Blaise Gnimassoun
email: blaise.gnimassoun@univ-lorraine.fr.

John C Anyanwu
email: j.anyanwu@afdb.org.

The Diaspora and Economic Development in Africa *

Keywords: The African Diaspora, International migration, Economic development, Africa. JEL classification: F22, F63, O55

While the dominant collective belief asserts that brain drain is detrimental to the development of small economies, new studies hold the reverse view. This paper aims at studying the role of the African Diaspora in the economic development of Africa. It analyzes both the overall effect and the specific effect of emigration according to the level of education of emigrants. Then, through a deeper investigation, the paper analyzes the main channels through which the Diaspora influences economic development in Africa. The results show that the African Diaspora contributes positively, significantly and robustly to the improvement of real per capita income in Africa. These findings challenge the dominant collective belief since the higher the educational level of the emigrants, the greater the impact of the Diaspora on the level of economic development. Improvement in human capital, total factor productivity and democracy are effective transmission channels of this impact. Finally, the results show that while high-skilled emigrants have an overall greater impact on economic development and democracy, those with a low level of education contribute more to remittances to Africa. The establishment of an annual African Diaspora Summer School (ADSS) by the AfDB in partnership relevant international and regional stakeholders as a channel for the transfer of knowledge, technology and experience would further strengthen the role of the Diaspora in Africa's economic development.

Introduction

International migration has increased significantly since the early 1990s and is now an integral part of globalization. One of the main characteristics of this migration is the size of the youth involved. Indeed, the examination of the architecture of international migration clearly shows a strong dominance of the mobility of the active population (see Figure 1). Africa, which is largely involved in this phenomenon, is characterized by high emigration to developed OECD1 countries. Moreover, the highly skilled emigration rate from Africa -especially West and East Africa -is among the highest in the world (see OECD, 2015). Figure 2 shows the remarkable increase of migration from African countries to the OECD countries with a growing share of highly skilled migrants. This brain drain from Africa nourishes the stock of the highly skilled African Diaspora. 2The impact of brain drain from developing countries is a controversial issue on which two paradigms are opposed. The first paradigm, the oldest and most widespread, posits that brain drain is detrimental to the economic development of the countries concerned [START_REF] Bhagwati | The brain drain, international integration of markets for professionals and unemployment[END_REF][START_REF] Miyagiwa | Scale economies in education and the brain drain problem[END_REF][START_REF] Haque | Human capital flight": impact of migration on income and growth[END_REF]. The argument is that emigration of an individual with a higher than average level of education and income results in a decrease in the average level of income and education. Thus, GDP per capita is reduced and the average human capital that is needed for future growth is also reduced. In addition, it results in a tax loss for sending countries and a loss of externalities related to the education of the individual. The second paradigm is more recent and challenges the first by putting forward two brain drain effects that could be beneficial to sending countries [START_REF] Meyer | Network approach versus brain drain: lessons from the diaspora[END_REF][START_REF] Kerr | Ethnic scientific communities and international technology diffusion[END_REF][START_REF] Spilimbergo | Foreign students and democracy[END_REF][START_REF] Docquier | Emigration and the quality of home country institutions[END_REF][START_REF] Agrawal | Brain drain or brain bank? The impact of skilled emigration on poor-country innovation[END_REF]. The first is an incentive effect, which takes place ex-ante of emigration itself, and leads families to invest in the education of their children in the perspective of future emigration. As all high-skilled individuals do not emigrate, the stock of human capital could increase with this incentive effect. The second effect is ex-post and relates to the links that the Diaspora maintains with the country, by their financial transfers or their possible return, but also and especially by their participation in scientific, political and business networks. Such networks potentially generate trade and capital flows, technology transfers, and can also contribute to the dissemination of social and institutional norms conducive to development [START_REF] Rapoport | Le "brain drain" et son incidence sur les pays en développement[END_REF]. Given these two divergent paradigms, predictions of the macroeconomic impact of the African Diaspora or emigration on economic development in Africa are purely speculative if they are not based on rigorous empirical studies. Also, due to the initial lack of consensual data on migration by skill level, empirical studies on brain drain are relatively recent. Notes: We use the IAB brain-drain database. Emigration is computed as the sum of emigrants from country i (from Africa) to OECD destination countries j at time t (see [START_REF] Brücker | Education, gender and international migration: insights from a panel-dataset 1980-2010[END_REF].

This paper aims to study the impact of the Diaspora on economic development in Africa. In order to shed some light on the issue of brain drain versus brain gain, we study both the global impact and the specific impact according to the level of education of the emigrants. To ensure the quality of our estimates and conclusions, we use a set of powerful econometric tools to overcome identification problems that are likely to characterize the relationship between the Diaspora and economic development. For example, the causality between emigration and economic development can go both ways. Indeed, while the Diaspora can contribute to the economic development of the countries of origin, the low level of economic development of these countries generally constitutes an incentive for emigration. To deal with this endogeneity issue, we use an instrumental variable (IV) approach. We rely on two complementary IV strategies: i) a gravity model predicting a country's emigration rate out of a set of reasonably exogenous bilateral variables and ii) internal instruments using both DIF-GMM and SYS-GMM estimations. Finally, using the income decomposition proposed by [START_REF] Hall | Why do some countries produce so much more output per worker than others?[END_REF], we analyze the main incomerelated channels through which the effect of the Diaspora passes. Thus, once the human capital channels, capital intensity and total factor productivity are analyzed, we then explore two additional channels by studying the impact of the African Diaspora in developed OECD countries (by level of education) on remittances and democracy in Africa.

The results show that the African Diaspora contributes positively, significantly and robustly to the improvement of real per capita income in Africa. By distinguishing the impact of the Diaspora by skill level, our estimates show that the higher the education level of emigrants, the greater the impact of the Diaspora. These findings challenge the dominant collective belief and are rather compatible with the new paradigm that emphasizes the effects of networks as well as the financial and technological transfers that the Diaspora allows. Then, through the decomposition of income, we show that all components of income are positively affected by the Diaspora, but the intensity of human capital and total factor productivity are the predominant channels. Finally, while the results highlight the overall greater income impact of high education level emigrants, those with a low level of education contribute more to remittances to Africa.

The rest of the paper is organized as follows. Section 2 presents a brief review of the literature on the impact of brain drain or the Diaspora. Section 3 describes our empirical strategy. In Section 4, we present and discuss our main results, and provide some robustness checks. We propose and analyse of transmission channels in 5. Finally, Section 6 concludes the paper and presents some policy implications.

2 Impact of the Diaspora: The two sides of the coin

The literature on the impact of emigration and therefore of the Diaspora has often focused on the effect of brain drain, i.e. emigrants with a high level of education.

The general belief is that brain drain is detrimental to the countries of origin (see [START_REF] Docquier | Globalization, brain drain, and development[END_REF]. The main argument relies on the theories of endogenous growth that emphasize the key role of education in growth [START_REF] Lucas | On the mechanics of economic development[END_REF]. Thus, the emigration of skilled workers (brain drain) represents a considerable shortfall for growth in countries of origin. For example, the seminal model of [START_REF] Bhagwati | The brain drain, international integration of markets for professionals and unemployment[END_REF] shows that brain drain causes a loss for the developing countries. This loss occurs through the fact that increasing international mobility induces high-skilled workers of poor countries to bargain for higher wages, and low-skilled workers respond by adjusting their wage requirements. Persuaded of the negative effects of the brain drain, some studies propose to tax the incomes of migrants abroad in order to limit the burden on the sending countries [START_REF] Bhagwati | Taxing the brain drain[END_REF][START_REF] Bhagwati | Income taxation and international mobility[END_REF]. [START_REF] Miyagiwa | Scale economies in education and the brain drain problem[END_REF] builds a theoretical model of brain drain and shows that brain drain increases education and income levels in the host country. Its impact is all the more important as, contrary to the presumption that brain drain hurts unskilled individuals left in the home country, it is rather professionals with mid-level capacities who are more adversely affected. [START_REF] Haque | Human capital flight": impact of migration on income and growth[END_REF] also find that brain drain jeopardizes the economic growth of the country of origin by reducing the rate of growth of effective human capital that remains in the economy.

In sum, the early models on the consequences of the brain drain (in the endogenous growth framework) find that brain drain raises inequality at the global level, with developing countries becoming poorer for the benefit of the richer ones.3

While these early studies emphasize the negative effect of brain drain, several recent studies highlight some benefits from brain drain [START_REF] Mountford | Can a Brain Drain Be Good for Growth in the Source Economy?[END_REF][START_REF] Stark | A brain gain with a brain drain[END_REF][START_REF] Stark | Human capital formation, human capital depletion, and migration: a blessing or a "curse[END_REF][START_REF] Vidal | The effect of emigration on human capital formation[END_REF][START_REF] Meyer | Network approach versus brain drain: lessons from the diaspora[END_REF][START_REF] Beine | Brain drain and human capital formation in developing countries: winners and losers[END_REF][START_REF] Kerr | Ethnic scientific communities and international technology diffusion[END_REF][START_REF] Giuliano | Remittances, Financial Development and Growth[END_REF][START_REF] Spilimbergo | Foreign students and democracy[END_REF][START_REF] Docquier | Emigration and the quality of home country institutions[END_REF][START_REF] Agrawal | Brain drain or brain bank? The impact of skilled emigration on poor-country innovation[END_REF][START_REF] Docquier | Emigration and democracy[END_REF]. The main channels are the incentive to invest in education that the prospect of future emigration entails, resulting remittances that provide an alternative way to finance investment, benefits that home country can acquire from human capital of returning migrants, and the transfer of knowledge and institutional norms through the Diaspora. However most of the theoretical contributions in this recent literature indicate that brain drain can have a positive impact only under certain conditions. For example, for [START_REF] Mountford | Can a Brain Drain Be Good for Growth in the Source Economy?[END_REF], this effect operates in the context of uncertainty about the success of emigration. He shows that when human capital accumulation is endogenous and when successful emigration is not a certainty, brain drain may increase average productivity and equality in the source economy even as average productivity is a positive function of past average levels of human capital in an economy.

