Global competitiveness of Europe: A robust assessment

Fifeková, Elena; Nežinský, Eduard; Nemcová, Edita Article Global competitiveness of Europe: A robust assessment DANUBE: Law, Economics and Social Issues Review Provided in Cooperation with: European Association Comenius (EACO), Brno Suggested Citation: Fifeková, Elena; Nežinský, Eduard; Nemcová, Edita (2018) : Global competitiveness of Europe: A robust assessment, DANUBE: Law, Economics and Social Issues Review, ISSN 1804-8285, De Gruyter, Warsaw, Vol. 9, Iss. 4, pp. 245-260, https://doi.org/10.2478/danb-2018-0015 This Version is available at: https://hdl.handle.net/10419/242135 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0 DANUBE: Law, Economics and Social Issues Review, 9 (4), 245–260 DOI: 10.2478/danb-2018-0015 245 GLOBAL COMPETITIVENESS OF EUROPE: A ROBUST ASSESSMENT Elena Fifeková1, Eduard Nežinský2, Edita Nemcová3 Abstract National (global) competitiveness became the central issue during the global crisis. Using the values of the three main subdimensions of the Global Competitiveness Index, we propose alternative DEA-based competitiveness indicators. In our approach, the index is nested in the more general measure of the competitiveness-given-performance indicator. We find that globally competitive European countries do not transform competitiveness into income per capita efficiently. Decomposition of the scores suggests that most of the relative inefficiency concentrates in innovation activity. The results proved robust against the CCR model used in previous research as well as principal component analysis. Keywords Global Competitiveness Index, Economic Performance, Data Envelopment Analysis, European Union I. Introduction In the course of globalization, economic competition between countries has intensified in the past decades both within the European Union and worldwide. National (global) competitiveness became the central issue during the global crisis not only for small open economies. Exposure to external shocks made every country of the global network vulnerable and forced to compete for resources, environment, or markets. The notion of competitiveness itself has evolved from a microeconomic feature of the exporting firm4to the broader concept of global competitiveness which characterizes the national economy. 1Center of Social and Psychological Sciences of the Slovak Academy of Science, Institute for Forecasting, Šancova 56, 811 05 Bratislava; University of Economics in Bratislava, Dolnozemská 1, Bratislava, Slovakia. E-mail: fifeko[email protected]. 2Center of Social and Psychological Sciences of the Slovak Academy of Science, Institute for Forecasting, Šancova 56, 811 05 Bratislava; University of Economics in Bratislava, Dolnozemská 1, Bratislava, Slovakia. E-mail: nezinsky[email protected]. 3Center of Social and Psychological Sciences of the Slovak Academy of Science, Institute for Forecasting, Šancova 56, 811 05 Bratislava, Slovakia. E-mail: [email protected]. 4Ability of a firm or a nation to offer products and services that meet the quality standards of the local and world markets at prices that are competitive and provide adequate returns on the resources employed or consumed in their producing (Business Dictionary). 246 Elena Fifeková, Eduard Nežinský, Edita Nemcová: Global Competitiveness of Europe: A Robust Assessment Global competitiveness has, however, been far from being defined and construed in a universally accepted way. Krugman (1994) identified competitiveness with productivity and expressed skepticism about the term itself. Berger (2008) lists the “ability of a nation to sell its goods to another nation”, “ability of a nation to earn”, “ability to adjust to changes in the external environment” and the “national ability to attract scarce mobile resources”. Recent assessments of competitiveness build on the ideas articulated in Porter (1990) and later combining inputs (often from government investments) and incentives (competition, openness) as drivers of higher productivity along with the quality of local demand conditions and the presence of the related and supporting industries into an integrated framework. Empirical work is represented by Delgado et al. (2012) employing parametric regression analysis for determining the impact of significant factors attributed to competitiveness affecting productivity. A competitiveness index is then constructed from the regressors as a weighted sum with fixed weights based on estimates. The causal link between economic growthandcompetitivenessin114countries