CROSS-NATIONAL DISTANCE AND THE AFFECT OF SOCIAL INEQUALITY:
AN INSTITUTIONAL APPROACH
(Wilkinson & Pickett, 2010)
University of Amsterdam – FEB Student: Jerel Jarvis
Supervisor: Dr. Ilir Haxhi Student Number: 10521046 Second Reader: Dr. Johan Lindeque Masters of Business Studies Final Version: 03/03/2014 Track: International
Management
March 2014
Abstract
In previous literature, comparison of social problems and how they directly connect to social inequality in a given country have been performed. Seemingly it is better to live in a country like Sweden, then The United States. Countries similar to Sweden, with low income inequality improves the quality of life of its people; as citizens in more equal societies live longer, have better mental health, and improved chances to a better education regardless of their background. Studies show it is not the rich societies that tend to do better, but the equal ones, in terms of social and health indicators. However, despite our review of previous literature, the process shows not enough systematic empirical and theoretical examination of social inequality,
especially how institutions, organizations or countries contribute to, or affect
inequality. Social inequity affects both developed and developing countries, but how can we test social welfare effectively, efficiently, and globally? We argue out of nine cross-national institutional distances (economic-, financial-, political-, administrative-, cultural-administrative-, demographic-administrative-, knowledge-administrative-, global connectedness-administrative-, and geographic
distance), six distances help to provide greater insight on the affects of social
inequalities. No pervious literature has married these two concepts before; therefore, we pose the question, how does cross-national distance affect social inequality? To test this research question, we analyze archived data theoretically using a unified framework to outline the dependent variable of the social welfare function and apply an empirical approach by using the six dimensions of cross-national institutional distance (economic-, political-, cultural-, knowledge-, global connectedness-, and geographic distance) as our independent variables. Gathering a sample of 50 host country distance dimensions, we took an average of the years from 2008 to 2011. From these countries, we selected six additional home countries for further analysis: France, Germany, Japan, Sweden, The United Kingdom, and The United States of America. We based this section on the variances in perceived social inequality the nations have. We found: economic-, knowledge-, and global connectedness distances affect social welfare the most, meaning social welfare is affected by some aspects of cross-national distance. Therefore, our contribution to this study is we provide further systematic empirical and theoretical literature with information on how countries can alleviate social inequalities. This is also relevant for managers, where the more
money businesses make, the more concern and contribution to social welfare prevails. Keywords: Social Welfare, Social Welfare Function, Social Inequality,
March 2014 TABLE OF CONTENTS
1. INTRODUCTION 6
2. LITERATURE REVIEW 9
2.1 Social Inequality and Social Welfare
Figure 1: Difference between Social Inequality and Social Welfare
2.1.1 The Social Welfare Function (SWF) 2.2 Location Determinants of Social Inequalities 2.2.1 Economic Distance 2.2.2 Financial Distance 2.2.3 Political Distance 2.2.4 Administrative Distance 2.2.5 Cultural Distance 2.2.6 Demographic Distance 2.2.7 Knowledge Distance
2.2.8 Global Connectives Distance 2.2.9 Geographical Distance 2.4 Conceptual Model
Figure 2: Research Model 3. METHOD
3.1 Data Collection
Table 1: Constructs, variables, and sources 3.2 Variables 3.3.1 Dependent Variable 3.3.2 Independent Variable 3.2.2.1. Economic Distance 3.2.2.2. Political Distance 3.2.2.3 Cultural Distance 3.2.2.4 Knowledge Distance
3.2.2.5 Global Connectedness Distance 3.2.2.6 Geographic Distance
3.3 Method
Table 2: Stepwise Regression on Social Welfare Function/Cross-National Distance Variables 4. RESULTS AND ANALYSIS
Table 3: Collinearly Statistics -Dependent Variable: Social Welfare Function
4.1 Results Factor Analysis
Table 4: Principle Component Factor Analysis 4.1.1 Economic Distance
4.1.2 Political Distance 4.1.3 Cultural Distance 4.1.4 Knowledge Distance
4.1.5 Global Connectedness Distance 4.1.6 Geographical Distance
4.2 Results Regression Analysis
Table 5: Correlation matrix of Social Welfare and Cross-
9 10 10 11 11 12 12 13 13 14 14 15 15 15 16 20 20 21 23 23 23 23 24 24 25 25 25 26 26 26 27 27 28 28 28 29 29 29 29 29 30
March 2014 Institutional Distance Variables
4.3 Results of Country Selection 4.3.1 France
Table 6: France -Dependent Variable: Social Welfare Function
4.3.2 Germany 4.3.3 Japan 4.3.4 Sweden
4.3.5 The United Kingdom
4.3.6 The United States of America 5. DISCUSSION
Figure 3: Supported Research Model 6. LIMITATIONS 7. FURTHER RESEARCH 8. CONCLUSION References Appendix
Table 7: List of 50 Countries
Table 8: Germany – Dependent Variable: Social Welfare Table 9: Japan - Dependent Variable: Social Welfare Table 10: Sweden - Dependent Variable: Social Welfare Table 11: The United Kingdom - Dependent Variable: Social Welfare
Table 12: The United States of America - Dependent Variable: Social Welfare 30 31 31 32 32 32 33 33 34 34 36 37 38 40 55 55 56 57 58 59 60
March 2014 Acknowledgements
We would dearly like to thank our supervisor, Dr. Ilir Haxhi, for his continual help, support, and encouragement throughout this FUN process! Secondly, we would like to thank Dr. Johan Lindeque, who was another teacher inspiring us in the
International Management track through such dedication and enthusiasm in his teachings. Special thanks goes out to our fellow classmates: it was a great team effort to keep each other motivated. Last but not least, major gratitude goes out to our friends and family for all the love and support: we could not have done it without you!
March 2014 1. INTRODUCTION
In the last decade, an interesting phenomenon surrounds the debate of social inequalities in relation to social problems. Social inequalities are proving to be of greater issue, for instance in helping businesses make more money, which in turn increases social welfare. However, an accumulating group of researchers contradict this claim. For example, Baurnard’s (2010) states, it is “better to live in Sweden than the US” due to the equality of societies: equal societies, like Sweden, tend to do better than unequal societies, like the United States (US). Countries similar to Sweden, with low income inequality improves the quality of life of its people; as citizens in more equal societies live longer, have better mental health, and improved chances to a good education regardless of their background. Rodgers (1979) found the average life expectancy for a fairly egalitarian and fairly in-egalitarian country can be as much as five to ten years. Additionally, the community life becomes stronger where the income gap is narrow; children perform better at school and are less likely to become teenage parents. With reduced inequality, people distill more trust in each other, creating less violence and lower rates of imprisonment (The Equity Trust, 2013a).
