Trade in Services and Income Inequality in Africa: The Role of Information and Communication Technologies

doi:10.21203/rs.3.rs-2540366/v1

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Trade in Services and Income Inequality in Africa: The Role of Information and Communication Technologies

https://doi.org/10.21203/rs.3.rs-2540366/v1

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With the advent of the technological revolution, trade in services is of paramount importance in major international trade issues. This paper analyses the effects of trade in services in general and ICTs in particular on income inequality in 46 African countries over a decade 2009–2018. It focuses in particular on the channels through which trade in services affects income inequality. The results from the estimates show a robust and significant inverse relationship between trade in services and income inequality in Africa. Greater participation in international trade in services reduces inequalities in intra-national income distribution in Africa. The results suggest that the interaction of trade in services and internet penetration has a positive impact on income inequality in Africa.

JEL Classification: F10, D31, C23

International Business

Trade in services

ICT

income inequality

panel data

Africa

International trade has been and remains one of the main drivers of global economic growth. As the structure of international trade has continued to evolve, trade in services now plays an important role. Its share in world GDP has increased from 6 per cent in 1960 to 13 per cent in 2017 (Luong, 2020). Moreover, Gervais and Jensen (2019) suggest that the potential welfare gains from trade liberalization in services are significant. This increased importance of trade in services prompts an effort to understand the drivers of this surge. There are several possible channels to explain the increase in services trade, such as time zone (Christen, 2017), geography (Anderson et al. 2014), or information and communication technologies (Luong, 2020). The latter includes the Internet, wireless networks, mobile phones and other means of communication. The rapid development of ICTs has transformed the world in the age of the digital economy, bringing significant benefits (World Bank 2016).

The relationship between trade, inequality and poverty within countries is not without controversy. Free trade in goods leads to an equalisation of factor prices between countries according to the factor price equalisation theorem. In the traditional literature on the neoclassical growth model, capital and labour play the central role as the two main factors of production. From the conventional single- sector neoclassical growth theory, international linkages are unimportant, but from trade, they are the key determinants. With the wave of trade globalization in recent decades, developing economies have rapidly integrated into the world economy. These countries have increased the volume of trade and foreign capital flows. This growth has had an impact on macroeconomic aggregates including gross national income. During this period of increased integration, changes have been observed in income inequality within these developing economies.

A few studies have examined the impact of international trade on income distribution (Hartmann, 2019; Feenstra and Hanson, 1996; Goldberg and Pavcnik, 2007; Topalova, 2005; Meschi and Vivarelli, 2009). Following the empirical studies of Katz and Murphy (1992) and Lawrence and Slaughter (1993), a large body of work attempts to estimate the impact of international trade on income inequality. But existing empirical studies focus only on international trade in manufactured goods, while very few studies examine the specific case of international trade in services. These sectors have traditionally been treated in economics as purely non-negotiable sectors. Over the past two decades, the telecommunications revolution has changed the fundamental nature of the activities that can be traded: while some services are non-negotiable, others have become tradable at no additional cost, regardless of distance (Head et al, 2009). According to the OECD, services account for more than 20 per cent of international trade. Services are highly affected by outsourcing: Multinational enterprises are moving their headquarters from activities outside high-wage centres in OECD countries to back-office services in places such as India (OECD, 2004; UNCTAD, 2004).

Trade in services contributes to new opportunities for the economy, creating more jobs and making a significant contribution to the GDP of the economy. More attention is being paid to services exports in developing countries' growth strategy papers. On the import side, they can help improve the performance of domestic firms through greater competition. Besides, services imports provide access to international best practices, investment capital, better skills and technology in the economy.

In Africa, trade in services grew faster than the trade in goods between 2005 and 2017, at an average rate of 5.4% per year according to the World Trade Organisation (WTO) data1. Commercial presence is the main mode of supply worldwide, accounting for nearly 60% of turnover in 2017. It is also important to underline that the added value of services accounts for almost half of the value of international trade in goods and services. According to the WTO, world imports of commercial services grew by 7.4% in 2018, with Africa leading the way at 13%, accounting for 3.1% of world imports of commercial services. Exports grew by 7.7%, a rate slightly lower than that of world merchandise exports in 20182.

