metadata: authors: 0: author: Fontagné,Lionel
1: author: Lebrand,Mathilde Sylvie Maria
2: author: Murray,Siobhan
3: author: Ruta,Michele
4: author: Santoni,Gianluca
count: Africa
docna: 0: docna: Trade and Infrastructure Integration in Africa
docty: Policy Research Working Paper
abstracts: cdata!: Economic integration of the African
            continent rests on two pillars: the ratification of an
            ambitious trade agreement and massive investment in
            transportation infrastructure. Leveraging a newly created
            city-level database on African exporters&#x2019;
            transport times, transport route optimization and general
            equilibrium modeling of international trade, the paper
            quantifies the impact of greater trade and transport
            integration in Africa. A pan-African agreement, such as the
            African Continental Free Trade Area, would increase African
            countries&#x2019; exports by an average of 3.4 percent
            and increase gross domestic product by 0.6 percent.
            Complementing trade integration by reducing transportation
            time on roads, ports and border posts would increase exports
            by 11.5 percent and increase gross domestic product by 2
            percent. Major transport investments are necessary to reap
            the full benefits of the African Continental Free Trade Area.
display_title: Trade and Infrastructure Integration
            in Africa
listing_relative_url: /research/2023/11/34198137/trade-infrastructure-integration-africa
topicv3: Infrastructure and Growth m1326244 1083,Transport Impact on Trade m1326911 1814,Trade Policy and Integration m1327407 775
publishtoextweb_dt: 2023-11-16T00:00:00Z
dois: 10.1596/1813-9450-10609
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: NA
1: author_key: 1457978
2: author_key: 892365
3: author_key: 1245806
4: author_key: 1196032
model_extractions: Policy Research Working Paper 10609

# Trade and Infrastructure Integration in Africa

_Lionel Fontagné Mathilde Lebrand Siobhan Murray Michele Ruta Gianluca Santoni_ Africa Region & Infrastructure Chief Economist Office November 2023
Policy Research Working Paper 10609

# **Abstract**

Economic integration of the African continent rests on two pillars: the ratification of an ambitious trade agreement and massive investment in transportation infrastructure. Leveraging a newly created city-level database on African exporters’ transport times, transport route optimization and general equilibrium modeling of international trade, the paper quantifies the impact of greater trade and transport integration in Africa. A pan-African agreement, such as the African Continental Free Trade Area, would increase African countries’ exports by an average of 3.4 percent and increase gross domestic product by 0.6 percent. Complementing trade integration by reducing transportation time on roads, ports and border posts would increase exports by 11.5 percent and increase gross domestic product by 2 percent. Major transport investments are necessary to reap the full benefits of the African Continental Free Trade Area.

This paper is a product of the Office of the Chief Economist, Africa Region and the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at mlebrand@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Trade and Infrastructure Integration in Africa<sup>∗</sup>

Lionel Fontagn ́e (Bank of France, CEPII and PSE)<sup>†</sup> Mathilde Lebrand (World Bank)<sup>‡</sup> Siobhan Murray (World Bank)<sup>§</sup> Michele Ruta (IMF)<sup>¶</sup> Gianluca Santoni (CEPII)<sup>‖</sup>

**Key Words:** infrastructure; preferential trade agreements; structural gravity; general equilibrium. **JEL Codes:** F14, F15.

> ∗The views expressed in this paper are the authors’ and do not necessarily reflect those of the institutions they belong to. We thank Antoine Bou ̈et, C ́eline Carr`ere, Thierry Mayer, Tristan Reed, Ariell Reshef, Hakobyan Shushanik, Farid Toubal and participants to various seminars and conferences who provided very useful comments on previous versions of this paper. Lionel Fontagn ́e thanks the support of the EUR grant ANR-17-EURE-0001.

> †lionel.fontagne@banque-france.fr.

> ‡Email: mlebrand@worldbank.org.

> §Email: smurray@worldbank.org.

> ¶Email: mruta@imf.org.

> ‖Email: gianluca.santoni@cepii.fr
# **Introduction**

Economic integration of the African continent rests on two pillars: large investments in transportation infrastructure and the ratification of an ambitious pan-African trade agreement. Both aim at reducing barriers to trade, but in different ways. Better transport infrastructure reduce transport costs between African countries and between them and the rest of the world, and trade facilitation programs reduce queuing times at borders. Preferential trade agreements (PTA) reduce frictions directly between trading partners by reducing tariffs and harmonizing policies on e-commerce, investment, and intellectual property. Infrastructure improvements and trade reforms are therefore potentially complementary: reducing trade frictions alone may not be sufficient if the contracting parties are poorly connected; similarly, improving infrastructure connectivity alone may not be sufficient in the presence of trade barriers (tariff and non-tariff). The contribution of this paper is to address the question of how the effects of both pillars of integration operate in combination using granular data on transport infrastructure, information on the depth of regional trade agreements and general equilibrium trade modeling.

To quantify the joint impact of trade reforms and infrastructure investment in Africa, we first build a new citylevel database of travel times using a geographic information system (GIS). We map the current infrastructure and border delays for the entire continent (both within Africa and between Africa and the rest of the world). We then add the infrastructure projects as listed in the Program for Infrastructure Development in Africa (PIDA) and assess their impacts on reducing transportation times. For trade policy, we rely on data from the World Bank’s Deep Trade Agreements database (Mattoo, Rocha & Ruta 2020); which contains detailed information on the content of trade agreements on a range of topics, including competition policy, investment, capital movements, and intellectual property rights protection. We follow the cluster analysis on the distribution of provisions done by Fontagn ́e, Rocha, Ruta & Santoni (2023) to sort the agreements according to their ambition into Shallow, Medium, and Deep treaties. We use both datasets to quantify the combined trade and welfare impacts of the infrastructure investments as planned in the PIDA and an ambitious (i.e., Deep) trade agreement for the African continent.

We quantify the impacts in four steps. First, we estimate a structural gravity model (Anderson & van Wincoop 2003) in which we include the level of ambition in trade agreements to explain bilateral trade flows. We include both domestic and international trade flows, as well as the trade deflection effect due to border crossings (an inverse measure of deepening globalization).

Second, we give a functional form to the unobserved bilateral component that depends on transportation time, contiguity, language proximity, past colonial relations, and other unobservable country characteristics for the year 2018. This strategy allows us to determine the elasticity of domestic and international sales to transportation time.

Third, after verifying that the elasticity resulting from our data is consistent with estimates widely used in the literature (Hummels 2007, Hummels & Schaur 2013), we quantify the effect of planned infrastructure investments on transportation.

As a fourth and final step, we construct counterfactuals for trade policy and the transportation network. For infrastructure, we construct two alternative transportation networks based on the projected investments for 2
roads alone and for the combination of roads, ports and land borders. For each of these networks we obtain a new matrix of transport times between African countries and with the rest of the world. On trade policy, we consolidate the various existing trade agreements in Africa into a new “ambitious” trade agreement, such as the African Continental Free Trade Area (AfCFTA). Then we simulate the single and combined impact of these reforms on trade and gross domestic product of African economies. We show that an ambitious trade agreement would increase African countries’ exports by an average of 3.6 percent and their GDP by 0.6 percent. Complementing trade integration by reducing transportation time on roads, ports and border posts would increase exports by 11.5 percent and GDP by 2 percent. These results suggest that infrastructure improvements are essential to reap the full benefits of the AfCFTA.

Our paper draws on several strands of research. First, there is a growing body of literature that examines the impact of transportation infrastructure projects on transportation costs, including Donaldson & Hornbeck (2016), Donaldson (2018), Fajgelbaum & Redding (2022), Allen & Arkolakis (2022), and Fan, Lu & Luo (2023). While most of this research focuses on one country and one transportation network, our work covers most African countries (and the rest of the world) and considers roads, ports and land borders simultaneously. A related stream of research examines the impact of transportation costs on exports as mediated by infrastructure, (Limao & Venables 2001, Martincus, Carballo & Cusolito 2017).<sup>1</sup> A second strand of literature aims to quantify the income gains from new transportation infrastructure using general equilibrium models of trade (Co ̧sar & Fajgelbaum 2016, Fajgelbaum & Schaal 2020). We use on a general equilibrium model of an endowment economy to uncover the trade and income effects of a fall in the cost of trade that induces a reshaping of the geography of trade flows. This fall in the trade cost is the result of a combination of regulatory change (a regional trade agreement) and infrastructure investment.