Many of these theories are based off of the seminal novel, “The Spirit Level”, by Wilkinson and Pickett (2009), where they focus their research on rich developed countries marked democratic. Wilkinson and Pickett (2009) compare an array of social problems and state how they directly connected to a scale of social inequality in a given country. More specifically, it is not the rich societies that tend to do better, it is the equal ones, in terms of social and health indicators (Wilkinson & Pickett, 2009). From an economic perspective, the higher income inequality leads to lowered social cohesion, which in turn produces poorer health statuses and social welfare issues (Coburn, 2000). Political issues take place where Anglo-Saxon countries, like the United Kingdom, or the United States, have weaker commitment to redistribution policies and therefore higher social inequality issues (Navarro & Shi, 2001). Or from a knowledge perspective, the patent systems encourages innovation over time, benefitting all people and thus also improving social welfare, as social welfare is usually higher in non-patent states (Bergin, 2011).
Despite our review of previous literature, the process shows not enough systematic empirical and theoretical examination of social inequality, especially how
institutions, organizations or countries contribute to, or affect inequality (Third International Conference on Institutional Work, 2013). Social inequity affects both developed and developing countries, but how can we test social welfare effectively, efficiently, and globally?
Based on the literature, we see a gap. We believe distances affects social inequalities because different countries may be distant from each other in more ways than just a geographical sense, but also in an economic, cultural, or political sense. Suitably, Berry et al.’s (2010) supply nine location determinants or dimensions, which could be applied to help aid in justifying social inequalities. These location determinants are: 1) economic, 2) financial, 3) political, 4) administrative, 5) cultural, 6) demographic, 7) knowledge, 8) global connectedness, and 9) geographical (Berry et al., 2010). Through using Berry et al.’s (2010) cross-national institutional distances theorizing approach, we can eliminate limitations as this process encompasses a rich variety
March 2014 amongst countries and how they differ from one another (Jackson & Deeg, 2008,
Pajunen, 2008). Moreover, in international business, researchers have used cross-national distance as a main explanatory variable for years (Werner, 2002). It is important we have different dimensions to define and measure cross-national distances to enhance the understanding of how diverse types of distances have a positive or negative effect in relation to a country’s social welfare.
Therefore, with the proficient background information from the previous literature outlining the direct relationship between social problems and social inequalities, and Berry et al.’s (2010) nine dimensions of distance, all come together and help us outline a method of assessing social welfare efficiently, effectively, and globally. The main goal of our study is to determine what institutional distances affect the Social Welfare Function (SWF): the measure of a society’s overall welfare calculated as the product of GDP per capita. Social welfare is the well being of the entire society. It is mainly concerned with how total income is divided among different individuals. This study further explores the increasing problem of inequality and social welfare
growing in different countries. We aim to fill the gap by attempting to relate cross-national distance, where social inequality will be researched by the most prominent proportions taken from Berry et al.’s (2010) nine dimensions of cultural distance: economic-, political-, cultural-, knowledge-, global connectedness-, and geographical distance. We plan to evaluate the individual distance between 6 different home countries (France, Germany, Japan, Sweden, The United Kingdom, and The United States) tested against 49 other host countries (Table 2) in response to their social inequalities. In completing this investigation, we created the following research question:
RQ How does cross-national distance affects social inequality through an
institutional approach?
To test this research question, we analyze archived data theoretically using a unified framework to outline the dependent variable of the SWF and apply an empirical approach by using the nine dimensions of cross-national distance from Berry et al. (2010) as our independent variables. We accomplish this through collecting data from an average of 50 host countries (Table 7) from the years 2008 to 2011; attempting to study the affects of social inequality through an institutional approach.
What do the dimensions from Berry et al. (2010) mean and what are the variables they are composed of? Economic distance compares economic development and macroeconomic characteristics and is composed of income, inflation, exports, and imports variables (Berry et al., 2010, Caves, 1996). Strong financial institutions in a country indicate money within the nation, redistributed and re-dispersed to equate societies and aid economic growth. Political institutions are the ones mainly responsible and have the power in establishing and resolving issues of social
inequalities in their societies. Administrative institutions look into the differences in colonial ties, language, religion, and legal systems. Culture is the extent to which shared norms and values in one country are different in another (Hofstede, 1984). It has shown to have an effect on social inequalities (Szlendak & Karwacki, 2013). Countries differ widely from each other in demographics (life expectancy, birth rate, size, and structure). The knowledge dimension in a country is all about differences in patients and scientific production (Guler & Guillen, 2010). Knowledge represents the
March 2014 changes in patents and scientific production, like “innovations previously established, education systems, and linkages between educational institutions and firms” (Berry et al., 2010). The location determinant of global connectedness encompasses the
openness of a country to the rest of the world. This would be the differences in tourism and internet usage (Berry et al., 2010). The geographic dimension takes physical distance into account. This can be measured through longitude and latitude (Chen, 2004). Additionally, according to Anderson (1979) and Deadorff (1998), it has been documented in having an influence on trade, foreign investment, and supplementary forms of economic activity that transpire between countries. Thereafter, we choose to focus on the most prominent cross-national distance
dimensions to be: economic-, political-, cultural-, knowledge-, global connectedness-, and geographical distance; as we think these distances help describe the affects of social welfare. We will be using the Berry et al.’s (2010) gravity model method to get a good outline of the spatial relationship amongst countries, and it allows us to test the affects of the cross-national institutional distances on the SWF. Limitations can also be eliminated as we made this process encompasses a rich diversity amongst countries and how they differ from one another (Jackson & Deeg, 2008, Pajunen, 2008). It is critical we have different dimensions to define and measure cross-national distances to comprehend how different types of distances have a positive or negative effect in relation to its social welfare. Through being methodological, we can study a more direct comparison between national differences and social welfare.
The first step in employing the traditional gravity model (Anderson, 2011) is to conduct a principal component analyses (PCA): to reduce data, to help prevent severe correlations, and to uncover the greatest suitable sub-variable would best represent the distance in subject. In order to eliminate bias, we collected all potential variables for the distance dimensions. Conversely, we needed to compile the data needed into one variable, as every independent distance dimension may have started with one to four variables that could represent it. The second step of the traditional gravity model is to apply hierarchical linear regression analysis. Therefore, we select six home countries: France, Germany, Japan, Sweden, The United Kingdom, and The United States of America. Then, we put them under extended analysis, where six hierarchical linear regression analyses will take place to see what effects the distances (economic-, political-(economic-, cultural-(economic-, knowledge-(economic-, global connectedness-(economic-, and geographical distance) have on the SWF of the participating countries. The dependent variable is computed by the difference in the SWF between one of the selected home countries and the host countries (for each of the 49 other host countries). The independent variable distance dimensions are computed by the difference between the home country’s constants and the host country’s constants.
