declining poverty rate and widening income gap in...

A Contemporary Business Journal

ISSN: 2232-0172 Vol 5 Issue 2, August 2015

pp. 165–191

Taylor’s Business Review, Vol. 5 Issue 2, August 2015 165

Correspondence: Siti Soraya Iskandar, Taylor’s University, Malaysia. Email: [email protected]

Declining Poverty Rate and Widening Income Gap in Indonesia & Brazil

Siti Soraya Iskandar and Rabi’ah Abd RahmanTaylor’s University, Malaysia

© The Author(s) 2015. This article is published with open access by Taylor’s Press.

Abstract: This paper examines the relationship between three globalisation-related variables, which are economic integration, personal contact and technological advancement, with income inequality level in Indonesia and Brazil. In this study, the level of income inequality is measured by Gini coefficient, economic integration is represented by the ratio of inward foreign direct investment to gross domestic product, personal contact is represented by the value of international tourism receipts whereas technological advancement is represented by the number of Internet users per 100 people of the country. Using ordinary least squares regression, this study proved that technological advancement has a significant and positive relationship with income inequality in Indonesia but a significant yet negative relationship with income inequality level in Brazil. Economic integration is also shown to share a positive and significant relationship in Indonesia. However, this study did not find evidence of a significant relationship between personal contact and inequality in Indonesia. This paper also employs Granger causality test to determine the direction of causality between the variables. It is found that there are causality and unidirectional relationships between technology and income inequality as well as personal contact in Indonesia. Technological advancement Granger causes income inequality, whereas economic integration Granger causes personal contact and technological advancement in Brazil.

Key words: Income inequality, economic integration, personal contact, technological advancement, Granger causalityJEL classification: D31, D63, I3

1. INTRODUCTION

Inequality generally refers to the gap between the rich and the poor or the haves and the have-nots, be it in terms of income, education, health or opportunities. As a multidimensional problem that can occur in numerous different strata of the society, inequality has become one of the most arduous challenges faced by not only the Third World, but also the First.

Siti Soraya Iskandar and Rabi’ah Abd Rahman

Taylor’s Business Review, Vol. 5 Issue 2, August 2015166

Economic inequality, more specifically, can be defined as the disparity in how assets, wealth or income are distributed among the populations (OECD, 2015). Of the highest concern faced by numerous countries in the world today, is the rise of income inequality level, a phenomenon in which the gap between the rich and the poor continues to widen due to the uneven distribution of income among the population. In fact, the global income inequality level is believed to have continuously increased since the 19th century (Ortiz & Cummins, 2011). The trend is evident and is proven by Milanovic’s study (2009) which showed that the global GINI coefficient estimates between the years 1820 and 2002 has indeed presented an increasing trend.

Figure 1. Global income inequality level over the decades Source: Milanovic (2009).

In 2014, income inequality levels in most of OECD countries has reached its highest level in 30 years, in which the richest 10% earn as much as 9.5 times more than the bottom 10% of the population (OECD, 2014). However, in emerging markets and developing countries (EMDCs), the pattern of inequality is deemed to be more varied, in which some countries such as the Philippines experience a downward trend of income inequality and other countries such as Indonesia show a rising level of income inequality.

1.1 Indonesia

Indonesia, a country that is often cited as one of the Tiger Cub Economies and one of the world’s largest emerging economies, has been experiencing rapid economic growth and significant poverty alleviation throughout the years, in which these accomplishments were predominantly more vigorous prior to the Asian Financial

Declining Poverty Rate and Widening Income Gap in Indonesia & BrazilSiti Soraya Iskandar and Rabi’ah Abd Rahman


Crisis 1997-1998. However, the level of income inequality in Indonesia has shown a unique pattern throughout the years. Prior to the Asian Financial Crisis, the level of inequality in Indonesia was perceived to be relatively stable and if not, declining. The trend, however, started to change after the crisis, in which income inequality level, which is measured by the Gini coefficient increased significantly from 0.30 in the year 2000 into a staggering 0.413 in the year 2013, indicating one of the fastest rising rates of inequality in the East Asia region (World Bank, 2014).

Figure 2. Indonesia’s Gini coefficient, 1995-2013Source: World Bank (2015)

1.2 Brazil

In general, the level of income inequality in Latin American countries underwent two significant trends or patterns from the 1980s to 2000s; income inequality was shown to be rising in 1980s until the late 1990s to early 2000s in which it started to decline (Gasparini & Lustig, 2011). Although the levels of income inequality in these countries are still considered to be significantly high, Tsounta and Osueke’s study (2014) found that Latin American and Sub-Saharan Africa are the only regions that have undergone decreases in the last decade.

Brazil has one of the highest income inequality levels in the world and once reached a Gini index of 0.63, which is one of the highest inequality levels in the history of the world (Gasparini & Lustig, 2011). However, with the introduction of market-oriented reforms and trade liberalisation that are believed to reduce income inequality in the country, Brazil’s Gini coefficient has showed a steady decline since the late 1990s, with the steepest decline between the years 2001-2007.



Figure 3. Brazil’s Gini coefficient, 1995-2012 Source: World Bank (2015).

The subject of rising income inequality in many countries has raised many concerns due to its possible adverse effects on economic development. According to Dabla-Norris, Kochhar, Suphaphiphat and Tsounta (2015), income inequality inhibits investment, causing a slowdown in economic growth and thus triggering a rise in economic, financial and political instability. It is also noted that when income inequality is prominent, economic growth drivers are affected because the lower- income households and individuals are incapable of accumulating both physical and human capital (Dabla-Norris et al., 2015). In addition, the widening income disparity could also lead to a slowdown in poverty reduction, severely damaging trust as well as the social cohesion within a particular country in the case of extreme inequality.

