Gani (2017) mentions that trade volume increases more significantly than the ratio of improvements in logistics infrastructure and services:

Authorities and authors emphasise the importance of infrastructure investments in the logistics sector, given their contribution to economic growth (Jumah et al. 2024) through their impact on production, consumption, and international trade. Similarly, Carruthers, Bajpai and Hummels (2003) highlighted the role of transportation and logistics infrastructure investments in promoting trade in Hong Kong and Singapore. Chinese exports have increased significantly thanks to changes in its logistics network (Wang, Kim & Kim 2021).

‘The work on the relationship between logistics clusters and trade is mainly focused on two main areas, namely LPI, infrastructure (mainly ports), and transport costs’ (Hamri & Mraihi 2026). Indeed, port logistics clusters have an important effect on port cities in China (Wang & Ma 2019) and for Asian countries (Gani 2017). Few works in the literature emphasise the impact of logistics service providers’ (LSP) concentrations on trade. We work on LSPs’ productivity to understand the impact of this concentration on trade. Empirical analysis of manufacturing firms has shown that regional advantages arising from the concentration of firms within the same industrial group, a Marshall–Arrow–Romer (MAR) externality, contribute to productivity gains and strong employment growth at the firm level.

Indeed, Marshallian agglomeration economics theory was important in China, South Korea, Egypt … because technological proximity reduced the cost of moving people, ideas, and goods (Badr, Rizk & Zaki 2019; Choi & Choi 2017; Howell 2017). The production function includes only space and labour as factors of production (Ciccone & Hall 1996). Therefore, we focus our study on capital and labour productivity of two samples: the first one comprised the LSPs collocated on Rades logistics seaport platform, the second comprised LSPs located at different industrial areas. The objective is to understand the impact of this concentration on Tunisian trade. Given that LSPs are clustered at the Rades platform, very close to the port of Rades, can we consider this co-location a determining factor in Tunisian exports? The World Bank considers the competence and quality of services in the private logistics sector to be a weak link in Tunisia’s logistics performance; to what extent can this explain these exchanges?

The first regression is performed using the ordinary least squares (OLS) method to assess the quality of our model. This method appears inadequate because we have both temporal and individual dimensions. It is necessary to verify whether there are individual effects or, more precisely, country-specific characteristics. We then perform the Hausman test to determine whether the model is random or fixed:

According to the results in the second and third columns, the GDP of the importing country is significant and positive. A 10% increase in the GDP of the importing country increases Tunisian exports by 47%. In the same context, a recent study by Ha, Chung and Seo (2016) showed that income elasticity had a significant positive effect on Korea’s trade volume with the ASEAN-5 countries. In another study on international grain trade, Yip (2012) found that the importer’s GDP led to a much faster growth in grain trade than the exporters’ GDP. A 10% increase in the importing country increases trade flows to 4% according to the empirical work of Iwanow and Kirkpatrick (2009); to 8.2% for the work of Puertas et al. (2014), to 0.2% for the study of Gani (2017), and to 9.5% for the work of Hausman et al. (2013). Indeed, an increase in the partner country’s GDP raises purchasing power and consumption, which in turn increases foreign demand for imports from its trading partners, including Tunisia. This means imports translated into an increase in Tunisia’s exports. In 2017, importers accounted for 71% of the total value of Tunisia’s exports, approximately 24 483.6 million dinars. Indeed, most of it is devoted to EU countries, mainly France, Italy, and Germany. In the Maghreb, Algeria is better positioned than Morocco and Libya to receive Tunisia’s exports. We did not consider the value of Tunisia’s imports to these countries, given the potential of exports to influence growth. However, we should not overlook the role of imports in the trade balance. For example, the latter is as follows for 2017 (million dinars): Germany (−21.7); Algeria (−680.9); Morocco (157.1); France (2964.8); Belgium (−17.9); Italy (−2118.7); Spain (500); and Libya (866.4). The product groups exported to these countries are (in descending order of value): mechanical and electrical products; textiles, clothing, and leather products; energy and lubricants; mining and phosphates; and agricultural and agri-food products. Indeed, for textiles (hosiery, clothing, artisanal carpets, shoes, etc.), only 20% of exports are under the general regime, and the rest (80%) are under the offshore regime. On the other hand, there is no offshore regime for the export of energy products and lubricants (including crude oil and refined products). The same is true for mining products and phosphate (crude phosphate, phosphoric acid, salt, etc.). In addition, the export of agricultural and agri-food industry products (olive oil, fish industry, dates, and citrus fruits) is 95% for the general regime. Finally, 40% of mechanical and electrical products are exported under the offshore regime. The evolution of exports to these countries by product group shows that the market share of mechanical and electrical products has been gradually decreasing since 2011 among the main importers, namely France, Italy, and Germany. We also report a drop in imports since 2014 in the market shares of Libya and Algeria, which was previously due to smuggling along the border. The results also show that the capital productivity of LSPs at the Rades platform is significant and negative. Indeed, if this productivity increases by 10%, this would reduce trade by 47% to the eight countries. Furthermore, if the productivity of capital increases, this means that capital becomes more productive, and therefore its contribution to the production of LSPs increases throughout the study period. We recall that this variable was calculated based on the ratio of turnover to logistics space. It represents the fixed capital which constitutes the essential determinant of the productivity of the capital of an LSP, given the nature of the activity. Indeed, for the first sample, these are service providers that carry out basic logistics activities such as transport, warehousing, grouping, and ungrouping. This can be explained by the fact that, in a stable logistics environment, turnover continues to increase from 1 year to the next, resulting in improved logistics activity and associated revenues for the company. The turnover, composed of two essential elements (the selling price and the number of units of goods or services), increased; this can be explained by an increase in the selling price of logistics services, by an increase in output, or by both at the same time. The increase in capital productivity can also be explained by the simultaneous increase in turnover and logistics spaces, except that the logistics spaces increase at a slower rate than turnover. Then we found that a few LSPs invested in expanding logistics capacity. Indeed, the government developed enormous logistics spaces to establish LSPs near the port. We highlighted the problem of oversized logistics spaces, which may explain the negative effect of capital productivity on exports. We noticed that when demand increases, LSPs resort to seasonal labour. We also note that the contribution of logistics space does not only lie in its size (space) but rather in the adaptation of its layout (warehouse zoning, number of docks, etc.) to the activity of the LSPs. In addition, this result can be explained by the logistics and customs policies implemented in Radès LP, the restrictive regulations or bureaucratic procedures which can limit the efficiency of capital productivity, leading to negative effects on exports. The capital productivity of LSPs located outside the LPs is significant and positive. If it increases by 10%, exports amount to 3.4%. Indeed, the second sample includes LSPs located in industrial zones. The increasing capital productivity of LSPs generally implies a growth in their logistics activities in quantitative terms (tonnage transported, tonnage stored, number of pallets processed in grouping or ungrouping, etc.) and qualitative terms (deadlines, etc.). This improvement in productivity makes the LSP services in this sample more profitable, more attractive and highly sought after by Tunisian exporters. The main sources of the improvement in the productivity of these firms (Courlet 2008) can be linked to internal economies of scale which are explained by the decrease in unit production costs due to the increase in production volume, and this increase is mainly linked to the production factors internal to the firm. These