In our results we provide a deeper understanding and analysis to how social welfare can be successfully evaluated through cross-national distance, and decisively provide further systematic empirical and theoretical literature with information on how countries can alleviate social inequalities. Categorically, we are affording to current social inequality literature by trying to find a relationship between the SWF on the cross-national institutional distances outlined by Berry et al. (2010). Our outcomes provide greater insight into how cross-national distance can affect social welfare. Conclusively, no other study is available of this kind: this study contributes to the existing body of literature because this comprehensive cross-national institutional
March 2014 approach has not yet been used to explain social inequalities and social welfare. Plus our added and extensive addition of observing 50 host countries globally and
quantitatively is of pronounced contribution.
We plan to complete this study providing and supplying sufficient background knowledge through looking at pervious literature. Then we move into forming hypotheses. Followed by the method of how we go about performing this study. We end with a discussion of our results and supply any limitations. Accompanied by future research ideas and our conclusions.
2. LITERATURE REVIEW
In order to properly access the influence cross-national distance on social inequality and social welfare, we think it is important to give a general review and the
distinction of the two concepts, along with the definition and explanation of the Social Welfare Function (SWF). We follow with a brief background suggesting the strong correlation between inequality, health, and social problems outlined by
Wilkinson & Pickett (2009). Next, we deliver a summary of the location determinants of social inequalities, describing the definitions and literature backgrounds to the nine dimensions of cultural distance from Berry et al. (2010): 1) economic, 2) financial, 3) political, 4) administrative, 5) culture, 6) demographic, 7) knowledge, 8) global connectedness, and 9) geographical. Then, we outline the gap in the literature and examine the research question. Finally, we provide a conceptual model clearly and visually outlining this study concludes.
2.1 Social Inequality and Social Welfare
Social inequality deals with the “inequality in access to health care, education, housing, food, economic resources, power structures, or areas of recreation;
degradation of living conditions, the environment, social structures, or relationships; and direct or indirect exploitation of groups on the basis of gender, race, ethnicity, nationality, socio-economic status, disability, or sexuality” (Third International Conference on Institutional Work, 2013). Social welfare has been around as long as humanity itself. The definition and scope of social welfare may vary from country to country, due to the historical development and evolution of administrative
organizations and structure. To set a basis, social welfare is the well being of the entire society. It can comprise quality of life, which is comprised of factors such as the quality of the environment (air, soil, water), level of crime, extent of drug abuse, availability of essential social services, as well as religious and spiritual aspects of life. This social welfare analysis is mainly concerned with how total income is divided among different individuals (Bellu, 2006). Social welfare encompasses that amount of total income available in a society (otherwise equivalently, the mean level of income). Additionally, it takes into account the degree of inequality.
We specify a clear and simple distinction between the social inequality measurement and social welfare beneath in Figure 1.
March 2014 Figure 1: Difference between Social Inequality and Social Welfare
Social Inequality Measurement Social Welfare
• Main concern: how total income is divided among different individuals
• Factors in: nothing associated with mean income
• Requires: only inequality measures
• Main concern: how total income is divided among different individuals
• Factors in: the mean level of income and degree of inequality • Requires: the SWF and indexes
measuring the level of welfare embodied in a given income distribution
(Bellu, 2006) 2.1.1 The Social Welfare Function (SWF)
Economically individualistic in form and originally discovered by Amartya Sen in 1973 (Sen & Hoffmann, 1973), Champernowne and Cowell (1998) describe social welfare through the means of the “Social Welfare Function” (SWF). This is a real-value function; symmetrically comprehensible and consistent in the ordering of social states in relation to their desirability of alternative allocations of health (Maestad & Norheim, 2012): it ranks conceivable social states from lowest to highest. It is believed standard the SWF equates “greater equality of health outcomes is always desirable” (Maestad & Norheim, 2012). Champernowne and Cowell (1998) use the term ‘social’ as it relates to an entire community under consideration. In defining the health profile, any variables considered affecting the economic welfare of society are contributions to the function in regards to the allocation of health among the members of society. It could be described in a vector (hi, …., hn); where h is the health of i, the individual, and n is the number of individuals. A SWF “ranks alternative health profiles in terms of their social desirability" (Maestad & Norheim, 2012).
Historically, two major distinct but comparative forms of the SWF are present: 1) Bergson (1938) and Smauelson (1947) created the traditional SWF, which takes welfare as a given set of individual preferences or welfare rankings; and 2) Arrow’s SWF, shows welfare through different possible sets of individual preferences or welfare rankings and with outwardly rational axioms constraining the function. Following them, a more classical formation of the SWF was created, where causes of individual welfare (like income and health) have been frequently used as arguments of the SWF. Dolan (1998) was one to realize a health related SWF uses health as its arguments.
This formula lets the SWF take the following form:
W = W ( u ( x1) , … , u ( x n ) )
It entails both the classical and the applied approaches; where ui = u(xi) embodies the welfare of a individual i, as a function of or a vector of valued goods xi (for example, like income health). Conclusively, as a health related WF, health becomes the direct
March 2014
source of social welfare (technically, hi replaces u(xi) as arguments in the social welfare function)(Maestad & Norheim, 2012).
This study’s focus on the cardinal SWF, as we calculate social welfare at country level, not individual level. Therefore, we use the SWF from Sen & Hoffmann (1973), emphasizing total incomes and helping to outline prospective patterns of combined choice compared to alternative social states; discussed further in 3.2 of the methods section.
2.2 Location Determinants of Social Inequalities
Many scholars have recognized the multi-dimensions of national distance. Some conclusive evidence is countries may be distant from each other not only in an economic sense, but also by geographic, cultural, social, or political differences (Dunning 1993). Empirical work on cross-national distance in relation to culture has been looked at, mainly by using Hostede’s (1980) cultural distance data in their empirical analysis. These are authors like: Kogut and Singh (1988); Barkema et al. (1996); Hennart and Larimo (1998). Ghemawat (2001) attempted to broaden the study of cross-national distance by considering cultural, administrative, geographic, and economic distance. Though a gap had fallen where no other previous scholars took into account the financial, political, demographic, or global connectedness of distances. Only recently has this been done with the guidance provision on how to measure all of the intuitional distance dimensions (Barry et al., 2010).
From an institutional perspective, we found Berry et al.’s (2010) nine dimensions of distance create a broader perspective of other dimensions having an influence on social welfare. These dimensions are: economic-, financial-, political-,
administrative-, cultural-, demographic-, knowledge-e, global connectedness-, and geographical distances. Below we have a brief description of each dimension: 2.2.1 Economic Distance
Economic distance encompasses the difference in economic development and macroeconomic activity. We can capture this dimension by looking at: the GDP per capita (income), GDP deflator (inflation), exports of goods and services (exports), and imports of goods and services (imports). Additionally, it consists of consumer purchasing power and preferences, and the openness of the economy to external influences. Among other variables, economic distance has been found to influence firm survival and performance (Berry et al. 2010, Caves, 1996).
