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Social Indicators ResearchAn International and InterdisciplinaryJournal for Quality-of-Life Measurement ISSN 0303-8300 Soc Indic ResDOI 10.1007/s11205-013-0275-1

Does Good Governance EnhanceHappiness: A Cross Nation Study

Roma Mitra Debnath & Ravi Shankar

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Does Good Governance Enhance Happiness:A Cross Nation Study

Roma Mitra Debnath • Ravi Shankar

Accepted: 18 February 2013� Springer Science+Business Media Dordrecht 2013

Abstract The purpose of this paper is to analyze the efficiency of the countries over the

period of 10 years by applying data envelopment analysis (DEA). Based on rational and

factual parameters such as freedom of press, freedom of religion, percentage of export in

Gross Domestic Product (GDP), index of globalization, life expectancy at birth, gender

ratio etc., this paper attempts to measure the efficiency of happiness. A combination of

social and economic factors has been used to measure technical efficiency. The contri-

bution of this paper is twofold. First, it measures the relative efficiency of all the countries

included in the study. The nations have been ranked as per their relative efficiency and the

peer group has been formed. Second a comparison between the rich and the poor countries

have been done to test empirically whether the economic growth enhances the happiness

among people. Presently, more than 3,000 studies have been published on happiness and

Veenhoven in 2004 created a database called World Database of Happiness. The World

Database of Happiness has attempted to present the available research findings on hap-

piness. Part of the findings on happiness in nations is available in ‘States of nations’. For

the research purpose, States of Nations and the data published by have been considered.

Although happiness has been quantified and the existing literature has sufficient empirical

evidences of the same, in the present context, the relative efficiency has been calculated for

the countries on basis of objective and subjective happiness parameters. As per the liter-

ature, happiness has two aspects (1) objective and (2) subjective. Objective parameters are

external to the individuals and covers material living parameters viz. GDP growth, income,

nutrition, mortality rate, literacy etc. However, Subjective indicators measure the quality of

life of the individuals. These are summarized as ‘‘subjective well-being’’. The various

parameters considered in the study capture different aspects of happiness. The result shows

how the government can increase the happiness of the people by analyzing the behavior

R. M. Debnath (&)Indian Institute of Public Administration, I.P.Estate, Ring Road, New Delhi 110 002, Indiae-mail: roma.mitra@gmail.com

R. ShankarDepartment of Management Studies, Indian Institute of Technology Delhi,Hauz Khas, New Delhi, Indiae-mail: ravi1@dms.iitd.ac.in

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and expectations. People express their preference explicitly about political parties, religion

believes, law and order situation, trust in official institutions etc. Although, the behavior of

people largely depends upon availability of goods and services in the market, the gov-

ernment can make budgets according to priority or preferences of people. Another way to

increase happiness can be done by analyzing the peer group, which is an outcome of DEA.

This shows the nations which are similar in terms of their economic and social conditions.

The government can compare the prevailing conditions in different countries that improve

the condition in their respective country. This could be an effective method as some of the

parameters can be replicable in order to make people happier. The limitation of this study

is lack of availability of data for many countries. As the number of countries increases, a

change in the relative efficiency can be observed. Therefore, a future study can be con-

ducted where the relevant data can be collected and a more global result can be obtained.

Keywords Governance � Happiness � Cluster analysis � Data envelopment analysis �Cross national

1 Introduction

‘Happiness’ has been a dominant area of research in the area of psychology for a long time.

However, it started gaining popularity among economists with the published work of

Easterlin (1974). Since twentieth century, the researchers are exploring the interrelation-

ship between happiness and individual characteristics. Happiness is considered as one of

the common theme in cross national research (Kalmijn and Veenhoven 2005). Buchanan

(1953) compared the happiness of nine countries for the first time. Cantril (1965) under-

took the second comparative study where fourteen nations were studied. This attempt was

followed by Gallup (1976), where the global happiness was studied and assessed. How-

ever, several international survey programme such as Euro Barometer, which is conducting

survey on happiness since 1973, World Value Survey since 1980 etc. are few in the field of

measuring happiness across nations. In recent past, Michalos (1991) and Diener et al.

(1995) compared among university students at a large scale. Veenhoven in 2005 collected

all these research on happiness and created world database of happiness. The existing

studies have focused on measuring the level of happiness in different nations (Veenhoven

2005).

‘Quality of life’ (QOL) means that life of a person although the term is widely used for

aggregates viz. quality of life of human beings or women etc. There have been a significant

number of researches carried out by sociologists, economists, psychologists, healthcare

professionals and others in the field of QOL. In 1789, Bentham suggested that a mea-

surement system is to be designed to measure the pleasure and displeasure with political

actions of each individual. Cummins (1993) defined QOL as an aggregate of seven

objective and subjective components like material well being, health, productivity, inti-

macy, safety, place in community and emotional bonding. World Health Organization

(1995) has defined QOL as an individual’s perception of their position in life in the context

of the culture and value systems in which they live and in relation to their goals, expec-

tation, standards and concerns. This incorporates the person’s physical health, psycho-

logical state, level of independence, social relationships personal believes and their

relationship to salient features of the environment.