Third, trade models have been used extensively to quantify the effects of trade agreements (see Lim ̄ao 2016 for a survey), including recent Structural Gravity General Equilibrium models (Anderson & Yotov 2016, Fontagn ́e & Santoni 2021, Fontagn ́e et al. 2023), New Quantitative Trade Models (Costinot & Rodr ́ıguez-Clare 2014, Caliendo & Parro 2015) and Computable General Equilibrium models.<sup>2</sup> While most papers examine the impact of infrastructure investment and trade reforms in isolation, we quantify their combined impact for Africa.

Finally, by combining GIS analysis and trade modeling, our work relates to a final stream of research that uses granular geographic data to shed light on the consequences of major infrastructure projects. The analysis of China’s Belt and Road Initiative is a recent and striking example of this (de Soyres, Mulabdic & Ruta 2020, Baniya, Rocha & Ruta 2020, Bird, Lebrand & Venables 2020, Lall & Lebrand 2020). We depart from the latter works in three ways: (i) we extend the transportation network developed by de Soyres, Mulabdic, Murray, Rocha & Ruta (2019) to the entire African continent for both regional and international transportation; this provides us with an ideal setup for quantifying the effect of a change in relative trade costs. (ii) We expand the types of transportation projects analyzed. (iii) We use a structural gravity general equilibrium approach

> 1A related but distinct line of research studies the impact of infrastructure development on allocative efficiency in developing economies. Asturias, Garc ́ıa-Santana & Ramos (2019) use plant-level data and focus on domestic sales in India, while we use multi-country aggregate data and consider domestic and international sales.

> 2Examples of recent applications of New Quantitative Trade Model (NQTM) and Computable General Equilibrium models (CGE) to the Africa region are presented in the following studies Abrego, Amado, Gursoy, Nicholls & Perez-Saiz (2019) (for NQTM) and Bengoa, Mathur, Narayanan & Norberg (2021), World Bank (2020), African Development Bank (2019) (for CGE).

3
to study the effects of “deep” integration through preferential trade agreements combined with infrastructure investment. More generally, our work is also related to the findings of Goldberg & Reed (2022) on the role of demand constraints in national development and, in particular, on how these constraints can be alleviated through international market integration.

The remainder of the paper is organized as follows. Section 1 describes the data used in the analysis. Section 2 presents the ex-post quantification of the trade effects of trade and transport reforms in Africa. Section 3 quantifies the impact of planned infrastructure investments on transport time. Section 4 presents a general equilibrium counterfactual analysis. Concluding remarks follow.

# **1 Data overview**

Analyzing trade and infrastructure in a general equilibrium structural gravity framework requires specific data. The focus of this section is the development and validation of an original dataset on transport times of African exporters. Built using Geographic Information System tools and network analysis, it provides a detailed, finescale perspective on transport times. Given the critical role that transport times play in shaping trade patterns, the production of such data is a contribution in its own right. We also discuss the sources of trade and production data and how we combined them to get an accurate picture of domestic and international trade flows.

## **1.1 Transportation Time**

Transportation times become prohibitive not only when roads are in poor condition, but also when there are delays at borders or operations at ports are inefficient. To get a complete picture of transportation costs for African exporters, we take all these aspects into account and construct a new database of transportation times at the city level. We first map the current infrastructure network to obtain our baseline transport time matrix. We then use the list of investments from the Infrastructure Development Program for Africa (PIDA) in its long-term vision (2012-2040) to create our counterfactual.

We start by mapping the current transportation network to calculate transportation times between large cities in African countries and the rest of the world. The nodes in the global network, which serve as export destinations in the analysis, are all cities (worldwide) with a population over 500,000 and the two most populous cities, data permitting, in each African country. This gives a total of 1,037 cities connected to the transportation network and 32 cities with a population of less than 50,000. The subset for Africa includes 165 connected cities that serve not only as destinations but also as export origins. The final network is solved for each origindestination pair, and routing is determined by the shortest path, adjusting for a maritime preference parameter.

The construction of the transportation network at the city level proceeds as follows.<sup>3</sup> We first build a transportation network for Africa and the rest of the world that includes land and maritime connections. For Africa, we rely primarily on major roads from Open-Street Map, including different classes of roads for which we assume different speeds (see Table A1 in the Appendix). For the rest of the world, we use the land and maritime

> 3To be compatible with trade data, the city-level network is then aggregated to the country level using a theoretically consistent approach. The aggregation procedure, following Hinz (2017), is described in detail below.

4
networks and speed assumptions from de Soyres et al. (2019). In doing so, we limit the analysis to road and maritime routes for shipments within Africa, while considering rail and maritime connections for shipments to the rest of the world.<sup>4</sup> Two main reasons justify this approach. First, road is by far the main transport mode for trade within Africa, while most international trade in the rest of the world travels by sea and by rail.<sup>5</sup> Maritime shipping accounts for 80% in volume and 70% in value of global trade and rail is the second transportation mode used (International Transport Forum 2017). Second, most transport reforms in Africa included in the PIDA, as discussed in detail in section 3, consist of road and maritime infrastructure investments. This approach allows us to incorporate most of PIDA’s proposed interventions.

We then integrate the land transport network by including delays at (land and sea) borders and cargo loading and unloading times at seaports. For this purpose, we first use the World Bank’s Doing Business indicators, which provide country-level information on the ease of crossing land or sea borders by measuring the number of documents required to trade, days to cross the border, and costs associated with exporting and importing.<sup>6</sup> We limit our analysis to waiting times to import and export for the latest year available in the dataset. If a country has a maritime gateway, we consider both delays at the land border and delays at the maritime border.

For each country, we define both land and sea border delays if the country is coastal and only land border delays if the country is landlocked. Because data are reported only for the most used gateway per country, which for coastal countries is generally a port, we assume that unobserved land-border times are a fraction of reported sea-border times.<sup>7</sup> In addition to delays at the maritime border, ships may experience additional delays for loading and unloading, which we calibrate using the median number of days container ships stay in ports, derived from UNCTAD data.<sup>8</sup> Finally, to combine data on land borders and port delays, we rely on the following assumptions. (i) Trucks crossing a border must pass through customs in the country of departure and the country of arrival (total crossing times are equal to the sum of times at the border of departure and the border of arrival). (ii) Export and import procedures at sea borders add up to logistics delays at ports (total times at sea borders are the sum of the country’s sea clearance time plus delays at ports).

In order to reduce border delays, several countries have implemented One-Stop-Border Posts (OSBP) to enable goods, people, and vehicles to stop in a single facility in which they undergo necessary controls to exit one state and enter the adjoining state. We include such facilities using maps and status information from the PIDA dashboard which includes up-to-date implementation status and various sources (World Bank reports

> 4Air transport is likely to be the preferred option for perishable goods, especially flowers (HS06) or vegetables (HS07). These goods represent less than 0.04 percent of an African country’s total exports in 2018. If all chapters from HS01 to HS08 are considered as perishable goods, the share is still less than 0.09 per cent.

> 5The prevalence of roads as a mode of transportation is also reflected in the PIDA action plan, where most of the priority transportation projects concern the road network (or a combination of roads and borders). Railways, on the other hand, are typically associated with port hub development, except for the Djibouti-Addis corridor and the Kinshasa-Brazzaville road-rail bridge project. We also prioritize road transport because the rail network in Africa is composed of several disconnected components with heterogeneous market sizes, making them difficult to compare.

> 6Note that we use border compliance time and do not include export documentation compliance (which is a one-time cost). Whether the country of entry is the final destination of the shipment or just a transit point, the border compliance time is the same. There may be a risk of overestimating border compliance time for transit shipments. However, in a non-free trade zone, it is not obvious that transit trucks would have less wait time than trucks arriving at their final destination. In the context of a broad study of Africa, using a single border wait time seems a safe simplifying assumption.

