RELIGION AND WORK ETHIC

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Samantha Marinello

May 2011

RELIGION AND WORK ETHIC

Samantha Marinello

May 2011

Mathematical Economics

Abstract

The role of culture in economic activities and outcomes is a subject debated mainly in the fields of sociology, anthropology, and political science. Recently, more economists are applying economic theory to engender new models that incorporate various aspects of culture, including widespread beliefs, values, and attitudes. Adding cultural variables to economic models has the potential to develop a better understanding of consumer choices on a microeconomic level. In addition, beliefs, attitudes, and values have the potential to explain differences in economic policies, growth, and activities on an international level.

This thesis contributes to existing economic literature by 1) constructing a utility function for work ethic that includes religious and demographic variables, and 2) utilizing an Ordinary Least Squares regression with data from the World Values Survey. Controlling for socioeconomic status, income, health, education level, urbanization level, gender, and religious participation across 13 countries, religious denomination is not significant in determining work ethic. However, with the addition of interaction terms between religious denominations and demographic variables, certain religions have a significantly higher or lower work ethic than Protestants. In addition, almost all demographic variables are significant predictors of work ethic.

KEYWORDS: (Religion, Work Ethic, World Values Survey)

TABLE OF CONTENTS

ABSTRACT ACKNOWLEDGEMENTS 1 INTRODUCTION 1 LITERATURE REVIEW…………………………………………………………. 8 1.1 Religion’s Influence on Economic Attitudes and Behaviors……………….. 8 1.2 Economic Attitudes and Behaviors Shaping Economic Growth……………. 13 1.3 Connecting Religion Directly to Economic Growth………………………… 17 2 THEORY 21 2.1 The Model………………………………………………………………...... 22 2.1.1 Budget Constraint………………………………………………...... 23 2.1.2 The Lagrange Function…………………………………………….... 24 2.1.3 First Order Condition………………………………………………... 24 2.1.4 Solutions of C and H………………………………………………… 25 2.2 Why Cobb Douglas?...... 26 3 DATA 28 3.1 Data Problems……………………………………………………………….. 29 3.2 World Values Survey………………………………………………………... 30 3.3 World Development Indicators and the World Income Inequality Database.. 48 4 RESULTS 52 4.1 Problems with the Original Model…………………………………………... 53 4.2 Economics Problems with OLS……………………………………………... 53 4.3 Ordinary Least Squares (OLS) Regression Results…………………………. 53 4.3.1 Observations from Regression without Interaction Terms…………… 54 4.3.2 Observations from Regressions with Interaction Terms……………... 61 4.3.3 Conclusion……………………………………………………………. 63 5 CONCLUSION 64 5.1 Results Consistent With My Hypothesis…………………………………… 65 5.2 Results Inconsistent With my Hypothesis………………………………….. 66 5.3 Academic Implications……………………………………………………... 67 6 APPENDIX 68 7 WORKS CONSULTED 81

LIST OF TABLES

3.1 Work Ethic Scores…………………………………………………………… 32

3.2 Religion and Work Ethic…………………………………………………….. 34

3.3 Participation and Work Ethic………………………………………………... 34

3.4 Wage and Work Ethic……………………………………………………….. 36

3.5 Socioeconomic Status and Work Ethic……………………………………… 37

3.6 Education Level and Work Ethic……………………………………………. 39

3.7 Age and Work Ethic…………………………………………………………. 41

3.8 Health Status and Work Ethic……………………………………………….. 42

3.9 Countries and Work Ethic…………………………………………………… 44

3.10 Urbanization and Work Ethic……………………………………………….. 46

3.11 Gender and Work Ethic…………………………………………………… 47

3.12 Wealth by Decile Per Country…………………………………………….... 50

3.13 Savings Rates Per Country………………………………………………...... 51

4.1 Description of H, Y, and Age……………………………………………… 54

4.2 Description of Religious Variables………………………………………….. 55

4.3 Description of Gender Variables…………………………………………….. 55

4.4 Description of Country Variables…………………………………………… 55

4.5 Description of Urbanization Variables……………………………………..... 56

4.6 Description of Health Variables…………………………………………… 56

4.7 Description of Socioeconomic Variables………………………………...... 56 4.8 Description of Participation Variables……………………………………..... 56

4.9 Description of Education Variables…………………………………………. 57

LIST OF FIGURES

3.1 Distribution of y (Income) in Dollars………………………………………… 49

LIST OF EQUATIONS

2.1 General Cobb-Douglas Labor Supply Utility Function……………………… 21

2.2 Budget Constraint for General Labor Supply Utility Function……………… 21

2.3 The Model…………………………………………………………………… 23

2.4 Budget Constraint for the Model…………………………………………….. 23

2.5 Lagrange Function…………………………………………………………… 24

2.6 First Order Condition (H)……………………………………………………. 24

2.7 First Order Condition (C)……………………………………………………. 25

2.8 First Order Condition (ʎ)……………………………………………. 25

2.9 Solution for H………………………………………………………………... 25

2.10 Solution for C……………………………………………………………….. 26

4.1 Ordinary Least Squares Model……………………………………………… 54

CHAPTER I

INTRODUCTION

Why do some countries continue to grow and develop while others remain in persistent poverty? Throughout modern history economists, sociologists, and political scientists have attempted to answer this question. In the field of economics, growth models are used to understand short-run and long-run economic development1. In the neoclassical Solow growth model, capital, labor, and technology are used to predict the long-run economic output of a country. One of the predictions of the Solow growth model is that incomes of poor countries will eventually converge to incomes of wealthy countries.

According to this theory convergence occurs because the marginal product of capital in wealthy nations decreases due to diminishing marginal returns while capital in poorer nations is more productive due to lower capital-labor ratios. In other words, additional capital in poorer countries is more productive than in wealthy countries because they start out with less. Although there is evidence of convergence among wealthy nations, there is little evidence that poorer countries are catching up economically2. Because of this discrepancy, social scientists are studying other factors

1 Marcus Noland, "Religion and economic performance." World Development Vol. 33, no. 8 (2005): 1215-1232.

2 Dan Ben-David, ―Convergence Clubs and Diverging Economies.‖ Working Papers, Centre for Economic Policy Research (1997). 1

2 that influence growth such as a country‘s history, institutions, and culture. Culture specifically refers to widespread beliefs and values.

Since the advent of Max Weber‘s, The Protestant Work Ethic and the Spirit of

Capitalism sociologists and political scientists have paid a more attention to religion as a possible explanation for income disparities among countries. In this work, Weber attributes the development of capitalism to the Protestant Reformation, which established a new set of values in Europe3. Weber most important claim is that ―work ethic‖ in Protestant countries was radically altered. This new ―work ethic‖ is loosely defined as the widespread belief that all work is sacred because it contributes to the common good and is a means of praising God. Individuals were therefore encouraged to produce more, accumulate wealth, and devote themselves wholeheartedly to their trade because these actions glorified God. Moreover, economic success attained from hard work was no longer considered sinful, as in the former Catholic value system, but was regarded as a sign that an individual was predestine for heaven. Weber claims that this strong tie between work, success, God, and salvation was the impetus behind economic development in Western Europe.

Recently, the debate surrounding the effect of Catholic versus Protestant values‘ on economic development has expanded to all religions and other facets of culture such as attitudes toward entrepreneurship and technology, tolerance towards others, and levels of social cohesion. There are several reasons to focus on religion. To start, religion influences individual and group attitudes by rewarding and punishing

3 Maria T. Brouwer, ―Weber, Schumpeter, and Knight on entrepreneurship and economic development.‖ Vol. 12, no. 1-2, pp. 83-105 (2002)

3 behaviors, defining moral and ethical beliefs, and reinforcing power structures4. Ethical beliefs can in turn shape an individual‘s preferences, behaviors, and interactions with others. In a society, shared ethical beliefs can shape policies as well as institutions.

A second reason is that religion is still pertinent, even in developed countries.

This notion is in direct opposition to the popularized secularization theory, which claims that religion is merely a vestige of pre-modern times that fades as societies modernize5. The increase in church membership in the United States over the last two centuries is only one of many facts that discredit secularization theory6. Other examples include the rise of Islamic fundamentalism in the Middle East, the growth of

Protestantism in Latin America, and the expansion of evangelical Christianity in the

United States. This idea the prevailing role of culture and religion is also explored in

Hungtinton‘s seminal work, The Clash of Civilizations. His theory maintains that cultural and religious differences have been the main source of conflict in the post-Cold

War era and will continue to be in the future7. Furthermore, in their paper

Modernization, Cultural Change, and the Persistence of Traditional Values Inglehart and Baker (2000) find strong empirical evidence that ―the broad cultural heritage of a

4 Sarah Drakopoulou Dodd and Paul Timothy Seaman, "Religion and Enterprise: An Introductory Exploration." Entrepreneurship: Theory & Practice 23, no. 1 (1998): 71-86.

5 Gerhanrd Lenski, The Religious Factor. New York: Doubleday and Company, (1961).

6 Laurence R. Iannaccone, "Introduction to the economics of religion." Journal of Economic Literature 36, no. 3 (1998): 1466.

7 Huntington, Samuel P. The Clash of Civilizations and the Remaking of World Order. New York, NY: Simon and Schuster, (1996). 5Le nski 4 society—Protestant, Roman Catholic, Orthodox, Confucian, or Communism--leaves an imprint on values that endures despite modernization‖8.

A third reason for modeling religion cited is that it is a slow-moving component of culture9. In their paper Does Culture Affect Economic Outcomes? They claim that religion can even be treated as exogenous because in almost all cases it is ―inherited by an individual from previous generations‖ and changes over centuries. In support,

Becker (1996) contends that, ―culture is often ‗given‘ to individuals throughout their lifetimes‖10. This exogeneity assumption has two practical applications: 1) modeling religion‘s effect on economic outcomes becomes more feasible and 2) short run changes in economic growth explained by religion might be extended to the long run. In other words, if religion can explain disparities in economic growth for one period, it could possibly explain historical disparities.

Despite this discourse surrounding religion‘s influence on economic outcomes, there are relatively few economics papers published on this subject. Of course, the paucity of economics studies is not surprising given that religion falls under the tenuous sphere of culture. Cultural beliefs and values are often vague, difficult to measure, and so pervasive that testable hypotheses are often infeasible11. In addition to these difficulties, the field of economics‘ has its own ideology, which resists the incorporation

8 Ronald Inglehart and Wayne E. Baker, "Modernization, Cultural Change, and the Persistence of Traditional Values." American Sociological Review 65, no. 1, (2000): pp. 19.

9 Luigi Guiso, Paola Sapienza, and Luigi Zingales, "Does Culture Affect Economic Outcomes?" The Journal of Economic Perspectives 20, no. 2 (2006): pp. 2.

10 Gary Becker,‖ Preferences and Values‖, in Becker Gary (ed.), Accounting for Taste, Harvard University Press: Cambridge, 1996.

11 Guiso (2006), pgs 6-7.

5 of beliefs and values. Specifically the ―Chicago school‖, an influential body of neoclassical economic thought associated with the University of Chicago, has historically held a ―rational Marxian‖ view of culture. In accordance with Marx‘s theory that the prevailing economic circumstances govern the ―dominant culture‖ the

Chicago school ―explains culture as a mere outcome of economic forces‖12. Moreover, individual or societal preferences can always be explained through the price system. If price is the sole determinate of choices then beliefs and values are rendered inconsequential.

Many renowned works have repeatedly impugned this ―rational Marxian‖ view of causality, most notably Max Weber‘s The Protestant Ethic and the Spirit of

Capitalism (1905), Karl Polanyi‘s The Great Transformation (1968), and David

Landes‘ The Wealth and Poverty of Nations (1998)13. These authors represent a larger body of researchers who contend that culture can influence economic outcomes. The general argument within this theory follows that culture determines personal traits such as tolerance, trust in others, individualism, entrepreneurship, work ethic, etc. On the aggregate level these traits shape the economy through the labor market, economic transactions, savings and investment, human capital, and institutions. I will expand upon this process in the literature review section of this paper.

However, in recent years more economists have begun to take advantage of new survey data and incorporating cultural variables, such as religion, into their models.

Their main objective is to find empirical evidence that religion can influence economic

12 Ibid.

13 Robert J. Barro and Rachel M. McCleary, "Religion and Economic Growth Across Countries." American Sociological Review 68, no. 5 (2003): pp. 1. 6 outcomes. Generally, the economic literature on religion utilizes hedonic models and

OLS regression analysis. Although these models are useful, they are not founded in economic theory. However, many of these studies also introduce econometric tools such as instrumental variables to control for reverse causation. The reverse causation problem is rooted in the Marx vs. Weber debate concerning the direction of causality.

Two additional studies that apply economic theory to religion are Zak and Knack

(1998)14 and Congleton (1991)15. Zak and Knack (1998) use a multi-period growth model to predict efficiency gains from trust while Congleton (1991) incorporates game theory in a model for work ethic and productivity. The introduction of instrumental variables, growth theory, and game theory represent major steps toward the development of models for ―nonmarket‖ behavior16.

Modeling ―nonmarket‖ behavior, such as beliefs, norms, and values, is a relatively new branch of economics that can be enhanced by this line of research.

Developing modeling techniques will contribute to our understanding of how people or groups make decisions on a micro level and possibly explain economic circumstances at the macro level. Of course, some economists argue that this approach challenges the foundation of economic theory, which assumes that individuals have ―stable, well- defined preferences and make rational choices consistent with preferences in

14 Paul J. Zak and Stephen Knackm, "Trust and Growth." The Economic Journal 111, no. 470 (2001): 295-321.

15 Roger D. Congleton, "The economic role of a work ethic." Journal of Economic Behavior & Organization 15, no. 3 (1991): 365-385.

16 Iannaccone (1998), pg 1465.

7 markets‖17. However, the inclusion of beliefs, norms, and values can actually enhance the understanding of a person‘s ―rational‖ choice. For example, it might be ―rational‖ for a particular person to choose more hours of work over leisure if they believe that work glorifies God. If this preference for work is ubiquitous across a society, then it is possible to explain that society‘s economic growth at the aggregate level.

This last example leads to the research question proposed by this paper: does religious affiliation affect an individual‘s willingness to work? To examine this question

I construct a classic microeconomic utility model for work ethic that incorporates religious and demographic variables. However, due to low variance caused by too many variables and too small of a sample size, this model is not used to generate results.

In its place, a basic Ordinary Least Squares regression is used to estimate the effect of all the independent variables on work ethic scores. Overall the goal of this paper is to test whether work ethics across religions are significantly different from each other.

17 Colin Camerer, and Richard H. Thaler. "Anomalies: Ultimatums, Dictators and Manners." The Journal of Economic Perspectives 9, no. 2 (1995): pp. 209. 8

LITERATURE REVIEW

The purpose of this chapter is to review the literature of religion‘s influence on economic outcomes. There are two ways in which religion and economics are linked.

The first connects religion to attitudes and behaviors that are either conducive or inimical to economic growth. The second forges a direct link between religion and growth. Although there are a substantial number of papers that analyze religions‘ affect on attitudes, there are only a smattering that relates religion and attitudes directly to economic growth. This lack of research reflects economists‘ reluctance to include beliefs and norms into their models.

The first section of this chapter begins with micro and macroeconomic literature concerning religious influences on ―economic‖ attitudes, or attitudes shown to shape various facets of the economy. Specifically, I will focus on trust, tolerance of others, 9 entrepreneurship, and work ethic. Next, I discuss papers that relate these ―economic attitudes‖ to growth. Finally, the last section of the literature review will cover papers in which the relationship between religion and growth is made directly.