Although there is no consensus in the definition of QOL, however, in order to under-

stand the concept QOL, objective as well as subjective measures should be used (Cummins

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2000; Diener and Suh 1997; Felce and Perry 1995; Turksever and Atalik 2001). Gomes

et al. (2008) concluded that QOL corresponds to an individual perception that brings

together several dimensions such as quantitative, qualitative and social which comprise

objective, subjective and relational aspects. Ramırez and Tovar (2002) were in opinion that

perception reflects perspectives and social values. These perceptions which are either

subjective or objective, takes a shape of a global character.

However, in the twenty-first century, different quantitative and qualitative measurement

scales were developed, which included different scales of satisfaction concerning various

areas of life (Ferriss 2006). The word ‘Happiness’ has been used in the existing literature as

a generic term for all worth and is in this sense; it is synonymous with QOL or ‘well-being’

(Veenhoven 1999). McCall (1975) defined QOL as a necessary condition for happiness.

Frey and Stutzer (2002a, b) explored that happiness has a significant role in economic

outcomes. For instance, if a person is happy, he would be more productive and efficient in

his life, which would increase his personal income and health (Graham et al. 2004). Myers

(2004) defined happiness as a ratio of positive to negative feelings whereas; Layard (2005a,

b) defined happiness as a good and nice feeling about enjoying life and to maintain the

same feeling. Duncan (2010) found that Happiness has three independent but correlated

factors like subjective well-being, life satisfaction, and absence of depression or anxiety.

Subjective well-being is the feelings that people have of joy; life satisfaction caters to

qualities or circumstances of life and, finally, the absence of insecurity (Argyle 2001a, b).

The purpose of this paper is to assess the performance of various countries in achieving

happiness, that is, to find the relative efficiency of the initiatives taken by the different

government. The initiatives which have been considered for the present study are

parameters from the existing literature review.

The next section discusses the relation between happiness and governance. Section 3

contains a brief discussion on methodology used in the research. Section 4 reports the

result and a discussion of the same is included. The conclusion is presented in Sect. 5.

2 Happiness and Governance

According to World Bank (1992), governance is the manner in which power is exercised in

the management of a country’s economic and resources for development’’. Governance as

defined by Kaufmann et al. (1999, 2008) is the traditions and institutions by which

authority in a country is exercised. This includes the process by which governments are

selected, inspected and replaced. According to the authors, the governments should have

properties of accountability, political stability and absence of violence, effectiveness,

regulatory quality, rule of law, and control of corruption. Although there are many defi-

nitions of governance, most of the definitions have agreed on the importance of capable

state operating under the rule of law. Helliwell and Huang (2008a, b) used the terms

governance and government as equivalents as both terms are broad and includes admin-

istration by the governments and their legislation and jurisdiction.

It has been established in the extant literature that objective economic circumstances

have only a small though statistically significant effect on happiness (Andrews and Withey

1976; Argyle 2001a, b; Diener and Biswas-Diener 2002). This conclusion was reiterated by

various other researchers like (Frey and Stutzer 2002a, b; Layard 2005a, b by referring to

Easterlin’s paper, ‘Does economic growth improve the human lot?’ The Paradox states that

money, and by extension economic growth, have little effect on happiness.

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Easterlin (1974) studied US economy during 1946–1970 and concluded that higher does

not guarantee greater happiness. This study was compatible with other recent studies on

happiness (Diener et al. 1999; Veenhoven 1993; Ravallion and Lokshin 2000). A similar

study by Kenny (1999), happiness level in Japan between the year 1958 and 1988 was

almost the same even though the GDP per capita increased five times. In few Asian

countries like Chile, China and South Korea have double income per capita within 20 years

but over that period data shows a decline in life satisfaction, especially in South Korea with

a statistically significant decline from four surveys between the period of 1990 and 2005,

(Easterlin et al. 2010; Hilke et al. 2009).

Although a good financial state allows people to buy goods which provides material

well-being and other several opportunities, however, the happiness we receive from

material well-being also depends on several factors such as our past and present socio-

economical conditions and our future goals (D’Acci 2011). Researchers like Winkelmann

and Winkelmann (1998), Clark and Oswald (1994) and Veenhoven (1996) concluded that

unemployment and poor health as key reasons of reducing happiness and satisfaction.

On the contrary, Inglehart (1990) and Blanchflower and Oswald (2000) believe that on

average higher income makes people happier. In addition, Bolle et al. (2009) with an

empirical research concludes if income is up by $1,000, then happiness increases by 0.06

units.

Ott (2010) studied that as per utilitarian believes, the governments should aim to create

the greatest happiness for the greatest number by legislation, jurisdiction and adminis-

tration. The author studied two fundamental questions in his research. Firstly, can gov-

ernments can increase the happiness and secondly, if they can, should they do so. The

authors also found that happiness affects the quality of government rather than vice versa.

Headey and Wearing (1992) established the fact that people tend to disbelief that gov-

ernments contribute less to happiness. Majority of the citizens are doubtful about the

governments as they link it with inefficiency, bureaucracy, high taxes, corruption etc.

However, Veenhoven (2004) opined that a number of social factors over which govern-

ments have some authority and control such as respect for the rule of law and civil rights,

economic freedom and tolerance of minorities have a positive impact on happiness levels.

