Four years later in 2017 Boy Genius Report 2017 reported Apple's official .... The dataset from Hofstede (2015) that can be downloaded from his private .... The free gratification of basic and natural human drives are allowed ..... (2001); Swamy et al. ...... Retrieved from http://archive.epi.yale.edu/files/2014_epi_metadata.pdf.
MINIMAL INFLUENCE OF NATIONAL CULTURE TO CORRUPTION, POLLUTION, AND HARMFUL WORKING CONDITIONS: AN EMPIRICAL CONTRIBUTION TO BUSINESS ETHICS
VOLKER ANDREAS MÜLLER
JUNE, 2018
MINIMAL INFLUENCE OF NATIONAL CULTURE TO CORRUPTION, POLLUTION, AND HARMFUL WORKING CONDITIONS: AN EMPIRICAL CONTRIBUTION TO BUSINESS ETHICS BY VOLKER ANDREAS MÜLLER
DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF BUSINESS ADMINISTRATION IN BUSINESS ADMINISTRATION
YEDITEPE UNIVERSITY JUNE, 2018
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ABSTRACT This study is an empirical contribution to business ethics: It demonstrates on an ecological level that national culture (according to Hofstede) plays a minor role in determining the levels of the unethical business practices corruption, pollution, and harmful working conditions of countries. For that hypothesis were statistically tested. Together with strong explanatory macroeconomic indicators (gross national income per capita, economic freedom, democracy index, human development index, gini index, and employment in agriculture) all main components of Hofstede’s model were found to have no significant influence or very little explanatory power on the investigated unethical business practices. National Culture cannot explain the levels of unethical business practices in countries. More than 80 percent of the levels of corruption, pollution, and harmful working conditions in countries are explained by the macroeconomic indicators of this study. In international business especially in developing and threshold countries (besides in industrialized countries) it is still practice to accept the three unethical business practices in the supply chain by excusing it with culture. This study demonstrates that this argumentation is not scientifically tenable.
Key words: Culture, Hofstede, Corruption, Pollution, Harmful Working Conditions, Ethics
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ÖZET Bu çalışma „Sosyal Sorumluluk ve Ahlak “alanına deneysel bir katkıdır: Ekolojik bir düzeyde milli kültürün (Hofstede’ye göre) ahlaki olmayan iş uygulamalarının saptanmasında yani yolsuzluk, çevre kirliliği ve ülkelerdeki sağlığa zararlı çalışma koşullarının ülkeler düzeyinde belirlenmesinde asgari bir rol oynadığını gösterir. Bunun için hipotezler istatistiksel olarak test edilmiştir. Yüksek açıklayıcılığa sahip makroekonomik indikatörleri (Kişi Başına Düşen Brüt Milli Gelir, Ekonomik Özgürlük Endeksi, Demokrasi Endeksi, İnsani Gelişmişlik Endeksi, Gini Katsayısı Endeksi ve Tarımsal İstihdam) barındıran bir modelde Hofstede’nin her altı boyutunun ahlaki olmayan iş uygulamalarını üzerinde çok küçük ya da hiç denilebilecek bir etkiye sahip olduğu tespit edildi. Milli kültür ülkelerdeki ahlaki olmayan iş uygulamalarının boyutunu açıklayamıyor. Ülkelerdeki yolsuzluk, kirlenme ve zararlı iş koşulları boyutunun %80 inden fazlası bu çalışmanın makroekonomik göstergeleri ile açıklanabilir. Uluslararası ticarette, özellikle gelişmekte olan ülkelerde ve eşik ülkelerinde (bunun yanı sıra endüstri ülkelerinde) tedarik zincirindeki incelenen üç adet ahlaki olmayan iş uygulamasına bahane olarak ülkelerin milli kültürü gösterilmektedir. Bu çalışma bu savın bilimsel olarak savunulabilir olmadığını göstermektedir.
Anahtar kelimeler: Kültür, Hofstede, Yolsuzluk, Çevre Kirliliği, Sağlığa Zararlı Çalışma Koşulları, Etik
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TABLE OF CONTENTS Approval……………………………………………………………………………………..i Plagiarism…………………………………………………………………………...………ii Abstract………………………………………………………………………….…………iii Özet…………………………………………………………………………………….….iv Table of contents…………………………………..……….....……….……………………v List of tables…………………………………………………………………………….…ix List of figures…………………………………………………………...…………..……xiii List of Terms…………………………………….………………………………………..xv List of abbreviations…………………………………………………………………….xvii 1. INTRODUCTION.…………………………………………………………………….1 2. LITERATURE REVIEW…..………………………………………………………….6 2.1. Quantitative models of national culture………………………………………...…6 2.1.1. The Hofstede Model………………………………………………………..7 2.1.2. Other Models of national culture…………………………………………12 2.2. Models for corruption…………………………………………………………....19 2.2.1. Explanatory variables without culture………………………………...….21 2.2.2. Models with national culture………………………………………….…..22 2.3. Models for pollution…………………………………………………………..….27 2.3.1. Explanatory variables without culture…………………………...……….27 2.3.2. Models with national culture……………………………………………...29 2.4. Models for harmful working conditions………………………………………....31 2.4.1. Indicators of harmful working conditions……………………………..….31
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2.4.2. Causes of harmful working conditions without national culture…………34 2.4.3. Harmful working conditions and national culture…………………….….34 2.5. Models for child labor………………………………………...………………….35 2.5.1. Explanatory variables without culture……………………………………35 2.5.2. Problem of accessible dataset for dependent variable for child labour...…36 3. RESEARCH MODEL..…………………………………………………………...…..38 3.1 Model for corruption……………………………………………………..……….39 3.1.1. Corruption Perceptions Index as dependent variable for corruption…..…39 3.1.2. The gross national income per capita as independent variable for corruption…………………………………………………………………40 3.1.3. Index of economic freedom as independent variable for corruption……..41 3.1.4. The democracy index as independent variable for corruption……………42 3.1.5. National culture as independent variable………………………………....43 3.2. Model for pollution……………………...……………………………………….45 3.2.1. Environmental performance index as dependent variable…………….….45 3.2.2. The Human Development Index as independent variable……………..…46 3.2.3. Corruption as independent variable for pollution….………………………………………………………...……..47 3.2.4. National culture as independent variable for pollution……………...……48 3.3.Model for harmful working conditions…………………….……………………..50 3.3.1. Vulnerable employment as dependent variable for harmful working conditions…………………………………………………………………50 3.3.2. GINI index as independent variable for harmful working conditions…....51 3.3.3. Employment in agriculture as independent variable for harmful working conditions…………………………………………………………………51
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3.3.4. National culture as independent variable for harmful working conditions…………………………………………………………………52 3.4. Overall view of all hypotheses………………………………………………...…54 4. ANALYSIS OF THE DATA……..…………………………………………………..55 4.1. Results for corruption………………………………………………………….....55 4.2. Results for pollution……………………………………………………………...67 4.3. Results for vulnerable Employment………………………………………...……79 4.4. Overall view of all tested hypotheses and results………………….…………….88 5. DISCUSSION OF THE RESULTS……...…………………………………………...89 5.1. Macroeconomic indicators that explain corruption…………………………..…..89 5.1.1. National wealth measured by Gross National Income per Capita calculated by Atlas method (GNIPCA)……………………………………….89 5.1.2. Economic Freedom expressed by Index of Economic Freedom…...…..90 5.1.2. Democracy expressed by Democracy Index of The Economist Intelligence Unit……………………………...……………………………….91 5.2. Impact of national culture to corruption…………………………………………92 5.3. Macroeconomic indicators that explain pollution…………………………..……94 5.3.1. Development of countries is the strongest explanatory variable for pollution…………………………………………………………………….…94 5.3.2. Corruption as moderate explanatory variable for pollution……..……..95 5.4. Impact of national culture to pollution…………………………………….……..96 5.5. Macroeconomic indicators that explain harmful working conditions……………97 5.5.1. Employment in agriculture is the strongest explanatory variable for harmful working conditions…………………………………………..………97 5.5.2. Income inequality increases the risk for harmful working conditions……………………………………………………………………..97
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5.6. Impact of national culture to harmful working conditions……………….………98 5.7. Impact of national culture to unethical business practices……………….………98 6. CONCLUSIONS, ROCOMMENDATION, LIMITATION.…………………….....100 6.1. Conclusions for science…………………………………………………….…..100 6.2. Conclusions for Management……………………………………………….….101 6.3.Conclusions for police maker……………………………………………..…….101 7. REFERENCES….…………...………………………………………………………103 Appendix A……………………………………………………………...………………113 Appendix B……………………………………………………………………...………116 Appendix C…………………………………….………………………….…………….116 Appendix D……………………………………………………………………….……..117 Appendix E……………………………...……………….…………….………………..119
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LIST OF TABLES Table 1 Quantitative models of national culture…………………………………………..…..7 Table 2 Dimensions of the GLOBE -study……………………………………………..……13 Table 3 Results of Hofstede’s ecological factor analysis of 18 GLOBE dimension scores for 56 countries……………………………………………………………………………….…..15 Table 4 Correlations between GLOBE dimension factors, GNP/capita and Hofstede indices across 48 countries (30 for LTO)…………………………………………………………….16 Table 5: Determinants of corruption……………………………………………………...….20 Table 6 Results of Bryan W. Husted’s regression analysis……………………………..……22 Table 7 Results of James H. Davis’ and John A. Ruhe’s regression analysis….……….……23 Table 8 Results of Monica Violeta Achim’s models of corruption as a function of culture....24 Table 9 Findings in literature about national culture as cause of corruption…………..…….25 Table 10 Datasets used in different studies……………………………………………….….26 Table 11 Regression results of Park et al. (2007). Dependent variable is the ESI for the year 2001…………………………………………………………………………………..……….29 Table 12 Summary the results of Onel & Mukherjee (2014)……………………………..….30 Table 13 Studies which report a high proportion of occupational injuries and diseases among informal workers……………………………………………………………………………...32 Table 14 Indicators of Simas et. al. (2014) for quantifying the “Bad Labour Footprint”...….33 Table 15 Worst ten countries for child labour from UNICEF dataset and from child labour index from Verisk Maplecroft……………...…………………………………………………37
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Table 16: Hofstede’s data for Africa East………………………………………………...….38 Table 17: Hofstede’s data for Uganda, Tanzania, and Zimbabwe………………………..….38 Table 18 Issue areas and indicators of The Environmental Performance Index (EPI)……….46 Table 19 Overall view of all hypotheses……………………………………………………..54 Table 20 Pearson correlations for CPI and different measurements of national income.....…55 Table 21 Descriptive statistics for CPI and different measurements of national income .......56 Table 22 Simple regressions for CPI and different measurements of national income ....…..56 Table 23 Pearson correlations for CPI and all independent variables…………………….….57 Table 24 Descriptive statistics for CPI and all independent variables………………….……62 Table 25 Simple regressions for CPI and all independent variables………………..………..63 Table 26 Models of corruption as a function of gross national income per capita (GNIPCA), economic freedom (EF), democracy index (DI) and culture (PDI, IDV, IVR)…………..…..64 Table 27 Overall view about hypothesis tested in this chapter for corruption……………….66 Table 28 Pearson correlations for EPI and all independent variables………………………..67 Table 29 Descriptive statistics for EPI and all independent variables………………….……73 Table 30 Simple regressions for EPI and all independent variables…………………..……..73 Table 31 Models of environmental performance as a function of human development index (HDI), gross national income per capita (GNIPCA), Government expenditure on education (GEE), and national culture……………………………………………………………..……74 Table 32 Models of environmental performance as a function of human development index (HDI) and perceived corruption (CPI)…………………………………………………..……76
