The effect of foreign direct investment of China on the employment of North Africa

(1)

1

University of Amsterdam

Faculty of Economics and Business

The Effect of Foreign Direct Investment of China on

the Employment of North Africa

Name: R. Aguelmous

Student Number: 11018593

Bachelor track: Economics and Finance

Thesis supervisor: Ms. N.J. Leefmans

Date: 26-06-2018

(2)

2

Statement of Originality

This document is written by Student Rachid Aguelmous who declares to take

full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and

that no sources other than those mentioned in the text and its references have

been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision

of completion of the work, not for the contents.

(3)

3

Abstract

The aim of this paper is to analyse the relationship between Chinese Foreign Direct Investment (FDI) and employment in 4 North African (NAn) countries (Algeria, Egypt, Morocco and Tunisia). In doing so the focus is put on whether the effects of Chinese FDI differ on total employment and total youth employment. Investigating the relationship between (Chinese) FDI and youth employment is exactly what this research contributes to the existing literature on FDI and employment.

To analyse the effect of Chinese FDI on employment in North Africa (NA), two analyses are conducted. The first analysis is a descriptive analysis that is related to several important variables that focus on (un)employment and (Chinese) FDI which are available for the period 2006-2016. The second analysis is a cross-country regression analysis that tests the relationship between Chinese FDI and total (youth) employment by using annual panel data for the period 1991-2016 and by applying OLS.

The descriptive analysis demonstrates that the 4 NAn countries have high unemployment rates and high youth unemployment rates. The analysis demonstrates that Chinese FDI flows toward the 4 NAn countries are mostly allocated to unskilled labour-intensive industries, while FDI flows from other countries (except China) into NA were mostly allocated to higher-skilled services sectors. The regression results show that Chinese FDI has not enhanced total employment in the 5 African countries, since the relationship is insignificant. In addition, Chinese FDI does have a significant negative effect on total youth employment in the 5 African countries. The main finding of this paper can be summarized as follows. Chinese FDI is not very effective in tackling the unemployment problems in NA and generating the necessary employment in NA, probably due to its focus on sectors using unskilled labour, whereas a large fraction of the unemployed in NA concerns skilled labour.

(4)

4

List of Figures and Tables

Figure 1: Total Unemployment in North Africa ( % of total labour force)

Figure 2: Total Employment to Population ratio in North Africa (% for ages 15-64) Figure 3: Total Youth Unemployment in North Africa (% of total labour force ages 15-24)

Figure 4: Total Employment in Sectors in North Africa (% of total employment) Figure 5: Total net FDI flows into North Africa (% of GDP)

Figure 6: Total net Chinese FDI flows into North Africa (in millions of US dollars) Figure 7: Total Gross Capital Formation in North Africa (% of GDP)

Table 1: Overview of variables in the regression analysis and their expected sign

Table 2: Total Chinese FDI flows into North Africa per sector (2006-2016, millions of US dollars)

Table 3: Regression results

Table 4: Summary of the regression analysis including the signs and significance found

List of Main Abbreviations

BRICs: Brazil, Russia, India and China CBE: Central Bank of Egypt

CGIT: China Global Investment Tracker FDI: Foreign Direct Investment

GDP: Gross Domestic Product HFDI: Horizontal FDI

ILO: International Labour Organization NA, NAn: North Africa, North African NSBC: National Statistical Bureau of China OLS: Ordinary Least Squares

VFDI: Vertical FDI

(5)

5

1. Introduction

Worldwide flows of Foreign Direct Investment (FDI) have significantly increased from approximately $400 billion in the mid-1990s to approximately $1.75 trillion in 2016 (UNCTAD, 2017). Most of the flows of FDI come from developed countries. Since 2009, approximately half of worldwide FDI is going to developing economies (Krugman, Obstfeld, & Melitz, 2015). Most of the FDI flows in the world can be characterized as Brownfield FDI1 (Krugman et al., 2015).

One of the fastest-growing FDI segments are flows from China towards other developing countries, especially to Africa, which increased heavily since China adopted its ‘Going Out’ strategy in 1999 (Mourao, 2017). In 2006, China’s FDI flows to the rest of the world accounted for $16.1 billion (UNCTAD, 2007). In 2016, this increased massively to $183 billion (UNCTAD, 2017). According to the National Bureau of Statistics of China (NBSC), China’s FDI flows into Africa increased from approximately $519 million in 2006 to approximately $2.4 billion in 2016. Although this trend received considerable interest since the beginning of the century, the scientific research mostly focused on Sub-Saharan Africa. The effect of Chinese FDI on North Africa (henceforth NA) has however had not much attention by scientific researchers.

This is especially interesting, since the North African (henceforth NAn) economies are more developed than those in Sub-Saharan Africa (Pecoraro, 2010). This means that this paper can focus on the region that is more closely related to the Asian Tigers in terms of economic performance. The Asian Tigers also partly relied on FDI for their economic development. In addition, employment creation in the formal sector is an important aspect of the development of a country. By examining the effects of Chinese FDI on the employment of NA, the paper can contribute to the existing research of how African countries can follow the path of the Asian Tigers. Although NA mostly receives FDI from Western countries, China’s FDI flows into NA have been increasing more than other FDI flows (Liu, 2014).

NA is characterized as a region with high unemployment rates of approximately 10 to 15 % in the period 2006-2016 as indicated in figure 1.

1_{Brownfield FDI refers to a foreign firm acquiring a firm in the recipient country or merging with a firm in the}

(7)

7 Figure 1

Source: Graph constructed by author based on data from World Development Indicators, World Bank

Chinese FDI flows into NA could possibly help the NAn economies in their development path through solving the unemployment problems that are structural to these economies, especially youth unemployment and the unemployment of the educated population. For example, in 2016 the average youth unemployment rate in NA was 29.3 % (Figure 3, page 20), much higher than overall unemployment. High youth unemployment is problematic as the population of the ages 15-24 in NA represents 20% of the total population in NA (Barsoum, Wahby, & Sarkar, 2017). According to data in 2013 which are the most recent data for an NAn country, 31.1% of the total unemployed in Egypt were people who finished their tertiary education and for people who finished their secondary education the number was 47.1% . On the other hand, for people who finished their primary education or did not follow any education, the number was equal to 4.1% (Barsoum et al., 2017). This illustrates that unemployment is mostly present under the more skilled people.

The Chinese investments however could also lead to an increase in unemployment due to an increase of Chinese manufacturing firms in NA which enhances competition in the domestic market (Alden & Aggad-Clerx, 2012). Because the effects of FDI flows from China could both have a positive or negative effect on employment in NA, the effect of Chinese FDI on employment in NA is worth investigating (Pigato, 2009). So the goal of this paper is to examine the effect of Chinese FDI on the employment of NA. By doing so, this paper can give some insights to NAn policymakers about how to respond to Chinese FDI when it comes

to job creation and job destruction. Therefore, the following research question is formulated: 0 2 4 6 8 10 12 14 16 18 20

Total Unemployment in North Africa

(% of total labour force)

Algeria Egypt Morocco Tunisia

(8)

8

How do the flows of Foreign Direct Investment (FDI) of China into North Africa affect the employment of North Africa (Algeria, Egypt, Morocco and Tunisia)?

