Towards an index of industrial capability

Transcription

1 Towards an index of industrial capability Jesús Crespo-Cuaresma Bruno Dissmann Christian Helmenstein Jaroslava Hlouskova Philippe Scholtès Abstract An index of industrial capability is proposed which may serve as an early warning mechanism to the export performance of sectors producing manufactures with medium and high technological content and as a general index of competitiveness potential. In order to find an empirically plausible set of weights for the index, a panel of 61 countries with annual data ranging from 1970 to 1998 is used in order to determine the variables (Granger-)causing export performance as measured by the revealed comparative advantage index. Two alternative modelling strategies are adopted, and both in- and out-of-sample criteria are used as model evaluation instruments. The evolution of the index is reported for four Southeast Asian economies, and the country ordering induced by different specifications of the industrial capability index is presented for the time interval Keywords: Southeast Asia, industrial capability, real comparative advantage, panel data methods JEL Classification: L60, C23, C53 The authors acknowledge funding from the Ministry of Economics, Trade and Industry, Japan. The present piece of research has benefitted from the comments made at the presentation of the preliminary results in the Office of Industrial Economics, Ministry of Industry, Thailand. Jesús Crespo-Cuaresma, University of Vienna, Department of Economics, Brünnerstraße 72, A-1210 Vienna, Austria; address: jesus.crespo-cuaresma@univie.ac.at Bruno Dissmann, United Nations Industrial Development Organization (UNIDO), Industrial Policies and Research Branch, Vienna International Centre P.O. Box 300 A-1400 Vienna, Austria; address: b.dissmann@aon.at Christian Helmenstein, Institute for Advanced Studies; Department of Economics and Finance, Stumpergasse 56, A-1060 Vienna, Austria; address: helmen@ihs.ac.at Jaroslava Hlouskova, Institute for Advanced Studies; Department of Economics and Finance, Stumpergasse 56, A-1060 Vienna, Austria; address: hlouskov@ihs.ac.at Philippe Scholtès, United Nations Industrial Development Organization (UNIDO), Field Office in Vietnam, 72 Ly Thuong Kiet, Hanoi, Vietnam ; address: pscholtes@unido.org 1

2 1 Introduction The United Nations Industrial Development Organization (UNIDO) has developed a scoreboard on sustainable industrial development (UNIDO s Sustainable Industrial Development Indicators), a set of indicators which capture qualitative and quantitative aspects of a country s industrialization process, and project a fair image of its strength and sustainability. Data on 58 different indicators are collected for a sample of 87 countries for the time span These indicators include measures linked to economic fundamentals and business climate, performance indicators, industrial capability indicators, human resources indicators, capital-related and infrastructure indicators. 1 The use of a scoreboard of l variables on m countries implies defining cross-country comparisons on a set of m l-dimensional vectors at every period of time. A way to circumvent this geometric dilemma is to transform the vectors into scalars, by assigning weights to each one of their l elements. For instance, one may argue that annual growth rate of MVA is a relatively more important determinant of competitiveness, than industry share in total FDI inflows. The corresponding variables accordingly receive different weights and the vector eventually collapses to scalar, that lends itself to straightforward comparisons across countries or over time. The United Nations Development Programme s Human Development Index and the World Economic Forum s Competitiveness Index are both constructed that way; UNIDO s Sustainable Industrial Development Indicators is not, because its developers considered that information losses far outweighed convenience gains. The only composite indicator of overall industrial competitiveness in UNIDO s Sustainable Industrial Development Indicators is the Competitive Industrial Performance Index (CIP), which is the simple arithmetic mean of four normalized performance indicators (manufactured value added per capita, manufactured exports per capita, the share of medium and high-tech activities in manufactured production and the share of medium and high-tech products in manufactured exports). The aim of this piece of research is to obtain a synthetic (scalar) measure of industrial capability (in the sense of potential to increase revealed comparative advantage) in order to facilitate cross-country comparisons, where the weights corresponding to different variables are chosen based on sound econometric analysis. This measure can then be compared across countries to generate a ranking of industrial capabilities. If the index option is selected, a procedure must be defined to determine a weight distribution that affords a credible correspondence from a set of m vectors the scoreboard, to m scalars the index. Econometric analysis of the dataset beneath the scoreboard will be used to elicit a weight profile that is both economically coherent, and consistent with empirical observation. Two different approaches will be used and compared for the construction of the index. The first one relies on the specification of a production-function-type of specification 1 A detailed description of the dataset and the measures included can be found in UNIDO s Industrial Development Report (UNIDO, 2002), which can be downloaded from 2

3 for explaining revealed comparative advantage (henceforth, RCA) in the subset of firms in the high- and medium-tech sector whose product is export-oriented and the second one is based on searching among all possible models of RCA determination for those with a better statistical fit. The production-function approach calls, therefore, for variables related to the production factors of the aforementioned industry, among them capital formation, education proxies and labour force. The purely statistical-fit approach searches among all available economic variables in order to look for the best fit both in- and out-of-sample. This is done taking into account the multi-collinearity which could be present among the potential explanatory variables, and considering not only measures of in-sample fit, such as R 2, but also the forecasting ability of the resulting model. Given a chosen model, the second aim of the study is to compute a signalling index which is free of country-specific features other than those that the procedure has isolated as explaining industrial capability. The index may be used both as a general measure for cross-country comparisons of industrial capability and as an early warning mechanism for export performance in the high- and medium-tech sector, given the nature of the dependent variable in the econometric analysis. The step to be taken for creating the index once the model is set up relies on using the estimated parameters as weights on the explanatory variables. Desirable characteristics of the index are: - universality: the index should be computed as a (linear) combination of economic variables of the country of interest, independently of its identity; - prediction power: changes in the index of international competitiveness potential should precede changes in the revealed comparative advantage, in such a way that potential across countries can be compared and the index can be used as an early warning mechanism concerning export performance. Once a proper econometric specification of the data generating process for RCA is found, a forward-looking projection of the specification of RCA (free of countryidiosyncratic effects other than those implied by the quantifiable variables in the specification) fulfills the desired properties mentioned above, and will be the index used in order to compare industrial capability across countries and to investigate its evolution through time. The paper is organized as follows. Section two briefly describes the RCA index and explains the nature of the index developed in the study. The results and the ranking of countries induced by this industrial capability index are presented in section three, together with some more detailed analysis for a group of Southeast Asian countries. Section four councludes and indicates potential paths of future research. 2 Determinants of RCA and the Industrial Capability Index 2.1 The RCA index The RCA index was first introduced by Balassa (1965), as a measure of international trade specialization and, therefore, of international competitiveness. The RCA index 3

