

But what drives volatility of investments itself? Buthe and Milner (2008) examined potential

factors that can explain volatility in FDI flows. Particularly, they examined whether and what

political factors influence the decision of foreign investors to invest in the host country.

According to them, political institutions of international and domestic nature have some effect on

volatility of foreign direct investments. BITs and PTAs tend to reduce volatility somehow,

whereas GATT and WTO have no significant effect on it. Moreover, according to them,

democracy seems to have positive effect on reduction of volatility in developing counties

FDI trends and prospects

The next thing we wish to consider is how FDI changed over time. During the past two decades

we observe the increasing importance of foreign direct investment flows, FDI flows have raised

significantly, and developing countries, particularly, and experienced significant growth of

investments in productive assets, rather than portfolio investments in form of stocks and bonds.

In fact, if FDI flows accounted for 1% of GDP in the 70s in developing countries then before

current crisis this share increased to 3% of their GDP. In 2008 and early 2009 FDI inflows

decreased to 14% after a long period of their growth beginning from 2003 and ending in 2007. It

is interesting to notice that the record of FDI growth was mentioned in 2007 reaching $1979

billion. During first period of economic crises (2008) both inflows and outflows of FDI of

developed countries fell by 29% and 17% respectively reaching $962 and $1507 billions. FDI

continued to fall due to the global economic crises up to 2010. In 2009 FDI of the developed

countries have fell by 35% more and of the developing countries by 30%. Crises has affected an

FDI activity because it influenced firms capacity to invest.

We can see the dynamics of FDI flows on the graph belov

(taken from UNCTAD World investment Forum)

It is interesting to note that, in 2010, economies such as developing and transitional, for the first

time, has taken more than half of all world’s foreign direct investment inflows (UNCTAD World

investment report 2010). After global economic downturn, in 2011, global FDI exceeded the

level which it had before crises being $1.5 trillion, still remaining 23% below their peak level in

2007. Comparing FDI with 2010 it rose by 16 per cent and this rise covers both developed,

developing and transition economies, even though the reason s for such growth among those

clusters are different. The million of flows of FDI were to transition and developing economies

which grew by 12 per cent, being $777 billion, due to Greenfield projects mainly, whereas FDI

flows to developed countries as well grew (by 21 per cent) but foreign TNCs cross-border

M&As were the main source of this kind of growth. The inflow of FDI was about 45 percent in

developing and transition countries and reached 6 per cent of all global FDI.

(taken from UNCTAD World investment Forum)

The inflows into developing economies reached 684 billion and was mainly driven by

investments into Latin America, Caribben, and Asia.

According to UNCTAD these countries are going to experience that high levels of investment

over the past three years. Based on the UNCTAD World investment report 2012, foreign direct

investment flows were expected to be between $1,5 trillion and $1,7 trillion in 2012 and grow at

moderate steady pace to $1,8 trillion in the year 2013 and increase to the amount of 1,9 trillion in

2014, if no economic shocks will occur.

During recent times many different regulatory measures were introduces by governmenrs, the

graph below shows the dynamics of policy changes that are undertaken across countries in

respect of FDI.

(taken from UNCTAD World investment Forum)

.

FDI in Russia

Since the downfall of the Soviet Union in 1991, Russia has been grappling with the onerous task

of trying to restructure its foreign investment policies and repositioning itself into the global

trading system. After the economic and political slump of the Yelstin era, it was not until 2006

that Russia finally witnessed a significant growth in its FDI inflows. Majority of foreign

investment inflows into Russia is in the energy and manufacturing sector. Russia has bucked the

trend of services dominating the FDI inflows.The four major countries that invest in Russia are

Cyprus, Netherlands, United Kingdom and Luxembourg.

Since 1991, following Soviet Union downfall, Russia has been struggling with tough problem of

integrating into global trading system and in particular to trying to change its policies in the way

to attract foreign direct investments.

UNCTAD reported that FDI inflow went up from 1,4 per cent to 24,8 per cent of GDP in the

period from 1996 to 2011, Those figures witness that Russia has been gradually integrating into

world economy, and that foreign direct investments are one of major forces of that integration.

UNCTAD reports in 2011 FDI grew by 22 per cent reaching the value of $53 billion, which is

the third-highest level recorded.  Constantly growing Russian consumer market, large and

affordable market of labor force attracts large MNEs such as Uniliever, PepsiCO and

ExxonMobil, the value of cross-border mergers and acquisitions increased from $4,5 billion in

2010 to $33 billion in 2011. For now, Russia is the largest economy among European transition

countries that sees almost 75 per cent of total FDI inflow into 17 transition countries in Europe.

Nevertheless, in spite of witnessing the receipt of lion share of investments , FDI’s effect on

economic development is minimal, Russia is 66th among 79 economies, according to UNCTAD

study of FDI effect on development. Major countries investing in Russia Cyprus, Netherlands,

United Kingdom and Luxembourg. However barriers to FDI entry exist in Russia: whereas

restrictions on FDI in banking sector and trade in gas decreased , restrictions on electricity,

transport and insurance still exist. These restrictions as well as infrastructural issues,

administrative barriers and lack of control over intellectual property rights enforcement are those

problems that withhold FDI.

Factors that affect the spatial distribution of foreign direct investment (FDI) across regions

in Russia

Gonchar and Marik (2013) in their research concerning the factors that are important to FDI

inflows in Russia come to the following conclusions. Geographical pattern of FDI inflows in

Russia seems to depend on market related factors, as well as resource endowment of the region.

Moreover, importance of availability of resources appears to grow over time, in spite of shocks

connected with events like Yukos trial. Agglomeration locations with existing FDI stocks seem

to attract investors by generating positive sector-specific as well as cross-sector externalities.

And according to their findings service oriented investments are located in the same regions as

natural resource seeking investments.