The author defines happiness as the overall enjoyment of your life as a whole. The author

also concluded that happiness can be raised by incorporating appropriate policy actions.

Diener and Seligman (2004) showed in their research that democratic societies that respect

universal human rights tend to have happier people. Helliwell and Huang (2008a, b) found

that quality of government has a substantial impact on average happiness. Though there is

no consensus among the governments whether to evaluate the public policies to achieve the

goal to increase the happiness (Duncan 2010)

Easterlin (2003) opined that good health and social connectedness contributes to peo-

ple’s happiness than the pursuit of wealth mad material goods. Therefore, it becomes a

primary responsibility of the government to focus more on subjective well being and not on

economic growth. No relations have been found between a country’s GDP and happiness

levels of citizens. Oswald (1997) concluded that in a developed nation, economic progress

buys only a small amount of extra happiness. Ott (2010) found that the quality of gov-

ernance is more important for happiness than the size of governments. The relation

between quality and happiness is independent of size, while the relation between size and

happiness fully depends on quality. The author also concluded that government can pro-

mote happiness and reduce inequality by enhancing their quality of governance.

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There is no unanimous decision among the researchers about the statement that a good

economy contributes positively towards the happiness of the people as compared to non-

economic factors.

The Gross National Happiness (GNH) is an official measure of happiness in Bhutan.

The concept of GNH is based on four pillars such as sustainable socio-economic devel-

opment, preservation and promotion of cultural values, conservation of the natural envi-

ronment and establishment of good governance (Lepage 2009). Since Bhutan is a Buddhist

state, it advocates Buddhist concept of happiness which promotes detachment from

worldliness to overcome suffering and achieve salvation. As a contrast, the western con-

cept of happiness is oriented towards achieving life-satisfaction in the world as empirically

found in the existing literature.

Happiness is subjective and it refers to feelings of one person at a time, therefore,

aggregate scores from national surveys of happiness may not be best way to estimate the

happiness of the nation (Duncan 2010).

There is very little empirical research on to measure the effectiveness of governance to

promote happiness. Kacapyr (2008) used regression analysis to study satisfaction across

countries. Veenhoven (2007) reported that Denmark and Italy have a higher trend in

happiness whereas, UK has a stagnated happiness and Belgium is experiencing a gradual

downward shift in happiness. Although these statistics were reported, there were no further

explanation what the governments of Denmark and Italy are doing to make its citizens

happy or the governments of UK and Belgium are not doing to contribute towards

happiness.

To conclude whether good governance can increase the happiness is still under ambi-

guity. Not many contributions are there in the existing literature. This paper builds a cross-

national study, which attempts to measure the efficiency of the various countries. The

different initiatives taken by the countries have been considered as parameters to assess the

efficiency of the governments. This paper aims to measure the effectiveness of nations in

promoting happiness.

The objectives of the paper are to

1. Cluster the nations under study on basis of economy

2. To determine the efficiency of the governance of the clustered (similar) nations

3. To compare the efficient governance among the nations.

3 Methodology

3.1 Cluster Analysis

Cluster analysis involves the identification of groups (nations in the present context)

among a set of different objects (113 nations under study) as described by Kaufman and

Rousseeuw (1990).

The analysis is done by measuring distance between the different objects which dis-

tinguishes the objects which are most similar than those which are not. Clustering has been

used for a variety of applications in economics. Grouping of some cases have been reported

as individuals, industries, nations, or time periods (Hirschberg and Aigner 1987; Hirsch-

berg and Slottje 1994; Hirschberg and Dayton 1996; Borland et al. 2001, Hirschberg et al.

1991, 2000a, b). In the present study, all the countries were clustered into three categories

according to economy as depicted in Table 1 indicating the countries within groups are

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Table 1 Cluster of the 113nations based on happinessfactors

Sr. no. Cluster I Cluster II Cluster III

1. Algeria Australia Albania

2. Argentina Austria Angola

3. Bolivia Belgium Armenia

4. Bosnia & H. Brazil Azerbaijan

5. China Canada Bangladesh

6. Colombia Denmark Benin

7. Cuba Finland Botswana

8. Cyprus France Bulgaria

9. Czech R. Germany Burkina F.

10. Dom. R. Ireland Burundi

11. Ecuador Israel Cambodia

12. Egypt Italy Cameroon

13. El Salvador Japan Chad

14. Estonia Mexico Chile

15. Greece The Netherlands Ethiopia

16. Guatemala New Zealand Georgia

17. Honduras Norway Ghana

18. Hong Kong Singapore Haiti

19. Hungary Spain Indonesia

20. India Sweden Kenya

21. Iran Switzerland Kyrgyzstan

22. Jamaica United Arab Emirates Latvia

23. Jordan United Kingdom Macedonia

24. Kazakhstan United States Madagascar

25. Kuwait Venezuela Malawi

26. Lithuania Mali

27. Malaysia Moldova. R.

28. Mauritania Morocco

29. Nicaragua Mozambique

30. Panama Nepal

31. Poland Niger

32. Portugal Nigeria

33. Romania Paraguay

34. Slovak R. Peru

35. Slovenia Philippines

36. South Africa Russian F.

37. Taiwan Rwanda

38. Thailand Senegal

39. Trinidad Tob. Sierra L.

40. Uruguay Sri Lanka

41. Vietnam Tanzania

42. Togo

43. Turkey

44. Uganda

45. Ukraine

46. Zambia

47. Zimbabwe

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having similar economy. This grouping gives a group of homogenous countries for which

the relative happiness was analyzed by using DEA as explained in the next section.

