[Antweiler and Trefler (2000), Van Biesesebroeck (2005)].<sup>20</sup> . This paper takes a different approach in assessing the implications of technology trap for poverty trap. It assumes the existence of a technology trap on the basis of persistent ‘technology lag’ and assesses the implications of the structural technology deficit for the resilience of poverty using a principal component analysis (PCA) technique, which draws on linear transformation to achieve data compression without loss of information.

In essence, this data reduction technique displays the eigenvalue decomposition of the sample second moment of a group of series, following the linear transformation. The first principal component is computed as a linear combination of the series in the group with weights given by the first eigenvector. The higher the degree of co-movements between the original series, the fewer the number of principal components needed to explain the proportional variance of that set. Alternatively, the convergence process is very slow when the vectors in the series are completely uncorrelated. In this case a maximum number of eigenvectors is needed to explain most of the proportional variance observed. In theory, the covariance matrix associated with such vectors is equal to zero.<sup>21</sup> Inference from this technique is derived from the estimated eigenvalues and eigenvectors. The size of the first eigenvalue provides the direction of largest variance while the second eigenvalue, orthogonal to the first one, provides the direction of minimum variance in the data.<sup>22</sup> The reduction of the multidimensional vector to a two-dimensional space of low-technology trap and poverty trap allows one to assess the implications of low technology endowment for poverty from a reduced model. Under this model, inference on the welfare effects of technology trap is less affected by the stability of increasing returns estimates. Moreover, the methodology is highly data-intensive, and hence, may allow one to assess the implications of technology jumps on welfare.<sup>23</sup> Although a partial equilibrium analysis, this data reduction technique provides an empirical characterization of technology and poverty trap attributes. Conceptually, the reduction of the problem to a two-dimensional space allows a representation of countries on the path of poverty trap and technology trap. In particular, areas of low equilibrium of dual traps (poverty trap and technology trap) and areas of higher equilibrium (relatively high technology endowment and income) can be sketched on the same two-dimensional vector space, where each point on the space represents the level of aggregate output for a given level of technology.

Figure 2 below sketches that two-dimensional representation. Countries confronted with technology trap and poverty trap fall in the first quadrant (bottom left), where income and technology endowment are relatively low and consistently fall below the median point. In both panels the dual trap is characterized by pull-factors, symbolized by the arrows pointing towards the origins to 20 In fact, the range of Sub-Saharan African’s increasing returns estimates varied markedly between 2000 and 2005, and were higher than the OECD averages which were uniformly inferior to unity.

21 A detailed description and overview of the rationale for using the PCA methodology is provided in Annex. For further details see Morrison (1990).

22 For instance, should the largest eigenvalue be knowledge, proxy for stock of technology and technological progress, it would imply that most of the proportional variance observed in the global distribution of income is explained by the variation in the diffusion of technology and knowledge at the global level.

23 However, by design the proposed PCA methodology is static and based on a partial equilibrium analysis. In practice, a change in the level of technology should have implications on productivity and income. Moreover, the prospects of technology jumps depend on a prior shift in the threshold level of capital. In fact, for models of threshold externalities improvement of productivity depends on the scale of capital per worker, suggesting that productivity growth requires strong investment rates sustained over a long period [Kraay and Raddatz (2007)]. In practice, countries in SSA recorded low investment rates during most of the adjustment era, with a rapid depreciation of fixed capital stock [Akyuz and Gore (2001), Bu (2006), Bayraktar and Fofack (2007)].

11