> 7Details can be found in Table A2 in the Appendix A.

> 8UNCATD reports ship dwell time at ports, this information is constructed from Maritime Traffic data and can be accessed here: `https://unctadstat.unctad.org/wds/TableViewer/tableView.aspx`

5
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From the previous analysis we derived the (shortest) transport time between each pair of cities. As our trade and production data are at the country level, a consistent aggregation approach is required. We address this by considering three essential parameters: location-specific friction, trade elasticity, and trade elasticity with respect to transport time. We aggregate transport time between cities at the country pair level using a theoretically robust iterative method that integrates population weights and a calibrated friction elasticity, as illustrated in Hinz (2017).

The theoretically consistent aggregation for the frictions at the level of the city pairs is as follows: When aggregating at the level of a pair of countries _ij_ , with exporter country _i_ and destination country _j_ , we take into account both the relative population of cities _k_ in _i_ and _l_ in _j_ , as well as the transport time elasticity, denoted as _δθ_ .<sup>12</sup> The transport time elasticity is calibrated using a simple iterative procedure as follows. We start with an arbitrary initial value of the elasticity (i.e. _δθ_ = _−_ 1). Then, we estimate the elasticity in a cross-section gravity equation for the year 2018 with origin and destination fixed effects and controlling for PTAs, language proximity, and shared colonial history (border dummies are not included because border crossing time is already included in transport time).<sup>13</sup> We retrieve the transportation time coefficient and then use it as a parameter in the aggregation equation ( _δθ_ = _β_<sup>_transportation_</sup> ). In the next iteration, this new aggregation parameter is used.<sup>14</sup> Using a network analysis to quantify shipping times and the impact of new and improved transportation infrastructure has advantages and disadvantages. On the plus side, a network analysis can be a useful tool to support modeling of international trade flows. It can enhance the understanding of the impact of changes in infrastructure or other factors on an interconnected system allowing to extract information from spatial data. Specifically, a network analysis enables the measurement of the time it takes to go from one location to another taking into account the overall quality and quantity of infrastructure, physical obstacles (e.g. rough terrain) or other barriers (e.g. border delays), and relative performance measures (e.g. processing time at ports or when crossing a border). As with any modeling exercise, it should be acknowledged that our network is a simplified version of reality and results are affected by the quality of inputs. For example, due to limited data availability, we cannot include some elements that might influence the definition of optimal routes, such as freight service quality, service frequency, conflicts or adverse weather events.

# **1.1.1 Transportation Time Validation**

Before using it as an input in the general equilibrium counterfactual analysis of trade and infrastructure reforms, we validate the transportation time in our dataset using evidence from Hummels & Schaur (2013) and Hummels

> 12The elasticity is a combination of trade elasticity _θ_ and location specific friction elasticity _δ_ .

> 13To retrieve the aggregation parameter we rely on a PPML estimator.

> 14We repeat this process until the estimated coefficient remains unchanged in its 5th digit. We do this separately for transport times with and without the sea route preference assumption. The procedure converges in 7 iterations in both cases. Without sea preference, the estimated _δθ_ is -0.852, with sea preference, the estimated parameter is -0.780.

7
(2007). Table 1 reports the results of the two-step validation exercise.

First we aim at identifying the tariff equivalent of one additional day of shipment for African exporters with the rest of the world by regressing the log of trade in 2018 on the number of days ( _days_<sup>#</sup> _ij_<sup>)neededtoreachany</sup> of the destination markets in the sample.<sup>15</sup> The estimated equation reads: where _i_ is the exporter country, _j_ the importer country, _Dij_ a vector of bilateral trade frictions (excluding distance), with _i_ being restricted to African exporter countries while _j_ being any destination in the sample.

Results are shown in column 1 and 2 of Table 1. In column 1 we focus on the shortest path in the transportation matrix without restrictions while in column 2 we add, whenever possible, a maritime preference to the algorithm searching for the shortest path. The estimated coefficients are then translated into tariff equivalent using an elasticity of substitution of _σ_ = 6, according to the following formula: Tariff Equivalent = ( _exp_ [( _β_<sup>_days_</sup> _/ − σ_ ) _−_ 1]) _∗_ 100.<sup>16</sup> In our preferred specification, column 1, an additional day of transport is equivalent to a 2.4 percent tariffs _for African exporters_ , a plausible value considering that Hummels & Schaur (2013) estimate _for the United States_ a tariff equivalent in the range of 0.6 to 2.1 percent. With a preference for maritime routes in the calculation of the optimal transport time, we obtain a tariff equivalent of 1.8 percent (in column 2).

In the second step, that is, in column 4 of Table 1, we regress the number of days against distance (without maritime preferences) and then use the equivalent tariff estimates from column 1 to infer the elasticity of trade costs (which we do not observe) to distance as in Hummels (2007), using _exp_ [( _β_<sup>_days_</sup> _/ − σ_ ) _−_ 1]) _∗ βdist_ . The results in column 6 replicate the analysis on data with maritime preferences, using the equivalent tariffs derived from column 2. The estimated equation reads: Column 3 and 5 show the estimation using the log number of days as a dependent variable by sake of comparison. In both columns 3 and 5, the estimated elasticity is around one: the number of days is proportional to the distance, as expected.

The regression results on the number of days indicate an elasticity of trade costs to distance of 0.180 when transportation time is calculated without the maritime preference (column 4) and 0.178 when more weight is given to the maritime route (column 6). The implied elasticity for African exporters is therefore slightly higher than 0.151, the one estimated for the United States in Hummels (2007). The evidence presented here suggests that transport time data can be used with some confidence to quantify the economic impact of trade reforms and infrastructure investments in Africa.<sup>17</sup>

> 15Note that the trade data used for the transport time validation also include domestic sales. The construction of the trade data is detailed in Section 1.2.

> 16The average of elasticities of substitution significant at the 1 percent level is 6.3 in Fontagn ́e, Guimbard & Orefice (2022).

> 17Note that the estimates presented to date are for 2018, and repeating the analysis for 2014 produces virtually unchanged results. The corresponding tables are available on request.

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Table 1: Transportation time data validation

|Dep Var:|_log_(<br>(1)|_Xij_)<br>(2)|Days_ij_<br>log(#)<br>(3)|Days_ij_<br>#<br>(4)|Days<sup>_MAR_</sup><br>_ij_<br>log(#)<br>(5)|Days<sup>_MAR_</sup><br>_ij_<br>#<br>(6)|
|---|---|---|---|---|---|---|
|# Days_ij_|-0.143***<br>(0.012)||||||
|# Days<sup>_MAR_</sup><br>_ij_||-0.105***<br>(0.009)|||||
|log(Distance)_ij_|||1.116***<br>(0.062)|7.472***<br>(0.408)|1.203***<br>(0.066)|10.090***<br>(0.535)|
|Contiguity_ij_|2.309***|1.983***|0.403**|-0.950|0.174|-4.492***|
||(0.415)|(0.453)|(0.165)|(1.037)|(0.182)|(1.624)|
|Language Proximity_ij_|0.441***|0.360***|0.016|0.273**|0.001|-0.417**|
||(0.062)|(0.062)|(0.013)|(0.126)|(0.013)|(0.175)|
|Colonial Ties_ij_|0.718**|0.768**|-0.007|0.192|0.004|0.741|
||(0.316)|(0.323)|(0.079)|(0.608)|(0.074)|(0.689)|
|Observations|2038|2,038|2,038|2,038|2,038|2,038|
|# Origins|20|20|20|20|20|20|
|# Destinations|110|110|110|110|110|110|
|Period|2018|2018|2018|2018|2018|2018|
|Transportation|no MAR|MAR|no MAR|no MAR|MAR|MAR|
|Tarif Equivalent|2.4|1.8|||||
|Distance|||||||
|Elasticity||||0.180||0.178|

_Note:_ MAR indicates that the transportation times are estimated considering maritime preferences. Exporter( _i_ ) and Importer ( _j_ ) fixed effects are included in all regressions. Standard errors in parenthe*** ** * ses are clustered by country-pair, p _<_ 0.01, p _<_ 0.05, p _<_ 0.1. To compute tariff equivalents we assume _σ_ = 6. log(Distance) _ij_ , Contiguity _ij_ and Colonial Ties _ij_ variables are from Mayer & Zignago (2011); while for Language Proximity _ij_ we the use “lp” variable from Gurevich, Herman, Toubal & Yotov (2021) (to be more precise we use _log_ ( _lp_ + 0 _._ 01)).

