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ScienceDirect Procedia Economics and Finance 34 (2015) 500 – 507

Business Economics and Management 2015 Conference, BEM2015

Standard of Living as a Factor of Countries’ Competitiveness Peter Madzíka,*, Jana Pitekováa, Alena DaĖkováa a

Department of Management, Catholic university in Ruzomberok, 058 01 Poprad, Slovakia

Abstract This study examines the relations between competitiveness of selected countries and the standard of living of their populations. The focus of this study was determined by literature review, which showed that the mentioned relation has been usually identified only implicitly, showing less emphasis on empirical study. Our research is based on international statistical reports focused on factors of competitiveness of national economies and the metrics used in evaluating the standard of living. Secondary data from several reputable sources - such as the World Bank, the Gallup Institute, and OECD - are systematized and analyzed using several statistical procedures, so as to identify structural links between the various indicators. Based on the identified relations, we discuss areas suitable for increasing the competitiveness of the countries, and we also compare them with current macroeconomic trends. © 2015 2016 The TheAuthors. Authors.Published Publishedby byElsevier ElsevierB.V. B.V. © This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of BEM2015. Peer-review under responsibility of the Organizing Committee of BEM2015 Keywords: international competitiveness; living standards; relationship; social indicators; statistical analysis

1. Introduction In 2010 European leaders initiated and prepared the strategy concerning Europe development and called it Europe 2020 Strategy and its main goal was to encourage “smart, sustainable, inclusive growth brought about through greater coordination of national and European policy“ (EC, 2012). This step was a response to economic and social problems of several European countries. Consequently in 2012 and 2014 World Economic Forum published reports evaluating Europe’s competitiveness progress based on the Europe 2020 Strategy (EC, 2012). The authors of the reports agree that after the financial crisis was relatively successfully managed by means of monetary tools in European economic area it is not possible “to be complacent” but it is a must to take actions to increase Europe´s

* Corresponding author. Tel.: +421-904-375-425. E-mail address: [email protected]

2212-5671 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of BEM2015 doi:10.1016/S2212-5671(15)01660-3

Peter Madzík et al. / Procedia Economics and Finance 34 (2015) 500 – 507

competitiveness (WEF, 2014). On the other hand there is a relatively strong scientific criticism of “artificial growth of living standard” based on indebtedness (Afonso, 2010) or generous social system (Algan, 2010), which partially equilibrates accelerating tendencies based on countries macroeconomic performance. With reference to a wider context concerning countries competitiveness growth it can be stated that this effort is not characteristic only for Europe but is a worldwide trend (ýarnogurský, 2015) (Capello, 2015). Emphasis on competitiveness as one of key pillars of national, international and world development can be felt from both theoretical and practical aspect (EC, 2012). There are several (more individual) methodologies to measure international competitiveness, preferably used to measure countries macroeconomics performance (Durand, 1998). The most widespread and relatively the most exact is World economic forum methodology. World economic forum monitors competitiveness by means of 12 aggregate indicators: Institutions environment, Infrastructure, Macroeconomic environment, Health and primary education, Higher education and training, Goods market efficiency, Labor market efficiency, Financial market development, Technological readiness, Market size, Business sophistication, Innovation. Next very important organization – OECD – does not evaluate international competitiveness directly but prepares methodology of so called competitiveness indicators platform (OCIP), which contains 12 indicators consolidated in three areas: (1) endowments and conditions, (2) policy and (3) performance (Hong, 2014). Since more scientific studies present implicit relation between international competitiveness and standard of living (Okrepilov, 2015) (Yakovieva, 2015), also approaches concerning living standard measurement were reviewed within literature retrieval. Metrics to measure living standard are most frequently identified with the valuation of life quality or with measurement of wellbeing index (Kral, 2011). To evaluate or measure life quality several metrics of institutions as for example OECD, Gallup´s institute, Mercer, The Economist are used till now but they differ from each other considerably. Each of the above mentioned metrics uses its own interpretation of life quality and the results achieved by means of their application may be markedly different. With the aim to measure quality of life some institutions use objectively measurable indicators of macroeconomic, demographic or social character and some of them base the results on indicators of perception by means of nationwide research also called subjective wellbeing (Diener, 2003). The theoretical study about importance of so called social indicators measurement was introduced by Noll in 2004, and he stated that „social indicators research has succeeded to considerably improve the measurement of peoples’ quality of life as well as the monitoring of general social conditions and change“ (Noll, 2004). With regard to structure of both mentioned areas it is possible to state that the factor of population living standard - in the situation when competitiveness is assessed - is taken into consideration only partially – some indicators of international competitiveness are identical with the indicators of living standard or some indicators of living standard are implicitly included in the indicator of international competitiveness. From the scientific point of view this failing presents potential for relatively extensive empirical research, since several previous expert studies implicitly (Okrepilov, 2015) or explicitly (Easterlin, 2011) point out the relation between living standard and international competitiveness. The goal of this study is to research mutual connections between individual indicators of international competitiveness and indicators of life standard (quality of life). 2. Material and methods To review the relation between living standard and international competitiveness the availability of secondary datasets was checked. It was found out that the latest evaluation of competitiveness was realized by World Economic Forum in 2014, while publicly accessible data include 151 countries (Schwab, 2014). In each country 12 aggregate indicators created by 150 variables are monitored. The access to data concerning living standard was more complicated. Found datasets containing subjectively measurable indicators were not actual and stratified only according to several countries. The total dataset containing global data concerning living standard was not found – we managed to find only the dataset containing the sequence of countries in five categories of living standard (wellbeing) and the dataset of OECD countries. Finally the total dataset of 36 countries containing 11 aggregate variables concerning quality of life and 12 aggregate variables concerning international competitiveness was developed.