To answer the research question, part 1 will give a literature review which is divided in a theoretical part, a review of the qualitative literature and a review of the quantitative literature of the topic. The theoretical part will discuss the theoretical framework of the channels through which FDI affects employment, while both the qualitative and quantitative literature concern the empirical literature. In part 2, the data collection and analysis will be discussed. The paper combines two types of data analysis in part 2. First, a descriptive analysis of important variables related to (un)employment and FDI in NA will be given for the 4 countries for the period 2006-2016. This is useful for interpreting the results of the regression analysis, which is the second analysis. The regression analysis will include 2 Ordinary Least Squares (OLS) regressions on 5 African countries by using panel data from 1991 to 2016. The dependent variables in the 2 OLS regressions are the employment to population ratio and the employment to population ratio of the youth population. In the last part of the paper, the conclusion will be given and the limitations of the paper will be discussed to provide suggestions for future research.

(9)

9

Part 1: Literature

2. Literature review

This section explains the literature that is related to the effects of FDI on employment. First, the theoretical framework of the topic will be described. Then the qualitative research about (Chinese) FDI and its effect on employment will be described. At last, the quantitative research that is related to the effect of (Chinese) FDI on employment will be described.

2.1 Theoretical literature

The effect of FDI and international trade on employment began to play a more prominent role since the 1970s, when increasing FDI outflows from Europe were followed by high unemployment rates in Europe (Pflüger, Blien, Möller, & Moritz, 2013). Given the even stronger growth of FDI in recent years, as indicated in the introduction, the topic became more important.

One theory that looks at FDI, is the theory of the multinational corporation. This theory makes a distinction between horizontal FDI (HFDI) and vertical FDI (VFDI). HFDI means that the affiliate of the multinational corporation takes on the whole production process for the local market where the affiliate is located while with VFDI, the affiliate takes on a part of the production process in the market where it is located (Krugman et al., 2015). The effects of HFDI on employment are not that straightforward. With HFDI, the affiliate usually employs mostly labour from the country of origin of the multinational. However, the coordinating activities of the affiliate are more done by domestic labour (Pflüger et al., 2013). The employment effect of the recipient country is zero in the first case due to the fact that no additional jobs are created but jobs are also not necessarily lost, while the employment effect of the recipient country is positive in the second case. The effects of VFDI on employment are more straightforward, as VFDI is more focused on using those factors that are relatively cheap and in the case of relatively cheap human capital, this could have a positive effect on domestic employment (Pflüger et al., 2013).

The Core-periphery model by Krugman is one theory that takes into account the interdependency between the location of the affiliate firm and employment. His theory focused on the potential effects that the location of the firm could have on the region where it is located. The theory specifies that the affiliate firm can give rise to all kind of technological

(10)

10

spillover effects which in turn could benefit the productivity of other domestic firms (Pflüger et al., 2013). By increasing FDI inflows, the supply of capital in the economy where the affiliate is located increases which in turn can increase domestic investment. Through the positive relation between domestic investment and aggregate demand, the employment rates will increase as firms need more domestic labour to take on the increase in aggregate demand (Abdouli & Hammami, 2017).

Another positive effect of FDI inflows on employment in the recipient country is attributed to globalization. When a foreign firm acquires a firm in the recipient country, the domestic firm that is acquired is better connected to the world market. A better connection to the world market can lead to more demand for the products of the domestic firm than was the case before the takeover. To take on the increase in demand, the firm needs to employ more domestic labour which enhances employment in the recipient country.

On the other hand, FDI inflows could also have a negative effect on employment through an increase in competition in the domestic market. An increase in competition of foreign firms negatively affects the productivity of domestic firms. As these domestic firms see their profits shrank, they must reduce the labour force in their firms (Jude & Silaghi, 2016).

In sum, the theoretical literature indicates several channels through which FDI may either have a positive or a negative effect on employment in the recipient country.

2.2 Qualitative literature

The paper that is close to the descriptive analysis of this paper is from Alden & Aggad-Clerx (2012). They conducted a descriptive analysis on the effects of Chinese FDI on employment creation in NA for the period 2003-2012. They investigated China-NAn investment patterns across NA and assessed the impact of Chinese FDI on employment creation in a case study including Algeria and Egypt. They provided a descriptive analysis of the following variables: total trade between China and NA, total Chinese FDI flows into NA, total unemployment, total youth unemployment and the youth labour force participation rate. They found that Chinese FDI leads to employment creation in NA in the unskilled labour-intensive industries but not in skill-intensive industries due to the fact that most of Chinese FDI is allocated to the unskilled labour-intensive industries. They concluded that Chinese FDI could have more potential impact by creating employment in skill-intensive industries. This employment creation can solve the problem of unemployment of the growing educated population in NA,

(11)

11

which is a structural problem of unemployment in NA. For example in Algeria in 2011, 42 % of the university graduates in Algeria were unemployed.

Castel, Mejia, & Kolster (2011) also conducted a descriptive analysis to the case of NA, but then focusing on the effects of FDI from Brazil, Russia, India and China (BRICs). They described the following variables for the period 1980-2010: total trade between the BRICS and NA, total and sectoral FDI flows from the BRICs into NA. They found that FDI flows from the BRICs into NA, which is dominated by FDI from China, can tackle the unemployment problems in NA because of the increase in FDI of the BRICs in skill-intensive industries in NA. However, they also recognized that still the largest proportion of FDI is allocated to unskilled labour-intensive industries. In addition, most of Chinese investment projects are conducted by Chinese labour, meaning that there is little job creation in NA by Chinese FDI.

Renard (2011) investigated the effects of China’s trade and FDI on the economic development of Africa. She also conducted a descriptive analysis by focusing on the following variables for the period 1994-2007: total trade between China and Africa, total and sectoral FDI flows from China into Africa. She found that the effect of Chinese FDI on Africa is quite diverse, because it depends on the importance of the sectors of the country. African countries where the manufacturing industry employs relatively more labour, have suffered from increased Chinese FDI as the new Chinese manufacturing firms have led to more competition in the local market. This resulted in lower employment in the manufacturing industry (Renard, 2011). The author concluded that Chinese FDI could have an overall positive effect on employment if the African governments provide regulations about the minimum levels of local employment that each Chinese-owned firm should have. The regulations could indirectly generate jobs.

The analyses above demonstrated that there is much debate about the effects of (Chinese) FDI on employment. Depending on the country and the sector invested in, the studies find either a negative effect, no effect, or a positive effect on employment.

