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Estimating the New GCI model
The appropriate weighting of individual indicators in determining overall competitiveness is a crucial part of any index model.74 The New GCI model calculates weights based on a regression of the pooled dataset on country GDP per capita.The stability of the model is tested by reallocating individual indicators and assessing the stability of the weights and the overall score. Other similar indexes have almost invariably set weights based on subjective priors based on the literature.Yet differences in opinion in the academic literature leave the door open for different choices that can compromise the resulting rankings.
The New GCI is generated in a two-stage process.
First, the weights for aggregating individual indicator scores to an overall country score are obtained using panel data for over 130 countries and up to 7 years
(2001–07).The weights obtained from estimating the model are going to be kept constant over time.
Additional years of data will be used to check the stability of the model over time. Second, the index score for a particular country in a given year is calculated by summing its weighted indicator values.
The New GCI model uses principal component analysis (PCA) to aggregate individual indicators (or categories of indicators).The premise of the PCA method is that within a “conceptual category,” indicators are highly correlated and related to the underlying phenomenon that is being measured.Within the area of microeconomic competitiveness, the degree of correlation between indicators is very high.The New GCI model thus applies a two-step PCA procedure. First, a separate aggregation is computed for COS and for each of the four components of the NBE. Second, these five components are aggregated into an overall score for microeconomic competitiveness.75 Figure 7 provides a sample calculation, showing the specific scores calculated for the indicators in company operations and strategy using the current specification of the new GCI model.
Within the area of macroeconomic competitiveness, the degree of correlation between indicators is relatively high within SIPI and MP but not across them, because they capture distinct concepts.The new GCI model thus uses PCA to calculate a score for SIPI and a score for MP, but does not use PCA for further aggregation.
The second step in computing the New GCI is to measure the contribution to country competitiveness of
MICRO, SIPI, and MP. We specify a comprehensive regression that uses GDP per capita purchasing power adjusted (log) as the dependent variable, with scores for
MICRO, SIPI, and MP for each country and year as the main explanatory variables, controlling for with endowments and year effects.The model allows the coefficients to vary by country stages of development by interacting

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