hasbeenestablishedbyKordalskaandOlczyk (2016) using Granger causality tests. Various approaches would require the use of different indicators to assess competitiveness. In the following analysis, we adopt the definition of national competitiveness from WEF (2018) as “the set of institutions, policies and factors that determine the level of productivity”. This definition underlies the widely used indicator of competitiveness – the GCI. The Global Competitiveness Index (GCI) aspires to offer impartial information that allows policymakers from the public and private sectors to better understand the main drivers of growth. Theoretically, the GCI assumes productivity to be the main determinant of long-term growth. Therefore, identified by empirical and theoretical research, the factors and institutions determining improvements in productivity are evaluated in 114 indicators which are further grouped into twelve pillars comprising institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, goods market efficiency, labour market efficiency, financial market development, technological readiness, market size, business sophistication, and innovation.5These pillars are in turn organized into three subindexes: basic requirements, efficiency enhancers, and innovation and sophistication factors. In the final calculation of the overall GCI, the three subindexes are assigned different weights. These depend on each economy’s stage of development, proxied by its GDP per capita and the share of raw materials exports. To expand the idea of discrimination between the countries by means of different weights, we propose that the weights are assigned strictly individually. Each country would choose its weights so as to accentuate its better performance in each particular domain and maximize its relative-to-others score. In this sense, we break the link between weights and economic performance which underlies the construction of the GCI, but on the other hand, we allow for further extension of the model to take in economic performance indicators. Thus, we reject the idea of ex ante assigned weights 5For more on some of these topics, see e.g. Laboutková and Vymětal (2017), Ravšelj and Aristovnik (2017), or Uhrová and Skalka (2016). DANUBE: Law, Economics and Social Issues Review, 9 (4), 245–260 DOI: 10.2478/danb-2018-0015 247 letting a country’s economic policy preferences be reflected in the proposed indicator. Countries with the highest competitiveness index (potential) may not be able to transform it into economic performance to the full extent. Efficiency of the transformation could be measured in the same way as efficiency of production processes. A well-established non-parametric technique of data envelopment analysis (DEA) can be employed in this case. Šegota et al. (2017) used a basic CCR model in this framework which can suffer from untreated slacks. We improve on that approach by employing an SBM model to tackle possible slacks saving through CCR for a robustness test. We proceed by delineating two basic data envelopment analysis models for measuring efficiency in Section II. We argue that DEA models can provide deeper insight into factors contributing to the object evaluation than the commonly used synthetic index could. In SectionIII we outline asubsystemanalysisintheframeworkofDEA.Theseanalytical tools are employed to assess the competitiveness of 30 European countries within the global environment. The results are presented in Section IV and confronted with an additional statistical tool – principal component analysis. Section V concludes. II. Nonparametric approach: DEA models Besides the standard synthetic indices (or more complex productivity measures) with ex ante assigned weights of constituent subdimensions, we propose that weights are determined individually for each country. To assess technical efficiency, the general conceptual formula is used: efficiency =outputs inputs (1) Index measures can be arrived at by collapsing inputs in the expression (1) to a fixed value (most often the unit). In classical DEA, as originally proposed by Charnes et al. (1978), every subject under evaluation – called the decision-making unit, DMU – aggregates its inputs and outputs by means of individually set weights so that the ratio (1) is maximized. Alternatively, one can minimize the reverse fraction. In order to avoid unboundedness, a constraint is imposed so that the resulting efficiency score cannot exceed unit which should also hold if any of the remaining n−1 DMUs uses the weights µand νof DMU0 under