When considering the magnitude of income inequality problem in Indonesia, the number of studies that have been conducted in efforts to address this complex and precarious issue is rather limited. Studies and researches that mainly focus on Indonesia itself is deemed to be even scarcer as most of the existing literature are usually focused on the ASEAN region or developing countries as a whole instead of focusing on individual countries. The same problem also applies to Brazil, in which most studies normally focus more on the Latin American region as a whole, instead of specifically focusing on any particular country itself. Therefore, this study complements the existing pool of literature by giving a country-focused insight on the outlook of the income inequality problem in Indonesia and Brazil. In addition, previous publications are also not widely accessible internationally due to the nature of them being written in the mother tongue, Bahasa Indonesia. Therefore, this study will hopefully abridge the existing language barrier for international readers who are



interested in discovering more about the potential drivers of income inequality in the country.

Last but not least, the focus of this study is on the relationship of income inequality with three main determinants of globalisation, namely trade, social contacts and technology. There is a very limited number of studies that link Indonesia’s and Brazil’s income inequality levels with these factors as many of the previous literature focused more on endogenous drivers such as level of education attainment, industrial and agricultural employment shares and government policies and expenditures. Therefore, this study will provide a new perspective on Indonesia’s and Brazil’s income inequality problem.

The rest of this paper is organised as follows. Section 2 discusses various theories of inequality and critically analyses the relationships of globalisation-related variables with the level of income inequality. Section 3 presents data used for our analysis and the estimation strategy. Section 4 presents results of our analysis. Finally, Section 5 concludes with a discussion.

2. GLOBALISATION AND INCOME INEQUALITY

According to Lee (2014), globalisation refers to the phenomenon in which the economies of different nations become increasingly integrated at a global level through the advances made in the international movement of production factors that includes goods, services, capital and labor. In simpler terms, Tabetando (2014) defines globalisation as the degree of connectivity and openness of a country to the rest of the world. On the other hand, authors such as Al-Rodhan (2006) claimed that globalisation is indescribable as it encompasses so many different and complex aspects. Globalisation, according to the author, involves the integration of economies, cross-border policy transfers, knowledge transmission, cultural stability, reproduction, relations or discourses of power and more importantly, globalisation is a revolution.

Generally, there is a substantial amount of literature that examines the possible drivers of income inequality. The potential impact of globalisation on various social and economic aspects of the society, including income inequality, has also been a subject of interest among researchers although the association between these two factors remains highly debatable in literature. Several studies have proven that globalisation has caused a reduction in inequality, in which those in favor of it claimed globalisation is one of the major driving factors of significant global poverty reduction and has had a pivotal role in the reduction of income inequality across countries. This inverse relationship between globalisation level and income inequality was proven by Zhou, Bowles, Biswas & Suanders’ study (2011) that carried out an analysis of globalisation impact on the inequality levels of 60 countries through the regression of Gini coefficients with globalisation, education and urbanisation indexes. Therefore, the empirical evidence from Zhou et al’s study (2011) indicated



that a higher level of globalisation tends to result in the amelioration of a country’s income disparity. Similarly, Tayebi and Esfahani (2009) also confirmed this finding and claimed that globalisation has significantly and expectedly reduced the level of income inequality.

On the other hand, there are also critics of globalisation whose studies have concluded that globalisation in fact, tends to bring about negative impacts on a nation’s poverty and inequality levels. A study by Milanovic (2006) showed that globalisation in general is favorable for small and less populous countries whereas the opposite effect applies for bigger and more populous countries. The study concluded that as globalisation tends to have a negative impact on big, populous nations, thus increasing globalisation level will result in a rise of global inequality, indicating a positive relationship between income inequality and level of globalisation. In addition, Atif, Srivastav, Sauytbekova & Arachchige (2012), who applied econometrics techniques on a panel data of 68 countries for a time horizon of 20 years to analyse the trends of income inequality and globalisation, concurred with the positive relationship between these two factors.

Other studies such as the one done by Ekmekcioglu (2012) have resulted in mixed and conflicting findings in which globalisation has not resolved the predicament of rising income inequality entirely but has instead caused an increase in income inequality in developed nations; however, it has managed to close the income inequality gap of several developing countries. Nevertheless, the result of Ekmekcioglu’s (2012) research does not seem to apply in the case of Indonesia, a developing country in which the income inequality level continues to rise despite an increase in globalisation level.

In the case of Indonesia, a country that consists of 33 provinces, the interregional study by Tabetando (2014) has also produced mixed findings in which the level of globalisation has a decreasing impact on inequality in rural and less globalised provinces but increases income differential in more industrialised and highly-globalised provinces, particularly the Java-Bali region where economic activity is the highest in the nation. The author claimed that the widening of income inequality in highly globalised cities and areas are mainly due to the relatively large wage differentials between skilled and unskilled workers.

Amongst the first and earliest theories that examined the effects of international trade, which is a part of globalisation, on income inequality levels is the Hecksher-Ohlin model that claimed how in labor-abundant economies, international trade should bring about a decreasing effect on the levels of inequality. As written by Tabetando (2014), with the onset of trade liberalisation brought about by globalisation, the Hecksher-Ohlin model predicts that countries will specialise in the production of products in which the required resources or inputs are in abundant supply. As a result of this, labor-abundant countries, which are mainly developing countries, will export labor-intensive products whereas capital-abundant countries will export their



capital-intensive and skill-intensive products. Consequently, there should be a rise in returns for unskilled labor in the labor-abundant developing countries that ought to abridge the wage inequality between skilled and unskilled labor. Yet again, the assumption of the Hecksher-Ohlin model in the case of Indonesia has to be refuted due to the ever-increasing inequality level in reality, where more than two products and two countries are involved.