factors can be external to the company, and in this case, we speak of agglomeration economies; they are interpreted by Catin (1997) as being economies external to the firms and internal in relation to their geographical locations. In addition, the providers of the 2nd sample can benefit from agglomeration economies. Co-located providers near industrial centres have operational advantages resulting from the sharing of tangible assets such as transport capacity, equipment and warehouse space, as well as intangible assets such as knowledge and information. In the same context and in the case of the logistics sector, co-location offers additional benefits resulting from the reduction of transport costs, the increase in the level of customer service, according to Masson et al. (2007), the collaborative organisation of transport activities is not only a matter of minimising costs, but also of improving service levels and satisfaction of the end customer, sharing of resources, increasing the value of additional services (repair services, etc.) and high levels of employment (Rivera, Sheffi & Welsch 2014; Sheffi 2012; Van den Heuvel et al. 2013). The results also show that distance is significant and negative. A 10% decrease in distance increases exports by 16% (9.5% and this coefficient decreases with the increase in the country’s income (Çelebi 2017); 15% (Iwanow & Kirkpatrick 2009); 4% (Puertas et al. 2014); 13% (Hausman et al. 2013); 25% (Limao & Venables 2001). This is explained in the literature by the fact that where distance increases, transport costs also increase, which decreases trade flows. Other authors criticise this approach because, on the one hand, the distance between countries is a variable that does not vary over time, which means that the assessment of the contribution of variations in transport costs to the evolution of trade flows is brutal (Jacks & Pendakur 2010). On the other hand, distance is not a sufficient variable to translate transport costs, there are other opinions on the parameter of geographical distance including the attempt to include geographical factors in the gravitational equation such as the area of a country, whether it is landlocked or has access to navigable water bodies, as well as the colonial relationship and the common language because these variables are considered determinants of trade costs (Behar & Manners 2008). Nowadays, innovation and ICT can reduce the effect of distance (sharing the same information platform, same information systems, traceability, etc.). But since distance is considered as a proxy for transport costs, the improvement in this measure can be interpreted as efforts to reduce the freight rate (in dollars per km) for bilateral trade. Freight costs could be reduced by deregulating transport, expanding ports to increase their capacity, and promoting the growth of 3PL services for better consolidation of freight flows. Processing time and cost can be improved by process reengineering to eliminate waste and streamline other operations (e.g. introducing more parallel processing than sequential processing), introducing advanced information technologies (such as electronic customs clearance and document flow), using data mining and filtering methods to identify only high-risk containers for security inspections, and adopting advanced scanning (Hausman et al. 2013). The competence and quality of logistics services is significant and negative. Indeed, a 10% increase in this index decreases trade by 3.7%. Indeed, an improvement in the quality of services translates into an improvement in customer satisfaction. The latter should be accompanied by an increase in trade flows to these customers. However, these results clearly show that this improvement is not enough to satisfy our foreign partners, which leads to a loss of market share, resulting in a decrease in exports to these countries. The aim is to improve the quality of logistics services while focusing on international competitiveness. This result can also be interpreted by the fact that the logistics performance is inefficient because the private logistics sector is incompetent and represents the weakest link in terms of logistics performance. Indeed, the main Tunisian export sectors have not yet exceeded the 1PL and 2PL quality level for LSPs, and only offshore firms have more developed logistics service quality (3PL). Authors show that service quality is a key element of logistics performance for both developing and developed countries. A 10% increase in the exporter’s logistics services competence index increases developing country exports by 22% (Turkson 2011); 1.2% (Puertas et al. 2014); and 2.5% (Gani 2017); 9.7% (Çelebi 2017). Furthermore, the competence and service quality index of partner (importing) countries is significant and positive. A 10% increase in this index increases Tunisian exports by 10%, confirming previous findings. Hence, Tunisia derives significant benefits in terms of exports if it establishes trade with high-income (importing) countries. An increase of 3.9% (Çelebi 2017); an increase of 3.5% (Puertas et al. 2014). While the LPI is a global performance index, and most authors have analysed this index in detail, across its six dimensions, we have already mentioned that we focus our study on the competence and quality of the private sector logistics service. This is the dimension that best represents the logistics sector.

To measure the contribution of the Rades LP, particularly its LSPs, to Tunisian exports to the top eight importers between 2011 and 2017, we estimated a gravity model that shows an inverse relationship between the capital productivity of LPs co-located on the platform and Tunisian exports. In contrast, LSPs located near industrial zones are more productive and effectively contribute to increasing Tunisian exports. Furthermore, we report insufficient service quality from the private logistics sector, which does not contribute to increasing exports. However, the role of government is not limited to the development of enormous logistics spaces but also involves preparing the necessary infrastructure that corresponds to the activity of investors and also fostering collaboration between the various stakeholders co-located in the platform.