4 of sector i in country j is defined as RCA ij = x ij x i, where x ij refers to the share of exports of sector i in national exports of country j, and x i is the share of exports of sector i in world exports. The RCA index ranges from zero to infinity, with values below one implying de-specialization in sector i, and values above one indicating specialization of country j in sector i. The RCA index is, naturally, not free of criticism. The usual source of methodological discussion on the validity of the RCA index as an indicator of comparative advantage rests upon the simplifying assumption that lies behind the use of the RCA index, namely that exports express directly the competitive capability of an economy (see, e.g., Laursen, 1998). However, the use of the RCA as an indicator of international competitiveness is widespread in the economic profession, and no alternative index seems more suitable for the analysis performed in this study. For econometric applications in this piece of research, the variable actually used will be the natural logarithm of RCA, as recommended by, e.g., Vollrath (1991). See Laursen (1998) for other alternatives to logarithmizing the data. 2.2 Specification issues The starting point of the econometric analysis will be an equation of the general type: RCA i,t = z w j x j,i,t kj + ε i,t, (1) j=1 where i denotes cross-sectional units (countries) and t time (years), k j is the time-lag applicable to the variable x j, w j is the parameter associated to x j,t kj and ε i,t is assumed to be composed by a time-invariant, country-specific constant (µ i ), and a white noise error (ν i,t ), ε i,t = µ i + ν i,t. (2) Given a specification such as (1)-(2) and the vector of estimated parameters ŵ = (ŵ 1... ŵ z ), The Industrial Capability Index (ICI) for country i at time t is given by the scalar ICI i,t (f) = ŵ x i,t+f, where x i,t = (x 1,i,t k1 x z,i,t kz ) and f IN, 1 f min j {k j }. The ICI is thus an f-years ahead projection of the non-country-specific part of the estimated specification (1). Throughout the analysis, we will refer to the ICI index as the one-year ahead projection, that is, we will use f = 1. 4

5 2.3 The production-function and statistical approach If the RCA is assumed to be determined by a production-function specification, the exogenous variables that need to be included are those referred to factors of production in the exporting medium- and high-tech sector. This implies using (lagged) variables on the right-hand side of equation (1) such as: 2 - Secondary school enrollment, as a proxy for human capital, a decisive input factor for activities with higher technological content, - Foreign direct investment, that is, the external side of capital formation, - Value added per worker in the sector of interest, as a measure of productivity and - Share of medium and high tech industries employment in total manufacturing, which will reflect changes in labour supply rooted on the comparative profitability of the medium and high tech sector compared to the rest of the industry, as well as scale effects of the sector in consideration. The alternative approach to specifying a production function is to concentrate on those variables which offer the best statistical fit both in- and out-of-sample (this modelling strategy will be labelled the statistical approach, as opposed to the productionfunction approach ). This is done in three steps: - Multi-collinearity is detected and corrected: Those variables which present collinearity problems are detected using a procedure based on the eigenvalues of the matrix of regressors, as explained in the appendix; - Among those variables remaining, the insignificant ones are removed from the specification, and the model with a highest explanatory power (measured by the adjusted R 2 ) is chosen; - By means of an out-of-sample analysis, the different models resulting from the two steps mentioned above are compared in terms of their ability to forecast the not-yet-realized levels of RCA, and the one with a better performance is the chosen model. The statistical approach ensures that the chosen model exhibits certain optimal characteristics concerning goodness of fit and forecasting power, but has the disadvantage that the resulting specification is not based on any model which is directly imposed by economic theory, and may be difficult to interpret ex-post. We believe, however that both the production function and the pure statistical approach can shed a light on the factors underlying the realized level of competitiveness of the medium and high tech industrial sector of the economy. 2 Throughout the analysis, only lagged right-hand-side variables will be considered. The reason for the choice is twofold: the nature of the ICI implies using only past variables as explanatory variables (or, alternatively, using forecasts in order to compute the projections) and we are interested in exclusively in variables causing (in the sense of Granger-causality) RCA. 5