Religion‘s Influence on Economic Attitudes and Behavior

There are a few recent studies that test whether religious affiliation and religious participation can affect an individual‘s level of trust towards others. In their paper, Trust in Others, Does Religion Matter? (2008), Daniels and Marc von der Ruhr review survey data from 1975 to 2000 in the U.S.18. Using probit models, they find that Black

Protestants, Pentecostals, fundamentalist Protestants, and Catholics are less trusting of others than Christians who do not belong to a particular denomination. For these more conservative sects, affiliation, and not church attendance, is significantly correlated with low-levels of outward trust. On the other hand, liberal nondenominational Protestants trust in others becomes greater as church attendance increases19. Alesina and La Ferrara

(2002) also analyzed religion‘s affect on trust of others in the United States and find no correlation20. However, Daniels and Marc von der Ruhr argue that no relationship was found because Alesina and La Ferrara only used data from one year, did not separate

Protestants by sect, and did not include a measure for religious dedication.

18 Joseph Daniels, and Marc von der Ruhr. "Trust in others: Does Religion Matter?." Working Paper 0902 Marquette University , (2008).

19 Ibid. pg 4.

20 Alberto Alesina, and Eliana La Ferrara. "Who trusts others?" Journal of Public Economics 85, no. 2 (2002): 207-234.

10

Another study that tests for religious differences in trust and tolerance of others is People’s Opium? Religion and Economic Attitudes21. However, this study uses more expansive data than its predecessors; it includes 66 countries and three waves of surveys. They generate their results by regressing religious denomination, participation, a dummy variable for being raised religiously on various attitudes. Education, sex, health status, and income are also included to alleviate the possibility of spurious correlation. For trust, they find several intriguing results that support the claim that religion does impact trust. In general, Guiso et al (2006) find that only Christian religions cultivate trust, Protestants more so than Catholics. In terms of tolerance of others, there is a positive correlation between all religions and intolerance as compared to nonreligious individuals; Buddhists are the only exception. Specific point estimates indicate that, ―actively religious Hindus are 29 percent more intolerant than non- religious people, Muslims 19 percent, actively religious Protestants and Catholics 7 percent more‖22. Additionally, religious participation increases trust in government and trust in the legal system for all denominations. However, an important factor that is also examined is if the religious sect is part of the dominant or minority religion in each country. Overall, the impact of religion on trust significantly differs if the religion is a minority.

A penchant for entrepreneurial activities is another personal disposition is potentially influenced by religion. During my research I have only found three studies that examine this relationship. The first is Minns and Rizov‘s (2004) study on Canadian

21 Guiso et al. (2006), pg 10. 22 Guiso et al. (2006), pg 263.

11 self-employment over the last 100 years23. Although they find self-employment, a proxy for entrepreneurship, is positively correlated with human capital and negatively correlated with local employee earnings, they do not find any correlation with Christian affiliation. In contrast, a study of 90,000 India workers finds stark differences between self-employment of Muslims, Christians, and Hindus24. Holding other demographic variables constant, Christians and Muslims are much more likely to own a business than

Hindus. This circumstance is attributed to Hindus strong tie to the caste system, which strictly demarcates the types of work people may pursue. The caste system therefore discourages the vast majority of Hindus from starting a business because this is only expected of the Vyshyas, or the business class.

The last study that empirically tests whether religion is associated with different levels of entrepreneurship is Religion and Enterprise: an Introductory Exploration25.

10,000 British citizens are separated into entrepreneurs, wage/salary workers, and unemployed/out of the work force. Next, probit models test whether these groups differ significantly in religious affiliation, participation, and adherence. The results indicate that there is no significant difference between these groups in terms of religion.

The last attitude that I will cover is work ethic. Papers on the topic of work ethic and religion are exceptionally conflicting. In the Catholic versus Protestant debate, earlier studies that find no difference between Catholic and Protestant work

23 Chris Minns, and Marian Rizov. "The spirit of capitalism? Ethnicity, religion, and self- employment in early 20th century Canada." Explorations in Economic History 42, no. 2 (2005): 259-281

24 David Audretsch, and Werner Bonte, Religion and Entrepreneurship. Jena Economic Research Paper No. 2007-075, (2007)

25 Sarah Drakopoulou Dodd, and Paul Timothy Seaman. ―Religion and Enterprise: An Introductory Exploration‖. Entrepreneurship : Theory and Practice, Vol. 23, (1998).

12 ethic include Blackwood‘s Social Change and Commitment to the Work Ethic (1979)26,

Bouma and Dixon‘s Beyond Lenski: A Critical Review of Recent ‘Protestant Ethic’

Research (1973)27, and Ray’s The Protestant Ethic in Australia (1982)28. On the other hand, there are abundant empirical works that have contradictory results. To start, in a study on work values in Europe, Giorgi and Marsh (1990) question whether Protestants or Catholics endorse the vocational work ethic more29. Vocational work ethic is defined as a view of work that stresses ―rewards of self-fulfillment and social obligation‖30 .

Their results indicate that Protestants endorse vocational work ethic more than

Catholics, and that predominantly Protestant countries have greater vocational work ethic. This finding suggests that a country‘s religious culture also has an impact on a person‘s work ethic regardless of their personal affiliation.

A country-specific study that supports Giorgi and Marsh‘s results is Religion and Work Ethic in the Netherlands (Voert, 1993)31. Four distinct Dutch religious groups are analyzed to see if they have significantly different views toward the

Christian work ethic, job as a duty, and job achievement. Their findings provide evidence that orthodox Calvinists have the most positive view of the Christian work

26 Larry Blackwood, ―Social Change and Commitment to the Work Ethic.‖ Pp. 241-256 in The Religious Dimension: New Directions in Quantitative Research. New York: Academic Press, (1979).

27 Dixon, ―Beyond Lenski: A Critical Review of Recent ‗Protestant Ethic Research‘‖. Journal for the Scientific Study of Religion, (1973) pp. 141-155.

28 Ray J. John. ―The Protestant Ethic in Australia‖. The Journal of Social Psychology, (1982), Vol 116, pp 127-138.

29 Liana Giorgi, and Catherine Marsh, ―The Protestant work ethic as a cultural phenomenon‖. European Journal of Social Psychology, Vol 20(6), Nov-Dec 1990, 499-517.

30 Ibid. pg 514.

31 Ter Voert, ―The effect of religion on work attitudes in the Netherlands‖. Social Compass, (1993) Vol. 40 pp 33-44.

13 ethic and strong Christian believers have a more positive disposition toward work as a duty. Arslan (2001) also finds that Protestant Work Ethic (PWE) characteristics vary among Muslim, Protestant, and Catholic managers in Turkey, England, and Ireland.

PWE is measured by 19 traits, including ―hard work and success‖, ―leisure‖, and

―attitudes toward money and saving‖32. Muslim managers ranked first for PWE characteristics, followed by Protestant and then Catholic managers.

Economic Attitudes and Behaviors Shaping Economic Growth

One way in which intolerance of others can be detrimental to the economy is by reducing the quality of the labor force33. Discrimination can create inefficiencies when potential skills are lost and labor competition is lower. For example, in the Wealth and

Poverty of Nations, Landes argues that Spain‘s slow growth during the 16th and 17th centuries was partially due to religious intolerance. Widespread discrimination encouraged by the Catholic Church weakened the workforce by expelling many skilled workers34. The exclusion of large portions of society from the job market also hurts the labor force quality by diminishing the level of competition.

Poor international trade and immigration policies can also result from prejudice.

People who are less tolerant of others are more likely to support policies that restrict

32 M. Arslan. ―The Work Ethic Values of Protestant British, Catholic Irish and Muslim Turkish Managers‖. Journal of Business Ethics. Vol 31, Number 4 pp 323.

33 David S. Landes, The Wealth and Poverty of Nations: Why Are Some So Rich and Others So Poor? (New York: W.W. Norton, 1998), pp 544.

34 Luigi Guiso, Paola Sapienza, and Luigi Zingales, "People's opium? Religion and economic attitudes." Journal of Monetary Economics 50, no. 1 (2003): 225-282.

14 imports and immigration35. According to classic economy theory, low mobility of goods and labor causes market inefficiencies. Inefficiencies in the labor market occur because workers cannot move to places where they are most productive36. In terms of goods the Ricardian model explains inefficiencies in international trade37. The model demonstrates that open trade policies are most efficient because each country is allowed to specialize in producing certain goods. Specialization leads to less expensive and higher quality goods that can be traded internationally. According to this model, all countries benefit from open international trade.

Many social scientists also argue that trust impacts economic performance. For instance, individuals living in low-trust societies spend a larger portion of their resources protecting their assets38. This behavior shifts resources away from more productive sectors of the economy, engendering widespread inefficiencies. In their paper Trust and Growth (1998) Paul Zak and Stephan Knack support this theory, using a general equilibrium growth model in which consumers decide whether to produce or investigate brokers based on levels of trust. They find that low-trust societies suffer from reduced levels of investment and growth. In contrast, societies with high-levels of

35 Joseph P. Daniels, and Marc von der Ruhr. "God and the global economy: religion and attitudes towards trade and immigration in the United States." Socio-Economic Review 3, no. 3 (2005): 467-489.

36 Bob Hamilton, and John Whalley, "Efficiency and distributional implications of global restrictions on labour mobility : Calculations and policy implications." Journal of Development Economics 14, no. 1 (1984): 61-75.

Iregui, Ana Maria. ―Efficiency Gain from the Elimination of Global Restrictions on Labour Mobility: An Analysis using a Multiregional CGE Model‖. Working Papers UNU-WIDER Research Paper, World Institute for Development Economic Research (UNU-WIDER), (2003).

37 Economy Watch, Richardian Model of International Trade: An Overview. http://www.economywatch.com/international-trade/ricardian-model.html (date accessed: April 2011)

38 Zak and Knack (2001), pg 301.

15 trust have high levels of investment and growth39. This phenomenon also discourages innovation if entrepreneurs must divert their time to ―monitoring possible malfeasance of partners, employees, and suppliers‖ instead of developing ―new products and processes‖40. Further evidence is found in Knack and Keefer‘s Does Social Capital

Have an Economic Payoff? (1997). They implement a hedonic model that regresses measures of trust and civic engagement on investment and GDP. They find that high levels of trust and civic engagement are significantly and positively correlated with economic growth across 29 countries41. Helliwell and Putnam (1995) also find evidence that Italian regions with higher levels of ―civic community‖ experienced more growth from 1950 to 199042.

There are many definitions of entrepreneurship used across disciplines such as organizational theory, mathematical economics, and social anthropology. The interpretation I will use for this paper is behavioral definition of an entrepreneur. In this school of thought the entrepreneur is described as ―coordinator of production and an agent of change‖43. In addition, he or she is an innovator who seeks out profit opportunities in a precarious environment. Moskowitz and Vissing-Jorgensen (2002) explain the ability of an entrepreneur to pursue risky, sometimes low-profit endeavors because they are risk tolerant, overly optimistic, and receive a great deal of satisfaction

39 Ibid.

40 Stephen Knack and Philip Keefer. "Does Social Capital Have an Economic Payoff? A Cross- Country Investigation." The Quarterly Journal of Economics (1997), pp. 4. 41 Ibid.

42 Robert Putnam. ―Making Democracy Work: Civic Tradition in Modern Italy.‖ 1993.

43 Wim Naude. ―Entrepreneurship in Economic Development‖. UNU-WIDER Research Paper No. 2008/20, (2008), pp 5-6.

16 from non-pecuniary benefits44. These benefits include independence, control, and a greater variety of work.

Economists cite entrepreneurship as the main impetus for economic development. Hansen and Prescott (2002) argue that entrepreneurship is responsible for the shift from a pre-industrial, subsistence economy to a post-industrial economy where economic growth is generated by technology and human capital accumulation45.

Murphy et al. (1991) demonstrates that an entrepreneur‘s ability is directly related to firm size and economic growth46. Rada (2007) also supports the claim that entrepreneurship triggers economic development and modernization47. The study finds that entrepreneurship encourages the shift of production and capital from tradition to modern sectors of the economy. This shift causes greater demand for higher skilled labor, which in turn encourages more people to become entrepreneurs.

There are both obvious and subtle reasons that work ethic can influence an economy. According to neoclassical economic theory, individuals that work more will be wealthier than those who work less simply because they will earn more income.

This same logic follows for groups of people: a society that works more will be wealthier than other societies because they produce more goods48. In his study, The

44 T.J. Moskowitz, and A. Vissing-Jorgensen, ―The Return to Entrepreneurial Investment: A Private Equity Premium Puzzle?‖ American Economic Review, (2002) 92: 745-79

45 G.D. Hansen, and E. Prescott,―Malthus to Solow‖, American Economic Review, (2002) 92: 1205-17

46 K. Murphy, A. Schleifer, R. and Vishny,‗The Allocation of Talen: Implication for Growth‘, Quarterly Journal of Economics, (1991)106 (2): 503-30

47 C. Rada, ―Stagnation or Transformation of a Dual Economy through Endogenous Productivity Growth‖, Cambridge Journal of Economics, (2007). Vol. 31: 710-40

48 Congleton (1991), pg 365. 17

Economic Role of Work Ethic (1991), Roger Congleton makes the argument that a strong ―work ethic‖, or belief in the value of work for its own sake, also makes an economy more efficient. In my research The Economic Role of Work Ethic is the only empirical paper I found that links work ethic to economic growth. Using game theory for small developing economies and large developed economies, Congleton shows that work ethic can influence production49.

For instance, in a small developing economy two workers that need to complete a task together can benefit from the other‘s decision to work longer hours. If they simultaneously decide to work longer hours, then both would gain higher utility.

However, because of incentive structures their dominant strategy is to work fewer hours and Pareto optimum is not reached. Congleton suggests that, ―a proper work culture can reduce this problem… with status or other non-pecuniary compensation‖50.

The case is different for large, developed economies. To start, developed economies already have several advantages in production. These advantages include

―experience in team production‖ and that production teams tend to fall under the same firm. However, coordination and communication within a firm can still be exceedingly complicated. In developed economies, Congleton suggests that work ethic can increase efficiency in production by replacing ―centralized forms of monitoring and enforcement‖ with ―decentralized monitoring of a work culture‖51. In other words,

49 Congleton (1991), pg 366.

50 Ibid. pp. 374.

51 Ibid. pg 375. 18 high work ethic individuals are less likely to shirk their duties, and therefore do not require expensive forms of monitoring.

Connecting Religion Directly to Economic Growth

Instead of investigating specific channels through which religion can influence economic growth, there are several studies that incorporate dummy variables for religious denomination, beliefs, and dedication into growth models. All of these studies regress indicators of economic development on various aspects of religion, controlling for other major determinants of growth. Although they are distinct in their questions, models, data, and results, they all support the idea that religion can influence a nation‘s economy.

Barro and McCleary (2003) investigate how church attendance, belief in God, the afterlife, heaven, and hell is correlated with various indicators of economic development from 1981 to 1999 across 59, mostly developed, countries52. They employ a holistic view of development, using per capita income, fertility rates, urbanization rates, education levels, and life expectancy as dependent variables. To control for reverse causation, or economic development influencing religious variables, Barro and

McCleary instrument for the presence of a state religion, religious pluralism, the composition of religious adherence and religious regulation. They find that economic growth is positively related to beliefs in heaven and hell, while being negatively related to church attendance. From these results they conclude that, ―religious beliefs influence individual traits that enhance economic performance‖53. Similarly, Sala-i-Martin et al

52 Barro (2003).

53 Ibid. pg 1. 19

(2004) took data from 88 countries for periods between 1960 and 1996 in order to determine religious affiliations affect on per capita income growth54. He finds the fraction of the population that is Buddhist or Muslim has a positive impact on growth.

Noland (2005) also implements a standard model of economic fundamentals and includes dummy variables for religious denomination55. However, in addition to real per capita income growth Noland also includes Total Factor Productivity (TFP) growth as a dependent variable. He finds that the hypothesis that religious affiliation has no effect on economic performance is rejected for TFP and real per capita income.

However, robust patterns of coefficients for particular religious denominations are not observed. In addition, Noland emulates Barro and McCleary‘s model and finds the same sign and significance pattern for church attendance, belief in heaven, and belief in hell in more than half the cases. Furthermore, in agreement Sala-i-Martin, the coefficient for Muslim is positive and significant, contributing 0.02 - 0.03% to TFP growth. Data on a subnational level for three predominantly Muslim countries, India,

Malaysia, and Ghana, also supports Islam having a positive impact on TFP growth although none of these variables are significant.

