Joël Cariolle Ferdi

Camille Da Piedade

Digital Connectedness and Exports Upgrading: Is Sub-Saharan Africa Catching Up? 1 By

Keywords: Economic complexity, Internet, Connectivity infrastructures, Sub-Saharan Africa, Exports, Trade diversification

We highlight a new dimension of the submarine cable infrastructure network, termed 'digital connectedness', reflecting a country's digital proximity to main world markets, and assess its impact on export upgrading. Using an instrumental variables approach conducted on a sample of 60 developing countries-including 23 sub-Saharan African countries-over the period 1995-2017, we find that digital connectedness contributes positively and significantly to the export basket complexity, but also points out spatial heterogeneity within our sample. In fact, estimations stress that, compared to the Rest of the World, a 10pp increase in the share of world GDP directly cabled to SSA countries leads to a supplementary increase ranging from 4.6 index points to 5.3 index points in the export complexity index. Moreover, while everywhere else the positive effect of digital connectedness declines with distance from global markets, in Sub-Saharan Africa the benefit increases. Finally, in line with the literature, improved digital connectedness also translates into higher exports of differentiated goods and greater participation in the global value chain.

I. Introduction

Sub-Saharan Africa (SSA) plays a very marginal role in global trade. Possible reasons for this relative marginalization include high transaction costs, poor infrastructure network, and structural handicaps related to unfavourable geographic factors. Despite the rapid growth rates recorded over the last two decades, sub-Saharan African countries have not engaged in an industrialization path that has enabled post-independence income levels catch up [START_REF] Rodrik | Premature deindustrialization[END_REF]. The international context of high commodity prices, low interest rates, and China's increasing appetite for African natural resources has explained the concomitance of high growth rates with slow structural economic transformations and of increasing and upstream participation to agricultural global value chains with low and stagnant regional value chain [START_REF] Rodrik | Premature deindustrialization[END_REF][START_REF] Balié | Does trade policy impact food and agriculture global value chain participation of sub-Saharan African countries?[END_REF][START_REF] De Melo | Prospects and challenges for supply chain trade under the Africa Continental Free Trade Area[END_REF]. However, beyond the country's position in the global or regional networks of productive activities, to paraphrase [START_REF] Hausmann | What you export matters[END_REF], what SSA exports matters for its longterm economic growth and industrialization. In this regard, the agricultural and food industries in SSA weight about a quarter of its GDP, employs roughly two-thirds of a population mostly located in rural areas [START_REF] Balié | Does trade policy impact food and agriculture global value chain participation of sub-Saharan African countries?[END_REF], and tempering the regional prospects for structural change triggered by industrial sectors development and sophisticated goods and services exporting [START_REF] Rodrik | Premature deindustrialization[END_REF][START_REF] Lim | Global agricultural value chains and structural transformation[END_REF].

Could an improved access to information and knowledge spur structural change and export basket upgrading in the region? The literature stresses that improved access to information and knowledge produced in different parts of the world has the power to induce structural change in trade patterns, especially for remote low-income countries [START_REF] Akerman | The skill complementarity of broadband Internet[END_REF]. In particular, various studies have shown that trade is constrained by information frictions, and that these frictions increase with the geographical distance between potential trade partners (Rauch & Trinidade, 2003;[START_REF] Bahar | Neighbors and the evolution of the comparative advantage of nations: Evidence of international knowledge diffusion?[END_REF][START_REF] Akerman | The skill complementarity of broadband Internet[END_REF][START_REF] Lendle | Internet Technology and the Extensive Margin of Trade: Evidence from eBay in Emerging Economies[END_REF]. In fact, the rapid decay of information and knowledge diffusion with the physical distance makes neighbouring countries more likely to exchange similar products with similar and geographically proximate trade partners [START_REF] Rauch | Networks versus markets in international trade[END_REF]Rauch & Trinidade, 2003;[START_REF] Chaney | The network structure of international trade[END_REF][START_REF] Bahar | Neighbors and the evolution of the comparative advantage of nations: Evidence of international knowledge diffusion?[END_REF][START_REF] Jun | Bilateral relatedness: Knowledge diffusion and the evolution of bilateral trade[END_REF]. Access to communication networks, by reducing information frictions, facilitates the matching between producers and distributors, assemblers and suppliers, investment need and saving capacity, importers and exporters (Rauch & Trinidade, 2003;[START_REF] Akerman | The skill complementarity of broadband Internet[END_REF], incites firms to export diversified, differentiated, or more sophisticated products [START_REF] Rauch | Networks versus markets in international trade[END_REF][START_REF] Jun | Bilateral relatedness: Knowledge diffusion and the evolution of bilateral trade[END_REF] and, thereby contributes to the export basket complexification. Without access to these networks, patterns of exports quality upgrading and trade network densification are geographically sticky [START_REF] Jun | Bilateral relatedness: Knowledge diffusion and the evolution of bilateral trade[END_REF]. This is particularly true when the knowledge embedded in exports is "tacit" or "multifarious"2 , and, therefore, relies on more direct forms of human interactions [START_REF] Bahar | Neighbors and the evolution of the comparative advantage of nations: Evidence of international knowledge diffusion?[END_REF][START_REF] Hidalgo | Economic complexity theory and applications[END_REF]. Therefore, in the light of this literature, African economies' isolation from main world markets, explained by important trade costs and a poor access to information, is a critical obstacle to an increased participation to world exchanges.

However, with the recent and massive deployment of submarine cable (SMC) connectivity infrastructure in SSA and the resulting rise in Internet penetration [START_REF] Cariolle | International connectivity and the digital divide in sub-Saharan Africa[END_REF] (see Figure 1), information and communication technologies (ICTs) are increasingly seen as a game-changing solution for the region, given its potential for (service) trade in remote areas [START_REF] Lendle | Internet Technology and the Extensive Margin of Trade: Evidence from eBay in Emerging Economies[END_REF]. Empirically, ICT diffusion facilitates catching up with developed countries through the "leapfrogging" process and the rise of mobile telephony in Africa illustrates this point quite well [START_REF] Aker | Mobile phones and economic development in Africa[END_REF]. Keeping aside the plentiful, relevant but yet anecdotal evidence on successful African entrepreneurship3 , the empirical literature provides evidence that digitalization improves business performance and foster Internet spillovers [START_REF] Hjort | The arrival of fast internet and employment in Africa[END_REF][START_REF] Cariolle | Spatial internet spillovers in manufacturing[END_REF][START_REF] Paunov | Overcoming obstacles: The Internet's contributions to firm development[END_REF], 2016), reduces the size of the informal sector [START_REF] Jacolin | Informal sector and mobile financial services in emerging and developing countries: Does financial innovation matter?[END_REF], and facilitates job creation [START_REF] Hjort | The arrival of fast internet and employment in Africa[END_REF]. In relation to trade, [START_REF] Freund | The effect of the Internet on international trade[END_REF], and then Clark and Wallsten (2006), stressed that Internet diffusion has stimulated trade flows and foreign direct investment (FDI). But more recently, [START_REF] Lendle | Internet Technology and the Extensive Margin of Trade: Evidence from eBay in Emerging Economies[END_REF] have shown that the deterrent effect of geographical distance between trade partners was substantially lower (65%) when transactions were made on one of the world's largest online marketplaces, compared to total trade. Their results, therefore, support the "death of distance", predicted by [START_REF] Cairncross | The death of distance: How the communications revolution will change our lives[END_REF], in that modern digital infrastructures and technologies are now able to carry sufficient information to reduce distance-related international search costs. Looking more specifically at the contribution of the digital infrastructure deployment, the trade dividends are very large according to evidence from industrialized economies [START_REF] Röller | Telecommunications infrastructure and economic development: A simultaneous approach[END_REF][START_REF] Czernich | Broadband infrastructure and economic growth[END_REF], but the research focused on developing countries is scarcer and display more mixed findings. Focusing on an industrialized country like Norway, [START_REF] Akerman | Information frictions, internet, and the relationship between distance and trade[END_REF] exploit the staggered roll-out of local fiberoptic broadband access-points to estimate the causal effect of Internet adoption on Norwegian firms' bilateral exports. They find that the reduction in information friction induced by Internet access enlarges the choice set of exporters and importers, making demand for traded products more elastic to trade costs and to distance. In developing economies, [START_REF] Hjort | The arrival of fast internet and employment in Africa[END_REF] have brought strong evidence that SMC deployment in SSA has spurred trade and job creation, but looking at the separate effect of SMCs' bilateral deployment on firm's participation to bilateral exports in a sample of 48 developed and developing countries over the period 1997-2014, [START_REF] Imbruno | Digital Connectivity and Firm Participation in Foreign Markets: An Exporter-Based Bilateral Analysis[END_REF] show that this effect is heterogeneous: it increased the number of bilateral exporters from developed countries but reduced this number in developing countries, by 5.4% in sub-Saharan Africa. This finding suggests that exporters from developed and developing areas differ in their ability to undertake information technology upgrading, as previously stressed in the context of Argentinian-Brazilian exports by [START_REF] Bustos | Trade liberalization, exports, and technology upgrading: Evidence on the impact of MERCOSUR on Argentinian firms[END_REF].

It is worth noting that establishing a trade relation requires considerable effort to gather information that is not necessarily freely available but assimilated through search and learning efforts. Firms can face some additional obstacles, including non-tariff barriers and issues related to incomplete information or limited capability to process information [START_REF] Allen | Information frictions in trade[END_REF][START_REF] Dasgupta | Inattentive importers[END_REF], to establish a successful trade relationship.

Using data from Chilean exporters, [START_REF] Morales | Extended gravity[END_REF] found that extended gravity has a large impact on export entry costs. They estimate that having similarities with a prior export destination in terms of geographic location, language, and income per capita jointly reduce the foreign market entry cost by 69% to 90%. Introducing the principle of relatedness-a measure of the overall similarity between an activity and a location-the economic literature on the process by which countries learn how to produce what they export, has demonstrate how poor knowledge diffusion constrains the ability of countries to penetrate new export markets. Indeed, countries are more likely to start exporting products that are related to their current export basket or that of their geographical neighbours [START_REF] Hidalgo | The product space conditions the development of nations[END_REF][START_REF] Hidalgo | The building blocks of economic complexity[END_REF][START_REF] Bahar | Neighbors and the evolution of the comparative advantage of nations: Evidence of international knowledge diffusion?[END_REF][START_REF] Jun | Bilateral relatedness: Knowledge diffusion and the evolution of bilateral trade[END_REF]. 4 The importance of knowledge diffusion in the diversification of economic activities has also been observed in the development of regional industries, technologies, and research activities, suggesting that similarity between economic activities enables knowledge diffusion in general [START_REF] Hidalgo | The principle of relatedness[END_REF].