Since a large part of the trade in services comes from foreign direct investment, it also contributes to the inflow of capital, which is a scarce factor in African economies. Wonyra and Okah Efogo (2020) show that foreign direct investment (FDI) favours trade in services to the extent that the entry of FDI into Africa is accompanied by a need to import services. Therefore, the importance of services in international trade can no longer be discounted. However, despite its importance, little research has been conducted to explicitly examine the impact of trade in services on income inequality in developing economies. In this research, we analyse the effects of trade in services on income inequality in Africa. Specifically, we identify the channels through which trade in services affects income inequality in African economies and explore the relationship between trade in services and income inequality in Africa.

This article fills the gap in the economic literature regarding the measurement of the effects of trade in services, the importance of which was only revealed with the Uruguay Round negotiations with the creation of the World Trade Organisation (WTO) in January 1995. It emphasises the need to examine the impact of trade in services on income inequality separately from that of trade in goods on income inequality in Africa with a focus on the role of ICTs. The issues raised are both important and relatively new, particularly in the context of developing and less advanced economies. This is important because of the pattern of development observed in many emerging economies, where services, rather than the industrial sector, have become dominant in terms of shares in production, employment and international trade. As developing countries become much more integrated into the world economy with globalization, it is, therefore, less important to understand how the growing share of services in international trade affects income inequality.

The rest of the article is presented in three main sections. In addition to the introduction and conclusion, the first section presents a review of theoretical and empirical work on the issue of trade in services and its effect on income inequality. The second section presents the materials and method of analysis. The third section analyses and discusses the main findings.

[1] World Trade Organization database, http://stat.wto.org/Home/WSDBHome.aspx?Language=E 4

[2]https://afriqueexpansionmag.com/2020/04/07/le-role-crucial-du-commerce-des-services-dans-la-croissance-economique-africaine-lexemple-canadien

2.1 Trade in services: what are the special features?

Most previous studies assessing the link between trade and inequality or growth consider global trade without distinguishing between goods and services. While these two sectors do not impact the growth of the world's economies in the same way. This raises the question of how goods are different from services and whether we should treat trade in goods differently from trade in services. This issue has also received some attention in the economic literature.

The economic literature differentiates services from goods through the following characteristics: intangibility, non-exploitation, invisibility and transience. But services are considered to be heterogeneous so that not all services meet these characteristics. Hill (1977) argues that goods and services are distinct and belong to two different categories. This author stresses that consumption and production of services should take place simultaneously, as services cannot be stored, unlike goods. Melvin (1989) goes beyond this definition and points out that Hill's thesis is limited to services that require physical contact between the consumer and producers. For Melvin, some services can be incorporated into an object such as a diskette and videotapes. Therefore, for such services, production and consumption can be separated. Due to the technological revolution, these services which do not require physical proximity between producer and consumer are increasing rapidly. Stern and Hoekman (1987) point out that the service can be complementary to, substitute for, or unrelated to trade in goods. Therefore, for services to be traded, they must be embodied in objects, information flows or movements of people. All these characteristics of services determine how trade in services will take place.

Under the General Agreement on Trade in Services (GATS), international trade in services can be classified into four modes:

Mode 1: Cross-border supply in which no physical contact between the producer and consumer is necessary and it is the service that crosses the border by internet, telephone, fax, etc.
Mode 2: Consumption abroad in which the consumer of services travels to the place where the services are provided.
Mode 3: Commercial presence abroad, i.e. the service provider provides services by establishing a commercial presence in another country through branches or subsidiaries.
Mode 4: Movement of natural persons in which the individual travels temporarily to the country of the consumer to provide the services.

This World Trade Organization (WTO) definition has some observations. Labour mobility (where trade in services is conducted through Mode 4) and investment (where trade in services is conducted through Mode

3) are key to analysing the impacts of trade in services. Also, the determinants of trade differ from one sector to another.

The determinants of trade in services may also be different from those of trade in goods. Barriers to trade in services are different and higher than barriers to trade in goods. For example, restrictions on factor mobility and investment can be major barriers to international trade in services. Also, barriers to trade in services are sometimes difficult to quantify and measure. Many services cannot be traded across borders, like goods, and therefore tariff barriers are not important in the case of these services. Trade in services also tends to be reduced by the regulatory laws of countries that deny access to both foreign and domestic service providers. These regulatory laws often vary between and within the different sectors of countries. Concerning economies of scale, it is common to assume an increasing return to scale for goods, while this may not be true in the case of services, as services are assumed to be very heterogeneous. Some services may not exhibit increasing returns to scale. All these characteristics make the trade in services a different and complex concept from trade in goods.

