Data-driven policy-making

Data and evidence are critical factors that should drive feasible and effective policy-making. Among the seven pillars of the Enabling Trade Index 2016, Border Administration exhibits the second largest score differential—between the country at the bottom, Yemen, and top performers such as Singapore—after the ICT Infrastructure pillar (see Chapter 2). Moreover, modernizing border administration is, relatively speaking, less costly, less time consuming and politically easier than other interventions. Therefore, border administration appears to be an appealing choice for countries wishing to implement speedy reforms; in other words, a ‘low-hanging fruit’ for policymakers.

TF data is also important for targeting some US$40 billion of Aid for Trade (a WTO initiative) annual funding to where it has the most impact.¹ Case studies conducted by the World Economic Forum and World Bank suggest that the effect of reducing barriers is not a continuous function, but rather depends on tipping points being reached.² Broadly speaking, appropriate targeting appears to be happening at a macro level. Countries with the weakest performance on the ETI, including Burundi, Mozambique, Gambia and Madagascar, have indeed received the highest amount of Aid for Trade funding relative to the size of their economies.³ The outcome, as assessed by the OECD/WTO (2013), is that US$1 invested in aid for trade is associated with an average increase of US$8 in exports from developing countries.