This paper examines the implications of the recent and rapid deployment of SMCs along African coasts for African trade patterns and makes three contributions to the empirical literature. First, we highlight a new dimension of the SMC infrastructure deployment, termed 'digital connectedness', reflecting a country's digital proximity to world markets, and assess its impact on export sophistication. This indicator is the share of world GDP to which a country is connected through direct SMC connections, therefore considering the international connectivity infrastructure from a more qualitative perspective. In fact, we start from the premise that, while the number of SMCs that lay in a country matters, the size of economies to which a country is connected to should matter too. The mechanism emphasized is rather straightforward and is based on the literature on information frictions and export sophistication [START_REF] Rauch | Networks versus markets in international trade[END_REF]Rauch & Trinidade, 2003;[START_REF] Chaney | The network structure of international trade[END_REF][START_REF] Akerman | The skill complementarity of broadband Internet[END_REF][START_REF] Jun | Bilateral relatedness: Knowledge diffusion and the evolution of bilateral trade[END_REF][START_REF] Hidalgo | Economic complexity theory and applications[END_REF]: the greater the digital connectedness, the closer the country to main production and consumption centres, the easier for exporters to gather information on buyers, sellers, production technologies, inputs price and quality, market regulations and institutions, and so on, the larger the incentives and capacity to enter these markets and export more sophisticated products.

Second, we measure export sophistication using a measure of export basket complexity [START_REF] Hidalgo | Economic complexity theory and applications[END_REF]. To do so, we rely mainly on the Economic Complexity Index (ECI), calculated using the MIT's Observatory of Economic Complexity trade data set. As defined by Hartman et al. (2017), the ECI assesses the sophistication of the export structure of a country by combining information on the diversity of exported product and the number of countries exporting that product (ubiquity). Studying the effect of digital infrastructure deployment on economic complexity is of primary importance for economic development research, given the recent trends in ICT growth in developing areas, especially SSA, and also because complexity appears to be a strong predictor of a country's future growth path, wealth carbon emission, and income inequality (Hidalgo & Hausman, 2009;[START_REF] Hidalgo | Economic complexity theory and applications[END_REF].

Third, to address possible reverse causality between the shape of the SMC network and countries' integration in world markets, we use classical panel data econometrics methods and adopt an instrumental variables framework (2SLS). Our approach consists in instrumenting the connectedness variable by the number of (indirect) 2 nd order SMC connections, that is, the cumulative number of distinct SMC connections a given country's first-order SMC connections have. We also reduce the concern for omitted variable bias by including time and country fixed effects.

The first set of estimations conducted on a sample of 60 developing countries over the 1995-2017-of which 23 are from SSA-shows that while digital connectedness significantly increases the export basket complexity in all countries, there is geographical and temporal heterogeneity within our sample. In fact, IV estimations stress that the effect of digital connectedness on export complexity is particularly strong over the period 2006-2015, and point to SSA countries' catch-up. In fact, our results stress that, compared to the Rest of the World (RoW), a 10pp increase in the share of world GDP reached by SSA countries' direct SMC connections5 leads to a supplementary increase ranging from 4.6 index points (FE-OLS estimations) to 5.3 index points (IV-2SLS estimations). The overall increase in SSA's export complexity resulting from a 10pp increase in its connectedness equals 8.5pp, corresponding to 47% of the ECI sample standard deviation.

The second set of estimations aims at identifying the factors that accentuate or attenuate the effect of connectedness on export complexity. Building on the findings of the literature on information frictions and trade patterns, we first test whether the effect of digital connectedness is conditioned by the geographical and maritime distance of countries to major world markets and find evidence in support of this hypothesis. Our results highlight that the positive effect of connectedness declines with both geographical and maritime distances to world markets, except for SSA, where both distances increase the benefits of digital connectedness. This finding is reinforced by additional evidence on the positive contribution of declining maritime transport costs in SSA, as reflected in increased maritime connectivity, to the positive effect of connectedness on export complexity. Thus, these estimates add evidence to existing studies on the role of geographic distance in international trade [START_REF] Blum | Does the internet defy the law of gravity?[END_REF]. 6Third, we highlight a mediating effect of Internet penetration and human capital, not specific to SSA countries, which is consistent with studies highlighting the importance of digital absorptive capacity to take advantage on the digitalization process (Choi et al., 2020;[START_REF] De Melo | Structural transformation in MENA and SSA: the role of digitalization[END_REF]. The contribution of critical dimensions of digital absorptive capacity, such has Internet penetration and educational attainment, are investigated and found to mediate the effect of connectedness over long period, but not to explain SSA catch-up in export complexity.

Last, in a series of robustness checks, we extend our analysis to other dimensions of export upgrading. The results show that digital connectedness increases exports of differentiated goods and the participation in global value chains. Regarding the GVCs participation, the impact is much stronger on backward participation and larger for sub-Saharan African countries. This result, therefore, corroborates previous evidence based on the ECI.

The remainder of this paper is structured as follows. Section II is devoted to the methodology and data, as well as our identification strategy. In Section III, we interpret the empirical results. Section IV is dedicated to robustness checks, and Section V concludes on the main messages of the paper.

II. Empirical framework

Our analysis starts from the premise that the reduction in search, replication, transport, tracking, and verification costs resulting from telecommunication SMC deployment has spurred goods and services exports sophistication. We consider that the size of economies to which a country is connected through SMCs is critical for information and knowledge diffusion, and thereby, for the diversification and sophistication of exported products. Therefore, we highlight the contribution of a new dimension of the SMC network, that we term 'digital connectedness', reflecting a country's digital proximity to main production and consumption centres, and assess its impact on export sophistication. In particular, we question the role of distance and other structural determinants of trade, digitalization and industrialization, in channelling this relationship. Considering that digital connectedness and export complexity might be mutually reinforcing, we employ an original instrumental variable approach to identify causal relationships. The next subsections present the data used in this study and our empirical strategy.

1. Data

Economic complexity index

The alternative view7 of the development process provided by research combining the statistical physics of networks and development economics has delivered new analytical tools8 to quantify the economic relevance of the "historically disregarded productive structure". For this paper, we rely on one of these tools, i.e., the economic complexity index (ECI). As defined by Hartman et al. (2017), the ECI assesses the sophistication of productive structure of a country by combining information on the diversity of exported products and the number of countries exporting these products (ubiquity). The intuition behind ECI is that sophisticated economies are not only diversified, but they export products and services that are exported by few countries [START_REF] Hidalgo | Economic complexity theory and applications[END_REF].

Figure 2 presents the ECI by region. From left-hand side panel, we can easily notice that Western Europe, North America, and East Asia display higher level of complexity, while SSA displays the lowest. The right-hand side panel however indicates sub-Sahara as the region with the greatest increase of complexity between 1995 and 2014, and also the greatest volatility in the index from 2003 to 2015, period corresponding to world geopolitical and financial turmoil and high uncertainty upon commodity markets.

Note that, while the ECI is our main dependent variable, we also mobilize other measures of export upgrading such as the augmented Economic Complexity Index (ECI+), Rauch (1999)'s classification of exports goods, and forward and backward participation in global value chains (GVCs).

Digital connectedness

We use the telecommunication submarine cable (SMC) network as the international infrastructure driving knowledge diffusion, and thereby, as a critical source of economic complexity. To date, SMCs are the cheapest and fastest path for international telecommunications (OECD, 2014)9 , so that more than 95% of international telecommunications passes through this infrastructure. The SMC network is, therefore, a critical determinant of a country's Internet bandwidth, speed, stability, and affordability [START_REF] Hjort | The arrival of fast internet and employment in Africa[END_REF][START_REF] Cariolle | International connectivity and the digital divide in sub-Saharan Africa[END_REF][START_REF] Cariolle | Spatial internet spillovers in manufacturing[END_REF]. A direct SMC connection with a partner country will considerably smooth telecommunications and reduce bilateral information and communication costs, compared to non-connected ones. In fact, telecommunications destined to a non-connected partner have to be carried through indirect cable paths, and thereby, will suffer from a slower, narrower, and more expensive bandwidth. The search for low latencies, lower cost, traffic stability, and autonomy, has indeed been a critical incentive for deploying shorter and direct cables connections between OECD countries, and lately, with emerging and developing ones (OECD, 2014).

Therefore, direct cable connections to the largest economies will provide exporters with a better access to information on these markets and facilitate telecommunications between their components. To build a synthetic measure of digital proximity of a country to the main production and consumption centres, we use data on the SMC network worldwide, combine it with worldwide data on GDP, and build an original indicator measuring a country's cumulative share of the world GDP reached by direct-i.e., firstorder-cable connections, as schematized in Figure 3. In this figure, country i is directly (or first-order) connected (subscript 1) to countries a and b through SMCs (irrespective of their number), giving a global connectedness indicator consisting in aggregating the weight of countries a and b in world GDP. Plotting this indicator average evolution in the world and in sub-Saharan Africa, in particular in Figure 4, we can see that, despite a remarkable jump in the early 2010s, an African country is still, on average, connected to some 5% of the world's GDP in 2017 (7% excluding landlocked countries), against 20% for an average developing non-African coastal country, and 27% for an average high-income country. Acknowledging that trade is limited by information frictions, and that capabilities and knowledge diffusion constrain export complexification, our intuition is that the African very limited digital connectedness to the main world markets may explain a still low export basket's complexity. However, the recent and sharp growth in its digital connectedness to world markets would be expected to have spurred knowledge diffusion and contributed to a rapid catch up for the recent years.

Empirical strategy 2.1 The model

Combining our original data set on international connectivity with data from MIT's Observatory of Economic Complexity, we construct an unbalanced panel of 60 countries (including 23 sub-Saharan ones) over the period 1995-2017. Table A4 (in appendix) reports descriptive statistics of the variable used in our model, while Table A1 shows the sample composition. Our baseline model is specified as follows:

𝐸𝐶𝐼 𝑖,𝑡 = 𝛼 𝑖 + 𝛼 𝑡 + 𝛽 1 𝐺𝐷𝑃_𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑𝑛𝑒𝑠𝑠 𝑖𝑡 + (𝛽 2 𝐺𝐷𝑃_𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑𝑛𝑒𝑠𝑠 𝑖𝑡 × 𝑆𝑆𝐴 𝑖 ) + 𝛽 3 𝑋 𝑖,𝑡 + 𝜀 𝑖𝑡 (1)
Where, 𝑬𝑪𝑰 𝒊,𝒕 is the complexity index for country i at time t; 𝑮𝑫𝑷_𝒄𝒐𝒏𝒏𝒆𝒄𝒕𝒆𝒅𝒏𝒆𝒔𝒔 𝒊𝒕 is the cumulative percentage of world GDP reached by direct cables laid in country i at time t; 𝑆𝑆𝐴 𝑖 a dummy variable equal to 1 for sub-Saharan countries and 0 otherwise; 𝑿 𝒊,𝒕 is a set of control variables; 𝜶 𝒊 and 𝜶 𝒕 are, respectively, country and time fixed effects; 𝜺 𝒊𝒕 is the error term. Since we are interested in an eventual catch-up of SSA, our parameters of interest are β1 and β2. Following the related literature, we control for Internet penetration rates, country size and development level, trade remoteness to world markets (to account to eventual threshold effect in this variable we also control for its squared value), rents from natural resource exports, FDI inflows, trade openness, democracy, electricity access, and real effect exchange rates. The description, expected sign, related literature, and source underlying these control variables are provided in Table A2. These control variables' descriptive statistics are reported in Table A4.

