Chinese Business Review (ISSN 1537-1506) Vol.13, No.9, 2014

Chinese Business Review

Volume 13, Number 9, September 2014 (Serial Number 135)

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Chinese Business Review

Volume 13, Number 9, September 2014 (Serial Number 135)

Contents

Economics

Impact of Financial Development on the Environmental Quality in Iran 537

Hadi Esmaeilpour Moghadam, Mohammad Reza Lotfalipour

Sino-European Trade Competition in Latin America and the Caribbean 552

Wioletta Nowak

Threshold Effects in the Capital Account Liberalization and Foreign Direct

Investment Relationship 562

Gammoudi Mouna, Cherif Mondher

Management

Cooperation Between Co-operative Business Organization and Investor Owned Firm

to Stimulate Economic Growth of a Country: A Cooperative Advantage Approach 578

Chanchai Petchprapunkul

Characteristics of Organizational Leadership and Motivation As a Factor of Change

in the Public Health System 586

Slobodanka Krivokapić

Chinese Business Review, September 2014, Vol. 13, No. 9, 537-551 doi: 10.17265/1537-1506/2014.09.001

Impact of Financial Development on the Environmental

Quality in Iran

Hadi Esmaeilpour Moghadam, Mohammad Reza Lotfalipour

Ferdowsi University of Mashhad, Mashhad, Iran

In recent decades, undesirable environmental changes, such as global warming and greenhouse gases emission,

have raised worldwide concerns. In order to achieve higher growth rate, environmental problems emerged from

economic activities have turned into a controversial issue. The aim of this study is to investigate the effect of

financial development on environmental quality in Iran. For this purpose, the statistical data over the period from

1970 to 2011 were used. Also by using the Auto Regression Model Distributed Lag (ARDL), short-term and

long-term relationships among the variables of model were estimated and analyzed. The results show that financial

development accelerates the degradation of the environment; however, the increase in trade openness reduces the

damage to environment in Iran. Error correction coefficient shows that in each period, 53% of imbalances would be

justified and will approach their long-run procedure. Structural stability tests show that the estimated coefficients

were stable over the period.

Keywords: financial development, trade, Auto Regression Model Distributed Lag (ARDL)

Introduction

Environmental pollution and protecting the environment have been the global issues that have even now

entered the political domain of countries. According to the Kyoto Protocol (Retrieved from http://www.unfccc.

int), countries of the world have taken appropriate executive measures to preserve the environment as common

public goods, they have also introduced some penalties for the world’s major polluting countries. In Iran, due to

the existence of large reserves of fossil fuels, to save energy is not taken seriously. Climate change caused by

increasing concentrations of greenhouse gases is seen as an important factor in changing the world’s climate, to

the extent that in many cases, a small change in the weather condition may end in severe changes in the

intensity and number of natural disasters and economic loss. Therefore, this is especially important because of

some abnormal environmental effects at different stages of the production, conversion, and consumption of

energy. Pattern of development in the energy sector would be acceptable only with minimum damage to the

environment. Pollutants and greenhouse gases arising from the activities of energy sector have undeniable

environmental effects at the regional and global level. Pollutant gases cause acid rain, health risks to humans

and other creatures, climate change, and global warming. In this study, environmental quality index is a

combination of various contaminants that is obtained by Principal Component Analysis (PCA).

Hadi Esmaeilpour Moghadam, M.A. Student in Economics, Ferdowsi University of Mashhad, Iran. Mohammad Reza Lotfalipour, professor, Ferdowsi University of Mashhad, Iran. Correspondence concerning this article should be addressed to Hadi Esmaeilpour Moghadam, P.O. Box: 9177948974, Azadi Sq,

Mashhad, Iran. E-mail: [email protected].

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IMPACT OF FINANCIAL DEVELOPMENT ON THE ENVIRONMENTAL QUALITY

538

Many studies concentrated on the relationship between environmental pollution and economic growth in

recent years, and the impact of financial development on the environment has received little attention. However,

financial development through various channels could be effective on the quality of the environment: (1)

Financial development through providing the necessary capitals for industrial and factory activities may lead to

environmental pollutions (Sadorsky, 2010); (2) financial intermediaries may access to the environmental

friendly new technology that can improve the environment (Tamazian, Chousa, & Vadlamannati, 2009); (3)

financial development may provide more financial resources with less financial costs, for instance, for

environmental projects (Tamazian et al., 2009; Tamazian & Rao, 2010).

This study investigates the effect of financial development on environmental quality in Iran over the

period from 1970 to 2011. In this study, the environmental quality index is the combination of various

pollutants which is obtained with the PCA. The paper is organized as follows: Part 2 of theoretical framework

discusses the importance of economic and financial development for environmental quality, Part 3 presents a

review of the literature, Part 4 presents data description and the econometric procedure, and the last two parts

comprise the study results and conclusions.

Theoretical Framework

Environmental Kuznets Curve

Greenhouse gases emission from fossil fuels and other human activities are serious threat to global

temperature. Changes in weather patterns may disrupt the environment and human activities.

A number of studies argue that the relationship between economic growth and environmental degradation

follows an inverted U curve. This inverted U is known as environmental Kuznets curve (EKC). Accordingly,

the use of natural resources and energy to achieve high economic growth increases the primary stages of

industrialization process due to the high priority of production and employment over clean environment and

low-technology, and consequently enhances the emission of pollution. At this stage, economic agents cannot

supply the costs of reducing pollution due to the low per capita income, and thus the environmental impacts of

economic growth are ignored. However, per capita income will improve the quality of the environment in the

next stages of industrialization process after reaching certain level of per capita income, so that in such situation,

the indicators of environmental pollution reduce with regard to the importance of clean environment, high

technology, and appropriate environmental laws and regulations.

Also the relationship between financial development and environmental degradation can be expressed in

the form of an inverted U relationship. So in the primary stages of financial development, because of the high

priority of growth over clean environment, just financial development increases the volume of industrial

activities. But in the next stages and after reaching to favorable growth, financial development will improve the

quality of the environment, by investing in environmental projects and taking access to high technologies

(Shahbaz, Hye, Tiwari, & Leitão, 2013a).

The first experimental study on the EKC was conducted by Grossman and Krueger (1991) in a report

format as the environmental effects of the North American free trade agreement. They reviewed the

relationship between air quality and economic growth in the 42 countries and concluded that the relationship

between economic growth and the concentration of suspended particles in the air and sulfur dioxide is in the

form of inverted U. This study was the basis for the next studies in this field.

Several studies, including Shafik (1994); Selden and Song (1994); Cole, Rayner, and Bates (1997); Lieb


539

(2004); Aldy (2005); Song, Zheng, and Tong (2008); and Iwata, Okada, and Samreth (2009), tested the

hypothesis of EKC. Although the hypothesis of EKC has been confirmed in the most of studies, the results of

some studies suggest the existence of a uniform or third degree forms relationship between pollution emissions

and economic growth.

Impact of Financial Development and Trade on Environmental Quality

Despite many studies about the relationship between economic growth and environmental quality, a

number of researchers, including Tamazian and Rao (2010), Zhang (2011), Pao and Tsai (2011), Jalil and

Feridun (2011), Shahbaz et al. (2013a), and Shahbaz, Solarin, Mahmood, and Arouri (2013b), considered

financial development as an important factor affecting the environmental quality in recent years.

Well-developed capital markets and the strong banking system can promote the progress of technology and

productivity. Capital of technologies that need large sums of investment can easily be provided in the

developed financial systems (Tamazian et al., 2009). The financial markets provide the implementation of such

technologies with risk sharing for investors.

Further development of financial sector can facilitate more investment with low cost, which also includes

investment in environmental projects. Ability to increase such investments in environmental protection as the

work of the public sector can be important for states in the local, state, and national levels (Tamazian & Rao,

2010). Corporate access to advanced and clean technologies, with the financial development that decreases CO2

emissions and increases domestic production, financial and investment regulations are promoted for the benefit

of environmental quality (Yuxiang & Chen, 2010). Financial systems with better performance release

restrictions of the foreign financing provision which prevents industrial and corporative development and make

way for economic growth (Levine, 2005). Thus, financing provision for industrial large activities can increase

environmental pollutions.

The effects of trade liberalization on environment are separated into three effects: scale effect,

composition effect, and technology effect. The effect of scale represents the change in the size of the economic

activities, second effect represents the change in the composition or basket of the manufactured goods, and the

effect of technology represents the change in the production technology, especially shift to clean technologies.

The effect of the scale increases environmental degradation and the effect of technology reduces environmental

degradation in trade liberalization. The effect of composition depends on the type of relative advantage. So

according to the concept of comparative advantage, if a country has advantage in the polluting goods and has

expertise in its production, then composition effect negatively influences the environment due to the changes in

the composition of the country’s manufactured goods to polluting goods; and if due to comparative advantage,

the combination of a country’s manufactured goods changes to clean ones, then the composition effect will

have positive influence on the environment. Generally, following trade liberalization if the effect of technology

dominates the scale and composition effects (in a country with a comparative advantage in polluting industries)

or if the effect of technology and composition (in a country with a comparative advantage in clean industries)

dominates the effect of scale, then trade liberalization will lead to positive environmental outcomes (Grossman

& Krueger, 1991).

Literature Review

Many studies have been conducted on the relationship between economic growth and environmental


540

quality. A number of researchers have examined the role of factors such as energy consumption (Ang, 2007;

Alam, Fatima, & Butt, 2007), foreign trade (Halicioglu, 2009), electricity consumption growth and

population growth (Tol, Pacala, & Socolow, 2006), human resources and capital (Soytas, Sari, & Ewing, 2007)

on the environment. Financial development has been considered as one of the effective factors on the

environment.

Tamazian et al. (2009) examined the effect of financial development in the BRIC (Brazil, Russia, India,

and China) countries using the modeling approach of the standard reduced form during 1992 to 2004. Results

showed that higher levels of financial and economic development reduce environmental pollution, while

financial liberalization and financial openness are crucial factors for reducing CO2 emissions. In addition,

adopting policies relevant to financial liberalization and openness to attract greater levels of research and

development (R&D) and foreign direct investment (FDI) may reduce environmental pollution in these

countries.

Tamazian and Rao (2010) in their study examined the effects of financial and institutional development on

CO2 emissions in 24 countries in transition period from 1993 to 2004. The results confirmed the existence of

EKC. The importance of institutional quality and financial development on environmental performance was

also confirmed. Based on the results, financial development had a positive effect on the environmental

protection in the countries in transition. Results also indicated that financial liberalization might be harmful to

the quality of the environment if it is not implemented in a strong organizational structure. Trade openness in

these countries has led to an increase in pollution.

Using panel cointegration and Granger causality test for BRIC countries, Pao and Tsai (2010) examined

the relationship between long-term and dynamic causality of carbon dioxide emissions, energy consumption,

FDI, and GDP. The results indicate that in the long-run equilibrium, carbon dioxide emissions compared to

energy consumption are elastic and compared to FDI are inelastic. The results also confirm the EKC hypothesis

in the studied countries.

Zhang (2011) examined the effect of financial development on CO2 emissions in China during the period

from 1994 to 2009, and employed techniques such as Johansson cointegration vector, Granger causality test,

and variance analysis. The results show that the financial development of China acts as an important stimulus in

rising the greenhouse emissions. The size and scale of financial intermediaries were more important than other

indicators of financial development. Nevertheless, the effect of financial intermediaries is far weaker. The size

and scale of China’s stock market have relatively greater effect on carbon emissions, while FDI, due to its small

share from GDP, has the least effect on Carbon emissions. Using the ARDL model, Jalil and Feridun (2011)

also examined the effects of growth, financial development, and energy consumption on CO2 emissions in

China in the two periods 1953-2006 and 1987-2006. In their study, the share of cash debt from GDP, the share

of commercial bank assets from total assets of the banking system, and the share of foreign assets and liabilities

from GDP were used as indicators of financial development. The results showed that financial development

contributes to reducing environmental pollution in China. The results also confirmed the existence of EKC in

China.

Shahbaz et al. (2013b) examined the effect of financial development on economic growth and energy

consumption, CO2 emissions in Malaysia from 1971 to 2011. The results showed financial development in

Malaysia led to decrease in CO2 emissions, while, economic growth and energy consumption increased CO2

emissions. In another study, Shahbaz et al. (2013a) examined the effect of economic growth, energy


541

consumption, financial development, and trade openness on CO2 emissions in the period from 1975 to 2011 in

Indonesia. In their study, real per capita domestic credit to the private sector was considered as a measure of

financial development. Results showed that economic growth and energy consumption in Indonesia increased

CO2 emissions, while, financial development and trade will diminish them. Furthermore, inverted U

relationship between financial development and CO2 dissemination was also confirmed.

Ozturk and Acaravci (2013) examined the effect of financial development, trade, economic growth, and

energy consumption on CO2 emissions over the period from 1960 to 2007 in Turkey, using the cointegration

approach. Results showed that in the long term, trade increases CO2 emissions, and financial development

variable is not significant on the CO2 emissions. EKC hypothesis was confirmed in Turkey as well.

In Iran, many researchers have studied the factors affecting the environmental quality. A number of

studies have addressed the relationship between environmental quality and economic growth (Pazhouan &

Moradhasel, 2007; Pourkazemi & Ebrahimi, 2008; Salimifar & Dehnavi, 2009; Ghazali & Zibaee, 2009;

Mowlayi, Kavosi Kalashemi, & Rafiei, 2010), energy consumption (Behboodi & Barghi Golazani, 2008;

Lotfalipour, Fallahi, & Ashena, 2010), trade openness (Barqi Askooei, 2008; Behboodi, Fallahi, & Barghi

Golazani, 2010; Agheli, Velaei Yamchi, & Jangavar, 2010; Lotfalipour, Fallahi, & Bastam, 2012), factors of

the labor force and capital (Sharzaei & Haghani, 2009), the value added share of the industrial sector from GDP

(Nasrollahi & Ghaffari Goolak, 2009; Vaseghi & Esmaeili, 2009). Sadeghi and Feshari (2010) in an article

using Johansson’s cointegration approach over the period from 1971 to 2007 with regard to indices of carbon

dioxide emissions and arable land for the environmental quality concluded that in addition to long-run

equilibrium between the export and environmental quality indices, the variables of exports and FDI had a

significant negative impact on environmental quality indices.

Fotros and Maboodi (2010) used econometric approach of Yamamato, investigating the existence and

direction of causality among energy consumption, urbanization, economic growth, and carbon dioxide

emissions over the period from 1971 to 2006. Results indicate a causal relationship among energy consumption,

GDP, urbanization, and carbon dioxide emissions. Estimation of the relationship among carbon dioxide

emissions, energy consumption, urban population and GDP showed that U hypothesis about environmental

pollution and GDP in Iran is true. Sadeghi, Motafaker Azad, Pour Ebadelahan Kovich, and Shabaz Zade

Kheyavi (2012) addressed the causal relationship between carbon dioxide emissions and FDI variables, per

capita energy consumption and GDP in the environmental Kuznets hypothesis in Iran over the period from

1980 to 2008. Results verified the bilateral causal relationship between variables of CO2 emissions and per

capita energy consumption, and unidirectional causal relationship from GDP to per capita energy consumption.

Using panel data and generalized moments approach, Barqi Askooei, Fallahi, and Zhande Khatibi (2012)

estimated the impact of variables such as energy consumption, factory products, economic openness, FDI, and

economic growth on the carbon dioxide emissions for the period from 1990 to 2010 in D8 countries. The

results showed that in the approach of fixed effects, all variables except FDI had a positive and significant

relationship with carbon dioxide emissions.

Materials and Methods

Data

The ARDL can be used for short-term and long-term relations between the dependent and explanatory

variables of the model. The model in this paper is as follows:


542

EN FD FD GDP OP (1)

where EN is environmental quality index, FD is financial development, FD2 is square of FD, GDP is gross

domestic product and OP is trade openness.

Using PCA which is based on a linear combination of the original variables on the variance-covariance

matrix and using the following indices, this study tries to extract the general index for financial development

and address all aspects of financial development:

(1) index of financial development depth: the ratio of cash to GDP in current prices;

(2) basic index of financial development: the ratio of domestic bank assets to total assets of commercial

banks and the Central Bank;

(3) index of financial development performance: the ratio of private sector’s debt (to the banking system)

to GDP;

(4) instrumental index of financial development: the ratio of money held by the public to total money

supply;

(5) structural index of financial development: the ratio of banking system claim of private sector to total

banking system credit.

Trade openness index is the ratio of total exports and imports to GDP and environmental quality index is

combinations of Sulfur Oxide pollutants, SO2 and SO3, Nitrogen Oxides of NOX, Carbon Monoxide, SPM

suspended particles, and Carbon Dioxide which are examined in PCA approach. Data on emissions of SO2, SO3,

NOX, CO, and SPM were obtained from energy balance sheet of Ministry of Energy, Department of Power and

Energy. Data on CO2 were collected from Carbon Dioxide Information Analysis Center, data on GDP were

obtained from UNCTAD (United Nations Conference on Trade and Development), and data on indices of

financial development, the financial development squared and trade were obtained from economic reports and

balance sheet of the Central Bank. In this study, the period between 1970 and 2011 was examined, and Microfit

4.0 and Matlab 8.01 were used for the estimation and forecasting.

Financial Development and Trade in Iran

Figure 1 shows the trend of financial development in Iran. As shown in the period between 1970 and 2011,

financial development has declined and then increased due to imposed war. Overall financial development in

Iran has been increasing. However, the amount of financing for the industrial activities has increased over the

period. Figure 2 shows the amount of financing for the country’s industrial sector compared to other sectors and

activities with incremental growth, and shows that this sector has received more attention than other sectors in

financial development process.

Figure 3 shows the amount of exports and imports in Iran. Exports and imports have increased over the

years of the study.

Industries such as cement, glass, ceramics, iron and steel, pulp and paper, etc. apply a wide range of

environmental effects and release in the air plenty of oxides of Carbon, Sulfur, and Nitrogen.

According to Figure 4 and 5, exports of polluting goods have declined in the period between 1970 and

2011 and have a downward trend. However, the growth in imports of polluting goods compared to total

imported goods has risen. Therefore, the amount of pollutants produced during this period has a downward

trend.


543

Figure 1. Financial development and its trend in the period between 1970 and 2011. Source: Economic reports and balance sheet of the central bank.