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Fig. 1. Used datasets.

Consequently this dataset was used to carry out several purposefully oriented statistical analysis. In all cases of analysis the reliability of corresponding set of variables which entered the corresponding mathematic and statistic algorithm was tested. More detailed summary of empirical research in the form of research questions and their correlation to applied methodological approaches is provided in the table 1. Table 1. Summary of the research purpose and the way of its realization. Research question (what we wanted to find out) How strong is the mutual connection between the variables in the group of competitiveness and the variables in the group of living standard? Which areas of a country competitiveness and population living standard have a strong correlation relation?

Applied approach (how we found it out) Bivariate correlation analysis (metrics of Pearson´s correlation coefficient)

Would that be possible to reduce the number of indicators of international competitiveness or the number of living standard indicators?

Factor analysis (principal components method with varimax rotation)

Are there groups of „similar“ countries in the area of competitiveness and living standard?

Hierarchical cluster and K-means cluster with betweengroups linkage and squared Euclidean distance)

3. Results 3.1. Internal correlation structure In the initial analysis all numerical variables of the data set were the subject of bivariate correlation analysis, where the level of reliability was equal to 95% and the admissible error was 0,05. After reviewing variables which describe country competitiveness it can be stated that there is a very distinctive correlation between „business sophistication“ and „innovation“ (Pearson correlation coef. = 0,914), but also between „institutions“ and „goods market efficiency“ (0,883) and „technological readiness“ and „innovation“ (0,845). It confirms the existence of mutual dependence which is covered by the macroeconomic theory in the area of monetary policy (Calza, 2013), or in the area of markets effectiveness (Janiþková, 2013). In the same way also the variables describing living standard were reviewed. Strong correlations were identified between „life satisfaction“ and „jobs“ (Pearson corr. coef. = 0,709), „life expectancy“ and „jobs“ (0,698) and also „environment“ (0,624). Also distinctive variables were identified and were the subject of studies aimed at quality of life (Schalock, 2004), or in more details at personal

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outcomes (Claes, 2010), or metrics of living standard measurements (Robeyns, 2005). The finding that the group of indicators describing „market size“ was in a minimum correlation with the other variables – maximum value of correlation coefficient -0,221 was also interesting. It is possible to state that the market size is a factor which has the minimum impact on international competitiveness which was confirmed by both historical and empirical studies (Nafziger, 2012). 3.2. Correlation structure between international competitiveness (IC) and living standard (LS) This initial research was followed by the examination of relations between individual variables classified into two areas – IC and LS. Apart from expressly and logically correlating indicators (such as „business cost of crime and violence“ vs. „homicide rate“), also several strong correlation connections were identified. Correlation coefficients of aggregate variables are presented in the table 2. Since the aggregate variables were determined by more variables the final correlation coefficients were calculated as the average of absolute values of corresponding individual variables. Table 2. Correlation coefficients between variables IC and LS