Instrumental variable

Increased digital connectedness can be a trigger of economic complexity or a consequence of it. The econometric challenge, therefore, consists in solving an eventual reverse causality problem by isolating a causal link going exclusively from GDP connectedness to ECI. To do so, we adopt the IV approach exploiting information on the shape of the SMC network linking partners to whom a country is connected. We specifically use the number of distinct second-order SMC connections (schematized in Figure 6) -excluding duplicates and common partners (i.e., only plain lines considered) -as an instrumental variable (IV) predicting connectedness (Figure 7). We, therefore, estimate the previous (second-stage) Equation 1 with this first-stage equation, using the two-step least-square (2SLS) estimator:

𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑𝑛𝑒𝑠𝑠 𝑖,𝑡 = 𝛼 𝑖 + 𝛼 𝑡 + 𝛾 1 2𝑛𝑑𝑜𝑟𝑑𝑒𝑟_𝑐𝑜𝑛 𝑖𝑡 + (𝛾 2 2𝑛𝑑𝑜𝑟𝑑𝑒𝑟_𝑐𝑜𝑛 𝑖𝑡 × 𝑆𝑆𝐴 𝑖 ) + 𝛾 3 𝑋 𝑖,𝑡 + 𝜖 𝑖𝑡 (2)
Correcting for heteroscedasticity and clustering standard errors at the country-level.

Given its large costs underlying related investments and operations, the ability of a cable to link together a large number of countries depends on served countries/regions/continents' geographical characteristics, in particular, on the possible scale economies induced by bringing Internet to multiple countries, regions, and continents.10 This is the case for most cables connecting Africa to the rest of the world, such as the Africa-Coast-to-Europe (ACE), WACS, EASSy, WASC or TEAMS cables, deployed in the 2000s and 2010s to serve a large number of countries located in the same regions and/or along the path to connect Africa to other continents. This is also the case of the SEAMEWE-3/4/5 or AAE1 cables, connecting countries located on the path linking far-East Asia to Europe through the Middle-East and North Africa. This characteristic of the cable network, therefore, fulfils the conditions of a good instrument.

Our IV's rationale is, therefore, quite straightforward: countries that are connected to country themselves poorly (densely) connected world markets will display low (high) connectedness. Our exogeneity claim lies in the fact that the shape and density of the SMC network is determined by historical long-term conditions favourable to western industrialized countries' interconnectedness (which are excluded from the estimation sample), by geographical factors and aggregate economic considerations, independent from a given country's economic situation or policy (Eichengreen et al., 2016; World Bank, 2018; Cariolle, 2021). 11 This claim is plausible for first-order cable connections, but even more likely if we focus on the density of second-order cable connections, which is the rationale of our main instrument (Figure 6). Appendix C reports IV estimates using the number of first-order and second-order cable connections as instruments to test overidentification restrictions.

III.

Main results

1. Baseline estimations and regional effects dimension of our panel, we conduct the Inoe-Solon test for auto-correlated residual and detect the presence of order-1 autocorrelation in residuals (see Appendix B). Therefore, we report in column (3) estimates of Equation 1 with Driscoll-Kraay AR (1) standard errors, and do not find that correcting for AR (1) residuals reduces the significance of estimated relationships.

Given that the deployment of the SMC has occurred recently in sub-Saharan Africa, we split our sample into four periods in order to properly assess temporal and regional heterogeneity. Column 6, column 8, and column 12 show that the impact of connectivity is larger in sub-Saharan Africa than anywhere else, suggesting a catch-up effect at play on the continent. This effect is noteworthy inasmuch as SSA is compared to the bestperforming developing countries in our sample (China and South Korea), which exhibit connectedness levels similar to Western and North American countries (Figure 2). One should also note that the 2006-2015 period is the one in which the highest catch-up effect has been recorded (column 12 and column 13).12 With respect to controls variables, remoteness and natural rent prove to be detrimental to the export basket complexity while an increase in the income level, in trade openness or a depreciation of the REER is associated with an increase in the complexity of the export basket. All the remaining control variables are statistically not significant.

Table 2 reports FE-2SLS estimates, while Appendix C reports estimates of the same estimator using both first-order and second-order cable connections as instrument set.

The statistics regarding the quality of the instruments are satisfactory13 , rejecting the null hypothesis that the equation is under-identified and displaying high first-stage Fstatistics, well-above 10. Instrument estimates are positive and statistically significant at 1% in the first-stage regression. The FE-2SLS estimates support a positive causal effect of connectedness on export complexity. IV estimates indicate that the effect is statistically significant at 1% and slightly higher than FE estimates (Table 1). Estimates in column (4) endorse the SSA's technology catch-up over the 2006-2015 period, already documented in Table 1. In magnitude, a ten percentage points (pp) increase in the share of world GDP directly wired to SSA countries leads to an additional 8.4 points increase in the export complexity index. This increase is 5.3 index-points higher than the rest of the developing world, over-performance mainly explained by the lower performance of Latin America and South Asia (column 5). However, contrary to previous FE estimations, IV estimates in column (5) do not show any more SSA catching-up China and South Korea.14

Does distance still matter?

As pointed earlier, the question of whether digitalization of exchanges has spurred the death of distance in international trade is central [START_REF] Lendle | There goes gravity: eBay and the death of distance[END_REF][START_REF] Goldfarb | Digital economics[END_REF], and this subsection is aimed at reframing this as problematic within the connectedness-complexity nexus. In Table 3, we test whether the positive effect of connectedness is conditioned by the country's geographical distance to main export markets. To do so, we interact the trade remoteness variable used as control with digital connectedness and SSA dummy variables, applying the same interaction procedure with our instrument set. The results show that the positive effect of connectedness decays with geographic distance to world markets, and this conclusion holds whether we restrict the estimation span to the 2006-2015 period or when we consider only coastal countries in the analysis. However, they suggest, in a 10% confidence level, that the effect of connectedness on export complexity increases with world markets remoteness in SSA coastal countries (column 6). This series of estimations suggest that, despite digitalization and trade digitization, the geographical distance hampers trade complexification, but with a probable exception in SSA. We further this nonlinearity in the next regressions, using more sophisticated measures of distance-related trade costs. In a second step, we built an alternative variable reflecting the sea-distance to world markets, using data on bilateral maritime distances from the CERDI-Sea Distance Database [START_REF] Bertoli | The CERDI Sea-Distance Database[END_REF]. It is likely that the Euclidian distance between capitals, used in the remoteness index, improperly reflects the trade costs related to distance, and that considering sea distances would be more relevant for our problematic since many exports are merchandizes shipped and transported by boats overseas. Based on this data, we compute the average sea distance of country to its ten main trade partners (including imports and exports), and interact this variable with connectedness and SSA variables in Equation 1, and with the instrument in Equation 2. Results, reported in Table 4, are consistent with previous estimations based on the remoteness index. 15 They indeed support that sea distance dampens the positive effect of digital connectedness on export complexity. They also confirm a relationship that was only 10% significant with the trade remoteness variable (Table 4, column 6), stressing that, in contrast to other developing regions the effect of connectedness increases with sea distance in SSA. For example, an increase of 3,000km in sea distance to main trade partners (approximately one standard deviation) reduces the positive effect of connectivity on export complexity in non-SSA countries by 47% but increases by 75% the positive effect of connectivity on export complexity in SSA countries. This effect is not driven by the presence of South Africa in the sample (column 4), and is robust to the exclusion of trade remoteness from control variables (column 5). Moreover, the simple interaction of connectedness with the SSA dummy is associated with a negative and significant sign, suggesting that SSA's complexity catch-up is driven by increased connectedness in countries that are the farthest from world markets. Therefore, (sea) distance to world markets could have been a structural handicap for the complexification of African countries' export basket, which is being offset through the digital interconnection process. To avoid potential collinearity with the sea-distance interaction variable and ensure the comparability of results with Table 3, the squared term of the remoteness variable has been dropped from the econometric equation. † In column (4) South Africa was excluded from the sample. † † In column (5), the trade remoteness variable was excluded from the econometric equation.

To further understand the role of distance in our relationship, we investigate whether maritime transport costs could mediate the effect of digital connectedness on export complexity, using the UNCTAD's liner shipping connectivity index as interaction variable. This index measures a country's connectivity to global shipping network based on five metrics: the number of ships, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country's ports. Results are reported in Table 5 and stress that shipping connectivity is complementary to digital connectedness, i.e., it increases the contribution of connectedness to export complexity, and that this complementarity is stronger in SSA, especially when South Africa is excluded from the sample (column 4).

Therefore, this bunch of estimations stresses that distance still matter to explain the effect of connectedness on economic complexification nexus, but it does in a different way for SSA countries. While increased geographical or sea distance to world markets attenuate the positive effect of digital connectedness on export complexity in most developing economies, an increased distance is, however, found to accentuate this effect in SSA. This means that SSA catch-up in economic complexity is explained by the connectedness of the remotest African countries from world markets (excluding South Africa). These countries probably suffer from the greatest structural handicaps to trade, and therefore it is probably there that the return to increased connectedness (the reduction in information and transaction costs) could be the stronger. This explanation is corroborated by the positive, but less robust, contribution to the connectedness-complexity nexus of shipping connectivity, reflecting decreasing maritime shipping costs in SSA compared to other developing regions. . To avoid potential collinearity with the SCI interaction variable and ensure the comparability of results with Table 3 and Table 4, the squared term of the remoteness variable has been dropped from the econometric equation. † In column (4), South Africa is excluded from the sample

The absorptive capacity channel

In a third step, we study other key channel of the connectedness-complexity nexus, namely, the country's digital absorptive capacity. We posit that digital connectedness will trigger structural transformations and export's structure complexification if a country and its driving force are able to absorb technological change and transform access to digital technologies into transaction cost reductions. We consider that this absorptive capacity is reflected by the penetration of the Internet within the whole population on the one hand, and by a country's human capital level on the other hand. While Internet use in the population is a natural proxy for the familiarity of a given population with Internet related technologies, educational attainment has been pinpointed as being a critical factor of technology absorption [START_REF] Paunov | Overcoming obstacles: The Internet's contributions to firm development[END_REF], 2016;Choi et al., 2020), as evidenced by the literature on the skilled-biased technological and organization change [START_REF] Akerman | The skill complementarity of broadband Internet[END_REF].