6 3 Modelling strategy and results 3.1 Empirical results: the production-function and statistical approach Data on 10 variables for 61 (N=61) countries covering the period (T =29) has been used in the analysis. 3 The variables and countries in the sample are shown in Table 1 and Table 2 respectively, together with their abbreviations. Not all countries have complete time series for all variables. The appendix describes the method used to account for missing values in the data. The dependent variable throughout the empirical analysis is (logarithmized) RCA in the subset of firms in the high- and medium-tech sector, and a fixed effects model will be used for the panel. The systematic strategy used in order to select the final model will be based both on in-sample and out-of-sample characteristics of the model, and multi-collinearity and statistical significance of the estimated parameters will be taken into account prior to model choice. Assume that we have nine explanatory variables and we are interested in the existing relationships up to two years (lags). First the case with nine one-year-lagged explanatory variables is approached: multi-collinearity among them is studied, and those presenting evidence of multi-collinearity are excluded of the model following the method presented in the appendix. Among the remaining variables, only those which are significant will be included in the final model. The choice of significant variables is done in a stepwise fashion, eliminating one by one the variables whose coefficients appear insignificant at a given significance level (starting by the most insignificant in terms of the p-value associated to the t-test statistic of the coefficient) and re-estimating the model without the insignificant variables until all remaining coefficients are statistically significant. The procedure is then repeated for the following combination of lags, and the adjusted R 2 value will be the indicator used to choose among the models resulting from each lag combination. Notice that, for the case of nine variables and two lags, 2 9 (=512) models are evaluated and a single one is chosen. On the other hand, in order to choose among models with different explanatory variables the criterion we have decided to choose is based on the out-of-sample performance of the model (the idea of using out-of-sample statistics as a modelling tool in panel data analysis has been defended by, e.g., Granger and Huang, 1997): all competing models are re-estimated for the period {1, T 2} and {1, T 1}, the one presenting a lowest forecasting error (in terms of average root mean square error across countries) when predicting RCA i,t 1 and RCA i,t will be the chosen one. Firstly, we estimate a production-function-based specification, including the variables and lag lengths presented in Table 3. The lag length is expressed as an interval of lags. For example, 3-5 in Table 3 implies that different specifications are tried, in which FDI enters with lag three, four and five (one lag at a time). The lags have been chosen according to our a-priori intuition on the transmission time required for the variable to have an effect on the export performance of the sector, and the use of a lag range allows for some flexibility in the duration of such a process. If significance issues are not taken into account, the resulting weight structure corresponding 3 The choice of variables has been done based on the existence of long time series for as many countries as possible. 6

7 to the model with better explanatory power among all possible lag structures is ŵ = (ŵ fdit 3 ŵ sest 6 ŵ vpmt 1 ŵ smmt 3 ) = ( ). However, some of these weights included are not significant at the usual critical levels suggested by statistical inference. In fact, with the specification reported, the estimated parameters for the employment and productivity proxies are not significant at the usual 10% significance level. If the procedure is repeated and the variables corresponding to insignificant parameters are removed from the specification in a stepwise manner, as suggested above, the resulting weight vector is ŵ = (ŵ fdit 3 ŵ sest 6 ŵ smmt 3 ) = ( ). The weights are all 10% significant in this specification. The only difference between the results of the two production function approaches is that the impact of productivity changes does not appear in the model with only significant variables. The relative weight of the other variables stays approximately equal between the two approaches, and reveals the importance of human capital formation and foreign direct investment as determinants of international competitiveness in the medium- and high-tech sector. The next step to be taken is to construct a ranking of countries according to the industrial capability index induced by the estimated specifications. This is done as follows: given the chosen model, the procedure of estimating the weights is redone for the sample up to 1990, and the index is computed for each country by projecting the non-country-specific part of the parametrization one year ahead (it corresponds, thus, to the ICI(1) described above). This is done for each year between 1990 and 1998, and the countries are ordered from highest (first position) to lowest (last position) according to the value of the index. Notice that, as the index respects the universality requirement, it is actually comparable across countries. Table 4 shows the ranking of the countries included in the regression according to the dependent variable in the analysis (RCA index in medium and high-tech sector), and Table 5 and 6 present the ordering induced by the production function-based index with all variables and only the significant ones, respectively. At first sight, they do not correspond to the ranking scheme of the RCA index, as what is indicated by the industrial capability index is actually the potential to increase RCA, which need not have a one-to-one mapping with the actual level of RCA; that is, countries with a low RCA index may have a higher export performance potential than countries at the top of the ranking of Table 4, and viceversa. The message from both rankings concerning Southeast Asian countries is, however, relatively similar: during the 90s, the relative international competitiveness potential of Philippines, Malaysia and Thailand has improved considerably (in the first two cases, in an extraordinary manner), Indonesia s industrial capability has not experienced a visible improvement and two clusters have been formed concerning international competitiveness potential, one containing Philippines and Malaysia, at a higher capability level, and the other one with Thailand and Indonesia, at a lower level. Concentrating now on the purely statistical approach, three sets of variables (see Table 7) have been selected as a starting point to the modelling strategy described 7

8 above. In the first two columns of Table 7 the six starting variables for the first model can be found, together with their proposed lag length. In all cases, the dependent variable is the (logged) RCA index for the medium and high tech sector. Models 1 and 2 tend to concentrate on short- and medium-term dynamics, as some of the variables included are business cycle-driven. On the other hand, Model 3 is based on variables with a longer transmission mechanism to the productive activity (education, international investment). See Table 8 for the chosen specification for each set of potential explanatory variables, and the estimated weights of the index. A rather permissive significance level of 20% was chosen in the procedure, and the two last observations on RCA were used for computing the root mean square error in the one-step-ahead forecasting comparison. For the evaluation of the t-statistics, a White heteroskedasticity/serial correlation corrected variance covariance matrix was used. Each column reports the estimated parameters for the corresponding explanatory variable, which corresponds to the weight of the variable in the industrial capability index. Each specification is optimal among the possible specifications resulting of all the combinations of variables and lags in the corresponding column of Table 7 in the sense that it has the highest adjusted R 2. All of its parameters are significant at the significance level indicated above, and no multi-collinearity is present. The last row of Table 8 indicates the root mean square error (RMSE) of the one-step-ahead forecasts of the model for the last two values of RCA: that is, the chosen model is re-estimated for the period and forecasts for 1997 are produced, these are compared with the actually realized values of RCA and the root mean square error across countries of the forecast is computed as RMSE = (A i t Ft i ) 2 /N, i where A i t and Ft i refer to the actual value of (log) RCA in period t and its forecast value for country i, from the sample of N countries. That is, we just measure the average distance between the forecasted values and the true realizations and the model minimizing the average distance (thus, the model that better forecasts RCA on average) is chosen. The same procedure is repeated for 1998, and both RM SE values are averaged to give the number reported in Table 8. While Model 2 tries to capture the behaviour of RCA through short-term related variables with short lag lengths, Model 3 is based on structural variables with a longer lag span. Model 1, on the other hand, mixes both of the approaches. The forecasting performance of the exclusively short-term based Model 2 is significantly worse than the others, so only results for the index induced by Model 1 and 3 will be reported. For the case of Model 3, the index can be defined on projections up to three years ahead, while in the other cases the minimum lag (one) sets the limit of steps-ahead in the projection, so in this case we can easily differentiate an early warning mechanism aimed at the development of RCA in the immediate future (Model 1), but taking into account also variables whose effect is more delayed (e.g., educational variables), and an early warning mechanism aimed at the evolution of RCA within three years (Model 3). The extreme importance of human capital formation as a determining factor for industrial capability is visible in the estimated models, where the secondary school enrollment variable appears always highly significant (with six years lag), positive and 8