Empirical studies on the impact of brain drain or the Diaspora are relatively recent because of lack of global data availability. Several of them investigate the impact of the Diaspora from the point of view of host countries that are mainly advanced OECD countries (see, among others, [START_REF] Ortega | The causes and effects of international migrations: Evidence from OECD countries 1980-2005[END_REF][START_REF] Boubtane | Immigration, growth, and unemployment: Panel VAR evidence from OECD countries[END_REF][START_REF] Ortega | Openness and income: The roles of trade and migration[END_REF][START_REF] Alesina | Birthplace diversity and economic prosperity[END_REF][START_REF] Jaumotte | Impact of migration on income levels in advanced economies[END_REF]. 4 Studies on the impact of brain drain or the Diaspora on growth and income for sending countries are few. Based on recent US data, [START_REF] Beine | Brain Drain and LDCs' Growth: Winners and Losers[END_REF] examine the impact of brain drain on 50 developing countries and find an overall positive effect of brain drain. However, they find that there are winners and losers among sending countries according to their levels of human capital: brain drain appears to hurt home countries with large proportion of highly educated emigrants. In the same vein, [START_REF] Beine | Brain drain and human capital formation in developing countries: winners and losers[END_REF] use emigration data on 127 origin countries by education level to examine the impact of brain drain on human capital formation. They find evidence of a promoting impact of skilled emigration on human capital. [START_REF] Batista | Testing the "brain gain" hypothesis: Micro evidence from Cape Verde[END_REF] use household survey data in Cape Verde and show that a 10 percentage point increase in the probability of own future migration improves the probability of completing intermediate secondary schooling by nearly 4 percentage point for individuals who do not migrate before age 16. [START_REF] Giuliano | Remittances, Financial Development and Growth[END_REF] explore the growth-enhancing impact of emigrant remittances on 100 developing countries. They find that remittances promote growth in less financially developed countries by providing an alternative way to finance investment and by overcoming credit constraints. Recent studies point to the presence of Diaspora externalities in the transfer of knowledge and technology [START_REF] Meyer | Network approach versus brain drain: lessons from the diaspora[END_REF][START_REF] Kerr | Ethnic scientific communities and international technology diffusion[END_REF][START_REF] Agrawal | Brain drain or brain bank? The impact of skilled emigration on poor-country innovation[END_REF] and in the diffusion of democratic values and social and cultural norms. Empirical studies by [START_REF] Spilimbergo | Foreign students and democracy[END_REF] and [START_REF] Docquier | Emigration and democracy[END_REF] highlight the role of emigration in improving political institutions. Using an international dataset, [START_REF] Spilimbergo | Foreign students and democracy[END_REF] finds that foreign students play an important role in promoting democracy in the home country, but only if education is acquired in democratic countries. Using cross-section and panel data, [START_REF] Docquier | Emigration and democracy[END_REF] also find that general emigration from developing countries to OECD countries helps to improve institutional quality in the countries of origin.

Although Africa has a large Diaspora in developed countries with a high proportion of emigrants with high qualifications, no study to our knowledge has examined the impact of this Diaspora on economic development in Africa. This study fills this gap by analyzing both global and specific effects according to the emigrants' level of education as well as the main transmission channels.

Empirical strategy

The first step in our study is to empirically investigate the effect of the Diaspora -depending on the level of education -on economic development in Africa. The second step consists in analyzing the different channels through which this effect operates. We focus on the African Diaspora in the OECD countries, which represents the stock of people from Africa and living in OECD countries. We follow [START_REF] Docquier | Emigration and democracy[END_REF] by computing the Diaspora rate (or emigration rate) as the sum of emigrants from country i (from Africa in our case) to OECD destination countries j at time t, j Diasp ij,t , divided by the native population of country i, N i,t (proxied by the sum of the resident and emigrant populations). In this section we present our empirical baseline model (Section 3.1), expose the OLS estimation approaches (Section 3.2), discuss how we deal with endogeneity issues (Section 3.3) and describe the data sources used in our empirical analysis (Section 3.4).

Baseline model

Based on the paper's objective, our baseline model specifies income per capita as the dependent variable and the Diaspora (emigration rate) as the explanatory variable of interest.

lny = α + γDiasp E + k δ k X k + ε (1)
y is the real GDP per capita at chained PPPs (a measure of economic development), Diasp E is the Diaspora rate of education level E (low, medium, high) that is the sum of emigrants of education E from country i (from Africa) to OECD destination countries j, divided by the native population of country i, X are control variables, and ε stands for the error term. Our reference model is simple. It explains per capita income by the country's Diaspora rate and its size (population and area) as in the international trade model proposed by [START_REF] Frankel | Does trade cause growth?[END_REF]. This model has been widely used in recent studies on openness to trade and migration (see, among others, [START_REF] Noguer | Trade raises income: a precise and robust result[END_REF][START_REF] Freund | Trade, regulations, and income[END_REF][START_REF] Feyrer | Trade and income-exploiting time series in geography[END_REF][START_REF] Ortega | Openness and income: The roles of trade and migration[END_REF][START_REF] Docquier | Emigration and democracy[END_REF]. Then, our reference model is supplemented using other control variables. By following the literature, we use the control variables below:

• Trade openness, measured as the sum of imports and exports as share of GDP.

Since emigration is a measure of openness in the context of globalization, it is important to introduce trade openness to be sure that emigration does not capture other dimensions of openness (see [START_REF] Ortega | Openness and income: The roles of trade and migration[END_REF]Docquier et al., 2016). In addition, there is an abundant literature on the impact of trade openness on per capita income [START_REF] Frankel | Does trade cause growth?[END_REF][START_REF] Dollar | Institutions, trade, and growth[END_REF][START_REF] Noguer | Trade raises income: a precise and robust result[END_REF][START_REF] Freund | Trade, regulations, and income[END_REF].

• Country Size, measured by population and area. Size and trade openness are the control variables we consider in our reference model (1) as in the empirical studies mentioned above. Indeed it is important to control for the size of the country because larger countries are generally characterized to experience more internal migration and less international migration. As a result, larger countries will have lower shares of Diaspora regardless of whether they are more or less open to migration.

• Education Attainment, defined as the number of years of schooling that a child of school entrance age can expect to receive. Education is undoubtedly an important determinant of growth and development (see [START_REF] Barro | Education and economic growth[END_REF]. It is therefore important to include this variable among our control variables.

• Terms of trade, calculated as the percentage ratio of the export unit value indexes to the import unit value indexes. Given their strong specialization in the export of raw materials, African countries are heavily exposed to fluctuations in the terms of trade that affect their economic development [START_REF] Blattman | The terms of trade and economic growth in the periphery 1870-1938[END_REF].

• Financial development, measured by the domestic credit to private sector as share of GDP is used to account for the influence of financial market development on per capita income. This variable is considered to be one of the determinants of long-term growth [START_REF] De Gregorio | Financial development and economic growth[END_REF][START_REF] Calderón | The direction of causality between financial development and economic growth[END_REF].

• Capital openness, measured by the [START_REF] Chinn | What matters for financial development? Capital controls, institutions, and interactions[END_REF] index of capital account openness and is used to capture the influence of financial liberalization policies on economic development via the impact on saving and investment decisions. Like trade openness, it seems important to control the impact of emigration through financial openness.

• Historical and geographical controls. The historical controls are colonial origin dummies and the European settlement in the colonies, measured as the fraction of the population of European descent in 1900 (see [START_REF] Acemoglu | The colonial origins of comparative development: an empirical investigation[END_REF].

For geographic control variables, we use country latitude and a dummy for landlocked countries.

• Domestic and foreign investment rate. While domestic investment is a key determinant of growth and economic development [START_REF] Anderson | Investment and economic growth[END_REF], several studies have also highlighted the impact of foreign direct investment (FDI) on growth and economic development (see among others, [START_REF] Borensztein | How does foreign direct investment affect economic growth?[END_REF][START_REF] Alfaro | FDI and economic growth: the role of local financial markets[END_REF][START_REF] Li | Foreign direct investment and economic growth: an increasingly endogenous relationship[END_REF].

• Democracy index (quality of institutions) measured by the indices of civil liberty and political right.

• Official Development Assistance (ODA), measured as a percentage of GDP.

ODA is decisive for several African countries that depend on it to finance development projects and programs.

• Regional dummies are introduced to control for unobserved regional heterogeneity.

Although it is important to use control variables to avoid the bias of omitted variables, it is also well known that with the multiplication of explanatory variables, there is a risk of multicollinearity bias. For example, education is probably strongly correlated with emigration, latitude is correlated with colonial origin, the rate of emigration itself is strongly correlated with trade and many geographic variables, and democracy is correlated with latitude and several geographic variables. For this reason, as in [START_REF] Docquier | Emigration and democracy[END_REF], we will add the control variables by subset and not all at the same time in the same regression.

Cross-section and pooled OLS specifications

To analyze both the long-term and short-term effects of the Diaspora on per capita income in Africa, we use both cross-section and panel specifications of the baseline model (1).

Cross-section OLS specification

This approach allows us to analyze the long-term impact of the Diaspora on the level of development. In other words, with the cross-sectional approach, one can test whether the differences in the Diaspora proportions among African countries significantly explain their differences in terms of economic development. Estimates are made with the OLS estimator using the full-sample averages of the dependent and independent variables for each country:

lny i = α + γDiasp E i + k δ k X k i + ε i (2)
where i stands for country index. In this regression, each variable is the average of the corresponding annual variable for each country over the sample period from 1980 to 2014.

Pooled OLS specification

While the cross-sectional OLS approach is important for placing the relationship between the Diaspora and economic development in a long-term perspective, it does not allow for analysis of the short-term effects of the Diaspora. To account for this concern, we construct a panel that contains non-overlapping 5-year averages data for each country since our explanatory variables of interest (Diaspora rates) are also available for each five-year period.

lny i,t = α + γDiasp E i,t-1 + k δ k X k i,t-1 + ϑ r R r + θ t T t + ε it (3)
where i and t stand for country and period indices, respectively; R r represents the regional dummies and T t is time-fixed effect. In this pooled OLS specification, the explanatory variables are introduced with a lag of one period (ie 5 years) to account for their potential lagged effects (see [START_REF] Docquier | Emigration and democracy[END_REF]. This also allows to consider as a member of the Diaspora, an African emigrant who has resided in an OECD country for at least 5 years.