Barriers to trade in services consist mainly of regulations that limit the access of foreign suppliers to domestic markets or subject them to less favourable treatment than local suppliers. These barriers or obstacles arise under the principles of market access (Article 16) and national treatment (Article 17) of the GATS. Concerning the principle of market access, Article 16 stipulates that according to the four modes of supply identified in Article 1 of the GATS, each Member shall accord to services and service suppliers of any other Member treatment no less favourable, Most Favoured Nation (MFN) treatment. The restrictions take the form of limitations on : the number of service suppliers, whether in the form of numerical quotas, monopolies, exclusive service suppliers or the requirement of an economic needs test; the total value of service-related transactions or assets, whether in the form of numerical quotas or the requirement of an economic needs test; the total number of service transactions or the total quantity of services produced, expressed in specified numerical units, whether in the form of quotas or the requirement of an economic needs test; the total number of natural persons who may be employed in a particular service sector, or who may be employed by a service supplier and who are necessary for, and directly involved in, the supply of a specific service, in the form of numerical quotas or the requirement of an economic needs test; measures which restrict or prescribe specific types of legal entity or joint venture through which a service supplier may supply a service; foreign equity participation, expressed as a maximum percentage limit on foreign ownership of shares, or on the total value of particular foreign investments or aggregate foreign investments.

The principle of treatment, for its part, implies that each Member shall accord to services and service suppliers of any other Member, in respect of all measures affecting the supply of services, treatment no less favourable than that it accords to its own like services and service suppliers. Thus, barriers may be said to exist where there are conditions/limitations of competition in favour of the Member's services or service suppliers as compared to like services or service suppliers of any other Member.

The impact of trade in services on income inequality depends on the nature of domestic and foreign factors of production. Where substitutability exists, trade in services will provide access to foreign factors, which will reduce the demand for and output of domestic factors. Consequently, this would reflect a negative impact on income inequality. However, if domestic and foreign factors are complementary, access to foreign factors due to trade in services will increase the demand for domestic factors and have a positive impact on domestic factor returns as well as on income inequality. Some of the services are also used as intermediate inputs in the production of certain goods, so trade in these services may also have important implications for the distributional consequences of trade in services. It can therefore be stressed that there are many characteristics of trade in services that may have important implications for the distributional consequences of trade in services.

2.2 Trade in services and channels of income inequality

In this section, we identify the different channels through which trade in services can impact on income inequality in Africa. Knowledge of these channels could help to reduce income inequality due to trade in services in Africa.

Firstly, we note that some services are used as intermediate inputs in the production of certain goods and services. Trade in services will make these services available at a lower cost and increase their efficiency, which in turn will increase the productivity of the goods and other services sector. The increase in productivity will further affect factor returns and thus inequality in income distribution.

Second, there is an impact on income inequality through technological spill overs, the creation of competition in the economy, etc. since most transactions in trade in services take place through foreign direct investment (sales of foreign affiliates). The presence of these foreign service providers is likely to be concentrated in the most dynamic regions of the country, neglecting rural areas, thus increasing inequality between rural and urban areas of the country.

Third, trade in services leads to labour mobility that can affect income inequality in African countries. For example, if the services exported by this mode are skill-intensive, this will lead to a shortage of skilled labour in the economy in question, and the brain drain is known to have an impact on the distribution of income in the economy. Conversely, if the workers whose services are exported are unskilled or semi-skilled, this will create more jobs for unskilled workers and thus help to reduce inequalities in the economy.

Finally, trade in services can also have an impact on income inequality through the growth channel. Larger exports of services will contribute to higher growth in the economy, which in turn affects income inequality through the Kuznets curve relationship between growth and inequality.

Given all the above, it seems clear that there are channels through which trade in services can increase income inequality and some channels through which it can reduce it. To identify the contours of this phenomenon and to better understand the effects of trade in services on income inequality to formulate better economic policy recommendations, it is necessary to review the empirical literature on the subject.

2.3 Overview of empirical work on the effects of trade in services on income inequality

Numerous studies have been conducted on the relationship between openness to trade and income inequality. Although most of them analyse the case at the global level, the approaches are quite similar. However, these studies focus mainly on trade in goods. This can be explained by the lack of detailed and quality data on trade in services. Most empirical studies have observed that trade in services leads to increased inequality within countries.