Wilkinson and Pickett (2010) believe in advanced capitalist countries, where higher income inequality leads to lowered social cohesion, which in turn produces poorer health statuses and social welfare issues (Coburn, 2000). However, Wilkinson and Pickett (2010) clearly emphasize in their research economic distance (income: GDP per capita) shows no correlation between social inequality and public health because the average well-being of societies are no longer dependent on national income and economic growth. This is more important in poorer countries, but not in the rich developed world. Conversely, we believe economic distance to be highly important
March 2014 and relevant towards our study, as this dimension is the closest in relation to the
SWF, in they both deal with income (GDP per capita) in their equations. 2.2.2 Financial Distance
Countries have evolved over time varying in different financial systems; all of which have diverse implications for the method in that businesses and their competitors fund their operations (La Porta et al., 1998, Whitley, 1992). Financial systems affect both the ability of the state to support and guide industrial development and the nature of firms’ strategic choices and risk management (Zysman, 1983). Financial distance can be measured by using three variables: private credit (domestic credit to private sector in percentage of GDP), stock market capitalization (market capitalization of listed companies in percentage of GDP), and listed companies (number of listed companies per 1 million population) (Berglof, 1998, Berry et al., 2010, La Porta et al., 1998, Steinherr & Huveneers, 1994).
We believe a strong established and evolved financial system plays a role in affecting social inequalities. A highly developed and established financial system indicates countries can, if not already, take the proper steps to relieve issues of social inequalities; showing the relevance of the financial distance towards our study. However, each country operates under a different financial system, and unless the financial systems are the same and the distances between the countries are closer, the lower the SWF.
2.2.3 Political Distance
Dealing with changes in political stability, democracy, and trade bloc membership; political distance currently emphasizes national distance within countries is strongly related to political distance (Jackson, 2001)(Roe, 1994). This is due to national political system differences (Whitley, 1992). Political differences have been regarded by previous scholars in mostly dichotomous terms ,emphasizing the difference
between democratic and autocratic regimes (Berry et al., 2010). Furthermore, national diversity is thought to reflect various institutional constraints coming from coercive political regulation, isomorphism of cognitive models in response to uncertainty (Dobbin, 1994), or other normative pressures to establish legitimacy (Biggart, 1991, Hamilton & Biggart, 1988).
This form of distance can be calculated through grouping countries along continuous political dimensions, like institutional checks and balances (Demirbag et al., 2007, Dow & Karunaratna, 2006, Heinz, 2000), democratic character, the size of the state relative to the economy, and external trade associations (Brewer, 2007, Cingranelli & Richards, 2008, Hirschberg et al., 1994).
We believe political distance affects social inequality as it has a lot of say in how wealth is distributed. For instance, a country being left-wing or right-wing, determines how the political party in government influences a country’s level of social inequalities (Navarro & Shi, 2001). The later (right-wing being individualists against social welfare (where social welfare is undesirable and economically
damaging), the former being collectivists for welfare and public provisions. The main political positions for countries being for social welfare would be: Marxism,
March 2014 Conservatism, Liberal individualism, Socialism, Social democracy, and Fascism. If the political environment is unstable, greater issues of social inequity result. The relevance of political distance with respect to our study is to see whether political distance has an impact on social welfare.
2.2.4 Administrative Distance
Administrative distance deals with the differences in bureaucratic patterns due to colonial ties, language, religion, and the legal system (Ghemawat 2001, Henisz 2000, La Porta et al., 1998, Whitley 1992). Administrative distance has been measured by determining whether countries share a common language (Wolf & Weinschrott, 1973), a common legal system (Guillén & Suárez, 2005, La Porta et al., 1998), and whether they have had a colonial relationship (Bröcker & Rohweder, 1990). Associated with both cultural and political distance; administrative distance goes beyond national political systems and includes both formal and informal institutional arrangements transcend the purely political nature of the nation-state (Berry et al., 2010).
If a country were to share a common legal system, or colonial relationship, we theorize countries with common-law legal systems would most likely band together in the case of an emergency; and if one country were to be suffering miserably from a lack of social welfare, another country is more likely to help with aid directly.
2.2.5 Cultural Distance
This dimension of distance according to Ghemawat (2001) and Whitley (1992), deal with culture as a relevant dimension of cross-national comparison. It can be described as the extent to which shared norms and values in one country differ from those in another (Hofstede, 1984). Referring to the complex meanings, symbols and
assumptions about good versus bad, legitimate versus illegitimate; culture underlines’ the practices and norms in society (Licht et al., 2005). It has also been described as collective programing of the mind, distinguishing its members from one human group to another (Hofstede, 2003).
The most widely used dimensions of culture, measuring cultural distance, are those from Hofstede (1980)(Kirkman et al., 2006). He created five variables calculating the cultural distance between country A and B. These five key distinguishing aspects of national culture are: power distance, uncertainty avoidance, individualism vs. collectivism, masculinity vs. femininity, and long-term orientation. Individualism is the degree in which individuals are integrated into groups. Uncertainty avoidance deals with a society’s tolerance for uncertainty or ambiguity. Power distance is the extent to which the less powerful members of organizations and institutions accept and expect power to be distributed unequally. Masculinity refers to the distribution of roles between the genders (Hofstede, 1997). In using Hofstede’s approach, cultural distance has shown to impact the foreign expansion of the firm (Barkema et al., 1996, Hennart & Larimo, 1998, Johanson & Vahlne, 1977, Kogut & Singh, 1988, Werner, 2002). His approach gives contribution towards providing a set of cultural indicators for a large same of countries (Hofstede, 2003). In addition, Kogut and Singh (1998)
March 2014 extended Hofstede’s study by calculating the distance with the given variables from Hofstede’s study.
We believe cultural distance is of relevance to our study, as people share the same cultural system are more likely to work together in an efficient way and understand one another, helping to decrease social inequalities because of shared norms and values. Consequently, we hope to see if this dimension shows great relevance to our study.
2.2.6 Demographic Distance
This dimension is notified as a key dimension of cross-national distance (Whitley, 1992). Countries can differ widely in terms of size, growth, age structure, and qualities of their populations, etc. These listed dimensions have direct implications for market attractiveness and growth potential. Already research has been done in the international business area, using demographic variables to study patterns of
international corporate expansion and share prices (Caves, 1996, Huynh et al., 2006). It can be measured by using the variation in life expectancy rates, birth rates, and the age structure of population (Berry et al., 2010). However, the data is only available for most countries every few years. Especially when a population census is conducted (United Nations, 2006). We hope this dimension has relevance to our study by seeing if demographic distance plays a role in the SWF.