Another major theory in inequality that has been debated intensely is the Kuznets inverted-U curve that illustrates the curvilinear relationship between income inequality and economic development. Based on the data of numerous industrial societies, the Kuznets inverted-U curve hypothesises that as a country develops, income inequality initially increases and peaks at a certain point, and then starts to fall beyond that particular point (Kuznets, 1955). In numerous studies conducted on Western industrialised nations, Kuznets conjecture is deemed to be evident, such as in the case of England, Sweden, Germany and France but such a relationship does not manifest in Latin American and Asian countries (Acemoglu & Robinson, 2002).

Various past studies have utilised different variables to measure the level of globalisation in a particular country. One of the most commonly used set of variables is the KOF Index of Globalisation, as used by the studies of Tayebi and Esfahani (2009) and Zhou et al. (2011), which includes measures such as economic, social and political globalisation. These two studies have an advantage over other studies in that they identified the different types and forms of globalisation and the effects that each has on income inequality levels. However, one of the slight demerits of the variables used by the two studies was the inclusion of technological aspects into the social globalisation variable. Technology, as reported by Jaumotte, Lall and Papageorgiou (2008), is one of the most significant globalisation determinants that may affect the level of income inequality. Therefore, including technological aspects such as the number of internet users, internet hosts and secure server providers in a particular country as a measure of social globalisation may be misleading and may biasedly distort the relative importance of the social globalisation variable itself.

Heshmati (2005), however, used a similar yet slightly different Kearney Index of Globalisation that includes economic integration, personal contacts, political engagement and an additional variable of technological connectivity as a separate variable free of and separate from the personal contacts variable that is closely related and similar to the social globalisation variable used in the KOF index of globalization. Therefore, the choices of variables under Heshmati’s (2005) study is deemed to be more suitable for this particular study because the effect of technology, which is relatively significant variable, can be assessed and analysed appropriately and accordingly.



2.1 Economic Integration and Income Inequality: Foreign Direct Investment

Economic integration is often cited to have a significant impact on the level of income inequality and is considered as one of the most prevalent variables influencing income inequality. One of the most common measurements that were used to assess the level of economic integration is the ratio of inward Foreign Direct Investment (FDI) to the total gross domestic product (GDP) of a country. The definition of FDI according to UNCTAD (2013) is an investment that is made to acquire a long-term relationship and interest in enterprises that are operating outside of the investor’s economy, which in most cases, is made to obtain an effective voice in the management of the said enterprise(s).

The Dependency Theory is one of the most fundamental principles that may explain the relationship between FDI level and rising income inequality in developing countries. According to Todaro and Smith (2015), the Dependency Theory or also known as the Neocolonial Dependence Model is a model that proposes how underdevelopment in developing countries (periphery) exists because they are caught up in a dependence-dominance relationship with richer /developed nations (center). In the case of income inequality, this dependence-dominance relationship occurs in a way that the periphery becomes overreliant on FDI inflow from the center. This eventually causes structural distortions and suppression of autonomous policies within the peripheral countries, in which government intervention into the economies that could have promoted higher income distribution policies within the periphery is prevented to protect the interests of the center and in order to maintain continuous FDI inflow (Franco & Gerussi, 2013).

The volume of inward FDI has been used as a measurement of economic integration in numerous studies in the past, in which the results often claim that it shares a positive and significant relationship with income inequality level (Heshmati, 2005; Jaumotte, Lall & Papageorgiou, 2008; Tayebi & Esfahani, 2009). This indicates that an increase in inward FDI will bring about an increase in income inequality, exhibiting a deterioration of the existing income gap. The study by Jaumotte, Lall and Papageorgiou (2008) has also suggested that financial or economic globalisation with inward FDI in particular, increases inequality in both developed and developing countries, overall. The elaboration provided by the authors for this particular trend can be seen from the perspective of the host country in which FDI inflow is usually allocated to more modern sectors that cause an increase in demand for workers with higher skill premiums. Jaumotte, Lall and Papageorgiou (2008) claimed that this will cause a significant raise in the wage or income of that portion of the population that has relatively higher education and skills, thus exacerbating the income differentials between low or unskilled workers and highly-skilled ones. As mentioned by Lipsey and Sjoholm (2004), FDI benefited skilled workers more than unskilled workers in developing countries such as Indonesia, thus causing an increase in wage disparity. In tandem with the findings of Tabentando (2014), it was also noted that FDI



in Indonesia tends to cluster in several geographical areas only; thus, resulting in regional inequalities. This only goes to show that the distribution of FDI within a particular country is also crucial in determining the effect it has on income inequality levels.

Te Velde’s (2005) study on income inequality levels of Latin American countries also suggested that FDI in general does not hold a major income inequality- reducing role and in fact, it may have increased the level of income inequality instead. Similarly, in some countries such as Turkey, increasing FDI inflows is shown to exacerbate the level of income inequality initially for a short period of time instead, but this effect does not hold in the long run (Ucal, Bilgin & Haug, 2014).

2.2 Personal Contact and Income Inequality: International Tourism Receipts

Geogantzas, Katsamakas and Solowiej (2010) claimed that the spread of beliefs, ideas, social behavior and people are part of the complex dimensions of the phenomenon called globalisation. One component of social globalisation is personal contact, which measures the direct interaction of people living in different countries. The cross-sectional study of Heshmati (2005) highlighted that social contact is the single component that contributes most significantly to the reduction and variation in income inequality levels across countries.

One of the most common measures of personal contact is the level of international tourism (Heshmati, 2005; Zhou et al., 2011; Tayebi & Esfahani, 2009). Pant (2011) in his study discovered that international tourism tends to have a negative relationship with income inequality levels, therefore indicating that international tourism has an income inequality reducing effect. The relationship, however, is perceived to be relatively weak according to the study. This could be caused by the methodology of the study in which cross-country regression for a very limited period of time was carried out. A more representative and statistically significant result can be obtained by running data obtained over a longer time period, which unfortunately, is very scarce in nature.