Geographic locations of FDI in Russia

However geographic distribution of inflows of FDI quite uneven and there are only few locations

are integrated into world economy with investments and trade. Major locations are those

endowed with large stock of natural resources, human capital and good infrastructure and large

local consumer markets, most of them are urban cities in Western part of the country. Recent

researches show that major locations of FDI inflows are Moscow, Moscow region, the island of

Sakhalin and Saint-Petersburg. However, many firms can actually report nominal location while

production can be in different place, this can connected with taxations issues, relations with

government or some legal issues. Due to that reasons, concentration of firms in Moscow can be

overestimated, e.g. Moscow looks like center of mining industry and oil extraction, whereas in

reality it is not, only headquarters of such firms are located in Moscow.

It is also interesting, Gonchar and Marik (2013) report sharp and short-term increase in FDI in

some regions, which can actually be attributed either to some investment projects of carried out

through offshore or to specific short-term advantage given to some ( internal offshore Chukotka

and Kalmykia in 2000s or resource-endowed Magadan).

There exists high tendency for Russian firms to conduct their commercial deals through creation

of foreign holdings that have Russian subsidiaries under their control, this actually creates

distortions in estimation of level of FDI in Russia. Yakovenko (2012) indicates that 57 per cent

of Russian firms conduct their deals through foreign jurisdictions. Various surveys of Russian

entrepreneurs report them to be dissatisfied with domestic judicial and legislation systems.

From the sample of approximately 15000 Greenfield and M&A FDI which was used in Gonchar

and Marik (2013) we can see that top 3 regions Moscow, St. Petersburg and Moscow region

accumulate about 62 per cent of the total amount of FDI firms. On the contrary many firms while

being registered in Moscow operate in other regions (47% of total FDI). Therefore estimated

level of concentration of FDI in those three regions is biased upwards.

Other important point to note is economic split-off between the Western part and the Eastern part

of Russia. Only 9 per cent of FDI inflows are allocated in Eastern part, if we think of Ural

Mountains as the separations line.

Furthermore, territories located on borders of the countries, in contrast to inland territories host

larger FDI inflows. Border territories likewise enjoy more FDI inflows from countries which

they share border with, relative to other more distant countries. For instance, 85 per cent of

investments to Smolensk region are originated from Belarus (Smolensk is located on border with

Belarus),the same holds true for Belgorod which hosts 77 per cent of FDI from nearby Ukraine

and Bryansk which hosts 87 per cent of FDI from Belarus. Primorsk Territories (Far East) are

relatively more attractive to Chinese investors, than any other of the regions.

Distribution of FDI across sectors

From the first sight, statistics of inward Foreign Direct Investments across sectors shows that

FDI are attracted mostly by primary resources industries. In spite of regulations, which are

present, before crisis, more than half of all FDI inflows direct into quarrying and mining,

however in later periods major spheres that host FDI were real estate, manufacturing and

wholesale trade. The driving force of sharp increase in FDI inflows in Russia in the 2000s were

oil and gas sectors that experienced growth of prices that time. Those industries are still in top

positions in terms of accumulated FDI stock, although when considering number on foreign

entrants and their shares in the market (market share of FDI reached 14% in that industry,

whereas FDI in extraction industries account for only 7% of market share). Table below provides

a clear image

Source : Gonchar and Marik (2013)

However it is important to note that the fact that high concentration of inward FDI in natural

resources endowed regions, does not necessarily mean resource-rich territories would attract

FDI. It is because foreign capital accession is strictly regulated in extraction sectors, there is a

reason to say the environment is unwelcoming in that case, not unattractive. Second, significant

part of FDI inflows in those resources sectors are simply redirected capital flows of Russian

investors who seek European legal system protection or tax advantages. According to different

estimations it redirected Russian investments in FDI stocks ranges from 30 per cent to 70 per

cent. For instance, no large foreign investments were undertaken or mergers took place in oil

industry in 2008 (when investments reached its highest level ever recorded), that large amount of

FDI was mainly from Gazprom’s affiliate in the Netherlands, therefore, money were just

channeled back into Russian economy.

Empirical Research

Design Issues

There are two main issues that have to be pointed out concerning the investigation of FDI

determinants: specification of the model and endogeneity. Most of the studies are based on OLS

paradigm, however it does not predict any specific set of FDI determinants, that should be used

in the model. Based on the empirical literature which uses such factors as market size, GDP

growth, different measures of infrastructure, trade openness, financial openness, quality of labor

force, institutions and political factors we will include them in the specifications we examine.

Another difficulty concerns endogeneity. There are two main source of endogeneity. The first

one comes from the fact there exists simultaneity between FDI flows and the determinants. FDI

is likely to raise level of GDP growth, as well as it can influence level of trade openness.

Business environment is also likely to exhibit some endogeneuty, possibly higher levels of GDP

can lead to improvement of business environment. The second one arises from problem of

omitted variables in the specification, we can not guarantee to include all the factors in our

specification that can possibly determine FDI.

We follow Corcoran and Gillanders (2012) and use cross-sectional approach in our analyses,

because data we use suffers from very high correlation across time. We believe that cross-

sectional analyses will be superior to panel data analyses. Further correlation tables can be found

in appendix.