9 with a relatively high value. The ranking of countries according to the index of industrial capability derived from Model 1 (as a one-step-ahead projection of the non-constant econometric specification) is presented in Table 9. 4 The longer-run index of industrial capability derived from Model 3 induces the ranking presented in Table 10. Notice that, in this case, due to the nature of the optimal specification, the index is computed as a threesteps-ahead projection of the not-country specific part of the estimated relationship between RCA and the chosen variables (and is, thus, an ICI(3) instead of an ICI (1)). Concerning the performance of Southeastern Asian countries, the ranking resulting from Model 1 and 3 also induce the two clusters of high (Philippines and Malaysia) and low relative industrial capability (Thailand and Malaysia). In the case of Model 1, Malaysia performs much better than in the production function approach, ranking fifth highest in 1998 among the countries studied. 3.2 The index of industrial capability in Southeast Asia We would expect from a good signalling index both that it anticipates futures developments of the variable we are trying to explain (RCA) and that it gives us a deep insight into the relative distances among economic units in terms of the capability to grow in terms of the variable studied. Using the alternative specifications constructed for the index of industrial capability, we will investigate the development of international competitiveness of the medium- and high- tech sector in Southeastern Asia in the 90s. Figure 1 plots the (log) RCA index for the medium- and high- tech sector in the four Southeast Asian countries of interest for the period Figure 2 plots the development of the industrial capability index constructed out of the production function approach with significant variables, 5 and Figure 3 plots the resulting index for Model 1 of the statistical approach, chosen due to its superior forecasting power (see Table 8). The signalling power of both versions of the index can be inferred from the fact that, for instance, for Thailand there exists a correlation of approximately 0.85 and 0.91 between the present value of the index and the future (one-year-ahead) value of RCA for the production function ICI and the statistically oriented ICI, respectively. 6 An overall increase in export potential for medium- and high-tech industries (as measured by either index) can be observed for the whole period and for all countries in the Southeast Asian group, which has been realized in increases in the position for all four countries in relative competitiveness across the sample of countries used in 4 Notice that for creating the ranking, in each period τ the optimal model for the given variables was chosen for the data ranging from 1970 to τ (τ = 1990, 1991,..., 1998). In other words, the ranking is an out-of-sample exercise, as opposed to an in-sample exercise where the estimated parameters for the whole sample are used for the analysis a posteriori. 5 The results do not change substantially if the index with both significant and insignificant variables is used, so the results focus on the index where all the weights are statistically significant. 6 The statistical ICI presents, furthermore, a significant positive correlation between rates of growth of the index and rates of growth of RCA. 9

10 the study comparing 1990 with 1998 (Indonesia climbs up 8 positions, Malaysia 7 positions, Philippines 27 positions (!) and Thailand one position). As commented above, the four countries part from rather similar levels of industrial competitiveness, and two clusters of industrial competitiveness are created in the period , one at a high level, formed by Malaysia and Philipinnes (which lead in terms of RCA in 1998 among the Southeast Asian economies) and another one at a lower level, with Thailand and Indonesia as members. This feature, which is only visible in terms of RCA index from 1996 on (see Figure 1), appears already in in both indices of industrial capability. Figure 4 and Figure 5 present the evolution of the relative level of industrial capability among Southeast Asian countries measured in terms of the position in the rankings constructed for the ICI based on the production function and the statistical features of the model, respectively. Notice that the y axis is inverted, so that moving towards a better position in the ranking is mirrored by shifts upwards in the figure. The features of international competitiveness development described above can be easily found in these plots. It should be however mentioned that the Asian crisis resulted in a dramatic decrease of the rates of growth of the countries of study, but the effect on foreign direct investment has been very asymmetric across the Southeast Asian economies. While Malaysia and Indonesia have seen their levels of FDI fall to very low levels in the period , Thailand and Philippines have experienced the opposite development, which for the case of Thailand represents an increase at practically exponential rates (FDI in Thailand for 1998 is almost eleven times that of 1996). This fact, together with the steady increase of secondary school enrollment for this group of countries gives us an insight into a future path of development of industrial capability where Thailand possesses a momentum towards convergence with the leader group (Philippines and Malaysia), and Philippines tends to increase its industrial capability further. 4 Conclusions and paths of further research This study presents a proposal for an industrial capability index, which should serve as an early warning mechanism to export performance. Two different approaches have been taken for the econometric modelling of RCA, leading to relatively similar conclusions concerning the international competitiveness potential of Southeast Asian economies. The results underline the extreme importance of human capital formation and foreign direct investment in the process of acquiring and developing export-related industrial capability. The index can be used as a signalling mechanism for the export performance in the medium- and high-tech sector due to the fact that a) it is based on an econometric analysis, that is, the weights given to the variables and the choice of the variables themselves are not set ad hoc. Instead, they represent the actual (causal) relationship found; b) as out-of-sample fit is also taken into account, the index is found to be optimal in the sense that the original model from which the index is developed min- 10