9
# **1.2 Trade, Production and PTAs**

A fully-fledged general equilibrium structural gravity approach relies on a trade matrix that includes both intranational and inter-national manufacturing and agricultural trade flows. The inclusion of intra-national sales is critical, as the correct benchmark for trade integration is the domestic economy (Head & Mayer 2014, Yotov 2021). The trade database used for the empirical analysis includes both domestic and international sales of agricultural and manufacturing products. Domestic sales are calculated as the difference between the value of production and total exports (both measured in gross terms).<sup>18</sup> Manufacturing output and trade come from UNIDO INDSTAT2 (2022) and UN-ComTrade, respectively.<sup>19</sup> We follow standard practice for filling gaps in intra-national flows if output is missing or implies negative domestic sales. Specifically, we linearly interpolate production values between non-missing data points and extrapolate the remaining missing values using the ratio of gross output to value added as in Head & Mayer (2021).

Agricultural product flows come from the Food and Agriculture Organization of the United Nations Statistics Division (FAOSTAT).<sup>20</sup> The FAOSTAT reports a Detailed Trade Matrix in both quantities (in tons) and values (in thousands of US dollars) from 1986 to 2018 and a disaggregated agricultural production data. As done for the manufacturing sector, after filling in some missing values following the same procedure mentioned earlier, we combined production and trade data into a dataset containing both domestic and international sales.

Finally, we combine manufacturing and agricultural data and aggregate, for each year, the underlying flows by country of origin and destination. The final dataset includes, for each year, only countries with non-missing (or nonnegative) domestic sales. Overall, the estimation dataset covers 137 countries (34 of which in the African continent) on 1-year intervals from 1986 to 2018. The counterfactual exercises are computed for year 2018 and 2014.<sup>21</sup> Trade and production data are then combined with the list of in force Preferential Trade Agreement (PTAs) as provided in Fontagn ́e et al. (2023). Using various clustering techniques, Fontagn ́e et al. 2023 show that existing PTAs can be classified into three optimal groups based on the distribution of provisions included in each of the 18 policy areas covered by the World Bank Deep Agreement database. In what follows, we use this classification and estimate the elasticity of bilateral trade flows to different types of PTAs.

> 18If the resulting value of domestic sales is negative, it is considered missing. Head & Mayer (2014) discuss an alternative options based on ratios and proposed by Romalis (2007), and Caliendo & Parro (2015) .

> 19Manufacturing data are available at `https://stat.unido.org/` and `https://comtrade.un.org/`

> 20Agricultural data are available at `http://faostat3.fao.org/home/E` .

> 21In year 2018, the dataset covers 112 and (20 of which in the African continent); while in 2014 the total number of countries is 132 (30 of which in the African continent). Importantly, these data do not include informal trade, which can be a significant part of total agricultural trade (Bouet, Pace & Glauber 2018). The likely underestimation of trade flows between African countries may also have an impact on the estimated gains from the reduction of trade barriers between them.

10
# **2 Assessing Trade Impacts of PTAs and Infrastructure Investments**

In this paper, we employ a four-step strategy to investigate the trade implications of the AfCFTA and of the improvements in transportation infrastructure. The results of the first two stages are outlined in this section: i) estimating a structural gravity model where the ambition of _signed_ trade agreements explains bilateral trade intensity; ii) giving a functional form to the _unobserved_ bilateral frictions in relations between African countries and trading partners by making them dependent on transport time as measured in our data set. The third stage, which quantifies the impact of planned infrastructure investments on transport time, is presented in the section 3. The final stage, simulating the general equilibrium effects of policy and infrastructure integration in Africa, is discussed in section 4.

## **2.1 The Effects of Different Types of PTAs on Trade**

In this section, we estimate the trade impact of the three clusters of PTAs identified in Fontagn ́e et al. (2023) in a gravity framework in order to recover the partial effect on trade of existing PTAs associated with the different clusters.This estimation is performed on the worldwide sample of countries and the obtained elasticity is used in the trade policy counterfactuals addressing the economic impact of integration among African countries.<sup>22</sup> To this end we estimate the following structural gravity equation with PPML on panel data: Where X _ij,t_ includes both intra-national and international manufacturing plus agricultural trade flows on 1-year intervals from 1986 to 2018.<sup>23</sup> Bilateral flows are normalized with total absorption at destination (X _.j,t_ ), as to mitigate the differences in the penalization of large and small trade flows of the PPML estimator (Eaton, Kortum & Sotelo 2013, Head & Mayer 2014).

Our main variable of interest, PTA _ij,t_ , is sliced across clusters: PTA<sup>_z_</sup> _ij,t_<sup>.Thedummy</sup><sup>_INTL BRDRij_takes</sup> the value of one for international trade flows. We interact this dummy with decades indexed by _T_ (leaving the first period as reference). Exporter-time and importer-time fixed effects control for time-varying Multilateral Resistance Terms, whereas bilateral fixed effects control for time invariant unobserved characteristics of the country pairs, that can potentially lead to self-selection into PTAs (Baier & Bergstrand 2007). As an additional control, we also include the variable _Opted Out PTAij,t_ , to control for agreements that are no longer in force.

The results presented in Table 2 present the elasticity of bilateral trade to the different types of PTAs (as classified by the clustering procedure) in column 1 to 3 for manufacturing and agriculture, manufacturing only, and agriculture only respectively. Two results emerge. First, PTAs of different ambition have a different impact on trade, with PTAs in cluster # 1 having the largest impact and the PTAs in cluster # 3 the lowest respectively.

> 22The estimated elasticity captures the world average effect of trade integration. African countries register only two agreements within the “deep” class: Common Market for Eastern and Southern Africa (COMESA) and Economic Community of West African States (ECOWAS). Estimating a regional specific elasticity would likely be affected by such scanty sample.

> 23Although results are robust to the use of longer intervals, e.g. 4-year intervals as in Anderson & Yotov (2016), we privilege a specification on sequential data as suggested by Egger, Larch & Yotov (2022).

11
Second, the trade impact of the most ambitious PTAs in cluster # 1 is magnified for agricultural products, a sector where ensuring compatibility of norms and controls is an important challenge.

Table 2: Partial equilibrium impacts of different types of PTAs, PPML estimates

|Dep Var:<br>_Xijt/Xjt_|Manufacturing<br>and Agriculture<br>(1)|Manufacturing<br>only<br>(2)|Agriculture<br>only<br>(3)|
|---|---|---|---|
|PTA<sup>_z_#1</sup><br>_ij,t_|0.519***|0.447***|0.981***|
||(0.065)|(0.066)|(0.112)|
|PTA<sup>_z_#2</sup><br>_ij,t_|0.268***|0.262***|0.174***|
||(0.032)|(0.034)|(0.060)|
|PTA<sup>_z_#3</sup><br>_ij,t_|0.107***|0.011|0.121*|
||(0.035)|(0.036)|(0.072)|
|Opted Out PTA_ij,t_|0.157***|0.108**|-0.086|
||(0.043)|(0.045)|(0.107)|
|INTL BRDR_∗_1990|0.274***|0.233***|0.155**|
||(0.032)|(0.034)|(0.063)|
|INTL BRDR_∗_2000|0.761***|0.616***|0.825***|
||(0.043)|(0.048)|(0.079)|
|INTL BRDR_∗_2010|1.130***|0.963***|1.269***|
||(0.050)|(0.056)|(0.097)|
|Observations|486,623|413,265|388,594|
|R-squared|0.984|0.974|0.997|
|Period|1986-2018|1986-2018|1986-2018|
|Interval|1-year|1-year|1-year|
|FEs|it jt ij|it jt ij|it jt ij|

_Notes_ : Exporter-time ( _it_ ), Importer-time ( _jt_ ) and Exporter-Importer ( _ij_ ) fixed effects. Standard errors in *** ** * parentheses are clustered by country-pair, p _<_ 0.01, p _<_ 0.05, p _<_ 0.1. The excluded category for the international border dummy is the period 1986-1989.

