A project submitted to the graduate school in partial fulfillment of the requirements for the degree

NATIONAL REALIZED ABSORPTIVE CAPACITY:
COUNTRY COMPARISONS

BY
LINH THI THUY DO, B.E., M.S.

A project submitted to the Graduate School
in partial fulfillment of the requirements
for the degree
DOCTOR OF ECONOMIC DEVELOPMENT

Major Subject: ECONOMIC RESEARCH
Minor Subject: APPLIED STATISTICS

NEW MEXICO STATE UNIVERSITY
LAS CRUCES, NEW MEXICO
NOVEMBER 2017


“National Realized Absorptive Capacity: Country Comparisons,” a project prepared
by Linh Thi Thuy Do in partial fulfillment of the requirements for the degree Doctor
of Economic Development, has been approved and accepted by the following:

Loui Reyes
Dean of the Graduate School

Christopher Erickson
Chair of the Examining Committee

Date

Committee in charge:
Dr. Christopher Erickson, Chair
Dr. James Peach
Dr. Robert Steiner
Dr. Son Tran

ii


ACKNOWLEDGEMENTS
At first, I would like to emphasize my deepest gratitude to Dr. Christopher A.
Erickson, who is my supervisor, for his valuable comments and guidance during the
process of this study. Without his help, I cannot complete this project.
I am also grateful to my friends for the constructive discussions and useful
suggestions on any inaccuracy. All of them make a great contribution to the
completion of this project.
Finally, the sincere gratitude and appreciation go to my family for their
encouragement and mental support.

iii


VITA
February 24, 1987

Born in Thai Nguyen City, Vietnam

2005

Graduated from Thai Nguyen Specialized Upper Secondary

School, Vietnam

2009

Graduated from Foreign Trade University
Hanoi, Vietnam

2012

Graduated from University of Essex,
Colchester, the United Kingdom

2014-2017

Research Assistant and Teaching Assistant
Department of Economics, Applied Statistics, and International
Business, College of Business
New Mexico State University

Field of Study
Major Field:

Economic Development

Minor Field:

Applied Statistics

iv

ABSTRACT

This project investigates the association between the level of economic
activity and national realized absorptive capacity. Absorptive capacity is a dynamic
process that has four dimensions: acquisition, assimilation, transformation, and
exploitation of the external knowledge. From macroeconomic perspectives, national
absorptive capacity can be defined as the process by which a nation internalizes
external resources for its economic growth. The literature shows that many variables,
including development level, drive the dynamics of national absorptive capacity. We
distinguish between a nation’s potential and realized absorptive capacity, then use
data from the World Bank to empirically identify if there are significant differences in
realized absorptive capacity among nations grouped into the four income categories
defined by the World Bank. The association between the change in foreign direct
investment net inflows as the percentage of gross domestic product and a country’s
growth of real gross domestic product is used as the indicator of the nation’s realized
absorptive capacity. We estimate fixed effects regressions, which show that countries
are different in their national realized absorptive capacity. The regression results
reject the null hypothesis that countries at higher level of development have higher
national realized absorptive capacity. We further find evidence that investment in
infrastructure has positive associations with national realized absorptive capacity.
Keywords: national realized absorptive capacity, foreign direct investment, economic growth.

v


TABLE OF CONTENTS

List of Tables .............................................................................................................. vii
List of Figures ............................................................................................................ viii

1. Introduction ................................................................................................................1
2. Methodology ..............................................................................................................7
Step 1 – Estimating National Realized Absorptive Capacity ....................................7
Step 2 – Identifying Determinants of National Realized Absorptive Capacity .......11
3. Data and Estimation Techniques .............................................................................13
4. Results ......................................................................................................................16
Tests of stationarity, cointegration, and Granger causality ...................................16
Step 1- Estimates of National Realized Absorptive Capacity .................................20
Step 2 – Determinants of National Realized Absorptive Capacity .........................34
5. Conclusions and Policy Implications .......................................................................41
Appendix 1 – List of countries included in the sample ...............................................45
Appendix 2 –Descriptive Statistics ..............................................................................46
Appendix 3: Regression results for models in Table 3 ................................................48
Appendix 4: Estimates of national realized absorptive capacity .................................53
Appendix 5: STATA commands .................................................................................57
References ....................................................................................................................64