It is noted that the contribution of tourism industry in the Brazilian economy is significantly higher in municipal districts, where there are less than 100,000 inhabitants, compared to big cities (Farias et al., 2009). Therefore, it can be deduced that a negative relationship between tourism and inequality level is likely to occur in the case of Brazil as well. However, this may not be applicable in the case of Indonesia where the tourism industry is mostly concentrated in the regions of Bali, Jakarta and other big cities, as the country’s immense potential of rural tourism in the country is greatly hampered due to this clustering (ILO, 2012).

Other variables that are often used to measure personal contact according to the study of Heshmati (2005) and Zhou et al. (2011) as well as Tayebi and Esfahani (2009) are international telephone traffic and transfer payments. International tourism receipts, however, is chosen as the measurement of personal contact in



this literature instead. International tourism receipts refer to the expenditures spent by international inbound visitors for goods and services received in the destination country (World Bank, 2015). This particular variable is chosen as it allows the measurement of income generated from tourism-related activities and therefore, it can be related to the geographical dispersion of such activities. The validity of international tourism receipt as a measurement of international tourism level is also presented in Pant’s study (2011) that used the particular variable.

2.3 Technology Transfers and Income Inequality: Internet Users

The cross-sectional study of Jaumotte, Lall and Papageorgiou (2008) discovered that technology is one of the major driving factors behind the rise of income inequality that has an even greater effect than economic integration. Furthermore, it has a significant, positive relationship with income inequality level, thus indicating that greater technological transfers result in exacerbating the income inequality problem. The study suggested that the positive relationship shared by technological transfers and income inequality is because the advancements in technology will biasedly favor the part of population and workforce with higher skillset, thus aggravating the “skills gap” between the lowly-skilled and the highly-skilled workers or population and widening their income disparities as well. This was also supported by Furusawa and Konishi’s study (2013). Authors such as Jaumotte, Lall and Pegeorgiou (2008) and Ekmekcioglu (2012) often relate inward FDI and technological transfer, as the level of FDI inflow is highly concentrated in high-skill and high-technology sectors.

On the other hand, Heshmati’s (2005) cross-sectional study contradicted this view and indicated that technology transfers share a negative relationship with the level of income inequality in a particular country, which is also evident in the research done by Zhou et al. (2011).

The differences in findings amongst these authors may be caused by the differences in measurements of technology transfers in their respective research. For example, the study of Jaumotte, Lall and Papageorgiou (2008) used share of ICT in total capital stock, which measures investment flows into ICT-related assets whereas Zhou et al. (2011) and Heshmati (2005) used variables such as the number of internet users, secure internet hosts and servers to measure technology. However, as noted by Vu (2005), data on ICT-related investments would be virtually unavailable in the case of developing countries and in the case of some developed countries, the statistical methods that are used to derive the investment value are often different and vary across countries, thus resulting in highly varying and inaccurate values that may not truly reflect or represent the actual level of technology transfers in a particular country.

Furthermore, Alexander and Fong’s study (2014) indicated that technology and IT revolution have helped businesses to coordinate and communicate globally. This



may reduce the global inequality level due to the shift of production processes to regions with lower costs that will consequently increase the wage level of those living in those regions. However, Alexander and Fong (2014) also mentioned that this offshoring process has caused an exacerbation in income inequality within advanced and OECD nations as the demand for low and middle-skilled labor would fall and at the same time, put a higher premium on highly-skilled ones.

There also exists a paradoxical relationship between economic growth, technology transfers and income inequality levels. According to Pepper and Garrity (2015), ICT or technology should bring about positive effects on the growth of lower-income countries and populations. However, their study also concluded that the negative effect of ICT or technology on income inequality level on global and country levels seem to exist as technology causes an increased sophistication of the financial markets that are often attributed to further wealth accumulation by top income earners. This is again reflected in Dabla-Norris et al’s study (2015) that revealed how technology transfers disproportionately increase the income in the top end of the economic spectrum.

3. METHODS

This study empirically investigated the effects of globalisation on long-run income inequality in both Indonesia and Brazil and the relationship between globalisation- related variables and income inequality. In order to satisfy this objective, an explanatory and quantitative research methodology was used to conduct this study. The nature of this study is a time series with a time frame of only 18 years from 1995 to 2012 due to the scarcity of inequality-related data in online databases. The positivism philosophy was chosen as the applied research philosophy reflected in the deductive and scientific approach of this study.

3.1 Data Description

As the dependent variable of this study, Gini coefficient (GINI) was chosen to measure income inequality. According to World Bank (2015), Gini refers to an index that measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. The value of Gini coefficient ranges from 0 to 1, in which 0 represents perfect income equality and 1 corresponds to perfect inequality. In the context of a Lorenz curve, the Gini coefficient is defined as a ratio of the areas on the Lorenz curve diagram. The data for Gini coefficient used in this study are obtained from Center for Economic Development and Studies of the Padjadjaran University in Indonesia (CEDS, 2013).



Figure 4. Lorenz curve Source: Todaro and Smith (2015)

Net inflow of FDI refers to the value of inward direct investment made by non-resident investors in the reporting economy and for the purpose of this study, the inward FDI is converted into a ratio of GDP to indicate its value in comparison to the country’s GDP. The data for this variable is obtained from World Bank’s World Development Indicators.

In order to measure the level of personal contact, the variable International Tourism Receipts (TOUR) was chosen to indicate the value of international tourism in a country and roughly estimate the level of interaction or contact that the population of the country has with foreign citizens. International tourism receipts are expenditures by international inbound visitors, including payments to national carriers for international transport (World Bank, 2015). This variable is measured in US dollars.