3.2 Data Envelopment Analysis

Data envelopment analysis (DEA) is a popular method in the area of efficiency mea-

surement. Efficiency measurement is a commonly used tool to measure the performance of

any decision making unit (DMU) and to estimate the relative efficiency of the DMUs.

Generally simple efficiency can be calculated using a ratio of output and input as given in

Eq. 1.

Efficiency ¼ Output=Inputs ð1Þ

However, in DEA, multiple inputs and outputs are linearly aggregated using weights.

Therefore, the efficiency is measured as a ratio of

Efficiency ¼Weighted Sum of Outputs=Weighted Sum of Inputs ð2Þ

Efficiency ¼P j

j¼1 vjyjP j

j¼1 uixi

ð3Þ

where ui is the weight assigned to input xi and vj the weight assigned to output yj as given

in Eq. 3.

Data Envelopment Analysis (DEA) is a non-parametric approach, initiated by Farrell

(1957). Later on, Charnes, Cooper and Rhodes (CCR) in 1978 made a major breakthrough

in the same field. DEA has received widespread acceptability particularly in its application

to public sector operations, such as education and health care where the policy objectives

are vaguely defined as a functional form of the inputs-outputs relationships. DEA has been

applied in a number of different areas like hospitality, health care (hospitals, doctors),

education (schools, universities), banks, manufacturing, benchmarking, management

evaluation, energy efficiency, fast food restaurants, retail stores etc. (Cooper et al. 2000;

Fare et al. 1994; Thanassoulis and Dustan 1994; Tomkins and Green 1988; Sinauny-Stern

et al. 1994; Rhode and Southwick 1993; Cooper et al. 1996). Tim Anderson (available at

www.emp.pdx.edu) in 1995 compiled over 360 papers on application of DEA and there has

been a constant increase in the number of DEA applications. DEA is used to compute a

score which defines the relative efficiency of a particular decision making unit (DMU)

versus all other DMUs observed in the sample. The various inputs and outputs are assigned

optimal weights by which the output can be maximized.

DEA models assume constant returns to scale (CRS) and variable returns to scale

(VRS). In a CRS, the change in the output is proportionate to change in the input. How-

ever, in a VRS, the change in output is not proportional to the change in the input. Figure 1

shows various types of returns to scale (RTS).

Point A represents the units present in the region of increasing returns to scale. If we

assume that an increase in inputs will increase outputs above the dashed line that would

result in greater than proportionate increase in outputs. If the units increase their inputs, the

ratio of inputs to outputs will change so that the unit moves along the efficiency horizon

and the unit will move into the region of constant returns to scale. Point B falls into

constant RTS. Point C falls in the region of decreasing returns to scale or non-increasing

RTS. This implies that increases in inputs will result in a ratio of inputs to outputs that

continue to fall along the frontier. If that assumption holds, increases in inputs will result in

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proportionately smaller increases in output. The only point which is not identified by any

region is an inefficient unit (Fig. 2).

4 Analysis and Discussion

The DEA model which is used in this research is a BCC-output oriented model as the

objective is to maximize the output, which is happiness in the present context. In the

present context, happiness is being considered as an output variable and various indicators

of governance have been considered as input variables in DEA-output oriented model.

Helliwell and Huang (2008a, b), Kaufmann et al. (2008) and Ott (2010) segregated the

indicators of good governance into two parts (1) political quality and (2) democratic and

technical quality. The first quality includes freedom of expression, freedom of association

and a free media or accountability. It also includes political stability and absence of

violence. The latter one combines effectiveness of governance (quality of policy formu-

lation), regulatory quality (promotes private sector development), rule of law (prevention

of crime and violence) and control of corruption.

For the present study, the various outputs and inputs were considered from the study

conducted by Ott (2010). There are two outputs viz. (1) Average happiness which repre-

sents worst/best possible life and (2) Inequality in happiness in terms of dispersion of

happiness in nations under study. The four inputs considered in this study are (1) Technical

quality of governments: this includes government effectiveness, regulatory quality, rule of

law and control of corruption (2) Democratic quality of governments: this includes voice

and accountability, political stability and absence of violence, (3) Government consump-

tion as a percentage of government consumption in total national consumption and (4)

Government expenditures as a percentage of government expenditures in Gross Domestic

Product (GDP) .