11 imizes the forecasting error across all best models in-sample. This ensures a high correlation between variations of the index and future changes in export performance, allowing the early warning mechanism to actually act as a leading indicator of RCA; c) it allows for policy analysis and recommendations, as it sheds a light on the variables directly affecting export performance, its relative importance and the length of the transmission mechanism towards international competitiveness. The index of industrial capability would serve, furthermore, as a valuable piece of a more complete signalling mechanism both at a business cycle level and at a long-term economic growth level. Lastly, it is interesting to investigate whether the weight profile estimated for a country at time t can offer insight into future manufacturing performances. In other words, whether the index not only captures past evolution, but also provides an indication of future prospects.the dynamic structure adopted in the estimation suggests, as already sketched in the previous sections, that it can. The index therefore takes on a forward-looking dimension. Can it also be turned into a prescriptive tool for policy design? To illustrate the issue, suppose that in the weight profile ŵ = (ŵ i, i = 1,..., z), the j-th element stands out as far greater than the others. That is, (subject to an appropriate normalization of the variables), the j-th indicator is particularly important to explain industrial performance. Thus policy-makers a priori must pay special attention to this factor when elaborating industrialization strategies. However, the policy-maker has at his disposal a bounded set of resources: what matters is to allocate these where they will yield the highest returns in terms of bolstering competitiveness gains. In other words, he must assess the gradient, along the z instruments, of the reaction correspondence that links resources invested on the one hand, and competitiveness gains on the other. There is no reason why its supremum element should necessarily be the partial derivative with respect to the j-th variable. A formal depiction of the gradient calls for additional econometric analysis, featuring this time in the dataset the public resources deliberately invested by the policy-maker to influence the course of industrial development. The use of different variables and (eventually nonlinear) parametrizations of the specification of the dependent variable in the analysis can be easily implemented in the statistical framework proposed. A potentially fruitful path of further research would be based on modifications of the specification for the dependent variable, allowing for different types of cross-terms and nonlinear effects of explanatory variables in industrial capability. 11

12 Appendix Missing data and incomplete time series The original panel contains time series with missing data. In order not to lose the information of past observations by amputating those observations prior to the latest missing point, the missing values will be estimated in the initial stage of our study by applying a relatively simple method proposed, e.g., by Justel and Delicado (1999), and based mainly in the work of Maravall and Peña (1997). The incomplete time series will be handled as follows: a) The realization of the time series for which missing data are encountered will be assumed to follow an autoregressive process of first order. 7 b) Initially, the missing values are replaced by the average of the existing observations in the time series of interest. c) With this artificial data, an AR(1) process is estimated. d) The estimated process is taken to be the real data generating process, and the missing values are replaced by the conditional expectation of the estimated process at the time periods where the missing values are encountered. This procedure has been applied to all those variables that reported missing values in the period Multi-collinearity issues In order to take into account the statistical significance of the variables initially included in the econometric model as a choice mechanism for the relevant variables affecting industrial competitiveness, we need first to tackle the problem of multicollinearity. Consider the fixed-effects model (1)-(2). The Within transformation (see e.g. Baltagi, 1995) renders a model in matrix form such as where a typical element of ỹ is (RCA i,t RCA i, ), with RCA i, = ỹ = Xw + ñ, (3) T max(k j ) 1 RCA i,t, (T max(k j )) and the elements of X and ñ are obtained by applying the same transformation to the explanatory variables and the error term in (1). The transformation wipes out thus the country-specific constant. t=1 7 In the most general case, the time series is assumed to follow an AR(p) process, where p could be estimated using information criteria or a simple LM test on the significance of lags of the explained variable. However, our study will set p = 1 due to the fact that the database contains a relatively small number of time series observations per variable and country (28 in the best cases), and the inclusion of more lags of the variable would damage the accuracy of the estimated parameters of the AR process in stage c). 12

13 For the Within-transformed model (3) the OLS estimate of the parameter vector w is the best linear unbiased estimator (BLUE) of the true w, and is unique if the X matrix has full column rank. A reduced rank X matrix, however, leads to non uniqueness in the solution to the equation X ỹ = ( X X)w. Although such a situation (a reduced column rank X matrix) is unlikely to appear when working with real data, as it would imply that one of the variables we are using is a deterministic linear combination of the others (perfect multi-collinearity), high correlation among the variables is often a problem when dealing with economic data. Multi-collinearity affects the sampling variances of the parameter estimates, and thus the t statistics for testing the significance of the regression parameters. A method proposed by Belsley et alia (1980) and based on the principal components decomposition of ( X X), will be used to detect the existence of multi-collinearity among the explanatory variables included in the model. The determinant of ( X X) can be written as X X = λ 1 λ 2 λ K, where λ s, s = 1,..., K are the eigenvalues of ( X X). Small values of λ s will be associated with a near-singular ( X X) matrix. It is intuitive to use some function of these values as a detection mechanism for multi-collinearity. Belsley et alia (1980) propose the condition number of the X matrix [κ( X)], defined as κ( X) = λ max / λ min. (4) If X is normalized to have unit length per column, condition numbers ranging above 20 indicate dangerous collinearity among variables of the model. In our study, the condition number will be computed for each combination of explanatory variables potentially explaining the chosen RCA index, and if multi-collinearity is encountered, a variable will be excluded of the model (the choice criterium is the sample cross-correlation across explanatory variables) and the exercise repeated for the remaining variables, until no evidence of multi-collinearity is found. 13