Health

Life satisfaction

Safety

Work life balance

International competitiveness

Institutions 0,38 0,56 0,52 0,55 0,39 0,53 Infrastructure 0,39 0,74 0,34 0,30 0,37 0,40 Macroeconomic environment 0,12 0,14 0,45 0,02 0,16 0,20 Health and primary education 0,41 0,49 0,34 0,46 0,56 0,49 Higher education and training 0,42 0,57 0,44 0,59 0,57 0,51 Goods market efficiency 0,37 0,63 0,46 0,37 0,40 0,46 Labor market efficiency 0,34 0,57 0,55 0,49 0,40 0,54 Financial market development 0,29 0,48 0,49 0,36 0,22 0,42 Technological readiness 0,46 0,70 0,45 0,47 0,46 0,59 Market size 0,17 0,23 0,08 0,20 0,14 0,23 Business sophistication 0,40 0,77 0,53 0,40 0,33 0,44 Innovation 0,36 0,74 0,53 0,32 0,39 0,46 Average* 0,34 0,55 0,43 0,38 0,37 0,44 * average was calculated from absolute values of Pearson Correlation Coefficient

Civic engagement

Environment

Education

Community

Jobs

Income

Living standard Housing

Pearson Correlation

0,24 0,19 0,12 0,24 0,20 0,26 0,14 0,21 0,28 0,06 0,22 0,19 0,20

0,49 0,46 0,04 0,46 0,43 0,42 0,35 0,35 0,59 0,14 0,47 0,44 0,39

0,64 0,37 0,40 0,36 0,54 0,47 0,59 0,68 0,57 0,00 0,64 0,61 0,49

0,37 0,46 0,04 0,49 0,47 0,44 0,33 0,19 0,51 0,14 0,30 0,33 0,34

0,22 0,31 0,11 0,36 0,37 0,17 0,25 0,01 0,40 0,19 0,21 0,20 0,23

Average*

0,44 0,39 0,16 0,42 0,46 0,41 0,41 0,34 0,50 0,14 0,43 0,42

It results from the table that there is the strongest correlation between the group of indicators concerning the income of an individual with the indicators like „business sophistication“, „innovation“, „infrastructure“ and „technological readiness“. This dependence is confirmed by several studies in which the connection between business environment and country innovation is provable (Schneider, 2013) (Droppa, 2013). 3.3. Reduction of indicators The number of reviewed indicators was relatively high, what from the point of view of their utilization in macroeconomic analysis presents great benefit. The more data are monitored and evaluated the more problematic is their interpretation in a simple way. That was the reason why the data structure was verified through the data reduction algorithm. Both groups of aggregate variables (IC and LS) were suitable for this algorithm which was confirmed by Kaiser-Meyer-Olkin criteria – it reached the values 0,841 for IC and 0,688 for LS. In case of IC algorithm identified three latent factors through which almost 84,2% variability of original 12 aggregate variables might be explained. In case of LS three latent factors were extracted from correlation data structure and they explain 77,9% variability of original 11 aggregate variables. The results are presented in the following couple of tables and with the aim to improve their transparency correlation coefficients lower than 0,3 were not presented.

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Table 3. Factor analysis results. Rotated Component Matrixa Rotated Component Matrixb Component Component International competitiveness 1 2 3 Living standard 1 2 3 Technological readiness ,904 Jobs ,909 Health and primary education ,901 Income ,835 Infrastructure ,896 Life Satisfaction ,829 Higher education and training ,853 Health ,696 ,317 Business sophistication ,825 ,328 ,338 Environment ,694 ,362 Innovation ,822 ,365 Work-Life Balance -,849 Institutions ,785 ,521 Education ,685 ,509 Goods market efficiency ,750 ,488 Community ,484 ,618 Labor market efficiency ,622 ,594 Civic engagement ,788 Financial market development ,370 ,823 Safety -,437 -,761 Macroeconomic environment ,819 Housing Market size ,972 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a,b Rotation converged in a) 4 iterations b). in 8 iterations.

