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I would like to thank, without implication, Jacques Artus, Ulrich Baumgartner, Claude Bismut, Karl Habermeier, Joaquim Levy, Sunil Sharma, Alexander Hoffmaister and participants at a seminar held at the French Ministry of the Economy for helpful discussions and comments on an earlier draft. I am also grateful to staff members at the French Ministry of the Economy, INSEE, and the OECD for providing some of the data used in this study, and to Brooks Calvo for excellent research assistance.
Manufacturing is a key tradable goods sector in France (as in most industrial countries) and plays an essential role in external trade performance. In 1993, for instance, manufactured products (in volume terms) amounted to 71.4 percent of total imports of goods and 76 percent of total exports.
The geometric weighted average (with fixed levels of weights calculted through a double-weighting method applied to bilateral trade data) is the most frequently used formula. Traditional Paasche-type indices that do not account for changes in weights may, however, produce second-order approximation errors which may accumulate over time. See Guerrieri and Milana (1993) for an alternative technique based on time-varying weights.
France mostly trades with European Union (EU) countries. In 1993, trade with these countries represented about 60 percent of total exports and imports. Germany is the largest partner, and accounted for about 17 percent of exports and imports in 1993.
Unpublished Fund staff estimates indicate that a similar pattern has been observed in other industrial countries in Europe, including the United Kingdom and Italy.
There are a variety of limitations associated with profitability-based measures of competitiveness. As pointed out by Lipschitz and McDonald (1992, p. 39), relative profit margins may not provide reliable indicators regarding developments in rates of return on capital if there are significant differences in technology across countries: profits per unit of output may fall relative to competitors, but, if capital used per unit of output falls even more, profits per unit of capital will increase faster than for competitors. Furthermore, as indicated earlier, labor costs are only one factor affecting total production costs and thus profitability; non-labor costs (such as material input costs) are also an important component. Value added other than labor costs includes many elements in addition to pure profits. For example, a shift to more capital-intensive sectors may tend to push up the value-added deflator beyond any rise in unit labor costs, even though no individual sector has become more profitable (Turner and Van’t dack, 1993, p. 60).
Ideally, techniques of price index construction should take account of quality changes, with changed quality at the same price being recorded as a price movement at constant quality. However, currently used statistical methods do not capture these aspects adequately.
Moreover, fixed capital accumulation and non-tangible investment (R&D and education) expenditures are positively correlated with productivity performance, and thus unit labor costs.
The indicator focuses only on the quantitative aspect of capital accumulation and does not account for its efficiency, which depends in particular on its technological content and its sectoral orientation.
See, for instance, Bonnaz and Paquier (1993). It is worth noting that analyses along these lines have been at the forefront of the debate on competitiveness in France since at least the 1970s. See, most notably, Lafay (1976).
See most notably Bismut (1994), who found that the weight attached to competitors’ prices in export markets to be twice as large as the weight attached to domestic labor costs in the determination of French export prices in the long term.
Erkel-Rousse (1992) provides a recent study emphasizing the role of nonprice competitiveness (using an indicator very similar to the one used here) on the behavior of manufacturing trade flows in France and Germany during the 1970s and 1980s.
To construct the variance decompositions, the variables in the system must be ordered according to their degree of contemporaneous exogeneity. To check for robustness, different ordering of the variables are used below. In all alternative orderings, the first variable (the most contemporaneously exogenous variable in the system) is G-6 output and the last either the trade ratio or domestic output. Thus, in any given quarter, innovations in foreign demand are assumed to affect all of the other variables in the system, whereas innovations in other variables do not affect foreign demand in the same quarter; trade flows (or domestic output) are also assumed to be affected by contemporaneous innovations in all other variables.
In the preliminary stages of the analysis, experiments were performed with a vairety of other price- and cost-based indicators of competitiveness. Some measures (such as relative profitabiliy indicators) yielded poor results, whereas others (such as the real exchange rate index based on wholesale prices) yielded results that were close to those obtained when using normalized relative unit labor costs.
Sources of the data for the index of relative unit labor costs and the manufacturing trade ratio are described in Charts 1 and 10. France’s real GDP was obtained from the IFS database. G-6 real GDP was obtained from the World Economic Studies Division of the IMF Research Department. The quarterly series on nonprice competitiveness was obtained by interpolation from the semi-annual series.
The Johansen technique yielded implausibly high elasticities (almost 6, for instance, for domestic output in some of the tests that were performed). The OLS regression obtained by appyling the Engle-Granger procedure was, for the period 1980q1-94q2:
McKinnon’s (1991) critical values are -2.913 and -3.547 at the 5 and 1 percent significance levels, respectively.
See Lütkepohl (1985) for a discussion of alternative criteria for choosing the lag length in VAR models. Imposing equal lag lengths on each of the variables in the model implies that the resulting parameter estimates may suffer from bias or inefficiency, depending on whether the model is under- or over-parameterized.