Chapter IV

THE IMPACT OF BILATERAL INVESTMENT TREATIES ON INVESTMENT FLOWS

Farok J. Contractor*Sankaran Raghunathan**

________________________________________________________________________________*Professor, Rutgers University, School of Management**Asst. Professor, Rutgers University, School of Management

Mr. Chang-Su Kim, Ph.D student, provided valuable computer assistance.

Chapter IV. THE IMPACT OF BILATERAL INVESTMENT TREATIES ON INVESTMENT FLOWS

TABLE OF CONTENTS

A. The Environment for the Proliferation of Bilateral Investment Treaties

B. An Overview of the Research Objectives of the Statistical Analysis

C. Explanations for the Possible Role of Bilateral Investment Treaties in Foreign Direct Investment

D. Motivations and Determinants of Foreign Direct Investment1. Principal Variable and Hypothesis2. Secondary Variables and Hypotheses

a. Market Sizeb. Market Growthc. Exchange Rate Changed. Inflatione. Rate of Capital Formationf. Country “Risk” (Quality of Nation’s

Foreign Investment Climate)g. Other Variables

E. Summary of Hypotheses

F. Methodology and Data1. Two Stages of Analysis2. Countries in Data Set3. Variable Definitions

a. Dependent Variables: Stage 1b. Explanatory or Independent Variables: Stage 1c. Dependent Variables: Stage 2

4. Data and Data Sources5. Methodology and Statistical Techniques Used

G. Results and Discussion1. Multivariate Regression (Stage 1)2. N-dimensional Pattern Analysis (Stage 1)3. T-tests of Differences In Group Means (Stage 2: Time Series Data)

a. The Research Questionsb. Results and Conclusions of Stage 2 Analysis

H. A Summary of Conclusions

ReferencesAnnexes (Annex Tables 1 through 11)

Chapter IV. THE IMPACT OF BILATERAL INVESTMENT TREATIES ON INVESTMENT FLOWS

__________________________________________________________________

This chapter is principally concerned with the question of whether the flow of foreign direct

investment between countries is enhanced by the act of signing bilateral investment treaties, or

whether such treaties are only a necessary precondition for foreign direct investment. That is to say,

are BITs only one component of a business environment that facilitates foreign investment, seen by

some as increasingly necessary signal of a country’s receptivity towards foreign investment – or do

BITs actually result in an increase in the rate of foreign direct investment flow between the

countries in question ?

To answer this question, detailed analyses were performed to determine whether a statistical

association could be found between investment flows and the act of signing of BITs. There has

undoubtedly been an acceleration in foreign direct investment (FDI) flows in the last decade, as

well as an acceleration in the rate at which BITs were signed. But can a causal effect be established

between FDI and BITs ? Or are explanatory variables, other than BITs, relating to a nation’s

investment climate or its market size more powerful determinants of FDI flows than are BITs ?

A. The Environment For the Proliferation of Bilateral Investment Treaties

As we saw earlier in Chapter I, 161 nations had concluded 1310 bilateral investment treaties

through end-19961. Over 67 percent of these were concluded in the 1990s, indicating a surge in

interest in signing treaties in the last few years. At the same there has been an acceleration in

foreign direct investment flows in recent years.

In general, over the past twenty-five years, trade and foreign direct investment (FDI) grew

considerably faster than the growth rate of the average domestic economy. Between 1973 and 1996

world total merchandise exports multiplied nine fold, while FDI flows grew thirteen fold.

Inevitably then, foreign investment has become a significant component of overall investment in

1 Because of missing information fewer nations are included in the data set used for the analyses in this chapter. For example, 133 nations are included in the data set used in the first part of this analysis. The total count of BITs, upto June 1996, comes to 1571 because of double counting of a BIT by more than one government. That is to say, the way the data are set up, the 1571 indicates BIT signings by each nation, rather than the number of BITs. Unless otherwise indicated therefore, the number of BITs in this report refers to “number of times BITs are signed” rather than the actual number of BITs

many OECD, as well as developing nations, commanding the attention of governments of both the

FDI-source and FDI-recipient governments.

Many emerging nations (defined here as low and middle-income countries, including the

Central and East European nations of the former communist bloc) are now important players in

global investment flows. Total private capital flows to emerging nations exceeded $ 200 Billion for

the first time in 1995. $ 35 Billion of this was portfolio investment and approximately $ 60 Billion

private debt flows. The bulk, of about $ 112 Billion, is in the Foreign Direct Investment (FDI)

category. Of this, about $ 12 Billion was received by Central and East European nations, and about

$ 100 Billion by other developing nations worldwide. Emerging nations increased their world share

of FDI inflows from 22 percent in the 1988 - 92 period, to 42 percent in 1994, before sinking back

to 36 percent in 1995, in the wake of the Mexican Peso crisis. In the list of the twenty biggest

recipient nations of FDI over the period 1985 to 1995, a third were developing nations, including

China, Mexico, Malaysia, Argentina, Brazil, Singapore and Hong Kong.

One motivation for governments to sign BITs is to “signal” a liberalized and more open

environment for inward foreign investment (Salacuse, 1990). Starting in the early 1980s, many

formerly restrictionist policies in both OECD as well as developing nations were liberalized

(UNCTC, 1991). In the 1990s countries began to aggressively compete with each other for

investment. Contractor (1995) reported that over one hundred nations had government departments

specially devoted to FDI promotion. The key question, investigated by this chapter, is whether the

act of signing BITs as a competitive tool, used by developing countries to increase their inflow of

FDI, actually results in an actual increase in the rate of FDI flow to that nation, or to its share of

FDI. Alternatively, might the enormous increase in developing country BITs in the 1990s (see

Chapter I, particularly Figure 3) neutralized any comparative advantage that might have been

conferred on a nation by the act of signing a treaty ? Finally, in general, are factors other than BITs

more powerful explanations of the destination and volume of FDI ?

Governments of major FDI source countries, such as the United States, starting in the mid-

1980s, actively embarked on a policy of improving the investment and intellectual property

protection regimes on behalf of their companies, linking the signing of BITs and commercial

treaties with other foreign policy objectives. While the bulk of outflow of FDI from developed

countries continues to go to destinations in other developed nations, the share of emerging nations

as destinations for investment has grown. The share of developing countries in the share of the

largest five outward direct investing nations rose from 18 percent in 1990, to 28 percent in 1993 -

94 (UNCTAD, 1996). Thus the signing of BITs between the FDI source countries and the

governments of emerging nations is seen as a means to cement the commercial ties between them

and help stabilize the investment climate in the latter nations. A concurrent motivation is the desire

or push on the part of investment guarantee agencies in the FDI source nations, as well as by

MIGA, to have a BIT with the host country in which FDI insurance is sought.

Perhaps another lesser reason for the recent proliferation of Bilateral Investment Treaties

amongst emerging nations is that several of their own companies are now making foreign direct

investments. The perspective of a government changes when foreign investment becomes a two-

way flow, and when its national firms wish to have their interests protected (as outward investors)

in other nations. In the traditional international product cycle theory (Vernon, 1966) developing

nations were supposed to indefinitely remain mere recipients of successive waves of inward FDI. In

the investment-development cycle theory however, a stage occurs when a nation’s own companies

begin to make outward investments (Dunning and Narula, 1994). FDI outflows from emerging

countries have registered an impressive growth rate, albeit from a currently low base. Their share

has increased to 15 percent of the world flows in 1995. Much of this FDI goes to other emerging

nation destinations. Half of FDI from Asian developing nations goes to other Asian developing

nations (Kumar, 1995). Korean, Taiwanese, Singaporean and Hong Kong firms are also beginning

to make significant investments in the United States (Lall, 1991). But for the most part, emerging

nation firms appear to cut their teeth in other emerging nations before tackling the larger markets of

the OECD. Some 55 percent of Bilateral Investment Treaties (BITs) signed in the 1990s have both

signatory governments within the emerging nation group (UNCTAD, 1996). Please refer also to

Figure 3 in Chapter I which shows the extent of intra-regional treaties among developing and

transition economies.

In short, the environment for foreign direct investment in the 1990s is such that both FDI-

originating, as well as host country governments have felt it increasingly important to conclude

bilateral investment treaties.

B. An Overview of the Research Objectives of the Statistical Analysis

The principal objective of this chapter is to examine the link, if any, between bilateral

investment treaties and foreign direct investment flows. In summary, the broadest research

questions are (a) Do Bilateral Investment Treaties influence Foreign Direct Investment as measured

by its flow, stock, or share going to a particular nation subsequent to signing of a BIT ? (b) If so,

how long does it take subsequent to the signing of BITs for the effect on FDI to be seen ? (c) If the

effect of BITs on FDI is weak, or non-existent, what other variables influence FDI more strongly ?

Is the signing of BITs associated with an increase in FDI? In this first phase of the research,

this question was examined cross-sectionally with data on 133 countries2. Across countries,

questions were asked, such as whether the number of BIT signings in a particular year relate to FDI

inflows to that nation, or to the change in FDI inflows. Similarly, the cumulative number of BIT

signings by a nation is hypothesized to correlate with that nation’s inward FDI stock. For a nation

as a recipient of FDI, the cumulative number its BITs can be regarded an aggregate indicator of its

welcoming attitude towards FDI. The first phase of the analysis is cross-sectional. That is to say, it

treats data for all nations at once for a particular year.

The second phase of the analysis takes a time-series perspective, and examines data on pairs

of nations that signed a BIT. For a pair of nations, did the signing of a BIT result in a subsequent

increase in FDI between them ? The focus of this analysis is to compare periods before and after

the signing of the BIT.

Measures for FDI flows or stock may additionally be “normalized” or adjusted, by

dividing them by host nation GDP, or population, in order to provide alternative measures for FDI

which correct for the size of the country’s economy. Yet other measures include examining

whether the share of a particular destination nation in a source country’s outward FDI total

changed subsequent to the signing of the BIT – or similarly, whether a nation’s share in the

composition of inward FDI into a recipient nation changed after signing a BIT with the FDI source

country.

Another not unimportant research question is whether the response of investors to BIT

signings (if any) is lagged. There is no theory of the response lag of investors. Some studies such as

UNCTAD (1991) which traced the response of investors to changes in government policy

announcements suggest a quick response of one to two years. Others such as Kreinin, Plummer and

Abe (1997) seem to suggest a longer lag of up to five years. In the absence of theory or convergent

2 The actual number of countries in any particular statistical run may be less because of missing data. This is because each variable used exhibits its own pattern of missing entries. For some statistical analyses, the combined effect of missing entries over the entire variable set can produce a rather large drop in the number of usable cases (countries or country pairs). In such instances, alternative methods were used.

results of past studies, different lags (including a zero lag) were tried out to find the best statistical

fit. Lags were tried out in both the pairwise time-series analysis, as well as the cross-sectional

study.

If BITs do not provide a strong determinant for FDI, alternative explanatory variables from

the FDI literature ought to be utilized to see if they provide a stronger statistical determinant. These

included variables such as host nation market size and growth, country risk indices, change in

exchange rate, inflation rate, and capital investment in the host nation, to explain cross-sectional

variation in FDI across nations. (The reasons for the selection of these alternative variables are

mentioned in part IV.E later in this chapter).

C. Explanations for the Possible Role of Bilateral Investment Treaties in Foreign Direct Investment

This section is not intended to be a review of the provisions included in bilateral investment

treaties. For that, see Dolzer and Stevens (1995), or UNCTAD (1996), or WTO (1996) or Salacuse

(1990), or Kishoiyan (1994). Here we ask why BITs should have a possible effect on FDI flows and

what they may mean from the perspective of an investor.

To begin with, the signing of a BIT has an important symbolic value to prospective

investors – as a formal welcome. The 1990s may be described as an era when most nations

exhibited very competitive attitudes to promote and attract inward investment (Contractor, 1995).

The signing of a BIT provides valuable publicity and an important symbolic declaration of stronger

diplomatic and commercial ties between the two nations.

