Collaborative Approach to Trade

Regional science offers a rich toolkit for analysis and policy development for substantive applications. In this book, the science is applied to the challenge for global investment agencies seeking to enhance trade in lagging regions. The challenge is particularly acute for sea-locked and land-locked countries and striking examples are provided by the widely scattered island countries of the South Pacific on the one hand, and Uganda on the other. What is needed is an in-depth analysis of trade routes as a basis for evolving policies to increase efficiency and reduce costs. The two case studies presented here provide an excellent illustration of the power of regional science, from assembling data bases in difficult situations, through to developing and applying models of the trade system. We apply network analysis, spatial interaction modelling and agent based models supplemented by appropriate statistical techniques. Our final results for both countries allow for substantive policy suggestions to be made.

As mentioned in Chap. 1, our two case studies have some similarities as we will see, but they differ in scale. The SPICs case calls for an outward looking perspective and the scale is extensive; whereas Uganda is inward looking and demands a micro-scale of analysis. For the outward looking perspective of the South Pacific Islands we adopt network theory and a spatial interaction approach (Jackson 2008; Wilson 2008) through which we examine how trade is influenced by cumulative network interactions. The twofold interpretation of the cumulative network concept involves (1) representing the shipping network system; in this sense, elements are aggregated in categories whose combinations help to describe the complete network system; and (2) accounting for the vertical and horizontal interactions within the system. The inward looking perspective of Uganda uses agent based modelling (ABM) (Epstein 2006). ABMs are a class of model applied widely in agriculture economics (Happe et al. 2006; Berger 2001; Balmann 1997, 1999). The advantages of this methodology are that it allows us to employ data from different sources and different groups of heterogeneous agents who interact; exchange of information ensues and the adaptive behaviour responses to accommodate changes in the environment are taken into the model. Both of our selected modelling approaches, network theory and spatial interaction on the one hand, and agent based modelling on the other, can handle positive and negative feedback. Given that trade theory warns us of the importance of information asymmetry as an established factor that can hinder trade and limit growth, we also introduce a new concept based on information exchange. This new concept is operationalised through the multiplier attachment factor. In the next sections we set out the details of our two approaches.

In Chap. 1 we offered a preliminary review of the main economic parameters of the two regions under examination by focusing on GDP. Here, we extend the analysis beginning with the employment rate in Fig. 3.1. We note that, in relation to the South Pacific Island countries, Fiji has the lowest employment rate compared to other SPICs and also to Uganda. The Uganda employment rate is significantly high, and according to World Bank data, nearly 75% of the population is employed (Fig. 3.1). Uganda’s high employment rate is followed by PNG, with an employment to population ratio above 70%. This data confirms the trend in GDP growth experienced by PNG in the last 10 years. The Solomon Islands reports a lower (65%) employment ratio, which is mainly the result of low GDP growth rates and a large subsistence agriculture sector.

Our model of trade in sea locked countries is grounded on the concept that the flow of trade is characterised by integrated layers of networks. The Multilayer model is composed of three categories of layers: physical, economic and sociological, which will determine the main outputs of the analyses. These layers are interactive, so to comprehensively study their interrelationships and impacts on international and national trade, we have to construct the model based on two distinct approaches. First, horizontal layers are studied through the use of complex network theory (see inter alia, the works of Barabasi and Albert 1999; Watts and Strogatz 1998; Erdős and Rényi 1959). Thereafter, vertical integration is analysed through the spatial interaction approach of Wilson (1967, 1970). In this way we are able to investigate both horizontal and vertical interdependency among layers and determine the impacts of natural and man-made shocks, such as (for the SPICs case study) the introduction of a specific policy for the reduction of transport cost, the maintenance of infrastructure and the provision of inter-island shipping services.

This chapter is based on the main concepts that Caschili and Medda (2015) have developed to construct the Port Attractiveness Index, which is used in multiplier attachment to construct the shipping network in module II. Caschili and Medda (2015) define port attractiveness as the combination of the productive capacity of a port and its level of international competitiveness which together provide direct and indirect economic benefits. A port generates freight traffic through its connections with inland trade routes and with other regional and international ports. Thus, in order to be attractive and competitive, ports often need to be integrated vertically, i.e. have secure maritime routes and landside operations, and integrated horizontally, i.e. be highly specialised with a wide geographical market share. The implication here is that a port must be equipped with effective facilities, it must provide reliable services at the lowest price, and it needs to have an efficient productivity level. These characteristics combined comprise the reputation of a port as an intricate network of operators, investors and maritime brokers.

According to the Pacific Islands Forum Secretariat (2012), the shipping market in the SPICs is relatively contestable. Nonetheless, the trade flows are extremely imbalanced since imports usually far outweigh exports, and the freight rates are often significantly higher than in other parts of the world (ADB 2013). These inefficiencies in the trade markets certainly stem from the intrinsic spatial dispersion of the islands, their physical scale, thin trade flows, chronic infrastructure difficulties (above all relating to maintenance and operations) and limited trade connectivity in the domestic and international market. However, the available empirical evidence does not clearly address the entirety of the interrelated aspects which hamper trade, and for this reason we develop a scenario analysis in this chapter based on the topological structure of shipping networks in the SPICs. The objectives are to explore how to stimulate the consolidation of cargo and decrease transport cost by achieving economies of scale through the network structure. These objectives relate to our general hypothesis (1) in Chap. 4: Can the structure of the shipping network stimulate the consolidation of cargo and decrease transport cost?