Using a sample of 10 OECD countries for the period 1980–2005, Cassette et al (2012) examined the short- and long-term relationship between international trade in services and income inequality. These authors find that trade in services has long-term effects on income inequality in the OECD. Moreover, they find that this impact on income inequality is not only between the highest and lowest incomes but also between the median and low incomes. The work of Amoranto et al (2011) on the impact of services sector liberalisation on employment and wages in the Philippines over the period 1991 to 2004 shows that liberalisation negatively impacts the most vulnerable and less educated populations. On the other hand, it creates greater employment opportunities for higher-skilled men than for women, based on the assumption that value-added effects arise from the liberalization of services that subsequently increase productivity in other sectors and influence changes in average wages across industries.

Gourdon, Maystre and Melo (2008) attempt to prove the importance of factor endowments in analysing the relationship between trade and income inequality. They find that openness to trade is associated with increased income inequality in high-income countries and that it reduces inequality in low-income countries. Furthermore, greater openness to trade is associated with greater inequality in countries with abundant capital and high skills. There is ample evidence in the literature of a positive relationship between trade openness and inequality in developed countries.3 They obtain different estimates of the responsiveness of inequality to trade and financial openness, depending on the estimation sample, the statistical techniques adopted and the control variables included in the models. Lim and McNelis (2016) use a panel of 1992 annual data for about 40 countries with GDP per capita below the world average and find a Gini index elasticity of about 0.05, although it roughly doubles for low-income countries and becomes negative for upper-middle-income countries. Bumann and Lensink (2016) report an average elasticity of the same inequality index to financial openness, as measured by the Chinn and Ito index (2008), close to 2, considering 106 countries over the period 1973–2008 and controlling for inflation, trade openness, financial depth, GDP per capita, education and demographic indicators. They also adopt a generalized method of moments (GMM) estimator to address the possible endogeneity of some explanatory variables and conclude that financial liberalization improves income distribution in countries with higher financial depth.

Dabla-Norris et al, (2015) study about 100 countries, including the most advanced economies, over the period 1980–2012, and estimate an elasticity of the Gini index that is negligible concerning trade openness and 0.05 concerning financial openness. Their reference model includes among the control variables: education, financial depth and some indicators on the structure of the population and the labour market, other than public expenditure. Roser and Cuaresma (2016) estimate a model on a panel of 32 developed countries over the last four decades using the Generalized Method of Moments (GMM) and find a Gini index elasticity to trade openness of about 0.01, controlling for public spending, GDP growth, GDP per capita and the structure of international trade. Furceri and Loungani (2015) study the impact of capital account opening on inequality and find that liberalization of domestic financial systems can increase income inequality in both the short and medium-term.

Jaumotte, Lall and Papageorgiou (2013) study the impact of globalization on income inequality. Globalization is divided into two categories: trade openness and financial openness. They find that globalization as a whole has a minor effect on income inequality. This is because its components have countervailing effects: trade openness reduces income inequality, while financial openness exacerbates it. It is technological change that contributes most to increasing income inequality.

Stylised evidence on OECD countries suggests that trade and financial openness reduces growth differences between countries, but not income inequality and that the effects of finance are greater in high-income countries (D'Elia & De Santis, 2019). These authors also find that, nevertheless, low- and middle-income countries benefit more from international trade. The results of the estimates indicate that trade openness has significantly improved conditions in low-income OECD countries, mainly in the short and long term, in line with catch-up theory. It has also reduced inequality, but only in low- and middle-income countries. Differently, financial openness has had a positive and significant impact only in the short term on middle- income countries and has increased income disparities within countries in the short term in low-income countries and the long term in high-income countries.

2.4 Information and Communication Technologies and Income Inequalities

Information and communication technologies can contribute to the reduction of inequalities both within and between countries. However, there needs to be a clear focus on building human capacity and providing those at the bottom of the ladder with the skills to develop, otherwise, ICTs (like any other vehicle for development) risk widening the digital divide rather than narrowing it. It is when ICTs are used to bring information and knowledge and thus economic and social progress to the most disadvantaged groups, including people with disabilities, girls and women, that ICT technologies show their full potential to reduce inequalities.