The endogeneity issue

Although we want to estimate equations ( 2) and ( 3) by OLS, we keep in mind that such regressions raise a number of econometric problems that could lead to estimation bias. The main problem in using cross-sectional and pooled OLS regressions is the endogeneity of our main variable of interest, the rate of emigration (or proportion of the Diaspora). One can imagine that the level of economic development of countries affects the desire of individuals to emigrate. Indeed, most people prefer to leave their country to live in countries with a better standard of living. Thus, the relationship between emigration and economic development can be characterized by an inverse causality. Furthermore, unobserved characteristics of countries can jointly affect the rate of emigration and economic development. To account for these potential problems of simultaneity bias, we use two estimation strategies. We first use a two-stage least squares (2SLS) estimation strategy that requires finding suitable external instruments for migration in the first stage. Then we use GMM estimators with internal instruments as the second identification strategy. This latter strategy allows us to better account for unobservable heterogeneity and persistence in the lagged dependent variable and other regressors.

Gravity-based 2SLS approach

Initiated by [START_REF] Frankel | Does trade cause growth?[END_REF] in a per capita income equation, this approach has been adopted by several authors [START_REF] Dollar | Institutions, trade, and growth[END_REF][START_REF] Noguer | Trade raises income: a precise and robust result[END_REF][START_REF] Freund | Trade, regulations, and income[END_REF][START_REF] Feyrer | Trade and income-exploiting time series in geography[END_REF][START_REF] Ortega | Openness and income: The roles of trade and migration[END_REF][START_REF] Coulibaly | Growth-enhancing Effect of Openness to Trade and Migrations: What is the Effective Transmission Channel for Africa?[END_REF][START_REF] Alesina | Birthplace diversity and economic prosperity[END_REF][START_REF] Docquier | Emigration and democracy[END_REF]. Roughly speaking, it consists in constructing, on the basis of pseudo-gravity regressions, the geographybased prediction of the rate of bilateral migration from country i to country j. To do this, we consider the following pseudo-gravity model:

lnDiasp E ij =τ 0 + τ 1 lnDist ij + τ 2 lnP op i + τ 3 lnP op j + τ 4 lnArea i + τ 5 lnArea j + τ 6 (Landlocked i + Landlocked j ) + τ 7 Colony ij + τ 8 ComLang ij + e ij (4)
where Diasp E ij is the bilateral emigration rate (Diaspora rate, by level of education E) i.e., the stock of migrants born in country i in Africa and living in country j member of OECD as share of country i's population; Dist ij is the weighted distance that is equal to the distance between home country i and destination country j based on bilateral distances between the biggest cities of the two countries; P op i (P op j ) and Area i (Area j ) respectively denote the population (area) of origin (destination) country i(j); Landlocked is a dummy variable for landlocked countries; Colony ij is a dummy for colonial relationship; and ComLang ij , is a dummy for sharing a common language. In this gravity model, P op i (P op j ) and Area i (Area j ) are used to capture the capacity of home (host) country to send (receive) migrants. The migration costs are captured by geographic variables (such as Dist, Landlocked), linguistic and colonial ties (Language, Colony). The gravity model ( 4) is estimated by using both cross-sectional and panel data. To account for time-varying dimension in panel setting, we follow [START_REF] Feyrer | Trade and income-exploiting time series in geography[END_REF] and [START_REF] Docquier | Emigration and democracy[END_REF] by including both time and destination-country fixed effects and interactions between geographic distance and time dummies. The latter allows the effect of geographic distance to be time-varying, and thus to capture reduction in migration costs, for example, caused by improvements in aircraft technology (see [START_REF] Docquier | Emigration and democracy[END_REF].

Our gravity model is estimated by the Poisson Pseudo Maximum Likelihood (PPML) non-linear approach. As argued by [START_REF] Silva | The log of gravity[END_REF], contrary to the log-linearized model estimation by OLS, PPML estimation allows to address issues related to observations of the dependent variable with zero value and to heteroskedasticity. We rely on the procedure of [START_REF] Silva | On the existence of the maximum likelihood estimates in Poisson regression[END_REF] in order to deal with the identification problem of the (pseudo) maximum likelihood estimates of the Poisson regression models with non-negative values of the dependent variable (bilateral migration) and a large number of zeros on some regressors. After estimating gravity model in equation ( 4), we sum them up over destination countries j to obtain the predicted emigration (Diaspora) rate for each origin country i

( Diasp E i = j Diasp E ij ) in Africa.

GMM approach

Since our endogenous variable is likely to be characterized by a certain persistence (see [START_REF] Docquier | Emigration and democracy[END_REF], it seems important to take this phenomenon into account. Thus, we propose, in addition to the previous approaches, a dynamic panel specification as follows:

lny i,t = α i + βlny i,t-1 + γDiasp E i,t-1 + k δ k X k i,t-1 + ε it (5)
where i and t stand for country and period indices, respectively; α i is the country fixed effect. Note that under certain conditions, one can easily show that the short term specification (5) becomes that of the long term (2). Indeed, the dynamic specification (5) has often been used for other variables such as democracy, the stock of physical and human capital (among others [START_REF] Acemoglu | Income and democracy[END_REF][START_REF] Spilimbergo | Foreign students and democracy[END_REF][START_REF] Docquier | Emigration and democracy[END_REF]. Thus, if the explanatory variables of equation ( 5) are themselves persistent (e.g., Diasp E i,t = Diasp E i,ss and X i,t = X i,ss ∀t, where subscript ss stands for steady state) and if the coefficient of the lagged dependent is comprised between 0 and 1 (i.e., β ∈ [0, 1[), then the level of the dependent variable converges toward a long-run or steady state level (lny = (α + γDiasp E i,ss + δX i,ss )/(1 -β)), which characterizes the long-run relationship between income per capita and the right-hand-side variables (see [START_REF] Docquier | Emigration and democracy[END_REF] for the case of democracy). The coefficient γ/(1 -β) captures, in this case, the long-term effect of the Diaspora (emigrants) on economic development. Note also that equation ( 5) can easily be transformed into an equation of economic growth.

Although the introduction of the lagged endogenous variable among the explanatory variables allows to take into account the per capita income persistence, it becomes clearly inappropriate to estimate equation ( 5) by OLS since the lagged dependent variable is correlated with the time-invariant country-effects. It is therefore important to use a more robust method to take into account the dynamic effect of the income per capita, as well as problems of multiple endogeneity and error measurements. The generalised method of moments (GMM) for dynamic panel data is well-known and commonly used estimators to overcome the bias of fixed effects, endogeneity, and measurement. The difference-GMM and the system-GMM are two methods commonly used in the context of panel data. Difference-GMM was developed by [START_REF] Arellano | Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations[END_REF] following [START_REF] Holtz-Eakin | Estimating vector autoregressions with panel data[END_REF] and consists of transforming all regressors, usually by differencing, and uses GMM to correct the bias of correlation between the unobserved country-specific effects and the lagged dependent variables. System-GMM developed by [START_REF] Arellano | Another look at the instrumental variable estimation of error-components models[END_REF] and [START_REF] Blundell | Initial conditions and moment restrictions in dynamic panel data models[END_REF] [START_REF] Blundell | Initial conditions and moment restrictions in dynamic panel data models[END_REF] augments difference GMM estimator by making an additional assumption that first differences of instrument variables are uncorrelated with the fixed effects (see [START_REF] Roodman | How to Do xtabond2: An Introduction to "Difference" and "System" GMM in Stata[END_REF]. Accordingly, the efficiency can be considerably improved by the possibility of introducing more instruments. However, one can be confronted with the problem of proliferation of instruments which may be severe when the cross-section dimension is small. We use both GMM estimators. Time-specific effects are included in the regressions to capture common variations in the dependent variable and to reduce the asymptotic bias of the estimator in the presence of cross-sectional error dependence.

The Data

The dependent variable: The main dependent variable is the economic development measured by the per capita GDP at chained PPPs and obtained from the Penn World Table (PWT version 9.0).

The explanatory variables of interest:

The rate of population of African countries living abroad (the Diaspora or emigrants) is our explanatory variable of interest. This variable is also available by educational attainment (low, medium, high). Thus, in addition to the overall emigration rate, we also use emigration rates by level of education. All these variables are obtained from the IAB database [START_REF] Brücker | Education, gender and international migration: insights from a panel-dataset 1980-2010[END_REF]. The authors computed emigration stocks and rates of the population aged 25 years and older by gender and educational attainment in five-year intervals from 1980 to 2010. We selected emigration data from African countries (countries of origin)5 to 20 OECD countries (destination countries defined by the authors).6

The control variables: Several sources were used to mobilize the control variables. Trade openness (export + import/GDP ) and domestic investment are from PWT9.0. Data on the expected years of schooling are from the UNESCO Institute for Statistics. We use the [START_REF] Chinn | What matters for financial development? Capital controls, institutions, and interactions[END_REF] index of capital for capital openness. Our democracy variable is calculated by summing the indices of civil liberty and political right obtained from the Freedom House database. Indeed, these two variables are used by [START_REF] Docquier | Emigration and democracy[END_REF] as measures of democracy. We use the database from [START_REF] Acemoglu | The colonial origins of comparative development: an empirical investigation[END_REF] for the historical variables (colonial origin and the European settlement in the colonies). Data on latitude and landlocked as well as other variables of the gravity model (common language, colonial links) come from the CEPII database.

Empirical results

The baseline results of the OLS estimates are presented first. Then, we present their equivalents estimated through the gravity-based 2SLS strategy to deal with endogeneity. The results of the cross-sectional and pooled data estimates are presented simultaneously. We thus distinguish the short and long-term impact of the Diaspora on per capita income in Africa. Finally, we present the results of the dynamic panel estimates based on the GMM estimators.