14 References [1] Balassa, B. (1965): Trade Liberalization and Revealed Comparative Advantage, The Manchester School of Economic and Social Studies, 32, [2] Baltagi, B. H. (1995): Econometric Analysis of Panel Data. John Wiley & Sons, Chichester, England. [3] Belsley, D. A., E. Kuh and R. E. Welsch (1980): Regression Diagnostics, Identifying Influential Data and Sources of Collinearity, Wiley, New York. [4] Granger C.W.J. and H. Huang (1997): Evaluation of Panel Data Models: Some Suggestions from Time Series, Working Paper Series, 97-10; Department of Economics, UC San Diego. [5] Justel, A. and P. Delicado (1999): Forecasting with Missing Data: Application to Coastal Wave Heights, Journal of Forecasting, 18, [6] Laursen, K. (1998): Revealed Comparative Advantage and the Alternatives as Measures of International Specialisation, Danish Research Unit for Industrial Dynamics, Working Paper [7] Maravall, A. and D. Peña (1997): Missing Observations and Additive Outliers in Time Series Models, Advances in Statistical Analysis and Statistical Computing, JAI Press. [8] UNIDO (2002): Industrial Development Report 2002/2003, Competing Through Innovation and Learning, United Nations Industrial Development Organization. [9] Vollrath, T.L. (1991): A Theoretical Evaluation of Alternative Trade Intensity Measures of Revealed Comparative Advantage Weltwirtschaftliches Archiv, 127,

15 Variables Annual growth rates of GDP Gross fixed capital information as % of GDP FDI inflows as % of Gross Capital Increase Openness (Trade as % of GDP) Share of capital good imports as % GCI Value added per worker in medium and high tech industries(%) Annual rate of inflation School enrollment - secondary (% gross) Share of medium and high tech industries value added in total MVA (%) Share of medium and high tech industries employment in total manufacturing (%) Abbreviation dgd dfi fdi opn ica vpm inf ses sem smm Table 1: Potential explanatory variables of RCA Country Abbreviation Country Abbreviation Algeria dza Korea, Rep. kor Argentina arg Madagascar mdg Australia aus Malaysia mys Austria aut Malta mlt Barbados brb Mauritius mus Bolivia bol Mexico mex Brazil bra Morocco mar Canada can Netherlands nld Chile chl New Zealand nzl Colombia col Nicaragua nic Costa Rica cri Norway nor Cyprus cyp Pakistan pak Denmark dnk Panama pan Ecuador ecu Paraguay pry Egypt egy Peru per El Salvador slv Philippines phl Finland fin Portugal prt France fra Singapore sgp Greece grc Spain esp Guatemala gtm Sweden swe Honduras hnd Switzerland che Iceland isl Thailand tha India ind Trinidad Tobago tto Indonesia idn Tunisia tun Ireland irl Turkey tur Israel isr United Kingdom gbr Italy ita United States usa Jamaica jam Uruguay ury Japan jpn Venezuela ven Jordan jor Zimbabwe zwe Kenya ken Table 2: Countries and abbreviations 15

16 Production function Variables Lag fdi 3-5 ses 5-6 vpm 1-2 smm 3-4 See Table 1 for the corresponding abbreviation. Table 3: Alternative specifications for explaining RCA: Production function approach jpn jpn jpn jpn jpn jpn jpn jpn jpn 2 usa usa usa usa sgp sgp sgp sgp sgp 3 mlt mlt mlt sgp mlt mlt mlt usa usa 4 sgp gbr sgp mlt usa kor usa phl phl 5 che sgp gbr gbr kor usa kor mlt mlt 6 gbr che che kor mys mys gbr gbr gbr 7 swe fra fra fra gbr gbr mys kor mys 8 fra swe swe che swe che che mys kor 9 kor kor kor swe fra fra phl che fra 10 aut esp esp mys che mex fra fra che 11 esp aut mys esp mex swe swe swe mex 12 ita ita aut aut esp esp mex mex swe 13 can mys ita mex aut ita esp irl esp 14 mys can mex ita can can aut esp irl 15 fin isr can can ita aut irl ita nld 16 isr mex isr isr isr irl ita nld ita 17 nld nld nld nld irl fin can aut can 18 dnk fin fin fin fin nld fin can fin 19 mex dnk irl irl nld isr nld fin isr 20 bra bra dnk dnk tha tha tha isr aut 21 jor phl bra tha jor dnk isr tha tha 22 tha jor jor bra dnk jor dnk dnk dnk 23 nor tha tha jor bra bra jor bra bra 24 brb nor brb brb brb prt prt prt prt 25 zwe brb prt prt prt brb bra jor jor 26 mar zwe nor nor phl phl brb arg cri 27 tur mar zwe phl tur tur tur brb bol 28 tun slv slv arg ury nor arg tur arg 29 ind tur phl slv nor zwe slv cyp tur 30 phl ind tur tur arg arg cyp tto brb 31 ken arg arg zwe slv aus idn ind nor 32 arg tun gtm mar gtm ind ind nor cyp 33 slv gtm mar gtm cyp idn aus aus slv 34 gtm aus ind ind ind cyp gtm zwe grc 35 grc cyp tun tun aus slv nor slv tto 36 tto tto cyp aus tun gtm zwe cri zwe 37 aus grc aus ury idn tun bol gtm idn 38 ury col idn cyp mar grc tto idn gtm 39 cyp idn ury tto pak col tun grc ind 40 cri ury col idn nzl ury grc tun aus 41 pak pak tto pak zwe mar col col ury 42 pan cri pak grc grc nzl mar nic tun 43 col nzl cri col col isl nic nzl col 44 egy mus grc cri cri tto isl ury nzl 45 idn egy nzl nzl isl pak nzl isl mar 46 nzl mdg egy mdg ven cri ury mar ven 47 mus pan mus ven tto ken cri pak pak 48 isl chl ven chl ken nic pak egy egy 49 ven ven pan egy mus ven ken ken mdg 50 chl ken chl mus chl egy ven ven isl 51 mdg isl bol isl egy mus egy chl ken 52 per per isl ken pan bol hnd mus chl 53 nic nic ken pan bol pan chl hnd hnd 54 jam hnd per ecu ecu chl pan pan mus 55 dza jam hnd bol nic ecu mus ecu per 56 hnd pry nic nic hnd pry ecu per pan 57 pry dza mdg per per hnd per pry ecu 58 ecu ecu jam hnd pry per jam bol nic 59 bol bol dza pry dza jam pry jam pry 60 pry jam jam dza dza mdg jam 61 ecu dza mdg mdg mdg dza dza Table 4: Country ordering implied by RCA 16