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,955

As seen from the results although the algorithm identified 3 or 4 factors after rotations their interpretation is quite problematic. In case of IC the first factor is created mostly by 9 variables, the second one mostly by two variables (financial market development and macroeconomic environment) and the third one only by one variable (market size). While naming of the second factor (to for example „macroeconomic level of the country“) and the third factor (to the unchanged name „market size“) would be quite easy, it is very difficult to find a name which would represent its composition for the first factor. Similar results were found out also in case of LS, where the first factor mostly was created by 5 variables, the second one by three variables (work-life balance, education, community), the third one by two variables (civic engagement, safety) and the fourth factor only by one variable (housing). The same problem with naming of the first factor presents interpretative barrier as it was in the previous case. Factor analysis proved that from the interpretation aspect it is not suitable to reduce the number of variables (problematic total naming) but from the mathematic aspect reduction of data to three or four factors could help to identify countries with similar characteristics. That was the reason why factor score of each case calculated by means of algorithm was recorded in a form of individual variable used in cluster analysis. 3.4. Groups of similar countries So 7 variables entered cluster analysis (3calculated factors for international competitiveness and 4 calculated factors for living standard). Algorithm of hierarchical clustering based on dendrogram using Average Linkage (Between Groups) recommended to determine 8 relatively homogeneous groups and their structure may be seen in figure 2.

Fig. 2. Cluster analysis results (international competitiveness – on the left, living standard – on the right).


It concerns the countries with similar characteristics of international competitiveness and living standard, while the countries which are the most characteristic for the particular cluster (based on their distance to the centroid of the particular cluster) are marked in bold. Graphic intersection of clusters is caused by limitation of display space – cluster analysis was made in 7-dimensional space but maximum three dimensions can be displayed. The results show that similarity of countries is - apart from seven provided factors – partially given in culture (similar countries as CZE, HUN, POL, SVK) and partially by economic power (JPN, GBR, USA). 3.5. Wider context To measure and compare performance of individual economics macro-economy uses several aggregates. Gross domestic product is the most frequently used as the aggregate macro-economic indicator. Economic theory distinguishes the terms economic power and economic level. Under the term economic power they understand the total volume of articles produced by the economy usually during a year and under the term economic level of a particular country they understand the volume of produced articles fall to one inhabitant (Uramová, 2000). When individual economics are compared the sequence of countries based on their economic power is different (traditional leaders USA, Russia, China) from the sequence in comparison of their economic level (traditional leaders Switzerland, Norway, Sweden, Luxemburg, Belgium). Back in 1968 reasonable criticism of GDP appeared. Mankiw warned that: “Gross domestic product does not evaluate quality of child health care, education, free time utilization. It does not include the beauty of poetry or the power of marriages, intelligence of political discussions or incorruptness of state officials. Simply said it does not measure anything what makes our life meaningful and beautiful“(Mankiw, 2009). The result of this disproportion was determination of an indicator new economic welfare - NEW, which adds the value of free time, shadow economy to GDP value and distracts damages to environment from it. Its more detailed equivalent is the index of life quality which was applied in this study. Due to the fact that it is not possible to quantify individual items in an exact way the indicator gross national product is still used most frequently for international comparison. In general the aim of various indicators of economic or social character is to describe all-society system. To understand individual items of the system better the sets of economic, social, psychological or other theories are developed and supported by particular indicators they help understand interdiscipline connections and relations. It is easier to catch and work with the indicators with a relatively simple way of calculation or quantification that is why they are used more frequently. But as stated in scientific studies reduction of knowledge of a complex system of the world functioning (and people living there) usually brings a set of next questions (Nafziger, 2012). Connection between living standard and international competitiveness which was proved in this study is one of the possible ways leading to increase of scientific knowledge. 4. Discussion Previous scientific studies which dealt with the topic concerning connections between living standard and international competitiveness or macroeconomic performance result from so called Easterlin paradox – it means interconnection of „matter“, respectively economic performance and subjective feeling of satisfaction. In 70-ties of the last century American economist Richard A. Easterlin in reviewing relation between the amount of pension and feelings of satisfaction in several countries came to the following conclusions, which he later revised (Easterlin, 2010): x there is a strong positive correlation between the level of wealth and subjective feeling of satisfaction in a particular time, x in situation when the level of wealth goes up in different time period the connection with the increase of subjective satisfaction was not confirmed – indirect explanation of this result can be found by means of model Kano interpretation (Hrnþiar, 2014) x when feelings of satisfaction were compared in a particular time in various countries only small differences appeared, although the wealth of the countries was different.