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Table 33 Overall view about hypothesis tested in this chapter for pollution…………...……78 Table 34 Pearson correlations for VE and all independent variables………………….……..79 Table 35 Descriptive statistics for VE and all independent variables……………..…………84 Table 36 Simple regressions for VE and all independent variables……….…………………84 Table 37 Models of vulnerable employment as a function of employment in agriculture (EAC), income inequality (GINI), and national culture ……………………………………..85 Table 38 Overall view about hypothesis tested in this chapter………………………………87 Table 39 Overall view for all hypotheses…………………………………………………….88 Table 40 Hungary: While the level of democracy decreased the level of perceived corruption increased…………………………………………………………………...………………….92 Table 41 2014 data for countries with highest degree of perceived corruption………….…..94 Table 42 World’s worst pollution problem…………………………………………………..95 Table 43 Descriptive statistics of CPI from 2010, CPI from 2015, and Hofstede’s dimensions of national culture…………...……………………………………………………………....113 Table 44 Pearson correlations of CPI from 2010, CPI from 2015, and Hofstede’s dimensions of national culture…………………………………………………………………...………113 Table 45 CPI2010 and CPI2015 as functions of Hofstede’s significant dimensions……….114 Table 46 Models of environmental performance as a function of Education Index (EI), gross national income per capita (GNIPCA), and life expectancy at birth (LE)…………..………116 Table 47 Models of corruption (CPI) as a function of GNIPCA, EF, and DI…………...….116 Table 48 Pearson correlations of different measure of income……………………………..117
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Table 49 Pearson correlations of the Environmental Performance Index (EPI) with the Human Development Index (HDI) and its’s significant components……………………….118 Table 50 Models of environmental performance as a function of human development index (HDI), Education Index (EI), life expectancy at birth (LE), perceived corruption (CPI), and gross national income per capita (GNIPCA)………………………………………..………118 Table 51 Pearson correlations of THEFT and Hofstede’s dimensions of national culture………………………………………………………………………………………..119 Table 52 Descriptive statistics of THEFT and Hofstede’s dimensions of national culture………………………………………………………………………………………..119 Table 53 Simple regressions of THEFT and Hofstede’s dimensions of national culture………………………………………………………………………………………..120 Table 54 Models of theft as a function of culture of THEFT and Hofstede’s dimensions of national culture…………………………………………..…………………………………..120
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LIST OF FIGURES Fig. 1. Environmental Sustainability Index (ESI) as a function of Corruption Perception Index (CPI)…………………………………………………………………..………………………28 Fig 2 The Human Development Index (HDI)………………………………………….……..47 Fig. 3 Correlation between corruption (CPI) and gross national income per capita (GNIPCA) for Hypothesis 1………………………………………………………………………………58 Fig. 4 Correlation between corruption (CPI) and economic freedom (EF) for Hypotheses 2…………………………………………….……………..……………………..59 Fig. 5 Correlation between corruption (CPI) and democracy index (DI) for Hypothesis 3…………………………………………………………………………………..59 Fig. 6 Correlation between corruption (CPI) and power distance (PDI) for Hypothesis 5…………………………………………………………………………………..61 Fig. 7 Correlation between corruption (CPI) and individualism (IND) for Hypothesis 6……………………………………………………………………………..……61 Fig. 8 Correlation between corruption (CPI) and indulgence versus restraint (IVR) for Hypothesis 10……………………………………………………………………………...….62 Fig. 9 Correlation between environmental performance (EPI) and Human Development Index (HDI) for Hypothesis 11………………………………………………………………...……68 Fig. 10 Correlation between environmental performance (EPI) and level of perceived corruption (CPI) for Hypothesis 12………………………………………………..…………68 Fig. 11 Correlation between environmental performance (EPI) and power distance (PDI) for Hypothesis 14…………………………………………………………………………………70
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Fig. 12 Correlation between environmental performance (EPI) and individualism (IND) for Hypothesis 15……………………………………………………………………………...….70 Fig. 13 Correlation between environmental performance (EPI) and individualism (LTO) for Hypothesis 18………………………………………………………………………………....71 Fig. 14 Correlation between environmental performance (EPI) and individualism (IVR) for Hypothesis 19…………………………………………………………………………………72 Fig. 15 Correlation between vulnerable employment (VE) and Gini coefficient (GINI) for Hypothesis 20………………………………………………………………………..………..80 Fig. 16 Correlation between vulnerable employment (VE) and employment in agriculture (EAC) for Hypothesis 21……………………………………….…………………………….80 Fig. 17 Correlation between vulnerable employment (VE) and power distance (PDI) for Hypothesis 23……………………………………………………………………...………….82 Fig. 18 Correlation between vulnerable employment (VE) and individualism (IDV) for Hypothesis 24…………………………………………………………………………………82 Fig. 19 Correlation between vulnerable employment (VE) and individualism (LTO) for Hypothesis 27………………………………………………………………………………..82
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LIST OF TERMS Power Distance (PDI)…………………………………………………………….……………8 Individualism vs. Collectivism (IDV)………………………………………………………….9 Masculinity vs. Femininity (MAS)…………………………………………………………….9 Uncertainty Avoidance (UAI)………………………………………………………………….9 Long Term Orientation vs. Short Term Normative Orientation (LTO)…………………..…..10 Indulgence vs. Restraint (IVR)…………………………………………………….…………10 Global Leadership & Organizational Behavior Effectiveness (GLOBE)…………………….12 Corruption Perceptions Index (CPI)…………………………………………………….……39 Gross National Income per Capita calculated by Atlas method (GNIPCA)…………………40 Index of Economic Freedom (EF)………………………………………..…………………..41 Democracy Index (DI)…………………………..……………………………………………42 Environmental Performance Index (EPI)…………………………………………………….45 Human Development Index (HDI)……………………………………………………..…….46 Vulnerable Employment (VE)…………………………………………………………..……50 Gini coefficient (GINI)…………………………………………….…………………………51 Employment in Agriculture (EAC)……………………………………………………..…….51 Gross National Income per Capita on purchasing power parity (GNIPCP)……………….....55 Gross Domestic Product per Capita based on Purchasing Power Parity in international US Dollar (GDPPCP)……………………………………………………………………………..55
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Real Gross Domestic Product per Capita based on Purchasing Power Parity in 2011 US Dollar (RGDPPC)……………………………………………………………………………………55 Gross Domestic Product per Capita in current US Dollar (GDPPC)………...……………….55
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LIST OF ABBREVIATIONS cf. = confer et. al. = et alii (and other authors) n.a. = not available
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1. INTRODUCTION Unethical practices in international business play an important role in international business. Apart from an ethical point of view the involvement in unethical business practices of transnational companies leads to a bad image and results in a loss of revenue. In naive daily discussions about unethical practices in international business a connection or sometimes even treating as equivalent of culture and these practices is present. It is very important for international management to learn as much as possible about the sources and connections of unethical practices in global markets, for being able to response in an appropriate way that goes along with considered facts among scientists. This study is undertaken to test some aspects of theory: During the last 20 years a lot of researcher studied the influence of culture to especially corruption but also to other unethical business practices like pollution or child labor. Most of these authors tried to answer the research question whether there is significant impact or not. A lot of authors could find a significant impact of national culture. Some of these authors even conclude that culture plays a “major role” in explaining corruption (Achim, 2016). In the field of business ethics there is a debate between relativism and normativism: Daniels, Radebaugh and Sullivan (2015, p.448) introduce into the debate “relativism versus normativism” in their chapter about “Ethics and Social Responsibility”. They define relativism as “ethical truths depend on the groups holding them” and normativism as “universal standards of behavior that all cultures should follow”. Without being intended by the authors of the studies about the impact of national culture to unethical business practices, their results can be used to support the position of relativism. If culture would play a major role or very important role in explaining corruption or other unethical business practices, managers or policy makers could accept corruption or
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other unethical business practices like child labor or pollution as a common cultural practice in the sense of relativism. Because I’m supporting the position of normativism and I’m convinced that unethical business practices like corruption, pollution, harmful working conditions, and child labor are nothing else than just crime, I want to clear up the not yet asked research question: How strong is the impact of national culture to bad business practices? Is it minimal or does it play a major role? The research question is very important for ethical valuation of these unethical business practices. Managers in international business have to fail decisions about making contracts with suppliers all over the world. Are corruption, pollution, harmful working conditions and child labor in the supply chain acceptable, because they are part of the local culture? They are not acceptable, if the impact of culture is minimal. But in the current situation readers of scientific journals can find statements like “about half of the level of corruption in countries is explained by the national culture” (Achim 2016). Such declarations may entice managers to tolerate unethical business practices in the supply chain. Even international major companies have done this during not long ago: In 2013 the British newspaper The Guardian reported about child labour and harmful working conditions in Apple's supply chain: The company was confronted with several cases of child labour in their supply chain. One supplier in China employed 74 children, which were younger than 16 years. In that year all together 106 cases of child labour were found in Apple’s supply chain. Before that year already 70 cases of underage labour were reported. Besides in the same year several worker suicides came to light at Foxconn what is a Taiwanese company that assembles Apple’s products (Garside, 2013).