The number of Internet users per 100 people (INT) is used to measure the level of technological advances in a particular country. It is defined as the number of individuals who have used the Internet (from any location) in a year. Internet is accessed via computer, mobile phone, personal digital assistant, games machine, digital TV etc. Similarly with other explanatory variables used in this study, the data for internet users were obtained for World Bank’s World Development Indicators.

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3.2 Theoretical Framework

Figure 5. Theoretical framework

3.3 Estimation Technique

In order to identify which globalisation factor has a significant relationship with the level of income inequality and to explain the relationship between income inequality and explanatory variables, several econometrics tests were applied in this study.

Firstly, descriptive statistics and a simple correlation coefficient was applied using correlation matrix to analyse the strength and direction of the relationships amongst variables. Secondly, the variables were regressed and analysed further using ordinary least squares (OLS), in which individual coefficients were tested for its significance at 5% level. Based on the OLS output, further tests were also applied, which includes the Durbin-Watson test for serial correlation as well as t-test and f-test hypothesis testing at 5% significance level. Furthermore, the variance inflation factor (VIF) was also tested to observe the presence of multicollinearity. In the later part of this study, the Granger Causality test was applied to test for causality between the variables and the Augmented Dickey Fuller test was carried out for unit root testing.

Based on the reviewed literature in the second chapter of this study, the following functional form is proposed:

Y = F (FDI, TOUR, INT) (1)

The following model is then specified and tested:

LGINI=β0 + β1(FDI) + β2(TOUR) + β3(INT) + ε (2)

Although the level of income inequality problem has become an area of interest among researchers, data related to the study, such as the Gini coefficient, remains

RATIO OF FDI INFLOW TO GDP

INTERNET

INT. TOURISM RECEIPT

INT. TOURISM RECEIPT



highly scarce and inconsistent throughout different sources. For example, the values of Gini coefficient obtained from the World Bank’s databank is different from the estimates generated by the national statistics body of Indonesia, which is also different from the estimates generated by CEDS used in this study. Therefore, it can be very difficult to find the most appropriate and accurate estimates of the Gini coefficient in Indonesia. The Gini of CEDS was chosen for this study only because it is the most complete compared to all other sources that are available, providing a complete data set for Gini coefficient from 1992 to 2012. However, as the data on tourism and Internet usage on Indonesia is only available starting from 1995, the observation has to be taken from 1995 to 2012 instead. Furthermore, as there were some missing data in the case of Brazil, the Gini coefficient values need to be interpolated for several years by using the averages of the previous and following years. Gini coefficient is also only available for a very limited time frame, so the time series analysis could only be conducted with only 18 observations. A larger sample size is often associated with greater precision of the estimates. As the result of this data scarcity, this study is unable to enhance the precision as the maximum numbers of observations have been used.

4. RESULTS

Table 1. Descriptive statistics

IDNGINI IDNFDI IDNNET IDNTOURMean 0.352778 0.924305 4.319643 6.03E+09Median 0.345000 1.475477 2.493653 5.54E+09Maximum 0.410000 2.916115 14.52000 9.46E+09Minimum 0.300000 -2.757440 0.026109 4.26E+09Std. Dev. 0.032323 1.656537 4.517868 1.57E+09Skewness 0.248433 -0.819432 0.980754 0.973874Kurtosis 2.096763 2.599985 2.783914 2.829150

BRAGINI BRAFDI BRANET BRATOURMean 0.569494 2.734956 18.98442 3.52E+09Median 0.574450 2.724125 16.14063 3.03E+09Maximum 0.599000 4.987589 48.56000 6.89E+09Minimum 0.526700 0.618451 0.105138 7.44E+08Std. Dev. 0.025903 1.157924 17.38970 2.16E+09Skewness –0.368079 0.271970 0.361540 0.259091Kurtosis 1.600409 2.536430 1.627937 1.540492

Jarque-Bera 1.875589 0.383076 1.804052 1.799007



Probability 0.391490 0.825688 0.405747 0.406771

Sum 10.25090 49.22921 341.7196 6.34E+10Sum Sq. Dev. 0.011406 22.79340 5140.830 7.96E+19

Observations 18 18 18 18

Jarque-Bera 0.797035 2.134415 2.920653 2.867185Probability 0.671315 0.343968 0.232160 0.238451

Sum 6.350000 16.63749 77.75358 1.09E+11Sum Sq. Dev. 0.017761 46.64998 346.9893 4.19E+19

Observations 18 18 18 18

The descriptive statistics results show that Brazil indeed has a higher average Gini coefficient, which indicates that between the years 1992 and 2012, the average income inequality level in Brazil is perceived to be higher than in Indonesia. Similarly, the ratio of FDI to GDP and the number of Internet users per 100 people in Brazil are also relatively higher than in Indonesia. However, it is observed that the total tourism receipts in Indonesia are higher than Brazil’s.

Table 2. Regression results - Indonesia

Variable Coefficient Std. Error t-Statistic Prob. C -1.116122 0.050938 –21.91137 0.0000IDNFDI 0.030892 0.006001 5.147684 0.0001IDNNET 0.011631 0.003385 3.435920 0.0040IDNTOUR -1.41E-12 1.05E-11 –0.134307 0.8951R-squared 0.884074 Mean dependent var –1.045855Adjusted R-squared 0.859232 S.D. dependent var 0.091198S.E. of regression 0.034217 Akaike info criterion –3.719081Sum squared resid 0.016391 Schwarz criterion –3.521220Log likelihood 37.47173 Hannan-Quinn criter. –3.691798F-statistic 35.58874 Durbin-Watson stat 2.744977Prob(F-statistic) 0.000001

Table 1 (con’t)



From the OLS results, the estimated equation can now be expressed as:

LGINI = β0 + 0.0309(FDI) – 1.41E-12 (TOUR) + 0.0116 (INT) + ε

According to the OLS regression result shown above, it can be seen that in Indonesia, FDI has a positive and significant relationship with income inequality level, which is indicated by the positive coefficient and a p-value that is lower than 0.05. The coefficient of 0.0309 indicates that when the ratio of FDI to GDP increases by 1%, the GINI coefficient will increase by 0.031%, when all other explanatory variables are held constant. This shows that an increased level of FDI in Indonesia tends to be associated with the worsening level of income inequality in the country. The number of internet users in Indonesia per 100 people also exhibits a positive and significant relationship with income inequality level, with a coefficient of 0.0116, which means that for every 1 increase in the number of population using the internet per 100 people of relationship with income inequality level, with a coefficient of 0.0116, which means that for every 1 increase in the number of population using the internet per 100 people income inequality level is expected to rise by 0.012%, when all other explanatory variables are held constant. The coefficient of tourism receipts, however, is shown to be not significant at 5% as its p value of 0.8951 is much higher than 0.05.

Internet and FDI are shown to have significant relationships with income inequality level, perhaps as they are more concentrated in the urban and high-developing areas such as Jakarta and other Javanese regions of Indonesia. This uneven distribution of Internet access and capital accumulated from FDI in more rural areas can cause the worsening of income gap in the country. However, tourism may not be significant in Indonesia due to the relatively low competitiveness of Indonesian tourism even when it is compared with other ASEAN countries such as Malaysia, Singapore and Thailand. Although the country is the biggest amongst all ASEAN nations, various factors have been hampering the growth of Indonesian tourism sector, causing it to progress relatively slower when compared to other countries. According to ASEAN (2013), the slow growth of Indonesia’s tourism is due to several factors such as poor health and hygiene standards whereby tropical diseases such as malaria are still prominent in the country, poor tourism infrastructure and poor policies and regulations as well. These are the reasons why despite the intensive effort in boosting tourism level in the country by the Indonesian government, Indonesia’s tourism sector’s contribution to the economy can be said to remain relatively low and why it may not significantly impact or affect income inequality level in Indonesia.

The R square value represents how much variations in income inequality level is explained by the regression line, which in this case are the explanatory variables mentioned in this study. The R square value according to the regression output is 0.88407 and this indicates that approximately 88.4% of the total variations in income



inequality levels or Gini coefficient in Indonesia is explained by the explanatory variables. The intercept (C) indicates the value of income inequality when the explanatory variables are 0. In this case, the C value is 1.116, which means that the value of income inequality or Gini coefficient when all of the explanatory variables are 0 is hypothetically shown as 1.116.

Table 3. Regression results - Brazil

Variable Coefficient Std. Error t-Statistic Prob. C –0.506920 0.003888 –130.3900 0.0000IDNFDI –0.000833 0.000960 –0.866808 0.4007IDNNET –0.002126 0.000429 –4.957774 0.0002IDNTOUR –4.09E–12 3.42E-12 –1.196044 0.2515R-squared 0.993844 Mean dependent var –0.563980Adjusted R-squared 0.992525 S.D. dependent var 0.045784S.E. of regression 0.003958 Akaike info criterion –8.032818Sum squared resid 0.000219 Schwarz criterion –7.834957Log likelihood 76.29536 Hannan-Quinn criter. –8.005536F-statistic 753.4118 Durbin-Watson stat 1.312632Prob(F-statistic) 0.000000

From the OLS results, the estimated equation can now be expressed as:

LGINI = β0 – 0.000833 (FDI) – 4.09E-12 (TOUR) – 0.002126 (INT) + ε

The result on Brazil also indicates that only the number of Internet users significantly affects the level of income inequality in the country at the 5% significant level. The coefficient of Internet users in Brazil is shown as -0.00213, which indicates that when the number of Internet users per 100 people increases by 1, the level income inequality is expected to fall by 0.00213% given that all other explanatory variables are held constant. Therefore, it can be said that a higher number of Internet users in Brazil is expected to be associated with a lower level of income inequality in the country. However, as it is shown that FDI and tourism receipts do not share any significant relationships with Brazil’s income inequality, this indicates that factors that may be associated with the lower Gini coefficient in Brazil are not captured by the model; some examples of these factors or variables are redistribution programs which may involve transfer payments or public expenditure programs with progressive tax systems. Other factors such as education distribution may also play a part in the lowering of income inequality in Brazil. Furthermore, despite the p-values that are higher than 0.05, FDI and tourism receipt in Brazil are shown to have negative coefficients which indicates that they share a negative yet insignificant relationship with income inequality level.



As opposed to the case of Indonesia, the negative coefficients indicate that income inequality tends to decrease with increased FDI and tourism receipts. Perhaps it can be said that FDI flow is more equally distributed within the country, thus allowing backward rural areas to progress and catch up with the urban areas using the capital generated from FDI inflow. The same reasoning may be applied with tourism receipts, in which tourism opportunities in the country may be more equally dispersed. The R square value of the regression of Brazil indicates that 99.384% of the total variations in the dependent variable is explained by the regression line.

4.1 T-Test At 5% Significance Level

The Tk value for all of the variables in this study is summarised in the following table.

Table 4. T-test results

Indonesia BrazilTc= 2.145 Sig. Level:

5% D.F = N – K – 1= 14Tc= 2.145 Sig. Level:

5% D.F = N – K –1 = 14Variable Tk Decision for H0 Tk Decision for H0

C -21.91137 Reject H0 -130.3900 Reject H0

FDI 5.147684 Reject H0 -0.866808 Do not reject H0

NET 3.435920 Reject H0 -4.957774 Reject H0

LTOUR -0.134307 Do not reject H0 -1.196044 Do not reject H0

The T-test confirms the results of the OLS regression output. It can be seen that the Tk value of FDI and NET in the case of Indonesia is bigger than Tc. The null hypothesis of these two variables can be rejected and it can be concluded that FDI and NET indeed share a relationship with income inequality. However, as the Tk value of LTOUR is lower than the specified Tc from the t-table, the null hypothesis cannot be rejected and it can then be concluded that LTOUR does not share statistically significant relationship with income inequality.