The study is a cross national, in which 130 nations were studies. The data on various

inputs and outputs were collected for these nations and the data set represents both rich and

poor countries. The data has been collected from world database of happiness (Veenhoven

2010). A descriptive statistics of the data is provided in Table 1A. The descriptive statistics

reveals a huge disparity among the indicators across the nations that are similar in terms of

their economy. With cluster I, II and III categories of countries, the maximum and min-

imum values varies from negative to positive or sometimes between zeros to positive

Decreasing returns to scale (C)

Constant returns to scale (B)

Increasing returns to scale (A)

Input

Ou t pu t

Fig. 1 Various returns to scale in DEA

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Rescaled Distance Cluster Combine

C A S E 0 5 10 15 20 25Label Num

Mauritania 75Uzbekistan 125Slovak R. 105Hungary 50Iran 53Bosnia & H. 14Ecuador 35Montenegro 78Romania 97Colombia 27Trinidad Tob. 117Cuba 30Portugal 95South Africa 107Croatia 29Korea. R. 62El Salvador 37Uruguay 124Lebanon 68Burma/My. 19Vietnam 127Belarus 10Hong Kong 49Bolivia 13Estonia 38Kazakhstan 60Czech R. 32greece 45Pakistan 88Panama 90Jamaica 57Taiwan 112Argentina 4Jordan 59Chile 25Cyprus 31Kuwait 64Malaysia 73Thailand 115Algeria 2Slovenia 106guatemala 46India 51Poland 94Lithuania 69Egypt 36Honduras 48Nicaragua 84Dom. R. 34Benin 12Cambodia 21Chad 24Togo 116Ethiopia 39Sierra L. 103Cameroon 22Bulgaria 17Zimbabwe 130haiti 47georgia 42Burkina F. 18Niger 85Mali 74Tanzania 114Uganda 119Madagascar 71Azerbaijan 8Latvia 67Albania 1Nigeria 86Kyrgyzstan 65Mozambique 80Kosovo 63RussianF. 98Moldova. R. 77Zambia 129ghana 44Paraguay 91Ukraine 120China 26Indonesia 52Botswana 15Serbia 102Morocco 79Yemen 128Macedonia 70Palestina 89Philippines 93Turkey 118Peru 92Armenia 5Malawi 72Bangladesh 9Sri Lanka 109Kenya 61Angola 3Nepal 81Tajikistan 113Senegal 101Burundi 20Rwanda 99Laos 66Brazil 16Puerto Rico 96Venezuela 126Denmark 33The Netherlands 82france 41United Kingdom 122Italy 56Ireland 54United States 123Austria 7Spain 108Israel 55Saudi Arabia 100Australia 6Belgium 11New Zealand 83Switzerland 111Canada 23Norway 87Sweden 110finland 40germany 43Japan 58United Arab Emirates 121CostaRica 28Mexico 76

Singapore 104

Fig. 2 Dendogram of nationsunder study

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quantity. For instance, the indicator Technical Quality, the indicator ranges from negative

value to positive value showing the performance of the similar countries on this parameter

(Table 2).

It is a generally claimed that happiness is a political goal and the goal of giving

happiness to all people is of prime importance of the government. The findings presented

indicate governments to shift their priorities away from technical and democratic char-

acteristics to other social values. A very interesting pattern can be observed from the three

clusters which were formed on basis of economic position in the world. Cluster II repre-

sents developed countries whereas, cluster I and Cluster III represent developing and least

developed countries respectively.

Table 3 represents the efficiency of the governance of nations along with their return to

scale. As it can be observed that countries like Algeria, Argentina, China, Cuba, Egypt etc.

are having efficiency = 1. The efficiency has been calculated by using Eq. 3. This implies

that in these countries, the efficiency of the government is 100 %. The last column shows

the returns to scale (RTS). It can be also observed that the RTS of efficient governments

are either constant viz. Algerira, Cuba etc. or decreasing viz. Argentina, Egypt etc. Sim-

ilarly, Table 4 represents developed nations along with their efficiency and RTS. The same

trend can be seen as for the efficient government like Brazil, Denmark etc. are having

different RTS.

Table 5, which represents least developed countries like Angola, Cambodia, Chad,

Malawi, Nepal etc. the returns to scale also differ for efficient governments like Angola is

having a constant RTS whereas, Chile is witnessing a decreasing RTS. Most of the gov-

ernments are inefficient viz. Bangladesh is having 90 % efficiency; Georgia is having 86 %

etc. Figure 3 gives a more compact picture of efficiency of nations clustered as I, II and III.

The cluster I nations, which have been exhibited in Table 3 shows few governments like

Argentina, China, Egypt, Greece, Poland, Panama etc., are fully efficient. All these

countries can be classified as developing nations. However, a close examination of the

Table 2 Descriptive statistics of the indicators used in the study

Technicalquality

Democraticquality

Governmentconsumption

Governmentexpenditure

Averagehappiness

Inequalityhappiness

Cluster I

Max 1.73 1.08 9.82 94.6 6.42 3.2

Min -0.99 -1.5 1.98 0 4.77 1.36

Average 0.035952381 -0.012380952 6.176904762 67.11428571 5.666904762 2.104761905

SD 0.66839559 0.734661599 1.796116827 20.83535881 0.449917919 0.337908399

Cluster II

Max 2.13 1.51 7.42 89.6 8 2.62

Min -1.08 -0.83 1.12 2.2 6.49 1.15

Average 1.3688 0.8292 4.2416 46.244 7.1496 1.728

SD 0.800841 0.610442 1.692619 23.78199 0.379257 0.331433

ClusterIII

Max 1.27 0.9 9.72 94.4 6.24 2.34

Min -1.59 -1.64 0 40.1 3.24 1.4

Average -0.55596 -0.47894 6.624894 77.56383 4.354255 1.815532

SD 0.520146 0.617067 2.329196 13.64996 0.54724 0.216744

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Table 3 Returns to scale ofcluster I nations under study