2.3 Quantitative literature

The largest proportion of the quantitative literature related to the topic focuses on the effects of total FDI on total employment in developing economies. Moreover, the results of quantitative research done so far signify that there is little consensus about the effects. This

(12)

12

section will describe the research that is done and its results. The variables used in the quantitative literature, their signs and significance can be found in Appendix 8.

Most of the studies related to the topic, focus on the effect of FDI on total employment. For example, Ngwakwe (2017) used time series data from 1991 to 2004 to analyse the relationship between FDI inflows and employment in South Africa by performing an OLS regression. He found that FDI inflows in South Africa had an insignificant negative effect on employment in South Africa. According to him this is due to a reduction in domestic productivity, because of the increased competition of foreign firms.

But also a positive relationship between FDI and employment was found. Habib & Sawar (2013) applied the Johansen cointegration technique and the Granger causality test to study the effect of total FDI inflows on the employment rate of Pakistan for the period 1970-2011. They found that FDI inflows had a significant positive effect on the employment rate in Pakistan which is due to an increase in employment opportunities for the local population. The research of Habib & Sawar (2013) also demonstrated that causality only runs from FDI to the employment rate and not vice versa.

Megbowon, Ngarava, & Mushunje (2016) also applied the Johansen cointegration approach but for the case of South Africa for the period 1980-2016. They found an insignificant positive long run relationship between FDI and employment. The three authors found no causality links between FDI and employment in South Africa. They indicate that the results of their regression are due to the low level of technology spillover effects of FDI flows into South Africa and the weak effects that FDI has on the demand in the domestic market of the recipient country. The authors could not explain why this was the case with South Africa.

The research of Nahidi & Badri (2014) used panel data for several countries. They studied the effect of FDI on employment in the 6 countries over the period 2002-2010. They found that FDI had a significant positive effect on the employment to population ratio in the 6 countries, although the effect is small. According to them, this is mainly due to the development phase of the countries. The more developed a country is, the better it can absorb the positive effects of FDI.

Jude & Silaghi (2016) studied the effects of FDI on employment growth in 20 transition economies in Central and Eastern Europe by using panel data of the period 1995-2012. Their research is quite different compared with the research above, because they estimated a dynamic labour demand model that includes a fixed effect estimator and a GMM estimator. They found an short run negative effect of FDI on employment growth in the 20 transition economies that is significant, although the effect is small. According to Jude &

(13)

13

Silaghi (2016) this is probably due to the increased competition of foreign firms that negatively affects the productivity of domestic firms.

There are also studies that focus on the effect of sectoral FDI on employment. Inekwe (2013) used time-series data of the period 1990-2009 for Nigeria to analyse the relationships between FDI in the manufacturing & services sectors and employment by using the Johansen cointegration technique. His results demonstrate that FDI in the manufacturing sectors has a significant positive relationship with the employment rate in Nigeria. They state that this is due to the high share of unskilled labour in Nigeria which can be easily employed in the manufacturing industry. However, they found that FDI in services sectors have a significant negative long run relationship with the employment rate in Nigeria. They stated that this is due to the availability of low human capital, meaning that foreign firms employ more skilled labour from the country of origin. Inekwe (2013) also found that causality only runs from FDI in the manufacturing and services sectors to the employment rate by using the Granger causality test.

Coniglio, Prota, & Seric (2015) analysed the relationship between FDI and employment by using firm-level data in 2010 from 19 Sub-Saharan African countries. They applied the OLS method. They found a significant positive relationship between FDI and employment in the 19 sub-Saharan African countries because foreign firms (Chinese and other foreign firms) generate more employment than domestic firms, and more particularly they found a significant positive relationship between FDI and employment in labour-intensive industries due to the unskilled labour that is readily available to be employed.

Lipsey, Sjöholm, & Sun (2013) also found a significant positive relationship between FDI and employment growth in the manufacturing industry but for the case of Indonesia. They studied the effect of acquisition of domestic firms by foreign firms in the manufacturing industry by controlling for several plant characteristics. They applied the OLS method on data from 1975 to 2005. They found that most of the growth effects of employment already commenced when the domestic firm was acquired by the foreign firm but when a foreign firm is acquired by a domestic firm, the significant positive effect disappears again. The authors attribute the positive effect to the increase in innovation in the company and because the firms are better connected to the world market after the foreign takeover.

Another research that takes into account the sectoral distribution of employment, is the research of Nyen Wong & Cheong Tang (2011). They studied the effect of FDI on employment in the manufacturing and services sectors in Singapore by using quarterly time-series data of the period 1997-2005. They applied the autoregressive distributed lag

(14)

14

framework. They found a significant positive relationship between FDI and employment in both the manufacturing and services sectors. According to them, this can be explained by the fact that Singapore matches the education of their labour force with the labour demands of the foreign firms investing in the country. In the study of Nyen Wong & Cheong Tang (2011), there is a two-way causality between FDI and employment in the manufacturing and services sectors. This is due to the high human capital that is available in Singapore, which attracts foreign companies.

The only study that has taken into account the effects of Chinese FDI on employment instead of total FDI on employment, is the study conducted by Miniesy & Adams (2016). They studied the effects of Chinese FDI on the employment of seven African countries, but then focusing on the local level by investigating several Chinese investment projects in the seven African countries. They used the net additionality estimation method, which is a method that focuses on the net effect of FDI on employment at the local level. The results demonstrate that Chinese FDI does have a positive effect on employment in each of the seven African countries, both at the national level and the local level and both direct and indirect. However, according to the two authors, the indirect effect could be more positive if the seven African countries invested more in education.

In sum, the largest proportion of the quantitative literature demonstrated that FDI does have a significant positive relationship on total employment or on employment in several sectors/industries, but there are also cases where the relationship is insignificant or significantly negative. The size of the significant positive effect varies from a small to a more substantial effect in the studies reviewed.

(15)

15

Part 2: Data Collection and Analysis

3. Variables included

This section gives an overview of the variables included in the descriptive analysis and the regression analysis. The variables in the regression analysis are divided in main variables and control variables. This section also considers the effect that is expected from the independent variables on the dependent variables in the regression analysis.

3.1 Variables in the descriptive analysis

The variables that are studied in the descriptive analysis of this case study are based on the studies of Alden & Aggad-Clerx (2012), Soumaré (2015) and Castel et al. (2011) where they applied a descriptive analysis to the following variables: the total net FDI inflows into NA, the total net FDI flows from China into NA (total and sectoral), total employment to population ratio, total unemployment and total youth unemployment.

The following variables will also be described as performed in the research of Nyen Wong & Cheong Tang (2011) and Soumaré (2015): employment in the agriculture sector, employment in the industry sector, employment in the service sector and employment in the manufacturing sector. The breakdown between the industry sector and the manufacturing sector is relevant due to the importance of both sectors in NA and because of the strong position of China worldwide in the manufacturing sector (Soumaré, 2011). The FDI and (un)employment variables are considered as these are the most relevant variables in the research of this paper and these variables provide useful background for interpreting the results of the regressions.