Figure 2. The financing of industrial activities compared to other activities. Source: Economic reports and balance sheet of the central bank.

Figure 3. Exports and imports in the period from 1970 to 2011. Source: Economic reports and balance sheet of the central bank.

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Figure 4. Trend of exporting polluting goods over the period from 1970 to 2011. Source: Economic reports and balance sheet of the central bank.

Figure 5. Trend of importing polluting goods over the period from 1970 to 2011. Source: Economic reports and balance sheet of the central bank.

Model

In this study, Autoregressive Distributed Lag Modeling Approach was employed which was proposed by

Pesaran and Shin (1999). Most of recent studies suggest that ARDL approach is preferable to other approaches

such as Engel-Granger, in examining the cointegration and long-run relationship among the variables. Whether

the variables in the model are I(0) or I(1), this approach is applicable, and in small samples it is relatively more

efficient than other approaches. ARDL Model is as follows:

, ∑ , ′ (2)

where

, 1 (3)

, 1, 2, … , (4)

In the above relationships Yt is the dependent variable and Xit is the independent variable. L is lag operator

and wt is a vector of categorical variables including predetermined variables in the model, such as intercept,

dummy variables, time trend, and other exogenous variables. P is the number of lags used for the dependent

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545

variable and q is the number of lags used for the independent variables. Numbers of optimal lags for each of the

explanatory variables could be set by a measure of Akaike, Schwarz-Bayesian, Hanan-Queen, or adjusted

coefficient of determination. In this study, given the small size of the data set, Schwartz-Bayesian measure was

used. Long-run coefficients are calculated as follows:

,

,

1, 2 , …, (5)

ARDL approach consists of two steps to estimate the long-run relationships. First, the dynamic ARDL

model is tested for long-run relationship, and in the next step, long-run and short-run coefficients are estimated.

The second step is conducted only if the long-run relationship is verified in the first step. Having estimated

ARDL dynamic model, this paper tested the following hypothesis:

H ∑ 1 0

H ∑ 1 0 (6)

The null hypothesis implies the absence of a long-run relationship. Quantity t statistics requires to perform

the test as follows:

∑

∑ (7)

If t statistics obtained from the absolute critical values provided by Banerjee, Dolado, and Mester (2012) is

larger, then the null hypothesis based on absence of cointegration is rejected, and long-run relationship is

accepted (Nowferesti, 1999). In the second step, if the presence of cointegration is approved, the long-run

relationship would be estimated.

Study Results

Before the test, reliability of all variables is checked to ensure that none of the variables is I(2). If there is

any I(2) variable in the model, F statistics is not reliable. To ensure variables of time series used in the model

stationary or none-stationary, Augmented Dickey Fuller (ADF) test has been used. Table 1 shows the ADF

test’s results in the level for the variables. Usually the Schwarz Bayesian Criterion (SBC) saves the number of

lags. Therefore, in this study, the number of optimized lags is selected based on SBC criteria. OP variable in the

level, while without trend, is stationary, but for the variables of FD, FD2, GDP, and EN, Absolute Dickey Fuller

statistic in both cases is smaller than the critical values. Therefore, the variables in level are none-stationary and

the unit root hypothesis on the variables is not rejected.

Table 1

Results of Unit Root Tests in the Level

Variables With intercept and without trend * With intercept and trend **

Optimal lag ADF statistics Test results Optimal lag ADF statistics Test results

EN 0 -0.95 Non-stationary 0 -2.11 Non-stationary

FD 1 -0.74 Non-stationary 0 -2.79 Non-stationary

FD2 1 -0.72 Non-stationary 0 -2.44 Non-stationary

GDP 0 2.50 Non-stationary 5 0.25 Non-stationary

OP 9 -4.55 Stationary 9 -3.53 Non-stationary

Notes. * Critical value at the confidence level of 95% in cases without trend is -2.96; ** critical value at the confidence level of 95% in cases with trend is -3.56. Source: Research findings.


546

To find the stationary degree of the variables, ADF test was replicated for the first-order difference of the

variables. Test results showed that variables get stationary by making one deduction.

Table 2

Results of Unit Root Tests on the First Difference of the Variables

Variables With intercept and without trend * With intercept and trend **

Optimal lag ADF statistics Test results Optimal lag ADF statistics Test results

EN 0 -5.28 Stationary 0 -5.38 Stationary

FD 0 -3.94 Stationary 0 -3.71 Stationary

FD2 0 -3.84 Stationary 0 -3.60 Stationary

GDP 0 -3.81 Stationary 0 -4.24 Stationary

Notes. * Critical value at the confidence level of 95% in cases without trend is -2.96; ** critical value at the confidence level of 95% in cases with trend is -3.56. Source: Research findings.

Result of estimation of ARDL model is based on the three parts: dynamic, short-run, and long-run

relationships. The following equation as the dynamic relationships among variables can be specified and

estimated:

EN ∑ α EN ∑ FD ∑ FD ∑ GDP ∑ OP U (8)

To estimate the relationship, as the data are on annual basis, the maximum lags were taken two, and using

Schwarz-Bayesian criterion, dynamic relationships among variables were selected. The optimal lags for each of

the variables were set and the model was estimated as ARDL (1, 0, 0, 0, 0). To study the long-run relationship

of the variables, the value of computational statistics of Banerjee et al. (2012) is calculated in the following

way:

.

.3.78 (9)

The value of table of Banerjee et al. (2012) at confidence level of 90% for a model with intercept is equal

to -3.64; thus, the existence of long-run relationship among the variables is confirmed. Having ensured the

long-term relationship, results of estimation would be provided in Table 3.

Table 3

Result of Estimation of Long-run Relationship

Variables Coefficients Standard deviation t statistics Critical value

FD 14.55 6.33 2.30 0.028*

FD2 -0.11 0.06 -1.84 0.074**

GDP 20.84 3.14 6.63 0.000*

OP -38.81 8.56 -4.53 0.000*

Notes. * Significant at 95% confidence level; ** significant at 90% confidence level. Source: Research findings.

As the results of the classic test show the lack of successive correlation among components of disturbances,

properly specified equation and equal variance, the results of long-run relationship are reliable. Results

obtained from Table 3 show that all variables are significant at the 90% confidence interval. The positive

coefficient of GDP (20.84) shows that economic growth in Iran is primarily associated with emission increase.

Coefficient of financial development and trade liberalization is positive and negative respectively, which


547

implies that increase in financial development causes rise in environmental degradation; however, trade

increase promotes the quality of the environment. The coefficient of long term emissions relative to variable of

squared financial development is significant and negative (-0.11), which shows that the inverted U relationship

between financial development and environmental quality is true in Iran.

For a more detailed review of the results, changes in the environmental degradation index and financial

development could be estimated in the model according to the coefficients, and assuming that all other

conditions do not change, shown in Figure 6.

Figure 6. Inverted U relationship between financial development and environmental quality for Iran using Matlab. Sources: Research findings.

In this figure, the vertical axis and horizontal axis respectively represent the environmental emissions and

financial development. As it is seen, the curve for Iran is similar to an inverted U and the estimated model fully

meets the theoretical expectations. In the period between 1970 and 2011, Iran was in the first half of the curve,

and financial development for levels higher than 0.72 leads to improved environmental quality. The estimated

error correction model to study adjustment of short-run disequilibrium towards long-run equilibrium is

presented in Table 4.

Table 4

Results of the Estimation of Error Correction Model

Variables Coefficients Standard deviation T-statistics Critical value

dFD 7.76 3.79 2.04 0.049*

dFD2 -0.06 0.03 -1.71 0.096**

dGDP 11.12 3.25 3.42 0.002*

dOP -20.70 6.23 -3.32 0.002*

ECM(-1) -0.53 0.14 -3.80 0.001*

Notes. * Significant at 95% confidence level; ** significant at 90% confidence level. Source: Research findings.

The value of -0.53 was obtained for error correction coefficients in the model, which means a 53%

adjustment in each period to establish a long-run equilibrium. The results of CUSUM and CUSUMSQ tests for

evaluating the estimated coefficients and the results of stability test for short- and long-run coefficients over the

time were shown in Figure 7 and 8.


548

Figure 7. Plot of cumulative sum of recursive residuals. Sources: Research finding.

Figure 8. Plot of cumulative sum of squares of recursive residuals. Sources: Research finding.

As in both tests, the statistics were within the 95% confidence intervals, null hypothesis based on the

stability of the coefficients was accepted and at the confidence level of 95%, the obtained results are valid.

Conclusions

Economic growth is one of the most important concerns of human communities. Development process in

Iran, like other developing countries involves the use of the environment and at its degradation at the same

times. Financial intermediaries through financial development may increase technological innovation and

mobilize financial resources to identify the best production technology and make investments in projects

involving clean environment. Nevertheless, financial development may increase financing for industrial

activities which harm the environment.

Due to the different reliability degrees of the variables, long-run ARDL model was employed. The results

show that the coefficient of financial development is positive and significant at the 0.05% probability level, and

suggest that in addition to economic growth, financial development also affects environmental quality in Iran,

and has led to increase environmental pollution. Negative squared coefficient of financial development implies

that inverted U-shaped relationship between financial development and environmental quality is true for Iran.

Results show that Iran is on the upside half of curve and according to predictions made on the basis of the


549

financial development of approximately 0.72 in Iran, financial development will lead to improved

environmental quality. Given the high importance of development for developing countries, including Iran, to

support environmental policies is of low priority. Based on the Figure 2, in the years considered, financing for

industrial activity has increased compared to other activities and industries have been inefficient in protection

of the environment. Financial development has made way for destruction of environment. In fact, the

investments were only effective in increasing the size of the industrial activities and have not resulted in

technological advancement in the industry.

Results show that economic growth had a significant and positive impact on emissions. The study’s results

also suggest that increased trade openness has led to improvement of environmental quality in the country. This

could be due to that the goods which produce large quantities of pollutants in the manufacturing process are

imported from other countries like China. As a result, the pollution increases in the exporting countries, and in

Iran as an importing country, pollution reduces due to the reduction in production of polluting goods.

Furthermore, it might be due to decline in export of polluting goods, which reflects low production and reduced

pollution in Iran. The decline in the proportion of heavy polluting products’ export such as cement, glass,

ceramics, iron, and steel, which produce large amounts of pollutants in manufacturing process (Figure 5), and

increased proportion of imports (Figure 4) confirm the results of the model. In addition, economic openness

leads to an increase in imports of high tech intermediary and capital goods that create less pollution in

production process.

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Sino-European Trade Competition in Latin

America and the Caribbean

Wioletta Nowak

University of Wroclaw, Wroclaw, Poland

The article studies trade in goods between China and the Latin American and Caribbean (LAC) countries and

between the European Union (EU) and LAC during the years from 2000 to 2013. From the beginning of the 21st

century, big changes in LAC’s trade patterns have been observed. The article contains possible explanation of them.

The analysis is based on the ECLAC (Economic Commission for Latin America and the Caribbean) data.

Merchandise trade between China and LAC grew significantly over the period from 2000 to 2013. In 2013, the

value of merchandise exports from China was higher than from the EU-28 in the case of 12 LAC countries. Chinese

imports of goods surpassed the European ones in five countries in the region. In order to increase its exports of

manufactured goods and imports of natural resources and agricultural commodities, China combines trade

arrangements with foreign aid policy. Besides, a rapid development of bilateral diplomatic ties between China and

LAC is observed. The EU-LAC trade relations have worsened during the last decade mainly due to financial crisis

and development of the EU-Asia trade relations.

Keywords: China, merchandise trade, foreign aid, European Union (EU)

Introduction

The European Union (EU) and China (after the United Sates) are the most important trading partners for

the Latin American and Caribbean (LAC). Since the beginning of the 21st century, a rapid expansion of

Sino-Latin trade and economic relations has been observed. China is likely to surpass the EU and be LAC’s

second largest trade partner in a few years. China’s ties with Latin America are not new. However, their

dynamics in recent years were really spectacular. The significant increase in trade between China and LAC

countries was observed after the beginning of the financial crisis. Besides, from 2008, China has become a

major source of financing for many countries in the region. China uses its loans to develop bilateral trade

relations with the LAC states.

After the financial crisis, the EU was mainly concentrated on the struggle against its effects. In order to

restore its economy, the EU developed closer economic and trade relations with Asia. At the same time, the

EU-LAC trade relations have worsened. Although, the EU increased its aid-for-trade with LAC countries, it has

been gradually losing its importance for LAC as a destination for exports and as a source for imports.

In the literature, trade relations between China and Latin America and between the EU and LAC are

mainly examined separately (Bárcena & Rosales, 2010; Bárcena, Prado, Rosales, & Pérez, 2012; Roy, 2012).

Wioletta Nowak, Ph.D., University of Wroclaw, Wroclaw, Poland. Correspondence concerning this article should be addressed to Wioletta Nowak, Institute of Economic Sciences, Uniwersytecka

22/26, 50-145 Wroclaw, Poland. E-mail: [email protected].

DAVID PUBLISHING

D

COMPETITION IN LATIN AMERICA AND THE CARIBBEAN

553

The aim of the paper is to study China-LAC and the EU-LAC trade relations in the first decade of the 21st

century using the same data set. The analysis is principally based on the EULAC data.

Development of China-LAC and the EU-LAC Trade Relations

Trade between China and Latin America is dated back to the 1560s. At that time, Chinese ships sailed to

Acapulco in Mexico via Manila. China exported mainly silk, cotton cloths, jewellery, and gun powder to Latin

America, and imported wine, olive, oil, soap, and food. In 1815, this “silk-road” on the sea between China and

Latin America was closed due to an implementation of the Chinese export control policy (Jiang, 2006, p. 69).

In the 19th century, Sino-Latin American relations took a different form. The basis of ties was the Chinese

immigration. Hundreds of thousands of Chinese workers migrated to the Latin American countries (Mexico,

Brazil, Chile, Panama, and Peru) where they mainly worked in mines and on plantations (Ratliff, 2009, p. 2).

After the proclamation of People’s Republic of China in 1949, economic cooperation between China and

LAC was still limited. Trade exchange was insignificant, not to mention investments. The situation was a

consequence of a lack of diplomatic contacts at high governmental levels between China and LAC states and

the poor condition of the Chinese economy.

In the 1950s, Sino-Latin American ties were restricted to visits of individual Latin Americans. LAC

countries began to establish diplomatic relations with China just in the 1970s. The first countries that

recognized People’s Republic of China in the region were Cuba and Chile. Cuba established diplomatic

relations with China in 19601 and Chile 10 years later. Then, other major countries in the region began to

recognize China.2 It happened mainly because of two significant events: In 1971, the government of People’s

Republic of China was recognised as the legal representative of China in the United Nations (UN) and was

given the permanent seat in the UN Security Council; in February 1972, President Richard Nixon visited China,

after which the pressure from the United States declined and Latin countries which were always in the orbit of

the American influence could begin to establish diplomatic relations with China.

China-LAC trade relations accelerated after Deng Xiaoping’s reforms in 1978. China’s rapid economic

growth and its constantly increasing demand for natural resources, food and new markets caused that it had to

find new trade partners. China, among others, turned to resource-rich Latin America.

Until now, China has got preferential access to three markets in the region (Table 1). It signed free trade

agreement (FTA) with Chile, Peru, and Costa Rica. FTAs cover items on the World Trade Organization’s new

trade agenda. It means that they concern not only the deregulation and liberalization of goods markets but also

services and investment. Besides, in 2012, China declared readiness to negotiate with Mercosur (Argentina,

Brazil, Paraguay, Uruguay, and Venezuela) on a free trade area.

Sino-Latin American trade relations are developed and strengthened also during high-level visits. A

significant increase in official visits to LAC by the highest Chinese authorities has been recorded since 2001.

Diplomatic relations were developed and maintained by Jiang Zemin and his successors Hu Jintao and Xi

Jinping (Table 2).

1 After the Sino-Soviet split in the 1960s, Cuba chose the Soviet Union and froze its relation with China. Both countries normalised relations after the collapse of the Soviet Union. 2 In 2014, People’s Republic of China was recognized by 21 LAC countries: Cuba (1960), Chile (1970), Peru (1971), Mexico (1972), Argentina (1972), Guyana (1972), Jamaica (1972), Trinidad and Tobago (1974), Venezuela (1974), Brazil (1974), Suriname (1976), Barbados (1977), Ecuador (1980), Colombia (1980), Antigua and Barbuda (1983), Bolivia (1985), Grenada (1985), Uruguay (1988), Bahamas (1997), Dominica (2004), and Costa Rica (2007).


554

Table 1

Trade Agreements Between China and the Latin American Countries

Agreement name Type Coverage Date of entry into force

Chile to China FTA & EIA Goods & Services 01-Oct-2006 (Goods), 01-Aug-2010 (Services)

China to Costa Rica FTA & EIA Goods & Services 01-Aug-2011

Peru to China FTA & EIA Goods & Services 01-Mar-2010

Note. EIA: Economic Integration Agreement. Source: Retrieved from http://rtais.wto.org/UI/PublicAllRTAList.aspx.

Table 2

Development of Bilateral Ties Between China and Latin America and the Caribbean in the Years 2001-2014

Year Chinese authority Visited LAC countries

2001, April President Jiang Zemin Chile, Argentina, Uruguay, Brazil, Cuba, Venezuela

2003, December Prime Minister Wen Jiabao Mexico

2004, November President Hu Jintao Chile, Brazil, Argentina, Cuba

2005, September President Hu Jintao Mexico

2008, November President Hu Jintao Peru, Costa Rica, Cuba

2009, February Vice President Xi Jinping Mexico, Jamaica, Colombia, Venezuela, Brazil

2010, April President Hu Jintao Brazil

2011, June Vice President Xi Jinping Cuba, Uruguay, Chile

2012, June Prime Minister Wen Jiabao Brazil, Uruguay, Argentina, Chile

2013, May/June President Xi Jinping Trinidad and Tobago, Costa Rica, Mexico,

2014, July President Xi Jinping Brazil, Argentina, Venezuela, Cuba

Moreover, the China Council for the promotion of international trade has initiated so far eight China-Latin

America business summits which are also an important platform for trade cooperation between China and LAC.

The first was held in Santiago, Chile (2007), the second in Harbin, China (2008), and the next in Bogota,

Colombia (2009), Chengdu, China (2010), Lima, Peru (2011), Hangzhou, China (2012), San Jose, Costa Rica

(2013), and Changsha, China (2014).

