productivity level, followed by wholesale and retail trade, while mining and public utilities maintain the highest productivity levels. We note that productivity gaps are typical characteristics of underdevelopment. Economy-wide productivity gaps (as measured by the coefficient of variation of the log of sectoral labor productivities) tend to decline with sustained economic growth. Inter-sectoral productivity gaps are also the widest for the poorest countries in our sample. These indicate that structural change plays an important role in promoting convergence, both within and across countries.

Altogether, unlike BBC, the Asian economies show a particularly clear-cut case of growth-enhancing structural change. Figures 6 and 7 plot the (end-of-period) relative productivity of sectors against the change in their employment shares for the Asian and SSA countries. China, Thailand, and Indonesia represent distinct cases of growth-enhancing structural change, as evidenced by the sharply positive slopes of their scatter plots. In these economies, labor largely moved out of agriculture to higherproductivity activities, notably manufacturing and tradable services.<sup>27</sup> However, not all Asian countries exhibit this kind of pattern.<sup>28</sup> However, strong “within” productivity growth in Korea has given a boost to overall productivity. Turning to our African sample, Ethiopia, Tanzania, and Botswana show growth-enhancing structural change, while productivity gain from structural change is relatively weaker in Senegal and Ghana. In general, the pattern of structural change in Benin and Côte d’Ivoire looks more like those of Korea and Ghana. Burkina Faso exhibits growth-enhancing structural change; however, the slower pace of structural transformation and missing contribution of high-productivity manufacturing sets it apart from the typical pattern observed in Asia.

# **3. Decomposition of Productivity and GDP Growth**

In this section, we decompose GDP per capita growth into changes in labor productivity, employment rate, and demographic structure both at the aggregate and sectoral level. Next, we decompose productivity growth into within-sector and between-sector productivity changes. In addition, we split the structural change component into _static_ gains (or losses) in productivity (due to labor shifts from below- to above-average productivity _level_ sectors) (or vice versa) and dynamic gains (or losses) in productivity (due to relocation of workers from below- to above-average productivity growth sectors) (or vice versa). Finally, putting the pieces together, we disentangle the sources of per capita growth by tracing the contributions of changes in within-sector productivity, structural change (static and dynamic), employment rate, and population structure. Most studies<sup>29</sup> on structural change solely focus on decomposition of productivity growth, while the handful of studies that also decompose GDP growth fall short of decomposing structural change into its static and dynamic elements. In this paper, we adopt insights from Timmer _et al_ . (2014) to quantify the portion of productivity growth and economic growth that can be attributed to static and dynamic effects.

# **3.1 Defining the Contribution of Structural Change**

The empirical analysis in this paper employs the Shapley decomposition method.<sup>30</sup> As touched upon above, we move beyond the standard decomposition of productivity growth and rather pursue a broader approach that allows us to unravel the contributions of the above-mentioned contributory

> 27In China, however, the expansion of low-productivity service activities has dragged down economy-wide productivity growth. 28Korea particularly represents an anomaly as it saw its high-productivity manufacturing industries shrink in favor of relatively low-productivity service activities; hence the much flatter slope of the Korea plot.

> 29See, for instance, McMillan _et al_ . (2014), McMillan and Harttgen (2014), Timmer and de Vries (2009), and Timmer _et al_ . (2014). 30See World Bank (2012) for detailed technical discussions and illustrations of the Shapley decomposition technique. The estimates in this paper were obtained using the _Job Generation and Growth_ (JoGGs) _Decomposition tool_ � an Excel-based software. The Shapley decomposition first computes the marginal effect on output per capita growth of the sequential elimination of each of the contributory factors, and then the growth contribution of each factor is derived as the average of its marginal contributions in all possible elimination sequences. In other words, the growth contribution of a variable is interpreted as its impact on output per capita growth had the remaining contributing factors remained unchanged.

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