consideration. For nsubjects transforming minputs into soutputs the problem is formulated as: min z0(µ, ν)=Pm i=1xi0νi Ps r=1yr j µr (i=1,2, . . . , m)(2) s.t. Pm i=1xi j νi Pk r=1yr j µr ≥1(j=1,2, . . . , n)(3) µr≥0, νi≥0(r=1,2, . . . , s)(4) 248 Elena Fifeková, Eduard Nežinský, Edita Nemcová: Global Competitiveness of Europe: A Robust Assessment Linearized, the basic CCR output-oriented (CCR-O) model can be written as: min vTx0(5) s.t. −vTX+uTY≥0T(6) uTy0=1 (7) u≥0,v≥0 (8) Interpreted from the dual perspective of linear programming, the efficiency value can be viewed as an indirect distance measure from the efficiency frontier which envelops input and output data organized in matrices X and Y and is constructed from the best practice DMUs which are determined in the course of optimization and whose efficiency score (value of the objective function (5)) is equal to unit. An input-oriented CCR-I model can be shown to yield the same efficiency scores as CCR-O (e.g. Cooper et al., 2007). Alternative measure of the distance was proposed by Tone (2001). Defining slack variables as deviations of DMU’s inputs x0and outputs y0from the efficiency boundary as: s−=x0+Xλ s+=Yλ−y0,(9) a non-oriented slack-based measure (SBM) is determined by the optimization: min λ, s+,s−ρ= 1−1 mPm i=1s− i/xi0 1+1 sPs r=1s+ r/yr0 (10) s.t. x0=Xλ+s−(11) y0=Yλ−s+ λ≥0, s−≥0,s+≥0. One may give the model input or output orientation by omitting output or input slacks respectively from (10) obtaining thus SBM-I or SBM-O efficiency measures. Both CCR and SBM measures can be used to decompose the overall efficiency to contributing factors. DEA models provide efficiency values relative to other units in the selected sample. At this expense one obtains individual weights and benchmarks as a theoretical basis for possible policy action. III. Subsystem analysis of the EU countries To analyse EU countries from the global perspective, we borrow the idea of Thanassoulis and Portela (2001). We construct two efficiency frontiers – (i) global world frontier by asbmef model acting as a reference boundary and (ii) EU frontier modelled by sbmef eu that only comprises EU countries and providing within-group efficiency. This enables us to decompose overall efficiency to the component attributable to DMU (country) and the second attributable to the group. DANUBE: Law, Economics and Social Issues Review, 9 (4), 245–260 DOI: 10.2478/danb-2018-0015 249 Within-group DEA efficiency is calculated by considering DMUs belonging to the EU subsystem. The overall DEA efficiency is determined which cannot exceed the withingroup score attributable to the individual country. Efficient units of individual groups may prove inefficient relative to some global units. Dividing overall score by the group’s one yields component attributable to group, thus: sbmef (overall) score = sbmef eu score (attributable to country) ×EU/world (attributable to EU) In this way, the best practice of the subsystem is compared to that of the superior system. Graphical representation of the merit of the decomposition for an output-oriented model is depicted in Figure 1. Figure 1: Efficiency of the subsystem Source: Authors’ elaboration Schematically, inputs are represented on the horizontal while outputs are on the vertical axis. Output-oriented efficiency with respect to the global frontier efglob (A) =OA/OW, efficiency within the EU subsystem ef EU (A) =OA/OE. For country A, the ratio OE/OW presents the efficiency of the EU wrt the world . The value cannot exceed 1, and is equal exactly to 1 solely if a country constitutes both EU and the world boundary line as in the case of country B, whose efficiency ef glob (B) =ef EU (B) =1. Concerning the decomposition of DEA-based indexes, the same reasoning as for efficiencies applies. IV. Results and robustness check Empirical application of the techniques described above involve the calculation of optimizations (5)-(8) or (10)-(11). Data entering the models were adopted from WEF (2018) for three subindexes of GCI and World Bank (2018) for GDP per capita in PPP. Our dataset comes from those countries whose income p.c. exceeds 2000 USD. 250 Elena Fifeková, Eduard Nežinský, Edita Nemcová: Global Competitiveness of Europe: A Robust Assessment We believe it constitutes a sufficient global background for assessing EU countries. On the upper end, we excluded Luxembourg and Qatar from the dataset due to excessively outlying income p.c. values to prevent unrealistic benchmarking, leaving the dataset with n=87 countries (DMUs). Descriptive statistics