At last, the gross capital formation of each of the four NAn countries will be described as gross capital formation could have an effect on employment as the variable contains only domestic investment and no FDI (Nahidi & Badri, 2014).

3.2 Variables in the regression analysis

The main independent variables of interest that are specified in this paper are the total FDI flows from China and the total FDI flows from other countries (except China). Total Chinese FDI flows and total FDI flows from other countries did lead to a positive effect on employment in most, though not all, of the studies reviewed in 2.3. Therefore a positive

(16)

16

relationship is expected between these main independent variables of interest and the employment to population ratio.

Moreover, there should be an insignificant positive relationship between total Chinese FDI flows and the employment to population ratio for the youth. As already mentioned in 2.2, Chinese FDI does not create employment in skilled-intensive industries where most of the youth population in NA are searching for jobs, so that an insignificant relationship is expected. For the other main variable, a significant positive relationship should be expected with the employment to population ratio of the youth population. This is due to technology transfers which can create more skill-intensive jobs for the growing educated youth population in NA. Here, it is assumed that other countries except China consist mostly of Western countries and that the firms of Western countries are more innovated than those from China.

The first control variable of interest is the gross capital formation which is based on the research of Nahidi & Badri (2014). As the variable contains domestic investment it should lead to higher aggregate demand. This will have a positive effect on both dependent variables as the employment rates will increase as firms demand more domestic labour to take on the increase in aggregate demand.

Openness to trade is the second control variable of interest which is used as a control variable by Inekwe (2013) and which is expected to have a positive effect on both dependent variables as it can lead to more trade and exports which can increase aggregate demand. An increase in aggregate demand can lead to more demand for labour by firms.

Inflation on the other hand should have no effect on employment. This control variable could have a significant negative effect on employment in the short run by taking the Philips curve under consideration. But in the long run the relationship between inflation and the dependent variables is expected to be insignificant. This variable is based on the studies of Nahidi & Badri (2014) and Inekwe (2013).

The last control variable of interest is the growth rate of GDP per capita which is used by Habib & Sarwar (2013). This variable is expected to have a positive effect on both dependent variables. An increase in GDP per capita can lead to higher tax revenues as people gain in income. This increase in income can be used by the government to invest in more projects that focus on employment creation which are necessary for the youth population and the total population (Abdouli & Hammami, 2015).

The following table summarizes the variables used in the regression analysis and their expected sign.

(17)

17

Table 1: Overview of variables in the regression analysis and their expected sign Variable Employment to population

ratio

Employment to population ratio for the youth population

Total Chinese FDI + +/-

Total other FDI (except China)

+ +

Gross capital formation + +

Openness to trade + +

Inflation +/- +/-

GDP per capita growth + +

4. Descriptive analysis

This section first specifies the methodology of the descriptive analysis. Secondly, the characteristics of the data will be specified including what the sample is in the descriptive analysis and why this sample was constructed. At last, this section will display the descriptive analysis.

4.1 Methodology

The methods that are used in this paper are of great importance as it affects the validity of the research results. Especially as each data needs its own appropriate sample design and method. In the case of developing economies, this is of great importance as developing economies tend to have a lack of data for different variables.

The first part of the analysis is a descriptive analysis of the variables that are characterized in 3.1. The descriptive analysis can be described as a case study of four NAn countries, namely Algeria, Egypt, Morocco and Tunisia. This method is based on the previous research of Alden & Aggad-Clerx (2012). The descriptive analysis will discuss the differences and similarities between the four NAn countries for the period 2006-2016. Explanation of the use of the sample design of the descriptive analysis will be in 4.2. The data of the variables in the descriptive analysis will be described in several figures and tables to illustrate the trends more convenient. A descriptive analysis can provide background that is useful for analysing the results of the regressions.

(18)

18

4.2 Data and the sample design

The data that is used for the descriptive analysis consists of Algeria, Egypt, Morocco and Tunisia and consists of annual data for the period 2006-2016. Libya is not included in the sample because since 2011 Libya has a lack of data for several of the variables used in the descriptive analysis. This is mainly due to the unrest that followed after the outbreak of the Arab Spring. The sample period here differs from the sample period in the regression analysis. This is to keep the descriptive analysis more recent and more consistent as the period 1991-2005 consists of several lack of data of both the FDI and (un)employment variables for the four NAn countries.

Data of the employment variables in the descriptive analysis are obtained from the International Labour Organization (ILO) and the World Development Indicators (WDI). The Chinese FDI data and Chinese sectoral FDI data are obtained from the National Bureau of Statistics of China (NSBC) and China Global Investment Tracker (CGIT) respectively.

4.3 The analysis

4.3.1 Total unemployment in NA

Total unemployment is measured as % of total labour force. To consider the demographic trends, Appendix 1 and 2 display the total population of the ages 15-64 as % of the total population and the total population respectively for the 4 NAn countries.

Since 2006, Algeria, Egypt and Morocco followed a similar pattern of the unemployment rates with rates remaining at approximately 12 percent (Figure 1, page 7). Only in Algeria, the unemployment rate decreased in the period 2006-2016. In Egypt and Tunisia, the unemployment rate increased in the period 2006-2016, while in Morocco is stayed quite constant during the period 2006-2016. These high unemployment rates have remained high since 2006 due to the low economic growth in the three countries and due to the political unrest since 2011 (Barsoum et al., 2017). The case of Tunisia in 2011 is an outlier which can be due to the Arab Spring that reached its peak in Tunisia in 2011.

The case of Tunisia however is an exception and worth highlighting. According to Alden & Aggad-Clerx (2012), Tunisia should be in the best position when it comes to tackling unemployment as the country has the smallest population in NA and also decreasing fertility rates. However, they stated that FDI did not tackle this problem even though Tunisia is ranked in NA as the country with the best business climate

(19)

19 4.3.2 Total employment to population ratio in NA

Total employment is measured as a ratio compared to the total population. It is the proportion of a country’s population that is employed between the ages 15-64. The ages 15-64 are considered as the working-age population.

Figure 2

Source: Graph constructed by author based on data from WDI

The total employment to population ratio is under the 50 % for each of the four countries with Morocco ranking highest and Algeria ranking lowest (Figure 2). Compared with 2006, the employment to population ratio decreased steadily in Morocco and Tunisia in 2016. Only in Algeria and Egypt, the total employment to population ratio stayed relatively the same despite the increasing population in these two countries (Barsoum et al., 2017). According to Barsoum et al. (2017) these low ratios were driven by the low labour force participation rates of females in the region. NA has one of the lowest female labour force participation rates of all regions in the world (Alden & Aggad-Clerx, 2012).