BITs are a legal admission by a government, of foreigners’ claims to investment as a

valuable and contestable asset. It establishes the notion of recourse being made, not merely to the

legal and regulatory system of the host nation, but to international law, and ultimately to the FDI

source country government, as a means of bargaining power in a dispute or emergency. This

provides increased reassurance to nervous investors, especially in nations deemed by them to have a

higher commercial and political risk. Binding arbitration and dispute settlement provisions in BITs

(including increasing use of ICSID) should tip the balance towards investment in volatile political

or economic environments, and where the host nation legal system is deemed unreliable or

protracted.

Many BITs specify rules for nationalization or expropriation, and the principles for

determination of value, and the compensation to be paid. This may reduce both the actual

likelihood of nationalization (small as it may be in today’s FDI climate), but more importantly

reduce insurance premia as well. In some nations, BIT negotiations have been initiated at the

urging of investment insurance agencies so that they may then cover the investment risks.

Table IV.1: The Value of BITs As Seen By Investors

Declares Host Nation’s Welcoming Intent Formal Declaration of Foreigners’ Claim to Investment Assets and Legal Recourse Expropriation/Nationalization and Compensation Rules Defined and Clarified Predictability of Legal Environment and Dispute Resolution Mechanisms Stability of Regulatory Regime Equitable Treatment (Foreign vs. Domestic) (or MFN clause) Intellectual Property Provisions (in some BITs) Facilitates and Improves Returns on FDI Subrogation (in some BITs) Increases Pressure On Host nations to Have More Open and Liberal Investment

Regime

BIT negotiations are often accompanied by a discussion between the governments, of issues

relating to foreign investment in general. The acceptance by a host government, of common

provisions of BITs also increases the stability and predictability of the regulatory environment on a

range of issues from equity shares allowed to foreigners, to sectoral limitations, to the use of

expatriate personnel. From the point of view of a prospective investor, the BIT itself may only be

the visible, formalized tip of a larger set of underlying investment-related issues that the two

governments have discussed. However such bilateral negotiations often result in other investment

regulations and procedures not covered in the formal document, to be defined, regularized, clarified

and made stable.

In the eyes of several investors, BITs make a “level playing field” for domestic and foreign

investors -- that is to say provisions for equitable treatment of foreign and domestic investors. This

is especially so in the case of Most Favored Nation (MFN) clauses which the USA, in particular is

wont to insist upon (Vandevelde, 1993; WT, 1997).

OECD nations such as the USA have also pioneered the use of intellectual property

protection clauses in BITs. Other developing and transitional countries have emulated this. Much of

FDI is based on proprietary knowledge within the transnational firm which gives it an internalized

advantage over other companies. Such intangible assets, in the form of patents, copyrights and

brands are valuable to a wide spectrum of investors, particularly in high technology and service

sectors. The inclusion of intellectual property provisions in BITs often signifies not only acceptance

of the principle of intellectual property protection, but also that the signatories are committed to its

enforcement.

Other important provisions of BITs, from the perspective of investors, have to do with

remittance and repatriation of capital. This can make a crucial difference in cash flow projections

for prospective investments, in terms of discount rate used, the cost of borrowing, and insurance

premia. With respect to insurance premia, the provision of subrogation rights similarly reduces

insurance costs.

In the most general sense, the proliferation of bilateral investment treaties has increased the

degree of scrutiny on the inward foreign investment policies of developing and transitional nations,

by (a) the example set by one BIT signing being held up as a model in negotiations with another

government, thus setting up a virtuous cycle of liberalization from one country to another (b) the

inter-governmental negotiations acting as forums for the critical examination of restrictive

regulations. This is especially true for countries such as the US or ASEAN nations that have used

BIT negotiations to urge deregulation of FDI (c) BIT provisions being generalized by being

emulated in regional treaties.

D. Motivations and Determinants of Foreign Direct Investment: Explanatory Variables

1. Principal Variable and Hypothesis

From the foregoing discussion follows the principal hypothesis of this research project, that

BITs have a positive effect in promoting investment, and that the signing of BITs should be

accompanied by or followed by an increase in FDI flows to the recipient nation.

As noted, there is no theory or commonly accepted past observations which suggest how

quickly investors might respond to positive signals. Hence different time lags, ranging from zero

years, to one year, to two years, etc. will be tried out to see which provides the strongest statistical

association.

2. Secondary Variables and Hypotheses: Other Determinants of FDIThe effect of Bilateral Investment Treaties on Foreign Direct Investment may not turn out

to be significant. Even if significant, the effect of BITs may possibly be weaker compared to other

determinants of FDI. Hence the need to also use other commonly accepted host country variables in

the analysis -- such as market size and growth, exchange rate changes, inflation and capital

formation – as alternative determinants of FDI.

The literature on FDI determinants is broadly divided into firm-level analyses (e.g. Pugel,

1981) and analyses using country-specific variables (e.g. Lunn, 1983). The latter literature is, in

turn, divided into explaining patterns of outward investment from one source nation (eg.

Scaperlanda and Balough, 1983) versus cross-sectional analyses comparing inward FDI flows,

from all sources, across a sample of FDI recipient nations.

Our interest here, is in the latter type, where we will endeavor to explain variation in FDI

flows and stock across 133 host nations, based on (a) BITs signed by them and (b) the

characteristics of these FDI recipient nations. Identified below are host country-specific variables

commonly used in the academic literature to explain inflows of FDI to such nations.

a. Market Size

The size of the FDI-receiving market is one of the most frequently used variables in the literature.

It was significant in empirical studies by Kreinin, Plummer and Abe (1997), Scaperlanda and

Balough (1983), Kravis and Lipsey (1983), UNCTC (1991) and others. In one sense the notion that

FDI and market size should correlate positively, is intuitive. However, operationally speaking there

are questions and difficulties. Until recently some large markets of the former communist nations

were out of the ambit of global investment. Second, the argument of market size applies only to

local-market-oriented investments, and not to extractive, or export-platform-motivated investments

intended for markets other than the country in question. Third, operationalizing the market size

measure with GDP or equivalent measures (as most studies do) leaves the analysis open to all the

limitations of GDP as a surrogate for market size. Nevertheless, despite such caveats, market size

remains as one of the prominent determinants of FDI inflows in the academic literature.

b. Market Growth

Methodologically speaking, while FDI inflow is an appropriate dependent variable in itself,

one may also look upon it as a change in FDI stock. If the latter, then it is equally appropriate to

use, not just market size per se, but the change in market size as an explanatory variable. That is to

say, FDI flows are hypothesized to also respond to market and economic growth. Thus the change

in GDP variable was used in several studies such as Lunn (1983), Julius (1990), Kreinin, Plummer

and Abe (1997), etc. The GDP growth variable has not however produced consistent results in

various studies.

c. Exchange Rate Change

In pure theory, with exchange rates indexed to purchasing power parity (PPP), the

exchange rate variable should have no effect on FDI flow motivations. However persistent

deviations of actual exchange rates from PPP are well known. More pertinently, a sharp

devaluation creates an opportunity for foreign investors to buy assets in the country cheaply. This

was illustrated in the case of the sharp Mexican Peso devaluation after December 1994. On this

argument alone, a devaluation should be followed by a subsequent rise in FDI inflows into the

country. But the picture is, in reality, much more complicated. This is because a local currency

devaluation also impacts the future expected profit stream of foreign investors, as measured in

their own currencies, in ways that are by no means uniform. Export-oriented investments benefit

(but only so long as long as the currency remains relatively undervalued). The profits of local

market oriented investments, as measured in the foreign investor’s currency, may suffer for a

temporary or prolonged period, until price increases can be passed on to local customers; but this

depends on relative inflation, macroeconomic demand conditions accompanying the devaluation,

and price elasticity for the product in question. Hence a uniform hypothesis, applicable to all

countries is not advisable. Moreover, sharp devaluations are sporadic so there is no a priori reason

to expect the change in exchange rate variable to show up as significant, especially in a cross-

sectional study covering one year at a time. Nevertheless, since some studies like Froot and Stein

(1991) have found devaluation to be significant in explaining FDI flows, it was felt worthwhile to

try out this variable in this study.

d. Inflation

Once again, assuming PPP theory is working, i.e. that relative inflation is reflected in

continuous adjustments in the exchange rate, the inflation variable ought not, in theory, to be a

significant explanation for FDI flows. However, high inflation may in itself be a deterrent to

investors, by heightening economic uncertainty, and for this reason, a negative relationship may be

hypothesized between inflation and FDI.

e. Rate of Capital Formation

The rate of domestic capital formation is indicated as a possible FDI variable in UNCTAD (1993).

Two opposite interpretations of this variable are possible. If FDI is a complement to domestic

capital formation -- that is to say, if robust investment in a country is also accompanied by FDI

inflows -- then there should be a positive association between the two. On the other hand, if FDI is

a substitute for domestic capital formation, a substitution more likely in smaller economies, then a

negative association between the two may be expected.

f. Country “Risk” (Quality of a Nation’s Foreign Investment Climate)

Both a priori reasoning, as well as the results of previous studies (e.g. Schneider and Frey,

1985; Green and Cunningham, 1975) indicate that, ceteris paribus, the higher the perceived risk

associated with a nation, the lower would be the FDI to that nation. “Risk” scores for a country are

based on political, economic, or financial criteria which the rating agency tries to apply uniformly

across countries. Instead of the word “risk” which can be misleading, such scores are often better

regarded as indexes of the quality of the country’s investment climate The operationalization of the

country risk variable remain rather varied, depending on the agency that compiles comparative data

on the risk profiles of countries. (Examples of agencies that supply such ratings include the

Economist Intelligence Unit, Frost and Sullivan, the PRS Group, Moodys, and others). Typically,

risk scores for countries are calibrated on a 0 to 100 scale although some agencies use a letter

format such as “A”, “B”, “C”, etc. In general, the hypothesis used in previous studies is that

country risk is negatively associated with FDI.

g. Other Variables

A few additional variables have been used occasionally in other studies, such as

comparative labor rates, human capital (skills), and infrastructure development. For the most part

these variables are more relevant to certain sectors, or to particular types of investments such as

export-oriented FDI, and not to a study such as this one, encompassing large numbers of countries

and all sectors combined. On infrastructure, which is coming under increasing scrutiny as a

possible bottleneck to FDI, there is unfortunately no large scale comparative index that can be

applied across a large group of host nations. However, some “risk” rating agencies (see section

D.2.f above) attempt to include infrastructure in their assessment of a nation’s “economic” risk

score.

E. Summary of Hypotheses

To summarize, Foreign Direct Investment is deemed to be influenced by the following

explanatory variables. For each, a positive or negative sign in the parenthesis indicates its

hypothesized relationship to FDI

Box IV.1 Summary of Variables and their Expected Impact on FDI

FDI = f [BITs (+); Market Size (+); Market Growth (+);

Devaluation of Exchange Rate (+) (?); Inflation (-);

Capital Formation (?); Country “Risk” Score (-)3]

FDI is expected to be positively associated with BITs, market size, market growth,

exchange rate devaluation, and negatively associated with inflation and political risk3. The

relationship between FDI and Capital investment is hypothesized to be bi-directional.

The relationship between FDI and these variables is hypothesized to be stronger with a lag

of one to two years, following the results of UNCTAD (1991), although other studies propose

longer lags. Thus, for example, the values of independent variables in the 1994 would be expected

to expected to have the strongest relationship with FDI in 1995. Different lags will be tried out in

the time-series part of the analysis to determine the best fit.

F. Methodology and Data

3 However, note that some rating agencies such as the PRS Group, invert the scale, using a minimum of 1 for the most “risky” nation and a maximum of 100 points for the least “risky’ nation. In effect, the scale then is one describing a favorable foreign investment climate.

1. Two Stages of Analysis

The analysis was done in two stages. In stage one, a cross-sectional analysis of the

determinants of FDI, including BITs as an explanatory variable, was performed for 133 countries.