Europe and LAC are linked through historical, cultural, political, and economic ties which are dated back

to 1492. However, contemporary relations between the EU and LAC were regulated in 1999 during the first

EU-LAC summit which was held in Rio de Janeiro, Brazil. The main achievement of the summit was the

establishment of a strategic partnership between the EU and LAC. Since then, issues referring to a mutual

cooperation in the area of free trade between the regions are discussed during biannual EU-LAC summits. So

far six bi-regional summits have been held in Madrid, Spain (2002), Guadalajara, Mexico (2004), Vienna,

Austria (2006), Lima, Peru (2008), Madrid, Spain (2010), and Santiago, Chile (2013). The summits bring

together heads of state and government from both continents. In the years when the EU-LAC summits do not

take place, the EU and the Rio Group3 meet at ministerial level. In 2010, EU-LAC foundation was created in

order to assist in the implementation of main objectives of the strategic partnership between the regions. The

Foundation has 63 members: the 28 members of the EU, the 33 LAC states, and the EU institutions (Retrieved

from http://eulacfoundation.org/en/about-us).

The EU has privileged relations in trade with the Caribbean and countries of Central America. Besides, the 3 The Rio Group was established by Argentina, Brazil, Colombia, Mexico, Panama, Peru, Uruguay, and Venezuela in 1986. The Group eventually extended to 24 Latin American and Caribbean states.


555

EU signed special agreements with Mexico, Chile, Colombia, and Peru (Table 3). All trade agreements

between the EU and LAC relate to deregulation and liberalization of trade in goods and services.

Table 3

Trade Agreements Between the EU and LAC

Agreement name Type Coverage Date of entry into force

EU to CARIFORUM States EPA FTA & EIA Goods & Services 01-Nov-2008 EU to Central America (Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama)

FTA & EIA Goods & Services 01-Aug-2013

EU to Chile FTA & EIA Goods & Services 01-Feb-2003 (Goods)

01-Mar-2005 (Services)

EU to Colombia and Peru FTA & EIA Goods & Services 01-Mar-2013

EU to Mexico FTA & EIA Goods & Services 01-Jul-2000 (Goods)

01-Oct-2000 (Services)

Note. CARIFORUM includes 14 CARICOM members and the Dominican Republic. Source: Retrieved from http://rtais.wto.org/UI/PublicAllRTAList.aspx.

In 2000, the EU opened negotiations on free trade area with Mercosur. However, there are some obstacles

in negotiations concerning sectors which are sensitive for both sides. The EU protects its own production of

food via the common agricultural policy and Mercosur wants the access to the European market in agricultural

goods. In turn, Mercosur protects its own production of manufactured goods that are the EU’s basic export

commodities (Roy, 2012, p. 8).

Main Characteristics of Merchandise Trade Between China and LAC and Between the EU and LAC

Europe has been the second major trading partner (after the United States) for LAC for many years.

However, recently, an impressive increase in trade between China and LAC33 (33 countries) has been observed.

In the years 2000-2013, the value of China’s exports in goods to LAC33 increased about 19 times and Chinese

imports in goods from the region increased over 23 times. At that time, the value of the EU25 (25 countries)

exports in goods to LAC33 increased about three times. European merchandise imports increased 2.7 times.

Trends in merchandise trade between the EU25 and LAC33 and between China and LAC33 over the period

2000-2014 are presented in Figure 1.

In 2000, the value of European merchandise exports to LAC33 was 7.7 times higher than Chinese, but 14

years later only 1.2 times. At the beginning of the century, the value of European merchandise imports from

LAC33 was 9.3 higher than China’s imports from the region. In 2013, the EU25 imports value was merely 1.1

higher than the Chinese. The EU has been steadily losing its share in Latin American market to China. If the

average growth rate of Chinese trade with LAC will be maintained, China is likely to be the second important

trading partner for LAC in a few years.

After the beginning of global economic and financial crisis, both China and the EU decreased their trade

with LAC. In 2009 compared to 2008, the Chinese exports declined by 21% and imports by 10%. In the case of

the EU, the figures were respectively 24% and 31%.

The change in the Chinese merchandise trade with selected Latin American countries over the period from

2000 to 2013 is significantly higher. For instance, during last 14 years, the Chinese exports to Colombia and

Peru increased over 40 times. China’s imports from Venezuela and Colombia increased over 100 times. In the


556

case of Costa Rica, the increase in the Chinese imports was exceptionally high. In the considered years, the EU

increased its trade with the Latin American countries a few times (Table 4).

Figure 1. EU25 and China’s trade in goods with LAC33 in the years 2000-2013 (USD million). Source: Retrieved from http://www.cepal.org/comercio/ecdata2.

Table 4 Percentage Change in Value of China’s and the European Union’s Trade in Goods With Selected Latin American Countries in the Years 2000-2013

Country China EU25

Country China EU25

Exports Imports Exports Imports Exports Imports Exports Imports

Argentina 1,334% 554% 132% 111% Guatemala 964% 3,676% 132% 82%

Brazil 2,834% 3,249% 243% 155% Mexico 2,069% 1,997% 177% 242%

Chile 1,573% 1,447% 282% 149% Panama 752% 4,208% 153% 119%

Colombia 4,277% 11,158% 334% 356% Peru 4,188% 1,401% 387% 328%

Costa Rica 1,322% 46,041% 76% 116% Uruguay 856% 2,334% 182% 338%

Cuba 491% 522% 83% 71% Venezuela 2,264% 13,742% 96% 76%

Ecuador 3,863% 867% 509% 258%

Source: Retrieved from http://www.cepal.org/comercio/ecdata2.

The rise in the Chinese merchandise trade with selected Latin American countries is much more

impressive in longer period. During the last 20 years, China has been exponentially increased its trade with

countries in the region. China’s exports and imports of goods to its most important Latin American trading

partners are presented in Figure 2.

A fast growth of the merchandise exchange between China and LAC caused that China has already

overtaken the EU in trade with a few countries in the region. In 2013, China exported more commodities than

the EU to 12 Latin American countries: Chile, Panama, Venezuela, Peru, Uruguay, Guatemala, Paraguay,

Honduras, Jamaica, Nicaragua, Haiti, and Dominica. The Chinese imports value of goods was higher than the

European in the case of five countries: Brazil, Chile, Venezuela, Peru, and Uruguay.

Since the beginning of the 21st century, the importance of China as an export market significantly has

been increased in a few LAC countries (Table 5). In 2013, China absorbed almost one fourth of the Chilean

merchandise exports and one fifth of the Brazilian ones. Besides, over 17% of the Peruvian and 14% of the

Uruguayan exports were destined in China. A relatively big increase in the share of exports of goods to China

in total exports was also observed in the case of Colombia, Argentina, and Panama. China still became an

0

50000

100000

150000

200000

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Exports of goods

E25 China

0

50000

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150000

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2001

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2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Imports of goods

E25 China


557

unexploited market for Bolivia, Costa Rica, Mexico, or Paraguay. Over the period from 2000 to 2013, China

substantially increased its importance as a source of imports for the LAC countries. In 2013, the most

dependent countries on Chinese commodities were Paraguay (28.3% of its imports came from China), Chile

(19.7%), and Peru (19.4%).

Figure 2. China’s exports and imports of goods to Brazil, Chile, Mexico, and Venezuela (USD million) in the years 1994-2013. Source: Retrieved from http://www.cepal.org/comercio/ecdata2.

Table 5

Merchandise Trade of Selected LAC Countries with China in 2000 and 2013 (Percentages of Total Trade)

Country Exports Imports

Country Exports Imports

2000 2013 2000 2013 2000 2013 2000 2013

Argentina 3.0 7.2 4.6 15.4 Ecuador 1.2 2.3 2.2 16.7

Bolivia 0.4 2.6 3.1 12.1 Mexico 0.2 1.7 1.6 16.1

Brazil 2.0 19.0 2.2 15.6 Panama 0.2 6.1 0.6 7.9

Chile 5.0 24.8 5.7 19.7 Paraguay 0.7 0.6 11.5 28.3

Colombia 0.2 8.7 3.0 17.5 Peru 6.4 17.5 3.9 19.4

Costa Rica 0.2 3.3 1.3 9.6 Uruguay 4.0 14.2 3.2 16.9


The EU is still an important export market for most of the LAC countries. For instance, in 2009 over 20%

of Brazil’s, Honduras’ or Panama’s exports were destined in EU27. However, in the years from 2000 to 2009, a

percentage increase in merchandise exports to EU27 was observed only in Ecuador, Honduras, Panama,

0

10000

20000

30000

40000

50000

60000

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

Brazil

Exports Imports

0

5000

10000

15000

20000

25000

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

Chile

Exports Imports

05000

100001500020000250003000035000

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

Mexico

Exports Imports

0

5000

10000

15000

2000019

94

1996

1998

2000

2002

2004

2006

2008

2010

2012

Venezuela

Exports Imports


558

Paraguay, Venezuela, and four Caribbean countries. In the case of 18 countries in the region, the share of

merchandise exports of goods to EU27 in their total exports decreased over the considered period (Bárcena &

Rosales, 2010, p. 13).

An analysis of the structure of China’s and Europe’s trade with LAC shows some similarities. Namely,

both sell to LAC mostly manufactured goods and LAC countries send to them mainly resources and raw

materials. China’s exports of goods to Latin America consist principally of electronics, components and parts,

machinery and equipment, textiles and apparel. In other words, China is a source of imports of cheap

manufactured goods for the region (Table 6).

Table 6

Top Five Latin American Trade Partners of China in 2013

China’s exports of goods China’s imports of goods

No. Country Goods No. Country Goods

1 Brazil Telecommunication equipment, optical instruments, electrical machinery

1 Brazil Iron ore and concentrates, seeds and oleaginous fruit, crude petroleum, pulp andwaste paper, sugar and honey

2 Mexico Telecommunication equipment, optical instruments, automatic data processing machines

2 Chile Copper, ores and concentrates of base metals, iron ore and concentrates

3 Chile Telecommunication equipment, footwear, cloths

3 Venezuela Crude petroleum and oils, petroleum products

4 Panama Petroleum products, ships, boats and floating structure

4 Mexico Ores and concentrates of base metals, passenger motor vehicles, microcircuits, transistors

5 Argentina Telecommunication equipment 5 Peru Ores and concentrates of base metals, iron ore and concentrates, copper


Table 7

Top Five Latin American Trade Partners of the European Union in 2013

EU25 exports of goods EU25 imports of goods

No. Country Goods No. Country Goods

1 Brazil

Medicinal and pharmaceutical products, motor vehicle parts and accessories, petroleum products, aircraft and associated equipment

1 Brazil Iron ore and concentrates, feeding stuff for animals, seeds and oleaginous fruit, coffee, crude petroleum

2 Mexico Petroleum products, medicinal and pharmaceutical products, motor vehicle parts and accessories

2 Mexico Crude petroleum and oils, telecommunication equipment, passenger motor vehicles

3 Argentina Petroleum products, motor vehicle parts and accessories, medicinal and pharmaceutical products

3 Chile Copper, ores and concentrates of base metals, fruit and nuts

4 Chile Aircraft and associated equipment, passenger motor vehicles, crude petroleum

4 Argentina Feeding stuff for animals, chemical products, ores and concentrates of base metals

5 Colombia Medicinal and pharmaceutical products, aircraft and associated equipment

5 Colombia Crude petroleum and oils, coal, fruit and nuts


Latin America is important destination for the European medicinal and pharmaceutical products, motor

vehicles and aircraft (Table 7). Latin American exports mainly copper, iron and steel, oil, natural gas, coal,


559

soya beans, beef, bananas, and coffee to China and the EU. On average, the LAC countries sell more different

products to the EU than to China (Bárcena et al., 2012, p. 40). Of the LAC countries, only Mexico exports more

technology-intensive products to China and the EU.

Foreign Aid as a Tool of Promoting Trade With LAC Countries

China uses different methods to increase its trade in goods with other countries. One of the most effective

tools is foreign aid. Depending on the region and country, China provides grants, interest-free loans or

concessional loans to countries with which it trades. In the case of the LAC countries, low-interest loans

dominated in the Chinese foreign aid policy. It is estimated that China offered about USD 100 billion to the

region in the years from 2005 to 2013 (Figure 3).

Figure 3. China’s lending to Latin America and the Caribbean in the years 2005-2013 (USD billion). Source: Gallagher, Irwin, and Koleski (2012).

Over 90% of China’s lending to LAC was pledged after 2007. In 2010, China’s loan commitments to the

region were more than combined loans of the Inter-American Development Bank, World Bank, and United

States Export-Import Bank to LAC (Gallagher et al., 2012, p. 1).

During the financial crisis, China provided low-interest loans mainly to four resource-rich countries in the

region: Venezuela (USD 50.6 billion), Argentina (USD 14.1 billion), Brazil (USD 13.4 billion), and Ecuador

(USD 9.9 billion). China was the last resort of financing for countries like Argentina, Ecuador, and Venezuela

that were not able to borrow easily in international capital markets. The data about Chinese lending to LAC in

the years from 2005 to 2013 are presented in Table 8.

Table 8

China’s Lending to Selected Latin American and Caribbean States in the Years 2005-2013 (USD Billion)

Country Value of loans Country Value of loans Country Value of loans

Argentina 14.1 Colombia 0.075 Peru 2.3

Bolivia 0.611 Costa Rica 0.789 Uruguay 0.01

Brazil 13.4 Ecuador 9.9 Venezuela 50.6

Chile 0.15 Mexico 2.4

Source: Gallagher et al. (2012).

Besides, in July 2014, Chinese President Xi Jinping announced additional USD seven billion to Argentina,

USD five billion to Brazil, and USD 5.7 billion in loan and USD five billion in credit line to Venezuela (Lee,

0.231 0

4.8 6.3

13.6

37

17.8

3.5

15

2005 2006 2007 2008 2009 2010 2011 2012 2013


560

2014). A sharp increase in the Chinese financing provided to LAC in the second half of the first decade of 21st

century coincided with the rise of trade in goods between China and countries in the region.

The EU members and institutions have been provided official development assistance to Latin America

and the Caribbean for many years. Since 2001, European grants and concessional loans have been more often

directed to trade-related projects and programmes4. The EU increased its aid-for-trade disbursements to LAC

countries after the Hong Kong WTO Ministerial Conference (Nowak, 2014, p. 77). The European aid-for-trade

to the region in the years 2002-2011 is presented in Figure 4.

Figure 4. The European Union’s aid-for-trade commitments to LAC (USD million, 2011 constant). Source: Retrieved from http://dx.doi.org/10.1787/aid_glance-2013-en.

From the report Aid for Trade at a Glance 2013, it follows that in 2010, the EU provided aid-for-trade to

25 LAC countries. The volumes of aid varied across the states. The EU supported trade-related projects mainly

in the poorest countries of the region: The major ones were Peru, Haiti, Honduras, and Jamaica (Figure 5).

Figure 5. The European Union’s aid-for-trade disbursements to selected LAC countries in 2010 (USD million, 2011 constant). Source: Retrieved from http://dx.doi.org/10.1787/aid_glance-2013-en.

The level of the European aid was lower than China’s aid. It is worth noting that in the years from 2005 to

2010, from 12 LAC countries to which China exported more commodities than the EU in 2013, only Chile and

Venezuela were not supported by the EU under the aid-for-trade program.

Conclusions

The LAC countries are not the most important trading partners for the EU and China. In 2013, the EU’s

exports to LAC represented only 6.7% of total and imports 6.0%. Exports and imports figures for China were

similar 6.0% and 6.5%, respectively (Retrieved from http://www.cepal.org/comercio/ecdata2). However, the

region became the area of Sino-European trade competition especially since the second half of the 21st

century. 4 In 2001, the Development Assistance Committee announced The DAC Guidelines Strengthening Trade Capacity for Development.

821.2 956.6

1,446.7

2002-2005 2006-2008 2009-2011

63.854.8

30.4 29.7 23.6 22.7 20.8 20

Peru Haiti Honduras Jamaica Dominican Rep.

Suriname El Salvador Nicaragua


561

A significant growth in trade between China and LAC countries has been observed since the beginning of

the global financial crisis. China, providing a huge amount of low-interest loans to LAC countries and

developing diplomatic relations with them, has been constantly increasing bilateral trade in goods with the

region. At the same time, the EU was also provided grants and concessional loans to support LAC’s efforts in

expanding its trade. However, the EU more concentrated on development of trade relations with the Asian than

LAC countries. As a result, the EU has been losing its priority for LAC.

References Bárcena, A., & Rosales, O. (2010). The People’s Republic of China and Latin America and the Caribbean: Towards a strategic

relationships (Economic Commission for Latin America and the Caribbean, Chile). Bárcena, A., Prado, A., Rosales, O., & Pérez, R. (2012). Latin America and the Caribbean and the European Union: Striving for

a renewed partnership (Economic Commission for Latin America and the Caribbean, Chile). Economic Commission for Latin America and the Caribbean. (2014). Statistics and indicators. Retrieved from

http://www.cepal.org/comercio/ecdata2 European Union-Latin America and Caribbean Foundation (EU-LAC Foundation). (2014). Retrieved from

http://eulacfoundation.org/en/about-us Gallagher, K. P., Irwin, A., & Koleski, K. (2012). The new banks in town: Chinese finance in Latin America. Retrieved from

http://www.thedialogue.org/PublicationFiles/TheNewBanksinTown-FullTextnewversion.pdf Jiang, S. (2006). A new look at the Chinese relations with Latin America. Retrieved from

http://www.nuso.org/upload/articulos/3351_2.pdf Lee, B. (2014). Chinese President Xi Jinping brings billions on visit to Latin America. Retrieved from

http://www.ibtimes.com/chinese-president-xi-jinping-brings-billions-visit-latin-america-1635626 Nowak, W. (2014). Development effectiveness of foreign assistance. Nierówności Społeczne a Wzrost Gospodarczy, 38(2), 74-84. Organisation for Economic Co-operation and Development., & World Trade Organization. (2013). Aid for trade at a glance 2013:

Connecting to value chains. Retrieved from http://dx.doi.org/10.1787/aid_glance-2013-en Ratliff, W. (2009). In search of a balanced relationship: China, Latin America, and the United States. Asian Politics & Policy, 1(1),

1-30. Roy, J. (2012). European Union-Latin American relations in a Turbolent Era. The Jean Monnet/Robert Schumann Paper Series,

12, 1-33. World Trade Organization. (2014). List of all RTAs. Retrieved from http://rtais.wto.org/UI/PublicAllRTAList.aspx

Chinese Business Review, September 2014, Vol. 13, No. 9, 562-577

doi: 10.17265/1537-1506/2014.09.003

Threshold Effects in the Capital Account Liberalization and

Foreign Direct Investment Relationship

Gammoudi Mouna, Cherif Mondher

Université de Reims, Région Champagne-Ardenne, France

This paper examines how capital account liberalization (CAL) affects foreign direct investment (FDI) inflows.