4.3.3 Total youth unemployment in NA

Total youth unemployment is measured as % of the total labour force of the ages 15-24. The key determinant behind the high unemployment rates in the four countries are the high youth unemployment rates. Between 2006 and 2016, there have been some changes in the youth unemployment rate in Algeria, Egypt and Tunisia (Figure 3). Only in Morocco the youth unemployment rate stayed relatively constant. Morocco is the country with the lowest

30 32 34 36 38 40 42 44 46 48 50 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Total Employment to Population ratio in

North Africa (% for ages 15-64)

(20)

20

youth unemployment rate over the sample period. Moreover, compared to 2006, 3 out of the 4 countries had higher rates in 2016, namely Egypt, Tunisia and Morocco. Only in Algeria, the youth unemployment rate decreased when comparing 2016 to 2006. In 2016, the average youth unemployment rate was 29.3%, making NA the region with the second highest youth unemployment rate in the world. The case of Tunisia in 2011 is an outlier which can be due to the Arab Spring that reached its peak in Tunisia in 2011.

Figure 3

These rates are caused by the poor quality of the jobs for the youth population (Barsoum et al., 2017). Especially the disconnect between the education of the youth and the demands of the labour market is quite problematic (Alden & Aggad-Clerx, 2012). According to Soumaré (2015), this is one of the important aspects that introduced the Arab Spring in 2011 in NA.

4.3.4 Total employment in sectors in NA

Employment in the manufacturing sector, employment in the service sector, employment in the agriculture sector and employment in the industry sector are all measured as % of total employment and are provided as aggregate numbers for the four NAn countries as the NAn countries tend to have the same level of economic structure (Abdouli & Hammami, 2015). The industry sector contains manufacturing, resource extraction, construction and public utilities and the manufacturing sector contains the textile industry and other industries that

0 5 10 15 20 25 30 35 40 45 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Total Youth Unemployment in North Africa

(% of total labour force ages 15-24)

(21)

21 process or transform products.

Over the period 2006-2016, the population in the four countries were mostly employed in the services sector with an average employment rate of 42.5 % over the period in the service sector (Figure 4). Employment in agriculture has decreased since 2006 to 22.6 % in 2016. This is consistent with the development path of development economies. As a country goes further in its development path, less people tend to work in the agriculture sector. Employment in the manufacturing sector indicates almost no change which may be due to the strong position of China worldwide in the manufacturing sector (Brenton & Walkenhorst, 2010). The industrial sector on the other hand gained more employment.

Figure 4

Source: Graph constructed by author based on data from ILO and WDI

According to Alden & Aggad-Clerx (2012), employment in the services sector should be generated in skill-intensive sectors for the growing educated population in the four countries.

4.3.5 Total net FDI flows into NA

The total net FDI inflows in NA are measured as % of GDP. Total net FDI inflows as % of GDP has decreased in all the four NAn countries over the period 2006-2016 (Figure 5). This could be due to an increase in GDP in the period 2006-2016 for the four countries (Appendix 3), but also because of the first political protests that began around 2009 which could scare off foreign investors. In Egypt and Algeria, the shares were even negative in 2011 and 2015

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Total Employment in Sectors in North Africa

(% of total employment)

Manufacturing Agriculture Industry Services

(22)

22

respectively. In 2016, the number of all the four countries stood at 2%. Despite the shrank in the share of Egypt, Egypt remains the top flight destination for FDI inflows into NA (The Arab Investment & Export Credit Guarantee Corporation , 2017).

Figure 5

According to Noutary & Luçon (2016), most of the total net FDI inflows in the period went into the following sectors: energy, banking, real estate, tourism and consulting. So FDI flows from other countries (except China) into NA is mostly allocated to higher-skilled services sectors. They also state that France and the United States were the largest investors in the four countries in the period 2006-2016.

4.3.6 Total Chinese FDI flows into NA

The total net FDI flows of China into NA and the sectoral data of these total net FDI flows of China are measured in millions of US dollars. Figure 6 reports the total Chinese FDI flows into NA. Annual data of Chinese FDI flows into NA after 2011 is missing for both Morocco and Tunisia2.

Total Chinese FDI into the four NAn countries is gaining more presence in the inflows of FDI of the four countries. Egypt for example reported an inflow of Chinese FDI of $ 119.3 million in 2016 compared with just $0.8 million dollar in 2006 according to the Central Bank of Egypt (CBE). Algeria also reported several increases of Chinese FDI flows. The country reported in 2006 an inflow of Chinese FDI of $98.9 million and this reached a record in 2014

2_{Note: Annual Chinese FDI data after 2011 is missing for Morocco and Tunisia as the NSBC does not report FDI}

outflows to every country in the world -2 0 2 4 6 8 10

Total Net FDI flows into North Africa

(% of GDP)

(23)

23

of $665 million dollar. However in 2016, Chinese FDI flows to Algeria shrank to - $99.8 million. Only Tunisia did not report significant numbers of Chinese FDI inflows. The country only has Chinese FDI data for the period 2006-2011 and this period was characterized with mostly Chinese FDI inflows of approximately -$1 million. Morocco lies quite behind Algeria and Egypt when considering the inflow of Chinese FDI. In the period 2006-2011, the country reported Chinese FDI inflows of approximately $ 10 million annually, but in the period 2012– 2016 China invested over $2811 million in Morocco (The Arab Investment & Export Credit Guarantee Corporation , 2017).

Figure 6

Source: Graph constructed by author based on data from NSBC and CBE

Over the whole sample period it is clear that three sectors are of particular interest for China, namely the energy sector, the transport sector and the real estate sector (Table 2). According to Pigato (2009), China’s interest in the energy sector is related to China’s increasing demand for fossil fuels.

-100 -50 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Total net Chinese FDI flows into North Africa

(in millions of US dollars)

(24)

24

Table 2: Total Chinese FDI flows into NA per sector (2006-2016, millions of US dollars)

Sector Algeria Egypt Morocco Tunisia

Metals $ 420 $ 940 - - Logistics - $ 150 - - Real Estate $ 4230 $ 2900 $ 230 $ 110 Energy $510 $ 12970 $ 230 - Transport $ 14560 $ 4010 $ 930 - Utilities $ 160 $ 990 - - Tourism $ 1470 - - - Entertainment $ 160 - - - Other $ 280 $ 230 - -

Source: Graph constructed by author based on data from CGIT

4.3.7 Total gross capital formation in NA

Total gross capital formation in NA is measured as % of GDP and contains domestic

investment and no FDI.

Figure 7

The increase in the total gross capital formation as % of GDP in Algeria and its difference with the other three countries is the most striking result over the period 2006-2016 (Figure 7). Morocco reported a small increase in its gross capital formation as % of GDP.

0 10 20 30 40 50 60 20062007200820092010201120122013201420152016

Total Gross Capital Formation in North

Africa (% of GDP)

(25)

25

Egypt and Tunisia reported the smallest numbers over the period 2006-2016. These were also the only countries with a decrease in the gross capital formation as % of GDP when comparing 2016 to 2006.