Data were collected for the period 1985 through 1995. However, since the signing of BITs has

accelerated, with two-thirds signed recently in the 1990s, a cross-sectional analysis of FDI for the

year 1995 is presented below, with data for explanatory variables going further back in time to

check for time lag effects 4.

In stage two, the effect of BITs on FDI was analyzed using data on about 200 pairs of

source and recipient countries. This stage used data from 1966 to 1995, since BITs were signed in

the 1960s and even before. Clearly however, the overwhelming bulk of the time series data are

more recent covering the period from the mid-1980s till 1995.

2. Countries In Data Set

BITS are mainly signed between developed FDI source countries, and FDI recipient

nations in the developing and transitional economies. Out of the total of 188 countries, 133

countries were identified for this study as follows:

Total 188

Subtract:

Source Countries 16

No BIT information/No BIT concluded 30

No FDI information 9

Balance 133

A list of these 133 countries which formed part of the first stage analysis is given in Annex

Table 1. Source countries are defined as FDI source nations (all OECD members) which have

signed BITs with other countries except among themselves. A list of such source countries is also

given in the Annex Table 2.

For the second stage of the analysis, 200 observations of pairs of countries were used. In

this data set, 14 source countries and 72 recipient countries are represented. For each pair [FDI 4 The results for another year such as 1994 would be roughly comparable, as far as overall conclusions are concerned.

Source Country = FDI Recipient Country] data were sought for five years before, and five years

after the year in which the BIT was signed between the two countries involved.

3. Variable Definitions

In the first stage analysis, the following dependent and independent variables were used

for the years 1993 through 1995. The dependent variables—FDI flow and stock—for the year

1995 were used with the independent variables for the years 1993 through 1995 to test for any lag

effects.

a. Dependent Variables: Stage 1

FDI has been operationalized in terms of FDI flow and FDI stock. In addition to the

absolute value of FDI flow and stock, we normalized both the variables by population, and GDP.

This is to correct for the effect of large countries, and to see how BITs affect not just FDI, but FDI

per unit of GDP, or FDI per capita, across countries. Finally, the dependent variable FDI, is

expressed in terms of its growth rate to see if the growth rate is affected by BITs or other variables.

Each of the dependent variables shown in Table IV.2 will be regressed (one at a time)

against the explanatory variables shown in Table 1V.3 below.

Table IV.2 List of Dependent Variables (Stage 1: Cross-Sectional Analysis)

Variable UnitsFDI Flow Into recipient Nation Absolute Flow (Flow) Million $ Flow per capita (Fper) $ Flow per $1000 GDP (Fgdp$) $ Flow Growth (Fgrow) Change over previous year - Ratio

FDI Stock In Recipient Nation Absolute Stock (Stock) Million $ Stock per capita (Sper) $ Stock per $1000 GDP (Sgdp$) $ Stock growth (Sgrow) Change over previous year - Ratio

b. Explanatory or Independent Variables: Stage 1

The principal explanation for FDI that is being tested is its link with BITs. In Table IV.3,

BITs are measured in alternative ways, the number signed by the recipient in a particular year; or

the total number the FDI recipient nation has signed upto and including that year; or BITs per

million of population; or BITs per million of GDP. Tests will reveal which alternative measures

for the BIT variable have the strongest explanatory power to statistically explain FDI.

At the same time, alternative explanatory variables as determinants of FDI, other than BITs,

were tested, in case the BIT variables failed to provide sufficient explanatory strength. These

include population and various GDP measures, as indexes of market size. (Alternative measures for

the GDP variable can be tried, one at a time, such as GDP per capita, or GDP growth. In the latter

case, the hypothesis is that FDI is attracted by high growth rates). Other FDI determinants include

capital investment in the FDI recipient nation, and its inflation rate. Finally, investor perceptions

regarding a country’s political, economic and financial environment are said to influence FDI

flows to the nation. Table IV.3 shows investment or risk ratings for FDI recipient nations (on a 100

= Best … 1 = Worst scale) broken out separately for economic, political, and financial ratings, as

well as a composite score combining the former three criteria. (For further details on the criteria

used to

Table IV.3 List of Independent Variables (Stage 1: Cross-Sectional Analysis)

Explanatory Variables (FDI Recipient Nation)

Description

BITs Number of BITs signed in a particular year by recipient nation

Cumulative BITs Total number of BITs signed upto and including that year

BITs per capita BITs per million of populationBITs as a ratio of GDP BITs per billion of GDP in US$Capital Investment Value of Gross fixed Capital formation

($ Billion)Composite Country Rating 100 = Best Investment Rating; 1 = Worst

(Source: PRS Group)Political Rating 100 = Best Investment Rating; 1 = Worst

(Source: PRS Group)Exchange Rate Amount of local currency per US$ - average

rate that yearExchange Rate Change Change over previous year – ratio. Exchange

rates in local currency units per $Financial Rating 100 = Best Investment Rating; 1 = Worst

(Source: PRS Group)GDP in US$ GDP in local currency/Exch. Rate – millionsGDP growth Change over previous year – ratio

GDP per capita in US$ $ GDP/PopulationPopulation Population that year – millionsInflation Rate % - annual percentage change in CPIEconomic Rating For Country 100 = Best Investment Rating; 1 = Worst

(Source: PRS Group)construct these country ratings, please refer to Annex Table 3). In the statistical analysis, these

alternative investment risk indicators are not used simultaneously, but as alternatives to each other.

c. Dependent Variables: Stage 2

For the second stage analysis, each observation consists of a pair of countries [FDI

Recipient and FDI Source]

Table IV.4 List of Variables (Stage 2: Time-Series Analysis)

Variable DescriptionPaired FDI Inflows into a recipient country (from a particular FDI source nation)

FDI inflows ($ millions) into the recipient country for each of the five years before the BIT and five years after the BIT was signed

FDI inflow into a recipient country (from a particular FDI source nation) divided by GDP (in $1000s) for recipient country

For corresponding years

Share of FDI inflow from a source country as a % of total FDI inflows of a recipient country

For corresponding years

Share of FDI inflow of a recipient country as a % of total FDI outflows of a source country

For corresponding years

The focus of Stage 2 of the analysis is on comparing the time period before and after a pair

of nations signed a BIT, to see if FDI flows between that pair of nations increased following the

BIT signing. Four FDI indicators described in Table IV.2 are to be tried. The first is simply the

FDI flow between the pair of countries. The second is the ratio of FDI over GDP of the FDI

recipient nation. The FDI/GDP ratio corrects the imbalance created by large countries in a data set.

Since FDI flows have been increasing generally for most nations over time, especially in

the last decade, it may be argued that higher FDI figures in the years following a BIT may only

reflect the general rising trend, and not specifically the impetus of a BIT. To correct for this general

bias, one my take not he FDI figures, per se, but the shares that a nation occupies in FDI flows.

The third is the share of a source country, in a recipient nation’s total inflow. The fourth is the

share of a particular recipient country in a source nation’s total outflow. If either of these increase

following the signing of a BIT, with statistical significance, then we are on firmer ground for

asserting that it was the BIT that was associated with the increase, rather than FDI increasing

generally over time.

4. Data and Data Sources

Data were obtained from various sources. BIT information was obtained from UNCTAD.

FDI flow and stock information was also received from UNCTAD who had compiled them from

the International Monetary Fund, OECD, and official national sources. Exchange rate, GDP in

local currency, and Population data were obtained from International Financial Statistics

Handbook. Dollar exchange rates were calculated from the foregoing. The country investment

ratings—composite, economic, financial, and political—were purchased from PRS Group, a

private research data firm5. Further details on these ratings is given in Annex Table 3.

While some, or extensive, information is available on every one of the 133 FDI recipient

nations (shown in Annex Table 1), there is considerable missing information on some variables for

virtually every country. The more variables one adds to an analysis, the greater the number of gaps

in the data. In fact, only 17 countries had full information, for all years, for all the variables shown

in Table IV.2. However, this data problem is not so drastic, and is manageable, if fewer variables

are used at one time6.

5. Methodology and Statistical Techniques Used

Multivariate Regression (Stage 1): Given the hypotheses which postulate a relationship

between a dependent variable and multiple independent variables across countries, multiple linear

regression was considered an appropriate statistical technique to use. Each of the dependent

variables, described in Table IV.2 were, one at a time, regressed against independent variables

shown in Table IV.3.

For all independent variables loaded at once, using the stringent criterion that an entire row

of data are eliminated if any entry for a country is missing, only 17 nations are left in the data pool.

This is too small for any meaningful statistical analysis. To address this problem, statistical

software packages7 have “missing value substitution” procedures. These can involve, for example,

5 PRS Group website address is http://www.prsgroup.com and http://www.countrydata.com6 For example, using the stepwise regression technique.7 For this analysis, the SPSS package was used

replacing missing data with the mean value for a variable. This kind of substitution however lowers

the confidence one can place in the results of a study.

An often better alternative to the missing data problem is to use Stepwise Multiple

Regression, where independent variables are loaded in steps, or one at a time, starting for instance

with the independent variable that has the “strongest” explanatory significance, followed by the

independent variable that has the second “strongest” explanatory significance, and so on. The

statistical routine then stops at a point where only a sub-set of the most powerful of the explanatory

variables are entered, leaving out the rest. Stepwise multiple regression, in a situation such as this

analysis, has three virtues:

i. Because all the independent variable are not entered, the missing data problem is

reduced, and more cases are included in the analysis.

ii. The technique serves to identify the sub-set of explanatory variables that is best able

to statistically explain the dependent variable. In our analysis, it can serve to identify

(out of the nine or more explanatory variables in Table IV.3) which few were best in

explaining FDI flows.

iii. Most importantly for the purpose of this study, a stepwise regression using the

“strongest independent variable loaded first” specification would inform us whether

BITs – as opposed to other FDI determinants – were the better explanation. That is

to say, if the BIT variable were loaded first that would indicate (with caveats8) that

of al the variables the BIT variable was the most statistically important. On the other

hand, if the BIT variable is selected for entry in later steps, that would diminish its

importance relative to other FDI determinants. And if the BIT variable were not

selected at all, then BITs could not be said to play an explanatory role, for that

particular regression run.

It should be emphasized that even without using the stepwise regression technique, there are

several independent variables that should not, on a priori grounds, be entered together. That is to

say, some of the variables in Table IV.3 are redundant, and may be used as alternatives to each

other, and not together. As one example, country investment ratings are broken into several sub-

8 One can only draw this deduction weakly, and not with full assurance, since interaction effects with other variables and multicollinearity may sometimes cause a variable to be loaded in an early step even though it is “weaker” compared with the rest.

categories, such as “political”, “economic”, and “financial.” Scores were obtained for each country

on each of these sub-variables. However, both in theory, and in practice, it is difficult to

disentangle political, from financial, from economic risks. Such distinctions may provide some

value to investors who wish to track one country’s performance over time, but in a study involving

over a hundred nations, they may not be meaningful. Moreover, the sub-categories are often

strongly correlated. Hence they may be used as alternatives to each other, one at a time.

Additional variations to the regressions involved removal of three countries shown to be

egregious outliers (China, Singapore, and Hong Kong)9 and log transformation of independent

variables (in case some exhibited a non-normal distribution. See Annex table 4 for a break down of

the 24 x 8 = 192 regressions performed. Yet more variables, such as independent variables with

mean value substitution, and regression runs on a regional basis (as shown in Annex 5) raised the

total number of regression runs to 264 in number, overall.

Euclidean Distance (Stage 1): In addition to the regression technique, n-dimensional

Euclidean distance or “pattern analysis” was used to test for the relationship between FDI and the

profile of countries in terms of these independent variables. This technique involves identifying a

small top percentile of the countries in terms of FDI (or outcome variables) and determining the

profile of this ideal or desired group in terms of the mean values on the independent variables.

Once the desired profile is determined, then for each of the remaining countries, called the sample

countries, a distance measure is computed which measures the distance of each of these countries

from the ideal profile in a multi-dimensional space. If the distance from the ideal profile is greater,

then it is hypothesized that FDI will be lower; in other words, the correlation between the distance

and FDI is supposed to be negative and significant.

