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The purpose of this paper is to examine how the ranking of countries based on the World Economic Forum's (WEF') competitiveness index is changed when the underlying primitive data dimensions of this composite index are aggregated using weights that are endogenously determined for each country, instead of aggregated using the WEF's fixed set of weights applied to all countries.

The paper presents a method based on data envelopment analysis to determine weights for aggregating the underlying primitive data dimensions of any composite indicator. The approach determines endogenously the “best” weights a given observational unit (e.g. country) on the basis of its revealed performance on each primitive sub‐dimension underlying a composite index. The ranking of countries based on the values of a composite competitiveness index that uses the proposed endogenous weight method is then compared to the ranking based on the WEF's competitiveness index for the year 2006. The rankings are then compared and assessed to determine if the observed difference in the rankings are statistically significant.

A comparison of the ranking of countries on the basis of the value of each index reveals that countries do undergo a change in their competitiveness rank when endogenous weights are used. The results suggest the WEF's competitiveness index, which uses the same fixed weights applied to every country (or group of countries), creates a bias that favors countries that score high on the “technology” sub‐dimension of the index.

The study presents an alternative to the current practice of using a fixed set of weights applied uniformly to the basic unit of analysis. The method serves as a starting‐point for further research on the biases created by different weighting schemes to construct a composite indicator that aggregates primitive data, with the resulting composite index values then used to rank entities.

The method to determine endogenously the weights to be applied to each unit of analysis when constructing a composite indicator is novel and has wide applicability to the general issue of comparing performance across different units of analysis based on a composite index of performance (i.e. benchmarking).

Most composite indicators of national performance limit their scope to only economic performance criteria and aggregate primitive performance data using subjective fixed weight values applied uniformly to all countries. This paper proposes a weighting method to correct for biases inherent in the use of fixed and uniform weights, and it presents a composite performance indicator that encompasses both economic and non‐economic performance criteria.

The paper presents a method that endogenously determines country‐specific weights that explicitly take account of a country's own choices and achievements across primitive dimensions of performance. The method is then used to construct a composite inclusive index that combines economic performance with two other performance dimensions: environmental sustainability and governance.

Comparison of the endogenous weight method with the method of using fixed and uniform weights indicates a bias in the latter that penalizes countries, in terms of indicating lower relative performance, which are more diverse in their achievements among primitive performance dimensions. When the endogenous weight method is used, the performance ranking of countries is altered such that countries with greater diversity improve their relative performance while the relative performance of countries having less diversity may either rise or fall.

The weighting method discussed in this paper: is applicable at any level of analysis (e.g. nations, companies, business units, etc.), obviates objections about the “importance” of alternative primitive dimensions that can arise when subjective fixed weights are used; and indicates more accurately relative performance since each unit of analysis is first allowed to obtain its best performance before relative performance is assessed. The method can therefore assist policy makers, companies, etc. to more accurately benchmark performance and it can, in particular, assist companies to respond to perceptions of low performance or compliance among different performance dimensions when performance has been determined using the traditional method of fixed and uniform weights.