Authors use a dynamic panel model encompassing 14 Middle East countries over the period from 1985 to 2009. The

findings suggest that countries that are able to reap the benefits of the capital openness policy satisfy certain threshold

conditions regarding the level of financial development and institutional quality. Thus to promote FDI, governments

in this region should develop a set of policies that not only focus on financial openness, but also on the

improvement of the financial system and legal institutions.

Keywords: capital account liberalization (CAL), foreign direct investment (FDI), institutional quality, system GMM

estimator

Introduction

Capital account liberalization (CAL) has been one of the most important economic policies recommended

to developing countries for economic growth. Since the early 1990s, many countries in the Middle East have

established the measures of CAL to attract capital flows mainly foreign direct investment (FDI), which is

understood to be a major antecedent to economic development. Although, all members of them have witnessed

a substantial increase in the FDI inflows from 1985 to 2009 (Figure 1), it have been and continue to be poor in

comparison with the world and other developing regions. Furthermore, Figure 2 indicates a wide disparity in

FDI inflows and a notable difference in the process of CAL among Middle East countries. The question that

arises then is whether and under what conditions capital account policy promotes FDI. A few empirical studies

have been conducted to investigate this issue, which is still an open question until to date.

Studies have failed to establish a stable relation between capital account openness and FDI growth. Some

of them have found a positive impact of capital openness on FDI (Gastanaga, Nugent, & Pashamova, 1998) and

support the notion that countries with relatively liberalized capital accounts attracted more FDI inflows than

countries that are more closed. Butkiewicz and Yanikkaya (2008) reached the same result and concluded in

their study that capital restrictions reduce the benefits of FDI on growth in developing countries. Others have

doubted the robustness of this impact; Asiedu and Lien (2004) employed panel data for 96 countries over the

period from 1970 to 2000 and found that the impact of capital controls on FDI varies by region and has

Gammoudi Mouna, Ph.D. student in economics, Université de Reims, Région Champagne-Ardenne, France.

Cherif Mondher, Ph.D., professor, Université de Reims, Région Champagne-Ardenne, France.

Correspondence concerning this article should be addressed to Gammoudi Mouna, Université de Reims Champagne Ardenne

UFR des Sciences Economiques, Sociales et de Gestion 57 bis, rue Pierre Taittinger 51096 Reims Cedex, France. E-mail:

[email protected];[email protected].

DAVID PUBLISHING

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LIBERALIZATION AND FOREIGN DIRECT INVESTMENT RELATIONSHIP

563

changed over time and proved that capital controls have no effect on FDI to Sub-Saharan Africa and the Middle

East, but affect FDI to East Asia and Latin America adversely. This controversy has prompted research on the

evaluation of the possible pre-conditions under which CAL may spur FDI. From a theoretical point of view,

countries must reach a certain threshold in terms of institutional and economic development before they can

expect to benefit the CAL (Chinn & Ito, 2008; Noy & Vu, 2007; Alfaro, Kalemli-Ozcan, & Volosovych, 2005).

Broadly speaking, the most important preconditions for moving to CAL are: financial market development,

institutional quality, and macroeconomic stability. However, very little attention has been paid by scholars to

this argument. Recently, some empirical studies conducted by (Noy & Vu, 2007, Cherif, Ben, Goaied, &

Kamer, 2011; Okada, 2013) examine how the role of institutional quality as a key factor in explaining the

mixed results in the effect of CAL on FDI inflows has reached more positive conclusions.

Figure 1. FDI net inflows (%GDP) and capital openness index, 1985-2009. Source: World Development Indicators

and author’s calculations, Chinn and Ito (2011).

This paper seeks to contribute to this emerging body of knowledge by investigating the possible existence

of macroeconomic stability, financial development, and institutional quality threshold effects in the relationship

between financial openness and FDI. This paper focused on the two influential articles; the first pivotal article

is given by Noy and Vu (2007) and the second article is proposed by Okada (2013). Noy and Vu (2007)

constructed an annual panel dataset for 62 developing and 21 developed countries from 1984 to 2000 and an

empirical analysis for each group separately, given that the factors that affect FDI inflows are different across

the two groups, using a standard FDI determination model with fixed effect and adding the capital control

variable. Furthermore, in order to examine whether the impact of capital controls on FDI inflows is sensitive to

different institutional factors like corruption, financial risk, and political stability, they included interaction

terms between capital openness and corruption. They underlined that the liberalization of the capital account is

not sufficient to generate increases in inflows unless it is accompanied by a lower level of corruption or a

02

46

FD

I

1985 1990 1995 2000 2005 2010year

.4.6

.81

1.2

1.4

KA

OP

EN

1985 1990 1995 2000 2005 2010year


564

decrease in political risk. These results are obtained by using fixed effects and least squares estimators and

confirmed by GMM dynamic two-step panel estimator. Okada (2013) used a dynamic panel model to examine

how financial openness and institutional quality affect international capital inflows in the sample of 112

countries from 1985 to 2009. He found that while financial openness and institutional quality do not

individually have significant impacts on international capital inflows, their interaction effects are significant.

He confirmed the assumption that the partial effect of capital openness on FDI inflows is depending on the

level of institutional quality and concluded that capital account openness improves FDI inflow only in countries

with good institutional quality comparing to those with poor institutional quality. Furthermore, among

institutional factors, bureaucratic quality and law and order appear to play an important role in promoting FDI.

Figure 2. FDI net inflows (%GDP) and capital openness index in Middle East countries, 1985-2009 (Average). Source:

World Development Indicators and author’s calculations, Chinn and Ito (2008).

This paper complements previous studies that test the effects of CAL on FDI (Asiedu & Lien, 2004;

Alfaro, Chanda, Kalemli-Ozcan, & Sayek, 2004; Okada, 2013; Noy & Vu, 2007) and differs from researches of

Noy and Vu (2007) and Okada (2013) in two respects. Firstly, while Noy and Vu (2007) examined only one

aspect of institutional quality (corruption) and disregarded the role of other institutional quality which may be

important determinants of international capital inflows, this analysis is more comprehensive because the

measures of institutional quality reflect several characteristics of a country’s institutions, such as the

bureaucratic quality, law and order, government stability and investment profile. The second difference is that

although Okada (2013) disentangled how detailed components of institutions such as bureaucratic quality and

law and order can influence capital inflows, he disregarded the main role of financial development and

macroeconomic stability in promoting FDI. This study looks on the interaction effects between capital

openness and financial development on FDI inflows. The main contribution of the study is to investigate the

possible existence of macroeconomic stability, financial development, and institutional quality threshold effects

in the relationship between financial openness and FDI.

-50

51

01

5

Cyprus Egypt Iran Iraq Jordan Kuwait Lebanon Oman Qatar Saoudi Arabia Syria Turkey U.A.E Yemen

FDI KAOPEN


565

Authors employ a panel data of 14 Middle East countries1 over the period from 1985 to 2009. Several

studies have found that lagged FDI is correlated with current FDI. Authors therefore use the system GMM

methodology for dynamic panel data proposed by Blundell and Bond (1998). This dynamic panel approach

enables to consider the presence of unobserved country-specific effects as well as to deal with the problem of

reverse causality or simultaneity2.

The remainder of the paper is organized as follows: Section 2 presents the empirical methodology and data,

Section 3 discusses estimation results, and section 4 concludes the paper.

Empirical Methodology and Data

Empirical Methodology

This paper uses a panel of 14 countries from the Middle East region, covers the period from 1985 to 2009,

and considers the following benchmark regression presented by Okada (2013):

FDI𝑖𝑡 = 𝛽𝑖 + 𝛽1KAOPEN𝑖𝑡 + 𝜌FDI𝑖𝑡−1 + 𝛽′𝐶𝑉𝑖𝑡 + 𝜀𝑖𝑡 (1)

where i refers to countries, t refers to time, βi is the country-specific effect, and εit is the error term for each

observation. FDI is net FDI/GDP, FDIit-1 is the lagged value, KAOPEN is the indicator of CAL developed by

Chinn and Ito (2008)3, CV is a vector of controlling variables drawn from the empirical literature of FDI

determinants. According to Moosa and Cardak (2006), market size (GDPpc), trade openness (Open) and

infrastructure quality (Tele) are the most robust determinants of FDI, thus these variables form part of the basic

set of controlling variables and appear in all model specifications. Economic literature suggests that countries

which are endowed with natural resources would receive more FDI. This paper therefore includes the share of

fuel in total merchandise exports to capture the availability of natural resource endowments (Nat). This measure

of natural resources has been employed in several studies, including Jeffrey and Andrew (1997) and Asiedu and

Lien (2011) among others and those which are available at World Development Indicators from World Bank.

Furthermore, in order to examine whether CAL promotes FDI only under certain conditions such as

macroeconomic stability, financial depth, and political stability, this paper introduces multiplicative terms

(KAOPEN*k) where k represents respectively inflation consumer price (INF), financial development (DC), and

institutional quality (INST). Therefore, KAOPEN INF is the interaction between capital openness and

inflation consumer price, KAOPEN*DC is the interaction between capital openness and private credit to the

domestic sector, and KAOPEN*INST is the interaction between capital openness and institutional quality.

FDI𝑖𝑡 = 𝛽𝑖+𝛽1KAOPEN𝑖𝑡 + 𝜌FDI𝑖𝑡−1 + 𝛽2𝑘𝑖𝑡 + 𝛽3 KAOPEN𝑖𝑡 𝑘𝑖𝑡 + 𝛽′𝐶𝑉𝑖𝑡 + 𝜀𝑖𝑡 (2)

As previously mentioned, equations (1) and (2) make up a dynamic panel data model, where the dependent

variable is partly explained by its past value. This model involves two econometric problems. The first one

results from the dynamic nature of the data, which can introduce some correlations between the lagged

depended variable and the error term εit or between some of the variables of the CV vector and the specific term

βi. The second issue results from the potential endogeneity of the explanatory variables. So, the application of

static panel data estimation methods would lead to biased estimates with dynamic panel data models. Considering

1 Cyprus, Egypt Arab Republic, Iran Islamic Republic, Iraq, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syrian Arab

Republic, Turkey, United Arab Emirates, and Yemen. 2 The system GMM estimation allows to control for the potential endogeneity not only of FDI, but also of all other explanatory

variables. 3 This paper uses the KAOPEN index updated to 2011 which covers 181 countries from 1970 to 2011.


566

these aspects, the appropriate methodology to use is the GMM estimator for dynamic panel data models

suggested by Arellano and Bond (1991), which provides consistent estimates for such models. This estimator

often referred to the difference, as GMM estimator takes the first difference of the data and then uses lagged

values of the endogenous variables as instruments. This allows to get rid of country specific effects and

eliminates any endogeneity that may be caused by the correlation of these country specific effects and the

explanatory variables.

This paper therefore lagged independent and control variables for one period, the first difference

transforms the first equation (1):

FDI𝑖𝑡 = 𝛽1KAOPEN𝑖𝑡 + 𝜌FDI𝑖𝑡−1 + 𝛽′𝐶𝑉𝑖𝑡 + 𝜀𝑖𝑡 (3)

FDI𝑖𝑡 − FDI𝑖𝑡−1 =

𝛽1(KAOPEN𝑖𝑡 − KAOPEN𝑖𝑡−1) + 𝜌(FDI𝑖𝑡−1 − FDI𝑖𝑡−2) + 𝛽′(𝐶𝑉𝑖𝑡 − 𝐶𝑉𝑖𝑡−1) + (𝜀𝑖𝑡 − 𝜀𝑖𝑡−1) (4)

Consequently, the GMM difference has eliminated the country fixed effect. However, the

first-differencing equation (1) induces a new bias by constructing the new error term Δεit, which is correlated

with the lagged dependent variable ΔFDIit, therefore suggests the following moment conditions:

E [FDIit-s (εi εit-1)] = 0, for s ≥ 2; t = 1…, T;

E [KAOPENit-s (εi εit-1)] = 0, for s ≥ 2; t = 1…, T;

E [CVit-s (εi εit-1)] = 0, for s ≥ 2; t = 1…, T.

However, as pointed out by Arellano and Bover (1995), when the explanatory variables are persistent time,

lagged levels are often poor instruments for first differences. Blundell and Bond (1998) proposed a more

efficient estimator, the system GMM estimator, which mitigates the poor instruments problem by using

additional moment conditions. In the Blundell and Bond’s GMM estimator, the instruments for the regression

in levels are the lagged differences of the corresponding variables and the instruments for the regression in

differences are the lagged levels. Thus, Blundell and Bond (1998) and Arellano and Bover (1995) set the

following additional moment conditions:

E [(FDIit-s FDIi ,t-s) (βi + εit)] = 0, for s = 1;

E [(KAOPENit-s KAOPENit-s) (βi + εit)] = 0, for s = 1;

E [(CVit-s CVit-s) (βi + εit)] = 0, for s = 1.

In the study, it uses the system GMM approach which generally produces more efficient and precise

estimates compared to difference GMM by improving precision and reducing the finite sample bias (Baltagi,

2008).

To verify the consistency of the GMM estimator, authors consider two specification tests: first, the Hansen

test (J-test) for over-identifying restrictions. The hypothesis being tested is that the chosen instruments are

uncorrelated with the residuals. If the null hypothesis is not rejected, the instruments are valid. Second, the

Arellano-Bond test for autocorrelation examines the null hypothesis of no order serial correlation in the

first-differenced residuals. Authors check for second-order correlation AR(2)4

which should be not rejected the

null hypothesis.

Data

Data on dependent variable (FDI/GDP) and control variables, including trade openness (% GDP), GDP

per capita (current U.S. dollars.), the number of telephone lines per 1,000 inhabitants, inflation consumer price

4 By construction, the differenced error term is probably first order serially correlated, even if the original error term is not.


567

(annual %), domestic credit to the private sector (% GDP), and natural resource availability (share of fuel in

total merchandise exports) were collected from World Development Indicators published by the World Bank

(2011). Data on institutional quality were from the International Country Risk Guide (ICRG) published by the

Political Risk Services (PRS Group). ICRG ranges from zero to 100, the highest overall rating (theoretically,

100) indicates the lowest risk and the lowest score (theoretically, 0) indicates the highest risk. Furthermore, this

paper examines five unbundling institutional qualities among the subcomponents of political risk rating:

government stability, investment profile, corruption, law and order and bureaucracy quality. To ensure an easier

interpretation of the results, all indicators have been re-scaled to 0-1. The capital control measure (KAOPEN)

was taken from Chinn and Ito (2008) and updated to 2011. It is scaled in the range between -2.5 and 2.5, with

higher values standing for larger degrees of financial openness. One of the merits of the KAOPEN index is that

it refers to the intensity of capital controls, because it incorporates other types of restrictions such as current

account restrictions, not just capital account controls. The data were available for 181 developed and

developing countries from 1970 to 2008. Noting that the number of observations among countries is not steady,

it leads to an unbalanced panel data. Details on the variable definitions and data sources are available in

Appendix (Table A1 and A2).

Threshold Condition

The threshold effects are computed by using the partial differentiation of FDI on KAOPEN:

𝜕FDI𝑡𝜕KAOPEN𝑡

= 𝛽1 + 𝛽3𝑘𝑡

The positive effect of capital openness on FDI inflows is observed when:

𝛽1 + 𝛽3𝑘𝑡 > 0

Thus, the threshold effects in Middle Eastern countries can be computed as:

𝑘𝑡 > −𝛽1

𝛽3

The presence of the lagged depended variable in the model means that all the estimated beta coefficients

represent short period effects. The long period effects can be derived by dividing each of the betas by 1-, the

coefficient of the lagged depended variable.

Estimation Results

Descriptive statistics

Table 1 summarizes the descriptive statistics from the sample. For all variables, the cross-country variation

is very large, except openness to trade. The average of net inflows of FDI is 2.17% of GDP, with a standard

deviation of 3.66. The minimum value of net inflows of FDI concerns Yemen (-5.11 in 1995), whereas the

maximum value is for Jordan (23.53 in 2006). Concerning financial development, this paper observes that

average of domestic credit to the private sector is 45.66, with a standard deviation of 42.94. The minimum

reaches 1.8 (Iraq in 2004) and the maximum 269.66 (Cyprus in 2009). Macroeconomic instability seems critical

since the average of the annual percentage change of consumer prices equals to 12.75, with a standard deviation

of 20.5. The minimum value goes to Oman (-4 in 1987) and the maximum to Lebanon (99.8 in 1992). Cyprus

exhibits the highest value of institutional quality (highest scoring: 0.82), whereas the lowest index value is

observed in the Lebanon (lowest scoring: 0.1).


568

Table 1

Summary Statistics

Variable Obs Mean Std. Dev. Min Max

FDI 318 2.17 3.66 -5.11 23.53

KAOPEN 320 0.78 1.71 -1.83 2.5

Open 271 78.65 32.10 5.39 170.64

INF 270 12.75 20.50 -4 99.80

DC 323 45.66 42.94 1.8 269.66

LGDPpc 264 8.19 1.14 6.09 10.30

LTle 347 2.49 0.79 0.52 3.86

Nat 266 50.64 39.72 0.0003 99.73

INST 345 0.595 0.142 0.10 0.82

GS 332 0.67 0.19 0 0.95

IP 334 0.58 0.21 0 1

COR 331 0.42 0.14 0.16 0.83

LO 331 0.61 0.21 0.16 1

BQ 331 0.48 0.20 0 1

Notes. Open: trade openness; infrastructure quality: Tele; INF: consumer price index; DC: domestic credit to the private sector,

LGDPpc = Log (current U.S. dollars), Ltl = Log (1 + number of telephone lines per 1,000 inhabitants); Nat: natural resource;

INST: political risk; GS: government stability; IP: investment profile; COR: corruption; LO: law and order; and BQ: bureaucracy

quality.