Distance is computed as a squared Euclidean distance. However, given the differences in

the scales being used to measure the several independent variables – for instance, one variable

could be in $ Billions, while another is a percentage, or another a ratio -- another measure called

the Mahalanobis distance is more appropriate. The Mahalanobis procedure first standardizes each

9 Statistical practice often recommends removal of clear outliers in order to improve the statistics and significance of remaining variables. This requires no a priori theory. However, ex post, we know from similar FDI studies that China often stands out as a gross outlier. Hong Kong and Singapore also stand out in some studies because of the relatively small size of their populations.

variable by subtracting its mean and dividing by its standard deviation. This reduces each variable

to a comparable scale.

Box IV.2 Mahalanobis Distance n _

Distance = ( xsk - xik )/s.d.ik

k=1where, k=variable

n=number of variabless=sample group, and i=ideal group

_s.d.=standard deviation and x = mean

T-tests of Differences In Group Means (Stage 2): For the time series data covering some

200 pairs of nations, the intent is to see if FDI had increased subsequent to a BIT signed between

the pair of countries. For this purpose t-tests of differences in the means of the “before BIT” vs

“after BIT” year FDI is a strongly appropriate technique. Data were recorded on FDI for as much

as five years before and five years after a pair of nations signed their BIT. This enables several

comparisons to be made. For each of the dependent variables, FDI, FDI/GDP, FDI Share In Inflow,

FDI Share in Outflow, one can compare different time periods – for instance, Years (-5 to -1)

against Years (1 to 5), or Years (-2 to 1) against Years (2 to 5), where Year 0 is defined as the BIT

year, and so on, in a series of repetitive comparisons for the total time period sliced into different

segments for comparison. In fact 54 comparisons were made for each of the four variables for a

total of 216 comparisons.

G. Results and Discussion

1. Multivariate Regression (Stage 1)

For the first stage analysis, eight different dependent variables for 1995

Absolute Flow (Flow) Flow per capita (Fper) Flow per $1000 GDP (Fgdp) Flow Growth (Fgrow) Absolute Stock (Stock) Stock per capita (Sper) Stock per $1000 GDP (Sgdp) Stock growth (Sgrow)

were computed and regressed with independent variables shown in Table IV.4. for three different

years—1995, 1994, and 1993, for a total of 192 regression runs, using a variety of techniques such

as log transformed variables, and removal of outlier nations. For most readers however, the 192

combinations and their methodological details may not be of interest. These are summarized in

Annex Table 4.

Only salient results for and overall conclusions are presented in this section which covers

the Stepwise Regression method for all countries (including China, Singapore and Hong Kong)

and where untransformed variables on a normal basis were entered. As discussed above in section

IV.F on methodology, stepwise regression with forward inclusion is a technique helps to identify

the sub-set of explanatory variables that have the “strongest’ significance, i.e. explanatory power

vis a vis the dependent variable. The technique enters variables one at a time, starting with the

“strongest” until the adjusted R2 stops growing or until minimum loading criteria can no longer be

fulfilled to justify the loading of the rest. The sub-set of variables thus entered comprises the

resultant regression equation. The results are shown in Table IV.5 below for only three of the eight

dependent variables, “FDI Flow,” “FDI Stock,” and “FDI Over GDP,” The results for the other

five dependent variables were spotty and are only summarized in Annex 4.

In Table IV.5 order of loading of the independent variables is from left to right. The overall

conclusion is that indexes of FDI recipient market size, such as population and GDP, are the

leading determinants of FDI. It was only in equation No. 9 in Table IV.5 that the Bit variable is

loaded first. Overall, the Bit variable appears only twice in Table IV.5. In general, these results

suggest that BITs play only a secondary, and very minor role, in an analysis comparing a large

number of countries against each other, cross-sectionally. (Conclusions have not yet been drawn for

the time series analysis, which looks at individual countries over time).

Looking at individual dependent variables, FDI flow (Equation numbers 1,2, and 3) is

consistently a function of Population, GDP measured in $, and Capital Investment. When

regressed with lagged variables, Bit(’93) partially explains FDI flow with a two-year lag.

FDI stock (Equations 4, 5, and 6) is also consistently a function of Population, GDP

measured in $, and Capital Investment. Political Risk also seems to explain FDI stock in some

contexts.

Table IV.5 Regression Results

1. FDI Flow (‘95) = - 18.907 + 25.159 Population(’95) + 0.01911 GDP$(‘95) – 45.890 Capital(‘95)(-0.079) (27.422)** (5.527)** (-3.939)**

Adj. R2 = 0.992 F = 651.869 P = 0.0002. FDI Flow (’95) = - 0.634 + 27.180 Population(’94) + 0.02241 GDP$(’94) - 56.321 Capital(’94)

(-0.003) (28.873)** (5.306)** (-3.785)**Adj. R2 = 0.991 F = 651.869 P = 0.000

3. FDI Flow(’95) = - 493.582 + 25.634 Population(’93) + 0.02167 GDP$(’93) - 52.531 Capital(’93) + 162.744 Bit(’93)(-1.576) (24.971)** (5.973)** (-3.885**) (2.317)*

Adj. R2 = 0.994 F = 661.535 P = 0.0004. FDI Stock(’95) = 52544.739 + 0.433 GDP$(’95) - 963.008 Capital(’95) - 868.542 Political Rating(’95)

(1.989) (10.908)** (- 6.788)** ( - 2.199)*Adj. R2 = 0.955 F = 92.548 P = 0.000

5. FDI Stock(’95) = -4527.933 + 0.502 GDP$(’94) - 1230.342 Capital(’94) + 49.980 Population(’94)(-1.609) (10.316)** (-7.174)** (4.607)**

Adj. R2 = 0.949 F = 99.873 P = 0.0006. FDI Stock(’95) = - 3362.031 + 0.493 GDP$(’93) - 1187.801 Capital(’93) + 34.902 Population(’93)

(-1.377) (11.199)** (-7.264)** (3.265)**Adj. R2 = 0.960 F = 128.714 P = 0.000

7. Fgdp$(’95) = 15.625 + 0.02903 Population(’95) (6.614) (3.663)** Adj. R2 = 0.48 F = 13.417 P = 0.0028. Fgdp$(’95) = 15.628 + 0.02933 Population(’94)

(6.615) ( 3.660)** Adj. R2 = 0.437 F = 13.413 P = 0.0029. Fgdp$(95) = 6.706 + 3.019 Bit(’93) (1.909) (4.158)** Adj. R2 = 0.504 F = 17.24 P = 0.001Notes:a. The results represent Stepwise Regression and the variables are ordered in the sequence of highest contribution.b. In each regression result, the second row with numbers in parenthesis represents “t” values for the coefficients. Significance levels ** better than .01; * better than .05.c. Regressions were tried with each of the eight dependent variables for three years—1995, 1994, 1993—and only these nine regressions had large enough R 2 and P values.d. All coefficients of independent variables are significant at least at the 0.05 level.

Results for Fgdp$ (FDI flow per $ 1000 of GDP of recipient nation in Equations 7, 8 and 9)

are not as strong. The equations’ adjusted R2 is lower, albeit highly significant. However in

Equation 9 Bit(’93) was highly significant and loaded first.

Population and GDP, two indicators of market size, are positively and significantly

associated with FDI flow and stock as was hypothesized. Capital Investment is consistently,

negatively and significantly associated with FDI flow and stock. This result partially supports the

arguments made earlier on this relationship. When BIT was significantly associated with FDI flow,

it was positive indicating that an increase in BIT is associated with an increase in FDI flow, but

with a lag of two years, thus leading to an acceptance of the hypothesis. Political risk, while being

significant, is negatively correlated. Given the fact that the political risk factor was in increasing

order of favorable conditions (a score of 1 being high risk and a score of 100 being low risk), the

negative result does not support our hypothesis.

The data were next divided into five regions, 1. Africa; 2. Central and Eastern Europe; 3.

East and Southeast Asia; 4. Latin America and Caribbean; 5. West Asia. Additional regression

runs were performed on each region separately to see if there were any regional variations

regarding the significance of BITs. A summary of results are shown in Annex Table 5. BIT-related

independent variables were frequently included in runs involving only one dependent variable

Flow(’95), for all regions except West Asia. Moreover, because the division of data into regions

resulted in incomplete data, a mean value substitution procedure had to be instituted10. However,

we then cannot place too much reliance in the results.

The overall conclusion from Table IV.5 (and Annex Tables 4 and 5) is that BITs play a

minor and secondary role in a cross-country comparision of FDI determinants, and that in keeping

with other studies such as UNCTAD (1991) or Kreinin, Plummer and Abe (1997), Scaperlanda and

Balough (1983), market size appears to be the leading determinant of FDI.

2. n-Dimensional Pattern Analysis (Stage 1)

Pattern analysis measures the n-dimensional Euclidean distance for standardized variables,

to a desired or ideal point. In our analysis, it measures the “distance” between a nation and a

10 Without this no statistically significant results would have been obtained

desired subset of countries which exhibit a high FDI. From this one can deduce if BITs are

associated with high FDI.

Table IV.6. Pattern Analysis Correlation

Distance Measure Distance MeasureWith 1994 variables With 1995 variables

FDI Flow -95 0.394* 0.580** FDI Stock -95 0.478* 0.456* Flow/GDP$ -95 - 0.384* Stock/GDP$-95 - 0.417* Stock Growth-95 0.376* * Correlation significant at the 0.05 level (1-tailed) ** Correlation significant at the 0.01 level (1-tailed).

The pattern analysis results do not consistently support the hypothesis that a deviation from

an ideal BIT profile will necessarily result in lower FDI levels. The results are not only mixed in

direction but also not very strong. This could be due to the fact that some of the variables are

correlated among themselves.

3. T-tests of Differences In Group Means (Stage 2: Time Series Data)

In the second stage of analysis, the focus of the analysis is on how FDI flows between a pair

of countries changes over time. Did the FDI flows between just these two countries increase

following the signing of their BIT ? When pairs of nations are examined over several years, this

can provide a time-based perspective that the foregoing cross-country could not give. However,

obtaining FDI data between pairs of countries is not easy. These data on BITs and FDI, from 1971 -

1994, were obtained from a variety of sources including UNCTAD, OECD, and some country

governments. Data on 72 FDI recipient countries, and 14 source nations, thus comprise 200 pairs of

observations.

a. The Research Questions

For each pair of countries, FDI and other data were recorded for four years before, and five

years after, the BIT signing. For each pair Year 0 is called the BIT year, and the data go from Year

- 4 to Year +5.

Year -4 Year 0 Year +5|________________||___________________|

BIT Year

The main interest is on how the mean values of FDI in the years before the BIT was signed,

compare with the mean values of FDI in the years after the BIT. One can make comparisons for

periods spanning three, four and five years each, before and after. Thus, a total of 54 pairs of time

periods (before versus after combinations using different cutoff points) were tested over the 200

observations. Annex Tables 7 to 11 show the 54 combinations for different groups of countries.

The reason for computing 54 time period comparisons is to determine if lag effects can be seen.

The 200 observations were also later split into five regions, Africa; Central and Eastern

Europe; South, East and Southeast Asia; Latin America and Caribbean; and West Asia, to examine

any regional effects. Similar tests were done on each region.

Measurements were made for a pair of countries, over the above 10 year span on four

criteria:

1. FDI Flow between the pair of nations

2. Ratio of “FDI/GDP of Recipient Nation”

3. Ratio of “FDI Inflow into Recipient Nation from Particular Source Country in

Question/

Total FDI Inflow into Recipient Country” (i.e. the share of a particular source

country in a recipient’s overall FDI inflow)

4. Ratio of “FDI Outflow from a Source Country to a Particular Recipient Nation/

Total Outflow from Source Country” (i.e. this is the share that a particular recipient

nation occupies in a source country’s total FDI outflow)

Dividing FDI by GDP of the recipient nation corrects for the fact that FDI grows over time,

with economic growth. The FDI/GDP ratio is a better index of growing foreign investment in a

country. An even more focused indicator is the share that a particular source nation occupies in the

total FDI inflow into a country.