In the case of Brazil, only NET is shown to have a Tk value greater than the specified Tc. Therefore, we can reject the null hypothesis for this variable and conclude that NET indeed shares a significant relationship with income inequality in Brazil. However, as FDI and LTOUR in the case of Brazil does not have a Tk value greater than the Tc, we fail to reject their respective null hypotheses and therefore it can be concluded that FDI and LTOUR do not share a statistically significant relationship with income inequality.



4.2. F-Test at 5% Significance Level

Table 5. Summary of F-Test

Degrees of FreedomCountry F-Stat Numerator Denominator Fc (5%) Decision for H0

Indonesia 35.58874 3 18 3.16 Reject H0

Brazil 753.4118 3 18 3.16 Reject H0

It is observed that the F-statistics of both Indonesia and Brazil are shown to be well above the critical Fc value obtained from the F-table. Therefore, we can reject the null hypothesis of both countries and it can be concluded that the overall fit of the equations is relatively strong and both of the functions have coefficients that are highly significant in a joint setting.

4.3 Durbin Watson (DW) Test

In the case of Indonesia, the value of DW statistics according to the OLS output is 2.744; this value is shown to be higher than dU and according to the decision rules, if d > dU, we do not reject H0: ρ = 0. This indicates that there is no positive serial correlation for the case of Indonesia.

In the case of Brazil, the DW value from the OLS regression output table is shown as 1.312; this value is shown to be higher than dL but lower than dU. In other words, the value is shown as dL ≤ d ≤ dU and according to the decision rule, if dL ≤ d ≤ dU, the test is inconclusive. Therefore, it can be concluded that for the case of Brazil, the test for serial correlation is inconclusive.

Table 6. Multicollinearity - VIF Indonesia

Variable CoefficientVariance

UncenteredVIF

CenteredVIF

C 0.002595 39.89172 NAIDNFDI 3.60E-05 1.908052 1.435004IDNNET 1.15E-05 6.683267 3.396058IDNTOUR 1.11E-22 65.94381 3.969486

Brazil

Variable CoefficientVariance

UncenteredVIF

CenteredVIF

C 1.51E-05 17.36284 NABRAFDI 9.23E-07 9.268958 1.341973BRANET 1.84E-07 136.4030 60.30392BRATOUR 1.17E-23 226.7588 59.50969



As shown by the output tables above, it can be observed that the Centered VIF values for Indonesia are all below the value 5, thus indicating that there is less likely to be multicollinearity problem in the data set. However, in the case of Brazil, the Centered VIF values are shown to be higher than 5 for tourism receipts and Internet users variables, indicating a concern of multicollinearity in the case of Brazil.

Table 7. Correlation analysis

IndonesiaLIDNGINI IDNFDI IDNTOUR IDNNET

LIDNGINI 1.000000 0.792958 0.766073 0.794496IDNFDI 0.792958 1.000000 0.547114 0.425471IDNTOUR 0.766073 0.547114 1.000000 0.839007IDNNET 0.794496 0.425471 0.839007 1.000000

BrazilLBRAGINI BRAFDI BRATOUR BRANET

LBRAGINI 1.000000 0.090264 –0.990697 –0.995984BRAFDI 0.090264 1.000000 –0.051343 –0.125586BRATOUR –0.990697 –0.051343 1.000000 0.988840BRANET –0.995984 –0.125586 0.988840 1.000000

According to the output shown above, it can be seen that for the case of Indonesia, FDI, Internet users and tourism receipts are all positively correlated with the level of income inequality (Gini) and the correlations are moderately strong in general.

In the case of Brazil, it can be seen that the number of Internet users and tourism receipts are very strongly and negatively correlated with income inequality but the FDI level is shown to be very weakly correlated with the level of income inequality. Although there is no correlation with a value of precisely 1, the correlation coefficient of Brazil’s Internet users and tourism receipts to its income inequality indicates a strong negative relationship of -0.99, which is nearly 1 in absolute value. The results of the Granger Causality test are outlined in the table below.

Table 8. Granger CausalityIndonesia

Probability Causality Inference0.0367 NET ➡ LGINI Unidirectional0.0291 NET ➡ TOUR Unidirectional



BrazilProbability Causality Inference0.0061 NET ➡ LGINI Unidirectional0.0415 FDI ➡ TOUR Unidirectional0.0272 FDI ➡ NET Unidirectional

According to the test, it can be observed that the variable NET is said to Granger Cause both LGINI and TOUR in the case of Indonesia. In other words, there are causality and unidirectional relationships between the number of Internet users and income inequality as well as tourism receipts. The causality relationship between NET and LGINI supports the result of the OLS that is previously mentioned in the earlier section of this chapter, in which NET and LGINI share a positive and significant relationship. With the support of this Granger Causality test result, it can now be concluded that there is a causal relationship between NET and LGINI. The rationale behind this is that an increase in the number of Internet users indicates that technology-related competency is becoming even greatly distributed to more parts of the population. As more people have access to the Internet and information, this allows them to acquire greater skillset and enhance their literacy that will boost their skill premium. The result of the causality relationship between NET and TOUR is also rational and justifiable; this is because an increase in Internet tends to increase information for upcoming or potential tourists and therefore the likelihood of enhancing tourism receipts is also greater.