There are a few studies that focus solely on Protestantism and Catholicism to test Weber‘s thesis that Protestant values are more suitable for economic growth than

Catholic values. Grier (1997), for example, analyzes panel data from 1961 to 1990 of

54 Xavier Sala-i-Martin, Gernot Doppelhofer, and Ronald I. Miller. "Determinants of Long- Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach." The American Economic Review 94, no. 4 (2004): pp. 813-835. 55 Noland (2005).

20

63 former British, French, and Spanish colonies56. His objective is to see if changes in the percentage of Protestant adherents can explain variations in their economic success.

Like Sala-i-Martin, Barro, and Noland, Grier uses per capita income as one of his dependent variables, the other being real average GDP. Important independent variables that affect growth are also incorporated into the model. Grier finds strong evidence that growth of Protestantism is positively correlated to economic growth when both real average GDP and per capita income are the dependent variable. However, growth of

Protestantism does not explain the gap between former colonies when real average GDP is the dependent variable, but does explain the gap between British and French colonies when per capita income is the dependent variable. In other words, when growth of

Protestantism is added to the model, the coefficients of British and French colonies converge for per capita income.

Another study that looks at Protestantism versus Catholicism is Ulrich Blum and

Leonard Dudley‘s paper, Religion and Economic Growth, was Weber Right? (2001)57.

The primary interest of the paper is why real wages in Paris and London diverged even though literacy rates, a proxy for human capital, were identical. This situation lies in opposition of neoclassical models, which predict convergence in the marginal productivity of labor when human capital is equal. Applying data from 1500 to 1750, the authors test if the Catholic culture of Paris and the Protestant culture of London explain the divergence. They utilize non-cooperative game theory and a production function that incorporates network externalities in addition to human capital. The

56 Robert Grier, "The effect of religion on economic development: A cross national study of 63 former colonies." Kyklos 50, no. 1 (1997). 57Ulrich Blum, and Leonard Dudley. "Religion and economic growth: was Weber right?" Journal of Evolutionary Economics 11, no. 2 (2001).

21 results of their model indicate that Protestant economic success was not due to a greater propensity to work or save, but rather their ability to take advantage of trade networks.

Protestants could create better information networks across larger regions than

Catholics because they were more likely to honor contracts with people they did not know personally. CHAPTER II

THEORY

The purpose of this section is to expound on the theory behind my model. Due to the fact that there are no extant economic models for work ethic and religion, I decided to develop my own grounded in neoclassic economic theory. To start, I adopted a basic, Cobb-Douglas labor supply model, wherein, the consumer maximizes their utility based on their preferences for two goods: work and leisure. The variable for work (C) is measured by consumption because an increase in work generates more income, allowing for more consumption. The second variable, leisure (H), is proxied by work ethic; a person with low work ethic will have more leisure time than a person with higher work ethic.

EQUATION 2.1

U (max H, C) = H α C β H = Hours of Leisure/Proxy for work ethic C = Consumption

EQUATION 2.2

The general budget constraint used this utility function is: Y = - C + (24 - H) w Y = Income w = Wage

The rationale behind using this model is that ―work ethic‖ can also be viewed as an individual‘s preference for work vs. leisure. Presumably a person with a strong work ethic would derive greater utility from working longer hours than a person with a lower

22

23 work ethic, ceteris paribus. Therefore, a ―high‖ work ethic individual would choose more C than H compared to someone with ―low‖ work ethic. Of course, the idea of

―work ethic‖ is much more nuanced than number of hours devoted to work. My exact proxy for work ethic, however, will be discussed in the following chapter. A second reason for using a basic labor supply model is that when wages are allowed to vary over time, income and substitution effects can provide insight into an individual‘s work ethic. For example, when wages are increasing, a person with ―high‖ work ethic will choose to work longer hours because they can consume more. On the other hand, a person with ―low‖ work ethic would choose to work less hours because they know they can still maintain their former income. However, due to lack of time series data, I implement a static model in this study.

My next step in generating my model was to make modifications in order to incorporate other variables including religion. Due to the lack of economic theory concerning religion‘s influence on work ethic as well as the ambiguous nature of the this topic I decided to subsume additional variables into this model nonlinearly to allow for different relationships between the independent and dependent variables.

The Model

The below equation is a basic Cobb-Douglas utility function for work and leisure. Each variable is entered in multiplicatively and exponentially as powers of the dependent variables, H and C. The multiplicative parameter is represented by the lower case delta and the exponential parameter is represented by the lower case gamma.

24

EQUATION 2.3

HRB δHPB δHSB δHEB U (max H, C) = H ^ (ɣHRAR * ɣHPAP * ɣHSAS *ɣHEAE *

δHAB δHGB δHNB δHVB δHUB ɣHAAA * ɣHGAG * ɣHNAN * ɣHVAV * ɣHUAU ) * C ^

δCRB δCPB δCSB δCEB δCAB δCGB (ɣCRAR * ɣCPAP * ɣCSAS *ɣCEAE * ɣCAAA * ɣCGAG *

δCNB δCV B δCUB ɣCNAN * ɣCVAV * ɣCUAU )

H = Proxy for work ethic and hours of leisure, C = Consumption, R = Religious affiliation P = Religious participation, S = Socioeconomic status, E = Level of education, A = Age G = Gender, N = Country, V = Health status, U = Urbanization

EQUATION 2.4

Budget Constraint:

Y = - C + (X - H) w Y = Income, C = Consumption, H = Proxy for work ethic and hours of work, X = Maximum H value, w = Proxy for wage rate

The budget constraint is determined by how much the individual consumes as well as how much income they generate. The term (X-H) is a proxy for hours of work and w is a proxy for wage. X represents the maximum value of H, which is the lowest possible score for ―work ethic‖. Therefore, the higher a person‘s ―work ethic‖ score, the higher their income. It is important to note that the assumption that higher work ethic creates more income is substantial and unrealistic.

25

The Lagrange Function:

The below function is the Lagrange multiplier. It is used to maximize H and C given the budget constraint.

EQUATION 2.5

HRB δHPB δHSB δHEB L (H, C) = H ^ (ɣHRAR * ɣHPAP * ɣHSAS *ɣHEAE *

δHAB δHGB δHNB δHVB δHUB ɣHAAA * ɣHGAG * ɣHNAN * ɣHVAV * ɣHUAU ) * C ^

δCRB δCPB δCSB δCEB δCAB δCGB (ɣCRAR * ɣCPAP * ɣCSAS *ɣCEAE * ɣCAAA * ɣCGAG *

δCNB δCV B δCUB ɣCNAN * ɣCVAV * ɣCUAU ) + ʎ (Y - C + (X-H) * w)

First Order Conditions:

Each first order condition is the derivative of the Lagrange function with respect to H, C, and .

EQUATION 2.6

HRB δHPB δHSB δHEB δHAB 1) LH = (ɣHRAR * ɣHPAP * ɣHSAS *ɣHEAE * ɣHAAA *

δHGB δHNB δHVB δHUB HRB ɣHGAG * ɣHNAN * ɣHVAV * ɣHUAU )* H^ (ɣHRAR *

δHPB δHSB δHEB δHAB δHGB δHNB ɣHPAP * ɣHSAS *ɣHEAE * ɣHAAA * ɣHGAG * ɣHNAN

δHVB δHUB δCRB δCPB δCSB * ɣHVAV * ɣHUAU - 1) * C ^ (ɣCRAR * ɣCPAP * ɣCSAS

δCEB δCAB δCGB δCNB δCV B *ɣCEAE * ɣCAAA * ɣCGAG * ɣCNAN * ɣCVAV *

δCUB ɣCUAU ) - ʎ * H * w

26

EQUATION 2.7

δCRB δCPB δCSB δCEB δCAB 2) LC = (ɣCRAR * ɣCPAP * ɣCSAS *ɣCEAE * ɣCAAA *

δCGB δCNB δCV B δCUB δCRB ɣCGAG * ɣCNAN * ɣCVAV * ɣCUAU ) * C ^ (ɣCRAR *

δCPB δCSB δCEB δCAB δCGB δCNB ɣCPAP * ɣCSAS *ɣCEAE * ɣCAAA * ɣCGAG * ɣCNAN

δCV B δCUB HRB δHPB δHSB * ɣCVAV * ɣCUAU - 1) * H ^( ɣHRAR * ɣHPAP * ɣHSAS

δHEB δHAB δHGB δHNB δHVB *ɣHEAE * ɣHAAA * ɣHGAG * ɣHNAN * ɣHVAV *

δHUB ɣHUAU ) - ʎ * C

EQUATION 2.8

3) Lʎ = Y – C + (X-H) *w

Solutions for C and H:

The EQUATIONs for H and C are the solutions to the system of EQUATIONs of the first order conditions. These solutions are used to estimate all of the parameters.

EQUATION 2.9

* 2 2 HRB δHPB δHSB δHEB H = 1/wy + 1/x – (w ɣHRAR * ɣHPAP * ɣHSAS *ɣHEAE *

δHAB δHGB δHNB δHVB δHUB δCRB ɣHAAA * ɣHGAG * ɣHNAN * ɣHVAV * ɣHUAU )/ (ɣCRAR *

δCPB δCSB δCEB δCAB δCGB δCNB ɣCPAP * ɣCSAS *ɣCEAE * ɣCAAA * ɣCGAG * ɣCNAN *

δCV B δCUB ɣCVAV * ɣCUAU )

27

EQUATION 2.10

* δCRB δCPB δCSB δCEB δCAB C = ((w ɣCRAR * ɣCPAP * ɣCSAS *ɣCEAE * ɣCAAA *

δCGB δCNB δCV B δCUB HRB δHPB ɣCGAG * ɣCNAN * ɣCVAV * ɣCUAU )/( ɣHRAR * ɣHPAP *

δHSB δHEB δHAB δHGB δHNB δHVB ɣHSAS *ɣHEAE * ɣHAAA * ɣHGAG * ɣHNAN * ɣHVAV *

δHUB 1/2 2 2 3/2 δCRB δCPB δCSB ɣHUAU )) + (1/wy + 1/x ) * ((wɣCRAR * ɣCPAP * ɣCSAS

δCEB δCAB δCGB δCNB δCV B *ɣCEAE * ɣCAAA * ɣCGAG * ɣCNAN * ɣCVAV *

δCUB HRB δHPB δHSB δHEB δHAB ɣCUAU )/( ɣHRAR * ɣHPAP * ɣHSAS *ɣHEAE * ɣHAAA *

δHGB δHNB δHVB δHUB 1/2 HRB ɣHGAG * ɣHNAN * ɣHVAV * ɣHUAU )) - (w ɣHRAR *

δHPB δHSB δHEB δHAB δHGB δHNB ɣHPAP * ɣHSAS *ɣHEAE * ɣHAAA * ɣHGAG * ɣHNAN *

δHVB δHUB δCRB δCPB δCSB δCEB ɣHVAV * ɣHUAU )/ (ɣCRAR * ɣCPAP * ɣCSAS *ɣCEAE *

δCAB δCGB δCNB δCV B δCUB ɣCAAA * ɣCGAG * ɣCNAN * ɣCVAV * ɣCUAU

Why Cobb-Douglas?

There are several reasons for choosing a Cobb-Douglas function as my model.

First and foremost, it is a mathematically simply model that is easy to manipulate. It is also a widely accepted and used in the field of economics1. Additionally, the Cobb-

Douglas Function has several desirable mathematical properties. The first property is diminishing marginal returns: adding more of either good increases utility, but at a decreasing rate. Having diminishing marginal returns is essential because it is a fundamental tenet of economic theory. A second property of the Cobb-Douglas

1 Olivier Blanchard, Macroeconomics 3rd Edition (Prentice-Hall series in economics 2002), pg 240. 28 function is that it allows for constant, increasing, and decreasing returns to scale. This quality provides the model with greater flexibility, which is important because the exact relationships between the independent and dependent variables are unknown.

CHAPTER III

DATA

All of the data used for this research was compiled from three databases: 1) The

World Values Survey (WVS), 2) The World Development Indicators (WDI), and 3) The

World Income Inequality Database (WIID). The bulk of my data was collected from the World Values Survey (WVS) 2005-2007 wave1. It surveys a representative sample from 99 countries on topics such as family life, religious beliefs, and political views.

The purpose of the WVS is to understand sociocultural and political changes over time.

It is conducted by a global network of social scientists and is mainly funded by local research grants.

The second database, The World Development Indicators (WDI), consists of data compiled by the World Bank2. It includes 420 indicators of development, which fall under subjects such economic policy, education, and science and technology. In addition, this database covers 209 countries from 1960 to 2009. In order to stay consistent with the WVS, all data from the WDI was also collected in 2005.

The third source of information, the World Income Inequality Database (WIID), is compiled by the United Nation‘s University-World Institute for Development

1 World Values Survey. http://www.worldvaluessurvey.org (date accessed: October 2011)

2 World Bank. ―World Development Indicators‖.http://data.worldbank.org/indicator (data accessed: March 2011) 29

30

Economics Research3. Although this database contains income distribution data for 159 countries, the number of data points for each country is highly variable. Because of missing data points, all information gathered from the WIID ranges from 2000 to 2005.

I consider data from 2000 to 2004 acceptable because income distributions tend to change slowly overtime. Unfortunately, this temporal inconsistency does compromise the quality of the data. A second issue with the WIID is that it is compromised of manifold surveys conducted by different organizations, each having their own methods of measuring income and consumption. However, I chose to only include country data classified as ―household‖ and ―disposable income‖ to ensure some quality control.

Data Problems One problem with the data in general is that it is not representative of the world or countries included in the sample. Even though the World Values data provides weights to correct for nonrepresentative sampling, these weights became inaccurate after large quantities of observations were dropped. Observations were dropped if respondents did not fill in questions used in the study or if a group had very few observations (for example, Jewish people and Hindus). However, sample sizes for every variable are in the hundreds or thousands. Comparisons across each variable are therefore feasible because of fairly large sample sizes.

In the next section I will describe the variables from each of the three sources and explain why they are included in the model.

3 Nation‘s University-World Institute for Development Economics Research. ―The World Income Inequality Database‖. http://www.wider.unu.edu/research/Database/en_GB/database/ (date accessed: March 2011) 31

World Values Survey

Work Ethic (H)

To create a comprehension metric for ―work ethic‖, I combined the respondent‘s answers to five questions related to their disposition toward work. This metric I created for ―work ethic‖ is a secularized version of Weber‘s ―Protestant Work Ethic‖ described in the literature review section. The core ideas espoused by Weber transcend

Protestantism in 16th century Europe and can be applied to all religions and societies.

To begin, the belief that work serves the common good or, more generally has a purpose greater than the individual, is an integral part of PWE. Second, a strong work ethic must be a value that carries a great deal of importance in a person‘s life. Third, if the PWE is truly a cultural phenomenon, a person will instill this value in his or her children. Lastly, a person with a PWE believes that they will be rewarded for their hard work. A payoff received from working hard can either be spiritual, monetary, or simply personal satisfaction. Although this definition is not as comprehensive as Weber‘s, it is a reasonable proxy.

The first question simply asks the respondent to rate the importance of work in their life. The answers choices are 1) very important, 2) rather important, 3) not very important, and 4) not at all important. Clearly, someone with higher work ethic would rate work as more important than someone with lower work ethic. The second question asks the respondent what qualities they teach their children at home. The quality ―hard work‖ is either 1) mentioned or 2) not mentioned. Again, the rationale behind including this question is that if a strong work ethic is an integral part of an individual‘s culture and value-system, it would be important for them to teach it to their children. 32

The next question concerning work asks the respondent whether they view work as a ―duty toward society‖. The purpose of this question is to determine whether they believe their work also benefits their community. The answer choices are 1) strongly agree, 2) agree, 3) neither, 4) disagree, and 5) strongly disagree. The fourth question is a proxy for how the respondent views work in the context of their leisure time. Respondents are asked if they 1) strongly agree, 2) agree, 3) neither, 4) disagree,

5) strongly disagree with the statement ―work should always come first, even if it means less free time‖. Theoretically a person with a strong work ethic would choose the first two responses because they value work over free time.