Assessing the link between finance and inclusive development, the Global Financial Development Report 2014 (GFDR, 2014) shows the positive impact of financial inclusion on poverty. According to the report, financial inclusion can significantly reduce poverty and improve shared prosperity. The report also stresses that efforts to promote inclusion must be well designed.

In their study on the link between inclusive development (i.e. inequality and poverty) and mobile banking in 93 developing countries, Asongu and Nwachukwu (2018a) found a positive correlation between mobile banking and inclusive development when a certain threshold of the Human Development Index is reached. Using interactive ordinary least squares, the results show that the increasing use of mobile phones to pay bills has a positive relationship with the quality of growth in low- and middle-income countries. However, the correlation with inequality in Latin American countries is negative. The authors also find a negative association with the increasing use of mobile phones to receive and send money and poverty in two regions, namely: "Asia and the Pacific" and "Central and Eastern Europe".

Asongu and Odhiambo (2017) used interactive quantitative regressions to study the correlations between inclusive development and mobile banking throughout the conditional distribution of inclusive development. They found that, in most cases, increasing mobile banking mechanisms up to certain thresholds would also improve the quality of growth and reduce inequalities in the upper part of the distribution of inclusive development. They recommended that encouraging the use of mobile banking applications will play an important role in addressing the challenges of inclusive growth, inequality and poverty in developing countries. Asongu (2015a) complemented the qualitative and theoretical literature with empirical evidence on the income redistribution effects of mobile phone penetration in 52 African countries. The results suggest that mobile phone penetration is pro-poor.

Asongu et al (2018) assess the relationship between information technology and financial access using the generalised method of moments and quantitative regression as empirical techniques in a panel of 162 banks. They find that the diffusion of information mitigates the negative effect of market power on the quantity and price of loans. The authors provide policy thresholds at which the modulating effect of information diffusion on market power can improve access to finance in Africa. To determine whether mobile money can help firms reduce financial access constraints, Gosavi (2018) uses data from the World Bank's Business Survey Program for the year 2013 in Eastern Sub-Saharan Africa. The results show that businesses that use mobile cash have the advantage of easily obtaining lines of credit or loans. Further analysis reveals that businesses using mobile money are productive compared to other businesses in the sub-region. Abor et al (2018) used an instrumental variable technique and a seemingly unrelated probit model to assess two main objectives: (i) to investigate whether access to a wide range of financial services improves the ability of households to live in dignity and (ii) to analyse whether mobile telephony stimulates pro-poor development by helping households to reduce poverty and distribute consumption efficiently. The results show that financial inclusion and mobile phone penetration significantly reduce the likelihood of a household becoming poor and reduce per capita consumption of food and non-food items. By estimating the economic growth model using time series and cross-national data from 62 countries over the period 2000–2006, Yousefi (2011) found that the effect of economic growth on ICTs varies across different income groups. The author concluded that ICTs play an important role in the growth of high and upper-middle-income groups. However, ICTs did not contribute to the growth of lower-middle-income.

The studies and findings mentioned above suggest that there is no consensus on the effect of international trade in general, or trade in services in particular, on income inequality. Moreover, to our knowledge, there is little work in Africa. Therefore, a study-specific to the African continent should be conducted as part of this study to find out the effect in Africa. Moreover, unlike many previous studies, most of which were conducted in the 1990s and covered only two decades. This research reexamines the causal relationship between trade in services and income inequality in Africa over the last decade using longitudinal data.

We start from a specification of a linear panel model. Drawing on the work of Roser and Cuaresma (2016) who have worked on the causes of income inequality. The specification is presented by the following Eq. (1).

$${\text{l}\text{n}Income\_Ineq}_{it}={\alpha }_{0}+{\alpha }_{1}{{\text{l}\text{n}Income\_Ineq}_{it} }_{i,t-1}+ {\beta }_{1}{\text{l}\text{n}\text{T}\text{i}\text{S}}_{i,t}+{\beta }_{2}{\text{l}\text{n}\text{I}\text{C}\text{T}}_{i,t}+\sum _{j}{\delta }_{j}{X}_{ijt}+{\epsilon }_{i,t} \left(1\right)$$

The interaction model is represented by the Eq. 2.