In the case of Brazil, unidirectional relationships also exist between NET and LGINI, FDI and TOUR and FDI and NET. In other words, it can be said that the number of Internet users Granger Cause Income Inequality, whereas FDI Granger Cause tourism receipts and the number of Internet users. Similarly with the results of Indonesia, it can be seen that the number Internet users also shares a causal relationship with Brazil. Causality relationships also exist between inward FDI and the number of Internet users and tourism receipts. This indicates that the inward FDI in Brazil are likely to be allocated to the tourism industry and technology, which explains the causal relationship between the two variables.

4.4 Unit Root

To conduct the Unit Root test, the Augmented Dickey Fuller test was utilised and the level of significance of 5% was chosen. The result of the Unit Root test is summarised in the following table.

Table 8 (con’t)



Table 9. Unit Root

IndonesiaVariable Augmented Dickey Fuller (5%)

Level 1st Difference 2nd DifferenceRank

p-value p-value p-valueLGINI 0.387 0.01 – I0

FDI 0.0017 – – I0

TOUR 0.606 0.0013 – I0

NET 0.999 0.0001 – I0

BrazilVariable Augmented Dickey Fuller (5%)

Level 1st Difference 2nd DifferenceRank

p-value p-value p-valueLGINI 0.2618 0.071 0.0550 –FDI 0.3873 0.158 0.0214 I2

TOUR 0.222 0.0434 – I0

NET 0.0487 – – I0

For Indonesia, it can be seen that LGINI, TOUR and NET achieve stationary on first difference, whereas FDI in the case of Indonesia is already stationary at level. In the case of Brazil, however, LGINI remains non-stationary even after second differencing under the 5% level of significance as its p-value is still greater than 0.05. As we fail to reject the null-hypothesis for LGINI in the case of Brazil, we can assume that the variable LGINI has unit root and remains non-stationary. FDI of Brazil is shown to achieve stationary after second differencing, whereas TOUR and NET achieves stationary at first differencing and at level, respectively.

5. CONCLUSION

It can be concluded from this study that the effects of globalisation related-factors to income inequality generally varies from country to country. In Indonesia, it can be concluded that there are positive and significant relationships between the number of Internet users and inward FDI at the 5% significance level. The relationship between Internet users and income inequality is also shown to be a causal and unidirectional relationship as well. Therefore, it can be concluded that an increase in the level of FDI and number of Internet users in Indonesia tends to be associated with a higher level of income inequality.

On the other hand, in Brazil, only the number of Internet users is shown to share a significant relationship with income inequality; this relationship is also proven to



be a unidirectional and causal relationship as well. However, the coefficient of the number of Internet users in Brazil (NET) is shown to be negative, which is opposite from the result obtained in the case of Indonesia. Therefore, it can be concluded that a higher number of Internet users in Brazil tends to cause income inequality in the country to fall or decline.

The differences in results may be caused by the different structures, distributions and relative importance of each variable to the two different countries. For example, the level of income inequality tends to decrease with higher Internet users in Brazil perhaps due to better distribution of Internet access in the country as compared to Indonesia, in which FDI and technology are predicted to be mainly concentrated in urban areas especially in the Javanese regions and the capital city Jakarta. This is why the effect of FDI and income inequality in Indonesia has the opposite relationship and effect on income inequality, in which higher inward FDI and Internet access tend to be associated with higher income inequality instead.

The constant debate on the good and the bad of globalisation never seems to cease. However, it can be said that the process of globalisation itself is an irreversible force that will influence the global economy. Therefore, instead of attempting to stop the seemingly undesirable effects of globalisation on a country, what can be done is to maximise the opportunities it presents and adopt policies which will support a country’s economy in the context of our dynamic and ever-changing world.

As shown by the contradicting results of the effect of Internet users on income inequality of Brazil and Indonesia, it can be seen that technology indeed does not have to worsen the level of income inequality in a country and what matters instead, is how the government of each country manages the inflow of technology and FDI. For example, in the case of Indonesia, the project allocation on the Java island, which is one of the major five islands in the country, amounted to a total of Rp 70,0 trillion whereas projects allocated to four other major islands only amounted to a total of Rp 50,4 trillion in 2014 (Indonesia Investing Coordinating Board, 2015). In addition, four out of five cities where most of the FDI is channeled to are also located in the islands of Java. Therefore, one of the proposed policies that may alleviate the problem of income inequality in Indonesia is to redistribute project or FDI allocation into other areas aside from the Javanese regions in a more even manner. The government can achieve this by providing incentives for foreign investors to invest such as tax exemptions and free trade zones in less FDI-endowed areas and boost the growth of those particular areas.

Moreover, one of the reasons why Indonesia still struggles to attract FDI is also due to the complicated administration procedures and difficulties in obtaining licenses to obtain the necessary business permits and licenses. Therefore, shortening the necessary procedures may smoothen the flow of FDI into the country as well.

In addition, one of the secrets to Brazil’s success in reducing income inequality level that can be adopted by Indonesia is the effective redistribution and training



programs conducted by government. The Brazilian government is noted to spend and pump substantial investment in ICT and its Bolsa Familia social-welfare program that aids the less fortunate has also reported great success in supporting school enrollment. Consequently, these attempts exerted by the Brazilian government may have helped in bridging the wage gap due to skill premiums between highly-skilled and low-skilled workers, as more workers are now able to attain greater skillsets due to these programmes. The Indonesian government may attempt to adopt similar policies to improve access to technology in less developed areas as well, which in turn may enhance the skillset of workers located in those areas and reduce the wage gap. Expanding the research by including inequalities in other spectrums or other dimensions such as education and healthcare inequality into the focus of the study will also be interesting as it provides further insight into the inequality problems within different countries.

Open Access: This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0) which permits any use, distribution and reproduction in any medium, provided the original author(s) and the source are credited.

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