2.2.7 Knowledge Distance
This dimension captures country related differences in terms of its capacity to create knowledge and to innovate, with important implications for their role in the global economy (Furman et al., 2002, Nelson & Rosenberg, 1993). It has been argued and evidence has been noted, knowledge influences the location choice of multinational firms (Anand & Kogut, 1997, Berry, 2006, Guler & Guillen 2010). For instance, distance between two countries could be affected by talent, innovation, and creativity are known to be unevenly distributed across locations (Florida, 2002). Additional literature has recommended this distance dimension can influence the location choice of multinational firms because of its potential for spillovers (Anand & Kogut, 1997, Berry, 2006, Guler & Guillen, 2010, Nachum et al., 2008, Shaver & Flyer, 2000). Knowledge distance can be calculated by using measures like the number of patents and scientific articles per capita (Furnman et al., 2002, Berry et al., 2010, Nelson & Rosenberg, 1993). A patent system encourages innovation over time, benefitting all people and thus also improving social welfare, as social welfare is usually higher in non-patent states (Bergin, 2011).
The level of innovation, and openness to the world in a country is of relevance to this study because it may shed light on the level of social welfare. Countries with a high SWF are predicted to be less innovative, knowledgeable, and open to the rest of the world. Consequently, when a country is facing social welfare issues, the country is not in search of knowledge. If social welfare were to weigh down a country,
knowledge could be a dominant factor in trying to relieve the issue. We hope to see knowledge distance shows great relevance to our study, having an affect on the SWF.
March 2014 2.2.8 Global Connectedness Distance
Global connectedness distance deals with the connectedness of a country in relation to the rest of the world. It captures the ability of resident individuals and companies to interact globally, obtaining information, and diffusing a country’s own activities (Oxley & Yeung, 2001). To calculate this measure of distance is done through measuring the international tourism expenditures as a percentage of GDP, and internet users as a percentage of population (Berry et al., 2010, Guillen & Suarez, 2005).
We believe this distance dimension shows relevance and is predicted to be majorly affected by social welfare, as the goal of any nation seeks to maximize the social utility or social welfare subjected to whatever technological or resource constraints are relevant. Perdue et al. (1991) uncovered per capita income and quality of
available health care facilities to increase with increasing levels of tourism. Urtasun and Guiterrez (2006) also observed this pattern in overall social welfare terms, where tourism impacts are positive in regions with low levels of development in economic activities other than tourism, though negative in regions with a high level of non-tourism economic development. Therefore, the countries with a low SWF are more globally connected, having clearer communication channels, which help monitor social inequalities. Of relevance to this study is to see whether global connectedness distance has affect on the SWF.
2.2.9 Geographical Distance
Gravity models have been used by scholars in the international business and
international trade literatures, which has long been recognized in the important role geographical distance plays (Fratianni & Oh, 2009, Hamilton & Winters, 1992, Wolf & Weinschrott, 1973). This form of distance is important to have in our study as it has negative impact upon trade, foreign investment, and other types of economic activity taking place between countries (Anderson, 1979, Deadorff, 1998), where a choice between non-institutional dimensions as an independent variable are present. If geographical distance increases, it concludes an increase in costs for transportation and communication (Berry et al., 2010).
Different ways geographical distance has been calculated between pairs of countries has been taking the latitude and longitude of the main city in each region (Chen, 2004), in using the direct line distance (Krishna, 2003). Another way is by using the great circle method: the distance between two countries with regards to the
coordinates of the geographic center of the countries (Berry et al., 2010). Using the great circle method, we see if an influence of geographical distance on social inequality exists and whether it has an affect on the SWF.
2.4 Conceptual Model
Conclusively, from the literature gap we see previous studies are limited in the cross-national institutional dimensions they have used. This problem can be fixed with the approach of Berry et al. (2010), as their method encompasses the rich variations countries can vary from each other. We found the main drawback of Berry et al.’s (2
March 2014 010) model; it was not very applicable in rough times. However, using this model disaggregates the construct of distance by proposing a set of multidimensional measures can be easily accounted for. Thus, we need a new conceptual model, which can be seen in Figure 2 below. In this figure we can see the potential possible relationship between our independent variables (dimension) and its affect on the SWF.
Figure 2: Research Model
Independent Variables Dependent Variable
Economic Distance
Political Distance
Cultural Distance Social Welfare (SWF)
Knowledge Distance Global Connectedness Distance H1 H2 H3 H6 H5 H4
March 2014 Geographical Distance
We apply this model in finding the relationships between the SWF and the
differences of the institutional constructs. The relationship can be either positive or negative. Coming from a positive perspective, we would expect to see greater institutional distances, like economic-, and cultural-, and geographic distances to induce greater differences toward the SWF. From a compensational perspective, the greater institutional distances, like political distance, prompts smaller differences towards the SWF. In our case, high between country similarities towards the SWF is necessary to compensate for differences in institutional distances.
Given the conceptual model, we have formulated six different hypotheses. The first hypothesis addresses economic distance, where the relationship between cross-national economic distance and social welfare will be tested. Previous literature suggests economic distance is the “difference in economic development and
macroeconomic characteristics” (Berry et al., 2010). It encompasses the dimensions of: income, inflation, exports and imports. Income will most likely be the variable representing this dimension in the component factor analysis: we believe economic distance is a driver for the SWF, as income (GDP per capita) is additionally used in the calculation of the SWF (discussed more in detail in the data section (3.2)). We personally think a highly positive effect dominates the other constants. This is to say lower income correlates with social welfare: the greater the economic distance, the greater the social welfare.
For example, advanced capitalist countries, like the United States, have higher income inequality which leads to lowered social cohesion, thus in turn produces poorer health statuses and social welfare issues (Coburn, 2000). Therefore, we argue:
H1 Economic distance has a positive effect on the SWF.
The second hypothesis deals with political distance, where the relationship between cross-national political distance and social welfare will be tested. Previous literature suggests political distance is the difference in political stability, democracy, and the trade bloc membership (Berry et al., 2010). It consists of: policy-making uncertainty, democracy score, size of the state, world trade agreements, and regional trade
agreements. Democracies character (democracy score) or size of state (government consumption (% of GDP)) are most likely to be variables representing this dimension in the component factor.
We believe political distance is a driver for the SWF because a country’s political stance is an important influence in how they deal with their level of social
inequalities. We think the political distance will have a negative effect, as most countries are ruled democratically, rather than socially.
March 2014 For example, an Anglo-saxon country, like the United Kingdom, or the United States, who have weaker commitment to redistribution policies, have higher social inequality issues (Navarro & Shi, 2001). Thus, we foresee:
H2 Political distance has a negative effect on theg SWF.