vi


LIST OF TABLES
Table 1: Unit root tests for the %∆FDI and the %∆GDP ............................................17
Table 2: Results of unit root tests for other explanatory variables ..............................18
Table 3: Regression results for models with the dependent variable %∆GDP ............26
Table 4: Variance Inflation Factor of variables as in the regressions of Table 3 ........30
Table 5: Regression results using the averaging method .............................................37
Table 6: Variance Inflation Factor of variables as in the regressions of Table 5 ........39

vii

LIST OF FIGURES

Figure 1: National potential and realized absorptive capacity .......................................2
Figure 2: Graph of real FDI versus real GDP for the sample of 135 countries ...........14
Figure 3: Visualized Regression Results of Model I from Step 1 ...............................32

viii


1. Introduction
Absorptive capacity is defined as “a set of organizational routines and
processes by which firms acquire, assimilate, transform and exploit knowledge to
produce a dynamic organizational capability” (Zahra and George, 2002, pp. 186).
Absorptive capacity is a dynamic process involving the internalization of external
knowledge (i.e., technology) that has four dimensions: acquisition, assimilation,
transformation, and exploitation. The term “absorptive capacity” can be studied at
individual, group, firm, sector, and national levels. By aggregating upwards from the
firm level, Criscuolo and Narula (2008) define national absorptive capacity as the
process of internalizing external knowledge to grow an economy.
Zahra and George (2002) distinguish between a firm’s potential and realized
capacity. Potential capacity involves knowledge acquisition and assimilation; while
realized capacity involves knowledge transformation and exploitation. These four
capabilities are dynamic and influence the firm’s ability to utilize the knowledge for
organizational development. In particular, realized capacity allows the firm to
construct its competitive advantage; while potential capacity provides the firm with
flexibility to respond operationally to changing market conditions. Ben-Oz and Greve
(2015) use the survey data of 252 decision makers from 129 Israeli early-stage hightech organizations and find that realized absorptive capacity is affected by short-term
goals and has short-term effects, while potential absorptive capacity is mainly
affected by long-term goals and has long-term effects.

1


The concept of a firm’s potential and realized capacity can be generalized into
a nation’s potential and realized absorptive capacity. National potential absorptive
capacity is the maximum of a nation’s capability to acquire and assimilate knowledge
during a particular period of time. National realized absorptive capacity is the
nation’s current ability to transform and exploit the knowledge that has been acquired
and assimilated up to the current time. Visually, at a point in time, a national potential
absorptive capacity is the external bound, while its realized absorptive capacity lies
within the bound (Figure 1). The larger the green circle, the higher the efficiency ratio
– the ratio of realized absorptive capacity to potential absorptive capacity (Zahra and
George, 2002).

Figure 1: National potential and realized absorptive capacity. National potential absorptive
capacity is the maximum of a nation’s capability to acquire and assimilate the knowledge during a
particular period of time. National realized absorptive capacity is the nation’s current ability to
transform and exploit the knowledge that has been acquired and assimilated up to the current time. The
ratio of realized absorptive capacity to potential absorptive capacity is called the efficiency ratio (Zahra
and George, 2002). The larger the realized absorptive capacity, the higher the efficiency ratio.
Source: Authors.