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Predominant in industrial nations customers convict the companies for such behavior and some of them stop to buy from the company what means, after all the company has a decrease in revenue. Four years later in 2017 Boy Genius Report 2017 reported Apple’s official statement what happens when the company discovers that child labor laws have been violated at a factory building its devices: They spoke about a zero-tolerance concerning child labour in their supply chain and introduced their compensation for concerned cases: The supplier has to stop to employ the child and the Apple company will pay for the child’s school education. The school can be chosen by the family. Apple will also pay an income for the child’s basic needs up to the legal working age. In addition, a third-party organization will be given the task of monitoring the process and drawing up a report. The concerned supplier is committed to reemploy the child after reaching working age and completing the education at school. Apple is also proud to have found only three cases of child labour in 2015. The statement “about half of the level of corruption in countries is explained by the national culture” (Achim 2016) can also entice policy maker to tolerate unethical conditions in their countries or regions, because they can be interpreted as culture based or part of the own culture. From this point of view a clear identification of the role of culture and its impact to these practices is a valuable contribution to literature. In this sense a deductive strategy is applied: Theory from literature will be checked with secondary multi-cross-sectional data that is comparing countries for hypothesis testing. Thus, unit of analysis are countries. The research objectives are national culture as measured by Geert Hofstede (2015) and unethical practices in international business: corruption, pollution, harmful working conditions and child labor. The data is examined by statistical analysis.
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The aim of this research is to make an empirical contribution to business ethics that demonstrates on an ecological level that culture plays a minor role in determining the levels of the unethical business practices (corruption, vulnerable employment, pollution and child labor) of countries. This master dissertation is limited to research about national culture and unethical bossiness practices on an ecological level. Ecological level means that the characteristics of groups are measured, not the aspects of individual human beings. William S. Robinson (1950) explained the ecological fallacy: The ecological fallacy is made when scientists transfer characteristic of groups that means results of ecological data to the characteristics of individuals. National culture is understood as an ecological phenomenon that exists between countries. Ecological logics are seen as different from psychological, subjective logics. It is not possible to say “Germans are more punctual than a lot of other cultures. So, we can expect Hans Meyer to be punctual because he is a German.” – this is the ecological fallacy. There is on the one hand a lot of research that examines one of the four examined unethical business practices and national culture into a special, single countries or on the other hand a broad discussion about subjective causes of the four discussed unethical business practices. Both of them are not objective of this study and will not be discussed. This final thesis is organized in this way: The literature review introduces to quantitative models of national culture for business concerns and describes more detailed the model of Geert Hofstede (2015) and Hofstede’s understanding of culture. Then the researches of former authors to explain unethical business practices on an ecological level without and with national culture are presented and assessed. Most literature can be found for corruption. There are some contributions to pollution too, but harmful working conditions and child labor are not yet discussed on country level as far as we know. In the next chapter the models for the statistical analysis are built and the hypothesis are formulated. Altogether four models are
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built for corruption, pollution, harmful working conditions and child labor using secondary data from The Worldbank and other international research organizations or universities. In chapter four the results are presented. First an outstanding point is tested. Then for every of the four unethical practices first a Pearson correlation with all variables is done, then a regression with a model, that includes only dimensions of national culture from Geert Hofstede (2015, is calculated. Following that the significant cultural dimensions are include into a model that includes other important significant explanatory variables of the unethical practice. The results are discussed and interpreted in chapter five. This study comes to the conclusion that the impact of national culture is minimal.
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2. LITERATURE REVIEW In this chapter the prevailing literature will be examined regarding research question that asks how strong national culture influences the four unethical business practices of this study. The research takes place on an ecological level. Research objectives are countries. First a suitable quantitative model of national culture on country level has to be found. Then the impact of national culture to the four unethical business practices of this study will be examined regarding to the question: “How strong is the influence”. The four unethical business practices are corruption, pollution, harmful working conditions and child labor. This chapter is structured in this way. 2.1. Quantitative models of national culture Between 1980 and 2004 four models of national culture were published in literature: Geert Hofstede from Netherlands (1980) was the first who developed a model with five cultural dimensions, Alfons Trompenaars, also from Netherlands, followed him in 1993 with a similar model. Not as well-known as Hofstede and Trompenaars is the model of Shalom H. Schwartz (1994). The last publisher was the GLOBE-study from Robert House et. al. (2004). Table 1 shows Jürgen Rothlauf’s (2009) overview about the four models of national culture, that he has taken from Kutschker and Schmid (2008).
7 Table 1 Quantitative models of national culture 1st Publication Sample Size
Time
Questionaire
No. of countries
No. of dimensions Genesis of dimensions
Hofstede 1980 116,000 IBM employees Chinese Value Survey: 2,300 students 1966 – 1973 Chinese Value Survey: Early 80s 60 questions Chinese Value Survey: 40 questions 531 countries Chinese Values Survey: 23 Countries 5 (incl. Chinese Value) Correlation and factor analysis
Trompenaars 1993 30,000
Schwartz 1994 > 75,000
GLOBE study 2004 17,000
1983 – 1992
1988 – 1992 1992 – 2000
1994 – 1997
57 questions
Classification of 56 values according to their importance of live 67 countries
292 questions
55 countries
59 countries
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3
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Conceptual categories based on literature review, followed by empirical validation
Conceptual categories based on literature review, followed by empirical validation
Conceptual categories based on literature review, tested in pilot studies; empirical validation
Adopted from: Jürgen Rothlauf (2009), p. 61
2.1.1. The Hofstede Model Geert Hofstede’s dataset is the best-known study about national culture. Although it is the oldest research his dataset is most discussed and the majority of empirical research on national culture is done with Hofstede’s dataset. The accomplishment of his study is the creation of a model of national culture that had and has an important impact to international management. His concepts of Powerdistance, Uncertain Avoidance and IndividualismCollectivism are brilliant and inspired hundreds of scientist. Sine Hofstede in 2001 published his work “Culture’s Consequences: Comparing Values, Behaviors, Institutions, and
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Jürgen Rothlauf’s information about the Hofstede data refers to dataset that was published in Hofstede (2001). The dataset from Hofstede (2015) that can be downloaded from his private website gives 71 countries and 7 regions for the first four indicators (Powerdistance, Individualism, Masculinity versus Femininity, and Uncertain Avoidance), 92 countries and four regions for Long Term Orientation versus Short Term Normative Orientation, and 94 countries and three regions for Indulgence versus Restraint.