In the following, this paper reports results of the estimation using the system GMM estimator. Before

discussing the estimation results, the validity of the instruments must be confirmed. Indeed, the GMM system

regressions satisfy both the Hansen test of over-identifying restrictions and the second serial correlation test. In

all specifications of the Hansen test, this paper does not reject the null hypothesis that the instruments are valid.

Moreover, the AR(2) test fails to reject the null hypothesis that there is no second order correlation in the

first-differenced residuals. Then, the model seems correctly specified.

In Table 2, authors present results in which they take into consideration for macroeconomic instability (as

measured by inflation consumer price) and financial depth (as measured by domestic credit to private sector).

Table 3 provides results when taking into account of the institutional quality index. Table 4 summarizes the

results from the regressions running with five of the sub-components of the institutional quality index: law and

order, bureaucratic quality, corruption, government stability and investment profile both individually and

interactively.

Capital Account Policy, Macroeconomic Stability, Financial Development, and FDI

Column (1) in Table 2 shows the results of the benchmark equation where KAOPEN is the only

explanatory variable, authors control for lagged FDI, market size, trade openness, natural resource, and

infrastructure quality. It can be noted that ∂FDI/∂KAOPEN = β1 and therefore the parameter of interest is the

estimated coefficient of KAOPEN, β1, which is negative and significant at the 10% level suggesting that all else

equal, CAL has an adverse effect on FDI. One standard deviation increase in KAOPEN (sd = 1.71, see Table 1)

is expected to decrease FDI by about 1.52% points [∂FDI/∂KAOPEN = -0.892 1.71= -1.52]. This paper uses

an example to provide the reader with a better sense of the negative effect of KAOPEN on FDI in the region.

Specifically, it considers two countries in the Middle East countries that have extremely different levels of

capital openness Syria, and has the least capital openness country in the Middle East region and Qatar that has


569

the highest capital openness. The average value of KAOPEN from 1985 to 2009 is about -1.83 in Syria and 2.5

for in Qatar. Then, the estimation result of the regression (Column 1) shows that all else equal, an increase in

KAOPEN from the level of Syria to the level of Qatar will increase FDI by about 3.86% points in the short run

and by about 17.78% points in the long run. This follows from the fact that the short-run effect of a change in

KAOPEN on FDI is given by (β1 ) and the long-run effect is (β1 )/(1 ρ): Where is β1 the estimated

coefficient of KAOPEN and ρ is the estimated coefficient of FDIit-1. Here, = [2.5 (-1.83)] and from Table 2

β1 = -0.892 and ρ = 0.783, then ∂FDI/∂KAOPEN = [-0.892 (2.5 (-1.83) = -3.86] in the short period and 92.4

[-0.892 (2.5 (-1.83))/(1 0.783) = -17.78] in the long period.

In column (2), KAOPEN is interacting with a MENA dummy to test whether the effects of KAOPEN

variable on FDI are the same for both MENA and non-MENA countries. It takes the value of one if the country

is located in the MENA and zero otherwise. It finds that the coefficient of the interaction terms is negative and

insignificant, that is to say that there is no difference between MENA and non-MENA countries in how capital

account openness affects FDI inflows.

Table 2

First Differences GMM Estimates: Capital Account Liberalization, Macroeconomic Stability and FDI From

1985 to 2009

Variables (1) (2) (3) (4) (5) (6)

Lagged FDI 0.783

(0.078)*

0.864

(0.000)***

0.688

(0.004)***

0.932

(0.000)***

0.952

(0.000)***

0.750

(0.000)***

LGDPpc 0.519

( 0.313 )

-0.347

(0.514)

-0.341

(0.019)**

-0.521

(0.005)

-0.321

(0.007)***

-0.981

(0.015)**

Open 0.019

( 0.033)**

0.016

(0.031)**

0.005

(0.99)

0.004

(0.521)

0.007

(0.249)

0.007

(0.190)

LTel -0.253

(0.663 )

0.258

(0.378)

0.35

(0.021)**

0.579

(0.003)***

0.071

(0.753)

-0.05

(0.889)

KAOPEN -0.892

(0.062 )*

0.20

(0.126)

-0.981

(0.015)**

INF -0.021

(0.062)*

-0.003

(0.827)

Nat 0.0014

(0.873)

-0.001

(0.755)

-0.014

(0.154)

-0.006

(0.268)

0.001

(0.661)

0.011

(0.058)*

INF*KAOPEN 0.008

(0.589)

DC 0.014

(0.002)***

0.032

(0.030)**

DC*KAOPEN 0.020

(0.030)**

MENA*KAOPEN -0.001

(0.998)

Serial correlation test (p-value)a p = 0.48 p = 0.5 p = 0.66 p = 0.61 p = 0.67 p = 0.49

Hansen J test (p-value)b p = 0.5 p = 0.5 p = 0.87 p = 0.61 p = 0.86 p = 0.78

Number of instruments 9 10 9 11 9 12

Countries 14 14 14 14 14 14

Observations 186 186 171 169 188 186

Mean 12.75 45.66

Threshold Level 49

Notse. P-values are in parenthesis; the dependent variable is FDI/GDP; the data on the political risk note index is normalized to lie

between zero and one; a higher number implies more stability; the model is estimated with the two-step Arellano-Bond GMM

dynamic panel methodology which is asymptotically efficient and robust for all kinds of heteroskedasticity; ***, **, and * refer to

the 1, 5, and 10% levels of significance respectively; a The null hypothesis is that the errors in the first difference regression exhibit

no second order correlation; b The null hypothesis is that the instruments are not correlated with the residuals.


570

Table 3

First Differences GMM Estimates: Capital Account Liberalization, Political Stability and FDI From 1985 to

2009

Dependent variable: FDI/GDP (1) (2)

Lagged FDI 0.816

(0.000)***

0.767

(0.029)**

LGDPpc -0.378

(0.007)***

0.215

(0.628)

Open 0.014

(0.007)***

-0.015

(0.412)

LTel 0.237

(0.192)

0.713

(0.533)

Nat -0.005

(0.330)

-0.016

(0.176)

KAOPEN -13.989

(0.007)***

INST 2.43

(0.060)*

0.867

(0.901)

INST*KAOPEN 22.44

(0.007)***

Serial correlation test (p-value)a P = 0.6 P = 0.58

Hansen J-test ( p-value)b P = 0.34 P = 0.39

Number of instruments 9 12

Countries 14 14

Observations 188 186

Mean 0.59

Threshold Level 0.62

Notes. P-values are in parenthesis; the dependent variable is FDI/GDP; the data on the political risk note index is normalized to lie



the 1, 5, and 10% levels of significance respectively; a. The null hypothesis is that the errors in the first difference regression

exhibit no second order correlation; b. The null hypothesis is that the instruments are not correlated with the residuals.

This paper now discusses the direct effect of macroeconomic instability and the level of financial

development in the host country on FDI. In column (3) and (5), it includes separately the consumer price index

(INF) and the domestic credit to the private sector (DC) as explanatory variables. As seen that, while the

inflation coefficient is negative and significant at the 10% level, the estimated coefficient of financial

development is positive and significant at the 1% level, indicating a partial support for the standard proposition

that a higher domestic credit increases the FDI inflows, then financial development promotes FDI in Middle

East region.

On one hand, column 3 shows that all else equal, a one standard deviation increase in INF (sd = 20.5 see

Table 1) will decrease FDI by about 0.43% points (∂FDI/∂INF = -0.021 20.5 = -0.43). On the other hand,

column 5 shows that all else equal, a one standard deviation increase in DC (sd = 42.94, see Table 1) will

increase FDI by about 0.6% points (∂FDI/∂DC = 0.014 42.94 = 0.6). Here, it provides an example to illustrate

the catalyzing and direct effect of financial development on FDI. Considering two countries in the Middle East

that differ significantly in terms of financial development, Iraq, a country with very poor financial development

and Cyprus, a country with the best financial development in Middle East, the average values of the measures

of financial (domestic credit to the private sector) from 1985 to 2009 for the two countries are 3.66 in Iraq and

Cyprus in 156.56 (Table 5). Then all else equal, an improvement in the financial development of the level of


571

Iraq to the level of Cyprus will increase FDI by about 2.14% points [0.014 (156.56 3.66) = 2.14]. Ali, Fiess,

and MacDonald (2010) claimed that macroeconomic stability reduces the level of uncertainty encountered by

investors and increases the level of confidence in the economy, which encourages FDI.

Table 4

First Differences GMM Estimates: Capital Account Liberalization, Institutional Quality and FDI From 1985 to

2009

Dependent variable: FDI/GDP Law and order Bureaucracy quality Corruption Government

stability

Investment

profile

Lagged FDI 0.603

(0.000)***

0.970

(0.001)**

0.922

(0.002)***

0.562

(0.095)*

0.430

(0.021)**

LGDPpc -1.50

(0.006)***

-0.163

(0.798)

-0.187

(0.771)

0.672

(0.517)

-1.012

(0.149)

Open 0.024

(0.074)*

0.013

(0.427)

0.006

(0.553)

-0.00

(0.958)

-0.002

(0.820)

LTel 1.637

(0.047)**

0.307

(0.800)

0.108

(0.225)

0.478

(0.471)

0.16

(0.001)***

KAOPEN -5.25

(0.033)**

-7.94

(0.012)**

-4.53

(0.02)**

-7.88

(0.067)*

-2.90

(0.044)**

Nat -0.008

(0.392)

-0.016

(0.383)

0.012

(0.532)

-0.005

(0.747)

-0.014

(0.235)

INST 0.561

(0.856)

4.07

(0.576)

1.59

(0.750)

-3.01

(0.552)

-1.03

(0.676)

INST KAOPEN 8.20

(0.029)**

15.23

(0.009)***

9.78

(0.009)***

10.47

(0.05)**

5.44

(0.024)**

Serial correlation test (p-value)a p = 0.27 p = 0.18 p = 0.71 p = 0.3 p = 0.86

Hansen J test (p-value)b p =0.86 p = 0.84 p = 0.74 p = 0.22 p = 0.53

Number of instruments 12 12 11 6 12

Countries 14 14 14 14 14

Observations 186 186 186 186 186

Mean 0.61 0.48 0.42 0.67 0.58

Threshold Level 0.64 0.52 0.46 0.75 0.53

Notes. P-values are in parenthesis; the dependent variable is FDI/GDP; the data on the political risk note index is normalized to lie



the 1, 5, and 10% levels of significance respectively; a The null hypothesis is that the errors in the first difference regression

exhibit no second order correlation; b The null hypothesis is that the instruments are not correlated with the residuals.

The results so far have shown that CAL and macroeconomic instability undermine FDI and that financial

development has had a direct and positive effect on FDI. This paper now tests whether an improvement in

financial development will result in a significant reduction in ∂FDI/∂KAOPEN. Here, it estimates equation (2)

and reports the results also in column 5 (see Table 2).

Noting that [∂FDI/∂KAOPEN = β1 + β3 DC], the parameters of interest are the estimated coefficient of

KAOPEN, β1, and the estimated coefficient of the interaction term, β3. Estimations show that β1 is negative and

significant at the 5% level and β3 is positive and significant at the 5% level, suggesting that financial

development significantly reduces the adverse effect of capital account openness on FDI.

The marginal impact of KAOPEN is:

𝜕FDI𝑡𝜕KAOPEN𝑡

= −0.981 + 0.02 𝐷𝐶


572

Table 5

Countries Included in the Regressions

countries FDI KAOPEN Open LGDPpc Ltl Nat DC INF INST GS IP COR LO BQ

Cyprus 5.2 -0.31 100.14 9.32 3.62 2.57 156.56 3.42 0.71 0.68 0.80 0.67 0.73 0.90

Egypt 2.64 0.31 50.98 7.20 1.93 42.61 38.85 10.76 0.58 0.71 0.51 0.37 0.57 0.50

Iran 0.53 -1.09 39.24 7.33 2.45 81.96 24.54 20.24 0.54 0.63 0.44 0.50 0.60 0.46

Iraq 0.58 -0.80 na 6.79 1.51 89.37 3.66 na 0.33 0.53 0.39 0.25 0.27 0.10

Jordan 4.86 0.76 112.53 7.51 2.31 0.17 70.03 4.77 0.61 0.72 0.58 0.55 0.59 0.53

Kuwait 0.13 1.89 94.17 9.71 3.03 75.8 54.41 2.54 0.65 0.66 0.67 0.45 0.71 0.48

Lebanon 11.94 1.90 71.59 8.25 2.75 0.27 68.25 26.46 0.46 0.56 0.48 0.29 0.51 0.35

Oman 1.61 2.19 85.60 8.97 2.15 83.88 30.91 1.20 0.69 0.75 0.71 0.47 0.75 0.56

Qatar 2.34 2.50 89.02 na 3.17 86.23 38.47 3.52 0.65 0.73 0.66 0.35 0.77 0.43

Saoudi Arabia 1.44 1.89 72.35 9.13 2.49 89.91 26.19 0.35 0.61 0.70 0.68 0.35 0.78 0.57

Syria 1.13 -1.83 62.11 7.01 2.22 59.78 10.36 12.37 0.58 0.76 0.45 0.44 0.66 0.31

Turkey 0.92 -0.78 46.41 7.96 2.99 2.38 20.20 53.70 0.57 0.63 0.55 0.45 0.62 0.56

U.A.E 1.44 2.50 132.81 10.06 3.29 49.51 52.03 3.78 0.66 0.69 0.69 0.38 0.62 0.62

Yemen 1.63 2.06 78.48 6.22 1.05 88.57 5.88 27.96 0.60 0.73 0.58 0.43 0.42 0.35

Notes. na: indicates missing data; Open: trade openness; infrastructure quality: Tele; INF: consumer price index; DC: domestic

credit to the private sector, LGDPpc = Log (current U.S. dollars), Ltl = Log (1 + number of telephone lines per 1,000 inhabitants);

Nat: natural resource; INST: political risk; GS: government stability; IP: investment profile; COR: corruption; LO: law and order;

and BQ: bureaucracy quality.

Here again, it uses the sample of Iraq and Cyprus. It notes that the average value of financial development

is equal to 3.66 for Iraq and 156.65 for Cyprus. Then, the increase in KAOPEN by one standard deviation will

decrease FDI in Iraq by about 1.55% points [∂FDI/∂KAOPEN = (-0.981 + 0.02 3.66) 1.71= -1.55] (column

6). Now it supposes that Iraq implements policies lead to an improvement in its financial development, such

that the value of financial development increases to the level of Cyprus. Thus, a one standard deviation increase

in KAOPEN will increase FDI by 3.67% points [∂FDI/∂KAOPEN = (-0.981 + 0.02 156.65) 1.71 = 3.67].

It’s important to note that the estimated coefficient of financial development remains significant, suggesting

that financial development has a direct and indirect impact on FDI.

The total effect of one unit increase in KAOPEN for the Middle East region is calculated to be -0.067%

point using the average value of financial development in the Middle East region [∂FDI/∂KAOPEN = -0.981 +

(0.02 45.66) = -0.067]. The threshold level of financial development, separating negative and positive partial

impacts of KAOPEN on FDI inflows, is 49.05 [-(β1/β3) = -(-0.981/0.02) = 49.05]. Then, it can be concluded

that FDI inflows in countries with a sound domestic financial system benefit more from CAL than those in

countries with a fragile financial system. It appears that countries should first reform their domestic financial

system before liberalizing the capital account to allow for enlarged FDI inflows.

This paper also checks whether the impact of capital control on FDI inflows is sensitive to macroeconomic

instability, results show that the coefficient of the interaction term between the two variables is not statistically

significant, thus, macroeconomic instability does not seem to further the CAL-FDI relationship. The estimated

coefficient of lagged FDI is positive and significant in all regressions, an indication that FDI is persistent. It can

be concluded that dynamic GMM is an appropriate estimator (Baltagi, Demetriades, & Law, 2009). Per capita

GDP has perverse signs, showing significant negative effects on FDI inflows. Trade openness, as measured by

the trade-GDP ratio, has a positive and significant impact on FDI inflows, supporting the evidence that


573

countries that are more open can attract more FDI inflows (Buchanan, Le, & Rishi, 2012). Results show also

that the infrastructure quality as measured by the number of telephone lines per thousand population plays a

significant role in absorbing FDI, this result of which is confirmed by Moosa and Cardak (2006) and Ali et al.

(2010).

Capital Account Policy, Institutions, and FDI

Table 3 reports the results of the regression analyzing the direct effect of institutional quality on FDI

inflows (column 1) and their influence on the role of capital account policy in promoting FDI inflows (column

2). As shown in column (1), institutional quality plays a significant role in determining FDI inflows by a

positive and significant coefficient on the institutional quality variable at the 10% level (k = INST). This

implies that FDI is attracted by the countries where institutions are solid. The result is in line with Ali et al.

(2010) who have stressed the importance of institutional quality in determining FDI inflows.

A one standard deviation increase in institutional quality (sd = 1.14, see Table 1) will increase FDI by

about 2.77% points (∂FDI/∂INST= 2.43 1.14 = 2.77). As a sample, this paper uses again the samples of

Cyprus and Iraq, which differ significantly in terms of institutional quality. The average values of the measures

of institutional quality from 1985 to 2009 of the two countries are INST—0.71 for Cyprus and 0.33 in Iraq (see

Table 5). Then all else equal, an improvement in institutional quality of the level of Iraq to the level of Cyprus

will increase FDI by about 0.92% points in the short period [2.43 (0.71 0.33) = 0.92] and by about 0.12%

points in the long period [2.43 (0.71 0.33)/1 0.81 = 0.12].

Then, this paper uses KAOPEN index and its interaction with the institutional quality variable to look for

whether institutional quality matters for FDI and CAL relationship. Regression in column (2) indicates that the

coefficient of KAOPEN, β1, is negative and significant at the 5% level and the estimated coefficient of the

interaction term, β3, is positive, suggesting that institutional quality as financial development significantly

reduces the adverse effect of capital account openness on FDI. Thus, the marginal effect of financial openness

on FDI increases with institutions, and the threshold level of institutions between the negative and positive

partial effect is 0.62 (13.989/22.44 = 0.62) which is 65th percentile in this sample. That is to say that, in the

sample, at most 35% (at least 65%) of the observations are greater (smaller) than 0.62. The negative effect of

capital openness on FDI in the Middle East countries is significant because of the low level of institutional

quality in the region, the average value of institutional quality in Middle East countries is 0.59 (see Table 3)

which is lower than the threshold levels (0.62).