2


National absorptive capacity is abstract and difficult to measure, but it does
exist in that two countries of similar characteristics, exposed to similar external
knowledge, can experience different paces of growth. Then the country that grows
faster is considered to have higher absorptive capacity. Whether at the individual,

firm, sector or national level, exposure to external resources is a necessary condition
to exploit the new resources. The sufficient condition to fully exploit the external
resources is absorptive capacity as measured by the four dimensions of acquisition,
assimilation, transformation, and exploitation.
National absorptive capacity can be built from the bottom up in that national
absorptive capacity depends significantly on the individuals’, firms’ and sectors’
absorptive capacity. In particular, individuals’ absorptive capacity influences an
organization’s absorptive capacity (Cohen and Levinthal, 1990), individual firms’
absorptive capacity determines a sector’s absorptive capacity (Cieślik and Hagemejer,
2014), and sectors’ absorptive capacity establishes national absorptive capacity
(Criscuolo and Narula, 2008). Therefore, any factor that has impacts on absorptive
capacity of individuals, firms, or sectors potentially influences national absorptive
capacity. And indeed, the literature shows similar factors that affect absorptive
capacity at three levels – firm, sector, and nation. For example, important
determinants of adsorptive capacity at all levels include human capital; investment in
research and development (R&D); the similarity of the partners’ knowledge; and
presence and activities of multinational enterprises (MNEs) (Criscuolo and Narula,
2008; Castellacci and Natera, 2013; Cieślik and Hagemejer, 2014; Un, 2017).
3


We argue that difference in national absorptive capacity can help explain why
some countries are successful in exploiting foreign direct investment (FDI) for
economic growth, while others are not. For less developed countries that lack access
to capital and technology, external resources like FDI, are especially important for
economic growth. But the more important thing is whether those countries have
enough capacity to absorb the spillover effects of FDI. Focusing on attracting more
FDI without improving national absorptive capacity could lead to the waste of limited
resources and could even be counterproductive to the extent that FDI crowds out
domestic investment. In this sense, understanding the factors influencing national

absorptive capacity might help countries have proper policies to better exploit FDI.
National absorptive capacity is also influenced by macroeconomic factors.
According to Criscuolo and Narula (2008), national absorptive capacity is determined
by international technological situation, institutions and economic actors, and the
efficient use of markets. In particular, Criscuolo and Narula state that both developing
and developed countries increasingly rely on FDI as an explicit means to develop the
nation’s technological and economic competitiveness which play key roles in
improving national absorptive capacity over the stages of technological development.
Castellacci and Natera (2013) summarizes previous literature to find that beside
human capital, the dynamics of a nation’s absorptive capacity are driven by five other
dimensions, including income and development level, international trade,

4


infrastructures, quality of institutions and governance system, and social cohesion and
economic inequality1.
Countries at different level of development have different dynamics of these
factors. The interaction of these factors can lead to complex relationships between
FDI and economic growth across countries. Highly developed countries tend to have
better human capital, more investment in R&D, higher productivity, more advanced
financial and economic systems, better established infrastructure, and more efficient
governance structures than less developed countries. Therefore, the more developed
countries may have higher national realized absorptive capacity. This means that we
would expect FDI will have a greater impact in developed countries than in
developing countries. On the other hand, Criscuolo and Narula (2008) state that there
are “diminishing returns on marginal increase in national absorptive capacity” (pp. 4)
when a country approaches the development frontier, since “the knowledge available
for assimilation is smaller and the complexity involved in its exploitation is higher”
(pp. 13). This means that there is less scope for gains from FDI in developed

countries than in developing countries; so, the more developed countries may have
less national realized absorptive capacity. How these factors balance is an empirical
question. This research aims to use empirical data to test the null hypothesis that

1

In our study, we also add these variables in the model of the determinants of national realized
absorptive capacity. In particular, the countries are grouped into four development levels based on their
income (GNI per capita) levels. Net exports variable measures a country’s international trade. Fixed
telephone subscriptions indicate a country’s infrastructures. Bribery incidence shows the quality of
institutions and governance system. GINI index presents economic inequality.

