The technology lag ( _At_ / _At_ * ) , which measures the distance from the global technology frontier, is proxied by the proportion of high-tech exports of the sub-region. Capital accumulation is proxied by investments. Other variables in the model are consistent and accordingly defined.34 This specification takes into account the constraint of data availability. Unlike advanced economies, the data on R&D is not always readily available in most developing countries. In this framework they are estimated from a method which combines simulation and instrumented parameters from OECD data. More specifically, R&D elasticity of economic growth is estimated from a panel of OECD countries using the model. A random number generator is then used to construct an R&D vector consistent with R&D empirical distributions in Sub-Saharan Africa.

The simulated R&D vector and estimated R&D elasticity of growth from OECD countries are used to draw inference on the implications of technological advances for growth and welfare in SSA. In particular, a marginal and joint variation of R&D elasticity of growth around the mean value and uniform changes in R&D scale are considered in assessing the expected welfare effects. The impact of technological changes measured primarily by R&D increases on poverty is also considered, drawing on the recursive modeling assumption. In particular and following Kakwani and Pernia (2000), poverty indices at any given period depend on the rate in the previous period, the poverty and growth elasticity and per capita GDP growth rates.<sup>35</sup>

# **V. Estimation and policy implications**

Estimation and applications are based on a pseudo-panel data covering the period 1990-2001. The focus is inference on income and welfare effects of advances in sciences and technology through increased research and development expenditures. The results and calibration are based on equation (10). A marginal and joint variation of R&D elasticity of growth and uniform changes in R&D scale are considered, in turn. The link to micro data and household survey allows inference on poverty.

The first simulation assumes increases expenditures on research and development under constant R&D elasticity of growth. The regression coefficients in equation (10) are either elasticities or semielasticities and provide the proportionate change in the conditional mean of aggregate output when the specified regressor is subject to changes.<sup>36</sup> Under the base case scenario, the estimated R&D elasticity of growth is set at the average going rates across OECD countries (8 percent). The sign and magnitude of growth elasticity are consistent with estimates obtained in the literature, which establishes a positive association between R&D and economic growth [Mansfield (1972), Gittleman and Wolff (1995)].

Table 2 in the Annex reports the econometric results.<sup>37</sup> The likelihood test rejects the null hypothesis that the underlined model does not provide a good prediction of GDP. In particular, the F-statistic is 34 Aggregate output is estimated in constant 2000US$; openness is estimated as the sum of exports and imports expressed as a percentage of GDP; financial depth is proxied by M2 over GDP and macroeconomic instability is measured by inflation rate of consumer price index. ˆ 35 That is _Pt_ ( α ) = _Pt_ − 1( α ) ∗ 1( − δ ) θ ( _t_ , _GDP_ ) , where δ is the poverty and growth elasticity, θ<sup>ˆ</sup> ( _t_ , _GDP_ ) is the growth rate of GDP per capita and (0 ≤ α ≤ 2). We are particularly interested in the poverty incidence rate ( α = 0) . For further details on the methodology see Kakwani and Pernia (2000). 36 The parameters are elasticities when the regressors are taken in logs and semi-elasticities when they are not. 37 In addition to R&D the basic model (constant R&D elasticity of growth) considers five exogenous variables for their potential impact on growth: M2 in GDP to capture the financial depth, investment, inflation rate of consumer price index to capture macroeconomic volatility, the sum of imports and exports over GDP to capture the degree of economic openness, and high-tech exports as a share of total exports. This last variable also highlights the degree of economic diversification and integration into the global economy.

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