trdgdp2011 0.8987 0.9116 0.9242 0.9446 0.9492 0.9562 0.9850 1.0000 trdgdp2010 0.9126 0.9282 0.9398 0.9589 0.9624 0.9793 1.0000 trdgdp2009 0.9407 0.9546 0.9578 0.9649 0.9714 1.0000 trdgdp2008 0.9260 0.9468 0.9730 0.9852 1.0000 trdgdp2007 0.9477 0.9628 0.9848 1.0000 trdgdp2006 0.9628 0.9769 1.0000 trdgdp2005 0.9856 1.0000 trdgdp2004 1.0000 trd~2004 trd~2005 trd~2006 trd~2007 trd~2008 trd~2009 trd~2010 trd~2011

urb2011 0.9957 0.9969 0.9979 0.9986 0.9993 0.9997 0.9999 1.0000 urb2010 0.9967 0.9977 0.9985 0.9992 0.9996 0.9999 1.0000 urb2009 0.9977 0.9985 0.9992 0.9996 0.9999 1.0000 urb2008 0.9985 0.9992 0.9996 0.9999 1.0000 urb2007 0.9992 0.9996 0.9999 1.0000 urb2006 0.9996 0.9999 1.0000 urb2005 0.9999 1.0000 urb2004 1.0000 urb2004 urb2005 urb2006 urb2007 urb2008 urb2009 urb2010 urb2011

fo2011 0.8992 0.9186 0.9389 0.9420 0.9412 0.9753 0.9923 1.0000 fo2010 0.9132 0.9328 0.9539 0.9573 0.9538 0.9867 1.0000 fo2009 0.9346 0.9529 0.9763 0.9791 0.9796 1.0000 fo2008 0.9263 0.9465 0.9691 0.9683 1.0000 fo2007 0.9580 0.9752 0.9944 1.0000 fo2006 0.9694 0.9820 1.0000 fo2005 0.9912 1.0000 fo2004 1.0000 fo2004 fo2005 fo2006 fo2007 fo2008 fo2009 fo2010 fo2011

Moreover, different empirical studies use different dependent variables as measures of FDI. In

our research we will use both of two widespread variables: first is FDI as percentage of GDP

and the second is logarithm of FDI per capita.

We will consider two time periods in our analyses which correspond to the violation of FDI

inflows dynamics. Those two periods to consider are periods of FDI drop and its recovery

afterwards. The drop was caused by world economic crises and in 2008 when the crises began it

enormously affected the developed economies while it reached FDI of developing economies

only in the end of 2008. As developed and developing economies at that time were considered to

be somehow decoupled, investors were still confident in developing countries up to the forth

quarter of 2008, our analyses concerns developing economies exclusively thus it is reasonable

for our analyses to examine 2009 as a period of economic downturn(in that period developing

economy faced up to 30% drop in FDI). The second period of our analyses is going to be the

period of economic recovery and recovery in FDI especially. This period started in 2010 for

developing economies and our analyses will cover the examination till 2011 because of the

absence of the more recent data. Our analyses concerns the idea that factors which are generally

considered in literature to be important for FDI inflows during normal times can become more or

less significant than normally in crises times. Moreover factors that are important for stimulation

of investments are not the same that factors that are important for preventing drop in FDI flows.

Hypothesis

So as it was said above our nain hypothesis will be:

Ho: factors that are important for stimulating inflow of FDI are generally not the same as those

impeding outflow of Foreign Direct Investments.

Description of variables and data sources

For our analyses we take sample of 99 developing countries. We use two types of Dependent variable, first is FDI net inflows measured as percentage

of GDP (firms with at least 10% foreign ownership measured in current US dollar) and second is FDI net inflows per capita (firms with at least 10% foreign ownership measured in current US dollars) this data was taken from World Development Indicators, the World Bank.

As a proxy to infrastructural quality we will use electric power consumption (kwt per capita)

which measures the consumption of electric power by plants , internet users(per 100 people)

which measures the number of people having access to the worldwide network and urban

population as a proportion of the total population in the country (all taken from World Bank).

Another proxy we use is proxy to financial openness. In our analyses this is going to be Chinn-

Ito index, which measures the degree of capital account openness.

As a proxy to trade openness we use trade as percentage of GDP (sum of exports plus imports

divided by nominal GDP in current US dollars), data from World Bank.

We also use one worldwide governance indicator, as the proxy for institutional quality and

political risk. It is rule of law governance indicator (source: World Bank).

As a measure of quality of labour force we will use Human Development index (obtained

from Human Development Report, UNDP).

As a proxy to institutional quality we use measure of business environment Doing Business index from the World Bank. However we consider only one subindex to be important FDI determinant, it is Doing Business trade across borders index. It is based not only on nominal requirements, but on what is observed in real life. As a result, it appears to be really objective measure of business environment in the country, which can be used by firms, when they form their decision whether to enter one country or another. Therefore Doing Business index provide a clear image of how friendly countries business environment is for standard middle-size investor in terms of countries environment. Throughout our analyses we use distance to fronties measure, which illustrates countries level of business environment, 0 being the lowest performance and 100 being the frontier. Other explanatory variables as GDP growth GDP per capita which are proxies to economic

growth and market size respectively trade are also taken from the World Bank.

As it was said above we use cross-sectional data, thus as a dependent variable we will take

fdi2009 (for period of investments drop) or average of fdi 2010-2011 (for period of recovery),

the independent variables will be obtained by averaging the measures from 2004 to 2007 (for

the case of downfall) and by averaging measures from 2007 to 2009 (for case of recovery). By

including only previous periods into our analyses we partly overcome the problem of

endogeneity highlightened above. It should be noted that limited by 2004 year from below,

because publications of doing business index began in 2004 and the latest possible available data

of our dependent variable is 2011.

First thing to consider is the model specification appropriate for our analyses:

FDI t=β0+ β1 FDI t−1+β2V +ε (1)

FDI t−FDI t−1=β0+β2V +ε (2) ,

While model 1 considers FDIt-1 to be just one of the explanatory variables in the model, specification 2

examines the factors that influence the residuals. Model 1 and 2 are equivalent when β1=1, and this is

generally true for periods of stable growth, however in periods of economic distortions, β1≠1, and

specification 1 will be superior.