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Organizations Across Nations.” probably an endless number of students wrote their final thesis about Hofstede’s cultural dimensions. Hofstede’s data in his first publication about national culture (1980) was based on the re-analysis of an existing database that was created in the IBM Corporation in several countries. Employees were asked towards their attitude in all countries with an IBM company seat. 72 countries were examined between 1967 and 1973. In his later book (2001) the number of countries was expanded through replications to 76 countries. Hofstede’s results are based on an ecological factor analysis of the IBM-data (Hofstede, 2011). Geert Hofstede defines “culture” as “the collective programming of the mind that distinguishes the members of one group or category of people from others” (Hofstede, 2001, 2010). Hofstede’s main innovation was his idea of describing culture with dimensions of culture which represent basic problems to which different national societies have over time developed different answers (Hofstede, 2017). One of the biggest misunderstandings about Hofstede’s study is the application of his data on individual level. Here the ecological fallacy has been done. Hofstede data does not allow to answer questions about individuals. His study was entirely devoted to the study of culture at the national level (Hofstede, 2011). Another important point is about the scope of Hofstede’s study. His research only examined work-related values, not culture as a whole. It never was Hofstede’s aim to develop an all-round model of national culture. His model is adequate for management. Hofstede himself explains his six dimensions of national culture in this way: Power Distance (PDI) The extent to which the less mighty members of a community accept and expect unequal distribution of power is expressed in this dimension. The handling of inequalities
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among humans is here the basic issue. Individuals in societies showing a large degree of PDI obtain a hierarchical system in which everybody has his place and which needs no further argument. People strive to equalize the allocation of power and appeal justification for a lack of balance of might in societies with little PDI. (cf. Hofstede, 2001, 2010) Individualism vs. Collectivism (IDV) High scores in this dimension represent individualism and can be determined as a preference for a broad-meshed social framework in which people are expected to be cautious only themselves and their direct families. Collectivism in contrast symbolizes a preference for a densely conjoined framework in Society in which people can expect their members of family or joiner of a particular in-group to take care for them in exchange for absolute loyalty. In individualistic societies the members defines them self as “I”, in collectivistic societies as “we”. (cf. Hofstede, 2001, 2010) Masculinity vs. Femininity (MAS) Material benefit for success, achievement, assertiveness, and heroism are preferred in masculine societies, which are at large more competitive. This is one side of this dimension. Femininity on the other side stands for societies, which are at large more consensus-oriented. Their members prefer quality of life, modesty, caring for the weak, and cooperation. In business life MAS is sometimes also understood by "tough versus tender" cultures. (cf. Hofstede, 2001, 2010) Uncertainty Avoidance (UAI) The UAI dimension stands for the extent to which the humans of a society feel uneasy with unpredictability and ambiguity. Most important here is how a cultural group deals with the truth that the future can never be known. Should the society try to control the future or just let it happen? Countries with high scores for this dimension cultivate rigid codes of belief and
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behavior. Unorthodox behaviour and ideas are not welcome. The members of societies which have low scores in this dimension show a more relaxed attitude. Practice is more important than principles. (cf. Hofstede, 2001, 2010) Long Term Orientation vs. Short Term Normative Orientation (LTO) While dealing with the questions and problems of the present and the future every cultural group has to maintain the roots of its own past. These two existential goals are weighted differently. Low scores on this dimension represent societies which are short term orientated. The members of these society prefer to keep time-honoured traditions and norms. Societal change is perceived with mistrust. On the other side long term orientation expresses a more pragmatic access by encouraging thriftiness and engagement in modern education as a way to be ready for the future. Synonyms are normative versus pragmatic and monumentalism versus flexhumility. (cf. Hofstede, 2001, 2010) Indulgence vs. Restraint (IVR) The free gratification of basic and natural human drives are allowed relatively in societies that represent indulgence. These cultural groups are related to enjoying life and having fun. Societies which suppress fulfilment of needs and control it by means of rigid social norms stand for Restraint. (cf. Hofstede, 2001, 2010)
11
Is the Hofstede dataset stable over time? In the course of this literature review the reader will find the application of Hofstede’s old dataset from 1966 – 1973 with dependent variables that came from time periods between 1996 (Husted 1999) and 2014 (Achim 2016). There are at least 23 years between the datasets. How is it explainable that national culture is changing so slow that it is appropriate to combine it with dependent variables that change by themselves a lot during time? The Hofstede Center (2017) announces about this topic that the values for his dimensions are relations between the countries. So his country scores are the results of comparison. This means for example a power distance of 66 does not mean that 66 were measured for that country but the relative score in comparison to other countries. The Hofstede Center (2017) believes that this means that the values are quite stable over time. According to them cultural shifts tend to be global or continent wide. So if many countries are changing at the same time their relative positions stay same. The authors concede changes in the levels of wealth or education may let Hofstede’s cultural values of his dimensions change stronger but they are convinced that the relative positions change very slow. Sjoerd Beugelsdijk, Robbert Maseland and André Van Hoorn (2015, p. 223) argued similar. They found cultural change to be absolute rather than relative. With that they mean that Hofstede’s scores relative to the scores of other countries haven’t changed hugely in other words cultural differences between countries pairs are in most cases constant. But in literature also other opinions can be found: Linghui Tang and Peter E. Koveos (2008, p. 1045) argued different by identifying changes in economic conditions as the source of cultural dynamics. On the other hand they identified institutional characteristics to be the basics for cultural stability. In their study they found national wealth operationalized by GDP per capita having a curvilinear relationship with Individualism versus Collectivism (IDV),
12
Long Term Orientation versus Short Term Normative Orientation (LTO), and Power Distance Index (PDI) what means that these dimensions change with the economic conditions over time while Uncertainty Avoidance Index (UAI) and Masculinity versus Femininity (MAS) stay stable because they reflect institutional traditions. Following the argumentation of Tang et. al. (2008) at least three of the five dimensions have changed during time. To find an answer to the question how strong the results change over time some statistical analysis (see Appendix 8.1.).
2.1.2. Other models of national culture The GLOBE Model Robert House et. al. (2004) developed their model with data from 1994–1997. The model is discussed in literature more often than Trompenaars and Schwartz. The GLOBEstudy works with nine dimensions, four of them are taken from Hofstede’s work in which he took over two dimensions (Power Distance and Uncertain Avoidance) and modified two dimensions. Individualism was spitted into In-Group Collectivism and Institutional Collectivism. Instead of Hofstede’s Masculinity versus Femininity (MAS) the GLOOBEstudy worked with Gender Equality, which is completely different to Hofstede’s original dimension. Four new dimensions were created. Based on House et al. (2004) Jürgen Rothlauf drew up the overview shown in Table 2
13 Table 2 Dimensions of the GLOBE -study Uncertain Avoidance
Power Distance
Institutional Collectivism In-Group Collectivism Gender Egalitarianism Assertiveness Future Orientation
Humane Orientation
Performance Orientation
“… is the extent to which members of an organization or society strive to avoid uncertainty by relying on established social norms, rituals, and bureaucratic practices.” “… is the degree to which members of an organization or society expect and agree that power should be stratified and concentrated at higher levels of an organization or government.” “… is the degree to which organizational and societal institutional practices encourage and reward collective distribution of resources and collective action.” “… is the degree to which individuals express pride, loyalty, and cohesiveness in their organizations or families.” “… is the degree to which an organization or society minimizes gender role differences while promoting gender equality.” “… is the degree to which individuals in organizations or societies are assertive, confrontational, and aggressive in social relationships.” “… is the degree to which individuals in organizations or societies engage in future-orientated behaviors such as planning, investing, in the future, and delaying individual or collective gratification.” “… is the degree to which individuals in organizations or societies encourage and reward individuals for being fair, altruistic, friendly, generous, caring, and kind to others.” “… is the degree to which an organization or society encourages and rewards group members for performance improvement and excellence.”
Adopted from: Jürgen Rothlauf (2009, p. 58)
In contrast to Hofstede Robert House et al. (2004) measured every dimension two times. First as values, what means as the things should be. Second as practices, what means how the things are practiced. Jürgen Rothlauf (2009, p. 57) gives additional details about the research: Initiated in 1991 by Robert J. House the GLOBE-study is the most extensive cultural investigation in terms of scope, depth, duration and sophistication. 170 investigators explored cultural peculiarities of 62 societies. Aim of the study was to establish a theory how culture influences leadership styles and organization culture by collecting data from more than 17,000 managers from 951 different organizations. The companies work in the industries of financial service, food processing, and telecommunications, because these sectors were expected to exist irrespective of the economic condition. The questionnaires are constructed of 7-point scales which ask the responder to agree to a statement between 1 (low agreement) and 7 (strong agreement).
14
Jürgen Rothlauf (2004, p. 60) also criticized the model. He praised the model in contrast to former researcher on this topic for differentiating between cultural groups within one country: The GLOBE team collected data for West Germany, East Germany, Germanspeaking Switzerland, French-speaking Switzerland, black population of South Africa, and white population of South Africa. But Rothlauf (2004, p.60) criticizes that other heterogeneous countries such as USA, India and China were still not differentiated by subcultures. Rothlauf (2004, p.60) identified the choice of sample as most critical, because the GLOBE-study was restricted to interviewing only middle managers. Regarding to him it is questionable whether cultural peculiarities can be observed adequately by managers, because they received a broad education, have considerable experiences also on international level and can be expected to be more sensitive regarding cultural differences. Hofstedes (2006) summarizes his critics about GLOBE with the headline: “What did GLOBE really measure?” He sees further problems with the data of GLOBE research: “My main concern about the GLOBE research is that the questionnaire items used may not have captured what the researchers supposed them to measure.” Hofstede (2006) continues to criticize that “among all it’s over 800 pages the book does not reproduce the survey questionnaires, just one or two sample items per dimension. (…) The items are formulated at a high level of abstraction, rather far from the respondents' daily concerns.” Hofstede (2006) recognizes another problem by defining culture dimensions by psycho-logic: “I found that distinctions derived from comparing collective trends in respondents' answers across countries sometimes followed a different logic”. He continues to quote from his book: “'Cultures are not king-size individuals. They are wholes, and their internal logic cannot be under- stood in the terms used for the personality dynamics of individuals. Eco-logic differs from individual logic” (Hofstede, 2001: 17). Hofstede (2006) re-analyzed the GLOBE's dimension and was able to identify five factors as shown in Table 3.