As in Fontagn ́e & Santoni (2021), we derive a matrix of bilateral frictions on the basis of the estimates from column 1 of Table 2: where ( _Xij,t/X_<sup>�</sup> _ij,t_ ) is the ratio of observed to predicted trade from Equation 3. The “estibrated” _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_</sup> as well as the estimated inward and outward multilateral resistance terms represent the key elements for the baseline and the counterfactual exercise that follows. Note that given _σ_ = 6, the estimated term _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_</sup> should be interpreted as the reciprocal of the bilateral trade friction, denoted by _τij,t_ . Consequently, this term serves as a measure of 12
the ease with which trade occurs between countries _i_ and _j_ at time _t_ .<sup>24</sup>

# **2.2 The Role of Transportation in Shaping Bilateral Trade Frictions**

In this section we want to quantify to what extent transportation – as revealed by the network analysis above – is a source of bilateral frictions in trade relations between African countries and their trading partners. To proceed, we start from _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_,theestimatedtradefrictionmatrixfromthepreviousstepandregressthemon</sup> transportation time. In the regression, we include the existing PTAs (i.e. the baseline) and additional controls that capture geography and historical bilateral determinants of trade (i.e. language proximity, contiguity and colonial ties) and control for unobserved exporter and importer characteristics using fixed effects. The estimation is performed in cross section for the year 2018 because we do not have time variation in our analysis of transport networks.<sup>25</sup> Having identified the functional form for bilateral frictions, we will be able in the next section to build counterfactuals using the estimated elasticity of _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_</sup> to changes in transportation time to model our counterfactual scenarios. Table 3 presents the results of these estimates across specifications. Column 1 shows the average impact of transportation time. Column 2, on the other hand, distinguishes between domestic trade (namely _i_ = _j_ ) and cross-border trade, and shows that the elasticity of cross-border trade frictions with respect to transport time is significantly larger than that of domestic trade. In column 3 we introduce a maritime preference in the choice of the optimal route, which has a limited impact on the measured elasticity, as shown by the standard errors. Our preferred estimates are reported in columns 2 and 3. These coefficients are used in the next section to translate changes in transport time into changes in bilateral trade frictions.

In our preferred specifications, we include the PTA variable as a control variable. This ensures that the elasticity of trade frictions with respect to transport time is orthogonal to the trade agreement in question. Omitting PTAs from the equation would have a limited effect on the estimate of the cross-border elasticity, while the effect on the estimate of the domestic elasticity would be slightly reduced. 26 However, it could be argued that there is an interdependence between PTAs and transportation time. Columns 4 and 5 examine this relationship by introducing an interaction between these two variables. The resulting interaction term is negative and statistically significant. For example, in column 4, the elasticity of cross-border trade frictions increases slightly when the interaction with PTAs is taken into account ( _−_ 1 _._ 836 _−_ 0 _._ 581 = _−_ 2 _._ 417). Interestingly, the elasticity of domestic trade frictions also increases when the interaction term is included ( _−_ 1 _._ 024). These results suggest that the expected benefits associated with improved connectivity and synchronized trade policies

> 24All other things being equal, an increase in one of the determinants of trade frictions, such as distance or transportation time, implies a decrease in the ease of trade. The underlying values of _β_<sup>�</sup> and _μ_ � _ij_ are calculated over the entire period and are therefore time-invariant, although both the baseline and the counterfactual are calculated in a cross-section (either 2018 or 2014, depending ondata availability). Since 0.6% of the estimated _μ_ � _ij_ are unidentified, we replace missing trade costs with standard gravity regression predictions using log distance, common border, colonial ties, and language proximity as explanatory variables.

> 25When we use year 2014, although the regional coverage increases to 30 African countries, the estimated elasticity of _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_</sup> to transportation time hardly changes. In 2014, without maritime preferences (corresponding to column 4 of Table 3) the coefficient for log(Transportation Time) _i_ = _j_ is -2.369 (se 0.119); whereas the estimated coefficient for log(Transportation Time) _i_ = _j_ is -0.606 (se 0.157). With maritime preferences (corresponding to column 5 of Table 3) the estimated coefficient for log(Transportation Time) _i_ = _j_ is -2.029 (se 0.101); whereas the estimated coefficient for log(Transportation Time) _i_ = _j_ is -0.651 (se 0.157).

> 26Our preferred specification in column 2 of Table 3 without the PTA variable reports an estimated coefficient of -2.454 (se 0.130) for cross-border trade and -0.408 (se 0.169) for domestic trade. The corresponding general equilibrium tables are available upon request.

13
may be larger when this relationship is incorporated into the general equilibrium analysis. As a result, using the estimates in columns 2 and 3 leads to more conservative effects.

Table 3: Elasticity of bilateral frictions to transportation time

|Dep Var:|||�<br>_τ_ <sup>1</sup><sup>_−σ_</sup><br>_ijt_|||
|---|---|---|---|---|---|
||(1)|(2)|_,_<br>(3)|(4)|(5)|
|log(Transportation Time)_ij_|-1.572***<br>(0.105)|||||
|log(Transportation Time)_i_=_j_||-2.252***<br>(0.135)|-1.867***<br>(0.115)|-1.836***<br>(0.236)|-1.515***<br>(0.228)|
|log(Transportation Time)_i_=_j_||-0.678***<br>(0.181)|-0.734***<br>(0.189)|-1.024***<br>(0.252)|-1.043***<br>(0.266)|
|Language Proximity_ij_|0.206***|0.171***|0.151**|0.164***|0.151**|
||(0.059)|(0.058)|(0.060)|(0.058)|(0.060)|
|Colonial Ties_ij_|0.795**|0.735**|0.737**|0.690**|0.692**|
||(0.329)|(0.334)|(0.343)|(0.339)|(0.349)|
|PTA_ij_|1.085***|0.777***|0.870***|2.500***|2.447***|
||(0.163)|(0.168)|(0.170)|(0.723)|(0.824)|
|PTA_ij ×_ log(Transportation Time)_i_=_j_||||-0.581**<br>(0.242)|-0.495*<br>(0.256)|
|Observations|2,220|2,220|2,220|2,220|2,220|
|# Origins|20|20|20|20|20|
|# Destinations|110|110|110|110|110|
|Period|2018|2018|2018|2018|2018|
|Transportation|no MAR|no MAR|MAR|no MAR|MAR|
|Estimator|OLS|OLS|OLS|OLS|OLS|

_Note:_ MAR indicates that transportation time is estimated considering maritime preferences. Exporter( _i_ ) and Importer ( _j_ ) fixed effects are included in all regressions. Standard errors in parentheses are clustered by country-pair, *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Colonial Ties _ij_ variable is from Mayer & Zignago (2011); while for Language Proximity _ij_ we the use “lp” variable from Gurevich et al. (2021) (to be more precise we use _log_ ( _lp_ + 0 _._ 01)). The dependent variable _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_</sup> is the unexponentiated reciprocal of bilateral trade friction _τij,t_ and indicates trade ease between countries _i_ and _j_ : increasing transport time implies decreasing bilateral trade easiness.