$${\text{l}\text{n}Income\_Ineq}_{it}={\alpha }_{0}+{\alpha }_{1}{{\text{l}\text{n}Income\_Ineq}_{it} }_{i,t-1}+ {\beta }_{1}{\text{l}\text{n}\text{T}\text{i}\text{S}}_{i,t}+{\beta }_{1}{\text{l}\text{n}\text{I}\text{C}\text{T}}_{i,t}+ {\beta }_{3}{\text{l}\text{n}\text{T}\text{i}\text{S}\text{*}\text{I}\text{C}\text{T}}_{i,t}+\sum _{j}{\delta }_{j}{X}_{ijt}+{\epsilon }_{i,t} \left(2\right)$$

The term error is broken down into individual and idiosyncratic errors.

$$\epsilon it = \vartheta i +{\mu }_{it} \left(3\right)$$

In this equation, the dependent or explained variable is the logarithm of the income inequality noted lnIncome_Ineq measured by the Gini index. The variable lnTiS measures the logarithm of total trade in services as a percentage of GDP. ICT captures the infrastructures of telecommunications and is measured by the use of internet and telephone. X represents the vector of control variables that also affect income inequality. In this paper, we have drawn on the work of Dabla-Norris et al, 2015; Bumann and Lensink, 2016 using variables such as access to education, health, and electricity. To capture the cross-cutting role of telecommunications services, we interact with the trade in services variable with the internet access variable. i = 1, 2, …, N and t = 1, 2, … , T. ϑi is the individual error term and is IID (0, σ2) while μit is IID (0, σ2) are all independent of each other. The data come mainly from the World Bank database (World Development Indicators, 2020). They cover ten years from 2009 to 2018 and are based on a sample of 46 African countries3.

The model is estimated using dynamic panel data estimation techniques. The inclusion of the lagged dependent variable as one of the explanatory variables suggests a problem of endogeneity (Ametoglo et al, 2018; Wonyra and Okah Efogo, 2020). One of the solutions is the use of the Generalised Moment Method (GMM). (Arellano and Bond, 1991; Arellano and Bover, 1995; Blundell and Bond, 1998). The GMM estimator addresses issues of dependent variable shifts, unsupported fixed effects, independent regressor endogenous effects, as well as the presence of heteroskedasticity and autocorrelation between and within individuals or countries (Roodman 2009). However, the GMM method is only asymptotically effective for 'small T and large N'. Thus, it is not suitable for small samples. An alternative method is the least squares estimator with corrected dummy variables (Least Square Dummy Variable Corrected, LSDVC). The method calculates the corrected bias of the LSDV estimators for the standard autoregressive panel data model using the bias approximations in Bruno (2005 a), which extends the results by Bun and Kiviet (2003), Kiviet (1999) and Kiviet (1995). This method is an appropriate dynamic panel data technique for small samples where GMM cannot be applied effectively. We can rewrite the dynamic panel data model expressed in Eq. 1 as follows:

$${y}_{it}=\gamma {y}_{i,t-1}+{X{\prime }}_{it}\beta +{\eta }_{i}+{\epsilon }_{it} \left(4\right)$$

Where yit is the dependent variable, X_it is the set of independent variables, ηi is an unobserved individual effect, and ε_it an unobserved disturbance white noise.

The model can therefore be rewritten in the following way:

$$y = W\delta + D\eta + \epsilon \left(5\right)$$

With $W = \left[y\right(-1\left) \right|X];$ W is the matrix of independent variables and the lagged dependent variable, D is the matrix of individual dummy variables (NTxN), η is the vector of individual effects (Nx1), δ is the vector of coefficients (kx1), and ε the usual error term.

The LSDV estimator is as follows:

$${\delta }_{LDSV}={\left({W}^{{\prime }} AW\right)}^{-1}{W}^{{\prime }}Ay \left(6\right)$$

where A represents a transformation that captures individual effects.

Chignon and Kiviet (2003) describe the bias associated with the LSDV estimator as:

$$E\left({\delta }_{LDSV}-\delta \right)= {c}_{1}\left({T}^{-1}\right)+{c}_{2}\left({{N}^{-1}T}^{-1}\right)+{c}_{3}\left({{N}^{-1}T}^{-2}\right)+O\left({{N}^{-2}T}^{-2}\right) \left(7\right)$$

In their Monte Carlo simulations, Bun and Kiviet (2003) and Bruno (2005a) examine the three possible nested approximations of LSDV bias, which in turn are extended to the first, second and third terms of Eq. (4)6.