5


countries at higher level of development have higher national realized absorptive
capacity. It will turn out that our hypothesis is not confirmed.
This study contributes to the literature in two aspects. First, this research
quantifies national absorptive capacity, particularly national realized absorptive
capacity, as the correlation between the external resources as measured by FDI net
inflows and economic growth. A set of national data is used to discover if there are
significant differences in realized absorptive capacity between nations of different
development levels. Then, we try to determine the factors affecting national realized
absorptive capacity, taking into account the distinction in development level between
countries. We do this using a panel data collected by the World Bank for a set of 135
countries over the period from 1990 to 2015 as the evidence of our arguments.
The remainder of the research is structured as followed. Section 2 is the
methodology, which clarifies the theoretical background and the two steps used in our
analysis. Section 3 presents the data and estimation techniques used for the analyzing
process of the specific data. Section 4 discusses the results and limitations of

analyses. Section 5 concludes and suggests some appropriate policies to improve
national realized absorptive capacity.

6


2. Methodology
The analysis on the association between the development level and national
realized absorptive capacity consists of two steps, each step using different empirical
models and different econometric techniques. In step 1, the absorptive capacity is
estimated. In particular, the estimated coefficient on the net inflows of foreign direct
investment (FDI) from a regression relating gross domestic product (GDP) growth to
FDI is used as a measure of the nation’s realized absorptive capacity. A separate
absorptive capacity is estimated for each country by using an interactive term
between the country indicator variable and absorptive capacity. In step 2, the
coefficient on FDI estimated in Step 1 is used as a measure of national realized
absorptive capacity in that the coefficients are modeled as dependent on selected
socio-economic variables, including the focus variable – the development level.
Step 1 – Estimating National Realized Absorptive Capacity
Economic Growth Model and Economic Growth Rate
We model the macro economy using the income identity. A nation’s GDP at
time t is expressed as the summation of four components as follows:
𝐺𝐷𝑃𝑖𝑡 = 𝐼𝑖𝑡 + 𝐶𝑖𝑡 + 𝐺𝑖𝑡 + 𝑁𝑋𝑖𝑡

(1)

where 𝐺𝐷𝑃𝑖𝑡 is gross domestic product in time period t for country i, I is capital
investment, C is consumption, G is government spending and NX is the excess of
exports over imports. All variables in equation (1) as well as in the following are
adjusted for purchasing power parity.

7


National capital investment can be decomposed into domestic capital
investment and foreign investment. If FDI is foreign direct investment, FII is foreign
indirect investment, and DI is domestic investment, then 𝐼𝑖𝑡 = 𝐹𝐷𝐼𝑖𝑡 + 𝐷𝐼𝑖𝑡 + 𝐹𝐼𝐼𝑖𝑡 ,
and equation (1) can be rewritten as:
𝐺𝐷𝑃𝑖𝑡 = 𝐹𝐷𝐼𝑖𝑡 + 𝐷𝐼𝑖𝑡 + 𝐹𝐼𝐼𝑖𝑡 + 𝐶𝑖𝑡 + 𝐺𝑖𝑡 + 𝑁𝑋𝑖𝑡

(2)

Knowing that gross domestic product in previous time is:
𝐺𝐷𝑃𝑖,𝑡−1 = 𝐹𝐷𝐼𝑖,𝑡−1 + 𝐷𝐼𝑖,𝑡−1 + 𝐹𝐼𝐼𝑖,𝑡−1 + 𝐶𝑖,𝑡−1 + 𝐺𝑖,𝑡−1 + 𝑁𝑋𝑖,𝑡−1 (3)
Subtracting (2) from (3), then dividing both sides by 𝐺𝐷𝑃𝑖,𝑡−1 , results in the
following equation:
𝐺𝐷𝑃𝑖𝑡 − 𝐺𝐷𝑃𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

=

𝐹𝐷𝐼𝑖𝑡 − 𝐹𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

+

𝐷𝐼𝑖𝑡 − 𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

+

𝐹𝐼𝐼𝑖𝑡 − 𝐹𝐼𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

+

𝐶𝑖𝑡 − 𝐶𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

+

𝐺𝑖𝑡 − 𝐺𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

+

𝑁𝑋𝑖𝑡 − 𝑁𝑋𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

(4)