For example, if after Zambia and Japan sign a BIT, Japan’s share

in Zambia’s FDI inflow increases, then we can suggest that the BIT the

two nations signed was associated with Japan’s increased share of FDI in

Zambia.

By the same token, the share that a particular recipient nation occupies in a source country’s

total FDI outflow is another focused indicator.

For example, if after Bangladesh and Italy sign a BIT,

Bangladesh’s share in Italy’s total FDI outflow increases, than we can

suggest that the BIT signed by the two countries was associated with

Bangladesh’s increased share in Italy’s FDI outflow.

Unfortunately, the data for a single pair of countries is too small a set of observations to

allow for statistical testing. Therefore testing has to be done for groups of nations, and for the entire

global set of observations. For each before/after comparison, a T-test of differences in group means

was conducted to test for significant differences. While simple, this is very robust technique whose

results cannot be questioned (even in the face of a moderate amount of missing data). Since our

hypothesis is that BITs increase FDI, a one-tailed test is appropriate.

For each set of tests, for each of the four test measures indicated above, there is a maximum

of 54 comparisons of group means. (Please refer to Annex 6 for further details).Two salient

questions were asked:

1. How many of the 54 tests in each category were statistically significant ?

(The larger the number, the stronger the association between BITs and

subsequent FDI increases. These results are summarized in Annex Table

6).

2. What lags can be deduced by examining the pattern of statistically significant results

?

(This can be seen in Annex Tables 7 - 11)

Table IV. 7 Comparison of Before and After Means for FDI/Inflow Variable (ALL NATIONS) (37 out of 54 Maximum Possible Comparisons Were Significant As Shown Below)Number Mean FDI share for period Mean FDI share for period Difference P

1 2yrs before BIT to 1yr before BIT BIT year to 1yr after BIT -7720.21 .033**2 2yrs before BIT to 1yr before BIT BIT year to 2yrs after BIT -10992.5 .029**3 2yrs before BIT to 1yr before BIT BIT year to 3yrs after BIT -13483.3 .05**4 2yrs before BIT to 1yr before BIT 1yr after BIT to 2yrs after BIT -15413.1 .041**5 2yrs before BIT to 1yr before BIT 1yr after BIT to 3yrs after BIT -17639.4 .065*6 2yrs before BIT to 1yr before BIT 1yr after BIT to 4yrs after BIT -12680.6 .054*7 2yrs before BIT to 1yr before BIT 2yrs after BIT to 3yrs after BIT -21609.4 .061*8 2yrs before BIT to 1yr before BIT 2yrs after BIT to 4yrs after BIT -14304.8 .057*9 2yrs before BIT to 1yr before BIT 2yrs after BIT to 5yrs after BIT -9152.24 .083*

10 3yrs before BIT to 1yr before BIT BIT year to 1yr after BIT -1830.69 .073*11 3yrs before BIT to 1yr before BIT BIT year to 2yrs after BIT -8101.55 .048**12 3yrs before BIT to 1yr before BIT BIT year to 3yrs after BIT -10600.5 .080*13 3yrs before BIT to 1yr before BIT 1yr after BIT to 2yrs after BIT -12345.4 .064*14 3yrs before BIT to 1yr before BIT 1yr after BIT to 3yrs after BIT -14627.9 .093*15 3yrs before BIT to 1yr before BIT 1yr after BIT to 4yrs after BIT -9761.5 .095*16 3yrs before BIT to 1yr before BIT 2yrs after BIT to 3yrs after BIT -18054.9 .083*17 3yrs before BIT to 1yr before BIT 2yrs after BIT to 4yrs after BIT -10928.4 .097*18 4yrs before BIT to 1yr before BIT BIT year to 1yr after BIT -8812.81 .041**19 4yrs before BIT to 1yr before BIT BIT year to 2yrs after BIT -11814.9 .024**20 4yrs before BIT to 1yr before BIT BIT year to 3yrs after BIT -14205.5 .042**21 4yrs before BIT to 1yr before BIT 1yr after BIT to 2yrs after BIT -16240.7 .033**22 4yrs before BIT to 1yr before BIT 1yr after BIT to 3yrs after BIT -18380.3 .055*23 4yrs before BIT to 1yr before BIT 1yr after BIT to 4yrs after BIT -13556 .049**24 4yrs before BIT to 1yr before BIT 2yrs after BIT to 3yrs after BIT -22335.6 .047**25 4yrs before BIT to 1yr before BIT 2yrs after BIT to 4yrs after BIT -15254.9 .049**26 4yrs before BIT to 1yr before BIT 2yrs after BIT to 5yrs after BIT -10250.8 .097*27 2yrs before BIT to BIT year 1yr after BIT to 3yrs after BIT -49261.3 .095*28 2yrs before BIT to BIT year 2yrs after BIT to 3yrs after BIT -19323.1 .046**29 2yrs before BIT to BIT year 2yrs after BIT to 4yrs after BIT -13094.7 .042**30 2yrs before BIT to BIT year 2yrs after BIT to 5yrs after BIT -8684.93 .073*31 3yrs before BIT to BIT year 2yrs after BIT to 3yrs after BIT -17985.2 .055*32 3yrs before BIT to BIT year 2yrs after BIT to 4yrs after BIT -11819.9 .060*33 4yrs before BIT to BIT year 1yr after BIT to 2yrs after BIT -49583.2 .094*34 4yrs before BIT to BIT year 1yr after BIT to 3yrs after BIT -50635.2 .087*35 4yrs before BIT to BIT year 2yrs after BIT to 3yrs after BIT -21321 .033**36 4yrs before BIT to BIT year 2yrs after BIT to 4yrs after BIT -15152.2 .032**37 4yrs before BIT to BIT year 2yrs after BIT to 5yrs after BIT -10775.1 .065*

**. Significant at the level .05 (one-tailed), *. Significant at the level .10 (one-tailed)

For further details refer to Annex Table 8.

b. Results and Conclusions of Stage 2 Analysis

The Overall Pattern of Significant T-tests : (This summarizes the overall conclusions from Annex Table 6 which interested readers may scrutinize).

The results are not strong11, but unmistakably ubiquitous over all four variables

(FDI, FDI/GDP, FDI/Inflow, and FDI/Outflow) to suggest that BITs do have an effect on FDI.

Of the four variables, FDI/Inflow and FDI/Outflow registered the relatively largest number of

statistically significant results. This suggests that BITs may serve at the margin, to redirect the

share of FDI from /to BIT signatories.

For all nations taken together, the strongest results are in the FDI/Inflow category

where as many as 37 out of 54 tests were significant. These results are summarized in Table

IV.7 and their statistics shown. Further details may be seen in Annex Table 8. The negative sign

for the “difference” is because, as per expectation, the FDI share before the BIT was lower than

the FDI share after the BIT. The consistent negative sign in this category (and indeed in

virtually all results in other Annex tables) is a sign of an unmistakable, if weak11, effect.

The fact that these two FDI share variables produced superior results to FDI alone or

the FDI/GDP ratio, is gratifying because the share measures are far more unequivocal measures

of the role of BITs than the other two variables.

In terms of specific regions, BITs with African nations appear to have more effect

than in other areas. In particular, The share of FDI inflow from a particular source country is

more likely to be affected by BITs when the FDI recipient signatory is an African nation.

In the case of South, East, and Southeast Asia, BITs may be more instrumental in

redirecting the share of FDI outflows from source nations.

Time Lag Effects:

(This summarizes the pattern of results in Annex Tables 7 through 11).

By examining which time period comparisons are significant, one may deduce the lag

response between BIT signing (Year 0) and FDI response. Table IV.7 (and related Annex Table 8)

for the FDI/Inflow variable for all countries, reveals no particular pattern at all in the many

significant results. On the other hand, for the FDI variable for all countries (Annex Table 7) shows

11 Some may assert that significance between the 0.05 and 0.1 level is somewhat weak. However its use is common in many similar studies. Moreover, t-tests of differences in group means is a robust and unambiguous enough technique, that even a better than 0.1 significance is acceptable.

significant results only in Year 2 after the BIT. For Africa, with the FDI/GDP variable (Annex

Table 9) a similar pattern is seen, where it is mainly comparisons involving the period starting in

Year 2 that show significance. On the other hand, for Africa with the FDI/Inflow variable, the

significant entries are in Year 1 after the BIT, or in Year 0 (the BIT year itself). Finally, in Central

and Eastern Europe (Annex Table 11) the pattern of significant entries again is in comparisons

starting in Year 2 after the BIT.

The results are sufficiently mixed that only a tentative and hesitant conclusion can be

drawn, namely that the response lag after the signing a BIT may be as little as zero, but is more

likely to be two years.

H. A Summary of Conclusions

In a cross-country comparison of FDI determinants, the overall conclusion is that BITs play

a minor and secondary role, compared with other determinants of FDI, of which the size of the

recipient nation’s market is most prominent. The finding that the size of the market and economy is

an important factor is in keeping with several previous studies such as UNCTAD (1991), Kreinin,

Plummer and Abe (1997), and Scaperlanda and Balough (1983).

The time series data, using pairs of BIT signatory countries, showed somewhat stronger

results. The role and influence of BITs is weak but unmistakable, especially in redirecting the share

of FDI received from BIT signatory nations. That is to say, following the signing of a BIT, it is

more likely than not, that the FDI recipient nation will marginally increase its share in the outward

FDI of the source country. The effect however, is usually small.

How long does it take for the effect, if any, to be seen following the signing of the BIT ?

The analysis can only supply an extremely tentative conclusion to this question, that while the

response of investors can be immediate, it is more likely to be in the one to three year range.

Clearly, the signing of BITs is no panacea. Moreover, since many such treaties have been

signed frequently in recent years (two-thirds in the 1990s) the distinctiveness of a treaty, as a

competitive signal to attract investment, may have been somewhat eroded. Rather, BITs may, in the

future be simply regarded as a necessary condition, as a normally expected feature on the part of

investors.

REFERENCES

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Annex Table 1: List of 133 Recipient Countries included in the study Albania Algeria Antigua and Barbuda Argentina Armenia Bahrain Bangladesh Barbados Belarus Belize Benin Bolivia Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Chile China Colombia Congo Costa Rica Cote d'lvoire Croatia Cuba Cyprus Czech Republic Dominican R Dominica Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Gabon Gambia Ghana Greece Grenada Guinea

Guinea-Bissau Guyana Haiti Honduras Hong Kong Hungary Iceland India Indonesia Iran, Islamic Republic Iraq Israel Jamaica Jordan Kazakhstan Kenya Korea, Republic of Kuwait Kyrgyzstan Lao, People's Democratic Republic Latvia Lebanon Lesotho Liberia Lithuania Macedonia Madagascar Malawi Malaysia Mali Malta Mauritania Mauritius Mexico Moldova, Republic of Mongolia Morocco Namibia Nepal New Zealand Nicaragua Niger Nigeria Oman

Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Romania Russian Federation Rwanda Saint Lucia Saint Vincent & the Grenadines Saudi Arabia Senegal Sierra Leone Singapore Slovakia Slovenia South Africa Spain Sri Lanka Sudan Swaziland Syrian Arab Republic Taiwan, Province of China Tajikistan Tanzania Thailand Togo Trinidad & Tobago Tunisia Turkey Uganda Ukraine United Arab Emirates Uraguay Uzbekistan Venezuela Viet Nam Yemen Zaire Zambia Zimbabwe

Annex Table 2:

List of Source Countries and the number of BITs signed by them

Country Number of BITs

1. Germany 1032. United Kingdom 853. Switzerland 754. France 735. Netherlands 566. Italy 457. Belgium and Luxembourg 398. United States of America 379. Denmark 3610. Sweden 3311. Finland 2912. Austria 1813. Norway 1514. Australia 1415. Canada 10

16. Japan 4

Annex 3 Definition of Variables - Country Investment Ratings

The ICRG Risk Rating criteria developed by PRS(Political Risk Services) Group. The ICRG Risk Rating System rates political, economic, and financial risks, breaking each are down into its key components, as well as compiling composite ratings and forecasts.