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The results prove that high living standard and the feeling of satisfaction connected with it are not only the result material support of an individual. More extensive term which includes the level of needs satisfaction so that it considers also social, ethical, ecological and human aspects of life is its quality. The above mentioned connections open a wide field for detailed scientific research and currently several interesting questions are open-end: x To what extent increasing economic performance respectively increasing competitiveness might present a barrier to living standard respectively quality of life (high level of stress, lack of free time, divorces, long-term separation)? x How intensively do national politics focus on increase of material support of their inhabitants and how intensively they pay attention to the growth of their citizens satisfaction? x Is it possible to develop a measurement system which would include indicators of performance (hard data) and indicators of satisfaction (soft data) and which could help in creation of national politics? Based on the research results it might be concluded that correlation of some aspects of living standard and international competitiveness proved their mutual relation. Relatively strong is the impact of income on total competitiveness of a country, but connections might be found also in environment (average correlation = 0,44), or life satisfaction (0,49). There are several prestigious studies concerning opportunities to increase international competitiveness and their recommendations concern economic (business support), social (social system) or environmental (utilization of natural sources) area (Salvatore, 2009). The study points out that there is another potential source of international competitiveness and that is benchmarking. The results of cluster analysis present categorization of monitored countries into eight groups, while each group contains countries with similar characteristics – either from the point of view of international competitiveness or living standard. If the results of countries competitiveness individual items were compared it would be possible to assess which country got the best results in which item (considering other countries in the same cluster). In this way „leaders“ in particular areas of competitiveness could be identified (within „red cluster“ Estonia is the leader in the area of „Labor market efficiency“ and for example in the area „Business sophistication“ the leader is the Czech Republic). Since leaders achieve better results in these areas than the we may predict they assure the area in „some“ proper way. The „some way“ is called „best practices“ in studies which focus on benchmarking (Hrnþiar, 2008). So leaders in individual clusters might present the source of best practices, which may be adapted by other countries with relatively small effort (since they are in the same cluster, their environment is similar). Current direction of effort aimed at increasing of international competitiveness has more technocratic character (Cardona, 2013), but is considerably limited by economic performance of a country. But gradually the space for other ways ensuring competitiveness growth is being opened and indicated international cooperation of similar countries might present one of the above mentioned ways. 5. Conclusion The aim of this study was to examine the relations between competitiveness of selected countries and the standard of living of their populations. It results from the long-term statistics that there is correlation between GDP per capita and population living standard, what was proved in our study in a form of the highest correlation between the income and other corresponding indicators. Implications of empirical research results to the theory development and indication of future scientific research directions present one of the most important contribution of the study. References Afonso, A. F. (2010). Government size, composition, volatility and economic growth. European Journal of Political Economy, 26(4), 517-532. Algan, Y. C. (2010). Inherited trust and growth. American Economic Review, 100(5), 2060-2092. Calza, A. M. (2013). Housing finance and monetary policy. Journal of the European Economic Association, 11, 101-122. Capello, R. C. (2015). Global trends and the economic crisis: Future alternative European growth strategies. Technological Forecasting and Social Change, 98, 120-136. Cardona, M. K. (2013). ICT and productivity: Conclusion from the empirical literature. Information Economics and Policy, 25(3), 109-125. Claes, C. e. (2010). Quality of life measurement in the field of intellectual disabilities: Eight principles for assessing quality of life-related personal outcomes. Social Indicators Research, 98(1), 61-72.

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