OLS and 2SLS estimation results

Table 1 presents the baseline results obtained with the OLS estimators. These results show that the African Diaspora contributes significantly to improve the real income per capita in Africa. The impact of the Diaspora increases with the level of education of African emigrants. Indeed, the impact of the Diaspora does not seem to be significant in the cross-sectional regressions when the level of education of emigrants is low. The estimation results on the pooled data confirm the positive and significant impact of the Diaspora on the level of economic development in Africa. In these results, the impact of the Diaspora remains significant and positive whatever the level of education of emigrants. But the higher the level of study of emigrants, the greater the impact of the Diaspora on per capita income. These results contrast with the general belief that brain drain is detrimental to Africa. Obviously, these results are not sufficient to conclude definitively given the potential endogeneity bias. We therefore go further with the results, which are based on 2SLS estimations. Before turning to the results in Table 2, it is important to make some comments on the results of the gravity model that are useful in 2SLS regressions. Table A-1 shows the results of the different estimates (cross-section and panel) of the gravity model. These results are interesting for several reasons. First, they are consistent with the theoretical predictions. The distance, the isolation and the size of the population of the country of origin have a negative and siginificant impact on the emigration rate of African countries to OECD countries. The latter increases with the size of the population in the country of destination or when the African and OECD countries have a common language or colonial ties. Second, the results of the panel data estimates show that the impact of distance decreases over time. They thus confirm the results of [START_REF] Docquier | Emigration and democracy[END_REF] which show that improvements in transport technologies have led to a reduction in the cost of migration. More interestingly, the results show that the cost of distance varies according to the level of education of the emigrants. Indeed, for the same distance, emigrants with a high level of education face a lower cost than those with a low level of education. This result shows that Africans with high skills migrate easily to developed OECD countries. This may be a consequence of the migration policy of the countries of destination.

Table 2 presents baseline results on the impact of the Diaspora using the gravitybased 2SLS approach. These findings show a greater positive impact of the Diaspora on real per capita income in Africa. Indeed, the impact of the Diaspora is about twice as high with the instrumental variables (IV) method than with the OLS method with both cross-sectional data and panel data. African emigrants with medium skills and those with high skills contribute more strongly to improving the standard of living in Africa. The impact of low-skilled emigrants is only significant in the short to medium term. Given the relatively close coefficients for medium-and high-skill emigrants, we conduct additional regressions by considering the two variables simultaneously (columns 5 and 10) in order to check if one of them has a greater impact. The results show that highly skilled emigrants contribute most to the improvement of real per capita income in Africa. The latter therefore contrasts with the widespread belief that emigration of highly skilled people (brain drain) is detrimental to the economic growth of developing countries. Obviously the question of the robustness of these results arises. It is for this purpose that we perform a series of additional regressions that distinguish three subgroups of control variables given the relatively large determinants of per capita income.

The first control subgroup is composed of geographical and historical variables (European settlers in 1900, colonial origins, distance to the equator, landlocked dummy) often used to explain the origin of the quality of institutions in different countries [START_REF] Acemoglu | The colonial origins of comparative development: an empirical investigation[END_REF][START_REF] Sachs | Institutions don't rule: direct effects of geography on per capita income[END_REF][START_REF] Porta | The Economic Consequences of Legal Origins[END_REF]. These variables are important in explaining the contemporary economic performance of countries. Since the validity of geographically-constructed instrumental variables is weakened by the fact that geographical features may directly influence income per capita or indirectly through other channels than migration, it is prudent to include these control variables (see [START_REF] Rodriguez | Trade policy and economic growth: a skeptic's guide to the cross-national evidence[END_REF]. Table 3 presents the results of this first control group. The Diaspora contributes to a significant improvement in per capita income in Africa regardless of the educational level of the emigrants and whatever the estimation approach (cross-section or panel). As before, emigrants with a high level of education play a predominant role. It should be noted, however, that the validity tests of the instruments shows a certain fragility of our instruments in the cross-sectional approach except for the specification inculding high-skilled emigrants. This may be due to the limited number of cross-sectional observations since the panel specifications all pass these tests successfully.7

The second control subgroup is composed of internal variables depending on the economic and institutional policies of the countries (financial development, domestic investment rate, inflation rate, education, democracy index, capital openness) and which influence real per capita income. These variables are widely used in the literature as determinants of growth and income of countries (see, among others, De [START_REF] De Gregorio | Financial development and economic growth[END_REF][START_REF] Calderón | The direction of causality between financial development and economic growth[END_REF][START_REF] Barro | Education and economic growth[END_REF]. Table 4 shows the results of the estimates with this subgroup of control variables. The results of the cross-country approach show that only highly skilled emigrants have a positive and significant impact on per capita income. However, the panel approach shows that the overall African Diaspora -regardless of level of education -contributes significantly to improving the level of development in Africa. The impact of the Diaspora with a high level of education is always the largest. These results are thus globally consistent with the previous ones.

The last subgroup of controls we consider relates to a set of relatively external variables (terms of trade, foreign direct investment, official development assistance) over which countries have little or no control. Indeed, African countries depend heavily on these variables. The terms of trade affect economic development in African countries (see [START_REF] Blattman | The terms of trade and economic growth in the periphery 1870-1938[END_REF] and many African countries rely on ODA to finance development programs and projects. Finally, some studies highlight the importance of FDI in the development of countries [START_REF] Alfaro | FDI and economic growth: the role of local financial markets[END_REF][START_REF] Li | Foreign direct investment and economic growth: an increasingly endogenous relationship[END_REF]. Table 5 presents the results of the regressions with this subgroup of control variables. No significant changes are observed compared to baseline results. The African Diaspora contributes to a significant improvement in economic development in Africa, irrespective of the level of education of emigrants. The cross-sectional approach fails to differentiate between moderately and highly skilled emigrants, but the panel approach shows the greater impact of highly skilled migrants. Yes Yes Notes: Heteroskedasticity-robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5% and 1% confidence level, respectively. K-P F-stat is the [START_REF] Kleibergen | Generalized reduced rank tests using the singular value decomposition[END_REF] rk Wald F-stat test of weak identification. SY 10% max IV size and SY 10% max IV size are the [START_REF] Stock | Testing for weak instruments in linear IV regression[END_REF] critical values under the i.i.d. assumption. In the panel regressions, the explanatory variables are lagged by one period. Yes Yes Notes: Heteroskedasticity-robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5% and 1% confidence level, respectively. K-P F-stat is the [START_REF] Kleibergen | Generalized reduced rank tests using the singular value decomposition[END_REF] rk Wald F-stat test of weak identification. SY 10% max IV size and SY 10% max IV size are the [START_REF] Stock | Testing for weak instruments in linear IV regression[END_REF] critical values under the i.i.d. assumption. In the panel regressions, the explanatory variables are lagged by one period except the dummy variables. Yes Yes Notes: Heteroskedasticity-robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5% and 1% confidence level, respectively. K-P F-stat is the [START_REF] Kleibergen | Generalized reduced rank tests using the singular value decomposition[END_REF] rk Wald F-stat test of weak identification. SY 10% max IV size and SY 10% max IV size are the [START_REF] Stock | Testing for weak instruments in linear IV regression[END_REF] critical values under the i.i.d. assumption. In the panel regressions, the explanatory variables are lagged by one period. Yes Yes Notes: Heteroskedasticity-robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5% and 1% confidence level, respectively. K-P F-stat is the [START_REF] Kleibergen | Generalized reduced rank tests using the singular value decomposition[END_REF] rk Wald F-stat test of jointly weak identification. SY 10% max IV size and SY 10% max IV size are the [START_REF] Stock | Testing for weak instruments in linear IV regression[END_REF] critical values under the i.i.d. assumption. In the panel regressions, the explanatory variables are lagged by one period.

All the previous results show that the African Diaspora makes a positive and significant contribution to the economic development of Africa. We also show that the higher the education level of emigrants, the greater the impact of the Diaspora. These results are robust to set of control specifications. They thus break with the old theoretical paradigm which argues that the emigration of highly skilled people from developing countries is detrimental to their economic development [START_REF] Bhagwati | The brain drain, international integration of markets for professionals and unemployment[END_REF][START_REF] Miyagiwa | Scale economies in education and the brain drain problem[END_REF][START_REF] Haque | Human capital flight": impact of migration on income and growth[END_REF]. Our findings are more consistent with the new theoretical and empirical developments that highlight several channels through which emigration can be beneficial to the countries of departure. For African countries, as for most developing countries, the prospect of future emigration could encourage families to invest in education, thus contributing to improving human capital [START_REF] Beine | Brain Drain and LDCs' Growth: Winners and Losers[END_REF][START_REF] Beine | Brain Drain and LDCs' Growth: Winners and Losers[END_REF]. In addition to this potential effect, the African Diaspora could contribute to the economic development of Africa through several channels. Indeed, the Diaspora is a vector of transmission of financial capital (remittances) and of human and technological capital. In addition, some recent studies show that emigration plays an important role in the improvement of political institutions in developing countries (see [START_REF] Spilimbergo | Foreign students and democracy[END_REF][START_REF] Docquier | Emigration and democracy[END_REF]. Finally, in African international institutions, several high-level authorities have been part of the Diaspora and play an important role in the definition and implementation of development policies. All these reasons have undoubtedly explained the very important impact of the Diaspora that our study highlights.

GMM estimation results

Since previous estimates do not take into account the persistence of per capita income, we use dynamic panel estimates to deal with this phenomenon. In addition to being the most appropriate for dynamic panel estimation, GMM estimators also provide internal instruments to address the endogeneity problem between the emigration rate and the economic development of countries. Therefore, they usefully complement previous results by providing additional robustness tools. Table 6 presents the results of the estimates based on the system-GMM and difference-GMM techniques. These results show that, even taking into account the persistence that characterizes per capita income, the African Diaspora contributes positively and significantly to the improvement of the income in Africa whatever the approach used. Once again the impact of highly skilled emigrants is the highest. It should be noted that there is no evidence of second order serial correlation in the first-differenced residuals and the Difference Hansen test does not reject the validity of instruments. The dynamic panel estimates are thus consistent with the previous results. Notes: Heteroskedasticity-robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5% and 1% confidence level, respectively. L.Ln GDP per capita is treated as pre-determined and emigration variables are treated as endogenous. All variables are instrumented using their own 1st to 3rd lags. Due to the difference operator in the Difference-GMM approach, time-invariant variables are dropped. The area is not completely constant for all countries in our sample. This is the case for Ethiopia and Madagascar. The explanatory variables are lagged by one period except for variables that are invariant over time.

The transmission channels

In previous section, we have shown that the African Diaspora contributes significantly to the improvement of the standard of living in Africa. In this last section, we examine the channels through which this positive effect occurs. We therefore analyze the channel of income decomposition, the channel of remittances and that of political institutions.

The channel of income decomposition

We propose an income decomposition to further explore the channels through which the Diaspora affects economic development in Africa. Such an analysis provides a more precise view of the role of the Diaspora in economic development in Africa.