(1) (2) (3) (4)VARIABLES fdigdp2009 fdigdp2007 fdigdp0809 fdigdp1011

fdigdp0407 0.523*** 0.719*** 0.523***(0.0795) (0.0594) (0.112)

fdigdp0406 0.986***(0.107)

Constant 1.533** 2.217*** 1.216*** 1.870**(0.588) (0.699) (0.439) (0.827)

Observations 139 138 139 139R-squared 0.240 0.386 0.517 0.137

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

ALB

AGO

ARM

AZE

BGD

BLRBOL

BIH

BWABRA

BGR

CMR

CHL

CHNCOL

COGCRI

HRVDOM

ECU

EGY

EST

ETH

GAB

GEO

GHA

GTM

HND

IND

IDN

JOR

KAZ

KEN

KGZ

LBN

LTU

MKD

MYS

MEX

MDA

MNG

MAR

MOZ

NAMNICNGA

PAK

PANPRY

PER

PHLPOL

ROU

RUSSEN

ZAF

LKA

SYR

TJK

THA

TUN

TUR

TKM

UKR

URY UZB VNM

YEM

ZMB

-4-2

02

Res

idua

ls

0 50 100 150 200trade1

Period of FDI downturn (2009)

Before constructing the main regressions we first wish to run regression of the following form

and look at its residuals:

FDI 09=β0+β1 FDI0407+ε,

Where FDI09 is logarithm of FDI per capita in 2009 and FDI0407 if logarithm of FDI per capita in 2004-

2007 (average).

We will plot residuals on y-axes and different explanatory variables on x-axes to indicate the relationship

we expect to observe in main specifications.

First graph indicates clear positive relationship between economic growth and residuals from the

regression. The greater was amount of FDI inflow in 2009, relative to average FDI amount between 2004-

2008, and the higher will be the value of residuals observed. Thus, there is clear positive relationship

between level of GDP growth and amount of FDI inflow in2009 relative to average between years 2004-

2007.

Graph 1.Average GDP growth between years 2004-2007 on x-axes, residuals from regression on y-axes

Graph 2.Average Trade openness (trade as percentage to GDP) between years 2004-2007 on x-axes,

residuals from regression on y-axes

ALB

BGD

BLR

BOL

BIH

BWA

BRA

BGR

CMR

CHL

COL

COG

CRI

HRV

DOM

ECU EGY

EST

GAB GEO

GHA

GTM

HND

IND

IDNJOR

KAZ

KENKGZ

LBN

MKD

MEX

MDA

MNG

MAR

MOZ

NAMNIC NGA

PAK

PAN

PRY

PER

PHLPOL

ROU

RUSSEN ZAF

LKA

SYR

THA

TUN

TURUKR

URYUZB

VNM

YEM

ZMB

-50

510

Res

idua

ls

2 4 6 8 10gdpgrowth1

ALB

AGO

ARM

AZE

BGD

BLRBOL

BIH

BWABRA

BGR

CMR

CHL

CHN COL

COGCRI

HRVDOM

ECU

EGY

EST

ETH

GAB

GEO

GHA

GTM

HND

IND

IDN

JOR

KAZ

KEN

KGZ

LBN

LTU

MKD

MYS

MEX

MDA

MNG

MAR

MOZ

NAM NICNGA

PAK

PANPRY

PER

PHL POL

ROU

RUSSEN

ZAF

LKA

SYR

TJK

THA

TUN

TUR

TKM

UKR

URYUZBVNM

YEM

ZMB

-4-2

02

Res

idua

ls

20 40 60 80 100urbanpopulation1

Graph 2 indicates clear positive relationship between trade openness and residuals from the regression.

The greater was amount of FDI inflow in 2009, relative to average FDI amount between 2004-2008, and

the higher will be the value of residuals observed. Thus, there is clear positive relationship between level

of trade opennesss and amount of FDI inflow in2009 relative to average between years 2004-2007.

Graph 3.Average percentage of urban population relative to country’s population between years 2004-

2007 on x-axes, residuals from regression on y-axes

Graph 3 shows obvious positive relationship between trade openness and residuals from the regression.

The greater was amount of FDI inflow in 2009, relative to average FDI amount between 2004-2008, and

the higher will be the value of residuals observed. Thus, there is clear positive relationship between urban

population and amount of FDI inflow in2009 relative to average between years 2004-2007.This results

corresponds to results obtained in further regression analyses.

ALB

AGO

ARM

AZE

BGD

BLRBOL

BIH

BWABRA

BGR

CMR

CHL

CHN COL

COGCRI

HRVDOM

ECU

EGY

EST

ETH

GAB

GEO

GHA

GTM

HND

IND

IDN

JOR

KAZ

KEN

KGZ

LBN

LTU

MKD

MYS

MEX

MDA

MNG

MAR

MOZ

NAM NICNGA

PAK

PANPRY

PER

PHLPOL

ROU

RUSSENZAF

LKA

SYR

TJK

THA

TUN

TUR

TKM

UKR

URYUZB VNM

YEM

ZMB

-4-2

02

Res

idua

ls

-2 -1 0 1 2financialopenness

Graph 4.Average level of financial openness between years 2004-2007 on x-axes, residuals from

regression on y-axes

Graph 4 shows negative relationship between financial openness and residuals from the regression. The

greater was amount of FDI inflow in 2009, relative to average FDI amount between 2004-2008, and the

higher will be the value of residuals observed. Thus, there is clear positive relationship between urban

population and amount of FDI inflow in2009 relative to average between years 2004-2007.

Our first specification is of the following form:

FDI 09=β0+β1 FDI0407+β2 GDP+β3V +ε

Where FDI 09 is the logarithm of FDI per capita in 2009, FDI 0407 is average level of logarithm FDI

per capita between 2004-2007, GDP stands for GDP per capita, V is vector of control variables. V takes

different values from model 1 to 9, in those models TRADE1 is measure of trade openness (trade to GDP,

average between periods from 2004 to 2007), URBANPOPULATION is measure of urban population as

percentage to total population in the country (average across 2004-2007 years), FINANCIALOPENNESS

(The Chinn-Ito index), average between 2007-2008 years, HDIINDEX (one year, 2007) , INTERNET

(users per 1000), one year, 2007, ElE (electric consumption), 2007 and GDPGROWTH (level of GDP

growth average between 2004-2007 year).