No. Nations Efficiency RTS

1 Algeria 1 Constant

2 Argentina 1 Decreasing

3 Bolivia 0.955329 Constant

4 Bosnia & H. 0.946562 Decreasing

5 China 1 Constant

6 Colombia 1 Decreasing

7 Cuba 1 Constant

8 Cyprus 0.965572 Decreasing

9 Czech R. 1 Constant

10 Dom. R. 1 Decreasing

11 Ecuador 1 Constant

12 Egypt 1 Decreasing

13 El Salvador 0.931795 Decreasing

14 Estonia 0.834891 Constant

15 Greece 0.995185 Decreasing

16 Guatemala 1 Constant

17 Honduras 1 Decreasing

18 Hong Kong 0.883497 Decreasing

19 Hungary 0.884065 Decreasing

20 India 0.959243 Decreasing

21 Iran 1 Constant

22 Jamaica 1 Decreasing

23 Jordan 1 Decreasing

24 Kazakhstan 0.949904 Decreasing

25 Kuwait 1 Constant

26 Lithuania 0.934145 Decreasing

27 Malaysia 0.958952 Decreasing

28 Mauritania 0.901014 Decreasing

29 Nicaragua 1 Constant

30 Panama 1 Decreasing

31 Poland 0.932083 Decreasing

32 Portugal 0.927332 Decreasing

33 Romania 0.894634 Decreasing

34 Slovak R. 0.820172 Decreasing

35 Slovenia 0.923676 Constant

36 South Africa 0.868031 Decreasing

37 Taiwan 0.984633 Decreasing

38 Thailand 0.970101 Decreasing

39 Trinidad Tob. 0.960802 Decreasing

40 Uruguay 0.925042 Decreasing

41 Vietnam 0.88986 Decreasing

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economy shows some interesting result. For example, Argentina suffered recurring eco-

nomic crises, high inflation, ever increasing external debt, although it was one of the

world’s wealthiest countries 100 years ago. The estimated GDP growth is 7 % and the

unemployment rate is around 7 %. The economy has recovered from the 2009 recession,

but the government’s reliance on expansionary fiscal and monetary policies risks deteri-

orate inflation, which remains under-reported by official statistics. This statistics do not

portray well managed governance; however, its citizens are happy. On the other hand,

China has moved from a closed, centrally planned system to a more market-oriented one

that plays a major global role. Today, China stands as the second-largest economy in the

world after the US, having surpassed Japan in 2001. Although it is witnessing a major

development, the Chinese government faces numerous economic challenges like sustaining

adequate job growth for tens of millions of migrants and new entrants to the work force,

reducing corruption, containing environmental damage and social changes due to econ-

omy’s rapid transformation etc. In 2009, when the world witnessed a global economic

downturn, China rebounded quickly, outperforming all other major economies in 2010

with GDP growth around 10 %. The DEA result shows (Table 1) that China has achieved

full efficiency (efficiency = 1), although its RTS is constant. This implies, if any change is

done in the input variables, in this case, governance quality, there would be a proportionate

change in the output variable that is happiness.

Table 4 Returns to scale ofcluster II nations under study

No. Nations Efficiency RTS

1 Australia 0.943594 Decreasing

2 Austria 0.966464 Decreasing

3 Belgium 0.94295 Decreasing

4 Brazil 1 Constant

5 Canada 0.949098 Decreasing

6 Denmark 1 Decreasing

7 Finland 0.963313 Decreasing

8 France 1 Constant

9 Germany 0.902356 Constant

10 Ireland 0.952092 Decreasing

11 Israel 1 Constant

12 Italy 1 Constant

13 Japan 0.865044 Decreasing

14 Mexico 0.921673 Decreasing

15 The Netherlands 0.99529 Decreasing

16 New Zealand 0.962866 Decreasing

17 Norway 0.972437 Decreasing

18 Singapore 0.853083 Decreasing

19 Spain 0.933585 Decreasing

20 Sweden 1 Constant

21 Switzerland 0.965533 Decreasing

22 United Arab Emirates 0.900306 Decreasing

23 United Kingdom 0.906385 Decreasing

24 United States 0.958976 Decreasing

25 Venezuela 1 Constant

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Table 5 Returns to scale ofcluster III nations under study