Finally, the last question determines whether the respondent believes that people are rewarded for their hard work. The respondent chooses a number between one and ten depending on which statement they most agree with. Choosing the number one signifies that they completely agree with the statement ―in the long run, hard work usually brings a better life‖ and ten indicates they agree with the statement ―hard work doesn‘t generally bring success—it‘s more a matter of luck and connections‖.

According to my definition, one component of ―work ethic‖ is the belief that one will be rewarded for their hard work. However, in societies with rampant corruption it is possible that someone with a high work ethic would agree more with the second statement.

An amalgamation of these five questions assigns a work ethic score to each respondent. The scores range from 5 to 26: 5 representing an extremely high work ethic and 26 representing a very low work ethic. One drawback of this variable is that it must 33 be treated as continuous, given that it is the dependent variable in the model. Below,

TABLE 3.1 describes the distribution of work ethic scores for the sample.

TABLE 3.1

WORK ETHIC SCORES

H Freq. Percent

5 142 2.2 6 208 3.22 7 297 4.59 8 372 5.75 9 476 7.36 10 580 8.97 11 691 10.68 12 672 10.39 13 647 10 14 642 9.93 15 501 7.75 16 441 6.82 17 321 4.96 18 208 3.22 19 129 1.99 20 69 1.07 21 47 0.73 22 12 0.19 23 7 0.11 24 6 0.09

Religious Affiliation (R) and Religious Participation (P)

―Religious Affiliation‖ and ―Religious Participation‖ are two components of religion that I am investigating in this study. The reason I am including both affiliation and participation is that someone who associates with a religion but does not attend services could have different values than a member of the same religious community 34 who regularly attends services. Overall, the purpose of participation is to construct a more nuanced measure of religiosity.

The options for religious affiliation are as follows: Evangelical, Muslim,

Orthodox, Protestant, Roman Catholic, and member of the Church Of Sweden.

Religious participation is determined by the frequency in which the respondent attends religious services not including weddings and funerals. The answer choices are 1) more than once a week, 2) once a week, 3) once a month, 4) only on special holy days, 5) once a year, 6) less often, and 7) Never. Based on the studies cited in the literature review, I would expect work ethic to be significantly different between some religions.

I also predict that people who attend services more often will have a higher work ethic than people who attend services less often. One reason is that people who are more involved in community organization tend to be more hard working. A second reason is that all religions place a high value on working hard and contributing to one‘s community. Therefore, someone who attends services more often might be more likely to apply these teachings to their secular lives.

Refer to TABLE 3.2 and TABLE 3.3 for a description of religion and religious participation variables. ―Frequency‖ refers to the number of observations, ―Percent‖ shows the frequency of each variable as a percent of the total sample, ―Average Work

Ethic‖ is the average work ethic score of each variable on a scale of 5 to 26, and ―Std.

Dev.‖ is the standard deviation of average work ethic.

35

TABLE 3.2

RELIGION AND WORK ETHIC

Religion Frequency Percent Average Work Ethic Std. Dev.

Evangelical 602 9.31 12.32724 3.4915 Muslim 66 1.02 11.72727 3.584348 Orthodox 1770 27.36 10.87119 3.580507 Protestant 1012 15.64 12.71047 3.22824 Roman Catholic 2487 38.45 12.82147 3.500071 The Church of Sweden 531 8.21 13.43315 3.200936

TABLE 3.3

PARTICIPATION AND WORK ETHIC

Participation Freq. Percent Average Work Ethic (H)

More than Once a Week 354 5.47 11.87853 Once a Week 1,122 17.35 12.16667 Once a Month 1,032 15.96 11.93992 Only Holy Days 1,493 23.08 11.94106 Once a Year 541 8.36 12.68207 Less Often 973 15.04 12.23947 Never 953 14.73 13.16264

Wage (w)

Due to limitations of the World Values Survey there is no individual level data on the respondent‘s wages. I therefore used two questions that describe the respondent‘s work as a proxy for how much they earn. The first question asks the respondent to rate of a scale of one to ten whether their job is mostly manual or mostly cognitive. The second question asks the respondent to indicate whether their tasks at 36 work are mostly routine or mostly creative. A lower number on both questions signifies that a job is mostly manual and mostly routine while a higher number signifies that the job is mostly cognitive and creative. Using these questions, I am assuming that someone whose job is more creative and cognitive will earn a higher wage4. Although this statement is generally true, it is also possible that a factory work or soldier earns a higher wage than someone with a more cognitive and creative job, such as a teacher or artist. Another limitation of this variable is that it is treated as continuous instead of categorical. Unfortunately, this problem was unavoidable given the nature of the model.

Because people with more creative and cognitive jobs have more pecuniary incentives to work hard and are generally more educated, I hypothesize that they will have a higher work ethic than people with more routine and manual jobs. Refer to

TABLE 3.4 for a description of wage and work ethic.

4 William T. Dickens, and Lawrence F. Katz. ―Interindustry Wage Differences and Industry Characteristics‖. National Bureau of Economic Research, Working Paper No. 2014 (1987). 37

TABLE 3.4

WAGE AND WORK ETHIC

Wage Frequency Percent Average Work Ethic Std. Dev.

2 929 14.34 11.49839 3.952336 3 169 2.61 11.71006 3.250268 4 336 5.19 12.34524 3.528118 5 188 2.9 12.4734 3.715027 6 449 6.93 12.03786 3.524264 7 328 5.06 12.32927 3.321894 8 304 4.69 12.15461 3.478237 9 346 5.34 12.85549 3.39854 10 526 8.12 12.69202 3.375572 11 639 9.86 12.07981 3.678909 12 355 5.48 12.29014 3.350148 13 265 4.09 12.72075 3.449493 14 229 3.54 12.44978 3.285182 15 237 3.66 12.58228 3.462661 16 306 4.72 13.01634 3.336297 17 230 3.55 12.5913 3.36796 18 233 3.6 12.71674 3.609915 19 119 1.84 11.71429 3.362664 20 290 4.48 12.07931 3.71529

Demographic Variables The demographic variables included in the model are perceived socioeconomic status, level of education, age, country, health status, gender, urbanization, income, and consumption. Each is used to create an unbiased estimate for the effect of religion on work ethic. If a demographic variable is not included in the model and is correlated with both religion and work ethic, then the estimate for religion will be higher or lower than the ―true‖ value. This phenomenon is called spurious correlation. For example, if the majority of Muslims live in highly urbanized areas and individuals living in urbanized areas have a higher work ethic, then it could appear that Muslims have higher 38 work ethic than others. However, this finding would be misleading since some of the positive correlation between urbanization and work ethic would be subsumed in the variable ―Muslim‖.

Perceived Socioeconomic Status (S)

The question related to perceived socioeconomic status asks each respondent to describe the class in which they belong. The answer choices are 1) Upper Class, 2)

Upper Middle Class, 3) Lower Middle Class, 4) Working Class, and 5) Lower Class.

Refer to TABLE 3.5 for the distribution of socioeconomic class in the sample:

TABLE 3.5

SOCIOECONOMIC CLASS AND WORK ETHIC

Socioeconomic Class Frequency Percent Average Work Ethic Std. Dev.

Upper Class 52 0.76 12.09615 3.0759 Upper Middle Class 1583 23.18 12.30891 3.407802 Lower Middle Class 2555 37.41 12.31272 3.552665 Working 2123 31.09 12.24164 3.697507 Lower Class 516 7.56 12.3314 3.624172

One‘s socioeconomic status can affect one‘s work ethic in several ways. First, in general people from higher socioeconomic classes have more opportunities to receive an education and subsequently attain better-paying jobs with more opportunities for upward mobility. Presumably, a person with more opportunities to advance in his or her career would have higher work ethic because there are more incentives to work hard. Some of these incentives might include a bonus, promotion, or higher salary. Of course, this is not always the case. It is also possible that someone earning more could decide to work less because they can still support themselves financially. Furthermore, 39 belonging to a lower class could serve as motivation to work even harder to overcome economic or social disadvantages.

In addition to differences in opportunities and incentives, there are a plethora of studies that examine how each class teaches distinct values and social norms. Attitudes towards the importance wealth, success, and time dedicated to work have been shown to vary across classes. One study that supports this claim is Furnham and Muhiudeen

(1984)5. In their study they look at differences in Protestant Work Ethic scores in

Britain and Malaysia. They find that in both countries, working class respondents scored significantly higher than middle class respondents. In spite of the findings of

Furnham and Muhiudeen, I predict that people from higher classes will have a stronger work ethic score than people from lower classes because they are generally more educated and have more pecuniary incentive to work hard.

Level of Education (E)

For level of education, there are nine answer choices: 1) No formal education, 2)

Incomplete primary school, 3) Complete primary school, 4) Incomplete secondary school: technical/vocational type, 5) Complete secondary school: technical/vocational type, 6) Incomplete secondary education: university-preparatory type , 7) Complete secondary: university-preparatory type, 8) Some university, without degree, and 9)

University, with degree. Refer to TABLE 3.6 for a description of educational level.

5 Adrian Furnham. ―The Protestant work ethic in Britain and Malaysia‖. Journal of Social Psychology, (1984) 122:2, pp. 157-161.

40

TABLE 3.6

EDUCAION LEVEL AND WORK ETHIC

Education Frequency Percent Average Work Ethic Std. Dev.

No Formal 26 0.38 12.57692 3.910735 Incomplete Primary 356 5.21 11.64888 3.628956 Complete Primary 638 9.34 12.23511 3.439254 Incomplete Secondary: technical 603 8.83 12.72637 3.513583 Complete Secondary: technical 1529 22.39 11.96991 3.636153 Incomplete Secondary: prepatory 467 6.84 12.87366 3.340333 Complete Secondary: prepatory 1242 18.19 12.25845 3.557973 Some University: without degree 545 7.98 12.68991 3.467972 University: with degree 1423 20.84 12.30921 3.609445

Similar to socioeconomic class, the level of one‘s education often determines opportunities for upward mobility, wages, and the nature of their work. For example, someone with little education doing manual labor has fewer opportunities than an office worker to receive higher salaries or advance to a higher position. Without many rewards for working harder, there is less incentive to do so. Also, intuitively speaking a person with higher education places a greater importance on success because they are investing time and money into their education while forgoing income in the short run.

Importance of success is most likely correlated with work ethic.

Another aspect of education is the values one is taught while in school. In general, teachers and professors try to instill a strong work ethic in their students by assigning challenging tasks and rewarding students for high performance.

There are several studies that support the claim that education influences work ethic. Almost all studies find a strong positive relationship between level of education and work ethic. Yousef (2001) finds that among Muslims in 30 organizations located in 41 the United Arab Emirates, higher education is strongly and significantly related to measure of work ethic6. Another study of Muslims in Palestine also finds that education positively relates to the amount of pride one takes in one‘s work, his or her job commitment, and attitude toward financial success (Abboushi 1990)7. In agreement with the former studies, Frick (1995), and Rowe and Snizek (1995) show that level of education and occupational prestige are the most important determinants of work ethic8.

Again, because more educated people most likely value success, earn higher wages, and have more opportunities for job advancement I hypothesize that more education is positively related to work ethic.

Age (A)

In my regression there are six categorical variables for age: 15-25, 16-35, 36-45,

46-55, 56-65, and 66 and older. Refer to TABLE 3.7 for a description of each age group.

6 Darwish A. Yousef, ―Islamic work – A moderator between organizational commitment and job satisfaction in a cross-cultural context‖. (2001) Personnel Review, Vol. 30 Iss: 2, pp. 152-169.

7 S. Abboushi, ―Impact of individual variables on work values of Palestinian Arabs‖. International Studies of Management and Organization. (1990) Vol. 20. No. 3, pp 53-68.

8R. Row and W.E. Snizek, ―Gender differences in work values-perpetuating the myth.‖ (1995) Work and Occupations, Vol. 22 No. 2, pp 215-29.

42

TABLE 3.7

AGE AND WORK ETHIC

Age Frequency Percent Average Work Ethic Std. Dev.

15-25 649 9.5 12.37442 3.696849 26-35 1,341 19.6 12.71365 3.660605 36-45 1,700 24.9 12.43647 3.512539 46-55 1,464 21.4 12.12637 3.550016 56-65 901 13.2 12.14206 3.512488 66 and older 774 11.3 11.64083 3.39167

For almost all individuals work ethic varies throughout their lifetime. One‘s motivation to work can depend on several factors that vary with age. Some of these factors include career opportunities and having a family. One possible scenario is that before a person decides to start a family they are more ambitious because they want to establish themselves in a field and have more time to dedicate to work. On the other hand, for many people becoming older and more experienced can provide them with greater motivation to work harder. Financial burdens, career advancement opportunities, a clearer understanding one‘s goals can also generate greater motivation as a person ages. A second reason for differences among age groups is that each has their own shared experiences that affect their view on work, success, and wealth.

There are several studies that support the notion that work ethic changes varies across age groups. Most of these studies show that age is positively and significantly correlated with work ethic. Susman (1973) find older workers take greater pride in their job accomplishments and have greater overall job satisfaction9. Yousef (1995) also

9 G. Susman, ―Job enlargement: Effects of culture on worker responses‖. Industrial Relations, (1973). 12, 1-15.

43 finds a positive relationship between age and work ethic among Muslims living in the

United Arab Emirates10.

Based on economic intuition (more experience often leading to more opportunities for job advancement and higher salaries) and the findings of earlier studies, I predict that older age groups will have higher work ethic scores than younger age group.

Health Status (V)

In terms of their health status, respondents must choose between 1) very good health, 2) good health, 3) fair health, and 4) poor health. Refer to TABLE 3.8 for a description of health status.

TABLE 3.8

HEALTH STATUS AND WORK ETHIC

Health Observation Percent Average Work Ethic Std. Dev.

Very Good 1,733 25.38 12.33699 3.539223 Good 3,136 45.92 12.36193 3.517839 Fair 1,529 22.39 12.24199 3.657853 Poor 430 6.3 11.74419 3.671176

A person‘s health can have a profound effect on their ability to work. Someone with excellent health is physically more capable of working vigorously over long periods of time than someone with poorer health status. However, health status does not directly affect work ethic. Someone with good health can place a low value on work while someone with poor health can place a high value on work. Even though health status might not directly influence a person‘s value of work, it could provide

10 Yousef (2001), pg 160. 44 healthy people with more opportunities to work. On the other hand, it is possible that someone with poor health would focus their time and energy on other endeavors while pushing work into the periphery.

One study that supports the claim that health affects work ethic is O‘Brien and

Kabanoff (1979)11. Their results indicate that individuals with lower health are more likely to be unemployed and have lower work ethic. In this study, work ethic is defined in two ways: ones desire for influence, and desire for interaction and pressure at work.

Because unhealthy people are disadvantaged at work and most likely have fewer incentives to work harder, I predict that healthier people will have a higher work ethic score.

Country (N)

The countries included in this study are the United States, Canada, Germany,

Italy, Spain, Romania, Poland, Uruguay, Chile, Georgia, Bulgaria, Brazil, and Sweden.

Refer to TABLE 3.9 for a description of countries in the sample. Also, for religious demographics by country see TABLE 1 in the Appendix section.

11 Gorden E. O‘Brien, and Boris Kabanoff. ―Comparison of unemployed and employed workers on work values, locus of control and health variables‖. Austrailian Psychologist. Vol 14, Issue 2 (1979), pgs 143-154. 45

TABLE 3.9

COUNTRIES AND WORK ETHIC

Country Frequency Percent Average Work Ethic Std. Dev.