The left-hand side is the percentage change in GDP (%𝐺𝐷𝑃𝑖𝑡 ) while the
other variables are expressed as percentage of GDP. In the empirical model, FII, C, G
and NX are not included directly but are modeled by substituting for these variables
as follows:
𝐺𝐷𝑃𝑖𝑡 − 𝐺𝐷𝑃𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

= 𝛼0 + 𝛼1

𝐹𝐷𝐼𝑖𝑡 − 𝐹𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

+ 𝛼2

𝐷𝐼𝑖𝑡 − 𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

+ 𝛼3 𝑋𝑖𝑡 + 𝜀1𝑖𝑡

(5)

where X is a vector of other explanatory variables included as controls in regressions,
and 𝜀1 is the error terms.
Crowding Effects and Countries’ Economic Growth
FDI is a source of capital, thus, promotes economic growth directly through
capital deepening. But in addition to this direct influence, FDI may also influence
growth through its effect on domestic capital. There can be both crowding-in and
crowding-out effects, by which FDI either encourages or discourages domestic
8


investment (Jan and Vladimr, 2002; Wu, Sun, and Li, 2012). For example, Gallagher
and Zarsky (2007) state four ways by which foreign direct investment can crowd in
domestic investment: Backward linkages—multinational enterprises create higher
demand for specialized inputs produced by local firms; Forward linkages—products
of multinational enterprises enhance inputs to other domestic industries; Knowledge
spillovers—the presence of multinational enterprises generates productivity and
technology spillovers to the domestic firms so that they become more competitive;
Multiplier effects—multinational enterprises create more jobs and increase local

spending on domestically produced goods and services. On the other hand, Gallagher
and Zarsky (2007) argue that crowding-out effect can occur: Competition—domestic
firms are out competed by more efficient foreign companies leading to the loss of
local market shares; Borrowing costs—higher interest rates due to foreign investors’
borrowings in domestic financial markets discourage domestic firms from borrowing;
Labor market effects – multinational enterprises start “hiring domestic entrepreneurs
and skilled workers away from domestic firms (pp. 25).”
Whether foreign direct investment inflows crowd in or crowd out domestic
investment is an empirical question that is among the issues addressed in this project.
Agosin and Machado (2005) answer the question by considering the contribution of
foreign direct investment to overall investment, which comprises both foreign and
domestic investment. If a-dollar-increase in foreign direct investment leads to one
dollar increase in total investment, foreign direct investment’s influence is neutral. If
foreign direct investment crowds in domestic investment, then the increase in total
9


investment should be more than the increase in foreign direct investment. If the
increase in total investment is smaller than the increase in foreign direct investment,
crowding-out effect occurs. Another approach is carried out by Acar, Eris and Tekce
(2012) who consider the direct impact of foreign direct investment on domestic
investment. In a regression model where domestic investment is the dependent
variable and foreign direct investment is an explanatory variable, a negative
coefficient shows crowding-out effect while a positive coefficient represents
crowding-in effect. In particular, the share of domestic investment in national GDP
could be written as a function of the share of foreign direct investment in national
GDP as follows:
𝐷𝐼𝑖𝑡 − 𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

= 𝛾0 + 𝛾1

𝐹𝐷𝐼𝑖𝑡 − 𝐹𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

+ 𝛾2𝑍𝑖𝑡 + 𝜀2𝑖𝑡

(6)

where DI and FDI have the same meanings as stated in equation (2), Z is the
vector of controls, and 𝜀2 is the error terms. Note that 𝛾1 can be either positive or
negative depending on the net effect of crowding in and crowding out.
Substituting (6) into (5) leads to the new model:
%𝐺𝐷𝑃𝑖𝑡 = 𝛼0 + 𝛼1

𝐹𝐷𝐼𝑖𝑡 − 𝐹𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

+ 𝛼2 [𝛾0 + 𝛾1

= (𝛼0 + 𝛼2 𝛾0 ) + (𝛼1 +𝛼2 𝛾1 )