The GVSL-corrected estimator (GVSLC) is equivalent to:

LSDVC = ${\delta }_{LDSV}-{B}_{i}$, i = 1,2,3 (8)

In this article, we correct for them the most complete and precise (B3 in chignon and from Kiviet (2003) and Bruno (2005 a) ratings). We perform the bias correction with the Anderson-Hsiao estimator. (Ametoglo et al, 2018).

[3] Algeria, Angola, Benin, Botswana, Burkina, Burundi, Cape Verde, Cameroon, Chad, Congo, Côte d'Ivoire, Democratic Republic of Congo, Djibouti, Egypt, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea Bissau, Equatorial Guinea, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, Somalia, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe.

This section presents the main findings of our study. Figure 1 below presents the relationship between trade in services and income inequality in Africa. This figure allows us to classify the countries, the subject of the study, into three broad categories. The first category of countries consists of Cape Verde with a very high share of trade in services in GDP (more than 50%) and a relatively low GINI rate in the country. This could be explained by the quality of institutions and stable macroeconomic management giving priority to the development of human capital and efficient use of external financing focused on results. The second category of countries includes countries such as Somalia, Zambia, Benin, Uganda, Botswana, Malawi, Mali, Mozambique and Madagascar that have a high Gini index while the share of trade in services in GDP remains low. The last category concerns other countries that are predominantly below the median line with lower income inequality, so these countries have a low share of trade in services in GDP. There is a need for African countries to rethink their trade policies by orienting them more towards trade in services, a sector that employs the face of trade globalisation.

Table 1 below shows the results of the LSDVC regressions with the GINI index as the dependent variable. Overall, our coefficients have the expected signs and are significant. All the models in Table 1 show that the effects of trade in services on income inequality are negative and significant at the 1% threshold. In other words, an increase in the volume of trade in services as a share of GDP contributes to a reduction in income inequality in Africa. These findings reinforce the conclusions of the previous figure and validate the last transmission channel described in the literature review (increased services exports lead to an increase in the level of growth in the economy, which in turn leads to a reduction in income inequality). This result is consistent with the findings of Gourdon et al (2008) which show that greater openness to trade is associated with an increase in the incomes of the upper class in countries with highly skilled workers and capital.

Looking at the second model, it can be seen that investments in road infrastructure reduce income inequality. Moreover, the more people have access to basic infrastructure such as electricity, the more inequalities are reduced in Africa. These results confirm the first transmission channel for infrastructure. These results also confirm the work of Cassette et al (2012) which examines the short- and long-term relationship between international trade in services and income inequality. Investment in related infrastructure is very important to foster the role of trade in services in growth and inequality reduction. Indeed, it is in response to this that UNCTAD's most recent reports focus on proposing ways to unleash the potential of the services sector to foster growth and transformation in Africa (UNCTAD, 2018; UNCTAD, 2015).

Model 3 highlights the interaction variable between trade in services and Internet penetration in Africa. When this variable is included in the model, it is found that the impact of trade in services on the reduction of income inequality increases significantly. Other interesting results are also obtained in this model. It is found that the interaction of trade in services and the internet penetration rate has a positive but not significant impact on income inequality in Africa. This confirms the third channel relating to labour. Starting from the observation that trade in services in Africa is driven by mode 3, trade presence, with the influx of foreign direct investment (Wonyra and Okah Efogo, 2020), the latter generally provides employment and therefore provides sources of income to nationals of the countries receiving these investments. Furthermore, it is important to stress that the use of telecommunications services facilitates the business environment through the digitalization of economies and means of production. It should therefore be emphasised that the opening up of telecommunications markets in Africa at the beginning of the 2000s has encouraged the development of new trades, particularly business services, tourism, repair services and activities related to information and communication technologies. In most of the works that reflect the vision of emergence in African countries, the increase in international trade and the promotion of exports occupy a prominent place. Some works prescribe that African countries should focus on tradable and value-added services such as tourism (UNCTAD, 2018; UNCTAD, 2015).