Output of table 1 shows there are five significant coefficients in this specification, which explains

the dependence of FDI 2009 at the time of its decrease due to world economic crises to the

factors listed in the table below. The first factor to have positive significant coefficient is the

coefficient before previous level of FDI per capita, it is actually what we expected it to be. 1 per

cent change in FDI per capita 0408 is associated with approximately 0,9 per cent change in FDI

per capita in 2009.

Coefficients of four other factors turned out to be significant as well. Let’s first look at the

coefficient which is related to the trade openness. The coefficient is positive which shows that

trade openness has positive effect on FDI in that period. This is quite reasonable relating the

nature of MNEs activities. MNEs while producing goods in the host country use lots of

intermediate goods, which may be shipped from other countries as well as producing final goods

they may ship abroad after production. Consequently, trade openness of host country may play

crucial role at the moment when MNEs taking a decision whether to invest in this country or not.

Trade openness is measured as export plus import as percentage of GDP and, therefore, one

point increase in the ratio of trade to GDP leads to approximately 0,67 percent increase in FDI

on average. As we can see from the results greater trade openness leads to higher level of FDI

per capita in 2009, thus, trade openness counteracts the decrease of the FDI during crises times.

Next coefficient to examine is a percentage of urban population to total population in the

country. As we mentioned above urban agglomerations enjoy better infrastructure and large

pools of labor, thus we expect it to affect positively on FDI per capita. Therefore, from our

regression we can conclude that one percent increase in the urban population leads to the

increase of FDI by approximately 0.02 at the time of reduction of investments due to economic

downfall. In model 4 and 9 we conclude that the coefficient of financial openness was negative

and significant. This coincides with our intuition that greater financial openness of the country

leads to the greater movements of FDI flows while at the normal times it allows an investor to

enter the country more easily, it also allows to take away their asset more easily in times of

market instability. GDP growth is slightly significant only in the 9th model. Countries which

showed stable economic growth in past will be less affected by crises in terms of level of FDI

inflows. Model 9 shows that one percent increase in GDP growth leads to approximately 0,06

percent increase of FDI in 2009. Coefficients of HDI, internet and electricity are negative but

insignificant.

  (1) (2) (3) (4) (5) (6) (7) (8) (9)

VARIABLESlnfdipc0

9 lnfdipc09lnfdipc0

9lnfdipc0

9lnfdipc0

9lnfdipc0

9 lnfdipc09lnfdipc0

9 lnfdipc09                   lnfdipc0407 0.887*** 0.986*** 0.799*** 0.974*** 0.918*** 0.923*** 0.886*** 0.873*** 0.982***

(6.376) (6.693) (5.603) (6.864) (6.283) (6.441) (6.217) (6.332) (6.261)

trade1-

0.00689*-

0.00658*(-1.809) (-1.758)

Gdp-2.38e-

05 -3.74e-05-4.90e-

05-2.75e-

05-6.08e-

06-4.93e-

06-2.40e-

05-1.82e-

05 -4.62e-05(-0.641) (-1.004) (-1.278) (-0.757) (-0.137) (-0.119) (-0.536) (-0.493) (-0.935)

urbanpopulation1 0.0189** 0.0216**(2.039) (2.300)

Financialopenness -0.181** -0.165*(-2.093) (-1.874)

Hdiind -1.371 -1.137(-0.731) (-0.580)

Internet -0.0102 -0.00602(-1.042) (-0.598)

Ele 9.89e-07 5.30e-05(0.00742

) (0.396)gdpgrowth1 0.0556 0.0647*

(1.556) (1.880)Constant 0.572 0.830* 0.140 0.278 1.179 0.579 0.573 0.219 0.179

(1.219) (1.718) (0.278) (0.581) (1.235) (1.235) (1.199) (0.424) (0.182)

Observations 69 69 69 69 69 69 69 69 69R-squared 0.576 0.596 0.602 0.603 0.579 0.583 0.576 0.591 0.674t-statistics in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table 1.

Our second specification is of the following form:

FDI 09=β0+β1 FDI0407+β2 V +ε

Table 2 ahows the output of this specification. FDI 09 is the ratio of FDI to GDP in 2009,

FDI 0407 is average ratio of FDI per to GDP between 2004-2007, V is vector of control variables. V takes

different values from model 1 to 9, in those models TRADE1 is measure of trade openness (trade to GDP,

average between periods from 2004 to 2007), URBANPOPULATION is measure of urban population as

percentage to total population in the country (average across 2004-2007 years), FINANCIALOPENNESS

(The Chinn-Ito index), average between 2007-2008 years, HDIINDEX (one year, 2007) , INTERNET

(users per 1000), one year, 2007, ElE (electric consumption), 2007 and GDPGROWTH (level of GDP

growth average between 2004-2007 year). The only thing why this specification is fundamentally

different from first is that it uses FDI as percentage of GDP as dependent variable

In this specification we again examine the factors that affect FDI in periods of financial

downturn. As a dependant variable we took FDI as a percentage of GDP. It is quiet obvious that

FDI 2009 strongly depends on the average of FDI from 2004 to 2007. Coefficient of urban

population is significant and has positive sign in model 9. However in model 3 it has positive

sign as well but it is insignificant. Regarding our findings in previous specification we can

conclude that urban population has positive effect on FDI inflows and in times of economic

crises counteracts FDI shocks. All the rest coefficients appeared to be insignificant. The sign of

coefficients of trade, financial openness , HDI index and internet coincide with the signs in

previous specifications which we consider to be superior in terms of its ability to explain the

dependence of FDI in crises period on different factors. However we observed the difference in

sign of GDP growth and electricity. In case of GDP growth this is unexpected since persistent

GDP growth should make investors more confident in the economy and protect countries from

the decrease of FDI flows.