No. Nations Efficiency RTS

1 Albania 0.923584 Decreasing

2 Angola 1 Constant

3 Armenia 0.880923 Decreasing

4 Azerbaijan 0.985993 Decreasing

5 Bangladesh 0.905626 Decreasing

6 Benin 0.742906 Decreasing

7 Botswana 1 Constant

8 Bulgaria 0.943432 Decreasing

9 Burkina F. 0.752687 Decreasing

10 Burundi 0.962138 Decreasing

11 Cambodia 1 Constant

12 Cameroon 0.883853 Decreasing

13 Chad 1 Constant

14 Chile 1 Decreasing

15 Ethiopia 1 Constant

16 Georgia 0.864004 Decreasing

17 Ghana 0.970778 Decreasing

18 Haiti 0.918779 Decreasing

19 Indonesia 0.981178 Decreasing

20 Kenya 0.870904 Decreasing

21 Kyrgyzstan 0.949054 Decreasing

22 Latvia 0.971387 Decreasing

23 Macedonia 0.977207 Decreasing

24 Madagascar 0.763551 Decreasing

25 Malawi 1 Decreasing

26 Mali 0.780023 Decreasing

27 Moldova. R. 0.976169 Decreasing

28 Morocco 0.925065 Decreasing

29 Mozambique 0.898253 Decreasing

30 Nepal 1 Constant

31 Niger 0.790933 Decreasing

32 Nigeria 1 Constant

33 Paraguay 1 Constant

34 Peru 1 Decreasing

35 Philippines 1 Decreasing

36 Russian F. 1 Decreasing

37 Rwanda 0.864193 Decreasing

38 Senegal 0.875233 Decreasing

39 Sierra L. 0.892096 Decreasing

40 Sri Lanka 0.882955 Decreasing

41 Tanzania 0.793478 Decreasing

42 Togo 0.836143 Decreasing

43 Turkey 1 Decreasing

44 Uganda 0.828286 Decreasing

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Panama is another country lists in Table 3 having efficient governance. Its dollar-based

economy rests primarily on a well-developed services sector that accounts for three-

quarters of GDP. As far as economic condition is concerned, about 30 % of the population

lives in poverty, around 7 % of the population is unemployed. Also official corruption

remains a major problem for the government. Surprisingly, the scale efficiency is

decreasing RTS. A unit is said to operate at decreasing returns to scale (DRS) if a pro-

portionate increase in all of its inputs results in a less than proportionate increase in its

outputs. This implies that, even if the Panama Government tries to improve its governance

policies, the happiness among its citizen would be increasing at a decreasing rate.

Cuba and Czech Republic are two countries which are fully efficient (efficiency = 1).

Both the countries are having a constant return to scale, which indicates that any changes in

the governance would bring a proportionate change in the happiness. A quick observation

to their respective governance, it can be observed that the government of Cuba heavily

depends on service sector, whereas, Czech government is dependent on export. With an

unemployment rate of 2 %, the Cuban government announced it would eliminate 500,000

state jobs by March 2011 and has expanded opportunities for self-employment. The Czech

government is having 7 % unemployment rate. Some of the challenges faced by the Czech

government are rapidly aging population, funding of the unsustainable health care system

etc.

The second cluster (Table 4) has countries like Australia, Brazil, Denmark, Germany,

Spain, UK, US etc., which are developed countries as per the definition of UN. Some of the

countries have achieved 100 % efficient viz. Brazil, Israel, Italy, Sweden etc. indicating

fully efficient governance, whereas, countries like Australia (0.9435), Austria (0.9664),

Belgium (0.9429), Japan (0.8650), Mexico (0.9214) etc. do not have fully efficient gov-

ernments, as their efficiency ratio are less than 1. A close look at their RTS reveals that

among the efficient nations, the scale varies. For example, Brazil, France, Italy, Israel,

Sweden are having a constant returns to scale, whereas, Denmark has a decreasing scale.

Denmark being a modern market economy is having a high-tech agricultural sector

along with world leading industry highly dependent on foreign trade. Danish economy is

characterized by extensive government welfare measures and an equitable distribution of

income. Within the European Union (EU), Denmark is among the strongest supporters of

trade liberalization. Brazilian government, being a fully efficient government, outweighs

all other South American countries and is expanding its presence in world markets. Over

the years, the government has steadily improved economic stability by building up foreign

reserves and reducing its debt. However, a constant RTS implies that an increase in the

governance quality, would lead to a proportionate change in the happiness among its

citizens.

France is also having a constant RTS although its GDP mainly depends on industry and

services and around 90 % of the populations is dependent on industry and services too. It is

the most visited country in the world and maintains the third largest income in the world

from tourism. The French government has tried to recover its economic policy by giving

stimulus as a remedial measure to the economic crisis. However, an improvement in the

Table 5 continuedNo. Nations Efficiency RTS

45 Ukraine 0.963866 Decreasing

46 Zambia 0.980298 Decreasing

47 Zimbabwe 1 Decreasing

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governance policy would only lead to a proportionate improvement in the happiness

among the French people. Sweden has achieved an enviable standard of living under a

mixed system of high-tech capitalism and extensive welfare benefits. Except for unem-

ployment rate, which is 8.3 %, Sweden does not have any major transnational issues.

However, the constant RTS shows that the happiness cannot be increased beyond a certain

proportion. The Italian economy is driven in large part by the manufacture sector, whereas

industry and service sectors jointly contribute around 95 % of the GDP. Even though, in

2010, the unemployment rate had been around 8.5 %. The Italian government has strug-

gled to limit government spending, but Italy’s exceedingly high public debt remains above

115 % of GDP, and its fiscal deficit—just 5 % of GDP in 2009. It is an important gateway

for cocaine a major challenge for the government is to monitor money laundering by

organized crime and smuggling. As per DEA model, this country also has a constant RTS,

indicating, that happiness can be proportionately increased, if changes are done in the

governance.