To do this, we follow [START_REF] Hall | Why do some countries produce so much more output per worker than others?[END_REF] by using the decomposition of income based on a simple Cobb-Douglas production function. Specifically, we consider the following function:

Y = K ρ (AH) 1-ρ (6)
where Y stands for output, K is the stock of physical capital, H denotes the amount of human capital-augmented labor, A is a labor-augmenting measure of productivity, and ρ represents the labor share in income. Rewriting this production function in terms of output per worker and using the logarithmic transformation, we get:

lny = ρ (1 -ρ) ln( K Y ) + lnh + lnA (7)
where y = Y /L denotes output per worker, and h = H/L is human capital per worker. The level of productivity, it is calculated from the production function assuming that ρ = 1/3 in line with standard neoclassical approach.8 Moreover, following [START_REF] Hall | Why do some countries produce so much more output per worker than others?[END_REF], we consider human capital as a function of returns to schooling (H = e φ(S) L) as estimated in a Mincerian wage regression. φ is a function reflecting the efficiency of a unit of labor with S years of schooling. Each of the components on the right-hand side of Equation ( 7) contributes to the improvement of income per worker. Data on output, human capital (based on years of schooling and returns to education), capital stock and number of workers are from PWT9.0. The productivity is calculated directly from the production function once the other variables are known.

Table 7 reports the results of the 2SLS estimation of Equation 7 considering successively each term (respectively the log of income per worker, the log of the capitaloutput ratio, the log of human capital per worker, and the log of productivity) as the dependent variable. We present here the results on emigrants with a high level of education since they have the most significant impact according to our previous results. However, the results for the other levels of education (low, medium) are presented in Table A-2 in Appendix. In line with our previous findings, a high rate of Diaspora in developed countries contributes significantly to improving per-worker income in Africa. We find that the African Diaspora contributes to improving per-worker income in Africa mainly through an improvement in human capital per worker and higher productivity. The impact on physical capital intensity is positive but weaker. Specifically, our estimates indicate that a rise of one percentage point in the highly skilled emigration rate increases the contribution of the intensity of physical capital to income of 0.07 percentage point whereas it increases the contribution of human capital of 0.31 pp and that of productivity of 1.14 percentage point. Estimates for other levels of education of emigrants (low, medium) give qualitatively similar results (see Table A-2). Thus, the impact of the Diaspora on income per worker mainly passes through the enhancement of human capital and productivity. [START_REF] Kleibergen | Generalized reduced rank tests using the singular value decomposition[END_REF] rk Wald F-stat test of weak identification. SY 10% max IV size and SY 10% max IV size are the [START_REF] Stock | Testing for weak instruments in linear IV regression[END_REF] critical values under the i.i.d. assumption.

Diaspora and Remittances to Africa

We extend the analysis of transmission channels by also investigating the channel of remittances. Indeed, in addition to channels based on the decomposition of income, it is now well known that the African Diaspora is a provider of financial capital (remittances) that can be used to reduce financing constraints in Africa. Since the importance of the level of education of emigrants in improving income in Africa has been highlighted, we investigate whether remittances are also affected by the level of education of emigrants.9

Table 8 reports the results of estimations on the impact of the Diaspora on remittances10 according to the level of education of the emigrants. These results are edifying. They show that the remittances of the Diaspora depend on the level of education of the emigrants. Indeed emigrants with a low level of education contribute more significantly to remittances in Africa. In cross-country regressions, only low-skilled emigrants make significant remittances to Africa. Although in panel regressions the impact of emigrants with a medium or high level of education appears to be significant in explaining remittances (columns 7 and 8), this impact disappears when control variables are introduced in the regression (columns 11 and 12). This result could be explained by the fact that high-skilled emigrants have strong incentives to integrate into the host community, to bring family to the host country, and to undertake long-term life projects (long-term borrowing for home or vehicle purchases) in the destination country. Similar results were found by [START_REF] Faini | Remittances and the Brain Drain: Do More Skilled Migrants Remit More?[END_REF] and [START_REF] Niimi | Remittances and the Brain Drain: Skilled Migrants Do Remit Less![END_REF]. Both studies show that highly skilled migrants send less money to their home countries. [START_REF] Faini | Remittances and the Brain Drain: Do More Skilled Migrants Remit More?[END_REF] indicates that remittances decrease with the proportion of highly qualified individuals among emigrants. These results were confirmed by [START_REF] Niimi | Remittances and the Brain Drain: Skilled Migrants Do Remit Less![END_REF] who show that remittances decrease for tertiary-educated migrants even when addressing the issue of endogeneity. Yes Yes Notes: Heteroskedasticity-robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5% and 1% confidence level, respectively. K-P F-stat is the [START_REF] Kleibergen | Generalized reduced rank tests using the singular value decomposition[END_REF] rk Wald F-stat test of weak identification. SY 10% max IV size and SY 10% max IV size are the [START_REF] Stock | Testing for weak instruments in linear IV regression[END_REF] critical values under the i.i.d. assumption.

The channel of political institutions

The recent literature on the impact of brain drain considers that one of the contributions of the Diaspora in the countries of origin lies in its role in improving the quality of institutions. [START_REF] Docquier | Emigration and democracy[END_REF] support this point of view by showing empirically that emigration from developing countries to OECD countries helps to improve democracy in the countries of origin. We therefore consider this additional channel by examining the impact of the African Diaspora on democracy in Africa. To this end, we consider three variables of democracy: civil liberties and political rights from the Freedom House database, as well as the composite index of democracy -Policy 2 -from POLITY IV. Political rights ratings are based on an evaluation of three subcategories of issues related to the functioning of the institutions: electoral process, political pluralism and participation, and functioning of government. With respect to civil liberties ratings, four subcategories of issues are considered: freedom of expression and belief, associational and organizational rights, rule of law, and personal autonomy and individual rights. Each country is assigned a numerical rating from 1 to 7 for both political rights and civil liberties, with a higher score (7) indicating less freedom. For its part, the Polity IV indicator (Polity 2) is conceived on the basis of three essential and interdependent elements: the presence of institutions and procedures through which citizens can express effective preferences about alternative policies and leaders; the existence of institutionalized constraints on the exercise of power by the executive and the guarantee of civil liberties to all citizens in their daily lives and in acts of political participation. Countries have an index of democracy ranging from -10 (strongly autocratic) to +10 (strongly democratic). Note that while the increase in the indices of political rights and civil liberties refers to a deterioration of democracy, that of the Policy 2 index refers to an improvement in democracy.

Table 9 reports the results of 2SLS regressions in which we adopt as before the same identification strategy based on the gravity model. As with income, estimates are controlled for historical and geographic variables that may be related to the quality of institutions. These results unambiguously show that the African Diaspora in developed OECD countries contributes significantly to the improvement of democracy in Africa. This positive and significant effect of the Diaspora is observed whatever the educational level of the emigrants. Moreover, the results are significant at 1% significance level whatever the index of democracy considered. However, the size of the coefficients of estimation indicate that the higher the level of education, the greater the impact of the Diaspora on democracy. These findings confirm those of [START_REF] Docquier | Emigration and democracy[END_REF] and support the view that improving institutions is one of the channels through which the Diaspora contributes to improving the standard of living in Africa.

Conclusion and policy implications

Emigration from Africa to developed OECD countries has accelerated over the last thirty years with an increasingly high proportion of highly skilled migrants. This dynamic -called brain drain -feeds the African Diaspora and raises several questions, in particular the question of the impact of this Diaspora on economic development in Africa. The discussion remained theoretical for a long time because of the lack of data to conduct empirical studies and the dominant thought foresaw a detrimental effect of the brain drain for developing countries.

This paper uses a bilateral emigration database from African countries to 20 developed OECD countries to study the impact of the African Diaspora on economic development in Africa. It examines both the overall impact and the specific impact of the Diaspora according to the level of education of the emigrants in order to verify the existence of a possible harmful effect for the emigration of people with a high level of education. To address the problem of endogeneity between economic development and emigration, we use two IV approaches. The first is the gravitybased 2SLS approach with external instruments determined from the geographical characteristics of the countries and the second is the GMM approach, which is based on internal instruments.

The results show that the Diaspora contributes positively, significantly and robustly to economic development in Africa and this effect increases with the level of education of emigrants. More in-depth analyzes show that the impact of the Diaspora on the real per capita income goes mainly through the improvement of human capital and especially increase in productivity. Moreover, although the income-improving effect of emigrants with a high level of education is the highest, emigrants with a low level of education contribute more to remittances in Africa.

These results have two major implications. The first is related to the political sense given to the interpretation of these results. Indeed, they should not be interpreted as an incentive for brain drain. By helping to significantly improve the level of income in Africa, the Diaspora reduces the incentive to emigrate because the latter increases when income levels are low in the country of origin. The second area relates to policy measures that could further enhance the impact of the Diaspora on economic development in Africa. Two main virtuous measures could be considered. The first is the institution of the annual African Diaspora Summer School (ADSS) as a vector for the transmission of development drivers (knowledge, technology, experiences in all fields, etc.). The second is the establishment of a Diaspora savings account in banks in developed countries with the aim of alleviating the constraints of financing for development in Africa.

Furthermore, our findings lead to additional implications. Given that medium-and high-skill values of the Diaspora are associated with higher economic development in the sending country, there is need to upskill through the promotion of higher levels of education attainment, including through vocational education. This implies the implementation of both demand and supply policies. Supply-side policies should include increasing teachers' incentives, enhancing the basic quality of schools' physical infrastructure, and researching and implementing teaching methods to increase the learning performance of students who do not do well when left to their own devices. As for side-demand policies, they must include scholarships conditional on attendance, bringing in excluded groups, and developing the accountability of schools and teachers to students, parents, and the broader society to help ensure effective service provider behavior.

Since lower skills are associated with higher inflow of remittances, reducing the cost of remittances transfer should be an important policy target by both sending and receiving economies. Indeed, given the weaknesses of the infrastructure supporting remittances, technological improvements in the banking sector could significantly reduce transaction costs. Furthermore, new banking technologies that can expedite check clearance, reduce exchange losses, and improve disclosure, especially in rural areas in developing countries, can be particularly helpful. New technology would offer potential for greater efficiency, lower costs, and extended outreach. Innovative financial products such as debit cards and mobile telephony add-on services and pre-paid cards are new additions with huge potential.