In Table 6, we report estimations of the digital absorptive capacity channel. In columns (1) to (3), the share of population using Internet is used as proxy for this capacity and interacted with the connectedness and SSA dummy variables. Result stress that, in line with our expectation, Internet penetration is found to drive the positive effect of digital connectedness. However, this conditioning effect appears to be less significant over 2006-2015 period (column 3). Moreover, estimation in column (3) suggests that rising Internet penetration rates in SSA are not a factor explaining the observed catch-up in economic complexity, probably because of persistently low Internet penetration rates over the subcontinent.

Another critical dimension of the digital absorptive capacity is human capital, especially education level (Choi et al., 2020). In columns (4) to ( 12), we proxy educational attainment by the primary, secondary, and tertiary gross enrolment rates 16 , and interact separately these variables with the connectedness and SSA dummy variables. First, estimations stress the mediating effect of school enrolment, especially primary enrolment, is significant over the whole 1995-2017 period rather than 2006-2015. Moreover, estimates in column (5) support that increasing primary school enrolment in well-connected SSA countries is particularly beneficial to export complexity. Second, estimates in column (4) and column (5) stress that reaching a minimum primary enrolment rate is necessary for the positive effect of digital connectedness on export complexity to be felt. Based on estimates in column (4), this rate is established at 99%, which corresponds to the first quartile of the sample distribution. Third, the mediating effects of secondary and tertiary enrolment rates are positive and significant, in a 10% or 5% significant level and over long period only, but are not found to differ in SSA.

IV. Robustness analysis

In this final section, we test the robustness of our results and interpretations using complementary measures of the export sophistication process. First, we test whether previous regional effects in the connectedness-complexity nexus hold for differentiated exports, exports exchanged on organized markets, or exports with reference price, using Rauch's product classification [START_REF] Rauch | Networks versus markets in international trade[END_REF]. Second, we investigate whether these relationships are corroborated by increased global value chains participation. Third, we use additional measurements of export upgrading, such as the ECI+ or the Hausman et al. ( 2007)'s export sophistication index (EXPY). All regression tables associated with this section are available in the

Digital connectedness and exports according to Rauch's classification

How does the increase in digital connectedness materialize in exports? To answer this question, we use the [START_REF] Rauch | Networks versus markets in international trade[END_REF] classification, which is widely used in empirical work on the relationship between ICT and trade. The Rauch classification consists of three product groups and it presents an important feature in that it allows us to distinguish between products whose exchange faces high information search costs (differentiated goods) and those facing moderate or low information search costs (homogeneous goods sold on an organized exchanges market or with a reference price). Rauch provides two classifications, a "conservative" one that minimizes the number of homogenous products while the "liberal" classification maximizes them. To construct our exports per group's category following the two classifications, we rely on the four-digit level of the Standard International Trade Classification (SITC, revision 2) provided by UN COMTRADE.

Table D1 displays the results based on the conservative classification17 ; columns (1) to ( 6) show that digital connectedness has a positive and statistically significant effect on exports of differentiated goods, while the impact on the homogenous goods remains not significant. This result is consistent with the literature and meets our expectation inasmuch as differentiated goods are characterized by higher search cost and are intensive in information. When it comes to the heterogeneity analysis, column (7) indicates that the beneficial effect on digital connectedness on exports of differentiated goods is statistically significant at 1% level and in magnitude larger in sub-Saharan Africa than anywhere else-no significant effect is found in MENA countries. Column (8) shows that, organized exchanges are not left out, a positive effect of the digital connectedness being recorded in SSA and the two Asian regions.

Digital connectedness and value chain participation

To fully understand the mechanism at play in sub-Saharan Africa, we continue our empirical investigation focusing on global value chain participation. For this purpose, we use the UNCTAD-Eora global value chains data from [START_REF] Casella | Improving the analysis of global value chains: The UNCTAD-Eora database[END_REF]. Using the EORA Multi-Region Input-Output (MRIO) data set, and following [START_REF] Koopman | Tracing value-added and double counting in gross exports[END_REF]'s gross export decomposition, these authors compute various trade-value indicators including the foreign value-added content of exports (FVA) and the indirect value-added exports (DVX). The former measures the part of exports from a country incorporating-value added previously imported from abroad and is widely used as a proxy of the backward GVC participation. The latter captures forward GVC participation and is computed as the portion of gross exports produced in the country that enters as an intermediate input in the value-added exported by other countries including re-imported value-added.

In Table E1 (in the appendix), columns (1) and ( 2) show that digital connectedness induces greater participation in global value chains and that the impact is much stronger in terms of magnitude on backward participation than on forward participation. Regarding the heterogeneity of the effect, column (5) suggests that digital connectivity increases backward participation in all regions except MENA countries. Moreover, the effect is much larger for sub-Saharan African countries, confirming the catch-up effect documented earlier in the sense that more intermediate goods are needed to produce complex goods. The same conclusion applies to a lesser extent when it comes to forward participation, except for MENA and South-East Asia countries where the effect is not significant.

Alternative export upgrading variables

This section focuses on the sensitivity of results to alternative measure of export basket sophistication. The results in Table F1 (in the Appendix) are based on ECI+, an augmented version of ECI that considers the difficulty of exporting each product. ECI+ is deemed equivalent to the fitness index proposed by [START_REF] Tacchella | A new metrics for countries' fitness and products' complexity[END_REF] and outperforms ECI when used to predict future economic growth [START_REF] Albeaik | Measuring the knowledge intensity of economies with an improved measure of economic complexity[END_REF]. The estimates of columns (1) to (4) show that using of an alternative measure does not change our results. In Table F2 (in the Appendix), we introduce EXPY as an alternative indicator of sophistication. As defined by [START_REF] Hausmann | What you export matters[END_REF], EXPY indicates the level of productivity associated with a country's pattern of specialization. Compared to the ECI index, EXPY has two limitations [START_REF] Valette | Do migrants transfer productive knowledge back to their origin countries?[END_REF]. First, it includes GDP per capita and is de facto correlated with it. Second, it does not take into account the proximity between products.

Despite these limitations, the estimates leave our conclusions about the positive role of digital connectedness in export sophistication and the negative role of maritime distance unchanged. However, the sub-Saharan Africa exception does not hold anymore, in the usual significance levels.

V. Conclusion

This paper focuses on the implications of the recent and rapid deployment of SMCs along African coasts for African trade patterns and makes three contributions to the empirical literature. First, we highlight a new dimension of the SMC infrastructure deployment, termed 'digital connectedness', reflecting a country's digital proximity to world markets. Second, we assess its impact on export sophistication using the economic complexity index. Third, we address possible reverse causality between the shape of the SMC network and countries' integration in world markets, using the number of (indirect) second-order SMC connections as instrument.

From a sample of 60 developing countries, including 23 sub-Saharan African countries covering the period 1995-2017, our results show that, while digital connectivity significantly increases the complexity of the export basket in all countries, there is geographic and temporal heterogeneity within our sample. Indeed, the effect of digital connectivity on export complexity is particularly strong in the period 2006-2015, indicating a catching-up of sub-Saharan African countries. Compared to the rest of the world, a 10pp increase in the share of world GDP reached by SSA countries' direct SMC connections leads to an additional increase ranging from 4.6 index points (FE estimates) to 5.3 index points (IV estimates). The overall increase in SSA's export complexity resulting from a 10pp increase in its connectedness equals 8.5pp, corresponding to 47% of the ECI sample standard deviation. The results also show that the positive effect of connectedness declines with both geographic and maritime distance to world markets, except for SSA, where these two types of distance actually increase the benefits of digital connectedness. For example, a 3,000 km increase in sea distance (roughly one standard deviation) reduces the positive effect of connectedness on export complexity by 47% in non-SSA countries but increases the positive effect of connectedness on export complexity by 75% in SSA.

Focusing on the additional channels through which digital connectivity operates, we document a mediating effect of Internet penetration and human capital, that is not specific to SSA countries. Finally, in exploring how digital connectedness materializes in exports upgrading, we found that digital connectedness increases exports of differentiated goods -goods for which the search costs are higher-and promotes both backward and forward participation in global value chains.

Online appendix

Appendix A: Additional tables VI.

Connectedness

Cumulative share of the world GDP reached by direct-that is, firstorder-cable connections.

Author's computation using SMC network worldwide.

Trade (% of GDP)

Trade is the sum of exports and imports of goods and services measured as a share of gross domestic product. WDI

Internet users (% of pop)

Internet users are individuals who have used the Internet (from any location) in the last three months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV, etc.

WDI

Remoteness

Remoteness from world markets, adjusted for landlocked-ness is the trade weighted average distance from world markets.

UN-CDP and FERDI's retrospective EVI series.

Sea distance

Average sea distance of country to its 10 main imports and exports trade partners

Author's computation using CERDI-Sea Distance Database [START_REF] Bertoli | The CERDI Sea-Distance Database[END_REF].

Shipping connect index

Liner Shipping Connectivity Index score indicates how well countries are connected to global shipping networks based on the status of their maritime transport sector.

UNCTAD Natural rents

Total natural resources rents (% of GDP) are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. Remoteness from world markets. Transportation costs and geographic distance have a crucial impact on international trade [START_REF] Falvey | Transport costs in the pure theory of international trade[END_REF][START_REF] Hummels | Transportation costs and international trade in the second era of globalization[END_REF]. Several empirical studies of bilateral trade have emphasized the negative relationship between distance and trade flows [START_REF] Brun | Has distance died? Evidence from a panel gravity model[END_REF][START_REF] Disdier | The puzzling persistence of the distance effect on bilateral trade[END_REF][START_REF] Krautheim | Heterogeneous firms, exporter networks and the effect of distance on international trade[END_REF][START_REF] Carrere | The distance puzzle and low-income countries: An update[END_REF])-and diversification is not an exception. [START_REF] Dennis | Trade facilitation and export diversification[END_REF] found that a reduction in export or international transport costs is associated with a gain in export diversification. In line with results from [START_REF] Parteka | Determinants of export diversification: An empirical investigation[END_REF] that positing remoteness form major markets as a robust determinant of export diversification, we resort to Remoteness Index for our empirical investigation. This index, sub-component of the United Nations' Economic Vulnerability Index (EVI) [START_REF] Cariolle | Has structural economic vulnerability decreased in Least Developed Countries? Lessons drawn from retrospective indices[END_REF], is the normalized minimum average distance to 33% of the world markets. 18 We expect export complexity to decrease with greater remoteness from world markets, but to account to eventual threshold effect in this variable, we also control for its squared value.