5. Regression analysis

This section first specifies which method is used in the regression analysis and the reasons behind it. Secondly, the models that are used in the regression analysis of the paper will be discussed and also how the variables in the regression analysis are measured. Further on, the characteristics of the data will be specified including what the sample is and why this specific sample was constructed. At last, the results of the regression analysis will be displayed and analysed.

5.1 Methodology

In this part of the analysis, the relationship between Chinese FDI and employment in NA will be empirically tested. This is done by using dynamic panel data on 5 African countries (Algeria, Egypt, Nigeria, South Africa and Sudan) for the period 1991-2016. Explanation of the sample design and why it differs from the sample design in the descriptive analysis will be given in 5.3. Dynamic panel data is useful as it can better measure cross-country effects which makes it more suited to study the dynamics of changes in the variables used (Nahidi & Badri, 2014).

The dynamic panel data will be tested with OLS. It is a method that is quite comprehensive due to its multiple use in research papers and it can lead to unbiased estimates when using smaller samples, as was the case with the research of Coniglio et al. (2015). The OLS method can still generate biased estimates due to simultaneous causality. However, the correlation matrix indicates that the correlation between the dependent variables and independent variables is quite low (Appendix 10). Moreover, by using lagged values on the independent variables, simultaneous causality can be reduced even more.

The Breusch-Pagan test proves that there is no heteroscedasticity present. The Ramsey RESET test proves there is no omitted variable bias present.

5.2 The models and measurement

The first model that is tested in the regression analysis, is a log-log model. This is due to the expected non-linear relation that might exist between the dependent variable and the

(26)

26

independent variables (Inekwe: 2013, Nahidi & Badri: 2014). Model 1 in this paper looks as follows:

L(EMPLi,t)= 𝛼 + 𝛽1 ∗ 𝐿(𝐹𝐷𝐼_𝐶𝐻𝐼𝑁𝐴𝑖,𝑡 − 1) + 𝛽2 ∗ 𝐿(𝐹𝐷𝐼_𝑂𝑇𝐻𝑖,𝑡 − 1) + 𝛽3 ∗

𝐿(𝐷𝑂𝑀_𝐼𝑁𝑉𝑖,𝑡 − 1) + 𝛽4 ∗ 𝐿(𝑂𝑃𝑖,𝑡 − 1) + 𝛽5 ∗ 𝐿(𝐼𝑁𝐹𝐿𝑖,𝑡 − 1) + 𝐵6 ∗ 𝐿(𝐺𝐷𝑃_𝐺𝑅𝑂𝑊𝑇𝐻𝑖,𝑡 − 1) EMPLi,t is the total employment to population ratio for country i (i=1,..,N) at time t (t=1,…,T), 𝛼 is the constant, FDI_CHINAi,t-1 are the total net FDI flows from China into country i at time t-1 in constant 2010 US dollars, FDI_OTHi,t-1 are the total net FDI flows from other countries (except China) into country i at time t-1 in constant 2010 US dollars, DOM_INVi,t-1 is the total gross capital formation for country i at time t-1 in constant 2010 US dollars, OPi,t-1 is the degree of openness of country i at time t-1 by measuring the total trade as % of GDP, INFLi,t-1 is the inflation rate for country i at time t-1 by taking the inflation rate from the consumer prices and GDP_GROWTHi,t-1 is the GDP per capita growth for country i at time t-1. The independent variables are stated in lagged values to reduce simultaneous causality. The independent variables related to FDI and domestic investment are measured in constant 2010 US dollars to mitigate the effect of inflation.

The second regression model is almost equivalent to model 1, instead that the dependent variable is now the employment to population ratio of the youth population. This is especially relevant in the case of NA due to the high unemployment rates of the youth population, with rates from 25 % to 35 % in both Algeria and Egypt between 2006 and 2016, judging from figure 3 on page 20. Model 2 looks as follows:

L(EMPL_YOUTHi,t)= 𝛼 + 𝛽1 ∗ 𝐿(𝐹𝐷𝐼_𝐶𝐻𝐼𝑁𝐴𝑖,𝑡 − 1) + 𝛽2 ∗ 𝐿(𝐹𝐷𝐼_𝑂𝑇𝐻𝑖,𝑡 − 1) + 𝛽3 ∗ 𝐿(𝐷𝑂𝑀_𝐼𝑁𝑉𝑖,𝑡 − 1) + 𝛽4 ∗ 𝐿(𝑂𝑃𝑖,𝑡 − 1) + 𝛽5 ∗ 𝐿(𝐼𝑁𝐹𝐿𝑖,𝑡 − 1) + 𝐵6 ∗ 𝐿(𝐺𝐷𝑃_𝐺𝑅𝑂𝑊𝑇𝐻𝑖,𝑡 − 1) EMPL_YOUTHi,t is the employment to population ratio for the ages 15-24.

5.3 Data and the sample design

The data that is used for the regression analysis consists of 5 African countries (Algeria, Egypt, Nigeria, South Africa and Sudan). Morocco and Tunisia are dropped from the sample due to the lack of annual Chinese FDI data after 2011. Therefore, 3 African countries are added to the panel data in order to have sufficient observations. The countries are included in the sample of the regressions as they are either part of the region NA (Algeria and Egypt), or close to the region (Sudan) or because their economic performance is quite close to those of

(27)

27

the four NAn countries in terms of GDP per capita (South Africa and Nigeria). The data for the 5 African countries consists of annual data of the period 1991-2016. Appendix 4, 5, 6 and 7 display the variables total employment, total youth employment, total Chinese FDI inflows and total FDI inflows from other countries (except China) respectively for the 5 African countries for the period 1991-2016.

The panel data that is constructed still consists of several holes as some countries lack data in several periods. However, this is not on influence on the validity of the research as STATA finds the panel data strongly balanced. Data of all the variables are retrieved from the WDI, except the total net FDI flows from China are retrieved from the NSBC.

5.4 Results and analysis

Table 3 displays the results of the two regressions. This table can also be found in Appendix 11. Appendix 9 and 10 display the descriptive statistics of the variables and the correlations between the variables respectively. The correlations in Appendix 10 prove that there is no multicollinearity as the correlations between each of the independent variables is not close to - 1 or 1.

Model 1 indicates that Chinese FDI does have a positive relationship with employment, however this relationship is insignificant which contradicts the hypothesis in 3.2. In model 2, Chinese FDI does have a significant negative relationship with youth employment. This is not consistent with the hypothesis in 3.2 as was explained that Chinese FDI does not create skill-intensive jobs which are necessary for the youth population, so that there should be no effect. In fact, a 1% increase in Chinese FDI will result in a decrease of approximately 0.048% in youth employment, ceteris paribus.