The third hypothesis is cultural distance, with the relationship between cross-national cultural distance and social welfare will be tested. Previous literature suggests
cultural distance is the difference in attitude toward authority, trust, individuality, and the importance of work and family (Berry et al., 2010). It consists of Hofstede’s (1984) four dimensions of distance: power distance, uncertainty avoidance, individualism, and masculinity. Power distance or uncertainty avoidance are most likely to be the variables representing this dimension in the component factor analysis as the Hofstede Dimensions can also be found to correlate with other data about the
countries in question. For instance, power distance naturally correlates with income
inequality, and uncertainty avoidance is associated with the legal obligation in
developed countries for citizens to carry identity cards. This is consistent with our
first two hypotheses concerning economic and political distance.
We believe cultural distance is a driver for the SWF because shared norms and values in one country about social inequalities are going to differ from those in another. Hence, we believe either power distance or uncertainty avoidance from Hofstede’s (1984) four dimensions of distance will have a positive effect on the SWF. We thus posit the greater the social welfare, the greater the power distance and/or uncertainty avoidance.
For example, the country Sweden has a power distance of 22, while France has a power distance of 61. Sweden has a very low power distance, where the country focuses on equal rights and power is decentralized. Whereas the power distance in France is fairly high, and therefore a society with a reasonable degree of inequality accepted. Accordingly, the greater the power distance, the greater the social
inequality. Thus, we contend:
H3 Cultural distance has a positive effect on the SWF.
The fourth hypothesis addresses knowledge distance, where the relationship between cross-national knowledge distance and social welfare will be tested. Previous
literature is saying knowledge distance is the difference in patents and scientific production. It consists of patents and/or scientific articles (Berry et al, 2010). Patents are most likely to be the variable will represent this dimension in the component factor analysis, as it may be easier to acquire the necessary information, compared to finding the research on scientific articles.
We believe knowledge distance is a driver for the SWF because the more knowledge and innovation a country has, the less social welfare occurs. Thus, we personally think knowledge distance will have a negative effect on SWF.
For example, the bigger countries like Australia, Canada, and the United States have huge patent systems which encourages innovation over time. Thus, it benefits all
March 2014 people in those countries and also improving social welfare, as social welfare is
usually higher in non-patent states (Bergin, 2011). Therefore, we predict:
H4 Knowledge distance has a negative effect on the SWF
The fifth hypothesis centers around global distance, where the relationship between cross-national global distance and social welfare will be tested. Previous literature is saying global connectedness is the difference in tourism and Internet use. It consists of: international tourism expenditure, international tourism receipts, and Internet use (Berry et al., 2010). International tourism expenditure is most likely to be the variable that will represent this dimension in the component factor analysis.
We believe global connectedness distance is a driver for the SWF because the more globally connected a country is, the more aware everyone can be aware of its’ social inequalities. Therefore, we think global connectedness will have a negative effect on the SWF. Hence, the greater the global connectedness, the lower the social welfare. For example, a country like Spain has high tourism. The tourism impacts are positive in the country’s regions with low levels of development in economic activities other than tourism, though negative in their regions with a high level of non-tourism economic development (Urtasun & Guiterrez, 2006). Thus, we anticipate:
H5 Global connectedness distance has a negative effect on the SWF.
The last and sixth hypothesis is geographical distance, where the relationship between cross-national geographical distance and social welfare will be tested. Previous literature is saying geographical distance uses the great circle distance to evaluate the different geographical centers between countries according to the coordinates. We believe geographical distance is a driver of the SWF because the more countries are globally connected; the more social inequality issues are brought to the surface and can be better evaluated. Thus, we personally think geographical distance will have a positive effect on the SWF: the greater the geographical distance, the greater the social inequalities.
An example of this would Canada and the United States: because they boarder one another; the better trade, foreign investment, and other types of activities take place (Berry et al., 2010), lowering social welfare between both countries. Thus, we antedate:
H6 Geographical distance has a positive effect on the SWF.
This study aims to compare cross-national distance to differences between nations SWF by: theoretically providing unified frameworks help to better explain social welfare amongst different countries; empirically and simultaneously paying attention to multiple dimensions of cross-national distance; and methodology allowing more direct comparisons between national differences and the SWF. It can be expected the greater the cross-institutional distances (such as economic-, political-, and knowledge distances) induces greater differences in the characteristics of social welfare amongst the fifty countries (Table 7).
March 2014 3. METHOD
Above, we discussed the literature review and literature gap. From this point on, we use the development of a separate conceptual model using Berry et al. (2010) as the foundation. In addition to the theory of dimensions by Berry et al. (2010), we have added a new perspective, namely the Social Welfare Function (SWF) and its effects on cross-national distance.
3.1 Data Collection
We did an archive study to conduct this research and we obtained them from several secondary databases (Cingranelli and Richards, 2008; CIA Factbook, 2013; Freedom House, 2013; Hofstede, 2001, and 2013; House et al., 2004; Transparency
International, 2013; UPSTO, 2013; WDI, 2013). These secondary data sources we selected because they are efficient and convenient, leaving time for greater analysis and interpretation of the results. Additionally, secondary data is likely to be of higher quality data and can be obtained individually (Stewart & Kamins, 1994). It provides us with a source of data, both permanent and available in a form that may be checked relatively easily by others (Denscombe, 2007). Although collecting individual
secondary data would be done with a specific purpose in mind, in order to answer the research questions and to meet the objectives. Therefore, a slight disadvantage of secondary data, it is initially collected for another specific purpose, with different research questions and objectives (Saunders et al., 2009).
We did this research selecting six home countries for further analysis. These countries were: France, Germany, Japan, Sweden, The Untied Kingdom, and The United States of America. The reason we selected these countries is they have different national business systems and social inequalities; therefore, eliminating the one sidedness of taking countries sharing the same national business systems. Consequently, creating a broad overview of the worldwide SWF and eliminating the bias of targeting one national business system. We found national business system in the particular arrangements of hierarchy-market relations, become institutionalized and relatively successful in one context (Whitney, 2000).
The data collection of the SWF and the institutional distances we collected from 50 host countries (Table 7). We gathered them from the years, 2008-2012, but we found not enough significant data was available for the year 2012; thus, we eliminated it. Also, because we established the study does not focus on a change in time, we took an average of the years 2008-2011. The data we collected is in bilateral form, where country pair A and B has a different SWF than country pair B and A. The main source of information we gathered from the WDI (2013). Data relating to the institutional arrangements we compiled through Berry et al. (2010). Conversely, we compiled the data into one variable, as every independent distance dimension may have started with one to four variables that could represent it. Thus meaning every variable was used to compile their distance having equal weight. Therefore, we eliminate bias through collecting all the potential variables for each distance dimension (Cingranelli and Richards, 2008; CIA Factbook, 2013; Freedom House, 2013; Hofstede, 2001, and 2013; House et al., 2004; Transparency International, 2013; UPSTO, 2013; WDI, 2013). For example, economic distance is composed of the four variables: income, inflation, imports, and exports (see Table 1 below); but
March 2014 will be represented by one or two of those variables depending on their significance. We do this through a principal component analysis (PCA) discussed further down (Section 4.1).