Figure 2 presents a visual picture of the marginal effect of KAOPEN on FDI, based on each country’s

value of the political risk index for the Middle East countries. It indicates that countries, such as Cyprus, Oman,

and U.A.E that show positive effects of financial opening, have attained a threshold level of political stability,

whereas countries with underdeveloped institutional infrastructure may hamper the FDI inflows. So, the sample

countries can be categorized into two: Category A refers to countries where capital openness policy may

promote FDI and category B refers to countries where an increase in capital openness may not result in an

increase in FDI, and may possibly reduce FDI. Results reveal that 35% of countries in the sample lie above the

threshold of political risk index (0.62) and, then fall in category A, which are Kuwait, Oman, Qatar, U.A.E, and

Cyprus and 65% in the sample lie below the threshold level, which is Egypt, Iran, Iraq, Jordan, Lebanon, Syria,

Saudi Arabia, Turkey, and Yemen, these countries fall in category B.

Until now, institutional quality is discussed as a composite index of political risks comprising 12


574

subcomponents, Okada (2013) asserted that this index is enabled to capture the appropriate effects on

international capital inflows thus, in the following, it is interesting to investigate the impact of the interaction

between the five main subcomponents of political risk and capital account openness on FDI inflows.

Specifically, it considers bureaucratic quality, law and order, corruption, government stability, and investment

profile which may be important determinants of international capital inflows. To ensure easier interpretation,

authors standardize all sub-indicators of the institutional index to range between 0 and 1 where a higher value

indicates a higher institutional quality, results are reported in Table 4. As seen in all specifications, the

coefficient interaction terms between financial openness and each sub-indicators of institutional quality are

significantly positive. This seems to confirm the finding and implies that the result is robust, that its

institutional quality matters for CAL and FDI inflows relationship, a result which is in the line with previous

studies by Noy and Vu (2007) and Okada (2013). However, in all cases, except in the specification where

investment profile is used as a proxy of institutional quality, Middle East region seems far from the threshold

(mean < threshold level). In panel (3), for example, the one unit increase in KAOPEN for the Middle East

region is expected to decrease FDI by 0.42% points, using the average value of corruption in Middle East

[∂FDI/∂KAOPEN = -4.53 + (9.78 0.42) = -0.42]. This is due to the high levels of corruption in the region.

Thereby, it can suggest that CAL is only efficient in generating more inflows of FDI in an environment of low

political risk (Noy & Vu, 2007).

Conclusions

By employing the data of 14 Middle East countries over the period from 1995 to 2009, this paper

investigates the effect of CAL on FDI inflows, taking into account the role of macroeconomic stability,

financial development level and institutional quality in each country. It finds that, while CAL has a negative

effect on FDI, good institutions and domestic financial developments (in particular, the domestic credit to

private sector) mitigate this adverse effect. It concludes that there are threshold levels of institutional quality

and financial development that are important determinants of the relationship between CAL and FDI. The

results reveal that capital openness facilities FDI in Kuwait, Oman, Qatar, U.A.E., and Cyprus, where political

risk is above the threshold level (0.62), but it has a negative effect on FDI in Egypt, Iran, Iraq, Jordan, Lebanon,

Syria, Saudi, Arabia, Turkey, and Yemen, where political risk is high and then below the threshold level. It

finds also, that although macroeconomic instability has a negative impact on FDI inflows, it does not seem to

further the CAL-FDI relationship.

With regard to policy, the results suggest that capital account policy in Middle East countries must be

embedded within a sound institutional and financial framework. Thus, governments in this region should

develop a set of policies that are not only focused on capital account openness but also on the improvement of

financial institution’s efficiency and political framework, which constitutes a necessary precondition for

successful the liberalization of the capital account and attracting foreign investors. Egypt, Iran, Iraq, Jordan,

Lebanon, Syria, Saudi, Arabia ,Turkey, and Yemen must undertake measures that can help to fight corruption,

enhance the protection of property rights, increase the respect for law and the impartiality of the legal system,

and improve other aspect of the institutional environment. The results are in line with Okada (2013) and Noy

and Vu (2007) who have stressed the importance of institutional quality in the CAL-FDI nexus. These results

have an important implication for countries in Middle East given that most of them are in dire need of FDI with

weak institutions.


575

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576

Appendix

Table A1

Variables, Definitions and Sources

Variable Definition Source

FDI

Foreign direct investment, net inflows (% of GDP): Foreign direct investment is the net inflows

of investment to acquire a lasting management interest (10 percent or more of voting stock) in

an enterprise operating in an economy other than that of the investor. It is the sum of equity

capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in

the balance of payments.

WDI5

GDPpc

GDP per capita is gross domestic product divided by midyear population. GDP is the sum of

gross value added by all resident producers in the economy plus any product taxes and minus

any subsidies not included in the value of the products. It is calculated without making

deductions for depreciation of fabricated assets or for depletion and degradation of natural

resources. Data are in current U.S. dollars.

WDI

Open Trade is the sum of exports and imports of goods and services measured as a share of gross

domestic product. WDI

Tel

Telephone lines are fixed telephone lines that connect a subscriber's terminal equipment to the

public switched telephone network and that have a port on a telephone exchange. Integrated

services digital network channels and fixed wireless subscribers are included.

WDI

INF

Inflation as measured by the consumer price index reflects the annual percentage change in the

cost to the average consumer of acquiring a basket of goods and services that may be fixed or

changed at specified intervals, such as yearly. The Laspeyres formula is generally used.

WDI

DC

Domestic credit to private sector (% of GDP) refers to financial resources provided to the

private sector, such as through loans, purchases of no equity securities, and trade credits and

other accounts receivable that establish a claim for repayment. For some countries these claims

include credit to public enterprises.

WDI

Nat Share of fuel in total merchandise exports WDI

KAOPEN Capital openness index measuring the extent of openness in capital account transactions Chinn-Ito6

INST

Political risk rating consists of the following 12 subcomponents: (A) Government Stability

(12points), (B) Socioeconomic Conditions (12points), (C) Investment Profile (12 points), (D)

Internal Conflict (12 points), (E) External Conflict (12 points), (F) Corruption (6 points), (G)

Military in Politics (6 points), (H) Religious Tensions (6 points), (I) Law and Order (6 points),

(J) Ethnic Tensions (6 points), (K) Democratic Accountability (6 points), (L) Bureaucracy

Quality (4points). Institutions which are defined as the sum of each component are ranged from

0 to 100 and a larger value means lower political risk.

PRS-ICRG7

5 World Development Indicators (2011) 6 Chinn- Ito (2011) 7 International Country Risk Guide


577

Table A2

Variables, Definitions and Sources (Continued)

Variable Definition Source

Government

Stability

This is an assessment both of the government’s ability to carry out its declared programs,

and its ability to stay in office. The risk rating assigned is the sum of three factors

(Government Unity, Legislative Strength Popular Support) each of them with a maximum

score of four points and a minimum score of 0 points. Thus the government stability

scores from 0 to 12 and a higher values corresponding to “low risk levels".

PRS-ICRG

Investment

Profile:

This is an assessment of factors affecting the risk to investment that are not covered by

other political, economic, and financial risk components. It is the sum of three

subcomponents (contract viability/ expropriation, profit repartition and payment delays).

Each of them with a maximum score of four points and a minimum score of 0 points, so,

investment profile measured on a scale of 0 to 12, with 0 represent the highest risk levels

similarly 12 represent the lowest risk levels.

PRS-ICRG

Corruption

This index aims at evaluating the degree of corruption within the political system. It

indicates the opinion of analysts on each country regarding the extent to which high

government officials are likely to demand special payments, and illegal payments

generally expected throughout lower levels of government in the form of bribes connected

with import and export licenses, exchange controls, tax assessment, policy protection, or

loans. It ranks nations on a scale from 0 to 6. A score of 0 represents maximum corruption

level, while 6 indicates minimum corruption level.

PRS-ICRG

Law and Order

It ranges from 0 to 6, where a higher number indicates a better system of law and order.

This variable “reflects the degree to which the citizens of a country are willing to accept

the established institutions to make and implement laws and adjudicate disputes”. Higher

scores indicate: “sound political institutions, a strong court system, and provisions for an

orderly succession of power”. Lower scores indicate: “a tradition of depending on

physical force or illegal means to settle claims”. Upon changes in government new leaders

"may be less likely to accept the obligations of the previous regime.

PRS-ICRG

Bureaucracy

Quality

It ranges from 0 to 4. High scores indicate “an established mechanism for recruitment and

training”, “autonomy from political pressure”, and “strength and expertise to govern

without drastic changes in policy or interruptions in government services” when

governments change.

PRS-ICRG

Chinese Business Review, September 2014, Vol. 13, No. 9, 578-585

doi: 10.17265/1537-1506/2014.09.004

Cooperation Between Co-operative Business Organization and

Investor Owned Firm to Stimulate Economic Growth of a

Country: A Cooperative Advantage Approach

Chanchai Petchprapunkul

Kasetsart University, Bangkok, Thailand

This study elaborates that the economic growth of a country depending on not only the business performance of the

investor owned firms (IOFs), but also the business surplus of the cooperative organizations (Co-ops). The policy

maker should have the level of understanding and competence to blend five different factors related to

organizational structure and business model of the Co-ops and the IOFs to the five similarities factors on the

managerial approach of them into one marked. The study investigated five similarities factors and included into a

conceptual structural model with its six measurement models, economic growth model, general national factor

model, market and industry factors model, Co-ops/IOFs opportunities/threat model, Co-ops/IOFs strength

/weakness model, and lastly the Co-ops/IOFs firm dynamic/active: sales, profit, and lost model. Reliability of the

six similarities measurement models was tested by the Delphi technique with a sample of 33 respondents. The study

found that, apart from the six measurement models, it also has two intervening factor variables that will reduce the

power and magnitude of the economic growth which will come from mismanagement of policy maker: These

factors are the different in international culture among countries, and the global warming and natural disaster from

the excess consumption and excess production. These selfish, competition and economic greedy of people will lead

to economic, social, and natural disaster problems. To reduce the socioeconomic disadvantages and global disaster,

board’s committee, and Co-ops manager as well as chief executive officer (CEO) of the IOFs must have a good

understanding on these five similarities factors. Appropriate management of these five similarities factors will lead

the firms to reach their high managerial efficiency, customer value, firm value, and finally economic growth.

Keywords: Co-operatives, investor owned firms (IOFs), economic growth, not-for-profit firms, general national

factors, market industry factor, five forces, value chain

Introduction

It was accepted worldwide that the world economic real growth rate had a trend to reduce gradually,

according to the instability in the growth rate of both gross domestic product (GDP) and gross national product

(GNP). In addition, the economic growth was subtracted by the natural disaster and socioeconomic problem.

In order to stimulate economic growth of a country, the policy maker must have a good understanding on

Chanchai Petchprapunkul, Ph.D., senior lecturer, Department of Cooperatives, Faculty of Economics, Kasetsart University,

Bangkok, Thailand.

Correspondence concerning this article should be addressed to Chanchai Petchprapunkul, Faculty of Economics, Department of

Cooperatives, Kasetsart University, Bangkaen Campus, Bangkok 10900, Thailand. E-mail: [email protected].

DAVID PUBLISHING

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what the engines for the economic growth are. One giant mechanism is the higher productivities in the supply

side of the whole economy. In the same time, this mechanism was down-sized by many negative trickles down

multiplier effects. The way to increase and maintain the real growth rate is to pursue the better economic

growth concept that is the sustainable growth to create green and constructive economy.

Not only the investor owned firms (IOFs), but also the co-op business performance is encouraging the

growth of the economy. In the year 2012, the United Nation (UN) proclaimed that the philosophy of the

cooperative business organization and the way of Co-ops business operation can be promoted to assist the

world economy. Co-op business organization had a philosophy to cooperate together to create growth with

stability. The goal of the Co-op organization is not looking for profit but only surplus. They allocated all

surpluses to their members. Thus, their member can have higher quality of life, lower income inequality, and

higher welfare. Since the firm itself has not looked for business benefit, the firm has no risk burden. But, in the

same time, most of the Co-ops, especially in the developing country are small and have a narrow business

volume to help their member and have only small effects on their member’s way of life. The best way to

stimulate the real economic growth is to blend the advantage of these two kinds of the firms into one marked to

get both of the benefit of the two business philosophy.

Research Objective

The research objectives are as follows:

to elaborate that though the Co-ops and the IOF had the difference in their goal and objective of the

business philosophy, the cooperation between the Co-ops business organization and its management and the

IOF business organization and its management together will create the real and sustainable economic growth

rate;

to identify that the five different factors between the co-ops and the IOF are: organizational structure, goal,

and objective; ownership advantage and financing policy; investment policy; dividend payout policy; and social

and mutual aspect;

to indicate that the five similarities factors between the co-ops and the IOF are: general national variables;

market and industry variables; entrepreneurial opportunity or threat; entrepreneurial capacity:

strength/weakness; and business or firm dynamic-active: sale, profit and lost

Theoretical Concepts and Conceptual Framework

There are 10 measurements: Five of them are similarities factors and five of them are different factors

between the Co-ops firms and the IOF firms.

Five Different Factors

Different in organizational goal, objective, and structure. (1) Organizational goal and objective.

Cooperative goal is that a not-for-profit (NFP) (Cobia, 1989; Petchprapunkul, 2006) organization has its

business objective to service their members, while the IOF goal is that the general capitalism looks for the

profit maximization (PM). The former therefore did everything for their member benefit not their organization,

but the latter did everything for their share holder wealth.

(2) Organizational structure. Co-op organizational structure is composed of their general members and the

whole member elected a group of 15 members to be the board of director. One in the 15 will be chairman, the

leader of the board committee. Board committee will hire some professional managers to be the Co-ops


580

manager, staff, and workers. Policy of the Co-ops mostly came from their annual general meeting. But the IOF

structure is composed of the share holder, executive board, and management.

Different in ownership advantage and financing policy. Co-ops share or stock is owned by their

member, called “member owner” principle controlled by their member or “member control” principle

distributing benefit to their member or “member benefit” principle (Cobia, 1989). The business ownership

within the Co-ops firms is owned by their members and administered by their elected board of member who

manage their businesses through the management team, but the ownership within the IOF is owned by the share

holders and managed by their executive officer hired by the company.

Different in the investment policy. The Co-op organization mostly has its investment policy to provide

service to their member, but the IOF investment policy is to find profit for the share owner. The investment

policy of the Co-op depends on the needs of their membership and the members’ economic and social problem.

For the IOF, their investment policy depends on the profit margin and the rate of return they can generate from

their cash inflow and income stream, which have to exceed their cost of financing.

Different in dividend payout policy. The board member of the cooperative at the year will allocate their

surplus to their member in the form of patronage refund and dividend payout as much as possible. But the IOF

firm will pay their profit to their share holder in the form of money return, according to the number of share or

stock they held. The Co-ops will not accumulate their surplus to their own organization but to their member,

because their investment is all for their member benefit, while the IOF is doing everything for the share holder

wealth.

Different in the social and mutual aspect. Co-op firm looks for member surplus and welfare, while the

IOF looks for the share holder wealth. The Co-op firm is always looking for the mutual aspect to reduce their

selfish. They are globally increasing their important role in local, regional, national, and international

socioeconomic development. But the IOF mostly looks for its own sides. They do not care the socialism but

mostly are concentrated on a perspective of the individualism, not collectivism.

All these five different factors are the measurement of why or how the Co-op was established and why and

how the IOF was established. These five different factors need five different approaches to identify and pursue

their business to reach the organizational goal or effectiveness. But from the past up to present, many weak

points to the economy can be seen, especially the income inequality and turbulence to the society and citizen.

Many of the policy makers in many countries in the world, not only the developed countries, but also the

developing countries, had made these mistakes. This is the most serious issue. The world had faced many

problems, both the nature and the socioeconomic ones, for examples, the global warming, the natural disaster,

over consumption of natural resource and excess consumption of economic resource, the inequality of income

distribution, and so on.

However, it still has five more similarity factors for the policy maker to blend and synthesize their

business managerial approach, to create their efficiency, to make their internal function meet their sale, revenue,

cost, profit, and lost.

Five Similarities Factors

It is the time to relocate misunderstanding and mis-policy management to turn the next decade into one

marked out by much faster and more sustainable growth of the corporation between the Co-op models and IOF

model to promote and carefully support the economic growth of every country. The solution is related to the


581

adjustment of the five similarities mechanism of the engine of these two kinds of firms.

General national factors: S-P-E-L-T. These general national framework conditions are what and how to

scan the general environment in five dimensions: S = social, P = political, E = economic, L = legal, and T =

technology.

Aguilar (1967) proposed a concept of PEST and this concept had been added with one more L (L = legal).

Anyway, the policy maker has to scan these five dimensions to evaluate that the existing environment have

created or generated any opportunities or threats to the organizations. The general national factors: S-P-E-L-T

are as follows:

Social openness;

political/government policy;

economic and financial condition;

legal condition;

technology, Research & Development (R&D).

If the environment of these all five factor created an opportunity to the firm, it’s the time to penetrate the

business into the market to gain the market share. The next step is to look for and assess what will penetrate to

the new market with the existing product or the new product (Ansoft, 1965).

Market and industry factors. Porter (1979, 1980) suggested a “five forces” model: He explained that

every firm has to evaluate the density of the rivalry in the industry or the market structure when doing business.

This concept is to calculate the competitive advantage level, which will be based on the other four independent

variables: firstly, the new entry which will come into the industry with the excess supply to the industry;

secondly, the substitute product, whether the product would be replaced by the future substitute goods or

services, the market will be lost; the third and fourth variables are the bargaining power of supplier and buyer,

which will reduce the market share or create the higher cost of production. As competitive advantages, five

forces model are:

rivalry in the industry;

new entry;

substitute product;

bargaining power of supplier;

bargaining power of buyer.