In addition, there exists a significant positive relationship between FDI from other countries (except China) and total employment. This may be due to that Western countries, which present a large proportion of FDI from other countries, invest more in skill-intensive industries in the services sector which is the sector where most of the youth population demands employment. This significant positive relationship is found in model 2 at a 5% level. In both models, a 1% increase in FDI from other countries (except China) will result in an increase of approximately 0.048% in total employment and total youth employment, ceteris paribus.

In model 1, only the control variable inflation does have a significant relationship with employment at a 1% level. This relationship is positive which is not consistent with the hypothesis before as with the finding of Nahidi & Badri (2014) as they found a negative

(28)

28

relationship between inflation and employment. The result is consistent with that of Inekwe (2013). In model 2, a significant positive relationship between inflation and youth employment is also found which is not consistent with the hypothesis stated before.

Table 3: Regression results

Variable Model 1: L(EMPLi,t) Model 2: L(EMPL_YOUTHi,t) L(FDI_CHINAi,t-1) 0.0030547 (0.0063287) -0.0484005* (0.0116428) L(FDI_OTHERi,t-1) 0.0483073* (0.0108893) 0.0475416** (0.0200328) L(DOM_INVi,t-1) -0.0067278 (0.0179323) 0.159643* (0.0329897) L(OPi,t-1) -0.009634 (0.0475595) 0.2717733* (0.087494) L(INFLi,t-1) 0.1025214* (0.017521) 0.1626254* (0.0322329) L(GDP_GROWTHi,t-1) 0.111585 (0.0131513) 0.0217468 (0.0241941) R2 0.5070 0.4562 Adjusted R2 0.4608 0.4052 F 10.97 8.95 RMSE 0.10225 0.18811 N 71 71

*Significant at 1%. **Significant at 5%. ***Significant at 10%. Standard errors are between parentheses

The variables domestic investment, the degree of openness and the GDP per capita growth are all insignificant in model 1 and the signs of the variables domestic investment and the degree of openness are also contradictory compared to the hypothesis stated before and the results of Nahidi & Badri (2014) and Inekwe (2013).

On the other hand, model 2 has three control variables that have a significant effect on total youth employment at a 1% level. These are domestic investments, the degree of openness and inflation. As expected, the variables domestic investments and the degree of openness both have a significant positive effect on youth employment. Only the control variable GDP per capita growth is insignificant in both models.

(29)

29

6. Summary of the analyses

Table 4: Summary of the regression analysis including the signs and significance found Variable Model 1: L(EMPLi,t) Model 2: L(EMPL_YOUTHi,t)

L(FDI_CHINAi t-1) + -* L(FDI_OTHERi,t-1) +* +** L(DOM_INVi,t-1) - +* L(OPi,t-1) - +* L(INFLi,t-1) +* +* L(GDP_GROWTHi,t-1) + +

*Significant at 1%. **Significant at 5%. ***Significant at 10%.

The findings in both analyses can be summarized for the case of the four NAn countries. The descriptive analysis demonstrated that the four countries can be characterized as countries with low employment to population ratios, high (youth) unemployment rates and where most of the working population is employed in the services sector. In addition, Chinese FDI flows towards Algeria, Egypt and Morocco has increased in recent years. Most of the Chinese FDI is allocated towards more unskilled labour-intensive industries in the four NAn countries, while FDI flows from other countries (except China) into NA is mostly allocated to higher-skilled services sectors.

Judging from table 4, it becomes clear that there is an insignificant positive relationship between Chinese FDI and employment in the 5 African countries. The effect of Chinese FDI on youth employment presents a different story. Here, Chinese FDI does have a significant negative effect on youth employment in the 5 African countries.

The results of the regression analysis can be translated to the case of NA. The results demonstrated that Chinese FDI does not enhance total employment in NA as Chinese FDI is mostly allocated to unskilled labour-intensive industries while the growing (educated) population in NA demands more skill-intensive jobs.

Finally, the estimated results demonstrated that Chinese FDI does have a negative effect on youth employment in NA. The negative effect can be the result of Chinese firms that employ more skilled labour from the country of origin for the skilled-intensive jobs, where the educated youth population are employed, in the firms they acquire in the recipient country (Inekwe, 2013).

(30)

30

Part 3: Conclusion and Discussion

7. Conclusion

This paper has scrutinized the impact of Chinese FDI flows into NA on employment in NA by providing a cross-country perspective including four NAn countries. As was noted, Chinese FDI flows into the NAn countries is growing vastly. As the data on employment in NA demonstrate that there is an unemployment problem, both for total unemployment and total youth unemployment, it is of special interest to examine whether Chinese FDI flows to NA could tackle the unemployment problems in NA. Therefore, the following research question was formulated: How do the flows of Foreign Direct Investment (FDI) of China into North

Africa affect the employment of North Africa (Algeria, Egypt, Morocco and Tunisia)?

The descriptive analysis demonstrated that there is an urgent need for job creation in more skill-intensive industries in NA as the quality of most of the current jobs in the region is poor or unskilled. Chinese FDI however is mostly allocated to more unskilled labour-intensive industries, especially to the energy sector. On the other hand, FDI flows from other countries (except China) into NA is mostly allocated to higher-skilled services sectors.

The results of the regression analysis stated in table 3 demonstrate that there is a positive relationship between Chinese FDI and employment in the 5 African countries. However, the relationship was found insignificant. Interestingly, the results of the regression analysis demonstrated that there is a significant negative relationship between Chinese FDI and youth employment in the 5 African countries.

Overall, both analyses combined have demonstrated that Chinese FDI does not have a significant positive effect on generating employment in NA. This is likely due to the allocation of Chinese FDI flows into unskilled labour-intensive industries while a large part of the population of the region demands skill-intensive jobs. Therefore, the policymakers in NA should carefully consider the direction of Chinese FDI flows in specific industries to make sure that unemployment is tackled in an effective way. Moreover, the research of this paper contributes to the recent discussion on job creation in developing countries by suggesting that developing economies should thoroughly examine the heterogeneous impacts of FDI inflows of different countries.

(31)

31

8. Limitations and discussion for future research

As is the case with many developing economies, this paper also faced a lack of data of the four NAn countries. Especially the lack of annual data of Chinese FDI for Morocco and Tunisia after 2011 where problematic. Although the regression analysis still contained sufficient countries by adding 3 other African countries, the results would be more complete if the paper could only focus on NA. Therefore, future research should conduct a manner to find usable recent Chinese FDI data for Morocco and Tunisia.

In addition, this paper did not take into account the difference between Brownfield FDI and Greenfield FDI with respect to the Chinese FDI flows into NA due to the fact that the NSBC does not characterize the Chinese FDI flows into each country as Brownfield FDI or Greenfield FDI. This is again related to the unavailability of specific datasets.

Moreover, the lack of data also caused figures for Chinese FDI in the period 1991-1995 to be unavailable (Appendix 6). This is due to the fact that the NSBC started reporting Chinese FDI flows to other countries more thoroughly since 1996. This reduced the observations of the sample in the regression analysis from 130 to 71. Future research should therefore conduct research with more recent data to avoid the lack of Chinese FDI data before 1996.