WDI

Country size and development level. Country size and development level, in particular through human capital development, are favourable to the enhancement of the size of export basket and the countries' diversification possibilities [START_REF] Hummels | The variety and quality of a nation's exports[END_REF][START_REF] Parteka | Determinants of export diversification: An empirical investigation[END_REF][START_REF] Starosta De Waldemar | How costly is rent-seeking to diversification: An empirical approach[END_REF]. To capture the role played by a large domestic market in increasing product variety and quality, we control for the logarithm of the population. We also use the logarithm of GDP per capita as a global proxy of the level of development. We expect these factors to exert a positive effect on export complexity.

Natural rents. While natural resource abundance was once considered a source of development [START_REF] Rostow | The Stages of Economic Growth: A Non-Communist Manifesto[END_REF], a vast literature on "resource curse" has highlighted the negative impact of natural resources on economic growth [START_REF] Frankel | The natural resource curse: A survey of diagnoses and some prescriptions[END_REF][START_REF] Van Der Ploeg | Natural resources: Curse or blessing?[END_REF][START_REF] Ross | What have we learned about the resource curse?[END_REF][START_REF] Venables | Using natural resources for development: Why has it proven so difficult?[END_REF]. An abundance of natural rents and a low level of economic diversification characterize resource-rich countries. Indeed, natural resources dominate export earnings and government revenues [START_REF] Ross | What do we know about economic diversification in oil-producing countries?[END_REF][START_REF] Bahar | One more resource curse: Dutch disease and export concentration[END_REF]. This results in a low level of economic diversification, making them vulnerable to economic shocks and conflicts [START_REF] Ross | How do natural resources influence civil war? Evidence from thirteen cases[END_REF][START_REF] Venables | Using natural resources for development: Why has it proven so difficult?[END_REF]. To account for the role of natural resources on the economic complexity, we include an indicator of total natural resources rents expressed as a share of GDP, provided by the WDI (and also rely on a decomposition of this indicator into oil, gas, mineral, and forest rents). We expect a negative sign for this variable.

FDI inflows.

Export complexity is more likely to be affected by FDI. By facilitating the transfer of knowledge, technology and managerial skills, FDI may promote the production and the export of more complex goods and services [START_REF] Hausmann | Economic development and the accumulation of knowhow[END_REF]. We draw upon FDI inflows retrieved from World Development Indicators. We expect this variable to have a positive impact on export complexity.

Trade openness is often associated with greater specialization [START_REF] Imbs | Trade, finance, specialization, and synchronization[END_REF], diversification [START_REF] Dennis | Trade facilitation and export diversification[END_REF][START_REF] Makhlouf | Trade openness, export diversification, and political regimes[END_REF], or greater complexity in export structure [START_REF] Keller | International trade, foreign direct investment, and technology spillovers[END_REF]. We use trade as a percentage of GDP, derived from the World Bank's WDI, as a measure of openness. Since the literature show that countries that are more open benefit most from technology diffusion, we expect a positive effect of openness on complexity.

Institutional quality. Institutions are important for the sophistication and complexity of the economy [START_REF] Makhlouf | Trade openness, export diversification, and political regimes[END_REF][START_REF] Saadi | Remittance inflows and export complexity: New evidence from developing and emerging countries[END_REF]. To capture this impact, we use the Freedom House imputed polity 2 index provided by the Quality of Government Institute (QOG) and deemed to perform better in terms of validity and reliability [START_REF] Hadenius | Cultural and economic prerequisites of democracy: Reassessing recent evidence[END_REF]. The index ranges from 0 to 10, 0 characterizing a less democratic country and 10 for the most democratic. We expect that an increase in the Polity 2 index will improve complexity of the export.

Internet and energy access are central for exports sophistication [START_REF] Cristelli | The virtuous interplay of infrastructure development and the complexity of nations[END_REF]. We control for internet users [START_REF] Lapatinas | The effect of the Internet on economic sophistication: An empirical analysis[END_REF] and access to electricity. Both data are derived from the World Bank's WDI database and are expected to influence positively exports complexity.

Real Effective Exchange Rate. The exchange rate is at the heart of the diversification strategy in developing countries. Studying export surges in developing countries, [START_REF] Freund | Export surges[END_REF] show that export accelerations are preceded by episode of large real devaluations and a reduction in exchange rate volatility. Thus, exchange rate depreciation increases entry into new products and markets and these new flows account for 25% of growth during surges. In the same vein, [START_REF] Iacovone | Shipping good tequila out: Investment, domestic unit values and entry of multi-product plants into export markets[END_REF] find that devaluations precede export "breakthroughs" in Mexican firms, while Tang and Zhang (2012) highlight the negative impact of exchange rate appreciation on firms' extensive margin. To account for the role of REER on complexity, we draw upon FERDI's Sustainable Competitiveness Observatory (SCO) data. The REER index is calculated as the weighted geometric average of the nominal exchange rate indices vis-a-vis the 10 main partners, total imports, and exports excluding oil of the country under consideration adjusted for relative prices. The weights are calculated according to the relative share of the partners over the period 2009-2013. A change below 100 reflects a real depreciation, and thus a tendency to undervaluation. In line with Freund and Pierola (2012) findings, we expect the REER to affect negatively the complexity of the export basket.

Figure

  Figure 1: Submarine cable deployment worldwide, 2000 versus 2017

Figure 2 :

 2 Figure 2: ECI evolution by region, 1995-2017

Figure 3 :

 3 Figure 3: Digital connectedness

Figure 4 :

 4 Figure 4: Trends in digital connectedness (% world GDP), 1995-2017

Figure 5 :

 5 Figure 5: Export complexity vs digital connectedness, 2017

Figure 6 :

 6 Figure 6: Second-order SMC connections

Figure 7 :

 7 Figure 7: Second-order SMC connections and digital connectedness, 1995-2017

Table 1

 1

	2nd-order	2nd-order	2nd-order	2nd-order
	Partner 2.a	Partner 2.b	Partner 2.c	Partner 2.d
	% 2.a of world	% 2.b of world	% 2.c of world	% 2.d of world
	GDP	GDP	GDP	GDP
		SMC connection
	SMC connection	SMC connection	SMC connection
		1st-order	1st-order
		Partner 1.a	Partner 1.b
		% 1.a of world	% 1.b of world
		GDP	GDP
		SMC connection	SMC connection
		Country i

presents OLS fixed-effect estimates of Equation 1 based on a sample of 60 developing and transition economies (including 23 from sub-Saharan Africa) covering the period 1995-2017. Overall, it appears that digital connectedness is positively related to economic complexity. Suspecting eventual autocorrelation given the large time

Table 1 : OLS fixed effect estimates

 1

	Dep var. ECI	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
	Period			1995-2017			2000-2017	2005-2017	2010-2017	2006-2015
	Connectedness	0.223 **	0.109 **	0.110 ***	0.128 ***	0.226 ***	0.128 ***	0.317 ***	0.205 **	0.137 ***	0.240 *	3.337 ***	0.268 ***	0.126
		(0.108)	(0.0527)	(0.0231)	(0.0470)	(0.0383)	(0.0470)	(0.0477)	(0.101)	(0.0487)	(0.134)	(0.878)	(0.0908)	(0.0945)
	SSA x connected				0.340 *	0.195	0.340 *	0.287	0.436 **	0.562 **	0.280	-2.947 ***	0.464 **	0.567 **
					(0.199)	(0.174)	(0.199)	(0.206)	(0.198)	(0.224)	(0.256)	(0.992)	(0.195)	(0.227)
	Lat Am x connected					-0.205 ***		-0.281 **		-0.209		-3.624 ***		-0.181
						(0.0536)		(0.140)		(0.146)		(0.890)		(0.167)
	MENA x connected					-0.131 ***		-0.125		-0.0376		-3.350 ***		0.0585
						(0.0341)		(0.0798)		(0.0985)		(0.872)		(0.129)
	South-East Asia x connected					0.0239		0.0404		0.240 ***		-3.009 ***		0.278 **
						(0.0466)		(0.0730)		(0.0803)		(0.924)		(0.112)
	South Asia x connected					-0.280 ***		-0.320 ***		-0.117		-3.396 ***		-0.0648
						(0.0560)		(0.0751)		(0.0797)		(0.911)		(0.112)
	Controls, country FE, year FE							Yes
	N	2497	1150	1150	1150	1150	896	896	633	633	363	363	528	528
	R 2	0.041	0.652	0.471	0.657	0.693	0.656	0.697	0.661	0.703	0.685	0.729	0.699	0.737

Notes: Standard errors are robust to heteroscedasticity and clustered by country except in column (3) where Driscoll Kraay AR (1) standard errors are reported. *** p<0.01, ** p<0.05, * p<0.1. FE dummy variable estimator, except in column (3) where within FE estimates are reported. Driscoll-Kraay standard errors are robust to very general forms of cross-sectional and temporal dependence when the time dimension becomes large.

Table 2 : 2SLS fixed-effect estimates -Regional effects

 2

	Dep var. ECI	(1)	(2)	(3)	(4)	(5)
	Period:		1995-2017		2006-2015
	(A) Connectedness (con)	0.161 **	0.124 *	0.304 ***	0.304 **	0.278
		(0.0682)	(0.0657)	(0.0531)	(0.123)	(0.205)
	(B) SSA x con		0.265	-0.0779	0.532 **	0.250
			(0.223)	(0.194)	(0.258)	(0.292)
	(C) Lat Am x con			-0.315 ***		-0.667 **
				(0.0738)		(0.261)
	(D) MENA x con			-0.169 ***		-0.0954
				(0.0603)		(0.358)
	(E) South-East Asia x con			-0.0721		0.0562
				(0.0586)		(0.136)
	(F) South Asia x con			-0.442 ***		-0.326 **
				(0.0907)		(0.153)
			First-stage estimates
	F-stat (A)	63.28***	82.58 ***	117.37***	50.06***	97.92***
	F-stat (B)		40.82 ***	19.41***	47.65***	30.14***
	F-stat (C)			25.70***		5.42***
	F-stat (D)			7.28***		2.02*
	F-stat (E)			345.76***		143.67***
	F-stat (F)			13.96***		615.46***
	Cragg-Donald F-stat	382.685***	503.183	121.077	115.980	6.482
	LM-stat	24.113***	31.373	20.245	17.771	10.44
	Controls, country FE, year FE			Yes
	N	1150	1150	1150	528	528
	R 2	0.650	0.657	0.689	0.698	0.731

Notes: Standard errors are robust to heteroscedasticity and clustered by country. *** p<0.01, ** p<0.05, * p<0.1. FE 2SLS dummy variable estimator. Control estimates are not reported in the table.