= 𝛽0 + 𝛽1

𝐹𝐷𝐼𝑖𝑡 − 𝐹𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

𝐹𝐷𝐼𝑖𝑡 − 𝐹𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

𝐹𝐷𝐼𝑖𝑡 − 𝐹𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

+ 𝛾2 𝑍𝑖𝑡 + 𝜀2𝑖𝑡 ] + 𝛼3 𝑋𝑖𝑡 + 𝜀1𝑖𝑡

+ 𝛼2 𝛾2 𝑍𝑖𝑡 + 𝛼3 𝑋𝑖𝑡 + (𝜀1𝑖𝑡 + 𝛼2 𝜀2𝑖𝑡 )

+ 𝛽2 𝑍𝑖𝑡 + 𝛽3 𝑋𝑖𝑡 + 𝜀𝑖𝑡 (7)

In this model, 𝛽1 = (𝛼1 + 𝛼2 𝛾1 ) reflects both the direct effect of FDI on
domestic GDP growth via capital deepening as well as the indirect effects from
10


crowding in and crowding out. If crowding out effect is sufficiently strong, then 𝛽1
can be small or even negative.
In order to find the national realized absorptive capacity of different nations,
dummy variables are included to specify the effects of individual country’s foreign
direct investment inflows on the national GDP growth. Accordingly, model (7) can be
extended as follows:
%𝐺𝐷𝑃𝑖𝑡 = 𝛽0 + 𝛽1

𝐹𝐷𝐼𝑖𝑡 − 𝐹𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

+ 𝛽1𝑖

𝐹𝐷𝐼𝑖𝑡 − 𝐹𝐷𝐼𝑖,𝑡−1
𝐺𝐷𝑃𝑖,𝑡−1

𝐷𝑖 + 𝛽2 𝑍𝑖𝑡 + 𝛽3 𝑋𝑖𝑡 + 𝜀𝑖𝑡

(8)

in which 𝐷𝑖 is a country indicator variable that is equal to 1 if the country is i, and 0
otherwise. Therefore, the estimator of national realized absorptive capacity of a single
country i will be the sum of the estimated coefficient of 𝛽1 and the estimated
coefficient of the dummy variable 𝛽1𝑖 . That is, the slope on FDI is allowed to vary by
country. According to Gelman and Hill (2007), varying slopes can be considered as
the interaction between the within-country explanatory variables and the country
explanatory variables, and the regression with coefficients that can vary by group is
called a multilevel regression. To avoid the dummy variable trap 2 in the regression
model, one of the dummies must be excluded. This will be discussed in the Results.
Step 2 – Identifying Determinants of National Realized Absorptive Capacity
Step 2 seeks to discover why national realized absorptive capacity is different
across countries, i.e. why foreign direct investment may have different effects on
GDP growth in different countries. The literature has identified some possible

2

Dummy variable trap occurs when the value of one dummy can be predicted from values of other
dummies (perfect multicollinearity).

11


determinants. According to Castellacci and Natera (2013), the dynamics of a nation’s
absorptive capacity are theoretically driven by six dimensions: income and
development level, international trade (or the openness of the national system),
human capital, infrastructure, quality of institutions and governance system, social

cohesion and economic inequality. The authors use the CANA data (Castellacci and
Natera, 2011) and conclude that only three factors (income per capita, infrastructure,
and international trade) are statistically significant.
The estimated coefficients of national realized absorptive capacity will be
used as the dependent variable in the second stage of estimation process using
multilevel modeling techniques (Steenbergen and Jones, 2002; Raudenbush and Bryk
2002). Lewis and Linzer (2005) state that estimated dependent variable model
induces heteroscedasticity3, and ordinary least squares method with White’s
heteroscedastic consistent standard errors can lead to more efficient estimates than the
weighted least squares method. We use the following model to run the pooled data
ordinary least squares regressions:
𝑁𝑅𝐴𝐶𝑖𝑡 = 𝜃0 + 𝜃1 𝐸𝑖𝑡 + 𝜃2 𝐺𝑅𝑖 + 𝜖𝑖𝑡