Another limitation of the paper is the non-use of time dummies in the models in the regression analysis. This is due to the fact that most of the quantitative literature did not use time dummies. For future research, it would be better to include year dummies in order to correct the trend in order to avoid a spurious regression.

Although the above limitations affect the validity of the research conducted in this paper, the paper still gives an indication of how Chinese FDI flows into NA affect the employment of NA. Future research could give a stronger indication of this effect by adopting the above suggestions that are proposed.

(32)

32

9. Reference list

Abdouli, M., & Hammami, S. (2017). The Impact of FDI Inflows and Environmental Quality on Economic Growth: An Empirical Study for the MENA Countries. Journal of the

Knowledge Economy, 8(1), 254-278.

Alden, C., & Aggad-Clerx, F. (2012). Chinese Investments and Employment Creation in Algeria and Egypt. Economic Brief: The African Development Bank Group, 1-23. Barsoum, G., Wahby, S. & Sarkar, A. (2017). Youth and Employment in North Africa: A

Regional Overview. The International Labour Organization. Retrieved from

http://www.ilo.org/wcmsp5/groups/public/---africa/---ro-addis_ababa/documents/meetingdocument/wcms_577306.pdf

Brenton, P., & Walkenhorst, P. (2010). Impacts of the Rise of China on Developing Country Trade: Evidence from NA. African Development Review, 22(1), 577-586.

Castel, V., Ben Salah, K. A., & Amimi, A. (2016). The African Development Bank Group in North Africa – 2016. The African Development Bank Group, 1-139. Retrieved from https://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/AfDB_North_ Africa_2016_Annual_Report_ENG.pdf

Castel, V., Mejia, P. X., & Kolster, J. (2011). The BRICs in North Africa: Changing the Name of the Game? North Africa Quarterly Analytical: The African Development

Bank Group, 286(1), 1-20.

Collins C. Ngwakwe. (2017). Foreign direct investment risk implication on employment in an emerging economy. Risk Governance & Control: Financial Markets & Institutions,

7(4), 148-152.

Coniglio, N., Prota, F., & Seric, A. (2015). Foreign Direct Investment, Employment and Wages in Sub‐Saharan Africa. Journal of International Development, 27(7), 1243-1266.

Habib, M. D., & Sarwar, S. (2013). Impact of foreign direct investment on employment level in Pakistan: A Time Series Analysis. Journal of Law, Policy and Globalization, 10, 46-55.

Inekwe, J. (2013). FDI, Employment and Economic Growth in Nigeria. African Development

Review, 25(4), 421-433.

Jude, C. & Silaghi, M. I. P. (2016). Employment effects of foreign direct investment: New evidence from Central and Eastern European countries. International Economics, 145, 32-49.

(33)

33

Krugman, P. R., Obstfeld, M., & Melitz, M. J. (2015). International Economics: Theory and

Policy (Tenth Edition). Harlow: Pearson Education Limited.

Lipsey, R., Sjöholm, F., & Sun, J. (2013). Foreign Ownership and Employment Growth in a Developing Country. The Journal of Development Studies, 49(8), 1133-1147.

Liu, T. C. (2014). China’s economic engagement in the Middle East and NA. Policy brief:

FRIDE, 173, 1-5. Retrieved from

http://fride.org/descarga/PB_173_China_economic_engagement_in_MENA.pdf Megbowon, E. T., Ngarava, S. & Mushunje, A. (2016). Foreign Direct Investment Inflows,

Capital Formation and Employment in South Africa: a Time Series Analysis.

International Journal of Economics and Finance Studies, 8(2), 175-189.

Miniesy, R., & Adams, J. (2016). Local employment additionality impacts of Chinese overseas foreign direct investment in selected African economies. Local Economy:

The Journal of the Local Economy Policy Unit, 31(6), 665-689.

Nahidi, M. M., & Badri, A. K. (2014). FDI and Employment in D8 Countries. Merit

Research Journal of Art, Social Science and Humanities, 2(2), 15-20.

Noutary, E., & Luçon, Z. (2016). 10 years of Foreign Investment and Business Partnerships in the Mediterranean (2006-2015). ANIMA Investment Network, 1-33. Retrieved from http://www.animaweb.org/sites/default/files/mipo_10years_en_a5.pdf

Nyen Wong, K., & Cheong Tang, T. (2011). Foreign direct investment and employment in manufacturing and services sectors. Journal of Economic Studies, 38(3), 313-330. Pecoraro, E. (2010). China’s strategy in NA: New economic challenges for the

Mediterranean region. Working Paper: EUGOV, Institut Universitari d'Estudis

Europeus Bellatera, 26, 1-28.

Pflüger, M., Blien, U., Möller, J., & Moritz, M. (2013). Labor market effects of trade and FDI: Recent Advances and Research Gaps. Jahrbücher Für Nationalökonomie Und

Statistik, 233(1), 86-116.

Pigato, M. (2009). Strengthening China’s and India’s Trade and Investment Ties to the Middle East and North Africa. World Bank, 1(1), 1-187. Retrieved from

http://documents.worldbank.org/curated/en/952201468221960884/Strengthening-Chinas-and-Indias-trade-and-investment-ties-to-the-Middle-East-and-North-Africa Renard, M. (2011). China’s Trade and FDI in Africa. Working paper: The African

(34)

34

Soumaré, I. (2015). Does Foreign Direct Investment Improve Economic Development in North African Countries? Working Paper: The African Development Bank Group, 1-48.

UNCTAD (2007). World Investment Report. Retrieved from http://unctad.org/en/docs/wir2007_en.pdf

UNCTAD (2017). World Investment Report. Retrieved from http://unctad.org/en/PublicationsLibrary/wir2017_en.pdf

The Arab Investment & Export Credit Guarantee Corporation (2017). Investment Climate in Arab Countries Report. Retrieved from http://dhaman.net/ar/wp-content/uploads/sites/2/2017/10/Climate-2017-En.pdf

(35)

35

10. Appendix

Appendix 1

Appendix 2

Source: Graph constructed by author based on data from WDI 56 58 60 62 64 66 68 70 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Total Population in North Africa of the ages 15-64 (% of total population) Algeria Egypt Morocco Tunisia 0 20 40 60 80 100 120 20062007200820092010201120122013201420152016

Total population of North Africa (in millions)

(36)

36 Appendix 3

Appendix 4

Source: Graph constructed by author based on data from WDI 0 50 100 150 200 250 300 350 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Total GDP of North Africa (in billions of US

dollar) Algeria Egypt Morocco Tunisia 25 30 35 40 45 50 55 60

Total Employment to population ratio of the countries in the regression analysis (as %)

Algeria Egypt Nigeria South Africa Sudan