of the data are provided in Table A2 in Annex. Concentrating further on the performance of the European countries, “EU” will be henceforth referred to as a group comprising EU-28 before Brexit less Luxembourg, and with Switzerland and Norway added (i.e. 29 countries in the EU subset). The models’ orientation was determined so as to extract information on competitiveness. Table 1: Overview of DEA models employed model type variables outputs inputs ccri CCR-O 3 sub-indices of GCI 1 sbmi SBM-O 3 sub-indices of GCI 1 sbmi eu SBM-O 3 sub-indices of GCI 1 ccref CCR-I GDP p.c. (PPP) 3 sub-indices of GCI sbmef SBM-I GDP p.c. (PPP) 3 sub-indices of GCI sbmef eu SBM-I GDP p.c. (PPP) 3 sub-indices of GCI Source: Authors’ elaboration Table 1 provides an overview of the models and variables used. Global models involve 87 optimizations to be calculated, while those for the EU are just 29. Evaluating the competitiveness of EU countries, we computed global and group models to determine global and EU frontiers (as described in Section IV) employing sbmi and sbmi eu models. In Annex table A1 sbmi scores for all countries are provided. There are three worldwide efficient countries – Singapore, Switzerland, and US – with sbmi scores equal to 1. This corresponds to the three countries scoring the best in the GCI. Here we can point out the relativeness of the DEA approach letting all three DMUs be potentially benchmarked against. Focusing on GCI, one would opt for the highest GCI (Switzerland) solely. A detailed view on the solutions for lambda in (10)-(11), however, reveal that Singapore and the US only present benchmarks for themselves, indicating outlying DMUs in the DEA sense. Other countries with a competitiveness indicator less than unit are clustered around Switzerland, which acts as a general benchmark. EU group members have thus no particular frontier and there is no difference between benchmarking against the world and the EU boundary line. Therefore, sbmi and sbmi eu scores are identical and EU/world is unit as Table 2 for selected countries states. This particular dataset was not therefore allowed to demonstrate the capabilities of DEA to the full. In the case of performance-given-competitiveness evaluation, the only globally efficient DMU is Singapore, for EU the best practice is Ireland (which would correspond to the point E in Figure 1). Global EU countries’ scores now deviate from their global score. From the ratio of EU vs world performance, one can state that over 70% of the efficiency is attributable to the EU, as described in Section III. sbmef eu scores present efficiency attributable to individual countries within the system as benchmarked against Ireland. DANUBE: Law, Economics and Social Issues Review, 9 (4), 245–260 DOI: 10.2478/danb-2018-0015 251 Comparing index and efficiency scores makes it clear that the best scoring and therefore most “endowed” EU countries – Switzerland (sbmi =1), UK (0.924), Germany (0.959) or Finland (0.936) or Netherlands (0.967) did not manage to transform their potential into high income p.c. – compared to Singapore with an efficiency equal to 1. The EU thus seems to address the issue and analyse sources of relative underperformance. Table 2: Selected results for EU countries index efficiency sbmi sbmi eu EU/world sbmef sbmef eu EU/world Austria 0.896 0.896 1 0.482 0.670 0.719 Belgium 0.884 0.884 1 0.442 0.616 0.719 Czechia 0.802 0.802 1 0.308 0.430 0.717 Finland 0.936 0.936 1 0.452 0.629 0.719 France 0.878 0.878 1 0.413 0.575 0.719 Germany 0.959 0.959 1 0.529 0.736 0.719 Hungary 0.696 0.696 1 0.229 0.319 0.717 Ireland 0.876 0.876 1 0.712 1 0.712 Italy 0.771 0.771 1 0.317 0.441 0.719 Netherlands 0.967 0.967 1 0.571 0.795 0.719 Poland 0.739 0.739 1 0.240 0.335 0.717 Romania 0.666 0.666 1 0.185 0.258 0.717 Slovakia 0.723 0.723 1 0.263 0.366 0.717 Spain 0.787 0.787 1 0.326 0.455 0.717 Switzerland 1 1 1 0.686 0.954 0.719 United Kingdom 0.924 0.924 1 0.442 0.615 0.718 Source: Authors’ calculation Decomposition of SBM score can help identify sources of inefficiency. The objective function (10) penalizes DMU for (the sum of relative) slacks. For SBM-I model, 1 mPm i=1s− i/xi0presents the total penalty, s− i/xi0can be thus viewed as the ith input contribution to overall inefficiency. We can therefore determine how particular domains assessed by GCI subindexes contributed to the overall score in relative terms. Higher values would be associated with relatively weak performance in the area. The results of decomposition for selected countries are exhibited in Table 