15 Table 3 Results of Hofstede’s ecological factor analysis of 18 GLOBE dimension scores for 56 countries Factor 1 (25,9% of variance): 0.90 Uncertainty avoidance practice -0.87 Uncertainty avoidance value 0.84 Future orientation practice -0.80 In-group collectivism practice 0.70 Performance orientation practice -0.61 Future orientation value (0.47) Gender egalitarianism value Factor 2 (13.5% of variance): -0.82 Institutional collectivism value 0.79 Institutional collectivism practice 0.63 Assertiveness value Factor 3 (13.3% of variance): 0.85 In-group collectivism value 0.75 Performance orientation value Factor 4 (13.2% of variance): 0.81 Human orientation value -0.67 Human orientation practice 0.57 Power distance practice -0.55 Power distance value 0.52 Assertiveness practice Factor 5 (9.9% of variance): 0.90 Gender egalitarianism practice Total variance explained: 75.7% (figures are loadings). Adopted from: Hofstede (2006, p. 889)
Later Hofstede (2006) correlated his dimension scores with the result of two factor analyses of the GLOBE data as shown in table 4.
16 Table 4 Correlations between GLOBE dimension factors, GNP/capita and Hofstede indices across 48 countries (30 for LTO)
GNP/capita
1 0.75***
5
Hofstede indices Power Distance -0.66*** Individualism 0.61*** 0.40*** Uncertainty avoidance -0.54*** Long-term orientation Masculinity Significance limits: ***p>0.001; **p chi2 (+)
0.063
Pr(Skewness) (++)
0.294
Pr(Kurtosis) (++)
0.342
Chi2 RESIDUEN (++)
2.03
Prob > chi2 (++)
0.362
+ Breusch-Pagan / Cook-Weisberg test for heteroscedasticity; ++ Skewness/Kurtosis tests for Normality Source: Own calculations based on the data provided by Yale University's Center for Environmental Law and Policy (2015), The Worldbank (2017) [1], and The Hofstede Centre (2015) *** Correlation is significant at the 0.001 level; ** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level;
77
The variable of Hypothesis 11 human development index (HDI) stays significant in all five models. With this mind Hypothesis 1 can be finally accepted. The variable perceived corruption (CPI) is not significant in two of the five models. But these models (model 2 and model 3) have only 66 cases. This confirms the study of Stephen Morse1 (2006) whose model had similar number of cases corresponding the Hofstede dataset and found out that “even the best regressions have R2 values of less than 20%, but perhaps this is not surprising given the data sets that form the basis of the analysis.” He was absolutely right. But in all models with a higher number of cases perceived corruption (CPI) is highly significant at the 0.001 level. The cause is the dataset. Hypotheses 12 can be therefore also accepted. But when the beforehand significant cultural dimensions are included in the function all of them lose significance in every model. Models 2, 3, 4 and 5 show that the variables power distance (PDI), Individualism (IDV), Long- versus Short-term Orientation (LTO), and Indulgence versus Restraint (IVR) are not significant in a model with human development index (HDI) and perceived corruption (CPI). Finally, after these multiple linear regressions Hypotheses 14 (PDI), Hypotheses 15 (IDV), Hypotheses 18 (LTO), and Hypothesis 19 (IVR) can be rejected too and the null hypothesis can be accepted. Because of it for Hypothesis 13 that stated a causal relationship between cultural factors and the level of corruption we can accept the null hypotheses: Cultural factors do not affect the level of environmental performance (EPI).
78 Table 33 Overall view about hypothesis tested in this chapter for pollution Hypothesis
r
R2
p
Result
Explanatory Power+
H11: The higher the Human Development Index (HDI) the higher the level of environmental performance (EPI).
0.882***
0.78
***
Accepted
Strong
H12: The higher the level of perceived corruption (CPI) the lower the level of environmental performance (EPI).
0.766***
0.58
***
Accepted
Moderate
H14: Power Distance (PDI) affects the level of environmental performance (EPI).
-0.514***
0.26
No
Rejected
H15: Individualism (IDV) affects the level of environmental performance (EPI).
0.614***
0.38
No
Rejected
H16: Masculinity (MAS) affects the level of environmental performance (EPI).
-0.010
0.01
-
Rejected
H17: Uncertain Avoidance (UAI) affects the level of environmental performance (EPI).
-0.041
0.01
-
Rejected
H18: Long- versus Short-term Orientation (LTO) affects the level of environmental performance (EPI).
0.326**
0.11
No
Rejected
H19: Indulgence versus Restraint (IVR) affects the level of environmental performance (EPI).
0.219*
0.05
No
Rejected
H13: Cultural factors affect the level of environmental performance (EPI).
r = coefficient from Pearson Correlation (Table 28), R2 = Explanatory power in simple regression (Table 30) p = Significance level in multiple linear regression (Table 31) *** Correlation is significant at the 0.001 level; ** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level; + in multiple linear regression (Table 31)
79
4.3. Results for vulnerable employment Table 34 shows the pairwise Pearson correlations between the variables of vulnerable employment (VE), income inequality as Gini coefficient (GINI), employment in agriculture (EAC), and Hofstede’s six dimensions of culture (PDI, IDV, MAS, UAI, LTO, IVR). Table 34 Pearson correlations for VE and all independent variables VE
GINI
EAC
PDI
IDV
MAS
UAI
LTO
VE
1
GINI
0.549***
1
EAC
0.906***
0.534***
1
PDI
0.441***
0.436***
0.539***
1
IDV
0.611***
-0.627***
-0.666***
-0.598***
1
MAS
0.053
0.055
-0.012
0.115
0.083
1
UAI
0.059
0.076
-0.113
0.229
-0.165
-0.061
1
LTO
-0.293*
-0.583***
-0.188
-0.001
0.123
0.030
-0.014
1
IVR
-0.202
0.037
-0.376**
-0.284*
0.136
0.066
-0.074
-0.452***
IVR
1
Source: Own calculations based on the data provided by The Worldbank (2017) [11], Human Development Report Office (2015), and The Hofstede Centre (2015) *** Correlation is significant at the 0.001 level; ** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level;
Table 34 shows that the highest correlations (which were significant at the 0,001 % level of significance) were found between employment in agriculture (EAC) and vulnerable employment (VE) (r=0.91) as stated in Hypothesis21 as well as income inequality measured by Gini coefficient (GINI) and vulnerable employment (VE) (r=0.55) of Hypotheses 20. Figs. 15, and 16 show the correlation graphical.
0
20
VE 40
60
80
80
0
10
20
30
40
50
GINI
0
20
VE 40
60
80
Figure 15 Correlation between vulnerable employment (VE) and Gini coefficient (GINI) for Hypothesis 20
0
20
40 EAC
60
Figure 16 Correlation between vulnerable employment (VE) and employment in agriculture (EAC) for Hypothesis 21
80
81
Hypothesis 20 states a relation between income inequality expressed by Gini coefficient (GINI) and vulnerable employment (VE). After pairwise Pearson correlation has been done, Hypothesis 20 can be accepted because the correlation coefficient is significant at the 0.001 level. Hypothesis 21 states a relation between employment in agriculture (EAC) and vulnerable employment (VE) and can be also accepted according to the perfect result of a very high coefficient (r=0.91) and best significance at the 0.001 level. Hypothesis 22 examines the relation between Hofstede’s six dimensions of culture (PDI, IDV, MAS, UAI, LTO, and IVR) and vulnerable employment (VE). The pairwise Pearson correlation shows that Hypothesis 25 (MAS), Hypothesis 26 (UAI), and Hypothesis 28 (IVR) can be rejected because the correlation coefficients show weak or almost no linear relationships which are not significant. But the three independent variables of Hypothesis 23 (PDI), Hypothesis 24 (IDV), and Hypothesis 27 (LTO) have a significant relation. PDI, and IDV are significant at the 0.001 level. The relations are graphical expressed in Figs. 17 and 18. Power Distance (PDI) and Individualism (IDV) show moderate linear relationships with vulnerable employment (VE).
0
20
VE 40
60
80
82
0
20
40
60
80
100
PDI
0
20
VE 40
60
80
Figure 17 Correlation between vulnerable employment (VE) and power distance (PDII) for Hypothesis 23
0
20
40
60 IND
Figure 18 Correlation between vulnerable employment (VE) and individualism (IDV) for Hypothesis 24
80
100
83
Also, according to Table 32, the variable Long- versus Short-term Orientation (LTO) of Hypothesis 27 is significant, but only at the 0.05 level. The weak linear relationships with
0
20
VE 40
60
80
vulnerable employment (VE) that can be seen in Fig. 19.
0
20
40
60
80
100
LTO
Figure 19 Correlation between vulnerable employment (VE) and individualism (IDV) for Hypothesis 27
Therefore, for Hypothesis 23. (PDI), Hypothesis 24 (IDV), Hypothesis 27 (LTO) the null hypothesis can be rejected. Hypothesis 22 that examines the relation between Hofstede’s six dimensions of culture and vulnerable employment (VE) can keep accepted. Table 35 shows the data’s descriptive statistics. Table 36 shows the results of simple regressions.