Finally, as openness promotes higher economic development, greater trade openness (trade integration) should be encouraged by all stakeholders through the elements of African Development Bank's (AfDB's) "Integrate Africa" High Five priority, elimination of tariff and non-tariff barriers, finance for trade, infrastructural development especially multinational ones, policy harmonization, and product/market diversification. .530 Notes: Heteroskedasticity-robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5% and 1% confidence level, respectively. K-P F-stat is the [START_REF] Kleibergen | Generalized reduced rank tests using the singular value decomposition[END_REF] rk Wald F-stat test of weak identification. SY 10% max IV size and SY 10% max IV size are the [START_REF] Stock | Testing for weak instruments in linear IV regression[END_REF] critical values under the i.i.d. assumption.

Figure 1 :

 1 Figure 1: Age distributions of world population and international migration

Figure 2 :

 2 Figure 2: Migration from Africa to OECD countries by level of education

Table 1 :

 1 Per capita income regressions -Baseline OLS results Heteroskedasticity-robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5% and 1% confidence level, respectively. In the panel regressions, the explanatory variables are lagged by one period.

	Variables	Cross-sectional OLS regression		Panel OLS regression
	Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Diaspora	0.104				0.121***
		(0.065)				(0.028)
	Low		0.097				0.114***
			(0.064)				(0.029)
	Medium			0.544*				0.655***
				(0.317)				(0.144)
	High				0.960***				1.060***
					(0.204)				(0.098)
	Trade openness	0.014*** 0.015*** 0.012**	0.011**	0.008*** 0.008*** 0.006*** 0.006***
		(0.005)	(0.005)	(0.005)	(0.005)	(0.002)	(0.002)	(0.002)	(0.002)
	Ln Population	0.080	0.079	0.067	0.050	-0.002	-0.005	-0.011	-0.023
		(0.132)	(0.139)	(0.133)	(0.113)	(0.059)	(0.062)	(0.058)	(0.053)
	Ln Area	-0.063	-0.090	-0.054	0.010	-0.025	-0.052	-0.019	0.034
		(0.087)	(0.090)	(0.091)	(0.076)	(0.038)	(0.039)	(0.039)	(0.034)
	Constant	6.775*** 7.133*** 6.894*** 6.383***	7.612*** 7.979*** 7.745*** 7.276***
		(1.553)	(1.631)	(1.489)	(1.325)	(0.695)	(0.720)	(0.660)	(0.598)
	Observations	49	49	49	49	294	294	294	294
	R-squared	0.582	0.559	0.582	0.674	0.523	0.496	0.530	0.604
	Regional dummies	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Time Fixed Effect					Yes	Yes	Yes	Yes
	Notes:

Table 2 :

 2 Per capita income regressions -Baseline IV results

	Variables		Cross-sectional IV regression			Panel IV regression
	Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
	Diaspora	0.388***					0.350***
		(0.138)					(0.054)
	Low		0.336					0.331***
			(0.212)					(0.086)
	Medium			1.823***		-0.241			1.397***		-0.482*
				(0.489)		(0.563)			(0.217)		(0.263)
	High				1.562*** 1.664***				1.395*** 1.653***
					(0.229)	(0.345)				(0.174)	(0.243)
	Trade openness	0.016*** 0.019***	0.010	0.012**	0.013**	0.008*** 0.009***	0.004**	0.006*** 0.007***
		(0.005)	(0.005)	(0.006)	(0.005)	(0.006)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
	Ln Population	0.184	0.172	0.114	0.079	0.075	0.052	0.045	0.007	-0.012	-0.021
		(0.116)	(0.105)	(0.126)	(0.096)	(0.096)	(0.058)	(0.058)	(0.058)	(0.052)	(0.053)
	Ln Area	0.102	-0.010	0.106	0.095	0.078	0.107***	0.026	0.065	0.078**	0.056
		(0.091)	(0.092)	(0.093)	(0.066)	(0.074)	(0.040)	(0.043)	(0.040)	(0.035)	(0.037)
	Constant	2.893	4.552***	4.067**	4.845*** 5.133***	5.103*** 6.192*** 6.453*** 6.597*** 6.989***
		(2.073)	(1.599)	(2.045)	(1.373)	(1.516)	(0.834)	(0.781)	(0.751)	(0.641)	(0.695)
	Observations	47	47	47	47	47	282	282	282	282	282
	R-squared	0.262	0.443	0.332	0.644	0.669	0.341	0.401	0.453	0.597	0.606
	Regional Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	K-P F-stat	6.185	3.542	12.32	23.80	2.985	31.29	23.65	16.82	75.69	9.161
	SY 10% max IV size	16.38	16.38	16.38	16.38	7.030	16.38	16.38	16.38	16.38	19.93
	SY 25% max IV size	5.530	5.530	5.530	5.530	3.630	5.530	5.530	5.530	5.530	7.250
	Time Fixed Effect						Yes	Yes	Yes

Table 3 :

 3 Per capita income regressions -IV results with historical and geographical controls

	Variables		Cross-sectional IV regression			Panel IV regression
	Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
	Diaspora	0.217***					0.203***
		(0.079)					(0.039)
	Low		0.199***					0.197***
			(0.069)					(0.035)
	Medium			1.208**		0.342			0.870***		0.082
				(0.499)		(0.436)			(0.213)		(0.175)
	High				1.256*** 1.132***				1.067***	1.026***
					(0.291)	(0.337)				(0.173)	(0.209)
	Trade openness	0.016***	0.016**	0.016**	0.011*	0.012*	0.007***	0.007***	0.006***	0.005**	0.005**
		(0.006)	(0.006)	(0.006)	(0.007)	(0.006)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
	Ln Population	0.225*	0.201*	0.228*	0.123	0.157	0.087	0.073	0.064	0.021	0.026
		(0.116)	(0.105)	(0.118)	(0.101)	(0.113)	(0.058)	(0.057)	(0.057)	(0.053)	(0.055)
	Ln Area	-0.032	-0.072	-0.036	0.024	0.033	-0.004	-0.036	-0.028	0.015	0.017
		(0.079)	(0.084)	(0.083)	(0.073)	(0.072)	(0.036)	(0.037)	(0.036)	(0.035)	(0.034)
	British colony	0.891***	0.875***	0.920*** 0.589*** 0.659***	0.840***	0.839***	0.798***	0.583***	0.598***
		(0.232)	(0.235)	(0.236)	(0.201)	(0.241)	(0.106)	(0.109)	(0.105)	(0.098)	(0.110)
	French colony	0.987***	0.836***	1.077*** 0.862*** 0.985***	0.891***	0.781***	0.831***	0.740***	0.762***
		(0.272)	(0.238)	(0.293)	(0.204)	(0.296)	(0.118)	(0.107)	(0.116)	(0.095)	(0.113)
	European settlers 1900	-0.000	-0.000	-0.000	0.001	0.001	-0.003	-0.003	-0.002	-0.001	-0.001
		(0.004)	(0.004)	(0.005)	(0.004)	(0.004)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
	Latitude (abs)	-1.612	-0.603	-1.642	-1.800	-2.295	-0.807	-0.053	-0.152	-0.616	-0.693
		(1.444)	(1.402)	(1.539)	(1.494)	(1.476)	(0.704)	(0.678)	(0.689)	(0.700)	(0.671)
	landlocked dummy	-0.434**	-0.463***	-0.433**	-0.405**	-0.395**	-0.532*** -0.551*** -0.540*** -0.493*** -0.493***
		(0.177)	(0.179)	(0.180)	(0.167)	(0.169)	(0.075)	(0.076)	(0.075)	(0.073)	(0.073)
	Constant	3.905**	4.854***	3.828**	4.906*** 4.199***	5.966***	6.595***	6.651***	6.840***	6.735***
		(1.525)	(1.274)	(1.608)	(1.331)	(1.574)	(0.766)	(0.720)	(0.724)	(0.632)	(0.683)
	Observations	45	45	45	45	45	270	270	270	270	270
	R-squared	0.713	0.719	0.685	0.764	0.746	0.661	0.657	0.657	0.705	0.704
	Regional Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	K-P F-stat	3.247	3.292	2.826	18.73	1.607	18.69	20.83	14.29	93.57	8.714
	SY 10% max IV size	16.38	16.38	16.38	16.38	7.030	16.38	16.38	16.38	16.38	19.93
	SY 25% max IV size	5.530	5.530	5.530	5.530	3.630	5.530	5.530	5.530	5.530	7.250
	Time Fixed Effect						Yes	Yes	Yes

Table 4 :

 4 Per capita income regressions -IV results with domestic control variables

	Variables		Cross-sectional IV regression			Panel IV regression
	Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
	Diaspora	0.162					0.194***
		(0.131)					(0.048)
	Low		0.059					0.178**
			(0.165)					(0.079)
	Medium			0.761		-0.902***			0.815***		-0.397
				(0.510)		(0.330)			(0.177)		(0.284)
	High				0.923***	1.464***				0.876*** 1.164***
					(0.278)	(0.246)				(0.107)	(0.242)
	Financial Development	0.009**	0.011***	0.010**	0.008**	0.008**	0.006*** 0.007*** 0.007*** 0.005*** 0.005***
		(0.004)	(0.004)	(0.004)	(0.004)	(0.004)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
	Domest. Invest. Rate	-0.000	0.019	0.001	0.008	0.028***	-0.007	-0.000	-0.002	0.005	0.009*
		(0.030)	(0.021)	(0.026)	(0.016)	(0.010)	(0.009)	(0.008)	(0.006)	(0.005)	(0.005)
	Inflation	0.034	0.020	0.035	0.025	0.007	-0.001	-0.001	-0.000	-0.003*	-0.004*
		(0.028)	(0.022)	(0.026)	(0.019)	(0.018)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
	Education	0.120**	0.151***	0.119**	0.100*	0.113**	0.097*** 0.115*** 0.106*** 0.095*** 0.097***
		(0.058)	(0.043)	(0.056)	(0.052)	(0.046)	(0.021)	(0.019)	(0.018)	(0.017)	(0.017)
	Democracy	0.091*	0.068	0.072	0.092**	0.095**	0.024	0.009	0.012	0.019	0.016
		(0.054)	(0.054)	(0.047)	(0.043)	(0.044)	(0.021)	(0.022)	(0.019)	(0.017)	(0.017)
	Capital openness	0.193	0.197	0.172	0.080	0.035	0.146*** 0.160*** 0.130***	0.079*	0.064
		(0.141)	(0.134)	(0.140)	(0.156)	(0.154)	(0.046)	(0.048)	(0.044)	(0.044)	(0.046)
	Constant	6.038*** 5.824*** 6.141*** 5.907***	5.535***	7.091*** 7.115*** 7.085*** 6.981*** 6.948***
		(0.968)	(0.793)	(0.931)	(0.714)	(0.649)	(0.346)	(0.336)	(0.300)	(0.275)	(0.272)
	Observations	47	47	47	47	47	250	250	250	250	250
	R-squared	0.631	0.707	0.668	0.724	0.768	0.593	0.604	0.662	0.710	0.713
	Regional Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	K-P F-stat	8.143	7.101	13.53	27.99	6.111	48.84	34.86	29.74	147.4	12.75
	SY 10% max IV size	16.38	16.38	16.38	16.38	7.030	16.38	16.38	16.38	16.38	19.93
	SY 25% max IV size	5.530	5.530	5.530	5.530	3.630	5.530	5.530	5.530	5.530	7.250
	Time Fixed Effect						Yes	Yes	Yes