(9)

in which NRAC is national realized absorptive capacity, that is, the coefficients 𝛽1 +
𝛽1𝑖 from equation (8) in Step 1, E is the vector of explanatory variables, GR is the
country group categorized based on the development level, and 𝜖 is the error terms.
From a statistical viewpoint, heterogeneity “is the opposite of homogeneity, which refers to sameness
or similarity…. While heterogeneity is a term commonly used by both ecologists and statisticians,
heteroscedasticity is a purely statistical concept which concerns a particular type of heterogeneity
restricted to inequality of variances” (Dutilleul and Legendre 1993, pp.152). In contrast,
homoscedasticity, which indicates equality of variances, is the synonym for homogeneity of
variances.
3

12


3. Data and Estimation Techniques

This study uses the data collected from the World Development Indicators of
the World Bank. Using the single data source is expected to eliminate any unexpected
errors due to the heterogeneity in recording systems of different countries. The data
source contains information on 218 countries and territories across the world from
1960, but the problem of missing data limits the study to 135 countries over the
period of 26 years (1990 – 2015). The two criteria including an observation are the
availability of data on foreign direct investment inflows for at least 20 years, and the
availability of the purchasing power parity factor (PPP) data, which is only available
since 1990.
Variables are converted to international real dollars using the purchasing
power parity factor. FDI could be negative because “data on FDI flows are presented
on net bases (capital transactions' credits less debits between direct investors and their
foreign affiliates). Net decreases in assets or net increases in liabilities are recorded as
credits, while net increases in assets or net decreases in liabilities are recorded as
debits. Hence, FDI flows with a negative sign indicate that at least one of the
components of FDI is negative and not offset by positive amounts of the remaining
components” (World Bank, undated). Figure 2 shows the intuitive relationship
between real FDI and real GDP, pooling the data of 135 countries. It shows that high
real FDI tends to be associated with high real GDP.

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Figure 2: Graph of real FDI versus real GDP for the sample of 135 countries

Source: World Bank, World Development Indicators.
World Bank categorizes countries into four income groups – low, lowermiddle, upper-middle, and high – based on gross national income (GNI) per capita for
the previous calendar year. The categorization remains fixed for the entire fiscal year,
but might change across years. For the 2016 fiscal year, low-income countries are
defined as those with GNI per capita in 2015 of $1,025 or less; lower middle-income

countries have between $1,026 and $4,035 GNI per capita; upper middle-income
countries have between $4,036 and $12,475 GNI per capita; high-income countries
have GNI per capita of $12,476 or more. Accordingly, out of 135 countries in this
study, the number of economies categorized as low, lower-middle, upper-middle, and
high-income is respectively 21, 43, 40, and 31 (see Appendix 1). By way of

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comparison, U.S. gross national income per capita was $50,070 and the global
average was $10,577.669 in 2015.
The descriptive statistics of the data used in this study are described in
Appendix 2. The data consists of 3,510 annual data for 135 countries observed over
26 years. However, due to the missing data, most of the variables have fewer
observations than 3,510. In the regressions, we base on two criteria to add the
explanatory variables into the regression models: the findings from literature and the
availability of the data. This means the variables with relatively more observations
are more likely to be added in the models. To ensure stationarity, we use the
“difference” form of explanatory variables in regression models.
We use linear regressions, which take the advantage of the ordinary least
squares method, for estimating the unknown parameters. But ordinary least squares
estimates are the best only when the assumptions are guaranteed: the errors have
normal distribution, homoscedasticity4 and no serial correlation5. Violations of these
assumptions might lead to inefficient estimates. Therefore, it is important to check the
assumptions of the regression models and fix the violations of the assumptions, if
any. The tests of stationarity and cointegration should be carried out before doing
regressions to avoid linear regression violations. The software STATA6 version 12 is
used to process the data.