84 Table 35 Descriptive statistics for VE and all independent variables Variable
N
Mean
Standard Deviation
VE
87
24.59
19.04
GINI
151
20.91
10.439
EAC
92
14.68
15.063
PDI
78
59.33
21.223
IDV
78
45.17
23.972
MAS
78
49.27
19.008
UAI
78
67.64
22.993
LTO
96
45.49
24.221
IVR
97
45.39
22.177
Source: Own calculations based on the data provided by The Worldbank (2017) [11], Human Development Report Office (2015), and The Hofstede Centre (2015)
Table 36 Simple regressions for VE and all independent variables Variables
P
R squared
Regression
Standard
coefficient
errors
t-stat
F
N
Dependent variable VE Independent variables EAC
< 0.000
0.82
1.100
0.059
18.75
351.71
79
GINI
< 0.000
0.30
1.332
0.236
5.65
31.92
76
PDI
0.001
0.19
0.2834
0.078
3.65
13.29
57
IDV
< 0.000
0.37
-0.377
0.066
-5.72
32.73
57
MAS
0.696
0.00
0.036
0.093
0.39
0.15
57
UAI
0.666
0.00
0.037
0.085
0.43
0.19
57
LTO
0.015
0.09
-0.242
0.097
-2.49
6.19
68
IVR
0.096
0.04
-0.186
0.110
-1.69
2.85
69
Source: Own calculations based on the data provided by The Worldbank (2017) [11], Human Development Report Office (2015), and The Hofstede Centre (2015)
Here too, the simple regressions confirm all findings of the pairwise Pearson correlation. But it can be seen that the independent variable employment in agriculture (EAC)
85
explains vulnerable employment (VE) best. It can explain 82% of vulnerable employment (VE). The income inequality as Gini coefficient (GINI) as sole explanatory variable can only explain 30% of vulnerable employment (VE). The significant cultural dimension Individualism (IDV) has the highest cultural explanatory power, it can explain 37% of the level of vulnerable employment (VE). Power Distance is able to explain 19%. Long- versus Short-term Orientation (LTO) explains only 9%. For proving that national culture has a minimal impact on vulnerable employment (VE) I build models of vulnerable employment (VE) as a function of employment in agriculture (EAC), income inequality as Gini coefficient (GINI), and culture (PDI, IDV, LTO). Table 37 shows the results. Table 37 Models of vulnerable employment as a function of employment in agriculture (EAC), income inequality (GINI), and national culture Variables
Model 1
Model 2
Model 3
Model 4
Model 5
EAC
0.966***
0.962***
0.935***
0.998***
0.893***
GINI
0.615***
0.655***
0.609***
0.438**
0.582**
PDI
0.0160
IDV
-0.022 -0.045
-0.093
MAS UAI LTO
-0.027
-0.035
IVR Prob.
< 0.000
< 0.000
< 0.000
< 0.000
< 0.000
Adjusted R squared
0.88
0.87
0.88
0.89
0.88
F
258.80
112.45
115.07
162.52
67.60
N
71
49
49
59
46
Source: Own calculations based on the data provided by The Worldbank (2017) [11], Human Development Report Office (2015), and The Hofstede Centre (2015) *** Correlation is significant at the 0.001 level; ** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level;
86
Model 1 explains VE without the influence of national culture as a function of EAC and GINI. Model 2 and Model 3 should actually include at least 60 cases, but no more data is available. In Model 2 the independent significant cultural variable PDI from the Pearson correlation of Table 32 is added as third variable to explain VE. Model 3 does the same with IDV instead of PDI. In Model four was done the same as in Model 2 and Model 3. Here the cultural variable is LTO. The number of cases is better than those in Model2 and Model 3. Actually, Model five would need 80 cases for explaining VE with four independent variables. But it was built to check the robustness of EAC and GINI. Model 1 is the best model for explaining the level of vulnerable employment (VE). The number of cases is 71. The model predicts 88% of the level of corruption. According to which Hypothesis 1 and Hypothesis 2 can be finally accepted. Both variables stay significant in all of the five models. But again, beforehand significant cultural dimensions lose significance when they compete with stronger explanatory variables. Models 2, 3, 4 and 5 show that the variables power distance (PDI), Individualism (IDV) and Long- versus Short-term Orientation (LTO) are not significant in a model with employment in agriculture (EAC) and income inequality as Gini coefficient (GINI). Finally after these multiple linear regressions Hypotheses 23 (PDI), Hypotheses 24 (IDV) and Hypotheses 28 (IVR) can be rejected too and the null hypothesis can be accepted. Because of it for Hypothesis 3 that stated a causal relationship between cultural factors and the level of corruption we can accept the null hypotheses: Cultural factors do not affect the level of corruption.
87 Table 38 Overall view about hypothesis tested in this chapter Hypothesis
r
R2
p
Result
Explanatory Power+
H20: The higher the income inequality (GINI) the higher the level of vulnerable employment (VE).
0.549***
0.30
***
Accepted
Moderate
H21: The higher the employment in agriculture (EAC) the higher the level of vulnerable employment (VE).
0.906***
0.82
***
Accepted
Strong
H23: Power Distance (PDI) affects the level of vulnerable employment (VE).
0.441***
0.19
No
Rejected
H24: Individualism (IDV) affects the level of vulnerable employment (VE).
0.611***
0.37
No
Rejected
H25: Masculinity (MAS) affects the level of vulnerable employment (VE).
0.053
0.00
-
Rejected
H26: Uncertain Avoidance (UAI) affects the level of vulnerable employment (VE).
0.059
0.00
-
Rejected
H27: Long- versus Short-term Orientation (LTO) affects the level of vulnerable employment (VE).
-0.293*
0.09
No
Rejected
H28: Indulgence versus Restraint (IVR) affects the level of vulnerable employment (VE).
-0.202
0.04
-
Rejected
H22: Cultural factors affect the level of vulnerable employment (VE).
r = coefficient from Pearson Correlation (Table 34), R2 = Explanatory power in simple regression (Table 36) p = Significance level in multiple linear regression (Table 37) *** Correlation is significant at the 0.001 level; ** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level; + in multiple linear regression (Table 37)
88
4.4. Overall view of all tested hypotheses and results Table 39 Overall view for all hypotheses
Hypothesis
Result
H1: The higher the gross national income per capita (GNIPCA) the lower the level of perceived corruption (CPI) H2: The higher the economic freedom (EF) the lower the level of perceived corruption (CPI). H3: The higher the democracy index (DI) the lower the level of perceived corruption (CPI). H4: Cultural factors affect the level of corruption (CPI). H5: Power Distance (PDI) affects the level of corruption (CPI). H6: Individualism (IDV) affects the level of corruption (CPI). H7: Masculinity (MAS) affects the level of corruption (CPI). H8: Uncertain Avoidance (UAI) affects the level of corruption (CPI). H9: Long- versus Short-term Orientation (LTO) affects the level of corruption (CPI). H10: Indulgence versus Restraint (IVR) affects the level of corruption (CPI). H11: The higher the Human Development Index (HDI) the higher the level of environmental performance (EPI). H12: The higher the level of perceived corruption (CPI) the lower the level of environmental performance (EPI). H13: Cultural factors affect the level of environmental performance (EPI). H14: Power Distance (PDI) affects the level of environmental performance (EPI). H15: Individualism (IDV) affects the level of environmental performance (EPI). H16: Masculinity (MAS) affects the level of environmental performance (EPI). H17: Uncertain Avoidance (UAI) affects the level of environmental performance (EPI). H18: Long- versus Short-term Orientation (LTO) affects the level of environmental performance (EPI). H19: Indulgence versus Restraint (IVR) affects the level of environmental performance (EPI). H20: The higher the income inequality (GINI) the higher the level of vulnerable employment (VE). H21: The higher the employment in agriculture (EAC) the higher the level of vulnerable employment (VE). H22: Cultural factors affect the level of vulnerable employment (VE). H23: Power Distance (PDI) affects the level of vulnerable employment (VE). H24: Individualism (IDV) affects the level of vulnerable employment (VE). H25: Masculinity (MAS) affects the level of vulnerable employment (VE). H26: Uncertain Avoidance (UAI) affects the level of vulnerable employment (VE). H27: Long- versus Short-term Orientation (LTO) affects the level of vulnerable employment (VE). H28: Indulgence versus Restraint (IVR) affects the level of vulnerable employment (VE).
Accepted Accepted Accepted Accepted Accepted Accepted Rejected Rejected Rejected Rejected Accepted Accepted Rejected Rejected Rejected Rejected Rejected Rejected Rejected Accepted Accepted Rejected Rejected Rejected Rejected Rejected Rejected Rejected
Explanatory Power Strong Moderate Moderate Weak Weak Weak
Strong Moderate
Moderate Strong
89
5. DISKUSSION OF THE RESULTS The research objective of this master dissertation is to identify the strength of cultural influence to the four unethical business practices corruption, pollution, vulnerable employment, and child labour. Former authors in literature could identify the influence of Hofstede’s cultural dimensions several times, especially for corruption. An influence to pollution could be also established. None of the authors tried to answer the question, how strong the cultural influence is. This thesis aims to give an answer to this question. The methodological design was taken over from the former authors for being able to resume to them and to answer open question and to iron the wrinkles out that goes along with the statement “about half of the level of corruption in countries is explained by the national culture” (Achim 2016).
