The remainder of the paper is organized as follows. The next section focuses on empirical analysis and particularly uses a nonparametric method to infer on the nature of the association between the distribution of external debt and capital flight. Section III discusses a methodological approach to infer on the causality between capital flight and external debt, with emphasis on error correction Granger causality models. Section IV discusses empirical results and policy implications. The last section concludes.

# **II. Data Analysis**

The external debt series is obtained from the World Bank Debt Table. The estimated capital flight series has been kindly provided by Ndikumana and Boyce (2008).<sup>8</sup> This last series is constructed from three different sources: the IMF’s Trade Statistics, World Bank World Tables and IMF Balance of Payments Statistics. The need to reconcile these different sources to construct the capital flight series arises from the difficulty of gathering accurate information on capita flight as a result of widespread use of trade misinvoicing. The recourse to trade misinvoicing is partly motivated by the desire to evade customs duties, import restrictions and capital controls, and has, to certain extent, reinforced the illicit nature of flight capital in the landscape of global capital flows. Although laborintensive, the integration of different sources of information reduces the discrepancy between recorded inflows and outflows of foreign exchange.<sup>9</sup> The resulting capital flight series is very comprehensive in its coverage; it includes low and middleincome, HIPC and non-HIPC countries, from a large cross-section. Though debt overhang and subsequent relief predominantly concerns low-income and severely indebted countries, capital flight affects both low- and middle-income countries in the region. Hence, the sample has 40 countries, accounting for most of the region’s foreign liabilities and capital flight, reflecting the inclusion of leading capital flight candidates in the sample.<sup>10</sup> The two series are annual averages spanning the years 1970-2004, included.<sup>11</sup> In particular, they cover the decade that saw a number of countries accede the HIPC Completion Point. In this regard, they provide the basis for assessing the dynamics of capital flight during the HIPC era. Additionally, the comprehensive coverage makes it possible to investigate the dynamics of capital flight across region, controlling for external debt.

This section focuses on the dynamics and dependency structure of capital flight and external debt. The empirical analysis assumes that the movements underlying these two series follow a bivariate distribution symbolizing the two faces of the same coin. Though this assumption of time-invariant pairing may appear as relatively strong, I would propose that we abide by it for now and then subject the coin to tossing; so that we can explore the dynamics interaction or path-dependency of the two series using graphical and nonparametric methods. The graphical method draws on a scatter plot diagram to assess the nature of the association between the two series. In contrast, inference from nonparametric methods is based on the Spearman Rank Correlation test, which does not necessarily require imposing any functional distribution to the data [Gibbons (1996)]. This test assesses whether the two series are dependent or alternatively not identically distributed.

> 8 This new series is based on a more refined methodology which adjusts the change in external debt to account for debt write-offs within the Enhanced HIPC initiative. In addition, this revised series is adjusted for underreporting of remittances. For further details on estimation, see Ndikumana and Boyce (2008). 9 As a result of this discrepancy, capital flight is often recorded as errors and omissions in the Balance of Payments statistics [IMF (2006)].

> 10 For instance, the 40 countries account for more than 96 percent of total stock of external debt owed by the region to the rest of the world. The sample selection is due to limited data. The few countries not sampled include: Equatorial Guinea, Eritrea, Gambia, Guinea Bissau, Liberia, Mayotte, Namibia and Somalia.

> 11However, there are sample variations across countries which result in an unbalanced design.

4