4

Homoscedasticity is a statistical concept that refers to equality of variances, as mentioned above. In
panel data, it means equal variation in the regression coefficients across both space and time.
5
In statistics, serial correlation or autocorrelation of a random process is the linear correlation between
values of different time periods.
6
STATA has some limitations. In identifying the probability of Granger causality in panel datasets,
Lopez and Weber (2017) state that the command xtgcause, proposed by Dumitrescu and Hurlin (2012),

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4. Results
Tests of stationarity, cointegration, and Granger causality
There are numerous tests for stationarity of panel data. Many of the tests
require a strongly balanced data, which is a problem for us as our panel is not
balanced. Fortunately, the Fisher-type (Choi, 2001) and Im–Pesaran–Shin (2003) tests
allow unbalanced panels. Both tests have the similar null hypothesis that all panels
contain unit roots7, but the alternative hypothesis is a little different between the two.
The alternative hypothesis in the Im–Pesaran–Shin test is some panels are stationary,
while the alternative hypothesis in the Fisher-type test is at least one panel is
stationary.
Table 1 shows the results for the unit root tests of the two variables of interest,
which are the change in real FDI as the percentage of real GDP in a previous year
(hereafter, the %FDI) and the percentage change of real GDP (hereafter, the
%GDP). In both the Im–Pesaran–Shin and Fisher-type tests, time trend8 is included.
Before doing this, we also tested the stationarity of real FDI and real GDP. The
results from both tests show that while real FDI is stationary, real GDP is not.
Moreover, we are also interested in the number of lags (or past period values) to be

has been inaccurately implemented and might lead to incorrect results. Therefore, we use the newly
suggested command pvargranger, proposed by Arbigo and Love (2016), for Granger causality tests. To
the best of our knowledge, however, the latest version of pvar commands is still in beta mode with no
warranties, and users take their own risks in doing so.
7
The presence of a unit root is a major cause of the violation of stationarity. Unit root processes are
those integrated of order 1 – I(1), which means differences are taken just 1 times for a process to be
stationary.
8
An upward or a downward trend in a time series is a common violation of stationarity. In case the
mean trend is deterministic, removing the trend from the data (or detrending) will make the process
stationary. Since we do not know the data is stationary around a time trend or not, we assume that it is,
and carry the tests accordingly.

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included in the regression models later on. Therefore, different lagged values are
added into the tests. It can be seen that two variables are stationary up to four lags
included, and the results are robust between the two tests.
Table 1: Unit root tests for the %∆FDI and the %∆GDP

Lags

%∆FDI

%∆GDP

Im-Pesaran-Shin

Fisher-type

Im-Pesaran-Shin

Fisher-type

0

-53.0330***

3723.7396***

-27.6359***

1648.9770***

1

-30.9087***

1878.2797***

-14.6034***

895.7623***

2

-18.1076***

974.5450***

-8.7018***

586.9636***

3

-14.5610***

830.3888***

-6.2811***

505.6838***

4

-7.4775***

440.5276***

-3.5663***

331.0568**

5

-4.3818***

354.6732***

-1.1343

329.6714**

6

NA

274.6852

NA

367.5693***

7

NA

275.5317

NA

253.1963

8

NA

293.1007

NA

292.4816

Note: * p<0.05; ** p<0.01; *** p<0.001. The presented statistic in the Im–Pesaran–Shin
test is W-t-bar, while the presented statistic in the Fisher-type test is inverse chi-squared.
Both tests have the similar null hypothesis that all panels contain unit roots. The
alternative hypothesis in the Im–Pesaran–Shin test is some panels are stationary, while
the alternative hypothesis in the Fisher-type test is at least one panel is stationary. NA:
mean and variance of t-statistics are not available with lags >5.

Table 2 shows the results for the unit root tests of the other explanatory
variables. No lag is included, while time trend is included9. The tests were carried out
for both the level (L) and differenced (D) of the data. It is not surprising that
comparable results are found using either the Im–Pesaran–Shin or Fisher-type test
when the variables are in term of percentage rate. For the others, there are some
considerable distinctions between two tests. Fisher-type tests tend not to reject the
9

For total population and fixed telephone subscriptions, time trend is not included since the test did not
run properly with the inclusion of time trend.

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