Italy 401 5.87 12.85786 3.444887 USA 441 6.46 12.61905 3.265522 Canada 589 8.62 12.80475 3.318815 Norway 582 8.52 12.9433 3.125909 Sweden 573 8.39 13.42583 3.192566 Poland 364 5.33 13.71154 3.318166 Brazil 560 8.2 12.06964 3.819328 Chile 529 529 12.73535 3.41092 Bulgaria 296 4.33 12.19595 3.548518 Romania 552 8.08 10.16123 3.61953 Uruguay 178 2.61 12.85955 3.448973 Georgia 1009 14.78 10.8107 3.396842 Germany 755 11.06 12.62649 3.539221

There are a myriad of reasons that one‘s country can affect one‘s work ethic.

The main purpose of including a country variable is to subsume all variability across countries to separate the effect of a country‘s culture from religious culture. Even if many religious teachings and values are similar across countries, they are also influenced by a countries culture, history, and economic circumstances. In addition, work ethic itself could be influenced by these country characteristics.

One indicator of work ethic disparities among nations is hours dedicated to work a week. For example, Japanese men work 45 hours per week while Spanish men only work 27 hours12 and since the 1970s Americans have worked longer hours than

12 Lutz Hendrick, ―Why do Hours Worked Differ Across Countries? Evidence from U.S. Immigrants‖. Working Paper, University of Iowa. (2004).

46

Europeans13. In terms of PWE attitudes and countries, there is conflicting evidence. In a study about work attitudes, Furnham (1984) finds no differences in several PWE measurements between British and Malaysian citizens14. Conversely, Furhman (1993) finds from a sample of 13 countries that wealthier countries have lower work ethic scores than poorer countries on average15. In addition, he finds that work ethic scores are highly correlated with country-wide beliefs concerning prestige, wealth, and power.

Despite the findings of Furham (1993) and Furnham (1984), I hypothesize that wealthier countries will have a higher work ethic than poorer countries. The rationale behind this prediction is the same as earlier predictions: people from wealthier countries are more educated and have more incentives to work hard.

Urbanization (U)

To indicate the level of urbanization of their hometown, respondents indicate the population size of where they live. The options for population size are 1) under 2,0000

2) 2,000 - 5,000 3) 5 - 10,000 4) 10 - 20,000 5) 20 - 50,000 6) 50 - 100,000 7) 100 -

500,000 and 8) 500,000 and more. Refer to TABLE 3.10 for a description of urbanization rates in the sample.

13 Orsetta Causa. ―Explaining Differences in Hours Worked among OECD Countries.‖ OECD Economics Department Working Papers, No 596, OECD Publishing (2008).

14 Furnham(1984), pg 158.

15 Adrian Furnham et al. ―A Comparison of Protestant Work Ethic Beliefs in Thirteen Nations.‖ The Journal of Social Psychology. (1993), Vol. 133, Issue 2, pp. 185-197. 47

TABLE 3.10

URBANIZATION AND WORK ETHIC

Urbanization Frequency Percent Average Work Ethic Std. Dev.

2,000 and Less 934 13.68 11.94647 3.49848 2,000-5,000 699 10.24 11.54363 3.582715 5,000-10,000 409 5.99 12.54523 3.755849 10,000- 20,000 534 7.82 12.56929 3.449158 20,000- 50,000 779 11.41 12.27985 3.531895 50,000-100,000 731 10.7 12.48974 3.468977 100,000-500,000 1621 23.74 12.55275 3.563077 500,000 and more 1122 16.4 12.30927 3.61305

In addition to cultural differences across countries, there are also major cultural differences within a country. Specifically stark distinctions in education, wealth, types of work, job opportunities, and social norms and values exist between urban and rural areas16. Therefore it is important to control for this cultural phenomenon in order to find the direct effect of religion on work ethic.

Many studies support the claim that there are economic and cultural differences between urban and rural areas. In general people living in urban areas tend to have higher wages, are more educated, and have more job options17. One study that examines work ethic and demographic variables is Furnham (1991)18. In the study,

Furnham scores 400 adolescents in Barbados for several measures of Protestant Work

Ethic. He finds no significant difference between rural and urban youths.

16 David Glaeser. Cities and Skills. Journal of Labor Economics (2001) Vol. 19 no. 2, pp. 5

17Ibid.

18 Adrian Furnham. ―The Protestant Work Ethic in Barbados‖. Journal of Social Psychology, Vol. pp. 131, 29-43.

48

Again, because people living in more urbanized areas earn higher wages and have more job opportunities, I believe they will have a higher work ethic score than people from more rural areas because they are rewarded more for working hard.

Gender (G) TABLE 3.11 describes work ethic by gender in the sample.

TABLE 3.11

GENDER AND WORK ETHIC

Gender Frequency Percent Average Work Ethic Std. Dev.

Male 3432 50.26 11.99883 3.525152 Female 3397 49.74 12.58316 3.585394

There are several theoretical reasons why gender can shape someone‘s work ethic. To begin, in almost all societies there are major discrepancies in how women and men are paid, their ability to receive an education, and opportunities for upward mobility in the workplace19. In addition, women are generally employed is less lucrative jobs and are more likely to work part time or not at all20. Because women are generally disadvantaged in all these aspects, most researchers hypothesize that women will have a lower work ethic. However, studies which measure differences in work ethic between men and women have found conflicting results. Frick (1995), Chusmir and Parker (1991), and Rowe and Snizek (1995) find no significant difference between men and women21. On the other hand, Ali et all (1995) find that men have slightly

19 William A. Darity Jr, and Patrick L. Mason. ―Evidence on Discrimination in Employment: Codes of Color, Codes of Gender‖. The Journal of Economic Perspecitives, Vol. 12, No.2 (1998), pp. 63- 90. 20 Ibid.

21 Frick (1995), pp. 675. 49 higher work ethics than women22. In addition, Maume (2006) finds that women prioritize family obligations in response to their husband‘s success and work efforts23.

Because women generally work fewer hours, prioritize family over work, and receive lower wages, I believe they will have lower work ethic than men.

World Development Indicators (WDI) and the World Income Inequality Database (WIID)

Income (Y)

Although the income of the respondent is not measured by the World Values

Survey, I was able to calculate an estimate through a multi-step process. First I found

GDP by country in the WDI database and percent of GDP owned by each decile by country from the WIID. All GDPs are from the year 2005 while wealth owned by each decile ranged from the years 2000 to 2005. Next I multiplied GDP by country by its corresponding decile percents in order to calculate GDP by decile for each country.

Then I divided each country‘s population by ten to calculate population per decile.

Next, GDP per decile per country was divided by the corresponding tenth of the population. Once I attained income by decile and country, I merged it with each respondent‘s decile and country in order to create the variable Y.

Barbara Parker, and Leonard H. Chusmir, Motivation needs and their relationship to life success. Human Relations. (1991) Vol 44, pp 1301-12

Rowe and Snizek (1995).

22 A.J. Ali, T. Falcone, and A.A. Azim, ―Work Ethic in the US and Canada.‖ (1995).

23 David Maume, ―Gender Differences in Restricting Work Efforts Because of Family Responsibilities‖. Journal of Marriage and Family. Vol 68, pp 859-869 (2006).

50

Like my earlier predictions, I predict that income will have a positive relationship with work ethic because people with higher incomes are more likely to receive formal education and pecuniary rewards for working hard.

Refer to FIGURE 3.1 for the distribution of income in the sample and TABLE 3.12 for wealth by decile per country.

FIGURE 3.1

DISTRIBUTION OF INCOME (Y) IN DOLLARS

8.0e-05

6.0e-05

Density

4.0e-05

2.0e-05 0

0 50000 100000 150000 Y

51

TABLE 3.12

WEALTH BY DECILE PER COUNTRY

Data Country Yr 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th

Sweden 2003 4.10 6.00 6.80 7.60 8.50 9.30 10.40 11.80 13.70 21.80 USA 2000 1.81 3.53 4.79 6.04 7.32 8.67 10.30 12.45 16.07 29.03 Uruguay 2005 1.77 2.96 4.00 5.11 6.35 7.77 9.52 12.12 16.63 33.76 Brazil 2005 1.00 1.98 2.83 3.73 4.84 6.23 7.89 10.61 15.89 44.98 Canada 2000 2.71 4.58 5.82 6.90 8.02 9.14 10.55 12.39 15.14 24.75 Chile 2003 1.40 2.46 3.23 4.05 4.99 6.15 7.70 10.09 14.97 44.96 Georgia 2002 0.96 2.60 3.90 5.13 6.45 7.86 9.84 12.44 16.67 34.15 Germany 2004 2.87 4.83 5.97 7.05 8.06 9.15 10.58 12.38 15.28 23.78 Italy 2000 2.17 4.10 5.31 6.51 7.71 9.12 10.43 12.43 15.33 26.90 Norway 2000 3.90 5.72 6.60 7.35 8.15 9.09 10.25 11.72 13.85 23.37 Poland 2003 2.24 4.29 5.43 6.55 7.68 8.95 10.44 12.39 15.46 26.57 Romania 2000 3.11 4.98 6.13 7.11 8.14 9.23 10.48 12.15 14.80 23.87 Bulgaria 2001 1.23 2.93 4.16 5.13 6.07 7.09 8.35 10.19 13.28 41.56

Consumption (C = (1-s)*Y)

Like income, consumption was not included in the World Values Survey. To

remedy this issue, I multiplied one minus the savings rate by each respondent‘s

calculated income. I found data for savings rates by country in 2005 from the World

Development Indicators database. However, because I could not find decile level

savings rates I had to assume savings rate was constant among income deciles. Clearly

this is an unrealistic assumption and a major limitation of the data. Refer to TABLE

3.13 for savings rates by country.

52

TABLE 3.13

SAVINGS RATES BY COUNTRY

Savings Country Savings Rate Country Rate

Sweden 0.255680433 Germany 0.221746499 USA 0.141450114 Italy 0.206147763 Uruguay 0.196292015 Norway 0.376373016 Brazil 0.198140496 Poland 0.185249745 Canada 0.257992714 Romania 0.122566452 Chile 0.307113911 Bulgaria 0.124619823 Georgia 0.156678227

CHAPTER IV

RESULTS

This chapter will explain the issues with the model described in the theory chapter of this paper as well as present and interpret the results of an Ordinary Least

Squares (OLS) model. To compensate from missing results from the original model, I decided to compare OLS results from my data to the results of other economic papers using similar regressions. Papers that implement an OLS model cited in my literature review are Guiso (2006), Voert (1993), Arslan (2001), Giorgi and Marsh (1990),

Noland (2005), Grier (1997), Blum and Dudley (2001), and Barro and Cleary (2003).

Unfortunately, using an OLS model assumes a linear relationship in the parameters between the dependent variable, work ethic, and all of independent variables.

Furthermore, it is not based on economic theory.

Because my data and variables differ enough from preceding studies, drawing comparisons in results is still a contribution to the current literature. However, because there is no consensus on the definition of work ethic among researchers, I will be extremely cautious in comparing my results with other studies‘.

The chapter is organized in the following way: problems with the original model will be explained, followed by the OLS regression, table of variables, results, and comparisons between my findings and previous ones.

53

54

Problems with the Original Model

Unfortunately, the parameters in the model cannot be estimated because there is not enough variation among the variables given the complexity of the model. There is not enough variation because there are too many variables and the sample size is too small. However, this problem can be remedied given more time.

Econometric Problems with OLS

A major problem with the OLS model is significant non-normality in the residuals. This problem could not be resolved by taking linear transformation of the continuous variables. Despite the fact that the regression does not meet the normality assumption, I will still interpret the results below.

Ordinary Least Squares (OLS) Regression Results

Due to problems with the model an OLS regression of all the independent variables on work ethic scores was run. However, consumption and wage are excluded because they are essentially measuring the same concept as income. Furthermore, several of the independent, categorical variables are aggregated in order to generate larger sample sizes and variances between groups. The OLS model is written below, followed by several tabless explaining the variables (TABLES 4.1, 4.2, 4.3, 4.4, 4.5,

4.6, 4.7, 4.8, and 4.9).

55

EQUATION 4.1

Regression without Interaction Terms

*Age1, Protestant, Male, USA, LowUrbanization, BadHealth, WorkingandLower, NotReligious, PrimaryorLess

TABLE 4.1

DESCRIPTION OF H, Y, AND AGE

Variable Name Variable Type Description H Dep; Continuous Proxy for Work Ethic Y Indep; Continuous Income Age Age1 (Omitted) Indep; Categorical Ages 15-25 Age2 Indep; Categorical Ages 26-35 Age3 Indep; Categorical Ages 36-45 Age4 Indep; Categorical Ages 46-55 Age5 Indep; Categorical Ages 56-65 Age6 Indep; Categorical Ages 66 and older

56

TABLE 4.2

DESCRIPTION OF RELIGIOUS VARIABLES

Variable Name Variable Type Description Religious Denomination Protestant (Omitted) Indep; Categorical Protestant Respondents Evangelical Indep; Categorical Evangelical Respondents Muslim Indep; Categorical Muslim Respondents Orthodox Indep; Categorical Orthodox Respondents RomanCatholic Indep; Categorical Roman Cathoic Respondents ChurchofSweden Indep; Categorical Members of the Church of Sweden

TABLE 4.3

DESCRIPTION OF GENDER VARIABLES

Variable Name Variable Type Description Gender Male (Omitted) Indep; Categorical Male Respondents Female Indep; Categorical Female Respondents

TABLE 4.4

DESCRIPTION OF COUNTRY VARIABLES

Variable Name Variable Type Description Country USA (Omitted) Indep; Categorical U.S. Respondents Germany Indep; Categorical German Respondents Canada Indep; Categorical Canadian Respondents Uruguay Indep; Categorical Uruguayan Respondents Georgia Indep; Categorical Georgian Respondents Poland Indep; Categorical Polish Respondents Romania Indep; Categorical Romanian Respondents Norway Indep; Categorical Norweigen Respondents Italy Indep; Categorical Italian Respondenst Sweden Indep; Categorical Swedish Respondents Brazil Indep; Categorical Brazilian Respondents Chile Indep; Categorical Chilean Respondents Bulgaria Indep; Categorical Bulgarian Respondents 57

TABLE 4.5

DESCRIPTION OF URBANIZATION VARIABLES

Variable Name Variable Type Description Urbanization Low Urbanization (Omitted) Indep; Categorical Population of 2,000 to 10,000 Medium Urbanization Indep; Categorical Population of 10,000 to 100,000 High Urbanization Indep; Categorical Population of 100,000 or greater

TABLES 4.6

DESCRIPTION OF HEALTH VARIABLES

Variable Name Variable Type Description Health Bad Health (Omitted) Indep; Categorical Fair or poor health Good Health Indep; Categorical Very good or good health

TABLE 4.7

DESCRIPTION OF SOCIOECONOMIC VARIABLES

Variable Name Variable Type Description Socioeconomic Status Working and Lower Class Working and Lower (Omitted) Indep; Categorical Respondents Lower Middle Class LowerMiddle Indep; Categorical Respondents Upper Middle Class UpperMiddle Indep; Categorical Respondents Upper Indep; Categorical Upper Class Respondents

58

TABLE 4.8

DESCRIPTION OF PARTICIPATION VARIABLES

Variable Name Variable Type Description Participation Attends services less than Not Religious (Omitted) Indep; Categorical once a year or never Attend services only on Not Observant Indep; Categorical holy days or once a year Attends services Somewhat Observant Indep; Categorical once a month Attends services once a week Observant Indep; Categorical or more than once a week

TABLE 4.9

DESCRIPTION OF EDUCATION VARIABLES

Variable Name Variable Type Description Education No formal education, incomplete primary, Primary or Less (Omitted) Indep; Categorical and complete primary Complete secondary education: Secondary Indep; Categorical technical or preparatory Some university education University Indep; Categorical or university degree

In addition to running all of my independent variables on my dependent variable, regressions with interaction terms are also run. The interactions include 1) religion and gender, 2) religion and participation, 3) religion and certain countries, 4) religion and education, and 5) religion and urbanization. These interaction terms allow for analysis of religious denomination‘s effect on work ethic across demographic variables. All regressions use the same omitted variables as above except for the regressions between religion and four countries: Germany, Romania, Chile, and

Canada. Regressions for the other countries are not used because the sample size of 59 only one religion is large. For religious demographics by country in the sample, refer to

TABLE 3.8 in the data section.