Co-op/IOF business opportunity-threat. According to those two external factors: spelt and five forces

which the firms can not adjust or change them, the firms can only evaluate whether those external factors have

created opportunity or threat to the firm or not. Therefore, the firm has to scan their internal factor, to find out

that his firm has any strength or weakness to pursue his business policy and plan, to be market leader, and to

gain market share (Ansoft, 1965).

Co-op/IOF strength/weakness. This is the step of scanning the firm internal functions or structure that

has any positive or benefit or advantage over the competitors. If it has no any strengths, but weakness, it has to

prepare to prevent, protect, or adjust the business process as early as possible. The overall business process of a

firm has included overall activities which will create value chain to the firm and its customer. Therefore, the

management has to explore the strength or weakness in all of the functions.

Business/firm dynamic-active (sale, profit/lost). Porter (1985) suggested that a firm must have to create

its value chain to both the firm and customer. Every firm has two parts of activities. The first one is the primary


582

activities and the second one is the secondary activities. Primary activities consisted of three parts. The first

part is the product interrelationship, the second part is marketing and sales, and the third part is service. Product

interrelationship is compost of inbound logistic, operation, out-bound logistics. Secondary activities are

composted of firm infrastructure, human resource management, technology development, and procurement.

Economic growth of a country. According to the measurement models, if a firm can manage, get, and

maintain the overall strength, it will reach the profit margin. Finally, this profit margin is both the Co-op and

the IOF surplus or profit respectively. At the same time, the growth of business sector will lead to demand for

labor or employment and economic growth from the supply side.

If policy makers of these two kinds of firms have a good understanding on both the differences and

similarities factors, by blending up them together, all these factors will generate the sustainable economic

growth to every country.

Business life cycle theory: Population ecology, resource dependence, institutionalism theory. How

those of firms can survive in the business arena depends on their competence and capability adapted in the

business and industry. According to the “population ecology” theory (Freeman & Hannan, 1977), if the firms

cannot adapt them, they will be dead after a short period of establishment or bird stage. After that, they have to

try to find access to some resources. According to the “resource dependent” theory, every firm has access to

some resource spent in their business and functions. If the firm can find the needed resources, they will then

expand their business, but if they cannot access to the needed resources, they will go to the contraction stage or

even the dead stage.

(1) Business at the birth stage: At this stage, the policy maker has to know or need to know that the firm

which will can survive in the industry must have and adjust its organization like an human being, the firm

which cannot adjust to the environment will not give a birth to itself in such market. This belongs to the

“population ecology” theory.

(2) Business at the stage of expansion: At this stage, the firm has to acquire resources such as capital for

investment and expand the market. If the firm cannot find the resources, they cannot be the market leader or

even challenger. This is the suggestion from the “resource dependence” theory.

Lastly, every firm has to know about the institutional theory, which explained that a well-systematic firm

has to challenge, adopt, or imitate the big firm to make themselves professionally as the general perfect institution.

Research Model

The researcher adopted the similarities factor together and constructed a research model as posited in

Figure 1. It can be elaborated that the successful Co-op and IOF performance will influence the economic

growth of a country depending on two groups of factors: The first group is the level of understanding on the

different structure, goal, objective; investment policy; dividend payout policy; and the social and mutual aspect,

which has to be managed as efficient as possible, matching the organizational goal and objective of the Co-op

and IOF case by case.

The second group of the variable is the management factors, as elaborated in the research model, if the

policy leader pertains their competence and can organize the five independent variables to explain the

dependent variable. The research model elaborated the structural equation modeling: SEM, which consists of

only one structural model and six measurement models. The structural model explained the five independent

measurement models and finally the economic growth of a country.


583

Figure 1. Factors determine the Co-ops business performance and the IOFs business performance to economic growth.

Dependent Variable

In this study, the dependent variable measurement (economic growth) has totally three sub-measurements:

the GDP, jobs/employment, and low income gap. These mean that the all independent variables composed

together can explain the high level of GDP, explain high level of job employment, and create low income

inequality.

Independent Variables

Independent variables are shown as follows:

General national factors;

Market/industry factors: five forces (Porter, 1980) and STP-marketing (Borden, 1964);

Co-ops/IOFs business opportunities and threat;

Co-ops/IOFs business strength and weakness;

Co-ops/IOFs business dynamic-active (sales, profit, and lost).

Intervening Variable

In the model, they are also the intervening variables, which will change the relationship between the

dependent variable and the independent variables as the following:

International culture;

Global warming/natural disaster.

General

national

factors

Co-ops/IOFs opportunities/

Threat

Co-ops/IOFs strength/

weakness

Co-op/IOF

Firm

dynamic/active:

Sales, profit/

lost: Job/employ.

Low income gap

GDP/GNP

Jobs/employment

Busi

nes

s bir

ths

stag

e

Expan

sion s

tage

Contr

acti

on s

tage

Busi

nes

s dea

ths

stag

e

Tec

hnolo

gy/R

&D

Rel

ated

leg

al

Eco

nom

ic/F

inan

ce

Poli

tic/G

ovn

’t p

oli

cy

Soci

al o

pen

nes

s

Pri

mary

act

ivit

ies

Su

pp

ort

ive

acti

vit

ies

Economic

growth

Inte

rnat

ion

al c

ult

ure

Mar

ket

lea

der

Mar

ket

shar

e

Market and industry

factors

New

en

try

Su

bst

itu

te p

rod

uct

Bar

gai

nin

g p

ow

.er

of

sup

Bar

gai

nin

g p

ow

er

of

bu

y

Mar

ket

seg

men

tati

on

Tar

get

cu

sto

mer

Po

siti

on

ing

Glo

bal

war

min

g/n

atu

ral

dis

.


584

Research Methodology

This study employed the Delphi Technique which is a systematic forecasting method involving structured

interaction among group of experts on a subject (Caplan, 1950). Delphi Technique typically includes at least

two rounds of experts answering questions and giving justification, providing the opportunity between rounds

for changes and revisions. The multiple rounds, which are stopped after a pre-defined criterion is reached,

enable the group of experts to arrive at a consensus forecast on the subject being discussed.

According to the literature review, the researcher had constructed an open-end questionnaire to ask for the

expert opinion on what should be the major factors contributed from Co-ops and IOFs performance to create

the economic growth.

Conclusions

According to the results from the first round survey, the overall 25 factors are gained. These 25 factors

were observed and classified into the six latent variables. These six latent variables match the co-operatives

theory and the IOFs theories as well (Table 1).

Table 1

Dependent and Independent Variables

Latent dependent variables with its measurement Independent variables with its measurement or observe variables

Economic growth (GNP/GDP, job employ, income gap) Firm dynamic/active: (1) sales; (2) profit/lost; (3) job employment

Co-ops/IOFs, firm dynamic/active: sales, profit/lost, job

employment (within four stages of business lifecycle)

Co-ops and IOFs’ business opportunities/threat

Co-ops and IOFs’ organizational strength/weakness

Co-ops and IOFs’ business opportunities/threat General national factor “S-P-E-L-T analysis factors”

Market and industry factor: (1) Five forces; (2) STP-marketing

Co-ops and IOFs organizational strength/weakness

Market and industry factor: (1) Five forces; (2) STP-marketing

Firm specific factors: (1) primary activities: inbound/operation/out

bound; marketing-sales-service. (2) supportive activities: infra

structure; technology; HRM; procurement

Apart from the first round, the researcher had contacted back again to the 33 persons who are the 11

lecturers in the area of cooperative economics, 11 doctoral international students, and 11 policy makers in the

Ministry of Agriculture and Co-operatives. This second round of survey comes up with the output of the study.

All of 33 respondents as posited in Table 2 reported that the overall 25 measurements influence both the

performance of Co-ops and IOFs firms.

Table 2

Types and Number of Respondents

Types Number %

Lecturer in the universities 11 33.33

Doctoral international students 11 33.33

Policy maker 11 33.33

Sum total 33 100.00

Even Co-ops are the social enterprise, they are different from the IOFs only in their goal, objective,

structure, and the way to do business. However, both of them were affected by the business factors.


585

References

Ansoft, I. (1965). Corporate strategy. Pittsburgh: Carnegie-Mellon University Press.

Caplan, D. (1950). Delphi method, operation research. Santa Monica: Rand Corperation.

Cobia, D. (1989). Cooperatives in agriculture. Englewood Cliffs: Prentice Hall.

Aguilar, F. J. (1967). PEST analysis: Scanning the business environment. NewYork: Macmillan Publishers Ltd.

Freeman, J., & Hannan, M. (1977). Population ecology theory. American Journal of Sociology, 82(5), 929-964.

Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competitors. New York: Free Press

Porter, M. E. (1985). The competitive advantage: Creating and sustaining superior performance. New York: Free Press.

Porter, M. E. (1979). How competitive force shape strategy. Retrieved from http://www.docin.com/p-291884535.html

Borden, N. H. (1964) The concept of the marketing mix. Journal of Advertising Research, 4, 2-7.

Petchprapunkul, C. (2006). Performance of agricultural marketing cooperatives in Thailand. Saarbrücken: Lambert Academic

Publishing.


Characteristics of Organizational Leadership and Motivation as a

Factor of Change in the Public Health System

Slobodanka Krivokapić

University Mediterranean, Podgorica, Montenegro

The purpose of this study is to investigate the characteristics of organizational leaders in the health system and the

factors that motivate health workers, in order to improve health care. The research was conducted in the public

health institutions in Montenegro. The objectives of the research were to investigate whether leaders affect the

motivation of employees in order to implement changes in the health system. The study was implemented through

the interview method on the representative sample of 603 employees in public health institutions. The factor

analysis revealed the latent characteristics of the organizational leader and the factors that motivate employees in

public health institutions. The result of study indicates that employees in public health perceive organizational

leadership to have a good quality. Result also exposes that the strongest motivation factor is a financial incentive.

Financial incentive is the main motivator for employees in the public health sector, while the participation in

decision-making was the least important motivational factor. The results obtained indicate that employees are

primarily focused on individual goals, which influence the acceptance of change within the health system. The role

of organizational leaders in motivating is poor because obtained factor scores are not correlated. The research has

shown that organizational leaders do not have a great impact on the motivation of employees in the public health

sector. This has effect on the process of accepting changes, where the roles of leaders are very important, especially

in providing support to employees.

Keywords: organizational leadership, characteristics of the leadership, motivation for change, change management,

public health

Introduction

Dynamic development of societies, development of science and technology require changes in the health

systems. Changes in the health systems are permanent, review the existing solutions, and also seek the new

ones that would effectively fulfill health needs. The reforms undertaken in the health care systems in transition

countries have limited effects in terms of realized measures and effect. There are numerous reasons for this, but

the main one is the inability of leaders to implement changes. Relatively insufficient research is conducted in

the public health sector that focuses on the characteristics of organizational leadership in the transition

countries that have a monopoly over the health system. The previous research conducted by UNDP shows that

beneficiaries of health services believe that the biggest obstacle in the implementation of reforms is a poor

Slobodanka Krivokapic, Ph.D., assistant professor at the Montenegro Business School, University Mediterranean, Podgorica,

Montenegro. Correspondence concerning this article should be addressed to Slobodanka Krivokapic, Podgorica, Vaka Djurovica bb,

Monenegro. E-mail: [email protected].

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motivation of the health care workers. Of the total number of surveyed beneficiaries, 17.8% of respondents

believe that the health care workers are not motivated for change (Retrived from http://www.me.undp.

org/content/dam/montenegro/docs/publications/DG/Corruption/Integrity%20Assessment%20of%20the%20Hea

lth%20Care%20System%20in%20Montenegro%20LOC.pdf). The analysis also shows that the implemented

reforms have not significantly changed the leadership style within the health care system. The health care

system of Montenegro consists of 31 public health institutions that serve 635,000 inhabitants of Montenegro.

The latest figure shows that there are 8,535 employees in the public health sector (Retrieved from

www.mostat.me). The health care system in Montenegro is a highly centralized and bureaucratically organized

structure, without the financial autonomy of health care organizations. The employees of public health

organizations cultivate a collectivist approach that includes a high level of formalization. The decision-making

process is centralized and any strategic decisions require involvement and confirmation of the highest

managerial level. There are not any relevant studies of organizational behaviour or quantitative research that

would highlight potential problems comparative to motivation of employees in the health care system.

In order to study the influence of organizational leadership on the motivation of employees within the

public health system, the research on the characteristics of leaders has been undertaken. It is particularly

important to emphasize that the characteristics of leaders have a special importance in the implementation of

organizational changes. These changes in the health system greatly depend on the characteristics of leaders at

all levels, as well as the motivation of employees to accept these changes. The perception of the beneficiaries of

health services regarding the implementation of reforms in the health system imposed a research problem,

which was defined within the research question. The starting point of the research question was to determine

what characteristics the organizational leaders have and to what extent they can motivate employees.

The objectives of the research were:

to determine the characteristics of organizational leaders within the public health system;

to consider the managerial variables which determine the problems within the health care institutions;

to examine the impact of health policy on the operation, autonomy and functioning of institutions;

to determine the motivational factors;

to investigate whether leaders affect the motivation of employees.

The research hypothesis was that organizational leadership motivates the employees in public health

institutions.

The importance of this topic is to determine how characteristics of the organizational leaders in the health

system for country in transition, impact quality of health care and overall system. By studying characteristics of

organizational leaders and motivation factors that impact employees, it can contribute to elements that reduce

resistance to change. At the same time, this will help in leading the activities in order to improve health services.

This is the first study that appears at characteristics of organizational leaders and factors of motivation in

Montenegro health system.

Literature Review

The literature in regards to organizational leadership and motivation suggests the existence of a large

number of theories, models, and theoretical approaches. Empirical analysis and research confirm or deny

certain theoretical positions. Most theories argue that individuals are different, but leaders’ behaviour plays a

significant role in motivating and enabling employees to perform effectively. Organizational leaders with


588

different concepts on the same issue have different characteristics (Senge, 1990). The most attracting areas of

organizational science in recent year have been in the area of leadership and motivation (Ivanicevich, 2005,

Schermerhorn, 2005). Current research focuses on transactional leadership in which the leader directs or

motivates the followers towards the established goals. The motivation studies in the public sector raise the

question of the role played by the leadership (Moynihan & Pandey, 2007a). They also emphasize that managers

have varying degrees of influence over different aspects of work. Wright, Moynihan, and Pandey (2012, p. 206)

pointed out that leaders can influence public service motivation through several mechanisms, including

engaging employees, existing values, infusing jobs with meaning, highlighting, and rewarding public service

values. Studying the role of organizational leadership in motivating employees is of great importance in the

public health systems. The leader can affect the motivation through different mechanisms. Perry and

Hondeghem (2008, p. 308) pointed out that the specific challenges worth investigation include how leaders rise

the salience of collective identities and values in followers, self-concept, linking the organizational mission to

organizational members and client’s identities and values, and liking member’s job behaviours to their

identities and values.

The theory suggests that different factors contribute to increased motivation. Most researches on

motivation focus on individual differences among employees (Brewer, 2000). Attention is also given to the

information by which the employees can monitor their own progress (Ivancevich & McMahon, 1982). Robbins

and Coulter (2005) cited the Gallup study which found that the single most important thing subject to the

change of performances is the quality of relationship between employees and their direct supervisors, rather

than loyalty, wages, or work environment. Perry and Hondeghem (2008) pointed out on the motives and action

in the public domain that are intended to do good for others and shape the well-being of society.

The study of motivation in the public sector focuses on the employees and the organizational environment.

Wright (2001, p. 368) pointed out that the researchers generally make a mistake when they study motivation

while separating public and private sector. Contrary to this position, Alonso and Lewis (2001) pointed out that

the link between the motivation in the public sector and the performances is not so strong. Perry and Wise

(1990) defined the motivation in the public sector as an individual’s predisposition to respond to motives

grounded primarily or uniquely in public institutions and organizations.

The motivational context is the core content of organizational behaviour, institutional and environmental

factors, as well as individual motives. Houston (2000) stated that public sector motivation exists and those

employees attach greater importance to rewards at work, as well as the feelings of fulfilment, which represent

an important predictor of organizational performances.

Methodology

The research was conducted on a sample of 603 respondents through the interview method in 31 public

institutions in Montenegro. A specially designed questionnaire contained the demographic variables with 12

items, organizational variables with 16 items, characteristics of leaders with 11 items, influence of the Ministry

of Health on the work of health institutions with nine items, motivation factors with 12 items, demonization

factors with 12 items, autonomy and teamwork with seven items, interpersonal relationships and awareness

with seven items, and performance and satisfaction appraisal with two items. Assessment of the impact of

environmental factors has not been done, except for the impact of the Ministry of Health, as the emphasis has

been given on the internal factors of organizations.


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The concept of the research provided for a large number of variables that were supposed to point out to all

aspects of the organization and management within the public health institutions. Given a large scope of the

variables, the response rate of respondents ranged differently with respect to individual questions, in the range

from 73% to 99%. The sample consisted of the managers of all public health institutions, starting from the

primary to the secondary level, as well as other health care workers.

Of the total number of respondents (Figure 1), 192 (32.8%) were male, and 412 (68.2%) female. Of the

total number of respondents, 356 were managers at various levels (n1), while 247 (n2) represented other health

care workers. The average age of the respondents ranged between 41 and 50 years of age. The specialist doctors

were dominant in the structure of respondents in the number of 151, which made up 25% of the total number of

respondents.

Figure 1. Structure of respondents by gender.

Figure 2. Structure of respondents by age.

The average length of service of the respondents ranged from 11 to 20 years of service, while the

managers occupied their positions for up to five years on average. Of the total number of executives, 64 (10%)

of them have some kinds of training in management, with 21 of them having postgraduate degree. Other

respondents indicated that self-study had been a way of professional development in the field of management.

Only 8.5% of managers had some types of formal education in management. When asked how the

organizational changes affect the functioning and work within the institutions, the respondents replied as


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follows: About 23% of them answered that they did not know, 20.4% of them responded that changes led to an

increase in employment and 19.2% responded that changes led to the reduction of the number of employees in

the institution.

Statistical Analysis

The statistical analysis of collected data was performed on the entire sample. The analysis was performed

with the help of the SPSS (statistical package for the social science) statistical programme within which the

specialized macros were used: Comfac2 and Spromax. The testing of scales from the questionnaire for the

nominal scale was carried out by the method of optimal scaling, and the method of latent dimensions was used

for ordinal scales. The discriminate analysis was used in the research at the project level, which attempted to

answer whether there are significant differences between managers at different levels and other health care

workers. The variance analysis was used to study the responses to scale questions. The studied characteristics

included nominal, ordinal, and five-point scale for the factors of motivation, demotivation, satisfaction, and

characteristics of organizational leaders. The variables included in the analysis were selected on the basis of the

expectations that they were related to the detected clustering and on the basis of their metric and statistical

characteristics.