  (1) (2) (3) (4) (5) (6) (7) (8) (9)VARIABLES fdigdp09 fdigdp09 fdigdp09 fdigdp09 fdigdp09 fdigdp09 fdigdp09 fdigdp09 fdigdp09                   fdigdp0407 0.427*** 0.428*** 0.409*** 0.454*** 0.441*** 0.454*** 0.431*** 0.469*** 0.502***

(5.034) (4.538) (4.753) (5.113) (5.049) (5.102) (4.846) (5.134) (4.517)

trade1-

0.000278 0.00373(-0.0208) (0.276)

urbanpopulation1 0.0256 0.0606**(1.154) (2.024)

Financialopenness -0.288 -0.317(-1.019) (-0.970)

Hdiind -2.461 -5.036(-0.721) (-0.773)

Internet -0.0224 -0.0277(-1.004) (-0.740)

Ele-4.56e-

05 7.33e-05(-0.162) (0.171)

gdpgrowth1 -0.163 -0.168(-1.216) (-1.183)

Constant 1.868*** 1.888 0.556 1.856*** 3.382 2.441*** 1.931*** 2.727*** 3.068(3.072) (1.657) (0.431) (3.053) (1.546) (2.928) (2.667) (2.930) (0.969)

Observations 71 71 71 71 71 71 71 71 71R-squared 0.269 0.269 0.283 0.280 0.274 0.279 0.269 0.284 0.354t-statistics in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table2.

Period of FDI recovery (2010-2011)

Before constructing the main regressions we first wish to run regression of the following form

and look at its residuals:

FDI 1011=β0+β1 FDI 0609+ε,

Where FDI1011 is logarithm of FDI per capita in 2010-2011(average between years) and FDI0407 if

logarithm of FDI per capita in 2006-2009 (average).

We will plot residuals on y-axes and different explanatory variables on x-axes to indicate the relationship

we expect to observe in main specifications.

Graph 5 indicates positive relationship between economic growth and residuals from the regression. The

greater was amount of FDI inflow in 2010-11, relative to average FDI amount between 2006-2009, and

the higher will be the value of residuals observed. Thus, there is clear positive relationship between level

of GDP growth and amount of FDI inflow in 2010-2011 relative to average between years 2006-2009.

PAKEGY

BGD ETH

ECU

KEN

ROUBIH

PHLTUN

HRV

SEN

BGR

SDNMDAZAF MARIND

BWA

LKA NGA

JORMEXTUR THA

SYRUKR HNDMKD

GTM

LTUGEO

COD

LVAEST

VNMARM

CMR

AZECOL DOMPOL

VENIDNNIC

RUS CRI

PRYCHN

KAZ

ZMBMYS

KGZ

NAMGHA

BOLPER

PANBRA

LBNCOG

MOZ

GAB ALBURYCHL

BLR

MNG

-2-1

01

2R

esid

uals

-5 0 5 10 15gdpgrowth

Graph 5.Average GDP growth between years 2006-2009 on x-axes, residuals from regression on y-axes

Graph 6.Average Electric consumption (kwat per capita) as measure of infrastructure between years

2006-2009 on x-axes, residuals from regression on y-axes

Graph 7.Average urban population as percentage to total population in the country between years 2006-

2009 on x-axes, residuals from regression on y-axes

Graph 6 and 7 shows positive relationship between electric consumption,urban population and residuals

from the regression. The greater was amount of FDI inflow in 2010-11, relative to average FDI amount

between 2006-2009, and the higher will be the value of residuals observed. While both graphs seem to

indicate positive relationship between electricity consumption/urbanpopulation and amount of FDI inflow

in 2010-2011 relative to average between years 2006-2009. Regressions of our 3-rd specification does not

PAKEGY

BGDETH

ECU

KEN

ROUBIH

PHLTUN

HRV

SEN

BGR

SDN MDA ZAFMARIND

BWA

LKA NGA

JORMEXTURTHA

SYRUKRHND MKD

GTM

LTUGEO

COD

LVAEST

VNMARM

CMR

AZECOLDOMPOL

VENIDN NIC

RUSCRI

PRYCHN

KAZ

ZMBMYS

KGZNAM

GHABOL

PERPAN

BRALBN

COG

MOZ

GABALB URYCHLBLR

MNG

-2-1

01

2R

esid

uals

20 40 60 80 100urbanpopulation

PAKEGY

BGDETH

ECU

KEN

ROU BIH

PHLTUN

HRV

SEN

BGR

SDN MDA ZAFMARIND

BWA

LKANGA

JORMEX TURTHA

SYRUKRHND MKD

GTM

LTUGEO

COD

LVAEST

VNMARM

CMR

AZECOL DOM POL

VENIDNNIC

RUSCRI

PRYCHN

KAZ

ZMBMYS

KGZNAM

GHABOL

PERPAN

BRALBN

COG

MOZ

GAB ALB URY CHL

BLR

MNG

-2-1

01

2R

esid

uals

0 2000 4000 6000ele

PAKEGY

BGDETH

ECU

KEN

ROUBIH

PHLTUN

HRV

SEN

BGR

SDN MDAZAF MARIND

BWA

LKANGA

JORMEXTURTHA

SYRUKR HNDMKD

GTM

LTUGEO

COD

LVAEST

VNMARM

CMR

AZECOL DOMPOL

VENIDNNIC

RUS CRI

PRYCHN

KAZ

ZMBMYS

KGZ

NAMGHA

BOLPER

PANBRA

LBNCOG

MOZ

GAB ALBURY CHL

BLR

MNG

-2-1

01

2R

esid

uals

0 20 40 60 80 100doingbusinesstab

always capture posiive relationship seen on the graph for electricity (in Table3 model 11 ELE has

negative sign), but actually captures this relationship for urban population.