17 pan brb jpn nld nld nld nld swe nld 2 jpn jpn dnk fin fin fin fin nld gbr 3 che kor nld dnk dnk dnk dnk gbr swe 4 fin che fin jpn nor nor irl irl irl 5 dnk nld usa aut che irl nor fin aus 6 nor dnk kor usa jpn can esp dnk nor 7 aut aut aut che aut che can nor mlt 8 kor usa irl can can aut aut esp fin 9 isl fin che nor usa esp che can esp 10 swe can brb esp irl brb fra che fra 11 fra fra nor kor esp jpn brb fra dnk 12 usa isr can irl kor fra nzl usa nzl 13 nld nor esp brb swe kor tto aut can 14 isr esp fra fra fra isl usa nzl che 15 can ita swe swe brb usa jpn jpn sgp 16 ita isl isr isr isl nzl swe kor aut 17 jam swe ita isl gbr swe isl brb usa 18 ind grc grc grc ita grc mlt mlt tto 19 brb bra isl gbr isr mlt kor tto kor 20 per aus gbr ita grc tto grc sgp jpn 21 zwe gbr aus aus mlt aus gbr aus brb 22 esp dza mlt nzl aus gbr aus isr prt 23 dza cyp ury mlt sgp isr isr ita ita 24 grc mlt arg tto nzl ita sgp isl isr 25 arg zwe bra cyp tto cyp ita grc isl 26 chl arg phl phl phl ury cyp prt grc 27 ury nzl dza ury ury arg ury cyp arg 28 tur ury cyp arg cyp sgp arg arg chl 29 cyp phl nzl sgp egy phl egy phl cyp 30 bra jor tto dza arg egy phl ury mys 31 jor ind zwe chl chl chl prt egy phl 32 col mex per egy dza prt chl mys egy 33 bol tto chl per per jam per chl per 34 ven tur ind mys mys per mys per ury 35 mlt ecu egy bra ecu mys jam ind pan 36 ecu jam mex mex jam dza pan jam col 37 mar per mys jor prt pan ecu pan jam 38 idn mys tur jam ind tur dza tur bol 39 gbr sgp idn zwe zwe ind mex col ind 40 mex idn jor ecu mex mus tur bra cri 41 aus col jam ind bra ecu mus mex idn 42 ken egy ecu tur tur col ind ecu bra 43 mys ven col prt pan bra zwe idn tur 44 mus bol sgp pan col mex col dza mex 45 pry mus prt idn idn idn idn cri ven 46 tto cri mar col mus zwe cri mus ecu 47 tun slv mus mus jor tun tun tha nic 48 slv tun slv tun cri jor nic zwe tha 49 mdg mar cri mar ven cri bra jor mus 50 pak pan tun cri tun ven jor tun dza 51 egy gtm pan slv mar tha tha nic tun 52 tha pak ven ven tha nic ven ven pak 53 hnd chl gtm nic nic bol bol bol jor 54 cri ken bol hnd slv mar mar pak zwe 55 nic pry pak tha hnd pry pak mar mar 56 phl tha hnd bol gtm pak pry gtm pry 57 nzl nic tha gtm bol hnd hnd pry gtm 58 gtm hnd ken pak ken gtm gtm hnd hnd 59 sgp mdg nic ken pak ken ken ken slv 60 pry pry pry slv slv slv ken 61 mdg mdg mdg mdg mdg mdg mdg Table 5: Country ordering implied by the production function approach with all variables 17

18 jpn jpn jpn nld nld nld nld swe nld 2 che nld nld fin fin fin fin gbr gbr 3 dnk che dnk jpn dnk dnk dnk nld swe 4 fin usa fin dnk che nor esp irl irl 5 pan dnk usa aut jpn esp nor fin aus 6 aut fin aut che nor aut irl dnk nor 7 nor aut irl usa aut che aut nor fin 8 nld kor kor can usa irl che esp esp 9 kor can che esp can can can che mlt 10 usa swe can nor esp jpn fra can fra 11 swe fra swe kor irl fra jpn usa dnk 12 fra esp nor irl swe usa swe aut che 13 can nor esp fra fra isl usa fra sgp 14 isl ita fra swe kor swe nzl jpn can 15 isr isr isr isr isl kor tto nzl usa 16 esp isl ita brb gbr grc gbr kor aut 17 ita grc grc grc isr gbr isl sgp nzl 18 brb gbr isl isl ita isr kor isr jpn 19 grc brb brb gbr grc brb grc mlt kor 20 jam aus gbr ita brb nzl mlt tto tto 21 per mlt aus aus sgp ita isr ita isr 22 gbr cyp mlt mlt mlt mlt brb aus ita 23 ind sgp ury tto aus aus sgp brb prt 24 arg ury tto nzl nzl ury ita isl brb 25 cyp arg arg cyp tto tto aus grc isl 26 ury per phl ury phl cyp cyp prt grc 27 mlt bra cyp phl ury sgp ury cyp mys 28 aus nzl nzl sgp cyp egy egy phl cyp 29 zwe tto per dza egy phl phl ury phl 30 dza ind dza arg chl arg prt arg arg 31 chl phl mex chl arg chl chl egy chl 32 tto mex egy egy dza prt arg mys egy 33 tur zwe chl per per per per chl per 34 bra jor bra mex mys jam mys per ury 35 mex dza ind mys jam mys jam jam pan 36 ecu jam zwe jor prt dza pan ind col 37 col mys mys jam ind pan ecu bra jam 38 jor ecu jor zwe zwe mex dza pan ind 39 mys tur tur prt mex ecu mex col bra 40 idn egy idn pan pan col col mex cri 41 mar idn jam bra bra ind zwe tur mex 42 ven col sgp ecu col tur ind dza bol 43 bol pan ecu ind ecu zwe tur idn idn 44 mus chl col col tur mus mus ecu tur 45 egy mar prt tur idn bra bra zwe ven 46 ken ven pan idn mus idn jor cri ecu 47 tun bol mar mus jor jor cri jor dza 48 slv slv mus tun ven tun idn mus nic 49 pry cri slv mar cri cri tun tha tha 50 phl mus tun slv tun nic nic tun mus 51 pak tun cri cri mar ven ven nic tun 52 mdg pak ven ven slv mar mar ven jor 53 nzl gtm gtm nic nic bol tha bol zwe 54 tha tha bol hnd tha tha bol pak pak 55 hnd ken hnd tha hnd hnd hnd mar mar 56 cri pry ken bol ken slv pak gtm gtm 57 nic hnd pak ken gtm pry pry ken pry 58 sgp nic tha gtm bol pak slv pry slv 59 gtm mdg nic pak pak gtm ken hnd hnd 60 pry pry pry ken gtm slv ken 61 mdg mdg mdg mdg mdg mdg mdg Table 6: Country ordering implied by the production function approach with only significant variables 18