Annex Table 3 : Criteria Underlying Country Investment Risk Indicators

Components Indicators of Country Investment Climate and Risk Political economic expectations vs. reality; economic planning failures; political

leadership; external conflict; corruption in government; military in politics; organized religion in politics; law and order tradition; racial and nationality tensions; political terrorism; civil war; political party development; and quality of the bureaucracy

Financial loan default or unfavorable loan restructuring; delayed payment of suppliers’ credits; repudiation of contracts by governments; losses from exchange controls; and expropriation of private investments

Economic inflation; debt service as a percent of exports of goods and services; international liquidity ratios; foreign trade collection experience; current account balance as a percent of goods and services; and parallel foreign exchange rate market indicators

Composite Rating

The Composite Risk Rating is determined by combining the political, financial, and economic risk ratings.

The highest overall rating (100 points) indicates the best investment climate and the lowest risk, and the lowest rating (1) indicates the highest risk and poorest environment.

Source: PRS Group

Annex Tables 4 and 5Details on Regression Runs Carried Out In Cross-Sectional Stage 1 Analysis

___________________________________________________________________

The body of the report shows only nine of the most significant regression equations

using the stepwise forward inclusion technique in SPSS (Statistical Package for the Social

Sciences), for three dependent variables, each for three years.

However, a total of 264 regression runs were undertaken in a comprehensive search for

determinants of FDI, including BITs. The results for 192 are summarized in Annex Table 4.

The results for a further 60 are summarized in Annex Table 5. The remaining 12 are not

shown.

192 Regressions : Annex Table 4

The 192 regressions result from the combination of

Eight Dependent Variables (Flow(95), Fper(95), Fgdp$(95), Fgrow(95),

Stock(95), Sper(95), Sgdp$(95), Sgrow(95))

Two Loading Methods (Total = All independent variables entered together;

Stepwise = Stepwise forward inclusion)

Three Years (1995, 1994,1993)

Countries Excluded/Included (All = All 133 countries; Without = Without

China, Singapore, Hong Kong)

Transformation of Independent Variables (Normal = Untransformed; Log =

Log transformed)

Annex Table 4 does not show the actual statistics relating to each equation, but only

summarizes which independent variable achieved at least a 0.05 significance level. In this way,

an overall picture or pattern emerges as to which of the independent variables, including BITs,

emerge as possible determinants of FDI. Many of the regression runs, indicated by blanks,

failed to have even one of the independent variables achieve significance. This was more so in

the case of log transformed variables. Apparently, the data did not need transformation in the

first place. At any rate, for a priori reasons explained in Chapter IV, the main focus in Annex

Table 4 ought to be on section V. Stepwise/All/Normal, where all variables, on a normal

untransformed basis, are entered stepwise. These indeed yielded the best results. Detailed

statistics for the equations in the shaded boxes in Annex Table 4 are shown in the body of the

chapter in Table IV.5.

60 Regressions : Annex Table 5

These 60 regressions resulted from a combination of

Four Dependent Variables (Flow(95), Fper(95), Fgdp$(95), Fgrow(95))

Five Regions (Africa; Central and Eastern Europe; East and Southeast Asia;

Latin America and Caribbean; West Asia)

Three Years (1995;1994;1993)

BIT-related independent variables (Nos. 1 and 2) are frequently included in runs

involving the dependent variable Flow(’95) for all regions except West Asia. Because the

division of data into regions resulted in few complete data, a mean value substitution procedure

had to be instituted, without which no statistically significant results would have been obtained.

Having done so however, we cannot place too much reliance in the results.

Annex Table 4 and Annex Table 5 are shown on the next page.

Annex Table 4SUMMARY OF INDEPENDENT VARIABLES a LOADED IN 192 REGRESSION RUNS FOR EIGHT DEPENDENT VARIABLES

Flow(95) Fper(95) Fgdp$(95) Fgrow(95) Stock(95) Sper(95) Sgdp$(95) Sgrow(95)I. Total / All / Normal 1.Independent variable(95) 13, 1,3,8 3,8,13 2.Independent variable(94) 3,6,8,11,12,13 3,8,9,10,11 9,11,13 1,2,4,8,9,10,

11,123,8

3.Independent variable(93) 13, 3,8 II. Total / Without / Normal 1.Independent variable(95) 2,3,8 2.Independent variable(94) 11, 3,9,10,11 9,11 7,9,10,11,12

,13 3.Independent variable(93) 1,2,3,6,9,10,11,12,13 3,8 III. Total / All / Log 1.Independent variable(95) 2.Independent variable(94) 3.Independent variable(93)IV. Total / Without / Log 1.Independent variable(95) 2.Independent variable(94) 3.Independent variable(93)V. Stepwise / All / Normal 1.Independent variable(95) 3,8,13 10, 13 3,8,12 1, 2.Independent variable(94) 3,8,13 10, 13 3,8,13 10, 1, 3.Independent variable(93) 1,3,8,13 10, 1 3,8,13 10, 1,VI. Stepwise/ Without / Normal 1.Independent variable(95) 3,8, 10, 10, 3,8 1, 2.Independent variable(94) 1,3,8,10 10, 1,8 3,8 3,8 3.Independent variable(93) 3,8 11, 3,8 3,8 1,VII. Stepwise / All / Log 1.Independent variable(95) 2, 6,13 6, 6,7,8,11 2,10 1,4, 2, 2.Independent variable(94) 10, 1, 6,8 2,10 3, 2, 3.Independent variable(93) 10, 1,10 2,VIII. Stepwise / Without / Log 1.Independent variable(95) 2, 6,13 6, 6,7,8,11 2,10 1,4 2, 2.Independent variable(94) 10, 1, 6,8 2,10 3, 2, 3.Independent variable(93) 10, 6,8 1,10 2,

Independent variables list 1. Bit 2. Bto 3. Cap 4. Com 5. Eco 6. Exgrow 7. Fin 8. Gdp$9. Ggrow 10. Gper$ 11. Inf 12. Pol 13. Pop

a Note : This table lists variables whose probability value is better than 0.05(one-tailed). Shaded boxes above correspond to Table IV.5.

Annex Table 5SUMMARY OF INDEPENDENT VARIABLES LOADED a IN 60 STEPWISE REGRESSIONS FOR SEPARATE REGIONS

REGION Flow(95) Fper(95) Fgdp$(95) Fgrow(95)I. Africa 1.Independent variables(95) 1, 11, 13 1, 10 2.Independent variables(94) 2, 3, 6, 13 8, 10 3.Independent variables(93) 4, 8, 13 8, 10II. Central & Eastern Europe 1.Independent variables(95) 2, 7, 8 7, 2.Independent variables(94) 2, 8, 12 7, 3.Independent variables(93) 1, 8, 13 4, 13III. East & Southeast Asia 1.Independent variables(95) 1, 2, 13 3, 10 10, 6, 11, 13 2.Independent variables(94) 2, 4, 8, 13 10, 10, 6, 3.Independent variables(93) 1, 7 3, 12 12, 3, 10, 12IV. Latin America & Caribbean 1.Independent variables(95) 1, 3, 8, 11, 13 12, 2.Independent variables(94) 2, 5, 6, 13 3.Independent variables(93) 8, V. West Asia 1.Independent variables(95) 3, 10, 2.Independent variables(94) 8, 13 10, 3.Independent variables(93) 8, 9

Independent variables list : 1. BITs in year 2. Cumulated # of BITs 3. Capital Investment 4. Composite Risk5. Economic Risk 6. Exchange Rate

Growth7. Financial Risk 8. GDP in $

9. GDP Growth 10. GDP per capita 11. Inflation 12. Political risk13. Population

Note a. Including variables whose probability value is less than 0.05(one-tailed) b. Mean value substitution procedure used.

Annex 6

Summary of Stage 2 Time Series T-test Results________________________________________________________________________________

For each criterion in Annex Table 6

1. FDI: FDI Flow between the pair of nations

2. FDI/GDP: Ratio of “FDI/GDP of Recipient Nation”

3. FDI/Inflow: Ratio of “FDI Inflow into Recipient Nation from Particular Source

Country in Question/

Total FDI Inflow into Recipient Country” (i.e. the share of a particular source

country in a recipient’s overall FDI inflow)

4. FDI/Outflow: Ratio of “FDI Outflow from a Source Country to a Particular

Recipient Nation/ Total Outflow from Source Country” (i.e. this is the share that a

particular recipient nation occupies in a source country’s total FDI outflow)

the analysis was performed on either all 72 recipient nations (Global Analysis) or on selected

regions (Regional Analysis).

Handling Missing Data: For each category, the words “No adjustment” refer to the fact that

the data are unadjusted and a calculation is removed by the computer if a value is missing. This is a

stringent approach and sometimes voids statistical results even if underlying patterns exist. The

missing data are not so extensive (see Raw data File), but the gaps are pervasively scattered

throughout the data. As a result, to use only data that are absolutely complete in every entry, would

mean a large loss of observations from 200 to below 50 which is inadequate for statistical analysis.

“Missing Value Adjustment” refers to a program which supplies a mean value in lieu of the missing

datum. Since the extent of the missing data was not large (albeit pervasive), this substitution is

acceptable, especially for as robust a test as a comparison of group means. In the OECD data

several entries are reported as a dash “ - “ which can mean either a zero value, or a missing entry.

In most cases it is likely that the reason for the missing entry was because the FDI flow was in fact

zero, or near zero. In order to try and improve the number of observations in the analysis, another

set of calculations were made replacing a dash “ - “ with a zero. Because of our uncertainty, results,

if different, must be taken with caution.

Interpreting Numbers in parentheses ( ) In Annex table 6: Each cell of the matrix for Parts

I and II summarizes the results of 54 t-tests. The numbers in the parentheses ( ) report how many

of the 54 t-tests were significant. For example, in Annex Table 6 Part II.1.2 for Africa and

FDI/GDP, shows eight (8)* t-tests as having reached a 0.1 level of significance, and nine (9)** t-

tests as having reached a 0.05 level of significance – for a total of 17 t-tests out of a maximum of

54. This is a summary of information from Annex Table 9 which actually shows the statistics.

17 significant out of 54 is somewhat low. On the other hand, for Africa, the FDI/Inflow

share variable shows as many as (26)* + (3)** = 29 out of 54 as being significant. This summarizes

information in Annex table 10. And for all countries, Part I.1.2 for the FDI/Inflow share variable as

many as (21)* + ( 16)** = 37 tests out of 54 were significant. This summarizes information in

Annex Table 8 and in Table IV.7 in the chapter.

_________________________

(The shaded portions of Annex Table 6 summarize information in Annex Tables 7, 8, 9, 10, and

11).