Table 3 : Digital connectedness and the geographical distance to main world markets

 3

	Dep var. ECI	(1)	(2)	(3)	(4)	(5)	(6)
	Sample:		All Countries		Coastal Countries
	Period:	1995-2017	2006-	1995-2017	2006-
				2015			2015
	(A) Connectedness (con)	0.330***	0.296**	0.889***	0.321***	0.290**	0.803***
		(0.118)	(0.119)	(0.233)	(0.120)	(0.125)	(0.243)
	(B) Con x remoteness	-0.0038** -0.0038** -0.016***	-0.0032*	-0.0035*	-0.014***
		(0.00185) (0.00187) (0.00424) (0.00193) (0.00193) (0.00426)
	(C) Con x SSA		0.570	-0.710		0.540	-0.974
			(0.742)	(0.889)		(0.798)	(0.927)
	(D) Con x SSA x remoteness		-0.00538	0.0263		-0.00277	0.0350*
			(0.0119)	(0.0174)		(0.0135)	(0.0192)
	Additional controls
	SSA x remoteness		0.0211	-0.0673		0.00723	-0.133
			(0.0577)	(0.0664)		(0.0831)	(0.118)
	Remoteness index	0.0758	0.0671	0.323***	0.0477	0.0460	0.252**
		(0.0535)	(0.0619)	(0.0938)	(0.0680)	(0.0685)	(0.111)
			First-stage estimations
	F-stat (A)	30.99	35.72	21.26	22.96	26.37	17.41
	F-stat (B)	33.22	33.97	38.19	33.91	28.26	33.07
	F-stat (C)		139.04	77.79		144.25	58.62
	F-stat (D)		94.45	41.9		102.11	36.96
	Cragg-Donald F-stat	220.269	194.980	36.606	156.917	137.18	23.743
	LM-stat	24.993***	19.48***	11.42***	21.721*** 24.13***	7.726***
	Controls, country FE, year FE			Yes
	N	1150	1150	528	1039	1039	484
	R 2	0.646	0.655	0.681	0.649	0.663	0.688

Notes: Standard errors are robust to heteroscedasticity and clustered by country. *** p<0.01, ** p<0.05, * p<0.1. FE 2SLS dummy variable estimator. Control estimates are not reported in the table. To address the mediating effects of remoteness, the squared term of the remoteness variable has been dropped from the econometric equation.

Table 4 : Digital connectedness and the sea distance to main trade partners

 4 Standard errors are robust to heteroscedasticity and clustered by country. *** p<0.01, ** p<0.05, * p<0.1. FE 2SLS dummy variable estimator. Control estimates are not reported in the table.

	Dep var. ECI	(1)	(2)	(3)	(4)	(5)
	Period:	1995--2017		2006-2015
	(A) Connectedness (con)	0.194	1.201 ***	1.593 ***	1.590 ***	-0.133
		(0.363)	(0.291)	(0.420)	(0.396)	(0.195)
	(B) Con x sea distance	-0.000004	-0.000178 ***	-0.00025 ***	-0.00025 ***	0.000015
		(0.00006)	(0.00005)	(0.00008)	(0.00007)	(0.00004)
	(C) Con x SSA		-2.437 ***	-2.612 ***	-2.695 ***	-2.417 ***
			(0.486)	(0.485)	(0.605)	(0.925)
	(D) Con x SSA x sea dist.		0.000348 ***	0.000398 ***	0.000408 ***	0.000271 ***
			(0.0000679)	(0.0000808) (0.0000909) (0.0000999)
	Additional controls
	SSA x Sea distance		-0.000912	-0.00167	-0.00152	0.000081
			(0.000804)	(0.00115)	(0.00140)	(0.00095)
	Sea distance	0.000154	0.000861	0.00203	0.00194	-0.00223 **
		(0.00067)	(0.00103)	(0.00146)	(0.00158)	(0.000875)
			First-stage estimations
	F-stat (A)	10.44	14.8	15.57	20.9	19.42
	F-stat (B)	10.68	16.77	18.7	31.19	18.72
	F-stat (C)		36.46	38.45	41.85	34.51
	F-stat (D)		29.72	29.84	37.32	34.75
	Cragg-Donald F-stat	61.486	47.031	28.296	30.515	90.671
	LM-stat	17.894***	8.326***	9.080***	8.387***	13.471***
	Controls, country FE, year FE		Yes		Yes †	Yes † †
	N	737	737	528	518	797
	R 2	0.624	0.674	0.707	0.708	0.774

Notes:

Table 5 : Digital connectedness and the shipping connectivity channel

 5 Standard errors are robust to heteroscedasticity and clustered by country. *** p<0.01, ** p<0.05, * p<0.1. FE 2SLS dummy variable estimator. Control estimates are not reported in the table

	Dep var. ECI	(1)	(2)	(3)	(4)
	Period:	1995-2017	2006-2015
	(A) Connectedness (con)	0.174	0.104	0.0228	-0.0324
		(0.265)	(0.161)	(0.209)	(0.196)
	(B) Con x SCI	0.00314	0.00370 *	0.00424 *	0.00468 *
		(0.00262) (0.00209)	(0.00243)	(0.00240)
	(C) Con x SSA		-0.277	-0.333	-0.743
			(0.573)	(0.635)	(0.609)
	(D) Con x SSA x SCI		0.0445	0.0568	0.109 **
			(0.0423)	(0.0478)	(0.0477)
	Additional controls
	SSA x SCI		-0.189	-0.413	-0.973
			(0.485)	(0.514)	(0.596)
	Shipping connectivity index	-0.328 *	-0.304 *	-0.258	-0.244
	(SCI)
		(0.179)	(0.178)	(0.186)	(0.213)
			First-stage estimations
	F-stat (A)	6.67***	20.24***	23.28 ***	10.47***
	F-stat (B)	35.49***	112.35***	88.97***	39.06***
	F-stat (C)		144.19***	143.52***	57.20***
	F-stat (D)		103.68***	143.09***	49.54***
	Cragg-Donald F-stat	19.19	46.389	38.863	40.42
	LM-stat	5.034**	8.360***	8.988***	8.972***
	Controls, country FE, year FE		Yes		Yes †
	N	681	681	492	482
	R 2	0.631	0.676	0.706	0.712
	Notes:

Table 6 : 2SLS fixed-effect estimates -The absorptive capacity channel

 6 Standard errors are robust to heteroscedasticity and clustered by country. *** p<0.01, ** p<0.05, * p<0.1. Control estimates are not reported in the table.

	Dep var: ECI	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	Absorptive capacity var:	Internet penetration	1ary enrolment rate	2ary enrolment rate	3ary enrolment rate
		1995-2017	2006-2015	1995-2017	2006-2015	1995-2017	2006-2015	1995-2017	2006-2015
	(A) Connectedness (con)	0.0425	0.000369	0.147	-2.932 ***	-2.552 **	-1.900	-0.357	-0.436	-0.292	-0.0947	-0.136	-0.0859
		(0.0758)	(0.0584)	(0.137)	(1.012)	(1.008)	(2.763)	(0.284)	(0.280)	(0.502)	(0.130)	(0.117)	(0.307)
	(B) Con x internet	0.00290 ***	0.00385 ***	0.00284 *
		(0.00076)	(0.00062)	(0.0016)
	(B) Con x e.r.				0.0297 ***	0.0257 **	0.0210	0.00620 *	0.00657 *	0.00660	0.00640 *	0.00669 **	0.00918
					(0.00990)	(0.00997)	(0.0257)	(0.00364)	(0.00367)	(0.00573)	(0.00352)	(0.00325)	(0.00726)
	(C) Con x SSA		0.343	0.634 *		-28.85 **	-2.676		-0.727	0.487		-0.358	-0.505
			(0.289)	(0.343)		(11.68)	(23.94)		(2.191)	(3.641)		(0.942)	(2.175)
	(D) Con x SSA x internet		-0.0480 ***	-0.0233
			(0.0176)	(0.0280)
	(D) Con x SSA x e.r.					0.281 **	0.0304		0.0131	0.000750		0.0141	0.0257
						(0.113)	(0.229)		(0.0264)	(0.0427)		(0.0259)	(0.0538)
	Additional controls
	SSA x internet		0.588 ***	0.214
			(0.173)	(0.318)
	SSA x e.r.					-0.0119	0.0112		-0.0147	-0.00647		0.0784	0.0251
						(0.0451)	(0.0503)		(0.0647)	(0.0626)		(0.159)	(0.139)
	Enrollment rate (e.r.)				-10.40 ***	-10.46 ***	14.11 **	3.581 ***	3.299 ***	-2.120 ***	5.011 ***	4.410 ***	-18.45 ***
					(1.400)	(1.413)	(5.643)	(0.517)	(0.523)	(0.772)	(0.716)	(0.756)	(6.709)
	Internet user (% pop)	-0.0495	-0.0126	-0.0741	0.00241	0.0358	-0.0401	0.0122	0.0395	-0.0405	0.0117	0.0500	-0.0375
		(0.0373)	(0.0439)	(0.0706)	(0.0387)	(0.0364)	(0.0674)	(0.0372)	(0.0369)	(0.0687)	(0.0373)	(0.0402)	(0.0682)
				First-stage estimations
	F-stat (A)	53.98	37.53	27.32	60.15	34.31	16.52	30.66	36.02	34.78	30.81	39.96	41.81
	F-stat (B)	97.12	58.14	67.52	59.18	34.8	16.91	30.59	38.24	41.57	30.94	41.12	51.24
	F-stat (C)		25.42	33.87		26.86	28.37		25.68	22.99		27.7	23.36
	F-stat (D)		7.64	7.63		26.8	28.7		24.38	22.54		23.49	21.04
	Cragg-Donald F-stat	122.986	139.992	56.312	131.977	270.721	53.755	186.542	274.393	76.958	185.623	262.082	84.706
	LM-stat	26.491***	2.930***	3.524*	29.340***	35.727***	15.763***	27.687***	32.018***	16.930***	27.593***	29.575***	18.200***
	Xit, country & year FEs						Yes
	N	1150	1150	528	1150	1150	528	1150	1150	528	1150	1150	528
	R 2	0.660	0.657	0.698	0.652	0.663	0.702	0.656	0.665	0.703	0.656	0.665	0.703

Notes:

Table A1 : Sample composition

 A1

	TableA2: Correlation table
	Variables		1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24
	(1) ECI		1.000
	(2) Connectedness	0.520 1.000
	(3) 2nd order	0.305 0.675 1.000
	Country Region (4) log (GDP p.c.)	Obs Country 0.629 0.365 0.309 1.000	Region						Obs
	AGO (5) Trade (% of GDP) Sub-Saharan Africa 0.287 0.186 0.110 0.279 1.000 15 KAZ Eastern Europe and post-Soviet Union		23
	CIV (6) Internet users Sub-Saharan Africa 0.483 0.449 0.551 0.618 0.216 1.000 23 BOL Latin America						23
	CMR (7) Remoteness Sub-Saharan Africa -0.081 -0.281 -0.338 -0.132 -0.039 -0.153 1.000 22 BRA Latin America						23
	COD (8) Sea distance Sub-Saharan Africa -0.316 -0.308 -0.066 -0.098 -0.174 -0.155 0.234 1.000 13 CHL Latin America					23
	COG (9) Shipping connectivity index 0.599 0.724 0.501 0.433 0.247 0.509 -0.198 -0.179 1.000 Sub-Saharan Africa 11 COL Latin America				23
	GAB (10) Natural rents Sub-Saharan Africa -0.298 -0.090 -0.034 0.171 0.029 -0.058 -0.130 0.367 -0.171 1.000 16 CRI Latin America			23
	GHA (11) Electricity (access) Sub-Saharan Africa 0.605 0.497 0.365 0.814 0.123 0.507 -0.205 -0.350 0.438 -0.065 1.000 23 DOM Latin America		23
	GIN (12) log (Population) Sub-Saharan Africa 0.139 0.376 0.269 -0.220 -0.370 -0.040 -0.110 -0.019 0.431 -0.208 -0.009 1.000 18 ECU Latin America	23
	KEN (13) FDI inflows Sub-Saharan Africa 0.089 -0.001 0.067 0.064 0.542 0.103 0.093 -0.001 0.051 0.049 -0.047 -0.223 1.000 23 GTM Latin America 23
	LBR (14) log (REER) Sub-Saharan Africa -0.091 -0.026 0.010 -0.042 -0.061 0.036 0.108 0.002 -0.008 -0.026 -0.034 0.028 -0.046 1.000 3 HND Latin America 23
	MDG (15) Internet penetration Sub-Saharan Africa 0.483 0.449 0.551 0.618 0.216 1.000 -0.153 -0.155 0.509 -0.058 0.507 -0.040 0.103 0.036 1.000 23 MEX Latin America 23
	MLI (16) 1ary enrollment rate Sub-Saharan Africa 0.083 0.319 0.582 0.095 0.076 0.478 -0.105 -0.016 0.068 0.143 0.116 0.064 0.145 0.056 0.478 1.000 3 PER Latin America 23
	MOZ (17) 2ary enrollment rate Sub-Saharan Africa 0.043 0.255 0.614 0.125 0.033 0.645 -0.113 -0.005 0.176 0.037 0.153 0.073 0.115 0.124 0.645 0.775 1.000 21 PRY Latin America 23
	MRT (18) 3ary enrollment rate Sub-Saharan Africa 0.045 0.273 0.623 0.123 0.040 0.646 -0.114 -0.002 0.175 0.053 0.151 0.073 0.131 0.126 0.646 0.805 0.975 1.000 13 SLV Latin America 22
	MUS (19) Oil rents (%GDP) Sub-Saharan Africa -0.154 0.041 0.046 0.368 0.047 0.017 -0.212 0.211 -0.063 0.914 0.172 -0.185 -0.033 -0.031 0.017 0.086 -0.008 0.005 1.000 3 URY Latin America 23
	NGA (20) FVA pc	Sub-Saharan Africa 0.402 0.276 0.197 0.348 0.788 0.344 -0.005 -0.162 0.446 -0.116 0.159 -0.143 0.440 0.004 0.344 0.056 0.065 0.065 -0.066 1.000 23 VEN Latin America 19
	SEN (21) DVX pc	Sub-Saharan Africa 0.449 0.282 0.277 0.610 0.637 0.555 -0.142 -0.078 0.390 0.142 0.311 -0.251 0.305 -0.010 0.555 0.159 0.163 0.167 0.201 0.802 1.000 22 DZA North Africa & the Middle East 9
	TGO (22) Diff exp. Pc Sub-Saharan Africa 0.425 0.270 0.206 0.411 0.793 0.394 -0.040 -0.150 0.429 -0.061 0.187 -0.168 0.424 -0.006 0.394 0.078 0.080 0.081 -0.013 0.935 0.834 1.000 17 EGY North Africa & the Middle East 22
	TZA (23) OE exp. Pc Sub-Saharan Africa 0.117 0.080 0.102 0.561 0.194 0.334 -0.152 0.194 0.019 0.644 0.272 -0.338 0.068 -0.012 0.334 0.151 0.106 0.115 0.672 0.148 0.607 0.244 1.000 23 IRN North Africa & the Middle East 13
	UGA (24) Ref Pr.exp.pc Sub-Saharan Africa 0.296 0.152 0.172 0.456 0.553 0.405 -0.086 -0.031 0.218 0.138 0.189 -0.250 0.273 -0.007 0.405 0.096 0.098 0.099 0.159 0.649 0.872 0.664 0.561 1.000 3 ISR North Africa & the Middle East 23
	ZAF	Sub-Saharan Africa	22 JOR		North Africa & the Middle East			18
	ZMB	Sub-Saharan Africa	23 MAR		North Africa & the Middle East			23
	ZWE	Sub-Saharan Africa	13 OMN		North Africa & the Middle East			23
	CHN	East Asia		18 QAT		North Africa & the Middle East			18
	KOR	East Asia		23 SAU		North Africa & the Middle East			23
	IDN	South-East Asia	23 TUN		North Africa & the Middle East			22
	KHM	South-East Asia	20 TUR		North Africa & the Middle East			8
	PHL	South-East Asia	23			Total							1150
	SGP	South-East Asia	23
	THA	South-East Asia	18
	BGD	South-East Asia	21
	IND	South-East Asia	23
	LKA	South-East Asia	17

Table A3 : Dependent and control variables, expected sign, and associated literature

 A3

	Variable	Definition	Source
	ECI	Economic Complexity Index.
			Observatory of Economic
	ECI+	Augmented Economic complexity Index taking into account the difficulty of exporting each product.	Complexity (OEC, MIT).

Table A4 : Descriptive statistics -Baseline sample: 1,150 observations

 A4 Variable's definitions and related literature are reported in TableA3(in the appendix).

				Appendix D: Rauch's exports
		Mean Std.Dev.	Min	Max	Source
	Dependent variable
	ECI	38.25	17.81	0.00	90.84	MIT's OEC
	Interest variable
	Digital connectedness	18.10	21.27	0.00	75.59	Authors
	Instrumental variable
	2 nd order cable connections	32.00	27.84	0.00	102.00 Authors. telegeography
	Control variables
	Log (GDP p.c.)	8.09	1.21	5.53	11.15	WDI
	log (Population)	16.88	1.41	13.29	21.05	WDI
	FDI inflows	3.67	4.52	-6.06	39.46	WDI
	Trade (% of GDP)	73.49	48.74	15.64	437.33	WDI
	Internet users	17.90	22.19	0.00	97.39	WDI
	log (REER)	4.68	0.40	3.04	14.65	FERDI
	Remoteness index	53.56	23.19	0.00	100.00	FERDI
	Natural rents	9.01	11.24	0.00	58.65	WDI
	Polity2	5.81	2.68	0.00	10.00	QOG
	Electricity (access)	73.26	30.33	3.44	100.00	WDI
	Sea distance	7678.01 2935.01 2494.37 18646.79	CERDI
	Shipping connectivity index 26.91	23.30	0.80	141.58	UNCTAD
	Absorptive capacity channel
	Internet penetration	17.90	22.20	0.00	97.39	WDI
	1ary enrolment rate	102.31	2.63	95.97	105.32	WDI
	2ary enrolment rate	78.39	6.65	67.79	90.21	WDI
	3ary enrolment rate	34.23	7.26	22.16	47.70	WDI
	Value chain participation
	FVA pc	0.79	4.21	0.00	43.10	UNCTAD-Eora
	DVX pc	0.46	1.13	0.00	9.96	UNCTAD-Eora
	Rauch's exports
	Diff exp. pc	1.23	5.21	0.00	51.43	UN COMTRADE
	OE exp. pc	0.78	1.86	0.00	17.94	UN COMTRADE
	Ref Pr.exp.pc	0.79	3.38	0.00	39.57	UN COMTRADE

Note:

Table D1 : 2SLS fixed-effect estimates -Digital connectedness and Rauch's exports -Conservative classification

 D1 Standard errors are robust to heteroscedasticity and clustered by country. *** p<0.01, ** p<0.05, * p<0.1. Control estimates are not reported in the table.

	Period: 2005-2017	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	Dep. Var:	Diff exp. pc	OE exp. pc	Ref Pr. exp. pc	Diff exp. pc	OE exp. pc	Ref Pr. exp. pc	Diff exp. pc	OE exp. pc	Ref Pr. exp. pc
	(A) Connectedness (con)	0.256**	0.0272	0.0443	0.131*	0.0117	0.0160	-0.193**	-0.0231	-0.0296
		(0.112)	(0.0204)	(0.0368)	(0.0728)	(0.00802)	(0.0164)	(0.0941)	(0.0143)	(0.0357)
	(B) SSA x con				0.235	0.0287	0.0526	0.584***	0.0570*	0.118
					(0.165)	(0.0237)	(0.0439)	(0.214)	(0.0317)	(0.0948)
	(B) Lat Am x con							0.497**	0.0280	0.00920
								(0.252)	(0.0278)	(0.0486)
	(C) MENA x con							0.0141	-0.00718	0.0115
								(0.145)	(0.0162)	(0.0336)
	(D) South-East Asia x con							0.272**	0.0344**	0.0403
								(0.128)	(0.0158)	(0.0410)
	(E) South Asia x con							0.416***	0.0283*	0.0639
								(0.138)	(0.0145)	(0.0618)
	Ref Pr. exports per capita	0.824**		0.236**	0.792**		0.228**	0.694**		0.200**
		(0.333)		(0.107)	(0.332)		(0.0998)	(0.288)		(0.0860)
	OE exports per capita	-0.322*	0.0583***		-0.336*	0.0565***		-0.350*	0.0524***
		(0.179)	(0.00588)		(0.177)	(0.00490)		(0.193)	(0.00818)
	Diff exports per capita		0.0511***	-0.0886		0.0513***	-0.0877		0.0469***	-0.102
			(0.0110)	(0.110)		(0.00984)	(0.108)		(0.0103)	(0.123)
					First-stage statistics
	Cragg-Donald F-stat	58.447	57.215	57.357	195.632	195.335	195.205	19.759	19.699	19.501
	LM-stat	10.524***	9.375***	9.595***	21.068***	20.915***	20.977***	14.628***	14.292***	13.758***
	Controls, country & year FEs	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	N	773	773	773	773	773	773	773	773	773
	R 2	0.816	0.318	0.252	0.847	0.349	0.276	0.857	0.363	0.286

Notes:

Table D2 : 2SLS fixed-effect estimates -Digital connectedness and Rauch's exports -Liberal classification

 D2 Standard errors are robust to heteroscedasticity and clustered by country. *** p<0.01, ** p<0.05, * p<0.1. Control estimates are not reported in the table.