Graph 8.Average trade openness population as percentage to total population in the country between

years 2006-2009 on x-axes, residuals from regression on y-axes

Graph 9.Average Doing Business index trade across borders in the country between years 2006-2009 on

x-axes, residuals from regression on y-axes

PAKEGY

BGDETH

ECU

KEN

ROU BIH

PHLTUN

HRV

SEN

BGR

SDN MDAZAF MARIND

BWA

LKANGA

JORMEXTUR THA

SYRUKR HNDMKD

GTM

LTUGEO

COD

LVAEST

VNMARM

CMR

AZECOL DOM POL

VENIDN NIC

RUS CRI

PRYCHN

KAZ

ZMBMYS

KGZNAM

GHABOL

PERPAN

BRALBN

COG

MOZ

GABALBURY CHL

BLR

MNG

-2-1

01

2R

esid

uals

0 50 100 150 200trade

Graph 8 shows positive relationship between trade openness and residuals. Graph 9 shows negative

relationship between doing business index and residuals from the regression. The greater was amount of

FDI inflow in 2010-11, relative to average FDI amount between 2006-2009, and the higher will be the

value of residuals observed. Both of this relations were captured by regression output of our third main

specification (table3) and are significant.

Our third main specification is of the following form:

FDI 1011=β0+β1 FDI 0609+β2 GDP+β3V +ε

Table 3 shows the output of this specification. FDI 1011 is the logarithm of FDI per capita in

2010-2011 average across periods, FDI 0609 is the logarithm of FDI per capita between 2006-2009,

GDP stands for GDP per capita, V is vector of control variables. V takes different values from model 1 to

9, in those models TRADE is measure of trade openness (trade to GDP, average between periods from

2006 to 2009), URBANPOPULATION is measure of urban population as percentage to total population

in the country (average across 2006-2009 years), FINANCIALOPENNESS (The Chinn-Ito index),

average between 2008-2009 years, HDIINDEX (one year, 2007) , INTERNET (users per 1000), one year,

2007, ElE (electric consumption), 2007 and GDPGROWTH (level of GDP growth average between

2006-2009 year), in this specification we also included ease of doing business index (trade across

borders)- as we consider that component to have the most significant influence on FDI average across

2006-2009 periods, and governance indicator rule of low, which is our proxy to institutional quality and

political stability (average across periods 2006-2009) .

Table3 shows that factors that are important for FDI recovery are past values of FDI, urban

population, GDP growth and one component of doing business index-trade across borders which

measures procedural requirements to import or export cargo of goods. While signs of three first

are clear, the sign of doing business trade across border is perhaps surprising. It is not past value

of FDI inflows is significant, while it is not surprising either that macroeconomic indicator (in

our model GDP growth), which measures past economic performance is significant as well. We

can expect that developing countries, which showed higher levels of growth in previous periods

are likely to attract more cross-border investments. According to our results, one point increase

in GDP growth should increase FDI to developing countries by 7 per cent on average.

Regression 3 and 11 suggest that percentage of urban population appears to be significant factor

for FDI flows recovery, this seems reasonable as we actually expect investors to return to those

countries which provide easier access to labor (which urban agglomerations supply) and better

infrastructure. According to our analyses 1 point increase in urban population will result in

approximately 2 per cent rise in FDI inflows on average. Coefficient before trade openness

although only in specification 11 appears to be significant, this is quite natural, as higher level of

trade openness is important for FDI inflows (which regularly need to transport intermediate

products across borders)

Doing Business Trade across borders index appears to be significant, but have negative sign,

which actually contradicts the intuition, that better business environment in terms of international

trade restrictions should increase FDI inflows. And probably this point needs further research.

Other variables which are rule of law, trade openness, financial openness, HDI index, internet

and electricity appear to be insignificant. However HDI index coefficient (which measures

quality of labor force) and coefficient before electricity (which is our proxy to estimate

infrastructure) and trade openesss have positive sign, and this is what we actually expect it to be.

  (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

VARIABLES lnfdipc1011 lnfdipc1011 lnfdipc1011 lnfdipc1011 lnfdipc1011 lnfdipc1011 lnfdipc1011lnfdipc101

1 lnfdipc1011 lnfdipc1011 lnfdipc1011                       lnfdipc0407 0.855*** 0.820*** 0.782*** 0.863*** 0.835*** 0.890*** 0.854*** 0.848*** 0.880*** 0.855*** 0.695***

(9.847) (8.679) (9.464) (9.562) (9.335) (9.930) (9.673) (9.897) (9.804) (10.07) (7.239)Gdp -1.65e-05 -1.14e-05 -4.40e-05** -1.63e-05 -3.14e-05 1.91e-07 -1.74e-05 -3.79e-06 -1.18e-05 -6.26e-06 -1.53e-05

(-0.738) (-0.496) (-2.000) (-0.724) (-1.142) (0.00763) (-0.617) (-0.164) (-0.520) (-0.279) (-0.496)Trade 0.00229 0.00439*

(0.933) (1.940)Urbanpopulation 0.0186*** 0.0201***

(3.554) (3.645)Financialopennes -0.0200 -0.0217

(-0.357) (-0.409)Hdiind 0.993 1.024

(0.934) (0.902)Internet -0.00882 -0.00892

(-1.423) (-1.389)Ele 4.67e-06 -1.42e-06

(0.0552) (-0.0169)Gdpgrowth 0.0456* 0.0501**

(1.732) (2.038)Ruleoflaw -0.157 0.193

(-1.075) (1.192)Doingbusinesstab -0.00828* -0.00896*

(-1.978) (-1.741)Constant 0.984*** 0.912*** 0.502 0.953*** 0.552 0.974*** 0.986*** 0.728** 0.767** 1.373*** 0.237

(3.287) (2.944) (1.634) (3.032) (1.002) (3.279) (3.247) (2.207) (2.124) (3.892) (0.384)

Observations 68 68 68 68 68 68 68 68 68 68 68R-squared 0.773 0.776 0.810 0.773 0.776 0.780 0.773 0.783 0.777 0.786 0.851t-statistics in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table 3

Our forth main specification is of the following form:

FDI 1011=β0+β1 FDI0609+β2 V+ε

Table 4 shows the output of this specification. FDI 1011 is the ratio of FDI to GDP in 2010-2011

average across periods, FDI 0609 is the ratio of FDI to GDP between 2006-2009, , V is vector of control

variables. V takes different values from model 1 to 9, in those models TRADE is measure of trade

openness (trade to GDP, average between periods from 2006 to 2009), URBANPOPULATION is

measure of urban population as percentage to total population in the country (average across 2006-2009

years), FINANCIALOPENNESS (The Chinn-Ito index), average between 2008-2009 years, HDIINDEX

(one year, 2007) , INTERNET (users per 1000), one year, 2007, ElE (electric consumption), 2007 and

GDPGROWTH (level of GDP growth average between 2006-2009 year), in this specification we also

included ease of doing business index (trade across borders)- as we consider that component to have the

most significant influence on FDI average across 2006-2009 periods, and governance indicator rule of

low, which is our proxy to institutional quality and political stability (average across periods 2006-2009)

As in the previous model we examine the factors influencing the recovery of FDI flows. As well

as in other models previous level of FDI inflows is significant and positively affects dependent

variable, and this is quite natural. Trade openness in this specification while being insignificant

in the 1st model is significant in the 11th model. This coincides with the results of the

specification 3 which shows the same results (significance of trade openness). One percent

increase in the level of trade openness leads to the increase of FDI recovery by 0,04 percent.

Urban population turned out to be significant as well. It shows that FDI flows return back faster

to those countries which are more urbanized because urbanization corresponds to the better

infrastructure which is crucial for investors. The findings suggest that one percent increase in

urban population leads to 0,0969 percent increase in FDI inflows according to model 11. This

model doesn’t indicate any clear relation between internet as a measure of infrastructure quality

and FDI flows. In general, measure of FDI as percentage of GDP seems to be worse than

logarithm of FDI per capita. This specification shows less amount of significant coefficient and

even none of persistent significance within a table. Doing business index which measures the

restrictions on trade across borders shows negative coefficient and this is perhaps surprising

however we got the same results in the previous specification. Just as we concluded above these

issue require further research.

  (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

VARIABLESfdigdp101

1fdigdp101

1fdigdp101

1fdigdp101

1fdigdp101

1fdigdp101

1fdigdp101

1fdigdp101

1fdigdp101

1fdigdp101

1 fdigdp1011                       fdigdp0407 0.292** 0.207 0.285** 0.301** 0.338** 0.386*** 0.349** 0.285** 0.325** 0.272** 0.177

(2.176) (1.405) (2.077) (2.154) (2.462) (2.779) (2.496) (2.114) (2.358) (2.065) (1.044)Trade 0.0291 0.0437*

(1.333) (1.968)Urbanpopulation 0.0112 0.0969*

(0.301) (1.950)Financialopennes -0.122 0.269

(-0.261) (0.504)Hdiind -7.654 -7.006

(-1.397) (-0.656)Internet -0.0744** -0.0578

(-2.048) (-0.892)Ele -0.000622 -0.000394

(-1.354) (-0.510)Gdpgrowth 0.143 0.0699

(0.749) (0.302)Ruleoflaw -1.095 1.074

(-1.021) (0.705)Doingbusinesstab -0.0645* -0.0665

(-1.930) (-1.291)Constant 2.984*** 0.951 2.399 2.976*** 7.667** 4.853*** 3.813*** 2.399* 2.330* 6.699*** 5.341

(2.948) (0.520) (1.093) (2.918) (2.191) (3.604) (3.237) (1.871) (1.946) (3.093) (0.922)

Observations 72 72 72 72 72 72 72 72 72 72 72R-squared 0.063 0.087 0.065 0.064 0.089 0.117 0.088 0.071 0.077 0.111 0.228t-statistics in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table 4

Results

In our analyses we actually obtained very important results, factors that are thought to attract

FDI inflows can behave differently in times of crises, and factors that are important for impeding

dramatic declines in FDI flows are not necessarily the same as those important for increase in

FDI.

In our first specification which showed the period of financial downturn ( 2009) we have found

that GDP growth has positive effect on FDI, urban population has a positive effect as well, trade

and financial openness have negative effect on inflow of FDI in times of crises. All this results

are quite natural and can be explained easely. Countries that showed stable economic growth in

previous period will be safer from investors point of view. As well as countries with large levels

of urban population which is in our analyses proxy to infrastructure will not suffer that much

from drop of investmests. Whereas trade openness and especially financial openness is beneficial

for country in normal times since investors can enter the country more easily. In times of

economic downturn openness in general and especially financial openness can be harmful for

developing countries economies. We have found as well that GDP growth, urban population

have persistent positive effect regardless of economic situation.

Conclusion

Importance of FDI has been persistently increasing all the time especially for developing

countries which attract more than half of world investments in general. The importance of FDI

inflows for developing countries is crucial since for lots of them FDI inflows are the main source

of financing. To adopt the policies reasonable policies directed to the FDI policy makers should

understand which factors are important for FDI attraction or FDI inflows drops. While there is a

lot of literature concerning inflow of FDI during normal times and factors that influence

literature of FDI inflows during period of turbulence is quiet poor.

Our main hypothesis was that factors that are generally accepted to attract FDI during normal

times can behave differently in times of economic difficulties. On the basis of recent economic

crises we examined the factors that are not only important for stimulation economic growth, but

also examined the factors that are important for impeding decreases in FDI. And we also

showed, that those factors are not necessarily the same.

In the research we have found out how different factors which are considered to be determinants

of FDI in significant literature can behave differently in times of economic down fall. We

actually got that greater financial openness that may be beneficial in normal times makes the

process of outflow of investment much easier. Thus, it negatively affects FDI inflow in times of

crises. Furthermore, higher levels of GDP growth and urban population (as a proxy to measure of

infrastructure), according to our analyses have positive effects on FDI inflows both in crises

times and in times of recovery either, therefore, those investments attract FDI in normal times as

well as prevent FDI from sharp drop in times crises times.

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