Annex Table 6OVERVIEW OF STAGE 2 RESULTS: Numbers In Parentheses Indicate the number of significant T-tests In Each Cell a

(1) FDI (2) FDI/GDP (3) FDI/Inflow (4) FDI/OutflowI. Global Analysis: 72 Recipient Nations 1. Full data set (200 observations) 1.1 No adjustment (Stringent criterion) *(1) *(1) *(21) *(6) , **(3) 1.2 Missing value adjust. (non-stringent) *(12) , **(3) *(21) , **(16) *(1) 1.3 Missing Adj. & Replace ' - ' with ' 0 ' *(12) , **(20) <1 year lag> *(3) *(24)

II. Regional Analysis 1. Africa 1.1 No adjustment *(2) *(10),**(4) 1.2 Missing value adjustment *(5) *(8) , **(9) *(26) , **(3) *(9) 1.3 Missing Adj. & Replace ' - ' with ' 0 ' *(7) *(5) , **(12) *(25) , **(7) *(8) , **(1) 2. Central & Eastern Europe 2.1 No adjustment **(2) *(3),**(1) 2.2 Missing value adjustment *(18),**(9) *(9),**(9) 2.3 Missing Adj. & Replace ' - ' with ' 0 ' *(1) , **(53) *(2) , **(51) *(15) *(9) 3. South, East & South-East Asia 3.1 No adjustment *(9),**(1) *(3),**(7) *(4),**(2) 3.2 Missing value adjustment *(8) *(2) *(3) *(14) 3.3 Missing Adj. & Replace ' - ' with ' 0 ' *(8) *(2) *(27),**(1) 4. Latin America & the Caribbean 4.1 No adjustment *(2),**(2) *(7),**(3) 4.2 Missing value adjustment *(1) *(14) , **(4) 4.3 Missing Adj. & Replace ' - ' with ' 0 ' *(1) *(1) 5. West Asia 5.1 No adjustment *(1) *(2) *(7),**(1) 5.2 Missing value adjustment *(1) *(8) 5.3 Missing Adj. & Replace ' - ' with ' 0 ' *(12)

Note: a Out of 54 max. in each cell * significant at the 0.1 level (one-tailed) ** significant at the 0.05 level (one-tailed)

AnnexTable 7

Detailed Results of All 54 Comparisions for GLOBAL ANALYSIS for Dependent Variable: FDINumber Variable M. of 1st. M. of 2nd. Difference P Numbe

rVariable M. of 1st. M. of 2nd. Difference P

1 b2b1 - 0a1 18.0551 12.8456 5.2096 0.235 28 b20 - a1a2 14.4567 14.3896 6.71E-02 0.4962 b2b1 - 0a2 17.5816 14.2376 3.344 0.327 29 b20 - a1a3 14.2711 19.6951 -5.4241 0.2173 b2b1 - 0a3 17.4132 17.7118 -0.2986 0.484 30 b20 - a1a4 14.1845 20.4497 -6.2652 0.1524 b2b1 - a1a2 17.485 14.6617 2.8233 0.389 31 b20 - a2a3 15.372 25.4493 -10.0773 0.1335 b2b1 - a1a3 17.1825 20.5268 -3.3443 0.357 32 b20 - a2a4 15.1536 25.1798 -10.0262 0.1016 b2b1 - a1a4 17.0652 21.3019 -4.2367 0.302 33 b20 - a2a5 14.9425 26.7617 -4.8192 .055*7 b2b1 - a2a3 19.6186 27.5297 -7.911 0.25 34 b30 - a1a2 13.2097 14.2968 -1.0871 0.4468 b2b1 - a2a4 19.3 27.1042 -7.8042 0.226 35 b30 - a1a3 13.0556 19.5681 -6.5126 0.1869 b2b1 - a2a5 19 29.1557 -10.1557 0.146 36 b30 - a1a4 12.9771 20.3188 -7.3417 0.132

10 b3b1 - 0a1 15.9468 12.6884 3.2585 0.32 37 b30 - a2a3 14.0072 25.2626 -11.2554 0.11811 b3b1 - 0a2 15.4456 13.9618 1.4838 0.418 38 b30 - a2a4 13.8103 24.9976 -11.1874 .093*12 b3b1 - 0a3 15.3231 17.3673 -2.0442 0.394 39 b30 - a2a5 13.6049 26.5711 -12.9662 .054*13 b3b1 - a1a2 15.0235 14.4667 0.5568 0.477 40 b40 - a1a2 12.57 14.2051 -1.6351 0.41914 b3b1 - a1a3 14.795 20.2494 -5.4544 0.276 41 b40 - a1a3 12.4323 19.4458 -7.0135 0.16515 b3b1 - a1a4 14.6964 21.0155 -6.319 0.226 42 b40 - a1a4 12.3582 20.1925 -7.8344 0.11716 b3b1 - a2a3 16.6861 27.0667 -10.3806 0.192 43 b40 - a2a3 13.4398 25.0821 -11.6424 0.10517 b3b1 - a2a4 16.4208 26.6557 -10.235 0.17 44 b40 - a2a4 13.2526 24.8216 -11.569 .085*18 b3b1 - a2a5 16.1425 28.6815 -12.5363 0.109 45 b40 - a2a5 13.0671 26.3102 -13.2431 .052*19 b4b1 - 0a1 13.6277 12.6618 0.9658 0.44 46 b2a1 - a2a3 14.2443 24.5 -10.2557 .092*20 b4b1 - 0a2 13.1353 13.9726 -0.8373 0.45 47 b2a1 - a2a4 14.0515 24.6756 -10.6242 .057*21 b4b1 - 0a3 13.0604 17.3322 -4.2718 0.272 48 b2a1 - a2a5 13.8692 26.5171 -12.6479 .023**22 b4b1 - a1a2 13.9872 14.6204 -0.6332 0.473 49 b3a1 - a2a3 13.3466 24.3311 -10.9845 .090*23 b4b1 - a1a3 13.7908 20.3168 -6.526 0.227 50 b3a1 - a2a4 13.168 24.5078 -11.3398 .059*24 b4b1 - a1a4 13.7007 21.0716 -7.3709 0.181 51 b3a1 - a2a5 12.9839 26.3394 -13.3555 .027**25 b4b1 - a2a3 15.5751 27.0328 -11.4577 0.154 52 b4a1 - a2a3 12.9062 24.1678 -11.2616 .083*26 b4b1 - a2a4 15.332 26.629 -11.297 0.137 53 b4a1 - a2a4 12.7352 24.3455 -11.6103 .057*27 b4b1 - a2a5 15.0926 28.5357 -13.4431 .088* 54 b4a1 - a2a5 12.5676 26.0953 -13.5272 .029**

**. significant at the .05 level (one-tailed) *. significant at the .10 level (one-tailed)

AnnexTable 8

Detailed Results of All 54 Comparisions for GLOBAL ANALYSIS for Dependent Variable: FDI/iNFLOWNumber Variable M. of 1st. M. of 2nd. Difference P Number Variable M. of 1st. M. of 2nd. Difference P

1 b2b1 - 0a1 -2572.98 5147.2291 -7720.21 .033** 28 b20 - a1a2 -3029.95 45129.95 -4.82E+04 0.102

2 b2b1 - 0a2 -2471.74 8510.7231 -10992.5 .029** 29 b20 - a1a3 -2953.24 46308.02 -49261.3 .095*

3 b2b1 - 0a3 -2430.03 11053.26 -13483.3 .05** 30 b20 - a1a4 -2934.67 41823.89 -44758.6 0.111

4 b2b1 - a1a2 -2631.03 12782.04 -15413.1 .041** 31 b20 - a2a3 -3381.27 15941.87 -19323.1 .046**

5 b2b1 - a1a3 -2554.21 15085.16 -17639.4 .065* 32 b20 - a2a4 -3332.96 9761.761 -13094.7 .042**

6 b2b1 - a1a4 -2535.7 10144.91 -12680.6 .054* 33 b20 - a2a5 -3286.02 5398.9159 -8684.93 .073*

7 b2b1 - a2a3 -2965.5 18643.88 -21609.4 .061* 34 b30 - a1a2 -1935.2 44838.79 -46774 0.107

8 b2b1 - a2a4 -2916.07 11388.71 -14304.8 .057* 35 b30 - a1a3 -1886.52 46016.77 -47903.3 0 .10

9 b2b1 - a2a5 -2868.26 6283.9787 -9152.24 .083* 36 b30 - a1a4 -1874.72 41562.49 -43437.2 0.117

10 b3b1 - 0a1 241.9438 5072.6317 -1830.69 .073* 37 b30 - a2a3 -2157.97 15827.18 -17985.2 .055*

11 b3b1 - 0a2 231.8639 8333.4165 -8101.55 .048** 38 b30 - a2a4 -2127.36 9692.5261 -11819.9 .060*

12 b3b1 - 0a3 227.1321 10827.68 -10600.5 .080* 39 b30 - a2a5 -2097.6 5361.1587 -7458.76 0.12

13 b3b1 - a1a2 247.3097 12592.67 -12345.4 .064* 40 b40 - a1a2 -5031.82 44551.36 -49583.2 .094*

14 b3b1 - a1a3 240.193 14868.1 -14627.9 .093* 41 b40 - a1a3 -4906.2 45729.17 -50635.2 .087*

15 b3b1 - a1a4 238.4801 9999.9831 -9761.5 .095* 42 b40 - a1a4 -4875.55 41304.34 -46179.9 0.102

16 b3b1 - a2a3 278.2711 18333.15 -18054.9 .083* 43 b40 - a2a3 -5606.9 15714.13 -21321 .033**

17 b3b1 - a2a4 273.6535 11202.01 -10928.4 .097* 44 b40 - a2a4 -5527.93 9624.2689 -15152.2 .032**

18 b3b1 - a2a5 269.2447 6182.6213 -5913.38 0.187 45 b40 - a2a5 -5451.14 5323.9263 -10775.1 .065*

19 b4b1 - 0a1 -3776.67 5036.138 -8812.81 .041** 46 b2a1 - a2a3 35714.61 14965.84 20748.76 0.28

20 b4b1 - 0a2 -3595.59 8219.2602 -11814.9 .024** 47 b2a1 - a2a4 35235.22 9172.1256 26063.09 0.223

21 b4b1 - 0a3 -3523.2 10682.34 -14205.5 .042** 48 b2a1 - a2a5 34768.53 5077.1274 29691.4 0.188

22 b4b1 - a1a2 -3831.81 12408.84 -16240.7 .033** 49 b3a1 - a2a3 35957.48 14864.72 21092.76 0.226

23 b4b1 - a1a3 -3723.11 14657.21 -18380.3 .055* 50 b3a1 - a2a4 35478.05 9110.9757 26367.07 0.219

24 b4b1 - a1a4 -3696.89 9859.1384 -13556 .049** 51 b3a1 - a2a5 35011.23 5043.7229 29967.51 0.184

25 b4b1 - a2a3 -4302.95 18032.6 -22335.6 .047** 52 b4a1 - a2a3 32819.02 14764.96 18054.06 0.304

26 b4b1 - a2a4 -4233.55 11021.33 -15254.9 .049** 53 b4a1 - a2a4 32384.33 9050.6381 23333.69 0.246

27 b4b1 - a2a5 -4166.34 6084.4822 -10250.8 .097* 54 b4a1 - a2a5 31961.01 5010.7553 26950.26 0.209**. significant at the .05 level (one-tailed) *. significant at the .10 level (one-tailed)

Annex Table 9Detailed Results of All 54 Comparisions for AFRICA REGION Dependent Variable: FDI/gdp

Number Variable M. of 1st M. of 2nd Difference P Number Variable M. of 1st M. of 2nd Difference P1 b2b1 - 0a1 -0.035 -0.2002 0.1653 0.406 28 b20 - a1a2 0.6585 -0.8275 1.49E+00 0.3672 b2b1 - 0a2 -0.035 -0.5201 0.4853 0.385 29 b20 - a1a3 0.6585 0.423 0.2354 0.4713 b2b1 - 0a3 -0.035 0.3848 -0.4196 0.386 30 b20 - a1a4 0.6285 1.4239 -0.7954 0.2964 b2b1 - a1a2 -0.038 -1.0297 0.9915 0.404 31 b20 - a2a3 0.6734 0.677 -0.0036 0.4995 b2b1 - a1a3 -0.038 0.4287 -0.4669 0.436 32 b20 - a2a4 0.6413 2.1745 -1.5331 .060*6 b2b1 - a1a4 -0.038 1.625 -1.6632 0.116 33 b20 - a2a5 0.6413 1.4583 -0.817 0.2747 b2b1 - a2a3 -0.059 0.7349 -0.7934 0.391 34 b30 - a1a2 0.4787 -0.7429 1.2216 0.388 b2b1 - a2a4 -0.059 2.5509 -2.6094 .052* 35 b30 - a1a3 0.4787 0.391 0.0877 0.4889 b2b1 - a2a5 -0.059 1.6663 -1.7248 .093* 36 b30 - a1a4 0.4587 1.309 -0.8503 0.267