A multivariate test was taken as the linear F-test > 2.00 with the inclusion of the cubic component at the

significance level = 0.05. The significance of the difference in pairs was determined by the modified LSD test

(Bonferroni test), which tested the difference among the pairs of arithmetic means. When checking the null

hypothesis, the t-test was used for parametric data and chi-square test for non-parametric data. The difference in

the distribution of respondents by individual characteristics was analysed by contingency tables. To test the

average canonical correlations among variables, the Cramer’s coefficient V (0 < V < 1) was used and V > 0.20

was considered; the contingency coefficient C > 0.30, with the significance level of p < 0.01 (highly

statistically significant) to p < 0.001(significant).

The analysis of hypothetical factors was performed with the confirmatory factor analysis, where the

analysis was based on the Proctrustean ProMax rotation, with the image analysis used as the starting solution,

but on the variables rescaled in the Harris Kaiser’s Matrix. It is believed that this analysis is the most

mathematically and statistically based method. The similarity of explorative and hypothetical factors was

checked by the correlation coefficient or the congruence coefficient.

Reduction of Characteristics

The number of items in the research was varied and dichotomous due to which the reduction was made by

the “image” method of the factor analysis with the direct oblimin method of oblique rotation. The number of

factors was determined by the Guttman-Kaiser criterion for the model image analysis. The choice of this

method was made because of the estimate of the statistical quality of selected variables which are of low

reliability. The image analysis served as a measure of belonging of a particular item to the analysed sample and

as a relative measure of the correlation amount before and after the Gutman-analysis, which is the obtaining of

the anti-image correlation. The factor analysis was performed using the method of principal components rotated

into the position, with the support of the “2macro-comfact2 software”.

The reduction process was carried out for the whole studied population, as well as for sub-samples. For the

purposes of this paper, the results on the whole sample are presented.


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The first phase included the processing of the total variance of items with the direct oblimin method of

oblique rotation as a method of factorization. Due to the large number of statistically significant factor loadings,

the factors whose factor loading is F > 10,401, and the characteristic values (eigen-value) > 0.60 (Meyer-Olkin

Kaiser-Measure of Sampling Adequacy) were selected for the analysis.

The second phase included testing and the validity of the measurement scales. In the case of ordinal

variables, the factor analysis was used and the factorization was not performed for the normalization of ordinal

variables, because the analyses did not indicate a need for this. These variables were subjected to an explorative

part of the SproMax macro, resulting in the orthoblique factors from the Harris-Kaiser’s image analysis.

The third phase involved the factoring of the obtained latent variables by the component model (principal

component analysis). Three factors were identified by the Guttman-Kaiser criterion and rotated by the direct

oblimin method.

The analysis was performed in data processing by health institutions, which showed that there were

significant differences in the evaluation of organizational leaders by health institutions, which was expressed as

highly significant (4.2) according to the F-test.

Characteristics of Organizational Leaders

The characteristics of organizational leaders were evaluated on the basis of 11 characteristics that identify

the organizational goals, ethics, communication, negotiation skills, communication skills, autonomy, and

respect from employees.

The characteristics were measured on the five-point scale, which included: from 1 (especially good) to 5

(extremelly unsatisfactory).

The arithmetic means of the observed characteristics of organizational leaders are about the average score

of two, which indicates that the leadership is good. A lack of structuring as a high correlation (shown in Table 1

which contains 474 cases) and the average score indicate that respondents were not discriminatory.

Table 1

Analysis of the Characteristics of Organizational Leaders

Variable Mean Standard deviation

Vision of managers (V) 2.21730 1.27309

Goal setting (G) 1.90717 1.12620

Ethics (E) 1.84177 1.11981

Autonomy in decision-making (AD) 2.09705 1.24701

Care for future managers (CM) 2.48101 1.36269

Communication skills (CS) 2.05274 1.27894

Negotiation skills (NS) 2.11603 1.29613

Encouraging the creativity of employees (EEC) 2.66878 1.44453

Giving an autonomy to employees (GAE) 2.33966 1.35490

Care for employees (CE) 2.15823 1.26352

Respect of employees (RE) 2.06962 1.20139

The analysis of variance characteristic of managers indicates that there are significant differences in the

evaluation of the characteristics of the managers by profession. The difference in the evaluation of the sense of

vision is the greatest (V = 1.8598). The best score was given by specialists and the worst by other health care

workers.


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When it comes to the characteristic regarding the goals of the institution, the difference among the

respondents by profession is V = 1.8598, which was also assessed best by specialist doctors and worst by other

health care workers with the high school diploma. The characteristic regarding ethics also shows the difference

by profession (V = 1.1473), and is best assessed by administrative workers (Mean = 2.0000). On the issue of

autonomy in decision-making, differences among professions are also evident (V = 1.6193) and the best

assessment was given by clinical specialists, which is also true for the characteristic taking care of future

leaders (V = 2.9558), while it was assessed worst by the medical technicians with high school diploma. The

characteristics of organizational leaders were generally assessed best by specialist doctors and worst by medical

technicians. An exception is the characteristic example for colleagues to follow which was assessed worst by

senior medical technicians (Mean = 1.7500).

Table 2

Correlation Matrix of the Characteristics of Organizational Leaders

V G E AD CM CS NS EEC GAE CE RE

V 1.00000

G 0.73221 1.0000

E 0.64702 0.66224 1.00000

AD 0.57797 0.47461 0.46219 1.00000

CM 0.61354 0.57882 0.51965 0.59081 1.00000

CS 0.64218 0.57292 0.56974 0.39977 0.53736 1.00000

NS 0.62531 0.60267 0.52249 0.43644 0.58718 0.82147 1.00000

EEC 0.65311 0.61005 0.54652 0.55189 0.67290 0.57021 0.61904 1.00000

GAE 0.55402 0.57769 0.57336 0.45844 0.54340 0.58747 0.58186 0.6251 1.00000

CE 0.67517 0.67447 0.68266 0.54440 0.61262 0.65682 0.64069 0.66628 0.67246 1.00000

RE 0.58032 0.63606 0.59437 0.49786 0.61745 0.64293 0.62613 0.57735 0.58419 0.70999 1.00000

Notes. Determinant of correlation matrix = 0.0002595; Kaiser-Meyer-Olkin measure of sampling adequacy = 0.93418; Bartlett test of Sphericity = 3868.2288; Significance = 0.00000.

The correlation analysis among 11 variables of the characteristic of organizational leaders is shown in the

Table 2 and all items highly correlate with each other from 0.39 to 0.82 (significant level p < 0.01). Negotiation

skills and communication skills have the highest correlation of 0.82, and then the respect from employees and

an example to follow, with 0.71.

Result of the factor analysis of these 11 factors of the characteristics of organizational leaders explained a

57% of variance which is shown in the Table 3. Features with these characteristics are called management

support. Communication skills have the bigest communality (0.73345), then example of conduct (0.70329),

while ethics in the work has the lowest communality (0.45599). These factor loadings and the variance

extracted confirm the convergent validity of the dimensions.

The obtained factor discriminates the respect of the manager and has a quantitative, rather than structural

significance in the analysis. The obtained factor represents a structural tool for the analysis of latent features

and the characteristics of managers.

The obtained factor represents a structural tool for the analysis of latent features and the characteristics of

managers. Crossing the factor scores obtained by the institutions is a significant difference in the responses,

especially for the variable “communication skills”, so that Phi = 0.50092, Cramer’s V = 0.25046 and

contigency coefficient = 0.44787, as well as “negotiation skills” (Phi = 0.53984; Cramer’s V = 0.26992;


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contigency coefficient = 0.47504) and “develop creative abilities of employees” (Phi = 0.53179; Cramer’s V =

0.26589, conigency coefficient = 0.46953). Similar differences in the characteristics of organizational leaders

occur at institutions in the variable “giving employees more autonomy” (Phi = 0.50877; Cramer’s V = 0.25438;

contigency coefficient = 0.45345). Highly significant multivariate F ratio was obtained in terms of the “vision

of the leaders” (F ratio = 4.130) and “goal settings” (F ratio = 3.8005). Researches showed significant

differences in assessment of profession leader’s characteristics on all variables. F ratio for the variable goals is

1.8598 where the differences are particularly manifested with medical specialists, as well as the ethics (F ratio

is 1.1473), encouraging the creativity of employees (F ratio = 2.9558), and ethic (F ratio = 2.1030).

Table 3

Results of the Factor Analysis for the Characteristics of Organization Leaders (12 Items)

Factor Variables F Communality

E = 6.99 Pct of V = 57.2% support management

Example of conduct 0.82353 0.70329

Vision of managers 0.79532 0.68513

Negotiation skills 0.79176 0.72534

Communication skills 0.78804 0.73345

Goal setting 0.77306 0.65045

Encouraging the creativity of employees 0.76783 0.64055

Respect from employees 0.76471 0.61748

Care for employees 0.72930 0.58658

Giving an autonomy to employees 0.72714 0.56426

Ethics at work 0.72607 0.57496

Autonomy in decision-making 0.61280 0.45599

Organizational leader’s at all hierarchical levels of the health system are determined by the higher

authority. Despite the formal announcement of the competition, all directors of public institutions are appointed

by the ministry. The following action is for directors of institutions (in consultation with the ministry) to place

managers at lower levels. Such a system of recruitment leads to a reduced influence of the leaders of on the

employees and their focus mainly on solving management problems, not the handling or initiating significant

change.

Motivational Factors

In this research, 12 factors were used to test the motivation, adapted to the situation in health system, and

measured by five-point scale. Motivational variables include salary, financial incentives, working conditions,

the affirmation in the profession, interpersonal relationship, status of the institutions, job security, participation

in decision making, the quality of management, the job by itself, acceptance of colleagues, and responsibility

which are shown in Table 4.

Analysis of mean of variables of work motivation shows that employees are highly motivated. Salary and

responsibility have the lowest arithmetic means and standard deviations, which the respondents consider the

best motivators, individual and univariate. The least important variable is participation in decision-making,

where at the same time the largest individual differences exist.

Correlation among 11 variables of work motivation (shown in Table 5) is positive and significantly

correlated to 0.06 at significant level of 0.55.


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Table 4

Analysis of Motivational Factors

Variables Mean Std. Dev

Salary (S) 1.15517 0.47273

Financial incentives (FI) 1.38314 0.78539

Working conditions (WC) 1.29310 0.59775

The affirmation in the profession (AP) 1.43870 0.68229

Interpersonal relationships (IR) 1.27011 0.52739

Status of the institution (SI) 1.60345 0.87722

Job security (JS) 1.40996 0.75154

Participation in decision-making (PDM) 2.11877 1.1929

The quality of management (QM) 1.32567 0.66245

The job by itself (JI) 1.51533 0.85670

Acceptance of colleagues (AC) 1.48276 0.68483

Responsibility (R) 1.20498 0.49385

Notes. Number of cases = 522; the boldface shows the highest mean and standard deviation.

Table 5

Correlation Matrix of Motivational Factors

S FI WC AP IR SI J S PDM QM J I AC R

S 1.00000

FI 0.55298 1.00000

WC 0.08327 0.18962 1.00000

AP 0.20510 0.31255 0.40887 1.00000

IR 0.0677 0.08794 0.38767 0.31014 1.00000

SI 0.19033 0.25996 0.34289 0.42591 0.37718 1.00000

J S 0.23659 0.27643 0.27035 0.26621 0.32540 4.7416 1.00000

PDM 0.13410 0.29361 0.30117 0.44546 0.30298 0.51675 0.43822 1.00000

QM 0.08961 0.12862 0.27228 0.28631 0.34106 0.31184 0.22093 0.38106 1.00000

JI 0.10549 0.09680 0.22922 0.28237 0.19261 0.31331 0.27641 0.37028 0.22117 1.00000

AC 0.10018 0.11223 0.24915 0.34279 0.39289 0.35762 0.31210 0.33866 0.45241 0.43884 1.00000

R 0.02792 0.12868 0.28373 0.27946 0.23654 0.27217 0.25409 0.20305 0.21798 0.28518 0.27437 1.0000

Notes. Determinant of corelation matrix= 0.04020535; Kaiser-Mayer Olkin measure of sampling adequacy = 0.83156; Bartlett test of Sphericity = 1635.6353; Significance = 0.00000.

The results of data processing showed that there were no significant differences between the groups

surveyed in respect of profession and the position occupied (managers and non-managerial staff) (V = 0.3182)

when it comes to motivational factors. Salaries and financial incentives are essentially important for all groups

of respondents, regardless of profession or position within the institution. The only significant difference occurs

in terms of the factor “status of the organization”, depending on the profession (V = 3.9711). The greatest

importance to this factor was given by specialist doctors (Mean = 1.6739) and the lowest by medical

technicians (Mean = 1.3913).

The image factors obtained from this group of variables are relatively weak, because the first factor

explains only 24% of the total variance and the other 4%, because the loadings of variables are relatively low.

The other factor called financial incentives is a variant that includes only two items, while other variables of the

motivator belong to the first factor. Direct correlation of factors is relatively low and amounts to 0.40. Low


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correlation is caused by reduced variability, because the Table 6 shows that all the factors are very important.

Salaries and financial incentives have emerged as two aspects of the same variable.

Table 6

Results of the Factor Analysis for Motivation

Factor Variables F Com.

Factor 1 E = 4.12 Interpersonal relationships Pct of Var = 24%

Acceptance of colleagues (AC) 0.59600 0.39211

Status of the institution (SI) 0.55757 0.41711

Interpersonal relationships (IR) 0.55311 0.31413

Participation in decision-making (PDM) 0.55046 0.43660

The quality of management (QM) 0.50980 0.29419

The affirmation in the profession (AP) 0.48435 0.36180

The job by Itself (JI) 0.47383 0.28523

Working conditions (WC) 0.46851 0.28077

Job security (JS) 0.44316 0.33934

Responsibility (R) 0.40472 0.18728 Factor 2 Financial incentives E = 1.49 Pct of Var = 4%

Financial incentives (FI) Salary (S)

0.52825 0.52318

0.38312 0.33989

Discussion

In practice, it is expected that leaders are the bearers and creators of change and that they possess a high

level of ethics and responsibility. It is particularly important that leaders in public health institutions are

mediators in relations with numerous stakeholders, among whom, the employees have special importance.

The results obtained indicate that respondents were not particularly discriminatory towards organizational

leaders, who received the average score of good, which does not indicate the specific characteristics of leaders.

Particular note is leader’s traits related to organizational behaviour, such as the autonomy of employees, the

attitude towards colleagues, ethics, and communication. Respondents with respect to these variables showed the

greatest differences both in institutions and occupation. This indicates a need to change organizational culture,

which is an important factor for improving existing practices and management styles.

Organizational leaders in the public health system have limited tools to motivate employees because of the

legal rules which do not allow the recognition of additional rewards, as an opportunity for increased

performance and achieved results.

The lack of structure, high correlation, and average scores (mean score is “good”) point to conclusion that

sample population was indiscriminative. The factors in Table 3 have quantitative meaning as oppose to

structural ones. The factors represent structural tools for analysis of potential characteristics and attributes of

organizational leaders. It is recommended to widen the scope of analysis of characteristics of organizational

leaders in order for more detailed representation.

When it comes to motivation, the perception of the employees is that interpersonal relationships and

financial motivators prevail. Within the interpersonal relationships, the most important factor is “acceptance

from colleagues” and from among the financial motivators, salaries make the strongest motivator. It is

recognized that due to the overall financial position of health care workers, the financial factors stand out and

dominate in relation to others, which results in low variability of other motivational factors.


596

Encouraging and implementing changes within the health system and the implementation of strategic

decisions cannot take place without the charismatic organizational leadership, which will change the processes

within the system through the initiative and responsibility for the tasks performed. Building trust and good

working conditions leads to improved interpersonal relationships and satisfactory motivation. Moynihan and

Pandey (2007a) pointed out that effectiveness of leaders may depend even more on the ability to communicate,

persuade, and inspire. They also emphasised that managers have varying degrees of influence over different

aspects of work motivation, with grates influence over job satisfaction, noting that managers could influence

work motivation by changing the employees’ perception of organization (Moynihan & Pandey, 2007b).

Pandey and Wright (2006) raised the question of whether the public sector cultivates motivation.

Challenges of the environment present demands to organizational leaders to direct the employees and seek

the best ways to increase performances and achieve goals, regardless of whether it is the public system or

private health organizations. Perry and Wise (1990, p. 368) defined motivation in the public sector as an

individual’s predisposition to respond to motives grounded primarily or uniquely in public institutions and

organizations.

It should be particularly noted that changes in service organizations occur much harder and building trust

between leaders and employees has an enormous importance. Changes within the public health systems require

taking measures for greater involvement of employees in the decision-making process. This was rated low by

the respondents as a factor that can motivate them. Selection of appropriate organizational leaders with

authority could certainly improve the existing situation. The obtained results suggest that stronger leadership is

necessary to motivate public sector employees to accept organizational changes which would improve the

system of motivation and generally lead to an increase in performance.

Conclusions

Motivation and leadership are very important issues for the health care system and for the research of

organizational behaviour. The research has shown that organizational leaders in the public health care system of

transition countries have been given an average score of good by employees, and that they do not influence

motivation significantly.

Research has shown that there are significant differences in attitudes about characteristics of

organizational leaders, both between medical institutions and employees.

Financial incentives have proved to be the strongest motivational factor, which due to legal rules restricts

the ability of leaders to use this motivating factor. Participation in decision-making is rated lowest by

respondents, which puts them in a passive position when it comes to changes and their acceptance.

For the implementation of comprehensive organizational changes within the public health care system, it

is necessary to identify the motives for accepting new ideas, so that changes can be better received and spread

throughout the organization. In the process of accepting changes, the role of leaders is very important,

especially in providing support to employees. Support to employees includes explanations of what is expected

from changes and motivation of employees who can contribute to the successful change.

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Chinese Business Review (ISSN 1537-1506) Vol.13, No.9, 2014

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