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# **3 The Effects of New Infrastructures on Transportation Times**

Before moving to the general equilibrium exercise, we need to assess how new transport infrastructure would impact transportation times in Africa. We draw upon the list of infrastructure investments from the Program for Infrastructure Development in Africa (PIDA) for our simulations.<sup>27</sup> PIDA has compiled a list of 232 priority projects for roads, ports, borders, airports, and rails, aiming to enhance regional integration and stimulate economic development by 2040. The list of active and future One-Stop-Border Posts is updated using the OSBP Sourcebook 2nd Edition May 2016.<sup>28</sup> From this list, we retain projects related to roads, ports, and border posts that can be geo-referenced and supplement it with 48 additional border posts from the OSBP Sourcebook for Africa. This results in 208 geo-referenced projects (roads, ports, and border posts).<sup>29</sup> Figure 2 displays the newly selected investments in roads, ports, and border posts. By comparing pre- and postPIDA scenarios, we can quantify the changes in shipping times induced by the new and improved transport infrastructure projects.

The implementation of PIDA projects would significantly increase the number of road and port connections and enhance the speed and processing times along numerous road segments, borders, and ports, thereby reducing shipping times for many African cities. We begin by constructing a new counterfactual transport network that includes the new road classes and investments along the land borders and ports.<sup>30</sup> First, we incorporate road improvements into the new scenario by increasing the speed from the current road type to the average speed of the region’s freeways, based on regional road speeds that are an average of country-level speeds used in Weiss et al. 2020.

Second, we reduce queuing time for border points in the new list of one-stop border posts by half, resulting in a reduction of average land border delays between African countries from 3.5 days to 2.9 days. Figure 3 reveals the large heterogeneity of delays across countries, displaying the average border delay per country. Eritrea, Saudi Arabia, Democratic Republic of the Congo, and Central African Republic have the longest current border delays, while Morocco has the shortest border delay. The countries that will benefit from the largest reductions in land border times are Democratic Republic of Congo, Congo-Brazzaville, South Sudan, Burundi, Cameroon, Malawi, and South Africa.<sup>31</sup> Third, when a project involves building a new port or upgrading an old port, we assume that the associated processing time decreases according to the following rule: The improved port delay equals the second-best performance in the region or the second-best performance in the continent if the port is already among the best in its region. Overall, new border and port improvements at maritime gateways will reduce the average sea border delays from and to Sub-Saharan African coastal countries by 16 percent on average, with larger gains in ports with currently the highest delays. The countries that will benefit from significant reductions in sea border times are Cameroon, Cˆote d’Ivoire, Congo-Brazzaville, Democratic Republic of Congo, Kenya, Togo,

> 27Access the PIDA projects dashboard online at `https://www.au-pida.org/pida-projects/` .

> 28Further details available at

> `https://www.tralac.org/documents/resources/african-union/1682-osbp-sourcebook-2nd-edition-may-2016/file.html`

> 29All projects included in the analysis are listed in the Appendix in Table B2 for the road projects, in Table B3 for the border improvements, and in Table B4 for the port projects.

> 30A detailed description of the assumptions used to create our counterfactuals can be found in Appendix B.

> 31Figure 3 is an average at the country level, with some border points that benefit from a reduction in queuing time, others not.

15
and Tanzania (Figure 4).

Table 4 presents the average transportation time ( _Baseline_ ) and the expected change with the implementation of all major transportation infrastructure projects, including road, port, and border improvements (with and without the preference for sea routes) for the countries in the estimation sample.<sup>32</sup> Changes in the average shipping time before and after the implementation of the PIDA projects vary across destination countries. Considering all possible investments, countries in the Middle-East and North African region benefit the most from the investments reducing border and port delays, mostly when shipping to other countries in the regions. Without imposing a preference for maritime routes, the proportional reduction in shipping time in the Africa region ranges from 2.57 percent with countries in East Asia to 19 percent within the Africa region. In general, when assuming a preference for maritime routes, changes in transportation times are more pronounced. The country-level results (shown in Appendix Table B6) exhibit significant heterogeneity, demonstrating why it is crucial to use a network approach to include interconnections between different projects and countries. For each country, transport time varies as much with the infrastructure projects that affect it as with those of neighboring countries traversed by its optimal routes.

The optimal mix of sea and land routes also changes between scenarios. In the baseline, on average, 86% of the total trip length is at sea if maritime routes are preferred, and 51% if no preference is assumed. After all infrastructure investments are made, the shares drop to 84% and 45%, respectively. For certain pairs of countries, there are significant mode shifts. For example, simulated trips between Gabon and Senegal are mostly by sea in the baseline, but switch to land corridors when transport and border investments occur (in the scenario without maritime preference). In fact, the reduction in border waiting times favors land routes, and trips that took place by sea in the baseline then switch to land. On the other hand, trips between Burkina Faso and Algeria are mostly made by land in the baseline, but switch to maritime routes when border times are reduced (in the scenario with maritime preference). Indeed, the reduction of border times between Burkina Faso and its coastal neighbors makes the maritime route more accessible.

In the next section, we present the results of three counterfactual scenarios that examine the combined effects of policy and infrastructure reforms in Africa. These scenarios explore various combinations of the implementation of the African Continental Free Trade Area (AfCFTA) and the Program for Infrastructure Development in Africa (PIDA), and their impact on the continent’s economic development.

> 32Appendix Table B7 details the incoming and outgoing transport shocks for each country in the estimation sample. Appendix Table B5 provides regional transport statistics for all African economies.

16
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Table 4: Proportional decrease in transportation time, aggregated by regions

|Origin|Destination|Baseline|Improvements in<br>Roads|
|---|---|---|---|
||||land borders<br>and ports|
|||# Days|% Change|
|Without Maritime Pref.||||
||Africa|17|-19.06|
||East Asia & Pacifc|33|-2.57|
||Europe & Central Asia|23|-5.89|
|Africa|Latin America & Caribbean|30|-4.77|
||Middle East|24|-6.30|
||North America|29|-6.69|
||South Asia|29|-4.35|
|With Maritime Pref.||||
||Africa|27|-22.77|
||East Asia & Pacifc|38|-7.39|
||Europe & Central Asia|28|-10.74|
|Africa|Latin America & Caribbean|34|-9.44|
||Middle East|30|-11.65|
||North America|33|-9.34|
||South Asia|33|-8.81|

_Note_ : city level transport time are aggregate at the country-pair level using a theory consistent populationweighted average.

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# **4 Quantification Strategy and Counterfactual Results**

To assess the impact of policy and infrastructure reforms, we use a general equilibrium structural gravity model of an endowment economy. In this model, bilateral trade responds to simulated reforms through: (i) a direct effect driven by the estimated parameters _β_<sup>_z_</sup> , with z = (1 _, . . . ,_ 3) in Equation 3; (ii) an indirect effect induced by third-country adjustments, such as the effect of simulated East African Community deepening on EUKenya trade. The usual trade diversion effect appears, corresponding to the “general equilibrium effects” of an endowment economy (Head & Mayer 2014). The Multilateral Resistance Terms (MRT) “ _`a la_ Anderson & Van Wincoop” 2003 serve as general equilibrium trade cost indices. They transmit local shocks to the overall trade friction matrix. The inward MRTs transmit the impact of the policy shock to consumers, while the outward MRTs transmit it to producers in the exporting country. We analyze three different scenarios to explore the impact of different policy and infrastructure reforms:

### **Scenario #1: Deepening trade integration in the African region**

This scenario examines the AfCFTA’s enactment without additional resources for enhancing Africa’s transport infrastructure. We assume that the existing transport network remains unchanged, focusing on the trade integration effects facilitated by the AfCFTA. The results provide insights into trade liberalization’s potential benefits, excluding improvements to transportation infrastructure.<sup>33</sup>

### **Scenario #2: Improving connectivity in the African region**

The second scenario evaluates the isolated impacts of PIDA’s anticipated infrastructure investments, including enhancements to roads, ports, and borders, without the successful completion of the AfCFTA. The primary objective is to assess the potential economic benefits arising from upgraded transport infrastructure across the continent, disregarding the AfCFTA’s trade integration effects.<sup>34</sup>

### **Scenario #3: Combining trade and infrastructure integration**

The final scenario combines both trade integration through the AfCFTA and transport infrastructure investment under PIDA. We differentiate between two alternative assumptions regarding the transport network: with and without a preference for maritime transport. Comparing these sub-scenarios’ outcomes, we aim to understand better how transport mode selection influences the overall consequences of combined reforms on Africa’s economic development.