Table 5 :

 5 Per capita income regressions -IV results with external control variables

	Variables		Cross-sectional IV regression			Panel IV regression
	Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
	Diaspora	0.218***					0.266***
		(0.052)					(0.032)
	Low		0.277***					0.352***
			(0.084)					(0.061)
	Medium			1.190***		0.600			1.282***		0.167
				(0.260)		(0.440)			(0.144)		(0.249)
	High				1.041***	0.566				1.210***	1.074***
					(0.175)	(0.360)				(0.104)	(0.219)
	Foreign Direct Inv.	0.057**	0.074**	0.033	0.033	0.031	0.006	0.012	-0.005	0.004	0.002
		(0.029)	(0.029)	(0.027)	(0.031)	(0.027)	(0.008)	(0.008)	(0.008)	(0.009)	(0.009)
	Terms of trade	-0.001	-0.002	0.000	-0.001	0.000	0.001	0.001	0.001	0.001	0.001
		(0.003)	(0.003)	(0.003)	(0.003)	(0.003)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
	ODA	-0.068*** -0.073*** -0.064*** -0.049*** -0.056***	-0.037*** -0.040*** -0.034*** -0.028*** -0.028***
		(0.016)	(0.017)	(0.014)	(0.012)	(0.013)	(0.006)	(0.007)	(0.005)	(0.004)	(0.004)
	Constant	8.240***	8.410***	8.143***	8.225***	8.145***	7.789***	7.831***	7.846***	7.806***	7.806***
		(0.381)	(0.396)	(0.369)	(0.414)	(0.378)	(0.202)	(0.215)	(0.198)	(0.189)	(0.189)
	Observations	47	47	47	47	47	269	269	269	269	269
	R-squared	0.695	0.670	0.706	0.754	0.746	0.556	0.492	0.588	0.675	0.673
	Regional Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	K-P F-stat	8.624	6.406	10.15	30.87	2.356	49.78	35.84	32.97	126.5	10.52
	SY 10% max IV size	16.38	16.38	16.38	16.38	7.030	16.38	16.38	16.38	16.38	19.93
	SY 25% max IV size	5.530	5.530	5.530	5.530	3.630	5.530	5.530	5.530	5.530	7.250
	Time Fixed Effect						Yes	Yes	Yes

Table 6 :

 6 Per capita income regressions -Difference-GMM and System-GMM results

			Difference-GMM regressions			System-GMM regressions
	Variables	1	2	3	4	5	6	7	8	9	10
	L.Ln GDP per capita	0.725*** 0.699*** 0.706*** 0.685*** 0.708***	0.861*** 0.818*** 0.788*** 0.941*** 0.956***
		(0.117)	(0.116)	(0.126)	(0.122)	(0.125)	(0.109)	(0.116)	(0.149)	(0.087)	(0.073)
	Diaspora	0.066*					0.161***
		(0.036)					(0.059)
	Low		0.042					0.168
			(0.072)					(0.147)
	Medium			0.295***		0.085			0.405***		0.006
				(0.075)		(0.124)			(0.149)		(0.125)
	High				0.313***	0.252**				0.409*** 0.553***
					(0.089)	(0.118)				(0.135)	(0.201)
	Trade openness	0.006***	0.005**	0.005**	0.004**	0.004***	0.009**	0.008**	0.006**	0.007**	0.005*
		(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.004)	(0.003)	(0.002)	(0.003)	(0.002)
	L.Ln Population	0.373**	0.401**	0.303	0.253	0.337*	0.009	0.129	-0.045	-0.154	-0.078
		(0.183)	(0.178)	(0.187)	(0.193)	(0.198)	(0.266)	(0.319)	(0.264)	(0.224)	(0.143)
	Ln Area	5.165	2.495	3.030	1.347	2.637	0.125	0.056	0.090	0.125	0.106
		(6.692)	(4.904)	(5.195)	(3.779)	(4.651)	(0.140)	(0.215)	(0.162)	(0.116)	(0.113)
	Education	0.010	0.016	0.017	0.023	0.011	0.028	0.026	0.046	0.048*	0.035*
		(0.018)	(0.018)	(0.018)	(0.017)	(0.017)	(0.042)	(0.041)	(0.029)	(0.027)	(0.020)
	Landlocked dummy						-0.575	-1.075	-0.269	-0.418	0.192
							(0.576)	(2.270)	(0.920)	(0.436)	(0.315)
	British colony						1.849**	1.874**	2.376**	1.370**	0.683
							(0.839)	(0.883)	(0.912)	(0.512)	(0.409)
	French colony						1.231	0.739	0.711	0.728	0.627
							(0.863)	(2.112)	(1.476)	(0.740)	(0.504)
	European settlers 1900						-0.000	0.003	0.028	0.003	0.009
							(0.014)	(0.019)	(0.023)	(0.010)	(0.006)
	Latitude (abs)						0.277	2.031	-0.497	0.128	0.681
							(2.415)	(4.439)	(3.583)	(1.816)	(1.513)
	Constant						2.281	1.602	3.968	4.964*	3.969**
							(3.103)	(2.469)	(3.307)	(2.484)	(1.551)
	AR(2) p-value	0.268	0.334	0.289	0.268	0.315	0.303	0.352	0.309	0.336	0.509
	Dif. Hansen P-value	0.934	0.745	0.618	0.223	0.182	0.962	0.752	0.487	0.572	0.407
	Number of instr.	24	24	24	24	34	36	36	36	36	50
	Observations	188	188	188	188	188	235	235	235	235	235
	Number of ident	47	47	47	47	47	47	47	47	47	47

Table 7 :

 7 Results of Income decomposition estimations -High skills

		Income per worker Phisical capital intensity Human capital intensity	TPF
	VARIABLES	lnY /L	(ρ/(1 -ρ))lnK/Y	lnH/L	lnA
	High-skilled Diaspora	1.168***	0.072*	0.310***	1.140***
		(0.193)	(0.038)	(0.071)	(0.175)
	Trade openness	0.007***	0.002**	0.001*	0.001
		(0.002)	(0.001)	(0.001)	(0.003)
	Ln Population	-0.116*	-0.005	-0.003	-0.065
		(0.062)	(0.020)	(0.014)	(0.077)
	Ln Area	0.112***	0.006	0.016	0.094**
		(0.040)	(0.016)	(0.011)	(0.040)
	Constant	-1.639**	-0.007	-0.688***	-1.440
		(0.752)	(0.223)	(0.201)	(1.042)
	Observations	281	282	228	228
	R-squared	0.585	0.113	0.486	0.578
	Regional Fixed Effect	Yes	Yes	Yes	Yes
	Time Fixed Effect	Yes	Yes	Yes	Yes
	K-P F-stat	76.85	75.69	264.6	264.6
	SY 10% max IV size	16.38	16.38	16.38	16.38
	SY 25% max IV size	5.530	5.530	5.530	5.530
	Notes: Heteroskedasticity-robust standard errors are in parentheses. *, **, and *** denote significance at the
	10%, 5% and 1% confidence level, respectively. K-P F-stat is the

Table 8 :

 8 The Diaspora and Remittances to Africa

		CS-IV regression (Long term link)		Panel IV regression		Panel IV regression with controls
	Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	Diaspora	0.461*				0.458***				0.458*
		(0.278)				(0.153)				(0.263)
	Low		0.787**				0.763***				0.831***
			(0.312)				(0.182)				(0.288)
	Medium			2.057				1.977**				1.515
				(1.498)				(0.831)				(1.370)
	High				1.440				1.520*				1.435
					(1.324)				(0.817)				(1.418)
	Trade openness									0.052*	0.050*	0.057**	0.058**
										(0.027)	(0.026)	(0.028)	(0.028)
	Financial development									-0.088*** -0.092*** -0.081*** -0.082**
										(0.031)	(0.030)	(0.030)	(0.032)
	Capital openness									-0.068	-0.013	-0.073	-0.143
										(0.347)	(0.346)	(0.348)	(0.374)
	Constant	0.078	0.112	-0.056	0.295	0.872	0.826	0.948	1.051	-0.463	-0.331	-0.651	-0.695
		(0.370)	(0.235)	(0.563) (0.434)	(1.782)	(1.781)	(1.787)	(1.804)	(1.311)	(1.290)	(1.349)	(1.396)
	Observations	47	47	47	47	242	242	242	242	238	238	238	238
	R-squared	0.181	0.188	0.177	0.160	0.145	0.152	0.136	0.124	0.212	0.223	0.198	0.189
	Regional Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	K-P F-stat	9.445	6.037	14.67	44.60	48.71	35.29	35.49	129.4	54.15	37.20	33.34	142.8
	SY 10% max IV size	16.38	16.38	16.38	16.38	16.38	16.38	16.38	16.38	16.38	16.38	16.38	16.38
	SY 25% max IV size	5.530	5.530	5.530	5.530	5.530	5.530	5.530	5.530	5.530	5.530	5.530	5.530
	Time Fixed Effect					Yes	Yes	Yes	Yes	Yes	Yes

Table 9 :