19 Model 1 Model 2 Model 3 Variables Lag Variables Lag Variables Lag fdi 4-5 opn 1-2 fdi 3-5 ses 5-6 vpm 1-2 ses 5-6 opn 1 inf 1-2 smm 3-4 vpm 1 dgd 1-2 ica 3-5 dfi 1-3 sem 3-4 dgd 1-2 See Table 1 for the corresponding abbreviation. Table 7: Alternative specifications for explaining RCA: Statistical approach Variable (lag) Model 1 Model 2 Model 3 ses (6) opn (1) vpm (1) dgd (1) dgd (2) fdi (4) smm (4) ica (3) sem (4) inf (2) Radj % 84.2 % 85.5% RMSE See Table 1 for the corresponding abbreviation. Table 8: Optimal statistical specifications for RCA: Estimated weights 19

20 sgp sgp sgp sgp sgp sgp sgp sgp sgp 2 nld nld nld nld nld irl irl irl irl 3 dnk dnk irl irl irl nld nld nld nld 4 brb tto mlt mlt mlt mlt fin swe mlt 5 aut mlt dnk dnk fin fin mlt fin mys 6 tto aut aut fin dnk dnk dnk gbr swe 7 isl mys can tto can can can mlt nor 8 mlt brb tto can nor nor isl dnk fin 9 can isl isl aut aut aut nor nor aus 10 jor can fin isl isl mys aut can gbr 11 ury che che nor che isl mys aut can 12 swe isr mys che mys tto che mys dnk 13 mys fin nor esp tto che tto isl aut 14 isr swe brb swe swe swe esp esp esp 15 che grc isr mys nzl esp swe che che 16 fin usa esp nzl esp brb fra tto tto 17 esp nzl nzl isr fra fra brb prt kor 18 gbr esp grc brb brb nzl nzl fra isl 19 usa nor swe grc grc gbr gbr brb fra 20 grc fra fra fra isr isr isr cyp prt 21 mus cyp usa usa usa grc grc phl phl 22 nor kor kor gbr gbr usa cyp nzl nzl 23 fra gbr gbr kor phl kor kor kor brb 24 nzl pan cyp cyp cyp phl phl isr cyp 25 cyp ury ury ury kor cyp usa grc mus 26 phl phl pan phl ita ita ita usa isr 27 kor chl jpn jpn chl ury prt ita grc 28 per jor mus aus ury jam ury nic ita 29 ita jam jam chl aus jpn mus ury usa 30 nic ita ita ita jpn aus jpn mus nic 31 jam mus phl mus mus chl aus jpn jpn 32 chl jpn aus pan egy mus chl aus cri 33 jpn aus chl jam jam prt nic chl tun 34 pan ecu jor jor pan egy egy jam ury 35 arg tun zwe ecu prt pan jam egy chl 36 aus zwe ecu per zwe per mex mex jam 37 bol cri prt dza per dza zwe pan zwe 38 ecu hnd per prt dza nic arg tun egy 39 hnd bol mex zwe ecu mex pan arg tha 40 tun arg arg arg arg ecu jor ecu dza 41 ven mex tun mex jor zwe dza cri mex 42 cri dza dza egy nic arg per dza pan 43 col col cri col mex jor ecu jor jor 44 zwe idn col tun tun tun tun zwe idn 45 mex mar egy nic cri cri cri per arg 46 egy ven bol cri col col tur tur tur 47 slv slv hnd ven hnd hnd idn hnd ecu 48 mar egy idn hnd idn tur hnd tha per 49 mdg per ven idn bol idn col col hnd 50 gtm gtm nic bol tur bol tha idn col 51 dza ken slv tur ven ken bol bol bol 52 idn mdg mar slv ken mar mar ven pry 53 ken nic tur mar mar tha ken pry ind 54 tur tha ken ken ind ven ven ind mar 55 pry pry gtm pry slv ind ind ken ven 56 bra tur tha bra bra pry pry mar pak 57 ind ind ind gtm pry slv bra bra ken 58 pak pak pry ind gtm bra slv slv gtm 59 tha bra bra tha tha gtm pak pak slv 60 mdg pak mdg pak gtm gtm bra 61 pak mdg pak mdg mdg mdg mdg Table 9: Country ordering implied by the index of industrial capability derived from Model 1 - short and long run variables- 20