## **4.1 Scenario # 1: Deepening Trade Integration in the Africa Region**

We now proceed to the general equilibrium analysis of the trade impact of deepening trade integration in Africa with the AfCFTA consolidating and extending all existing trade agreements between African countries, _absent the improvement of transport infrastructure_ .

Results for the first scenario, deepening regional trade integration in Africa at the extensive and intensive

> 33We consolidate the existing trade agreements in Africa – and add the missing ones – into a high ambition pan-African trade agreement, the AfCFTA. We impose that non-positive entries in PTA _z_ are switched to 1, with depth class z = 1 (the most ambitious type). We also deepen the existing agreements signed between African countries. Under this simulation, positive entries in either PTA _z_ =3 or PTA _z_ =2 are switched to zero while the corresponding entries in PTA _z_ =1 are set to 1 at the intensive margin, conditional on _i_ and _j_ being African countries.

> 34Operationally, to derive a counterfactual trade cost matrix, we combine the percentage change in transport time with the estimated elasticity from column 4 of table 3.

21
margins described above, are shown in Table 5. In column 1, we report the baseline share of intra-African export for each African country, followed by the changes in intra-African exports in column 2 and African exports with the rest of the world in column 3. Column 4 shows the change in total trade, and column 5 the change in GDP. All changes are expressed as percentage deviation from the baseline.

On average, our trade integration scenario increases intra-African trade by 34.7 percent, while reducing trade with the rest of the world by 1.3 percent (diversion effects). This translates into an overall increase in GDP by 0.6 percent. These values may be viewed as modest, but one must recall that intra-African trade is hampered by trade frictions (e.g. roads). In the next sections, we gradually add the simulated impacts of the infrastructure investments.

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Table 5: General-Equilibrium effects of deeper trade integration in the African region, no improvement of transport infrastructure

|||Regional Trade|∆_X_|_i_|∆_Xi_|∆_GDPi_|
|---|---|---|---|---|---|---|
|Country|Baseline|(%)<br>(1)|Regional<br>(2)|RoW<br>(3)|(4)|(5)|
|Algeria|2018|5.32|47.94|-0.82|1.77|0.28|
|Botswana|2018|15.70|33.98|-4.76|1.33|7.34|
|Burkina Faso|2014|27.44|35.02|3.29|11.99|0.67|
|Burundi|2018|16.75|40.77|8.94|14.27|0.28|
|Cabo Verde|2014|8.85|61.55|-1.28|4.26|0.26|
|Cameroon|2014|3.62|32.31|0.77|1.91|0.23|
|Congo, Rep.|2014|17.02|50.76|-5.98|3.67|2.70|
|Cˆote d’Ivoire|2018|7.25|26.33|-0.05|1.87|0.32|
|Egypt, Arab Rep.|2018|11.84|34.34|-1.65|2.61|0.32|
|Ethiopia|2014|3.79|19.70|0.51|1.24|0.06|
|Ghana|2018|8.89|44.65|-1.68|2.44|0.38|
|Kenya|2018|27.16|33.97|0.18|9.36|0.44|
|Lesotho|2018|39.69|32.70|-9.94|6.99|9.45|
|Madagascar|2014|7.69|43.69|0.28|3.62|0.46|
|Malawi|2014|31.40|28.68|2.21|10.53|0.88|
|Mauritius|2018|15.70|33.79|-1.90|3.70|1.13|
|Morocco|2018|4.08|42.72|-0.57|1.19|0.34|
|Mozambique|2018|14.18|44.02|-0.43|5.88|2.54|
|Namibia|2018|28.60|25.72|-6.32|2.84|7.42|
|Niger|2018|14.74|8.59|2.59|3.47|0.22|
|Nigeria|2018|11.27|34.59|-2.02|2.10|0.18|
|Rwanda|2018|14.23|59.26|11.56|18.34|0.85|
|Senegal|2018|17.50|38.32|-0.29|6.46|0.56|
|South Africa|2018|21.76|24.90|-1.33|4.38|0.68|
|Eswatini|2018|86.81|17.09|-18.96|12.33|9.96|
|Tunisia|2018|6.79|47.97|-1.58|1.78|0.78|
|Uganda|2018|44.72|38.64|-3.40|15.40|1.39|
|Tanzania|2018|40.24|30.45|-3.41|10.21|0.85|
|Zambia|2014|17.07|35.83|0.98|6.93|2.34|
|Zimbabwe|2014|67.28|17.23|-17.22|5.96|9.92|
|Overall|||34.68|-1.34|3.41|0.64|

_Note_ : “Regional” stands for intra African exchanges. In column 1 the Regional trade percentages are computed at the baseline levels. In column 2 to column 5 percent changes with respect to the baseline in total exports and GDP of participating countries. We assume _σ_ = 6. The reference country for the normalization is New Zealand.

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# **4.2 Scenario # 2: Improving Connectivity in the Africa Region**

In this section, we investigate the outcomes of a counterfactual scenario that focuses on improving connectivity in Africa _without creating an extensive and ambitious free trade area_ . Table 6 presents the results for this scenario, showcasing the impacts of improved roads, ports, and border delays by country (without mode preference) in columns (1) and (2), and the results assuming a preference for sea routes in columns (3) and (4).

Overall, improving infrastructure connectivity in Africa would increase trade by 7.56 percent and GDP by 1.15 percent, relative to the baseline scenario. This could reach up to 1.46 percent if maritime transport is prioritized. The gains are not evenly distributed across the three levers considered: roads, ports, and land borders. Although the implementation of one-stop border posts accounts for a substantial share of the estimated gains in the simulated scenario,<sup>35</sup> the impact of other levers is not trivial. Improvements in road infrastructure alone increase GDP by 0.11 percent on average. This increase in GDP rises to 0.33 percent when road infrastructure improvements are combined with reductions in port delays. Related infrastructure projects are particularly attractive from a cost-benefit perspective. According to the PIDA Priority Action Plan, the estimated investment in transportation infrastructure $25.4 billion.<sup>36</sup> Based on the GDP impacts estimated above, road infrastructure development could yield a cumulative rate of return of 4.1 percent for the African continent, calculated as the sum of the GDP gains for the countries in our sample. The rate of return for the continent rises to 12.6 percent if road development is complemented by port improvement projects.<sup>37</sup> At the country level, the results exhibit significant heterogeneity. When border delays are high, road network development primarily boosts internal connectivity. However, when combined with additional reductions in border and port delays, intra-continental and intercontinental connectivity significantly improves, generating substantial benefits. Major increases in trade flows are observed in Burkina Faso, Burundi, Cameroon, Cˆote d’Ivoire, the Arab Republic of Egypt, Kenya, Malawi, Senegal, Uganda, Zambia, and Zimbabwe. Uganda, Zambia, and Zimbabwe, being landlocked countries, indirectly benefit from port investments in transit countries such as Tanzania or Mozambique.

Differences in outcomes without and with maritime preferences stem from variations in the assumptions used to construct the transport network and its counterfactuals. For instance, Egypt experiences a significant decrease in transport costs when a maritime preference is assumed. In this case, flows transition from longer maritime routes in the baseline scenario to shorter land routes when land border delays are substantially reduced between Egypt and its neighbors. This explains why the average impacts are considerably larger with the maritime preference than without.

GDP effects arise from the interplay of complex general equilibrium effects, relying on a country’s characteristics and position in the global trade network. The countries that gain the most include Botswana, Cameroon, Namibia, Niger, and Zambia. Egypt and Tunisia also observe substantial GDP gains when assuming a mar-

> 35In line with previous studies, customs delays have been shown to be a significant barrier to trade. For example, Volpe Martincus, Carballo & Graziano (2015) find that if all exports in Uruguay had been subject to physical inspection, and if such inspections had lasted two days, then total exports in 2011 would have been 16.4 percent lower than they actually were. Using transaction-level data from El Salvador, Carballo, Graziano, Schaur & Martincus (2022) show that upgrading transit systems to streamline border processing in developing countries can be a high-return investment (with a back-of-the-envelope estimated return of US$3 to 1).