3. Clearly, in an efficient country (Singapore), no inefficiencies are present. For the other DMUs, one can observe different patterns of inefficiency distribution across the three areas evaluated by GCI subindexes. Inefficiencies add up to 1 (100%). BASICR stands for basic requirements, EFF for efficiency enhancers, and INNOV for innovation and sophistication factors. From a global perspective, the most inefficiency concentrates in innovation activity. Policy measures should be advisably based on the analysis of best performing benchmark DMU. For EU countries the same recommendations hold since the results do not deviate much from the global pattern, as the last row of Table 3 makes clear. 252 Elena Fifeková, Eduard Nežinský, Edita Nemcová: Global Competitiveness of Europe: A Robust Assessment There are, however, some significant individual deviations from the average pattern. For instance, the UK exhibits most inefficiency in BASICR while being very strong in EFF. Strong performance in EFF is apparent in Canada as well, with the most inefficient area being INNOV. In terms of innovation activity, Israel and Japan show the most efficiency. Interestingly, V4 members (Czechia, Slovakia, Poland, and Hungary) share a common pattern of inefficiency distribution with a slightly greater relative potential improvement in innovation. Table 3: Decomposition of inefficiency (selected countries) DMU Score Inefficiency BASICR EFF INOV Total Belgium 0.884 0.421 0.246 0.333 1 Bulgaria 0.703 0.268 0.225 0.507 1 Canada 0.894 0.329 0.066 0.605 1 Colombia 0.688 0.349 0.213 0.438 1 Czechia 0.802 0.263 0.220 0.517 1 Estonia 0.817 0.192 0.221 0.588 1 Finland 0.936 0.336 0.324 0.340 1 Germany 0.959 0.544 0.168 0.288 1 Hungary 0.696 0.285 0.208 0.507 1 China 0.808 0.282 0.222 0.496 1 Israel 0.901 0.504 0.314 0.181 1 Japan 0.928 0.553 0.207 0.240 1 New Zealand 0.905 0.178 0.129 0.693 1 Poland 0.739 0.265 0.203 0.532 1 Russia 0.734 0.275 0.212 0.513 1 Singapore 1 0 0 0 Slovakia 0.723 0.281 0.232 0.486 1 Spain 0.787 0.296 0.206 0.498 1 Ukraine 0.658 0.339 0.244 0.417 1 United Kingdom 0.924 0.532 0.073 0.395 1 World average 0.769 0.289 0.255 0.456 1 EU average 0.819 0.299 0.258 0.443 1 Source: Authors’ calculation For a robustness check we computed index and efficiency scores employing a CCR model (5)-(8). Since we are not interested in values of scores per se, believing them to be only a starting point for deeper analysis and formulating policy measures, we test whether various evaluation techniques generate similar ranking6. For this purpose, we produce 6Due to the construction of the objective function, SBM and CCR scores may significantly differ in values while correlate positively. One can show that SBM capturing slacks never exceeds CCR score (Tone, 2001). DANUBE: Law, Economics and Social Issues Review, 9 (4), 245–260 DOI: 10.2478/danb-2018-0015 259 EU sbmi rsbmi ccri rccri sbmef rsbmef ccref rccref GCI rgci pc1 rpc1 BASICR EFF INOV Sri Lanka 0 0.673 73 0.706 81 0.096 72 0.104 72 4.08 84 1.567 75 4.51 3.81 3.76 Sweden 1 0.942 6 0.951 8 0.529 10 0.588 10 5.52 7 −2.751 6 6.00 5.30 5.57 Switzerland 1 1 1 1 1 0.686 4 0.750 4 5.86 1 −3.677 1 6.39 5.65 5.86 Taiwan 0 0.905 13 0.922 17 0.492 14 0.521 14 5.33 15 −2.153 14 5.84 5.25 5.12 Tajikistan 0 0.661 80 0.689 84 0.024 85 0.026 85 4.14 79 1.765 81 4.40 3.74 3.72 Thailand 0 0.754 40 0.805 36 0.147 58 0.151 59 4.72 31 0.204 39 5.06 4.62 3.92 Trinidad and Tobago 0 0.674 71 0.728 69 0.237 42 0.248 42 4.09 82 1.497 71 4.40 4.24 3.52 Turkey 0 0.708 53 0.763 55 0.211 46 0.219 47 4.42 52 0.924 53 4.75 4.40 3.65 Ukraine 0 0.658 82 0.699 82 0.065 79 0.068 78 4.11 80 1.799 83 4.18 4.09 3.55 United Arab Emirates 0 0.901 16 0.942 13 0.662 5 0.676 6 5.30 17 −2.120 15 6.02 5.23 4.93 United Kingdom 1 0.924 10 0.949 9 0.442 22 0.479 22 5.51 8 −2.457 10 5.65 5.55 5.34 United States 0 1 1 1 1 0.639 6 0.717 5 5.85 2 −3.195 2 5.54 6.01 5.80 Uruguay 0 0.688 62 0.753 59 0.172 54 0.176 55 4.15 76 1.216 59 4.81 4.20 3.47 Viet Nam 0 0.678 69 0.733 68 0.053 83 0.055 83 4.36 54 1.418 68 4.52 4.24 3.49 Source: Authors’ calculation 260 Elena Fifeková, Eduard Nežinský, Edita Nemcová: Global Competitiveness of Europe: A Robust Assessment Table A2: Descriptive statistics of the data Statistics on Input/Output Data BASICR EFF INOV YPCPPP Max 5.92 6.44 7.00 93905.5 Min 3.61 3.99 4.14 2079.9 Average 4.92 5.42 5.83 31391.5 SD 0.58 0.56 0.75 19743.2 Correlation BASICR EFF INOV YPCPPP BASICR 1 0.89 0.87 −0.80 EFF 0.89 1 0.91 −0.73 INOV 0.87 0.91 1 −0.66 YPCPPP −0.80 −0.73 −0.66 1 Source: Authors’ elaboration