	Period: 2005-2017	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	Dep. Var:	Diff exp. pc	OE exp. pc	Ref Pr. exp. pc	Diff exp. pc	OE exp. pc	Ref Pr. exp. pc	Diff exp. pc	OE exp. pc	Ref Pr. exp. pc
	(A) Connectedness (con)	0.143 **	0.0303	0.114 *	0.0684	0.0129	0.0591	-0.122 *	-0.0233	-0.0785
		(0.0707)	(0.0211)	(0.0625)	(0.0462)	(0.00854)	(0.0402)	(0.0722)	(0.0148)	(0.0483)
	(B) SSA x con				0.138	0.0321	0.104	0.352 **	0.0609 *	0.267 **
					(0.114)	(0.0241)	(0.0656)	(0.152)	(0.0323)	(0.133)
	(B) Lat Am x con							0.336 *	0.0308	0.129
								(0.172)	(0.0295)	(0.0885)
	(C) MENA x con							-0.0304	-0.00788	0.0545
								(0.0988)	(0.0165)	(0.0729)
	(D) South-East Asia x con							0.146 *	0.0361 **	0.133 *
								(0.0847)	(0.0168)	(0.0768)
	(E) South Asia x con							0.267 ***	0.0289 *	0.164 *
								(0.104)	(0.0153)	(0.0844)
	Ref Pr. exp. per capita	-0.00288	0.0611 ***		0.00717	0.0631 ***		-0.0472	0.0569 ***
		(0.357)	(0.00860)		(0.365)	(0.00639)		(0.349)	(0.00775)
	OE exp. per capita	0.608 ***		0.388 ***	0.582 ***		0.373 ***	0.546 ***		0.328 ***
		(0.212)		(0.122)	(0.208)		(0.118)	(0.200)		(0.108)
	Diff exp. per capita		0.0562 ***	0.0155		0.0548 ***	0.0124		0.0518 ***	-0.0150
			(0.0127)	(0.201)		(0.0117)	(0.197)		(0.0117)	(0.210)
					First-stage estimates
	F-stat (A)
	F-stat (B)
	F-stat (C)
	F-stat (D)
	F-stat (E)
	F-stat (F)
		Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	N	773	773	773	773	773	773	773	773	773
	R 2	0.805	0.328	0.620	0.824	0.365	0.654	0.832	0.379	0.664

Notes:

Tacit knowledge is not codifiable and hard to communicate, while multifarious knowledge is knowledge specific to an economic activity or task[START_REF] Hidalgo | Economic complexity theory and applications[END_REF].

See, for example, Ouassi-Olsson, L. "Investing in the exceptional African creativity", Entreprenante Afrique,

October 2021. https://www.entreprenanteafrique.com/en/investing-in-the-exceptional-african-creativity/

In this line of research,[START_REF] Regolo | Export diversification: How much does the choice of the trading partner matter?[END_REF] shows that similarly-endowed trade partners tend to exhibit a more diversified trade structure than differently-endowed ones, which is explained by greater competition stemming from identical trade costs. We guess that this mechanism could be extended to information costs.

A scenario that is highly plausible since a new connection to China would represent a 15pp increase in this share. This actually happened in 2017 to Djibouti when the Asia Africa Europe-1 (AAE-1) cable was deployed to connect France, Italy, to the Middle East, Central Asia, India, South-East Asia and China.

See Goldfarb and Tucker (2019) for a review of research on the distance-trade nexus in a digitalization context.

According toHidalgo (2009: 2), the main takeaway of this research field can be summed up as follow: "what a country produces matters more than how much value it extracts from its products".

Developing new concept and measures such as Economic Complexity, Product Complexity, Product Relatedness, and Country Fitness[START_REF] Hidalgo | Economic complexity theory and applications[END_REF][START_REF] Tacchella | A new metrics for countries' fitness and products' complexity[END_REF].

In 2014, "A single intercontinental submarine fibre can potentially carry more data, with less delay than could be achieved by combining all the world's active geostationary communications satellites together."(OECD, 2014: 20).

As an illustration of the large costs related to this infrastructure deployment, the WACS connecting South Africa and the West African coast to Europe since 2012 cost US$600 million, while the AAE-1 connecting Asia, Africa and Europe since 2017 cost US$800 million. For more information, see: https://subtelforum.com/submarine-cable-map/ or https://www.submarinenetworks.com/en/insights/a-new-coming-for-submarine-cable-systems-the-independentinfrastructure-developers

 11 The concern for a possible influence of policy on SMC network density is further lowered controlling for internet penetration rates, which is the combined outcome of telecommunications policies and a country's digital absorptive capacity. Moreover, estimations additionally controlling for a critical component of the terrestrial infrastructure, i.e., the country number of Internet Exchange Points, remain strictly unchanged. Results can be provided upon request.

The period 2006-2015 also corresponds to the episodes of sharp increases in SSA's economic complexity highlighted in Figure2. The SSA's average ECI score is 27.13, an increase of 11.46% over the average score over the entire 1995-2017period.

 13 First-stage F-stat, Cragg-Donad F-stat, LM-weak test, and in Appendix C, Hansen

tests.14 When Latin-America is taken as reference group, interaction terms are positive and significant, except for South Asia, with SSA displaying the strongest marginal effect. Estimates can be provided upon request.

We obtain a 23% correlation between these two distance variables in our baseline sample (TableA2in the appendix).

To avoid sample attrition, we filled-in missing values through linear interpolation and extrapolation. It seemed reasonable to us using these technics since we expect these variables to change slowly over time. Estimates using the original primary enrolment rate variable (best documented) shows little difference with those using the interextrapolated one.

Estimates based on the liberal classification are available in TableD2in the appendix.

See https://www.un.org/development/desa/dpad/least-developed-country-category/evi-indicators.html and also CDP Secretariat. Note on measuring remoteness for the identification of LDCs. August 2015.

1 This paper has been prepared for and with the financial support of the African Economic Research Consortium. It also benefitted from the support of the French national research agency under program ANR-10-LABX-14-01. Results and their interpretation reflect the views of the authors alone.


Intercomprehension (also known as receptive multilingualism or lingua receptiva) occurs when people communicate with each other, each speaking his or her own language, while understanding the language of the other. This is possible when the speakers have learned the other language to some extent or when the languages are mutually intelligible, i.e. when they are similar enough to allow for a certain degree of spontaneous mutual understanding [START_REF] Gooskens | Measuring crosslinguistic intelligibility in the Germanic, Romance and Slavic language groups[END_REF]. Generally, intercomprehension works best when the speakers' respective native languages are close to each other in vocabulary, morphology and syntax, such as Italian and French or Czech and Slovak. Intercomprehension is widespread and well-studied in stable bilingual or multilingual settings such as immigrant families or border regions (e.g. Scandinavia). However, when it comes to interactions with or between mobile citizens, its potential is still far from being realised.

What does research tell us?

According to [START_REF] Braunmüller | Aspects of Multilingualism in European Language History[END_REF], the rise of the monolingual nation state has led to intercomprehension being used less frequently and becoming largely unknown by the wider public. But then, intercomprehension might be expected to become more frequent in the multilingual settings of European society, and one suspects that its spontaneous use is much more frequent than is generally assumed.

Intercomprehension has been found to be one of the fairest modes of multilingual communication because it allows each person to speak his or her own language.

Given that it takes less time to acquire receptive rather than productive skills (particularly in a language from the same language family), intercomprehension is also a rational mediation choice for people who travel often or move from one country to another; the development of reading skills often comes first, encouraging progress in other skills. Furthermore, intercomprehension allows for truly intercultural interactions, as both speakers adapt to the other.

Illustration and evidence

Over the past twenty years, several research projects, including some funded by the European Commission (e.g. EuRom4, EuroCom) have examined numerous facets of intercomprehension and resulted in the development of specialised teaching materials for language teachers or language learners. However, several questions require closer examination, such as the conditions of the spontaneous use of intercomprehension by highly mobile citizens without any particular language training. For this reason, the MIME project has focused its empirical studies about intercomprehension in two very specific multilingual settings.

Our main study has focused on Italian families who had adopted a non-Italian-speaking child. Observation of two Italian families adopting a child from Chile showed that intercomprehension was used spontaneously by all family members. When asked about this, the parents declared that they preferred intercomprehension to other strategies because it allowed them to express themselves accurately.

Machteld Meulleman Alice Fiorentino

Université de Reims Champagne-Ardenne

What is intercomprehension

and what is it good for?

Policy implications

Strong policy support is essential in order to move from potential mutual intelligibility to actual intercomprehension between speakers. The following initiatives can be suggested.

1. Make people aware that the many formal similarities between cognate languages provide direct access to information.

2.

Make people confident about their spontaneous intercomprehensive skills and encourage them to boost those skills through practice or training.

3. Encourage engaged in face-to-face interactions to express themselves in the languages they know best, whenever they are capable of understanding each other to a sufficient extent (whether because they are proficient in a closely related language or because they have some passive knowledge of the other's language or a combination of the two).

Of course, accuracy can only be communicated if the languages allow a sufficient degree of mutual intelligibility, as in the case of Spanish and Italian. In addition, a good deal of cooperation is necessary for oral intercomprehension to work. Parents mentioned that they initially adapted their speech in order to facilitate mutual understanding, adopting an inclusive attitude. In the context of child adoption, intercomprehension allows equal participation of all family members, which enhances bonding in the family.

Another study on the languages used by international football professionals in France indicated that the use of intercomprehension is often regarded with suspicion. The Argentine football trainer Marcelo Bielsa, for instance, who uses consecutive interpretation at press conferences, was asked about his intercomprehensive skills by a journalist, suggesting that his partial language skills manifest the lack of desire to speak French. Although intercomprehension is widely used and highly valued in informal contexts, it remains less accepted in professional contexts. Fortunately, studies of professional contexts show that the more intercomprehension is used, the better it is accepted [START_REF] Berthele | Receptive multilingualism in the Swiss Army[END_REF].

Journalist (in