 9 Impact of the Diaspora on democracy in Africa

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	VARIABLES	P R	CL	P 2	P R	CL	P 2	P R	CL	P 2	P R	CL	P 2
	All skills	-0.489***	-0.450***	1.327***
		(0.107)	(0.076)	(0.360)
	Low skills				-0.475*** -0.468*** 1.172***
					(0.099)	(0.074)	(0.307)
	Medium skills							-2.440***	-2.202***	6.201***
								(0.667)	(0.489)	(2.282)
	High skills										-2.865*** -2.156*** 7.958***
											(0.672)	(0.533)	(2.457)
	Trade openness	-0.004	-0.005	-0.003	-0.005	-0.005	-0.002	-0.004	-0.004	-0.004	0.000	-0.001	-0.015
		(0.005)	(0.004)	(0.018)	(0.005)	(0.004)	(0.018)	(0.005)	(0.004)	(0.018)	(0.005)	(0.004)	(0.016)
	Ln Population	-0.017	-0.167*	-0.261	0.014	-0.148	-0.433	0.015	-0.134	-0.433	0.137	-0.017	-0.704*
		(0.130)	(0.096)	(0.431)	(0.129)	(0.096)	(0.418)	(0.124)	(0.092)	(0.413)	(0.112)	(0.083)	(0.383)
	Ln Area	-0.193**	-0.095	0.767**	-0.117	-0.032	0.537*	-0.158*	-0.060	0.654*	-0.268***	-0.118	0.994***
		(0.095)	(0.069)	(0.346)	(0.092)	(0.068)	(0.324)	(0.096)	(0.070)	(0.350)	(0.102)	(0.074)	(0.371)
	British colony	-0.980***	-0.886***	2.493***	-0.978*** -0.908*** 2.405*** -0.921***	-0.826***	2.289***	-0.333	-0.338*	0.723
		(0.232)	(0.179)	(0.720)	(0.235)	(0.181)	(0.706)	(0.221)	(0.169)	(0.677)	(0.217)	(0.174)	(0.672)
	French colony	-0.879***	-1.188***	2.421***	-0.617**	-0.991***	1.535**	-0.867***	-1.159***	2.220**	-0.584**	-0.805***	1.658*
		(0.301)	(0.225)	(0.935)	(0.260)	(0.196)	(0.769)	(0.295)	(0.214)	(0.945)	(0.264)	(0.200)	(0.864)
	European settlers 1900	-0.028***	-0.021***	0.121*	-0.028*** -0.021*** 0.176*** -0.029***	-0.022***	0.187*** -0.032*** -0.024***	0.159**
		(0.007)	(0.005)	(0.063)	(0.007)	(0.005)	(0.059)	(0.007)	(0.005)	(0.060)	(0.007)	(0.005)	(0.066)
	Latitude (abs)	0.363	1.892	-1.981	-1.451	0.472	2.436	-0.546	0.968	-0.418	0.502	1.036	-3.470
		(1.930)	(1.453)	(6.453)	(1.723)	(1.326)	(5.818)	(1.853)	(1.410)	(6.578)	(1.920)	(1.479)	(6.460)
	Landlocked dummy	0.239	-0.017	-1.110*	0.285	0.022	-1.086*	0.249	-0.007	-0.964	0.125	-0.090	-0.712
		(0.196)	(0.133)	(0.614)	(0.196)	(0.133)	(0.617)	(0.197)	(0.135)	(0.618)	(0.200)	(0.137)	(0.613)
	Constant	10.296*** 10.997*** -15.761**	8.801***	9.898***	-10.012*	9.332***	10.020***	-11.587*	8.616***	8.751***	-11.099*
		(1.997)	(1.461)	(6.973)	(1.807)	(1.323)	(6.046)	(1.807)	(1.309)	(6.456)	(1.652)	(1.210)	(5.968)
	Observations	269	269	263	269	269	263	269	269	263	269	269	263
	R-squared	0.406	0.479	0.463	0.418	0.487	0.490	0.410	0.497	0.468	0.437	0.523	0.493
	Regional Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Time Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

  Heteroskedasticity-robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5% and 1% confidence level, respectively.

	lndistw	-1.66***	-2.02***	-1.63*** -0.94***
		(0.26)	(0.29)	(0.34)	(0.26)
	lndistw1980					-1.76***	-2.09***	-1.75*** -1.05***
						(0.11)	(0.12)	(0.16)	(0.13)
	lndistw1985					-1.73***	-2.06***	-1.70*** -1.01***
						(0.11)	(0.12)	(0.16)	(0.12)
	lndistw1990					-1.70***	-2.05***	-1.64*** -0.98***
						(0.11)	(0.12)	(0.16)	(0.12)
	lndistw1995					-1.69***	-2.04***	-1.63*** -0.96***
						(0.11)	(0.12)	(0.16)	(0.12)
	lndistw2000					-1.68***	-2.03***	-1.61*** -0.95***
						(0.11)	(0.12)	(0.16)	(0.12)
	lndistw2005					-1.67***	-2.04***	-1.58*** -0.92***
						(0.11)	(0.12)	(0.16)	(0.12)
	lndistw2010					-1.65***	-2.03***	-1.56*** -0.89***
						(0.11)	(0.12)	(0.16)	(0.12)
	Ln pop. origin	-0.08	-0.03	-0.30**	0.03	-0.05	-0.01	-0.24***	0.05
		(0.11)	(0.13)	(0.16)	(0.13)	(0.05)	(0.06)	(0.07)	(0.06)
	Ln pop. dest.	0.26	0.21	0.32**	0.37***	0.27***	0.22***	0.34***	0.36***
		(0.16)	(0.21)	(0.15)	(0.13)	(0.07)	(0.08)	(0.07)	(0.06)
	Ln area origin	-0.43***	-0.55***	-0.26**	-0.38***	-0.46***	-0.56***	-0.31*** -0.40***
		(0.08)	(0.10)	(0.12)	(0.10)	(0.03)	(0.04)	(0.05)	(0.04)
	Ln area dest.	0.21	0.10	0.29*	0.27**	0.21***	0.10	0.28***	0.28***
		(0.14)	(0.19)	(0.17)	(0.13)	(0.06)	(0.08)	(0.08)	(0.06)
	Sum landlocked	-0.96**	-0.76	-1.16**	-1.09***	-0.93***	-0.74***	-1.12*** -1.08***
		(0.45)	(0.52)	(0.49)	(0.32)	(0.20)	(0.24)	(0.21)	(0.14)
	Common off. lang.	1.40***	0.87**	1.39**	1.71***	1.38***	0.85***	1.37***	1.70***
		(0.44)	(0.40)	(0.59)	(0.38)	(0.18)	(0.17)	(0.25)	(0.16)
	Colonial ties	1.30***	2.05***	0.72	0.99***	1.33***	2.07***	0.73***	1.01***
		(0.36)	(0.41)	(0.48)	(0.35)	(0.15)	(0.17)	(0.21)	(0.15)
	Constant	10.61***	14.16***	7.94***	1.87	10.87***	14.33***	7.90***	1.96**
		(2.21)	(2.49)	(2.51)	(2.07)	(0.92)	(1.00)	(1.19)	(0.96)
	Observations	1,000	1,000	1,000	1,000	7,000	7,000	7,000	7,000
	R-squared	0.48	0.59	0.29	0.39	0.45	0.56	0.26	0.36
	Notes:

Table A -

 A 2: Results of Income decomposition estimations -Low and medium skills

			Panel IV regression for low Skills		Panel IV regression for medium Skills
		lnY /L	(ρ/(1 -ρ))lnK/Y	lnH/L	lnA	lnY /L	(ρ/(1 -ρ))lnK/Y	lnH/L	lnA
	Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Low-skilled Diaspora	0.262***	0.087***	0.113***	0.414***
		(0.075)	(0.019)	(0.037)	(0.124)
	Medium-skilled Diaspora					1.127***	0.207***	0.787***	2.323***
						(0.208)	(0.056)	(0.202)	(0.381)
	Trade openness	0.010***	0.002***	0.002**	0.002	0.006**	0.001	0.001	-0.000
		(0.002)	(0.001)	(0.001)	(0.003)	(0.002)	(0.001)	(0.001)	(0.003)
	Ln Population	-0.064	0.011	0.004	-0.038	-0.095	-0.002	-0.011	-0.084
		(0.068)	(0.020)	(0.015)	(0.077)	(0.067)	(0.020)	(0.017)	(0.078)
	Ln Area	0.064	0.028*	0.001	0.040	0.096**	0.020	0.034**	0.121***
		(0.045)	(0.015)	(0.013)	(0.045)	(0.044)	(0.016)	(0.017)	(0.046)
	Constant	-1.996**	-0.565**	-0.628***	-1.214	-1.789**	-0.236	-0.792***	-1.495
		(0.896)	(0.241)	(0.233)	(1.076)	(0.846)	(0.233)	(0.246)	(1.044)
	Observations	281	282	228	228	281	282	228	228
	R-squared	0.497	0.081	0.413	0.579	0.498	0.093	0.308	0.530
	Regional Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Time Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	K-P F-stat	23.27	23.65	17.36	17.36	16.86	16.82	11.49	11.49
	SY 10% max IV size	16.38	16.38	16.38	16.38	16.38	16.38	16.38	16.38
	SY 25% max IV size	5.530	5.530	5.530	5.530	5.530	5.530	5.530	5

OECD (Organisation for Economic Co-operation and Development) countries are home to the majority of the Diaspora (at least the highly skilled) from developing countries, including Africa.

Diaspora is defined as the stock of people born in a country and living in another one (see[START_REF] Beine | diasporas[END_REF].

[START_REF] Commander | The brain drain: curse or boon? A survey of the literature[END_REF] present a broader survey of the theoretical literature on brain drain.

As this paper examines the impact of the Diaspora from the point of view of sending countries, we will not go further on the literature on host countries.

Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo, Dem. Rep., Congo, Rep., Cote d'Ivoire, Djibouti, Egypt, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe.

Australia, Austria, Canada, Chile, Denmark, Finland, France, Germany, Greece, Ireland, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and United States.

The statistics from[START_REF] Kleibergen | Generalized reduced rank tests using the singular value decomposition[END_REF]'s test of weak identification are higher than the critical values of the[START_REF] Stock | Testing for weak instruments in linear IV regression[END_REF] at the usual confidence level.

The empirical value of this parameter we get for African countries is 0.30 with the panel data, which is close to the reference value.

We do not address the issue of the impact of remittances on economic development. This could be the subject of a separate article. See for example[START_REF] Catrinescu | Remittances, institutions, and economic growth[END_REF].

Remittances (% of GDP) are taken from WDI and are defined as the sum of personal transfers and compensation of employees.

Appendix