5.1. Macroeconomic indicators that explain corruption 5.1.1. National wealth measured by Gross National Income per Capita calculated by Atlas method (GNIPCA) National wealth measured by Gross National Income per Capita calculated by Atlas method (GNIPCA) has the strongest correlation with Corruption Perception Index (CPI) and is able to explain 66% of corruption in a simple regression. The value of the regression coefficient (see Table 26) is positive what means that the CPI rises with increasement of the GNIPCA. When the CPI rises the perceived corruption level decreases because a high score of CPI expresses a low level of corruption. Of course, the coefficient is very small, because the units of measurement are Dollar. The value of the coefficient (0.0004368) expresses, if the Gross National Income per Capita (GNIPCA) increases for 2289 Dollar the level of perceived corruption decreases for one unit. As far as we know Husted (1999) was the first scientist who
90
identified national wealth as determinant of corruption. His dataset included only 44 cases. This study supports his findings with a dataset of 162 countries (simple regression in Table 25) or rather 149 countries (multiple linear regression in Table 26). This research could also identify the best measurement for national wealth. In contrast to other measurement of national wealth Gross National Income per Capita calculated by Atlas method (GNIPCA) has the highest correlation (r=0.81) in Table 20 and the strongest explanatory power in simple regressions (R2=0.66) in Table 22. Depending on this finding the risk for managers and companies to come across corruption rises in poor countries. The poorer the population of a country, the higher is the risk for corruptive structures. If policy makers think about how to fight against corruption, the increasement of the national wealth seems to be the optimal way, what is of course a very theoretical statement. In practice, the humanity was not able to improve the situation of the poorest countries, although there were a lot of ideas and concepts, which did not work in real life.
5.1.2. Economic Freedom expressed by Index of Economic Freedom Regarding to this work economic freedom also seems to be very strong determinant of corruption. The Index of Economic Freedom of Heritage Foundation (2017 [1]) is able to explain 60% of corruption in simple regressions (see Table 25). The idea of Paldam (2002) is confirmed by this research. In contrast to Paldam (2002), who had a dataset of 86 cases, this paper had the chance to work with 164 countries in simple regression (see Table 25) and 149 countries in multiple linear regression (see Table 26). The value of the regression coefficient (see Table 26) is also positive what means that the perceived corruption level decreases (CPI increases) with increase of the Index of Economic Freedom. The value of the coefficient
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(0.697) expresses, if the Index of Economic Freedom (EF) increases for one unit the level of perceived corruption decreases for 0.7 units. This means for managers in practice that they can expect higher levels of corruption, if they work in countries whose economy is highly regulated. So, it is no surprise that North Korea (together with Somalia) has the highest level of perceived corruption in the dataset for 2014. Policy makers can learn from this academic work and the works of preceded author that more regulations of trade and economy go along with a higher risk for corruption. On the other hand, a cut of regulations and laws that try to control economic processes will help to fight against corruption. More economic freedom goes along with less perceived corruption.
5.1.2. Democracy expressed by Democracy Index of The Economist Intelligence Unit Having a look on the findings Democracy has an important impact to corruption in countries, too. The correlation coefficient of the Pearson Correlation in Table 23 is high (r=0.738) and significant at the 0.001 level. In the simple regression (shown in Table 25) the Democracy Index (DI) of the Economist Intelligence Unit (2017 [2]) is able to explain 54% of the Perceived Corruption Index (CPI). But the DI can improve the explanatory power of a model with GNIPCA and EF only for five percent (Table 40 in Appendix 8.3.). As shown in Table 23, DI and EF have a high correlation that is significant on the 0.001 level. As shown in Table 40 Economic Freedom (EF) is minimal stronger (one percent) in explanatory power than the DI. Also, the correlation coefficient of Table 23 is higher for EF (r=0.78) than for DI (r=0.74), both on the 0.001 level of significance. Because of the high correlation of EF and DI, Model 1 from Table 26 was checked for multicollinearity, however not found (see Table 40).
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Democracy is another motor for fighting against corruption. This means for a manager who has to work in an autocratic country that he has a high risk for corruption. Policy makers should establish democratic structures to fight against corruption. Table 39 shows the development for Hungary as an example. Table 40 Hungary: While the level of democracy decreased the level of perceived corruption increased. Year 2017 2016 2015 2014 2013 2012
CPI* 45 48 51 54 54 55
DI** 6.64 6.72 6.84 6.90 6.96 6.96
Source: Transparency International (2018), The Economist Intelligence Unit (2017). * high scores of CPI express less corruption, low scores express a high level of corruption ** the higher the score, the more democratic
5.2. Impact of national culture to corruption The literature review could not give an answer to my research question, but my own results for corruption in table 26 are giving a clear answer to the research question: Hofstede’s dimensions of national culture can improve the explanatory power of corruption for only 2 two percent, from 85% to 87%. The influence of national culture is minimal. The regression coefficient of the multi linear regression in Table 26 for Power Distance (PDI) is negative (-0.111). So, an increase of PDI for one unit decreases the CPI for 0.1 units what means that the level of perceived corruption increases. Because the explanatory power is minimal, it may be concluded, that countries with high scores in PDI have better underlying conditions for corruptive structures than countries with low values for PDI. A little bit stronger is the influence of Individualism (IDV) (see Table 23 and Table 25). The regression coefficient for Model 3 of the multi linear regressions in Table 26 is
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positive (0.142). Therefore, an increase of IDV for one unit increases the CPI for 0.1 units what means that the level of perceived corruption decreases. This influence is minimal but regarding to these results collectivistic countries seem to have better opportunities for corruptive behavior than individualistic countries. But the national wealth, and the degree of economic freedom and democracy, are much more important than the culture to estimate the chance for coming across corruption. The statement “about half of the level of corruption in countries is explained by the national culture” (Achim 2016) is misleading because the models of Achim (2016) included only Hofstede’s indicators. Other stronger explanatory variables, which were already identified before by other researchers, were completely ignored. For demonstrating the senselessness of such a research designs a model for explaining theft can be found in Table 47 in the appendix. The model is similar to Achim’s (2016) model and is able to explain 68% of theft with Hofstede’s dimensions of national culture. Table 39b shows the 15 worst countries concerning perceived corruption. All of them have a low or very low GNIPCA that goes along with low values for economic freedom and/ or low values for democracy.
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Table 41 2014 data for countries with highest degree of perceived corruption Country
CPI
GNIPCA
EF
DI
PDI
IDV
Korea (North)
8
n.a.
1
1,1
n.a.
n.a.
Somalia
8
n.a.
n.a.
n.a.
n.a.
n.a.
Sudan
11
1830
n.a.
2,5
n.a.
n.a.
Afghanistan
12
630
n.a.
2,8
n.a.
n.a.
South Sudan
15
1010
n.a.
n.a.
n.a.
n.a.
Iraq
16
6700
n.a.
4,2
n.a.
n.a.
Turkmenistan
17
7310
42,2
1,8
n.a.
n.a.
Eritrea
18
n.a.
38,5
2,4
n.a.
n.a.
Libya
18
n.a.
n.a.
3,8
n.a.
n.a.
Uzbekistan
18
2110
46,5
2,5
n.a.
n.a.
Angola
19
4470
47,7
3,4
n.a.
n.a.
GuineaBissau
19
620
51,4
1,9
n.a.
n.a.
Haiti
19
820
48,9
3,8
n.a.
n.a.
Venezuela
19
n.a.
36,3
5,1
81
12
Yemen
19
1440
55,5
2,8
n.a.
n.a.
Source: Transparency International (2014) [1], The World Bank Group (2017) [1], The Heritage Foundation (2017) [1], The Economist Intelligence Unit (2017) [2], The Hofstede Center (2015)
5.3. Macroeconomic indicators that explain pollution 5.3.1. Development of countries is the strongest explanatory variable for pollution Regarding to the results environmental sustainability depends for the most part on countries’ development. The regression coefficient of Model 1 in Table 31 (74.309) expresses a positive direction what means the higher the level of development the better the environmental performance. If we would adapt the scale of EPI and HDI, an increase of one unit in HDI would improve the EPI for 0,74 units. The Human Development Index (HDI) alone is able to explain 78% of environmental sustainability (see Table 30). Table 43 in Appendix 8.4. demonstrates that the HDI’s three dimensions Education Index (EI), Live
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Expectancy at Birth (LE) and Gross National Income per Capita (GNIPCA) as single variables lead to the same results. Hence, pollution depend almost entirely on human development. The results of this academic work Managers in international business have higher risks of being involved in pollution scandals, if they work with partners in undeveloped countries. Effective environmental protection requires development. Undeveloped countries lack resources to avoid pollution and lack possibilities to put environmental protection laws to practice and control their obeying. Having a look at the 2016 World’s Worst Pollution Problems Report (Green Cross Switzerland, Pure Earth 2017) the reader can see, most of the worst pollution is happening in undeveloped countries. Table 42 gives an overall view about the worst pollution problems. Table 42 World’s worst pollution problem Rank 1 2 3 4 5 6 7 8 9 10
Industry Used Lead-Acid Battery Recycling Mining and Ore Processing Lead Smelting Tanneries Artisanal Small-Scale Gold Mining Industrial Dumpsites Industrial Estates Chemical Manufacturing Product Manufacturing Dye Industry
DALYs 2,000,000 - 4,800,000 450,000 - 2,600,000 1,000,000 - 2,500,000 1,200,000 - 2,000,000 600,000 - 1,600,000 370,000 - 1,200,000 370,000 - 1,200,000 300,000 - 750,000 400,000 - 700,000 220,000 - 430,000
* DALYs = Disability-Adjusted Life Years in low- and middle-income countries Source: Green Cross Switzerland, Pure Earth (2017)
5.3.2. Corruption as moderate explanatory variable for pollution As first mentioned by Morse (2006) the findings of this study support the influence of corruption, measured by the Corruption Perceptions Index. In the simple regression model of Table 30 the CPI is able to explain 58% of The Environmental Performance Index (EPI). But
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having a look at the models of Table 31 it can be seen that the CPI is only able to improve the explanatory power of a model only with HDI (see Table 30, R2=0.78) only for four percent (Model 1’s R2=0.82 in Model 1). In Model 2 and Model 3 of Table 31 CPI lost even its’ significance. So, it can be concluded that the explanatory power is moderate. The regression coefficient of Model 1 in Table 31 (0.210) expresses a positive direction in the way that if the CPI increase for one unit (less corruption) the EPI increases for 0.2 units (better environmental performance). Regarding to these findings managers in business can understand that places or companies with high corruptive structures increase the risk for being involved in pollution scandals. Policy maker can understand that a reduction of corruption goes along with less pollution.