Table 1: Constructs, variables, and sources
Construct Construct Definition Variables Variable Description Source Dependent Variable Social Welfare Function (SWF) The difference between 1 and the society’s or countries gini coefficient (1-G)
Income GDP per capita (current US$)
WDI Gini index Adjustment for
purchasing power parity (1-G) Independent Variables Economic Distance The difference in economic development and macroeconomic characteristics
Income GDP per capita (current US$) WDI Inflation GDP deflator (% of GDP) Exports Exports of goods and services (% of GDP) Imports Imports of goods and services (% of GDP) Political
Distance The difference in perception of corruption and in several aspects democracy Corruption Percentage Index Indexes international transparency Transparency International Democratic
Character Democracy score Freedom House Freedom of Association Extent to which the freedoms of assembly and association are subject to actual government limitations Cingranelli and Richards (2008) Size of the State General government final consumption expenditure (% of GDP) WDI
March 2014 Distance in attitudes towards individuality, authority, distribution of roles between genders, and uncertainty avoidance which individuals are integrated into groups Hofstede (2001) & House et al. (2004) Power
Distance The extent to which the less powerful members of organizations and institutions accept and expect power is distributed unequally Masculinity Refers to the
distribution of roles between the genders Uncertainty Avoidance Measures society’s tolerance for uncertainty and ambiguity Knowledge Distance The differences in patents and scientific production Patents Number of patents per 1 million population UPSTO/WDI Global Connectedness Distance The differences in tourism and internet use International tourism expenditure International tourism, expenditures (% of GDP) WDI International tourism receipts International tourism, receipts (% of GDP)
Internet Use Internet users per 1000 people Geographical
Distance
The great circle distance between geographic center of countries Great circle distance Distance between two countries according to the coordinates of the geographic center of the countries CIA Factbook
March 2014 Subsequently, after we found the representing variables per distance, next came a
hierarchical linear regression (gravity model approach) to calculate the effects, which we discuss further in the methods section below (Section 4.3).
3.2 Variables
3.2.1 Dependent Variables
Social welfare we used as the dependent variable for this study and we described it through the Social Welfare Function (SWF)(Sen & Hoffmann,1973). To be reminded of the definition, it is the measure of a society’s overall welfare calculated as the product of GDP per capita. Additionally, it is the difference between 1 and the society’s or country’s gini coefficient (1-G). It is officially represented as the following:
Where G is the Gini index, a relative inequality measure.
We plan to take the data from the CIA for the N=50 countries listed in Table 7; where the GDP per capita numbers are adjust for purchasing power parity.
In building this SWF for this study, first the income distribution has to be sorted by income levels. Subsequently, a choose value for e, where it corresponds to the choice in the degree of inequality aversion is selected. If we find 50 host countries are less unequally adverse, then we must choose a value close to 0. If we find the host
countries are more inequality adverse, then we must select a relatively higher value of
e. For this study, the value of 0 was chosen, as the list of 50 countries (Table 7) were
less unequally adverse. Lastly, we calculate the formula from above to find the level of social welfare associated to the initial income distribution.
Some basic characteristics follow with the SWF function. It only relies upon
individual incomes and is therefore individualistic. The dependent variable represents the 50 host countries around the world. We collected it by looking at income and living conditions data, which is linked to country characteristics (taken from Eurostat and OECD) including spending on active labor market policies, benefit generosity, income inequality, and employment protection.
3.2.2 Independent Variables
Below we provide a brief description of the independent variables, used and explain how we have derived them; additionally providing clearer insight of the variables: 3.2.2.1 Economic Distance
Economic distance compares economic development and macroeconomic
characteristics, and is composed by income, inflation, exports, and imports variables (Berry et al., 2010, Caves, 1996). These four variables encompass the following: the
March 2014 income variable is calculated using GDP per capita, measured by using 2005 US$;
the inflation rate is constructed by using the GDP as the deflator as a percentage of GDP; exports is measured by exported goods and services as a percentage of GDP; and imports is measured as imported goods and services as a percentage of GDP (Caves, 1996). All the variables, differences are calculated for each dyad resulting in four continuous variables to be modeled. Therefore, the 50 different country
distances, for each of the four variables we process further in the PCA to find which variable(s) has the greatest significance, and then those that do, we use to represent economic distance in the hierarchical linear regressions (gravity model approach). 3.2.2.2 Political Distance
Political distance encompasses the variance in political stability, democracy, and the trade of bloc membership. It can be calculated by characterizing countries along continuous political dimensions. According to Berry et al. (2010) and Cingranelli and Richards (2008), political distance is comprised by: the Corruption Perception Index, Democratic character, Freedom of Association, and the Size of the State variables. Firstly, the Corruption Perception Index variable measures the international
transparency and represents a score between 0 and 10; where 0 is being the highest in corporation and 10 being the lowest or no corruption. The Democratic Character variable represents the democrat score of a country ranked between 0 and 7, zero being free and 7 is not being free. The Freedom of Association variable is opposed to strict legal protections. It indicated the extent to which the freedoms of assembly and association are subjected to actual government limitations or restrictions. Scoring is from a 0 to a 2; where 0 is being totally restricted and 2 is having no restriction. Lastly, the Size of the state variable shows the governmental consumption as a percentage of GDP. Conclusively, for all four variables, differences are calculated for each dyad resulting in semi-continuous variables, except for the Size of state variable, which is continuous. Thus, the 50 different country distances, for each of the three variables we process further in the PCA to find which variable(s) has the greatest significance, and then those that do, we use to represent political distance in the hierarchical linear regressions (gravity model approach).
3.2.2.3 Cultural Distance
Cultural distance can be defined as the extent to which the shared norms and values in one country differ from those in another (Hofstede, 1984). It uses Hofstede’s cultural distance, which represents, at a national level, the difference between a country’s attitude towards individuality, authority, distribution roles between genders, and uncertainty avoidance (Chui & Kwok, 2008, Kwok & Tadesse, 2006, Kirkman et al., 2006). Cultural distance is constructed by: individualism, power distance,
masculinity, and uncertainty avoidance variables. Firstly, the individualism vs. collectivism variable is calculated by using the degree of tendency to which
individuals tend to be integrated into groups. Secondly, the power distance variable uses the degree of tendency to which the less powerful members of organizations and institutions accept and expect power to be distributed unequally among them.