> 36Link to PIDA Priority Action Plan

> 37Considering that the general equilibrium analysis is done in comparative statics we do not apply a discount factor to compute the return.

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itime preference. Some of these countries directly benefit from infrastructure investments in their territories, while others gain from spillovers originating from investments in neighboring countries. Although some of these countries experience significant increases in their trade flows, others do not, highlighting the importance of employing a general-equilibrium model that considers the complex trade interactions among African countries and the rest of the world.

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Table 6: General Equilibrium Effects, improving connectivity of the African Region

|||Roa|ds, land bo|rders an|d ports|
|---|---|---|---|---|---|
|||Wi<br>|thout<br>|W<br>|ith<br>|
|||Mariti|me Pref.|Mariti|me Pref.|
|Country|Baseline|∆_Xi_<br>|∆_GDPi_<br>|∆_Xi_<br>|∆_GDPi_<br>|
|||(1)|(2)|(3)|(4)|
|Algeria|2018|6.63|0.93|11.10|1.76|
|Botswana|2018|2.98|11.81|2.86|10.36|
|Burkina Faso|2014|38.37|2.01|38.38|1.99|
|Burundi|2018|37.07|1.35|43.22|1.45|
|Cabo Verde|2014|0.12|0.01|2.55|0.17|
|Cameroon|2014|17.46|2.70|9.47|1.89|
|Congo, Rep|2014|5.24|4.60|8.43|6.24|
|Cˆote d’Ivoire<br>|2018|9.39|1.41|15.01|2.17|
|Egypt, Arab Rep.|2018|8.43|0.99|7.36|1.01|
|Ethiopia<br>|2014<br>|14.39<br>|0.59<br>|8.92<br>|0.36<br>|
|Ghana|2018|5.88|0.99|5.12|0.90|
|Kenya|2018|12.74|0.94|24.83|1.70|
|Lesotho|2018|0.60|-0.25|-0.16|-0.51|
|Madagascar|2014|0.12|0.01|0.74|0.09|
|Malawi|2014|25.22|1.99|17.07|1.36|
|Mauritius|2018|0.00|-0.03|-0.03|-0.02|
|Morocco|2018|2.20|0.59|2.09|0.61|
|Mozambique|2018|2.90|0.67|2.20|0.55|
|Namibia|2018|3.22|7.62|3.43|7.10|
|Niger|2018|4.28|4.15|1.59|4.51|
|Nigeria|2018|7.50|0.58|13.59|1.38|
|Rwanda|2018|34.36|1.60|36.46|1.50|
|Senegal|2018|9.84|1.05|13.98|1.48|
|South Africa|2018|4.77|0.58|3.89|0.57|
|Eswatini|2018|1.78|0.37|2.94|1.24|
|Tunisia|2018|2.90|1.19|3.05|1.36|
|Uganda|2018|8.97|1.06|14.49|1.72|
|Tanzania|2018|12.79|1.68|20.83|2.78|
|Zambia|2014|19.74|6.63|14.14|4.86|
|Zimbabwe|2014|11.81|18.85|10.02|15.76|
|Overall||7.56|1.15|9.61|1.46|

_Note_ : percent change compared to the baseline in total exports and GDP of participating countries. We assume _σ_ = 6. The reference country for the normalization is New Zealand.

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# **4.3 Scenario # 3: Combining Trade and Infrastructure Integration**

In the third scenario, we explore the potential outcomes of concurrently implementing both trade integration through the AfCFTA and transport infrastructure investment as per PIDA’s projections. By considering the combined effects of these two initiatives, we aim to provide a comprehensive understanding of the potential synergies that may arise from simultaneous policy and infrastructure development.

Two sub-scenarios are considered: the first without a maritime preference (columns 1 and 2), and the second with a maritime preference (columns 3 and 4) in Table 7. The results indicate that reducing both types of trade frictions simultaneously offers cumulative benefits that exceed the sum of the individual policy effects. On average, when no maritime preference is considered, exports increase by 11.49 percent and GDP by 2 percent. When a maritime preference is assumed, the expected gains are slightly higher, with total exports and GDP increasing by 14.80 percent and 2.27 percent, respectively.

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Table 7: General Equilibrium Effects, improving connectivity and deepen integration of the African Region

|||Dee<br><br>Wi<br>|p PTAs plu<br>and ports i<br>thout<br>|s roads, b<br>mproveme<br>W<br>|orders<br>nts<br>ith<br>|
|---|---|---|---|---|---|
|||Mariti|me Pref.|Mariti|me Pref.|
|Country|Baseline|∆_Xi_|∆_GDPi_|∆_Xi_|∆_GDPi_|
|||(1)|(2)|(3)|(4)|
|Algeria|2018|9.12|1.40|14.42|2.22|
|Botswana|2018|4.79|21.45|4.70|19.56|
|Burkina Faso|2014|56.63|3.09|56.00|3.02|
|Burundi|2018|59.20|1.83|66.42|1.89|
|Cabo Verde|2014|4.23|0.28|6.86|0.46|
|Cameroon|2014|19.96|3.01|11.63|2.14|
|Congo, Rep.|2014|9.57|8.28|12.91|10.12|
|Cˆote d’Ivoire|2018|11.57|1.85|18.74|2.58|
|Egypt, Arab Rep.|2018|12.00|1.50|13.30|1.67|
|Ethiopia<br>|2014|16.51|0.67|10.64|0.43|
|Ghana|2018|8.15|1.39|7.90|1.27|
|Kenya|2018|22.93|1.43|38.71|2.21|
|Lesotho|2018|7.50|9.10|7.09|8.75|
|Madagascar|2014|3.75|0.47|4.60|0.57|
|Malawi|2014|41.00|3.43|31.95|2.68|
|Mauritius|2018|3.67|1.07|3.70|1.07|
|Morocco|2018|3.97|1.13|3.97|1.15|
|Mozambique|2018|8.47|3.20|8.01|3.03|
|Namibia|2018|6.24|16.86|6.55|15.96|
|Niger|2018|9.15|4.45|9.51|4.73|
|Nigeria|2018|9.55|0.81|17.65|1.49|
|Rwanda|2018|60.82|2.88|61.22|2.73|
|Senegal|2018|18.08|1.78|24.73|2.26|
|South Africa|2018|9.34|1.36|9.37|1.35|
|Eswatini|2018|13.77|10.30|15.15|11.01|
|Tunisia|2018|5.59|2.47|5.91|2.66|
|Uganda|2018|25.47|2.65|32.16|3.29|
|Tanzania|2018|23.83|2.77|33.30|3.65|
|Zambia|2014|27.90|10.53|22.33|8.43|
|Zimbabwe|2014|17.83|32.58|16.08|28.97|
|Overall||11.49|2.00|14.80|2.27|

_Note_ : percent change compared to the baseline in total exports and GDP of participating countries. We assume _σ_ = 6. The reference country for the normalization is New Zealand.

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# **5 Conclusions**

Major trade reforms and infrastructure investments are essential levers of economic integration for Africa. Quantifying their contribution can be challenging because of the lack of data and the presence of general equilibrium effects. In this paper, we start by constructing a new granular dataset of transport times faced by African exporters to quantify the reduction in times from the implementation of the Program for Infrastructure Development in Africa, an ambitious list of transport investments for the next decade. We then use data from the World Bank’s Deep Trade Agreements project to assess the response of trade to different degrees of agreement ambition using a structural gravity model. We recover the elasticity of domestic and international sales to transportation time for African countries, with a functional form for unobserved bilateral frictions, making them depend on transport time and other unobservable characteristics of the origin and destination countries. Finally, we build on our calibrated model to simulate the trade and economic impacts of our investment scenarios in roads, ports, and land borders, and the consolidation of various trade agreements signed in Africa into the African Continental Free Trade Area.

Our results show that trade reforms and infrastructure improvements complement each other. The combination of tariff reduction and trade facilitation, which improves border delays and behind-the-border frictions, can substantially reduce trade costs and increase exports by 11.5 percent and GDP by 2 percent for countries in the continent.

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