Israel has a technologically advanced market economy and has a constant RTS.

Although, the Israeli Government responded to the present recession by implementing a

modest fiscal stimulus package and an aggressive expansionary monetary policy—

including cutting interest rates to record lows, purchasing government bonds, and inter-

vening in the foreign currency market.

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On the other hand, Venezuela, which is highly dependent on oil revenues, which

account for roughly 95 % of export earnings, about 55 % of the federal budget revenues,

and around 30 % of GDP; attempts to alleviate social ills like human trafficking, which is a

major problem. Apart from this, it is also an illicit producer of opium and coca. This has

significantly increased money laundering activities within the country. Like other efficient

countries in the cluster II, Venezuela also has a constant RTS.

Finally Table 5, which represent Cluster III countries is a group of least developed

countries, shows Chile, Nepal, Peru, Angola, Paraguay etc. like other two clusters, there

are fifteen countries which are having fully efficient governance (efficiency = 1) viz.

Angola, Malawi, Nepal, Chad etc. there are thirty two countries which are not gully

efficient as their efficiency is less than one viz. Zambia, Sri Lanka, Senegal, Uganda etc.

The same trend can be observed among the efficient countries in terms of RTS. The

RTS is either decreasing or constant. In none of the cases, it was found an increasing RTS.

Chile has a market-oriented economy characterized by a high level of foreign trade and

a reputation for strong financial institutions and sound policy that have given it the

strongest sovereign bond rating in South America. Chile has increasingly assumed regional

and international leadership roles befitting its status as a stable, democratic nation. Still it

has a decreasing RTS, this implies even if the Chilean government tries to improve its

governance policies, the positive change in the happiness will always be under propor-

tionate. Unlike Chile, Paraguay where large proportions (around 27 %) of the populations,

especially in rural areas are dependent on agricultural activity has a constant RTS. The

nation faces political uncertainty, corruption, limited progress on structural reform, and

deficient infrastructure. It is also infamous as an illicit producer of cannabis, from which

llucinogenic drugs are being produced.

Philippines and Peru both are having a decreasing RTS, whereas, their economy and the

system of governance are quite dissimilar. Peru’s economy reflects its multi-faced geog-

raphy and it is a country which is abundant in mineral resources. Peru has continued to

attract foreign investment. However, political disputes may impede development of some

projects related to natural resource extraction. Peru is now the world’s second largest

producer of coca leaf after Columbia, which has led to an increase in the domestic drug

consumption. Philippine is an archipelago, which is made up of 7,107 islands, is prone to

natural disaster like cyclonic storm, typhoon and volcanic eruptions. Some major chal-

lenges its government is facing are tax collection, worsening condition by new tax breaks

and incentives etc. Apart from unemployment rate which is around 7 %, the government

faces problems like, refugees and internally displaces persons as a result of internal conflict

between military and Maoists. Nepal being the poorest country in the world as per UN

report (2010) has almost one-quarter of its population living below the poverty line.

Agriculture is the mainstay of the economy. Industry contributes around 15 %, whereas,

33 % are contributed by agriculture. Although around three fourth of the population is

dependent on agriculture. Unemployment rate is as high as 46 and around 25 % of the total

population lives below poverty line. Additional challenges to Nepal’s growth include its

landlocked geographic location, civil strife and labor unrest, and its susceptibility to natural

disaster. Interestingly, the scale efficiency is characterized as constant RTS.

5 Conclusion

The present paper studies nations of various sizes, with various economic standings in the

world, with different culture and society to find whether good governance can maximize

R. M. Debnath, R. Shankar

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the happiness or not. As it has been discussed in the existing literature that happiness is

measurable and it is widely appreciable in different forms in various cultures. However,

here are few social and economic factors over which government can influence to maxi-

mize the happiness levels of the nation. Except Bhutan’s policy of Gross National Hap-

piness (GNH) and US’s Declaration of Independence to ‘the pursuit of happiness’, not

much has been firmly established by the other governments in terms of public policy. The

complexities of the research findings indicate that social and economic reforms to maxi-

mize the happiness are quite indistinct. Even though the governments adopt various pol-

icies to maximize the happiness, the change in the degree of happiness differs. For

instance, in the case of developed countries, the social and economic issues are rare. For

instance, unemployment, infant mortality rate, crime, corruption etc. are rare to be seen in

developed nations. However, the effective public policies are ineffective to increase the

happiness of the people. For example, a decreasing trend can be observed in Denmark. In

other countries happiness is constant. The unemployment rate is under control. However,

As a contrast, in some of the developing nations, like China, even though the government

is trying to deal with economic issues like unemployment, corruption migration, and health

problems etc., the happiness is increasing proportionately. Quite astonishingly, in Nepal,

where one quarter people live in below poverty line, is able to increase happiness pro-

portionately along with changes done in governance.

The inexistence of a clear classification of policies and regulations, it can be proposed

that only good governance cannot maximize the happiness. There are subjective indicators/

parameters, which could be important to measure the happiness. Therefore, the definition

of good governance must change from its existing characterization.

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