Observations from the Regression without Interaction Terms

In my first regression without interactions, there are several interesting findings.

Variables that are significant at the 10% level or less are Age2, Age4, Age5, Age6,

Female, Georgia, Poland, Romania, Brazil, Bulgaria, HighUrbanization, Healthy,

UpperMiddle, Observant, SomewhatObservant, NotObservant, Secondary, and

University. When interpreting the coefficients of these variables, it is important to remember that the results are ordinal, not cardinal. In other words, if one coefficient is twice the size of another coefficient it does not mean that a group has twice as much work ethic as another. Because my metric for work ethic is subjective and qualitative, it does not have constant returns to scale. Therefore I will rank various groups based on the findings. For complete regression results, refer to TABLE 1 in the appendix.

Age For age, 26 to 35 year olds have lower work ethic on average than 15 to 25 year olds. However, age groups 36-45, 56-65, and 66 and older have increasing more work ethic in comparison to 15 to 25 year olds. For the most part, these findings are consistent with all studies mentioned in the data section of this paper.

60

Gender In comparing male vs. female work ethic, females have a significantly lower work ethic than men. This finding is in agreement with Ali et al (1995)1 and Maume

(2006)2.

Country Georgia, Romania, Brazil, and Bulgaria all have higher work ethic in comparison to the United States while Poland has lower work ethic than the United

States. The ranking of these countries from highest to lowest work ethic is 1) Romania

2) Georgia, 3) Bulgaria, 4) Brazil, 5) The United States, and 6) Poland. To see if these countries were also significantly different than other developed countries, I omitted

Germany, Norway, Sweden, Italy and Canada and found the same results. Furthermore, all developed countries were not significantly different the United States. In general, these results are in agreement with Furnham (1993) who found that less developed countries had higher work ethic in comparison to developed countries3.

Urbanization

Results from urbanization levels indicate that locations with population ranging from 10,000 to 100,000 are not significantly different than areas with populations of

2,000 to 10,000. However, it is possible that the lack of significance is due to fact that this variable is aggregated and the sample size is small. Conversely, places with

1 A.J. Ali, T. Falcone, and A.A. Azim, ―Work Ethic in the US and Canada.‖ (1995).

2 David Maume, ―Gender Differences in Restricting Work Efforts Because of Family Responsibilities‖. Journal of Marriage and Family. Vol 68, pp 859-869 (2006).

3 Adrian Furnham et al. ―A Comparison of Protestant Work Ethic Beliefs in Thirteen Nations.‖ The Journal of Social Psychology. (1993), Vol. 133, Issue 2, pp. 185-197.

61 population sizes of 100,000 and greater had significantly lower work ethic than places with populations of 2,000 to 10,000. This finding is in disagreement with Furnham

(1991), which finds no difference between urban and rural youths in Barbados4.

Health Differences in work ethic between people with very good or good health and people with fair or poor health are highly significant. This finding is both in agreement with theory as well as O‘Brien and Kabanoff (1979)5.

Socioeconomic Class

The only categorical variable that is significant under socioeconomic class is upper middle class. In this sample people who consider themselves upper middle class have lower work ethic than people who consider themselves working or lower class.

These results are the same as Furnham and Muhiudeen (1984), who finds that working class respondents in Malyasia and Britain had high PWE scores than middle class respondents6.

Participation

Within religious participation, all categorical variables are significantly different that the omitted variable, ―Not Religious‖. Frequency of religious service is positively correlated with greater work ethic. On average, people who attend services once a week

4 Adrian Furnham. ―The Protestant Work Ethic in Barbados‖. Journal of Social Psychology, Vol. pp. 131, 29-43.

5 Gorden E. O‘Brien, and Boris Kabanoff. ―Comparison of unemployed and employed workers on work values, locus of control and health variables‖. Austrailian Psychologist. Vol 14, Issue 2 (1979), pgs 143-154. 6 Adrian Furnham. ―The Protestant work ethic in Britain and Malaysia‖. Journal of Social Psychology, (1984) 122:2, pp. 157-161.

62 or more have the strongest work ethic, followed by 1) respondents who attend services once a month, 2) respondents who attend on holy days or once a year, and 3) respondents who attend less than once a year or never. Unfortunately, there are no studies that make a direct link between participation and work ethic to compare to.

Education

Education variables are the only variables that deviate from the all of the literature. While almost all of the literature finds that education has a positive value on work ethic, higher levels of education are negatively correlated with work ethic. Both respondents with university level education and secondary education have a significantly lower work ethic than respondents with primary school level education or less.

Religion

The most interesting result in this regression is that none of the religion variables are significant. This finding is consistent with some studies including

Blackwood (1979)7, Bouma and Dixon (1973)8, and Ray (1982)9, but is contradictory to most recent studies.

7 Larry Blackwood, ―Social Change and Commitment to the Work Ethic.‖ Pp. 241-256 in The Religious Dimension: New Directions in Quantitative Research. New York: Academic Press, (1979).

8 Dixon, ―Beyond Lenski: A Critical Review of Recent ‗Protestant Ethic Research‘‖. Journal for the Scientific Study of Religion, (1973) pp. 141-155.

9 Row and W.E. Snizek, ―Gender differences in work values-perpetuating the myth.‖ (1995) Work and Occupations, Vol. 22 No. 2, pp 215-29

63

Observations from Regressions with Interaction Terms

Gender and Religion Interaction Variables

The second regression is exactly the same as the first plus interaction variables for gender and religion. These variables allow for comparisons among males and females of different religious denominations. When male and Protestants are omitted from the model, Muslim females have significantly lower work ethic than Protestant females while Orthodox females have significantly higher work ethic than Protestant females. These findings suggest that the religious variables in the first regression are biased, and that a model with interaction terms is more accurate. For complete regression results, see TABLE 2 in the Appendix.

Country and Religion Interaction Variables

The four countries in which interaction variables with religion are created are

Brazil, Germany, Canada, and Chile. The two dominant religions in the sample are

Roman Catholic and Evangelical in Brazil, Roman Catholic and Evangelical in

Germany, Roman Catholic and Protestant in Chile, and Roman Catholic and Protestant in Canada. In all four cases there are no significant differences between religious sects in term of work ethic. For complete regression results, see TABLE 3 in the Appendix.

Participation and Religion Interaction Variables

In this sample, there are no significant differences between religious denomination and work ethic among varying levels of religious participation. However, with the inclusion of the interaction terms, the variable for Roman Catholics becomes 64 significant and negatively associated with work ethic in comparison to Protestants. For complete regression results, see TABLE 4 in the Appendix.

Education and Religion Interaction Variables

When adding education interacted with religion to the model, several differences are found between religions. The results show that Orthodox Christians with university and secondary level education have a higher work ethic score in comparison to

Protestants with university and secondary level education. Additionally, members of the Church of Sweden with university level education have a higher work ethic score than Protestants with university level education. For complete regression results, see

TABLE 5 in the Appendix.

Urbanization and Religion Interaction Variables

Several of the interaction terms between urbanization and religion are highly significant in the model. Both Muslims and Orthodox Christians living in highly urban areas (population of 100,000 or more) have significantly lower work ethic scores than

Protestants in urban areas, Muslims more so than Orthodox Christians. Members of the

Church of Sweden living in highly urbanized areas, on the other hand, have significantly higher work ethic than Protestants. Another interesting observation is that the variable Muslim becomes significant and indicates that compared to Protestants,

Muslims have higher work ethic. For complete regression results, see TABLE 6 in the

Appendix.

65

Conclusion

In conclusion, approximately half of my predictions were realized in the results.

The findings for age, gender, religious participation, and health are consistent with my hypotheses while variables for country, urbanization, education, and socioeconomic status are not. The main variable examined in this thesis, religious denomination, has conflicting results. Although no religious denominations are significant in the first regression without interaction turns, Muslim, Orthodox and Roman Catholic become significantly different than Protestant when interaction terms are added. Additionally, across demographics the results suggest that religion is significant in determining work ethic.

A further analysis of the results and hypotheses are examined in the final chapter of this thesis. CHAPTER V

CONCLUSION

This final chapter will offer conclusions concerning this study in the context of the academic literature regarding this subject. The primary purpose of this study was to challenge the ―rational Marxian‖ view of culture, which assumes culture cannot affect economic circumstances and that prices are the sole determinate of an individual‘s choices. This perspective permeates all aspects of economic theory and is shown to be fundamentally flawed by academics primarily in the fields of sociology, psychology, and political science. There is a substantial body of work cited in the literature review of this paper, which finds that widespread values such as trust and tolerance of others, work ethic, and entrepreneurship can have a profound impact on economic policies and activities within a country. On a macro level, differences in values and attitudes have the potential to explain disparities in economic growth not accounted for in economic models.

The specific value analyzed in this study is work ethic and how it is affected by religiosity, defined by religious denomination and religious participation. There are several reasons for examining religion: 1) religion rewards and punishes behaviors, 2) defines moral and ethical beliefs, 3) is still pertinent even in developed economies, 4) is slow-moving component of culture, and 5) is usually ―given‖ to an individual at birth.

In order to accomplish this goal, I developed a nonlinear model for work ethic founded in microeconomic theory. The model takes the form of a Cobb-Douglas utility

66

67 function, where the consumer chooses between consumption and leisure. The variable for leisure is proxied by work ethic, which is measured by the respondent‘s answers to several work-related questions from the World Values Survey. The consumer then maximizes their utility based on their preferences for leisure and work.

Unfortunately, due to the small size of my sample, large number of variables, and the complexity of the model, I was unable to find estimations with my model.

However, I did run several OLS regressions. I was then able to compare results generated with my sample and variables to similar studies that also use OLS regressions.

The next part of this chapter will examine the results and hypotheses.

Results Consistent with my Hypotheses

The significant and positive relationship found between age, males, good health, religious participation, and work ethic all support the hypotheses as well as the current literature. The significance of age has two possible explanations: 1) that work ethic increase throughout an individual‘s lifetime or 2) older generations value work more than younger generations because of how they were raised and their shared experiences.

In the absence of time series data, the reason is unclear. For gender, the fact that men have higher work ethic than women also has two explanations. This finding either 1) supports the theory disadvantages in the workplace negatively affect work ethic or 2) that men are more inclined to work harder because society expects them to provide for their families and be financially successful.

My prediction for religious participation is matched by the data: respondents who attended religious services have a higher work ethic than people do not attend 68 services. Although the exact causes are unclear, religion‘s emphasis on hard work and community involvement could be related. There could also be other variables correlated with religious participation that were not included in the model. The significance of religious denomination with the addition of interaction terms is only partly consistent with my hypothesis and the literature. One of the problems with these variables is that certain religious denominations were both positively and negatively correlated with work ethic in comparison to Protestants depending on the interaction terms that are used. This contradiction shows that people of a single religious denomination are extremely varied. It is also possible that these findings are the result of a biased sample.

Results Inconsistent with my Hypotheses

The four variables with results opposing my hypotheses are country, urbanization, socioeconomic status, and education. One commonality between the variables is that individuals from groups that tend to be wealthier, more educated, and receive more pecuniary rewards at work (higher wages, promotions, bonuses etc.), have lower work ethic scores. Respondents from the more developed countries have lower work ethic than less developed countries such as Romania, Georgia, Bulgaria, and

Brazil; respondents from highly urbanized areas have lower scores compared to respondents from rural areas; respondents who consider themselves upper-middle class score lower than respondents who identify as working or lower class; respondents with university education have lower work ethic than respondents with primary education or less. The finding that individuals born in more advantageous circumstances have lower work ethic others has significant implications. One possible interpretation is these 69 individuals take their resources and opportunities for granted. Another is that monetary incentives are not sole determinant of work ethic; an individual‘s values also matter.

Some of these findings are also supported by the existing literature. Furnham

(1993)1 finds that poorer countries have higher work ethic compared to wealthier countries and Furnham and Muhiudeen (1984)2, find that working class respondents in

Malyasia and Britain have high PWE scores than middle class respondents. On the other hand, almost all literature is contrary to the education findings in the regressions.

Furthermore, the negative correlation between education and work ethic is very counterintuitive.

Academic Implications

The results in this thesis provide some evidence that incentives and prices are not the only factors that influence individual preferences. An individual‘s values can alter choices that are not consistent with economic theory. Although results could not be engendered by my model for work ethic, similar models that incorporate cultural components from surveys can greatly enhance to the field of economics and possibly produce new theories or alter existent ones. Understanding the role that values and attitudes play in economic outcomes could also explain disparities in economic development.

1 Adrian Furnham et al. ―A Comparison of Protestant Work Ethic Beliefs in Thirteen Nations.‖ The Journal of Social Psychology. (1993), Vol. 133, Issue 2, pp. 185-197.

2 Adrian Furnham. ―The Protestant work ethic in Britain and Malaysia‖. Journal of Social Psychology, (1984) 122:2, pp. 157-161. APPENDIX

TABLE 1

Religious Demographics by Country

Country Religion Frequency Country Religion Frequency

Italy Brazil Roman Catholic 398 Evangelical 118 USA Orthodox 2 Protestant 187 Protestant 12 Roman Catholic 130 Roman Catholic 397 Muslim 6 Bulgaria Orthodox 8 Muslim 23 Protestant 126 Orthodox 270 Roman Catholic 342 Protestant 2 Norway Romania Muslim 8 Orthodox 501 Orthodox 18 Protestant 24 Protestant 540 Roman Catholic 27 Roman Catholic 11 Uruguay Sweden Evangelical 24 Muslim 2 Protestant 11 Protestant 3 Roman Catholic 122 Roman Catholic 14 Georgia Church of Sweden 531 Muslim 21 Poland Orthodox 973 Orthodox 3 Roman Catholic 2 Protestant 3 Germany Roman Catholic 352 Evangelical 460 Chile Muslim 2 Protestant 104 Orthodox 7 Roman Catholic 424 Roman Catholic 268

70

71

TABLE 2

Regression without Interaction Terms

H (work ethic) Coef. Std. Err. t P>t

Y (Income) -4.03E-06 2.79E-06 -1.44 0.149 Evangelical -0.2207118 0.2777026 -0.79 0.427 Muslim -0.3269954 0.5124996 -0.64 0.523 Orthodox 0.0561794 0.3726727 0.15 0.88 Roman Catholic 0.1505141 0.1855761 0.81 0.417 Church of Sweden 0.2290004 0.7989031 0.29 0.774 Age 2 0.3084995 0.1730486 1.78 0.075 Age 3 0.0026776 0.1663289 0.02 0.987 Age 4 -0.278685 0.1697218 -1.64 0.101 Age 5 -0.4827471 0.1889184 -2.56 0.011 Age 6 -0.9211473 0.200813 -4.59 0 Female 0.5473548 0.0859193 6.37 0 Germany 0.0867042 0.2897288 0.3 0.765 Canada -0.0760345 0.245771 -0.31 0.757 Uruguay -0.0831941 0.3467768 -0.24 0.81 Georgia -2.232199 0.4284465 -5.21 0 Poland 0.8559986 0.292703 2.92 0.003 Romania -2.915726 0.4082749 -7.14 0 Norway 0.2724081 0.2515084 1.08 0.279 Italy 0.1164501 0.2810881 0.41 0.679 Sweden 0.3796308 0.8006649 0.47 0.635 Brazil -0.9883816 0.2913843 -3.39 0.001 Chile -0.3807562 0.2694727 -1.41 0.158 Bulgaria -1.041552 0.4560587 -2.28 0.022 High Urbanization 0.3099309 0.1102821 2.81 0.005 Medium Urbanization 0.1342796 0.1136254 1.18 0.237 Healthy -0.3719533 0.1055358 -3.52 0 Lower Middle Class -0.0398965 0.1029899 -0.39 0.698 Upper Middle Class -0.3255816 0.1300121 -2.5 0.012 Upper Class -0.5096346 0.4115914 -1.24 0.216 Observant -0.5953227 0.1305845 -4.56 0 Somewhat Observant -0.5499706 0.1371963 -4.01 0 Not Observant -0.2592991 0.1120694 -2.31 0.021 Secondary Education 0.2244159 0.1129753 1.99 0.047 72