5.4. Impact of national culture to pollution Already the literature review could give an answer to the question how strong the influence of national culture is for explaining pollution. In the research of Onel and Mukherjeee (2014) all Hofstede indicators lost their significance in regression models that include other, stronger explanatory variables. The calculations of this work came to the same results: Hofstede’s indicators are able to explain pollution only in models without other stronger explanatory variables. Table 31 shows that the Hofstede indicators also lost significance in all regression models with other non-cultural independent variables. Concerning pollution, the answer to my research question is very clear. There is no or very little impact of national culture to pollution. Pollution is not explainable or excusable with national culture.
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5.5. Macroeconomic indicators that explain harmful working conditions 5.5.1. Employment in agriculture is the strongest explanatory variable for harmful working conditions Almost 82% of Vulnerable Employment, that goes along with harmful working conditions, can be explained with the percentage of working population working in agriculture (EAC) as demonstrated in Table 34. The regression coefficient of table 35 (0.966) expresses, that if the percentage of people working in agriculture increases for one percent, the percentage of people working in vulnerable employment also increases for almost one percent. From there, companies and managers can conclude, that making business in the international agricultural sector goes along with a high risk for being involved in scandals about harmful working conditions. The more a country’s economy depends on agriculture, the higher the risk for harmful working conditions.
5.5.2. Income inequality increases the risk for harmful working conditions The explanatory power of income inequality is only moderate, but able to explain 30% of vulnerable employment in the simple regression of Table 33. In Model 1 of Table 35 income inequality measured by GINI Index (GINI) is able to improve the explanatory power of the model for 6 percent in comparison to a model only with EAC (see Table 34). The direction of the regression coefficient (0.615) in Table 35 is positive what means that an increase of the GİNI for one unit increase the share of vulnerable employment of working population for 0.62 percent.
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Depending on these results managers can expect higher risks for being involved in scandals about harmful working conditions, if they are making business in countries with strong income inequalities. Policy makers can understand from the results of this study, that a reduction of inequality helps to improve the working conditions.
5.6. Impact of national culture to harmful working conditions Here too, the Hofstede indicators lost significance in all regression models with other non-cultural independent variables (see Table 35). There is no or only minimal impact of national culture to the unethical business practice harmful working conditions, is the answer to the research question.
5.7. Impact of national culture to unethical business practices Depending on the results of this academic work it does not make any sense to discuss about national culture, if unethical business practices shall be explained. On the ecological level with countries as units of observation, macroeconomic indicators play the major role. Between 82 and 88 percent of the three unethical business practices could be explained with these indicators. I almost all cases the dimensions of national culture were not able to improve the models, they were not even significant. Only Hofstede’s Individualism could improve the model for corruption in Table 26 (see Model 1 and Model 5) for two percent what expresses a minimal impact. National culture is not able to explain pollution and harmful working conditions.
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There is a minimal impact of the cultural dimension Individualism (IDV) to the level of perceived corruption. National culture is abused by companies and managers to justify corruption, pollution or harmful working conditions with national culture. The results of this study demonstrate, that there is no empirical evidence for that claim.
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6. CONCLUSION, RECOMMENDATION, LIMITATIONS 6.1. Conclusions for science This is the first study that answered the research question: How strong is the influence of national culture to unethical business practices. The most active and first author that examined the influence of Hofstede’s dimensions of national culture is Brain W. Husted. He also included further indicators and build models for corruption and pollution. But his results never gave an answer to the question about the share of his indicators he only reported the explanatory power for his whole model. Other authors built models that included only cultural dimensions. This is good for showing that culture is able to explain unethical business practices, but it doesn’t say anything about the importance of these influence. As shown in the appendix (8.3.) also other kinds of crime as theft work in model with Hofstede’s dimensions. But nobody would serious state that there are some cultures that accept theft more than others. Scientists should have a critical view on research about national culture concerning unethical business practices. There are much more important and influential indicators than culture. After all national wealth, development, and political conditions seem to be the most important factors on an ecological level that compares countries. This study could explain between 82 and 88 percent of the bad business practices corruption, pollution, and vulnerable employment. Future researchers could be able to explain more than 82 to 88 percent of the three unethical business practices. There is also missing a study for child labour that works with a better indicator than the percentage of child labour in countries. Maybe the company Verisk Maplecroft will make their child labour index available for other researchers. This study also makes a contribution to the debate between relativism and normativism in business ethics. The findings, that the influence of national culture to
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unethical business practices is minimal weakens the position of relativism. Of course, there are some truths that vary in cultures. For example, the kind of dressing oneself, the things that are seen as edible and delicious, or the role of women vary a lot between the cultures. But unethical business practices are not a part of culture. They are the results of poverty, development and political conditions which can create optimal conditions for criminal subjects.
6.2. Conclusions for Management Managers in international business have to fail decisions about the supply chain. They have to fail decisions between suppliers which offer $0.39, $0.43, or $0.49 for one piece of their interest. The difference of $0.10 is important for high amounts. So, the managers are enticed to accept corruption, pollution, harmful working conditions or child labour not least of all because their bonus payment depends on the business success. Culture is a welcome excuse for failing a decision for the benefits of the lower price that brings corruption, pollution, harmful working conditions or child labour with it. Managers can learn from this study that there is no big influence of national culture that can be proved scientifically.
6.3. Conclusions for police maker But by understanding, that culture does not play a major role in explaining unethical business practices and seeing these practices as crime caused by subjective individuals, policy makers have a clear task to fight against them by making laws and putting these laws to practice. In my opinion Germany made a good example of Siemens Company. In 2007 the district court of Munich imposed a fine of 201 million Euro on Germany’s biggest electronics
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company for compiling black cash registers to pay bribe in foreign trade. Additionally, the corporation had to pay 179 million Euro taxes extra. (“Schmiergeldskandal”, 2007)
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8. APPENDIX Appendix A Comparison of Hofstede’s dataset between five years with CPI Table 36 and Table 37 show the descriptive statistics of the CPI for 2010 and the CPI for 2015. We can see a correlation coefficient between both CPIs of 0.98. The main difference is the scale. The CPI for 2010 is between 0 and 10, the CPI for 2015 is between Table 43 Descriptive statistics of CPI from 2010, CPI from 2015, and Hofstede’s dimensions of national culture Variable
N
Mean
Standard Deviation
Minimum
Maximum
CPI2010
100
4.69
2.27
1.4
9.3
CPI2015
100
49.76
20.53
11
91
Source: Own calculations based on the data provided by Transparency International (2017) Table 44 Pearson correlations of CPI from 2010, CPI from 2015, and Hofstede’s dimensions of national culture CPI2010
CPI2015
CPI2010
1
CPI2015
0.978***
1
PDI
-0.648***
-0.652***
IDV
0.623***
0.676***
MAS
-0.157
-0.172
UAI
-0.285*
-0.273*
LTO
0.165
0.209*
IVR
0.385***
0.328**
Source: Own calculations based on the data provided by Transparency International (2017) and Hofstede (2015) *** Correlation is significant at the 0.001 level; ** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level;
114 Table 45 CPI2010 and CPI2015 as functions of Hofstede’s significant dimensions Variables
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Dependent
CPI2010
CPI2015
CPI2010
CPI2015
CPI2010
CPI2015
-0.069***
-0.611*** 0.059***
0.571***
0.040***
0.305**
variable PDI IDV MAS UAI LTO IVR Prob.
< 0.000
< 0.000
< 0.000
< 0.000
< 0.000
< 0.000
R squared
0.42
0.42
0.39
0.46
0.15
0.11
F
47.70
48.73
41.85
55.39
15.34
10.59
N
68
68
68
68
90
90
Source: Own calculations based on the data provided by Transparency International (2017) and Hofstede (2015) *** Correlation is significant at the 0.001 level; ** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level;
There is a discussion in literature how stable the Hofstede data is over time. This question has been taken up beside the research question because it the answer to the question of stability is important for judging the meaningfulness of this research and its predecessors in literature. The results in table 38 show that the Hofstede data is actual relative stable over time. The regression model for explaining corruption as a function of culture gives almost the same results for the CPI for 2010 and the CPI 2015. The same Hofstede indicators stay significant in all cases. The significance level was also same in two times out of three. Only one result changed from p
