 ~ ln( _yit_ )   _it_  _Tt_   ln( _N it_ )  ln( _X it_ )    _I it_   _it_ (5)

# **IV. Empirical Results and Prospects for Convergence**

Following estimation of parameters in the first-stage regression, the model specified by (5) is fitted to a panel of 24 countries. Included in this panel are all the six GCC countries and a sub-sample of 18 countries in SSA. Data sources are World Bank, World Development Indicators and World Economic Outlook. The time series data covers the period 1970—2007 as the study focuses on longterm changes and not cyclical variations in output. Per capita GDP, public and private investments, foreign direct investment, net interest income, changes in real effective exchange rate and money supply data in the conditioning vector are from the World Development Indicators database. So is the natural resource variable _N it_ , which is proxied by country exports, assuming that natural resources continue to account for the lion’s share of exports in these countries.<sup>37</sup> Data on corruption index, political stability and political risk rating are obtained from International Country Risk Guide.

This section assesses the prospects for income convergence between GCC and SSA countries when institutional continuity is controlled for. Counterfactual simulations investigating plausible policies that could enhance income convergence between the two regions are undertaken. However, in order to infer on the convergence parameters, it is foremost important to derive an empirical model that best estimates the dynamics of output growth in these economies. Such an underlying model is provided by equation (5). This model assumes that the estimated parameters are identical for all countries in the sample, but some differences could exist in the constant terms.<sup>38</sup> Estimated coefficients and _t-values_ of this model are reported in Table 2.

Three response variables are considered in the first-stage regression to instrument institutional continuity. This sequential approach produces three set of results with two panels in each. Panel A (left column) refers to the first-stage regression for institutional continuity and panel B (right column) refers to the second stage growth regression. The results are robust to alternative measure of institutional continuity, as most coefficients are significantly different from 0 at the 5 percent significance level in either model. The standard errors of estimated coefficients corrected for heteroskedasticity (in parenthesis) are very small, reflecting the relative stability of these parameters. Similarly, the adjusted R-squared associated with either model is comparably high, over 98%.<sup>39</sup> Notwithstanding the overall performance of these models, inference is exclusively based on the model, which uses political risk rating to instrument for institutional continuity in the first-stage regression (columns 5 and 6). In effect, out of all three alternative measures of institutional continuity considered, this last model has the lowest _p-value_ , hence performs better on average than alternative specifications which rely either on corruption index or political stability. The coefficient associated with this variable is significant, with a very low standard deviation. Indeed, this variable has a coefficient that is positive and significantly different from 0 at the 5 percent level of significance. In

> 37 For most countries, including those which have entered the path of diversification, natural resources continue to account for over 90% of total exports. In part, this reflects the fact that the diversification process has simply reduced the marginal propensity to imports in most countries which continue to operate far off the global production possibility frontier as a result of their productivity and competitiveness gap.

> 38 Time dummies are also included in the different models, although the results are not reported in the Table due to space limitation. However, they can be made available upon request.

> 39 An alternative model with variables expressed in percentage of GDP is also fitted to the data. The results in Table 1 in Annex are not significantly different, though the R-squared value is smaller.

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