However, it seems clear that Africa is only marginally exploiting the potential of this sector. Precisely, the dynamics of the expansion of this sector are not stable. Exports of services, in nominal terms, grew by an average of 9% per year between 2005 and 2009, while imports grew at a rate of 17%. In 2011 and 2012, the growth of service exports and imports slowed to 4% and 12% per year respectively. This slowdown can be explained by weak liberalization in the services sector, in line with the results of some work which shows that the more restrictive regulations a country has, the less it imports and the less it exports (Andrenelli et al., 2018; OECD, 2015). This can also be explained by the lack of competitiveness and underdevelopment of the formal services sector. Reducing inequality through the promotion of trade in services will require action on the competitiveness indicators of the services sector, including regulatory flexibility to attract more investment, improving human capital through investment in education and health to make quality labour available.

Table 1

Regression results using the LSDVC method
VARIABLES	(1)	(2)	(3)
Ln (Gini Index lagged)	-0.237***	-0.250***	-0.239***
	(0.0526)	(0.00488)	(0.0526)
Ln (Trade in services as % of GDP)	-0.0671***	-0.0621***	-0.0897***
	(0.00938)	(0.0161)	(0.0135)
Ln (real GDP per capita)		-0.0208	-0.0185
		(0.0167)	(0.0510)
Ln (Health expenditure as % of GDP)		0.0147***	0.0130***
		(0.00303)	(0.000196)
Ln (Proportion of the population with access to electricity)		-0.0207	-0.0193
		(0.0207)	(0.0119)
Ln (Transport infrastructure, rails)		-0.0347***	-0.0354***
		(0.00674)	(0.00516)
Government Effectiveness		0.0323	0.0341
		(0.0260)	(0.0299)
Control of corruption		-0.00287	-0.00428
		(0.0370)	(0.0400)
Ln (official development assistance)		-0.0179***	-0.0133***
		(0.00172)	(0.00463)
Ln (% of the population using the Internet)		0.0217
		(0.0305)
Ln (Mobile phone subscription)		0.00292
		(0.0360)
Ln (Education expenditure)			-0.0362
			(0.0269)
Ln (Trade in services*Internet use)			-0.0205*
			(0.0142)
Number of observations	414	413	413
Number of ids	46	46	46

Note: Standard deviation in brackets, *** p < 0.01, ** p < 0.05, * p < 0.1. Source: Authors, result of estimates.

This paper examines the effect of trade in services on income inequality in Africa. Issues of the income distribution have been at the heart of debates in development issues around the world in recent decades. At the same time, there has been an increase in trade in general and services in particular with the advent of the technological revolution. Empirical analyses of the link between trade and income inequality become particularly difficult because of the endogenous nature of trade. This problem has been addressed in this paper by estimating the relationship between the Gini inequality index of African countries and trade in services. The results from the estimates indicate a robust and significant inverse relationship between trade in services and income inequality in Africa. Greater participation in international trade in services reduces inequality in the intra-national distribution of income. The strong negative relationship between trade in services and inequality cannot be attributed to the fact that countries with a low Gini index engage more in trade in services. Finally, growth provides a channel through which trade reduces inequality by increasing both initial income and subsequent growth.

Given all the above, political and economic decision-makers must implement appropriate policies to address the above-mentioned problems. Specifically, policymakers should promote human resource development that would improve the quality of the labour force by providing quality education and training. Given the current trend of increasing demand for skilled labour in developing countries, an increase in the supply of skilled labour would reduce income inequality. It is also important to stress the importance of a well-functioning and flexible labour market, where workers with better skills can find and obtain appropriate jobs. It is important to note that public policies in Africa must focus on income redistribution policies to achieve balanced growth. Finally, in the framework of trade policies at the national, sub-regional, continental and multilateral trading system levels, African countries must promote the development of the service industry by making their regulations more flexible and place particular emphasis on trade presence (mode 3), including the attractiveness of foreign direct investment by improving the regulatory environment for business.

Data availability

The data that support the findings of this study are available from World Bank database at https://databank.worldbank.org/source/world-development-indicators

Conflict of interest

The authors declared that they have no conflict of interest.

Funding Statement

This research received no specific grant from any funding agency, commercial or not-for-profit sectors.

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Trade in Services and Income Inequality in Africa: The Role of Information and Communication Technologies

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Abstract

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1. Introduction

2. Literature Review

2.1 Trade in services: what are the special features?

2.2 Trade in services and channels of income inequality

2.3 Overview of empirical work on the effects of trade in services on income inequality

2.4 Information and Communication Technologies and Income Inequalities

3. Material And Method

4. Results And Discussion

5. Conclusion

Declarations

References

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