10 b3b1 - 0a1 -0.2383 -0.1828 -0.056 0.464 37 b30 - a2a3 0.4882 0.6327 -0.1445 0.4811 b3b1 - 0a2 -0.2284 -0.4429 0.2145 0.441 38 b30 - a2a4 0.467 1.9969 -1.5299 .074*12 b3b1 - 0a3 -0.2284 0.3413 -0.5697 0.327 39 b30 - a2a5 0.467 1.3448 -0.8779 0.24113 b3b1 - a1a2 -0.2587 -0.8687 0.61 0.43 40 b40 - a1a2 0.4949 -0.7429 1.2378 0.37814 b3b1 - a1a3 -0.2587 0.3727 -0.6314 0.399 41 b40 - a1a3 0.4949 0.391 0.1039 0.48615 b3b1 - a1a4 -0.2587 1.3972 -1.6559 .082* 42 b40 - a1a4 0.4743 1.309 -0.8348 0.26916 b3b1 - a2a3 -0.2819 0.6436 -0.9255 0.352 43 b40 - a2a3 0.5051 0.6327 -0.1276 0.48317 b3b1 - a2a4 -0.2819 2.1815 -2.4634 .036** 44 b40 - a2a4 0.4832 1.9969 -1.5137 .074*18 b3b1 - a2a5 -0.2819 1.4316 -1.7135 .063* 45 b40 - a2a5 0.463 1.6393 -1.1762 0.1719 b4b1 - 0a1 -0.2088 -0.1633 -0.045 0.465 46 b2a1 - a2a3 -0.018 0.6879 -0.7056 0.36620 b4b1 - 0a2 -0.2004 -0.3951 0.1946 0.443 47 b2a1 - a2a4 -0.017 2.0497 -2.0666 .029**21 b4b1 - 0a3 -0.2004 0.3578 -0.5582 0.319 48 b2a1 - a2a5 -0.017 1.3976 -1.4145 .047**22 b4b1 - a1a2 -0.2256 -0.7735 0.5479 0.434 49 b3a1 - a2a3 -0.1416 0.6393 -0.7809 0.34723 b4b1 - a1a3 -0.2256 0.4114 -0.637 0.392 50 b3a1 - a2a4 -0.1357 1.9464 -2.0821 .025**24 b4b1 - a1a4 -0.2256 1.3894 -1.615 .072* 51 b3a1 - a2a5 -0.1357 1.3214 -1.4572 .038**25 b4b1 - a2a3 -0.2461 0.6713 -0.9174 0.344 52 b4a1 - a2a3 -0.1548 0.6393 -0.7941 0.34526 b4b1 - a2a4 -0.2461 2.136 -2.382 .032** 53 b4a1 - a2a4 -0.1484 1.9464 -2.0947 .024**27 b4b1 - a2a5 -0.2349 1.7395 -1.9744 .030** 54 b4a1 - a2a5 -0.1424 1.605 -1.7475 .020**

**. significant at the .05 level (one-tailed) *. significant at the .10 level (one-tailed)

AnnexTable 10Detailed Results of All 54 Comparisions for AFRICA REGION : Dependent Variable: FDI/Inflow

Number Variable M. of M. of Difference P Number Variable M. of 1st. M. of Difference P

1st. 2nd. 2nd.1 b2b1 - 0a1 -10605.5 16666.76 -27272.3 .052* 28 b20 - a1a2 -2019.85 42424.3 -4.44E+04 .072*2 b2b1 - 0a2 -10605.5 27272.78 -37878.3 .051* 29 b20 - a1a3 -2019.85 53535.4

9-55555.3 0.103

3 b2b1 - 0a3 -10605.5 39141.53 -49747.1 .077* 30 b20 - a1a4 -1960.43 35294.22

-37254.6 .090*

4 b2b1 - a1a2 -11666.1 46666.73 -58332.8 .064* 31 b20 - a2a3 -2082.98 50000.13

-52083.1 0.122

5 b2b1 - a1a3 -11666.1 58889.03 -70555.1 .090* 32 b20 - a2a4 -2019.86 32323.33

-34343.2 0.111

6 b2b1 - a1a4 -11289.8 38709.78 -49999.6 .076* 33 b20 - a2a5 -2019.86 24242.49

-26262.3 0.101

7 b2b1 - a2a3 -12068.4 55172.56 -67241 0.104 34 b30 - a1a2 -1470.33 41176.53

-42646.9 .078*

8 b2b1 - a2a4 -11666.1 35555.65 -47221.8 .089* 35 b30 - a1a3 -1470.33 51960.9 -53431.2 0.1099 b2b1 - a2a5 -11666.1 26666.73 -38332.8 .078* 36 b30 - a1a4 -1428.31 34285.8 -35714.1 .098*

10 b3b1 - 0a1 -6060.25 16666.76 -22727 .048** 37 b30 - a2a3 -1514.89 48484.96

-49999.9 0.129

11 b3b1 - 0a2 -5882 26470.64 -32352.6 .058* 38 b30 - a2a4 -1470.33 31372.63

-32843 0.121

12 b3b1 - 0a3 -5882 37990.29 -43872.3 .091* 39 b30 - a2a5 -1470.33 23529.46

-24999.8 0.115

13 b3b1 - a1a2 -6451.24 45161.35 -51612.6 .073* 40 b40 - a1a2 -5440.73 41176.53

-46617.3 .064*

14 b3b1 - a1a3 -6451.24 56989.38 -63440.6 0.103 41 b40 - a1a3 -5440.73 51960.9 -57401.6 .096*15 b3b1 - a1a4 -6249.63 37500.09 -43749.7 .091* 42 b40 - a1a4 -5285.28 34285.8 -39571.1 .081*16 b3b1 - a2a3 -6666.3 53333.46 -59999.8 0.121 43 b40 - a2a3 -5605.62 48484.9

6-54090.6 0.114

17 b3b1 - a2a4 -6451.24 34408.69 -40859.9 0.11 44 b40 - a2a4 -5440.74 31372.63

-36813.4 0.101

18 b3b1 - a2a5 -6451.24 25806.5 -32257.7 0.102 45 b40 - a2a5 -5440.74 23529.46

-28970.2 .090*

19 b4b1 - 0a1 -9558.26 16176.56 -25734.8 .031** 46 b2a1 - a2a3 5303.332 48484.98

-43181.6 0.159

20 b4b1 - 0a2 -9285.16 25714.34 -34999.5 .043** 47 b2a1 - a2a4 5147.357 31372.64

-26225.3 0.169

21 b4b1 - 0a3 -9285.16 36904.86 -46190 .075* 48 b2a1 - a2a5 5147.357 23529.47

-18382.1 0.182

22 b4b1 - a1a2 -10155.7 43750.06 -53905.7 .060* 49 b3a1 - a2a3 3578.675 47058.94

-43480.3 0.156

23 b4b1 - a1a3 -10155.7 55208.46 -65364.1 .089* 50 b3a1 - a2a4 3476.433 30476.27

-26999.8 0.163

24 b4b1 - a1a4 -9847.9 36363.73 -46211.6 .074* 51 b3a1 - a2a5 3476.433 22857.19

-19380.8 0.171

25 b4b1 - a2a3 -10483.3 51613.03 -62096.3 0.106 52 b4a1 - a2a3 -783.9183 47058.94

-47842.9 0.135

26 b4b1 - a2a4 -10155.7 33333.42 -43489.1 .091* 53 b4a1 - a2a4 -761.5151 30476.27

-31237.8 0.132

27 b4b1 - a2a5 -10155.7 25000.05 -35155.7 .079* 54 b4a1 - a2a5 -761.5151 22857.19

-23618.7 0.13

**. significant at the .05 level (one-tailed) *. significant at the .10 level (one-tailed)AnnexTable 11

Detailed Results of All 54 Comparisions for CENTRAL & EAST EUROPE REGION : Dependent Variable: FDINumber Variable M. of

1st.M. of 2nd.

Difference P Number Variable M. of 1st. M. of 2nd.

Difference P

1 b2b1 - 0a1 15.25 8.75 6.5 0.185 28 b20 - a1a2 3.5238 16.6429 -13.119 .064*2 b2b1 - 0a2 13.5 10.4167 3.0833 0.288 29 b20 - a1a3 3.5238 17 -13.4762 .057*3 b2b1 - 0a3 13.5 10.1944 3.3056 0.273 30 b20 - a1a4 3.5238 17.0952 -13.5714 .056*4 b2b1 - a1a2 4.2 5.4 -1.2 0.369 31 b20 - a2a3 3.3333 15.3846 -12.0513 .070*5 b2b1 - a1a3 4.2 5.1 -0.9 0.4 32 b20 - a2a4 3.3333 15.1795 -11.8462 .074*6 b2b1 - a1a4 4.2 5.4167 -1.2167 0.367 33 b20 - a2a5 3.3333 16.1026 -12.7693 .060*7 b2b1 - a2a3 4.2 5.9 -1.7 0.319 34 b30 - a1a2 3.5179 16.6429 -13.125 .064*8 b2b1 - a2a4 4.2 6.0333 -1.8333 0.309 35 b30 - a1a3 3.5179 17 -13.4821 .057*9 b2b1 - a2a5 4.2 8.4167 -4.2167 0.193 36 b30 - a1a4 3.5179 17.0952 -13.5773 .056*

10 b3b1 - 0a1 15.25 8.75 6.5 0.185 37 b30 - a2a3 3.3269 15.3846 -12.0577 .070*11 b3b1 - 0a2 13.5 10.4167 3.0833 0.288 38 b30 - a2a4 3.3269 15.1795 -11.8526 .074*12 b3b1 - 0a3 13.5 10.1944 3.3056 0.273 39 b30 - a2a5 3.3269 16.1026 -12.7757 .060*13 b3b1 - a1a2 4.2 5.4 -1.2 0.369 40 b40 - a1a2 3.6071 16.6429 -13.0358 .065*14 b3b1 - a1a3 4.2 5.1 -0.9 0.4 41 b40 - a1a3 3.6071 17 -13.3929 .059*15 b3b1 - a1a4 4.2 5.4167 -1.2167 0.367 42 b40 - a1a4 3.6071 17.0952 -13.4881 .057*16 b3b1 - a2a3 4.2 5.9 -1.7 0.319 43 b40 - a2a3 3.4231 15.3846 -11.9615 .071*17 b3b1 - a2a4 4.2 6.0333 -1.8333 0.309 44 b40 - a2a4 3.4231 15.1795 -11.7564 .076*18 b3b1 - a2a5 4.2 8.4167 -4.2167 0.193 45 b40 - a2a5 3.4231 16.1026 -12.6795 .061*19 b4b1 - 0a1 15.75 8.75 7 0.166 46 b2a1 - a2a3 5.7222 15.1944 -9.4722 .020**20 b4b1 - 0a2 13.8333 10.4167 3.4166 0.266 47 b2a1 - a2a4 5.7222 17.7593 -12.0371 .040**

21 b4b1 - 0a3 13.8333 10.1944 3.6389 0.252 48 b2a1 - a2a5 5.7222 20.6713 -14.9491 .049**22 b4b1 - a1a2 4.6 5.4 -0.8 0.411 49 b3a1 - a2a3 5.7194 15.1944 -9.475 .020**23 b4b1 - a1a3 4.6 5.1 -0.5 0.444 50 b3a1 - a2a4 5.7194 17.7593 -12.0399 .040**24 b4b1 - a1a4 4.6 5.4167 -0.8167 0.409 51 b3a1 - a2a5 5.7194 20.6713 -14.9519 .049**25 b4b1 - a2a3 4.6 5.9 -1.3 0.358 52 b4a1 - a2a3 5.7944 15.1944 -9.4 .021**26 b4b1 - a2a4 4.6 6.0333 -1.4333 0.345 53 b4a1 - a2a4 5.7944 17.7593 -11.9649 .041**27 b4b1 - a2a5 4.6 8.4167 -3.8167 0.217 54 b4a1 - a2a5 5.7944 20.6713 -14.8769 .049**

**. significant at the .05 level (one-tailed) *. significant at the .10 level (one-tailed)