In Chap. 6 we analysed how different shipping networks can stimulate trade flows and facilitate the emergence of a hub-and-spoke structure. The hub-and-spoke offers significant gains in trade, because by implementing consolidation, it increases competition and economies of scale, reduces transport costs, and therefore fosters a collaborative approach to trade. In this chapter we extend the argument still further and test how different shipping network structures can increase trade flows when we implement trade coordination agreements. In other words, our objective here is based on hypothesis (2): Do trade coordination initiatives provide greater long-term economic growth for the SPICs? In testing hypothesis 2, we revisit the three scenarios of Chap. 6 and introduce two types of trade coordination agreements. In general, trade agreements offer numerous gains in the development of trade and growth within a region; in fact, not only may a region capitalise on economies of scale and comparative advantage, but also with increases in foreign and private capital and access to larger trade markets a region can realise more efficient and effective production. Being a participant in a trade agreement certainly generates positive ripple effects in education and infrastructure endowments and the overall income of a region increases. In this chapter we therefore combine two main concepts: trade agreement and network structure, in particular the hub-and-spoke structure. The roots of this approach are found in the work of Wonnacott (1996) and Enders and Wonnacott (1996) who demonstrate that the gains from hub-and-spoke logistics networks are reduced according to type of trade agreement implemented. Under bilateral agreements in particular, Wonnacott (1996) finds that “the hub gets a larger percentage share of the region’s total income, whereas the spokes get a smaller share.”

The purpose of this chapter is to integrate the previous chapters in relation to policy analysis. We start by examining the vertical interactions within the Multilayer model framework, and in doing so, we return to hypothesis 3: How can trade be facilitated and investment and growth take place by leveraging transport logistics accessibility as well as the economic and sociological factors of the SPICs? In Chap. 5, we discussed on the basis of hypothesis 3, how to evaluate Port Attractiveness in order to foster economic growth and investment. Thereafter in Chaps. 6 and 7, we analysed the topological structure of the networks and identified the impacts of economies of scale and trade agreements. Our next endeavour is to integrate these different layers by connecting the factors already highlighted in the Port Attractiveness Index, and above all to test the analyses and results obtained in Chaps. 5, 6 and 7 for the networks.

Different studies demonstrate how market inland country delays often hinder trade more than the effect of foreign tariffs (Hummels 2001; Portugal-Perez and Wilson 2009; Djankow et al. 2010; Freund and Rocha 2010). This is especially evident when we consider Africa’s exports, and in particular, highly time-sensitive goods such as agriculture products. Within this context the study and implementation of trade facilitation solutions for Uganda, and in general East Africa, plays an important role. Uganda belongs to one of the largest clusters of 10 landlocked countries spanning Central and East Africa. From this perspective, the objective to increase and facilitate trade in landlocked African countries may also, like a ‘domino effect’, improve trade across the African continent.

We focus on the seven districts mentioned in Chap. 9, located in the Central and Eastern regions of Uganda—Luwero, Mpigi, Masaka, Iganga, Mitiyana, Kamuli, and Mukono. We use available data to define the spatial, geographical and demographic environment of the agent based system. The spatial and geographical environment is captured using a network structure. The nodes of the network represent the GIS locations of villages in Uganda. The links between the village nodes correspond to the available road infrastructures which are primarily dirt road or paved/gravel roads.

The trade reforms initiated in Uganda during the 1990s have gradually led to trade liberalisation aiming to improve competitiveness and reduce distortion in the markets. For instance, the granting of preferential treatment in the form of duty reductions to COMISA countries, and the East African Community Custom Union treaty signed in 2004 are two pro-trade initiatives that have helped lower the barriers to trade, such as restrictions and tariff controls, and have significantly increased the volume of traffic of non-traditional agriculture exports. The amount of non-traditional agriculture exports has risen strongly from 14% in 1990 to over 68% in 2012, in line with Uganda’s annual average real GDP growth rate of 7.5% over the last 25 years (Uganda Government Statistics 2013). Strong export rates convincingly show us that agriculture production is pivotal in the economic growth of Uganda.

We have seen in the previous chapter how high transport costs and lack of transport infrastructures act as impediments to trade, and how these conditions largely shape the structure of the agriculture sector and negatively impact on trade flows in Uganda. There is consensus among experts and scholars (USAID 2008) that improving access to markets and to trade can lead to a shift away from a supply-based situation of the agriculture sector where farmers/outgrowers merely sell their crop surplus, to a demand-driven agriculture where farmers produce for the markets. However, this shift is complex and beset by numerous constraints that still prevent Uganda’s farmers from fully participating in the market.

Crop price dispersion across farmers/outgrowers is one of the main problems arising when we examine price asymmetric information in the agriculture sector in Uganda (Gollin and Rogerson 2010; Calderon 2009; Minten and Stifel 2008; Mandl and Mukhebi 2002). Farm gate prices are often 50% less than the urban wholesale price, and a large portion of this increment is comprised by transport cost. However, it is precisely transport cost reduction and logistics chain consolidation which are two of the most important initiatives in the goal to reduce price asymmetry; and we would assert that the core of the problem—lack of information—needs to be tackled. Farmers and traders often have imperfect or no knowledge whatsoever of their market positions in relation to production completion, demand, standards, best practice, and exposure to risk. Moreover, the agriculture sector is heavily subjected to price and weather variations, and since farmers/outgrowers are significantly affected by the lack of information, they would prefer to reduce risk wherever possible. But as they reduce risk, so do farmers/outgrowers also shy away from new technologies and instead remain within the production segment of unprocessed products, which are generally bulky and of low value.

The task to define a single, consistent and accurate method for our analysis has posed a serious initial challenge, since our aim was to capture the complexities of multiple trade variables which continuously and simultaneously interact, change and adapt to specific contexts and trade chains. Our endeavours throughout this book have certainly led us to the need for a change in paradigm (Dubois and Prade 2004; Rotmans and Van Asselt 2001).