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This paper aims to derive and estimate a theory-based empirical specification that models a firm’s choices of its international diversification (ID) and product diversification (PD) and how they evolve over time in response to shocks that alter the relative cost and relative profitability of ID and PD.

We use longitudinal data on U.S. manufacturing firms from 1984 to 1999, a period of intense shocks associated with rapid globalization, to estimate a dynamic panel data Tobit model that permits lags in a firm’s adjustment to its optimal mix of ID and PD over time.

We find strong support for the theoretical framework underlying our empirical specifications and posited dynamics, with full adjustment estimated to require, on average, 1.5 years, a finding with implications for the time spacing of observations in empirical studies of ID and PD to avoid biased inferences. Among the globalization shocks during the time period studied, our results indicate that global competitive pressures and efficiency gains from global supply integration to be the more important factors driving U.S. firms toward greater ID relative to PD. Augmentation of firms’ organizational (managerial) and physical capital resources is also found to be important for supporting an expansion of ID relative to PD. Technological resource augmentation is instead found to favor expansion of PD relative to ID.

Our empirical specification is novel. It readily incorporates an often ignored but necessary theoretical condition that defines a firm’s optimal choices of its ID and PD, and it allows observed choices at a point in time to deviate from their optimal values.

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

Competitiveness is one of the most misunderstood concepts of the 1990s. It has drawn substantial attention from the government and business communities during the last 25 years. Morrisson et al. (1988) noted that between 1983 and 1987, the term competitiveness appeared more than 5700 times in the titles of newspapers and magazine articles. The growth of importance and interest can also be observed from the increase in the bibliographical entries in ABI/Inform database. From 1981 to 1986, the topic “international competitiveness” increased by about 26 listings per year (a total of 159 in 6 years) and the rate increased to 45 listings per year from 1987 to 1993. Academic interest in the area has also increased and as a result, new developments contemplating conceptualization and understanding of competitiveness are taking place. However, to no one's surprise, writers from different disciplines offer a variation in perspective when describing the concept, understanding, and postulation of competitiveness.

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).

In this paper, I address issues concerning the empirical estimation of a relationship between firm performance and its degree of multinationality. I argue for greater delineation of the underlying nature of firms’ multinationality and point to several statistical issues regarding estimation that appear to need resolution, but which appear to have been largely neglected in the literature that has examined for a multinationality–performance relationship. Among these are endogeneity of the multinationality construct in the performance relationship and the likelihood that the multinationality–performance relationship is heterogeneous across firms.

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.