Thirdly, the masculinity variable refers to the distributions of roles between the genders: It reinforces whether society does or does not strengthen the traditional masculine work role model of amyl achievement, which are control and power. Fourthly, the uncertainty avoidance variable measures society’s tolerance for
March 2014 uncertainty and ambiguity. Conclusively, every country receives points between 0
and 100 to indicate the strength of the particular dimension in subject. Zero being the weakest and 100 being the strongest. For all four variables, the differences can be calculated for each dyad resulting in four continuous variables to be modeled. Therefore, the 50 different country distances, for each of the four variables we
process further in the PCA to find which variable(s) has the greatest significance, and then those that do, we use to represent cultural distance in the hierarchical linear regressions (gravity model approach).
3.2.2.4 Knowledge Distance
This type of distance signifies the difference in a country’s capacity to create knowledge and to innovate. The patents and scientific articles variables measure knowledge distance. Firstly, the patents variable captures the number of patents registered per 1 million of the population. Secondly, the scientific articles variable captures the number of scientific articles published per 1 million of the population. Conclusively, we only be using patents for this study, as there was a lack of
information on the number of scientific articles available. Patents’ measures the differences for each dyad resulting in one continuous variable to be modeled. Hence, the 50 different country distances of the number of patents per million will be used and since there is only one variable representing knowledge distance, patents will skip the PCA and go straight to representing knowledge distance in the hierarchical linear regressions (gravity model approach).
3.2.2.5 Global Connectedness Distance
Global Connectedness distance focuses on the connectivity of a country with respect to the rest of the world. Measuring captures the connectedness of a country: the international tourism expenditures, international tourist receipts, and Internet users. The international tourism expenditures variable is calculated by using the tourism expenditures as a percentage of GDP, while the International tourism receipts variable is measured by using the tourism receipts as a percentage of GDP. The Internet user variable captures the number of Internet users per 1000 people. Conclusively, all three variables measure the differences for each dyad resulting in three continuous variables to be modeled. Thus, the 50 different country distances, for each of the three variables we process further in the PCA to find which variable(s) has the greatest significance, and then those that do, we use to represent global
connectedness distance in the hierarchical linear regressions (gravity model approach).
3.2.2.6 Geographic Distance
This last type of distance is the only non-institutional dimension as an independent variable. It represents the geographical distance between pairs of countries. It is comprised by the Great circle distance variable; entailing measuring the distance between geographic centers of countries by using the great circle method (Berry et al., 2010). This variable is continuous. Thus, 50 different country distances will be accounted, and since there is only one variable representing geographical distance, we skip the PCA for the Great circle distance and go straight to representing
March 2014 3.3 Method
In this study we have encompassed the relativity of the national institutional differences and the variances of social inequalities by using the traditional gravity model and logistical regression (Anderson, 2011). Traditionally, the model stands in close relation to Newton’s law of gravitation, explaining the distribution of goods or other factors across space as determined by gravity forces conditional on their size and distance between them. The initial model helped us to explain the migration patterns (Ravenstein, 1889) and trade flows (Tinbergen, 1962). The traditional
gravity model helped us explain a mass of goods, labor, or other factors of production supplied at the origin I, Y, is attracted to a mass of demand for goods or labor at destination j, Ej, but the potential flow is reduced to the distance between them, dij (Anderson, 2011). This creates the following formula:
The variables of this study consist of data is not continuous or semi-continuous. Therefore, the gravity model follows this formula:
Xij = β0 + β1*(Ecoij)+ β2* (Polij) + β3 *(Culij) + β4*(Knoij) + β5*(GloConij) +
Β6* (Geoij) + εij .
Conclusively, Xij is the variable of the SWF from country i to j. The dependent
variables are, in order of occurrence: Ecoij,which represents the economic distance
between country i and j; Polij, which represents the political distance between country
i and j; Culij, which represents the cultural distance between country i and j;Knoij,
which represents the knowledge distance between country i and j; GloConij,which
represents the global connectedness distance between country i and j; and Geoij,
which represents the geographical distance between country i and j. Additionally, β0
represents a constant, and εij symbolizes a distance error term between i and j.
Table 2: Stepwise Regression on Social Welfare Function/Cross-National Distance Variables
Model Income Inflation Freedom of
Association
Power Distance
Individualism Patents International
Tourism Exp. Great Circle Distance Model 1 X X Model 2 X X X Model 3 X X X X X Model 4 X X X X X X Model 5 X X X X X X X Model 6 X X X X X X X X
4. RESULTS AND ANALYSIS
Subsequently, after examining the literature and developing a unique conceptual model, we chose to apply the gravity model. However, we conducted previously preliminary analyses to ensure no violation of the assumptions (VIF) of normality,
March 2014 linearity, multicollinearity, and homoscedasticity. Collinearity diagnostics indicated a problem with high multicolinerarity. The VIF scores showed values above 19 and low tolerance scores seen in Table 3 underneath.
Table 3: Collinearly Statistics -Dependent Variable: Social Welfare Function Standard Coefficients t Collinearly Statistics
Beta Tolerance VIF
Income 1,043 ***27,176 0,156 6,402 Inflation 0,008 0,415 0,594 1,683 Exports -0,047 -0,520 0,028 35,965 Imports -0,004 -0,041 0,030 33,090 CPI -0,092 **-1,973 0,106 9,441 Demographic Character 0,018 0,690 0,330 3,032 Freedom of Association 0,013 0,809 0,863 1,159 Size of State 0,009 0,273 0,209 4,794 PDI -0,004 -0,131 0,307 3,258 IDV 0,016 0,478 0,202 4,950 MAS -0,017 -0,747 0,465 2,152 UAI -0,041 **-1,720 0,414 2,417 Patents -0,020 -0,881 0,438 2,283
International Tourism Exp. -0,006 -0,232 0,294 3,407 International Tourism Rec. -0,009 -0,376 0,375 2,667
Internet Use 0,038 0,751 0,092 10,895
Great Circle Distance -0,020 -1,044 0,600 1,667
Note: Significance is ⌃p<0.1, *p<0,05, **p<0.01, ***p<0,001.
The next step in employing the gravity model, we conducted a factor analysis to represent the variables per distance. In the second step, we applied a hierarchical linear regression analysis.
4.1 Result Factor Analysis
Due to cross-national institutional distances being composed of individual variables, it creates a singularity problem. To eliminate this problem we ran a Principal
Component Analysis (PCA)(Table 4, found underneath). Using PCA was also a variable reduction technique, like the Exploratory Factor Analysis (EFA); as it reduced the number of highly correlated variables, under the construct of the
independent variables (Table 3): it condensed the number of observed variables to a smaller number of principle components that showed which variable accounted for the most of the variance of the observed variables. We were then able to explore whether the indicators conveyed in a meaningful way. We did not use a single PCA (including all the variables), as each cross-national institutional distance (economic-, political-, and global connectedness distance) needed to be tested separately to see which variable was strongest for each dimension of distance to be tested more efficiently for further analysis. If two variables provided to be of significance, than both were used for further analysis. Table 4, found below, can be used to outline this section for the full factor analysis.