University Education 0.3484227 0.1300196 2.68 0.007 _cons 13.12321 0.3010071 43.6 0

TABLE 3

Female and Religion Interaction Terms

Work Ethic Score (H) Coef. Std. Err. t P>t

Y (Income) -3.69E-06 2.82E-06 -1.31 0.191 Age 2 0.3205639 0.1732848 1.85 0.064 Age 3 0.0112007 0.1665069 0.07 0.946 Age 4 0.2757787 0.1698089 -1.62 0.104 Age 5 0.4752511 0.1889377 -2.52 0.012 Age 6 0.9148034 0.2008371 -4.55 0 Female 0.8128183 0.197515 4.12 0 Evangelical 0.1214082 0.3312978 -0.37 0.714 Orthodox 0.3577793 0.3959732 0.9 0.366 Roman Catholic 0.2902707 0.2211523 1.31 0.189 Church Of Sweden 0.2982086 0.8239287 0.36 0.717 Muslim 0.8803061 0.5926225 -1.49 0.137 Female*Evangelical 0.1738741 0.3430216 -0.51 0.612 Female*Muslim 1.484899 0.8389473 1.77 0.077 Female*Orthodox 0.5966471 0.2588176 -2.31 0.021 Female*Roman Catholic 0.2589756 0.2406058 -1.08 0.282 Female*Church Of Sweden 0.0138165 0.3357904 -0.04 0.967 Germany 0.056584 0.288826 0.2 0.845 Canada 0.1065562 0.2455123 -0.43 0.664 Uruguay 0.0941856 0.3464717 -0.27 0.786 Georgia -2.245018 0.4244073 -5.29 0 Poland 0.8394678 0.2927719 2.87 0.004 Romania -2.927211 0.4053228 -7.22 0 Norway 0.2553468 0.25192 1.01 0.311 Italy 0.0943067 0.2809241 0.34 0.737 Sweden 0.3153231 0.8068347 0.39 0.696 Brazil -1.000392 0.2912377 -3.43 0.001 Chile 0.3908202 0.2692916 -1.45 0.147 Bulgaria -1.025433 0.4522164 -2.27 0.023 High Urbanization 0.3068781 0.1105394 2.78 0.006 Medium Urbanization 0.1294946 0.1136225 1.14 0.254 Healthy 0.3838059 0.1053784 -3.64 0 73

Lower Middle Class 0.0389029 0.102989 -0.38 0.706 Upper Middle Class 0.3198611 0.1300138 -2.46 0.014 Upper Class 0.5009489 0.4132671 -1.21 0.225 Observant 0.5832395 0.1308594 -4.46 0 Somewhat Observant 0.5343576 0.1373027 -3.89 0 Not Observant -0.257287 0.1119311 -2.3 0.022 Secondary Education 0.2196112 0.113024 1.94 0.052 University Education 0.3483688 0.1299532 2.68 0.007 _cons 12.98998 0.3140567 41.36 0

TABLE 4

Brazil and Religion (Roman Catholic Omitted)

Work Ethic Score (H) Coef. Std. Err. t P>t

Y (Income) -4.08E-06 2.79E-06 -1.46 0.144 Brazil*Evangelical 0.6891023 0.4662012 1.48 0.139 Brazil*Protestant -0.8914331 0.9818335 -0.91 0.364 Brazil*Muslim (omitted) Brazil*Church Of Sweden (omitted) Brazil*Orthodox 3.558066 2.632263 1.35 0.177 Protestant -0.1212169 0.1890442 -0.64 0.521 Muslim -0.535478 0.5231021 -1.02 0.306 Church of Sweden 0.0772424 0.7894697 0.1 0.922 Orthodox -0.1814536 0.3801874 -0.48 0.633 Evangelical -0.5966291 0.2497179 -2.39 0.017 Age 2 0.309387 0.1732021 1.79 0.074 Age 3 0.0041899 0.1665771 0.03 0.98 Age 4 -0.2757237 0.1698881 -1.62 0.105 Age 5 -0.4842238 0.1887991 -2.56 0.01 Age 6 -0.9099996 0.2009717 -4.53 0 Female 0.5503005 0.0859243 6.4 0 Germany 0.2334638 0.3024043 0.77 0.44 Canada -0.071899 0.2460836 -0.29 0.77 Uruguay -0.0411671 0.3481687 -0.12 0.906 Georgia -2.144599 0.4277583 -5.01 0 Poland 0.8762227 0.2936632 2.98 0.003 Romania -2.832264 0.4076868 -6.95 0 Norway 0.2479394 0.2518491 0.98 0.325 74

Italy 0.1264732 0.2821377 0.45 0.654 Sweden 0.3833147 0.7999241 0.48 0.632 Brazil -1.06962 0.3066948 -3.49 0 Chile -0.3744631 0.2700011 -1.39 0.166 Bulgaria -0.9580996 0.4554782 -2.1 0.035 High Urbanization 0.3058754 0.110309 2.77 0.006 Medium Urbanization 0.1342099 0.1136285 1.18 0.238 Healthy -0.3736354 0.1055111 -3.54 0 Lower Middle Class -0.046685 0.1030449 -0.45 0.651 Upper Middle Class -0.3356857 0.1302443 -2.58 0.01 Upper Class -0.5201289 0.4119519 -1.26 0.207 Observant -0.6224308 0.1309234 -4.75 0 Somewhat Observant -0.5530264 0.137174 -4.03 0 Not Observant -0.2582119 0.1120983 -2.3 0.021 Secondary Education 0.2314238 0.1130228 2.05 0.041 University Education 0.3653919 0.1302572 2.81 0.005 _cons 13.26988 0.3180932 41.72 0

TABLE 5

Canada and Religion (Protestant Omitted)

H Coef. Std. Err. t P>t

-4.04E- Y 06 2.79E-06 -1.45 0.148 Canada*Catholic 0.41062 0.390221 1.05 0.293 Canada*Orthodox -0.99016 1.059162 -0.93 0.35 Canada*Sweden (omitted) Canada*Muslim 2.17785 2.065238 1.05 0.292 Canada*Evangelical (omitted) Evangelical -0.3385 0.306134 -1.11 0.269 Orthodox 0.01838 0.396695 0.05 0.963 Muslim -0.55045 0.512237 -1.07 0.283 Church of Sweden 0.11607 0.801049 0.14 0.885 Roman Catholic 0.029986 0.22743 0.13 0.895 Age 2 0.313508 0.173318 1.81 0.071 Age 3 0.013713 0.166589 0.08 0.934 Age 4 -0.26696 0.169962 -1.57 0.116 Age 5 -0.46907 0.189146 -2.48 0.013 Age 6 -0.91145 0.201149 -4.53 0 75

Female 0.549793 0.085919 6.4 0 Germany 0.153563 0.301225 0.51 0.61 Canada -0.34102 0.33303 -1.02 0.306 Uruguay -0.02207 0.355647 -0.06 0.951 Georgia -2.2433 0.446204 -5.03 0 Poland 0.926636 0.304415 3.04 0.002 Romania -2.9263 0.423405 -6.91 0 Norway 0.225139 0.254631 0.88 0.377 Italy 0.186365 0.293553 0.63 0.526 Sweden 0.440188 0.799475 0.55 0.582 Brazil -0.91947 0.302639 -3.04 0.002 Chile -0.33374 0.275941 -1.21 0.227 Bulgaria -1.0412 0.471694 -2.21 0.027 High Urbanization 0.305992 0.110333 2.77 0.006 Medium Urbanization 0.12852 0.113708 1.13 0.258 Healthy -0.37075 0.105596 -3.51 0 Lower Middle Class -0.03891 0.103007 -0.38 0.706 Upper Middle Class -0.32815 0.129957 -2.53 0.012 Upper Class -0.49943 0.411952 -1.21 0.225 Observant -0.60072 0.130541 -4.6 0 Somewhat Observant -0.55238 0.137394 -4.02 0 Not Observant -0.26347 0.111997 -2.35 0.019 Secondary Education 0.22567 0.11299 2 0.046 University Education 0.353592 0.130031 2.72 0.007 _cons 13.1676 0.302859 43.48 0

76

TABLE 6

Chile and Religion (Protestant Omitted)

Work Ethic (H) Coef. Std. Err. t P>t

Y (Income) -4.05E-06 2.79E-06 -1.45 0.146 Chile*Roman Catholic -0.332817 0.4374619 -0.76 0.447 Chile*Evangelical (omitted) Chile*Church Of Sweden (omitted) Chile*Orthodox (omitted) Chile*Muslim (omitted) Evangelical -0.1343862 0.2937293 -0.46 0.647 Church Of Sweden 0.2982322 0.8041709 0.37 0.711 Orthodox 0.1068831 0.3736992 0.29 0.775 Muslim -0.2770399 0.5131219 -0.54 0.589 Roman Catholic 0.2388052 0.2105556 1.13 0.257 Age 2 0.3099283 0.1731174 1.79 0.073 Age 3 0.0047114 0.1664384 0.03 0.977 Age4 -0.2744847 0.1698394 -1.62 0.106 Age 5 -0.4772399 0.1890428 -2.52 0.012 Age 6 -0.9141736 0.2009644 -4.55 0 Female 0.5485978 0.0859629 6.38 0 Germany 0.0335972 0.2979172 0.11 0.91 Canada -0.1053021 0.2491958 -0.42 0.673 Uruguay -0.130005 0.3521584 -0.37 0.712 Georgia -2.249212 0.4284502 -5.25 0 Poland 0.8076803 0.2995367 2.7 0.007 Romania -2.931122 0.4082565 -7.18 0 Norway 0.3030772 0.2522364 1.2 0.23 Italy 0.0640502 0.2893566 0.22 0.825 Sweden 0.3428023 0.8044298 0.43 0.67 Brazil -1.035893 0.2981439 -3.47 0.001 Chile -0.1492294 0.4083574 -0.37 0.715 Bulgaria -1.057852 0.456199 -2.32 0.02 High Urbanization 0.3091385 0.1102855 2.8 0.005 Medium Urbanization 0.1316051 0.1136766 1.16 0.247 Healthy -0.3697273 0.1056726 -3.5 0 lowermid2 -0.039577 0.1030017 -0.38 0.701 uppermid2 -0.3247106 0.1299945 -2.5 0.013 upper2 -0.5067892 0.4120604 -1.23 0.219 obser2 -0.6043357 0.1310737 -4.61 0 77

oam2 -0.5522735 0.1373169 -4.02 0 notobser2 -0.2617897 0.112098 -2.34 0.02 mededu2 0.2254735 0.1129891 2 0.046 highedu2 0.3506435 0.1300409 2.7 0.007 _cons 13.0863 0.302838 43.21 0

TABLE 7

Germany and Religion (Roman Catholic Omitted)

H Coef. Std. Err. t P>t

Y (Income) 4.16E+06 2.79E-06 -1.49 0.137 Evangelical*Germany -0.50032 0.437373 -1.14 0.253 Muslim*Germany 2.692601 1.834767 1.47 0.142 Orthodox*Germany -2.39073 1.688758 -1.42 0.157 Protestant*Germany (omitted) Church of Sweden*Germany (omitted) Evangelical -0.07394 0.340553 -0.22 0.828 Muslim -0.64703 0.530671 -1.22 0.223 Orthodox -0.01917 0.383528 -0.05 0.96 Protestant -0.1431 0.186051 -0.77 0.442 Church of Sweden 0.06107 0.791309 0.08 0.938 Age 2 0.303219 0.173131 1.75 0.08 Age 3 0.003194 0.166407 0.02 0.985 Age 4 -0.27727 0.169681 -1.63 0.102 Age 5 -0.47898 0.188863 -2.54 0.011 Age 6 -0.91468 0.201184 -4.55 0 Female 0.546705 0.085986 6.36 0 Germany 0.215224 0.309882 0.69 0.487 Canada -0.07496 0.245842 -0.3 0.76 Uruguay -0.13015 0.350657 -0.37 0.711 Georgia -2.30882 0.430136 -5.37 0 Poland 0.859838 0.292954 2.94 0.003 Romania -2.98632 0.409065 -7.3 0 Norway 0.266877 0.251834 1.06 0.289 Italy 0.116639 0.281304 0.41 0.678 Sweden 0.395336 0.801485 0.49 0.622 Brzil -1.05076 0.297496 -3.53 0 Chile -0.38128 0.269565 -1.41 0.157 Brazil -1.10159 0.457721 -2.41 0.016 78

High Urbanization 0.302898 0.110503 2.74 0.006 Medium Urbanization 0.127559 0.113668 1.12 0.262 Healthy -0.37178 0.105388 -3.53 0 Lower Middle Class -0.03934 0.10295 -0.38 0.702 Upper Middle Class -0.32571 0.130184 -2.5 0.012 Upper Class -0.50815 0.411337 -1.24 0.217 Observant -0.60997 0.131057 -4.65 0 Somewhat Observant -0.55158 0.136911 -4.03 0 Not Observant -0.26031 0.111974 -2.32 0.02 Secondary Education 0.225391 0.113045 1.99 0.046 University Education 0.35619 0.130347 2.73 0.006 _cons 13.28252 0.318427 41.71 0

TABLE 8

Religion and Participation

H Coef. Std. Err. t P>t

Y (Income) -3.95E06 2.81E-06 -1.41 0.16 Evangelical*Observant -0.37717 0.526929 -0.72 0.474 Evangelical*Somewhat Obsevant -0.70853 0.528126 -1.34 0.18 Evangelical* Not Observant -0.3006 0.429893 -0.7 0.484 Muslim*Observant 0.57583 1.060661 0.54 0.587 Muslim*Somewhat Observant -1.72907 1.876422 -0.92 0.357 Muslim*NotObservant -1.29628 1.674433 -0.77 0.439 Orthodox*Observant -0.37157 0.399214 -0.93 0.352 Orthodox*Somewhat Observant -0.45706 0.432762 -1.06 0.291 Orthodox*Not Observant -0.04961 0.328511 -0.15 0.88 Roman Catholic* Observant -0.45072 0.349516 -1.29 0.197 Roman Catholic*Somewhat Observant -0.65087 0.409706 -1.59 0.112 Roman Catholic*Not Observant -0.3664 0.321054 -1.14 0.254 Church Of Sweden*Observant -1.03219 0.756715 -1.36 0.173 Church of Sweden*Somewhat Observant -0.09106 0.955222 -0.1 0.924 79

Church of Sweden*Not Observant -0.29372 0.391036 -0.75 0.453 Evangelical 0.093392 0.398021 0.23 0.814 Muslim -0.10972 0.614734 -0.18 0.858 Orthodox 0.235478 0.446364 0.53 0.598 Roman Catholic 0.49007 0.279296 1.75 0.079 Church of Sweden 0.408661 0.828553 0.49 0.622 Age 2 0.299011 0.173291 1.73 0.084 Age 3 -0.00473 0.166463 -0.03 0.977 Age 4 -0.28455 0.169861 -1.68 0.094 Age 5 -0.48812 0.18977 -2.57 0.01 Age 6 -0.9304 0.20225 -4.6 0 Female 0.548223 0.086217 6.36 0 Germany 0.087261 0.293561 0.3 0.766 Canada -0.08052 0.245977 -0.33 0.743 Uruguay -0.10608 0.347211 -0.31 0.76 Georgia -2.24169 0.434014 -5.17 0 Poland 0.88523 0.295974 2.99 0.003 Romania -2.91678 0.413242 -7.06 0 Norway 0.364079 0.269944 1.35 0.177 Italy 0.146003 0.282363 0.52 0.605 Sweden 0.454629 0.817474 0.56 0.578 Brazil -0.99136 0.295425 -3.36 0.001 Chile -0.40023 0.269393 -1.49 0.137 Bulgaria -1.06326 0.465543 -2.28 0.022 High Urbanization 0.311749 0.111142 2.8 0.005 Medium Urbanization 0.13436 0.114336 1.18 0.24 Healthy -0.37784 0.105881 -3.57 0 Lower Middle Class -0.03891 0.103161 -0.38 0.706 Upper Middle Class -0.32227 0.130088 -2.48 0.013 Upper Class -0.49781 0.412155 -1.21 0.227 Observant -0.27085 0.284855 -0.95 0.342 Somewhat Observant -0.05155 0.344314 -0.15 0.881 Not Observant -0.07306 0.244589 -0.3 0.765 Secondary Education 0.221432 0.113331 1.95 0.051 University Education 0.346354 0.13027 2.66 0.008 _cons 12.90125 0.338508 38.11 0

80

TABLE 9

Religion and Education

H Coef. Std. Err. t P>t

Y (Income) -4.78E06 2.82E-06 -1.7 0.09 Evangelical* University -0.3152 0.448416 -0.7 0.482 Evangelical*Secondary -0.48092 0.339668 -1.42 0.157 Muslim*University 0.098699 1.120435 0.09 0.93 Muslim*Secondary 1.086056 0.945393 1.15 0.251 Orthodox*University -0.75963 0.331952 -2.29 0.022 Orthodox*Secondary -0.53987 0.311166 -1.74 0.083 ChurchOfSweden*University -0.63081 0.34333 -1.84 0.066 ChurchOfSweden*Secondary -0.64665 0.420936 -1.54 0.125 Evangelical 0.010889 0.3109 0.04 0.972 Muslim -0.8455 0.805591 -1.05 0.294 Orthodox 0.60283 0.453094 1.33 0.183 Roman Catholic 0.159187 0.185748 0.86 0.391 Church of Sweden 0.656511 0.838472 0.78 0.434 Age 2 0.300584 0.173435 1.73 0.083 Age 3 0.000484 0.166798 0 0.998 Age 4 -0.2817 0.170249 -1.65 0.098 Age 5 -0.4881 0.189794 -2.57 0.01 Age 6 -0.94616 0.202095 -4.68 0 Female 0.546008 0.085975 6.35 0 Germany 0.115647 0.291117 0.4 0.691 Canada -0.09519 0.246004 -0.39 0.699 Uruguay -0.06558 0.347747 -0.19 0.85 Georgia -2.27464 0.428235 -5.31 0 Poland 0.896301 0.294195 3.05 0.002 Romania -2.97719 0.408719 -7.28 0 Norway 0.277618 0.252704 1.1 0.272 Italy 0.109279 0.281308 0.39 0.698 Sweden 0.379626 0.811669 0.47 0.64 Brazil -0.99691 0.291703 -3.42 0.001 Chile -0.38831 0.27012 -1.44 0.151 Bulgaria -1.06338 0.457345 -2.33 0.02 High Urbanization 0.325602 0.110583 2.94 0.003 Medium Urbanization 0.138308 0.11381 1.22 0.224 Healthy -0.37134 0.105665 -3.51 0 Lower Middle Class -0.03511 0.103332 -0.34 0.734 81

Upper Middle Class -0.31732 0.130523 -2.43 0.015 Upper Class -0.51088 0.412808 -1.24 0.216 Observant -0.59483 0.130595 -4.55 0 Somewhat Observant -0.55587 0.137328 -4.05 0 Not Observant -0.25551 0.112292 -2.28 0.023 Secondary Education 0.374688 0.142025 2.64 0.008 University Education 0.585979 0.160806 3.64 0 _cons 13.01243 0.307509 42.32 0

TABLE 10

Religion and Urbanization

H Coef. Std. Err. t P>t

-3.74E- Y (Income) 06 2.80E-06 -1.34 0.181 Evangelcial*High Urbanization 0.007275 0.387069 0.02 0.985 Evangelical*Medium Urbanization 0.253366 0.357281 0.71 0.478 Muslim*High Urbanization 1.846887 0.892964 2.07 0.039 Muslim*Medium Urbanization 2.028486 1.434236 1.41 0.157 Orthodox*High Urbanization -0.1686 0.240472 -0.7 0.483 Orthodox*Medium Urbanization 0.15833 0.287069 0.55 0.581 ChurchOfSweden*High Urbanization -0.75274 0.434936 -1.73 0.084 ChurchofSweden*Medium Urbanization -0.08385 0.443177 -0.19 0.85 Evangelical -0.32089 0.351534 -0.91 0.361 Muslim -1.38684 0.655792 -2.11 0.034 Orthodox -0.13214 0.408273 -0.32 0.746 Roman Catholic 0.144778 0.186163 0.78 0.437 Church of Sweden 0.746944 0.877732 0.85 0.395 Age 2 0.314043 0.173288 1.81 0.07 Age 3 0.001271 0.166654 0.01 0.994 Age4 -0.28757 0.170027 -1.69 0.091 Age 5 -0.48326 0.18908 -2.56 0.011 Age 6 -0.90928 0.201377 -4.52 0 Female 0.544315 0.086068 6.32 0 Germany 0.088025 0.29059 0.3 0.762 Canada -0.10771 0.246794 -0.44 0.663 Uruguay -0.07688 0.346354 -0.22 0.824 Georgia -2.02511 0.434626 -4.66 0 Poland 0.866499 0.293602 2.95 0.003 82

Romania -2.73407 0.414561 -6.6 0 Norway 0.253733 0.254344 1 0.319 Italy 0.1318 0.282478 0.47 0.641 Sweden 0.253161 0.807232 0.31 0.754 Brazil -0.9863 0.292835 -3.37 0.001 Chile -0.42995 0.272619 -1.58 0.115 Bulgaria -0.78545 0.468225 -1.68 0.093 High Urbanization 0.390789 0.15732 2.48 0.013 Medium Urbanization 0.047719 0.151963 0.31 0.754 Healthy -0.3665 0.105684 -3.47 0.001 Lower Middle Class -0.04605 0.103062 -0.45 0.655 Upper Middle Class -0.32515 0.130038 -2.5 0.012 Upper Class -0.52262 0.406035 -1.29 0.198 Observant -0.60357 0.13117 -4.6 0 Somewhat Observant -0.55571 0.137361 -4.05 0 Not Observant -0.25907 0.112175 -2.31 0.021 Secondary Education 0.232145 0.113095 2.05 0.04 High Education 0.36142 0.130274 2.77 0.006 _cons 13.12181 0.310458 42.27 0

WORKS CONSULTED

Abboushi, S. ―Impact of individual variables on work values of Palestinian Arabs‖. International Studies of Management and Organization. (1990) Vol. 20. No. 3, pp 53-68.

Alesina, Alberto and La Ferrara, Eliana. "Who trusts others?" Journal of Public Economics 85, no. 2 (2002): 207-234.

Ali, A.J., Falcone, T. and Azim, A.A.―Work Ethic in the US and Canada.‖ (1995).

Arslan, M. ―The Work Ethic Values of Protestant British, Catholic Irish and Muslim Turkish Managers‖. Journal of Business Ethics. Vol 31, Number 4 pp 323.

Audretsch, David and Bonte, Werner. ―Religion and Entrepreneurship‖. Jena Economic Research Paper No. 2007-075, (2007).

Barro, Robert J. and McCleary, Rachel M. "Religion and Economic Growth Across Countries." American Sociological Review 68, no. 5 (2003): pp. 1.

Becker, Gary. ―Preferences and Values‖, in Becker Gary (ed.), Accounting for Taste, Harvard University Press: Cambridg, 1996.

Ben-David, Dan. ―Convergence Clubs and Diverging Economies.‖ Working Papers, Centre for Economic Policy Research (1997).

Blackwood, Larry ―Social Change and Commitment to the Work Ethic.‖ Pp. 241-256 in The Religious Dimension: New Directions in Quantitative Research. New York: Academic Press, (1979).

Blanchard, Oliver. Macroeconomics 3rd Edition (Prentice-Hall series in economics 2002), pg 240.

Blum, Ulrich and Dudley, Leonard. "Religion and economic growth: was Weber right?" Journal of Evolutionary Economics 11, no. 2 (2001).

Brouwer, Maria T. ―Weber, Schumpeter, and Knight on entrepreneurship and economic development.‖ Vol. 12, no. 1-2, pp. 83-105 (2002)

Camerer, Colin and Thaler, Richard H. "Anomalies: Ultimatums, Dictators and Manners." The Journal of Economic Perspectives 9, no. 2 (1995), pp. 209.

83

84

Causa, Orsetta. ―Explaining Differences in Hours Worked among OECD Countries.‖ OECD Economics Department Working Papers, No 596, OECD Publishing (2008).

Congleton, Roger D. "The economic role of a work ethic." Journal of Economic Behavior & Organization 15, no. 3 (1991): 365-385.

Daniels, Joseph and von der Ruhr, Marc. "Trust in others: Does Religion Matter?." Working Paper 0902 Marquette University , (2008).

Daniels, Joseph P. and von der Ruhr, Marc. "God and the global economy: religion and attitudes towards trade and immigration in the United States." Socio-Economic Review 3, no. 3 (2005): 467-489.

Darity, William A. and Mason, Patrick L. ―Evidence on Discrimination in Employment: Codes of Color, Codes of Gender‖. The Journal of Economic Perspecitives, Vol. 12, No.2 (1998), pp. 63-90.

Dickens, William T. and Katz, Lawrence F. ―Interindustry Wage Differences and Industry Characteristics‖. National Bureau of Economic Research, Working Paper No. 2014 (1987).

Dixon, ―Beyond Lenski: A Critical Review of Recent ‗Protestant Ethic Research‘‖. Journal for the Scientific Study of Religion, (1973) pp. 141-155.

Drakopoulou, Sarah Dodd and Seaman, Paul Timothy. ―Religion and Enterprise: An Introductory Exploration‖. Entrepreneurship : Theory and Practice, Vol. 23, (1998).

Economy Watch, Richardian Model of International Trade: An Overview. http://www.economywatch.com/international-trade/ricardian-model.html (date accessed: April 2011)

Frick, H.L. ―The Relationships of culture, gender and occupation to work values of employees of an international organization.‖ Journal of International Business Studies, (1995) Vol 26. No. 3, p 673.

Furnham, Adrian et al. ―A Comparison of Protestant Work Ethic Beliefs in Thirteen Nations.‖ The Journal of Social Psychology. (1993), Vol. 133, Issue 2, pp. 185- 197.

Furnham, Adrian. ―The Protestant Work Ethic in Barbados‖. Journal of Social Psychology, Vol. pp. 131, 29-43.

85

Furnham, Adrian. ―The Protestant work ethic in Britain and Malaysia‖. Journal of Social Psychology, (1984) 122:2, pp. 157-161.

Giorgi, Liana and Marsh,Catherine. ―The Protestant work ethic as a cultural phenomenon‖. European Journal of Social Psychology, Vol 20(6), Nov-Dec 1990, 499-517.

Glaeser, David. Cities and Skills. Journal of Labor Economics (2001) Vol. 19 no. 2, pp. 5.

Grier, Robert. "The effect of religion on economic development: A cross national study of 63 former colonies." Kyklos 50, no. 1 (1997).

Guiso, Luigi and Sapienza, Paola, and Zingales, Luigi. "Does Culture Affect Economic Outcomes?" The Journal of Economic Perspectives 20, no. 2 (2006), pp. 2.

Guiso, Luigi Sapienza, Paola and Zingales, Luigi. "People's opium? Religion and economic attitudes." Journal of Monetary Economics 50, no. 1 (2003): 225-282.

Hamilton, Bob and Whalley, John. "Efficiency and distributional implications of global restrictions on labour mobility : Calculations and policy implications." Journal of Development Economics 14, no. 1 (1984): 61-75.

Hansen, G.D. and Prescott, E. ―Malthus to Solow‖, American Economic Review, (2002) 92: 1205-17.

Hendrick, Lutz. ―Why do Hours Worked Differ Across Countries? Evidence from U.S. Immigrants‖. Working Paper, University of Iowa. (2004).

Huntington, Samuel P. The Clash of Civilizations and the Remaking of World Order. New York, NY: Simon and Schuster, (1996). 5Le nski Iannaccone, Laurence R. "Introduction to the economics of religion." Journal of Economic Literature 36, no. 3 (1998): 1466.

Inglehart, Ronald and Baker, Wayne E. "Modernization, Cultural Change, and the Persistence of Traditional Values." American Sociological Review 65, no. 1, (2000): pp. 19.

Iregui, Ana Maria. ―Efficiency Gain from the Elimination of Global Restrictions on Labour Mobility: An Analysis using a Multiregional CGE Model‖. Working Papers UNU-WIDER Research Paper, World Institute for Development Economic Research (UNU-WIDER), (2003).

John, Ray J.. ―The Protestant Ethic in Australia‖. The Journal of Social Psychology, (1982), Vol 116, pp 127-138. 86

Knack, Stephen and Keefer, Philip. "Does Social Capital Have an Economic Payoff? A Cross-Country Investigation." The Quarterly Journal of Economics (1997), pp. 4.

Landes, David S. The Wealth and Poverty of Nations: Why Are Some So Rich and Others So Poor? (New York: W.W. Norton, 1998), pp 544.

Lenski, Gerhanrd. The Religious Factor. New York: Doubleday and Company, (1961).

Maume, David. ―Gender Differences in Restricting Work Efforts Because of Family Responsibilities‖. Journal of Marriage and Family. Vol 68, pp 859-869 (2006).

Minns, Chris and Rizov, Marian. "The spirit of capitalism? Ethnicity, religion, and self- employment in early 20th century Canada." Explorations in Economic History 42, no. 2 (2005): 259-281.

Moskowitz, T.J. and Vissing-Jorgensen, A. ―The Return to Entrepreneurial Investment: A Private Equity Premium Puzzle?‖ American Economic Review, (2002) 92: 745-79.

Murphy, K. Schleifer, A. and Vishny, R.‗The Allocation of Talen: Implication for Growth‘, Quarterly Journal of Economics, (1991)106 (2): 503-30.

Nation‘s University-World Institute for Development Economics Research. ―The World Income Inequality Database‖.

Naude, Wim. ―Entrepreneurship in Economic Development‖. UNU-WIDER Research Paper No. 2008/20, (2008), pp 5-6.

Noland, Marcus "Religion and economic performance." World Development Vol. 33, no. 8 (2005): 1215-1232.

O‘Brien, Gorden E. and Kabanoff, Boris. ―Comparison of unemployed and employed workers on work values, locus of control and health variables‖. Austrailian Psychologist. Vol 14, Issue 2 (1979), pgs 143-154.

Parker, Barbara and Chusmir, Leonard H. ―Motivation needs and their relationship to life success‖. Human Relations. (1991) Vol 44, pp 1301-12

Putnam, Robert. ―Making Democracy Work: Civic Tradition in Modern Italy.‖ 1993

Rada, C. ―Stagnation or Transformation of a Dual Economy through Endogenous Productivity Growth‖, Cambridge Journal of Economics, (2007). Vol. 31: 710- 40.

87

Row, R. and Snizek, W.E. ―Gender differences in work values-perpetuating the myth.‖ (1995) Work and Occupations, Vol. 22 No. 2, pp 215-29.

Sala-i-Martin, Xavier Doppelhofer, Gernot and Miller, Ronald I.. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach." The American Economic Review 94, no. 4 (2004): pp. 813-835.

Susman, G. ―Job enlargement: Effects of culture on worker responses‖. Industrial Relations, (1973). 12, 1-15.

Voert, Ter. ―The effect of religion on work attitudes in the Netherlands‖. Social Compass, (1993) Vol. 40 pp 33-44.

World Bank. ―World Development Indicators‖. http://data.worldbank.org/indicator (data accessed: March 2011) World Values Survey. http://www.worldvaluessurvey.org (date accessed: October 2011)

Yousef, Darwish A. ―Islamic work – A moderator between organizational commitment and job satisfaction in a cross-cultural context‖. (2001) Personnel Review, Vol. 30 Iss: 2, pp. 152-169.

Zak, Paul J. and Knack, Stephen. "Trust and Growth." The Economic Journal 111, no. 470 (2001): 295-321.