Are Poor People Less Happy? Findings from Melanesia

Simon Feeny

School of Economics, Finance and MarketingBuilding 80, Level 11

RMIT University445 Swanston Street

Melbourne, VIC 3000Australia

simon.feeny@rmit.edu.au

Lachlan McDonaldSchool of Economics, Finance and Marketing

Building 80, Level 11RMIT University

445 Swanston StreetMelbourne, VIC 3000

Australiaplach88@gmail.com

Alberto PossoSchool of Economics, Finance and Marketing

Building 80, Level 11RMIT University

445 Swanston StreetMelbourne, VIC 3000

Australiaalberto.posso@rmit.edu.au

* Correspondence to Simon Feeny, School of Economics, Finance and Marketing, Building 80, Level 11, RMIT University, 445 Swanston Street, Melbourne VIC 3000, Australia. Tel: +61 3 9925 5901; Fax: +61 3 9925 5624; Email: simon.feeny@rmit.edu.au.

Abstract

Happiness is increasingly being used to inform development policy. This is particularly

true in Melanesian countries where linkages between income and life satisfaction are

perceived to be weak. This paper examines the self-reported happiness of households in

two Melanesian countries: the Solomon Islands and Vanuatu. The focus is on whether

those living in poverty are less happy. Findings indicate that wealth, increases in

income, relative income, and living on communally owned land are all positively

associated with happiness. Household size and food insecurity have a negative

association. There is also strong support for poor households being less happy.

Keywords: Happiness; poverty; Melanesia; Solomon Islands; Vanuatu.

Acknowledgements

This paper is part of a project funded by the Australian Agency for International

Development through its Australian Development Research Award scheme. These views

expressed in the paper are those of the authors and not necessarily those of the

Commonwealth of Australia. The Commonwealth of Australia accepts no responsibility for

any loss, damage or injury resulting from reliance on any of the information or views

contained in the publication.

1. INTRODUCTION

Happiness, as a measure of well-being, has received great attention by policymakers

in recent years. In 2011, the United Nations General Assembly adopted a resolution entitled

‘Happiness: towards a holistic approach to development’ and the 2012 World Happiness

Report advocates for self-reported well-being and happiness to take precedence over GDP in

policymaking (Helliwell et al., 2012).1 Throughout the Pacific, happiness gained much

interest when Vanuatu topped the New Economics Foundations’ Happy Planet Index (HPI) in

2006. Moreover, the MNCC (2012) focused on happiness in developing alternative indicators

of well-being for Vanuatu. The report found that people living on custom land, that

participate in traditional ceremonial activities and who are active members of their

community are, on average, happier.

This paper seeks to identify the main determinants of happiness in two Melanesian

countries: the Solomon Islands and Vanuatu. Communities in these countries are distinct,

with the majority of households living semi-subsistence lifestyles on communally owned land

in rural areas. Virtually all households have access to a garden on which to grow their food

and systems of exchange, reciprocity and social networks are known to be very strong.

A specific objective of the paper is to examine whether there is a relationship

between poverty and happiness. To the authors’ knowledge the relationship between the two

has yet to be examined in a systematic manner since household surveys are usually designed

to focus on one at the expense of the other.2 Given the focus of the international community

on income poverty and the preference of policymakers in the region (and elsewhere) to find

alternative indicators, it is important to determine whether the two are related.

Defining and measuring poverty in Melanesian countries such as the Solomon

Islands and Vanuatu is a challenging task. One view is that poverty does not exist in these

countries since communally owned land systems, subsistence or semi-subsistence lifestyles

and strong social support networks prevent extreme hunger, homelessness and outright

destitution. A similar view is that subsistence affluence prevails in these societies. The term,

originally coined by Fisk (1971), relates to households being able to satisfy their needs with

very little labour input.3 It is for these reasons that people have been very reluctant to refer to

poverty when evaluating human well-being in these societies.

Yet lifestyles in these societies are changing and many households in these countries

face a number of challenges akin to poor households across the world. Malnutrition and

hunger exist, with households relying on cheap, sometimes poor quality imported food and

sometimes skipping meals. Monetisation is increasing the importance of income to enable the

payment of school fess and the purchase of basic household goods and services in order to

meet the basic needs of the family. Urbanisation combined with very few employment

opportunities has given rise to squatter settlements, high rates of unemployment, social

tensions and crime. Moreover, climate change threatens to exacerbate the problems faced by

these countries. Unfortunately, only a very limited number of surveys are conducted to

provide insights into these issues. Assessing well-being in these contexts is therefore timely

and important and can provide crucial information for policymakers and their social

protection policies.

The challenges facing these countries are borne out by some of their official

development indicators. While the World Bank classifies both the Solomon Islands and

Vanuatu as middle income countries, other indicators reveal relatively low levels of

development. Using basic needs poverty lines, in 2006, 23 per cent of the population of the

Solomon Islands and 16 per cent of the population of Vanuatu lived in poverty (AusAID,

2009).4 According to the Human Development Index (HDI), the Solomon Islands and

Vanuatu rank 143 and 124 respectively out of 186 countries (UNDP, 2013). Assessing

progress towards the United Nations Millennium Development Goals (MDGs) is difficult due

to a paucity of data but where data do exist they suggest that the Solomon Islands is unlikely

to achieve any of the goals while Vanuatu is on track to achieve just two: reducing child

mortality and combatting HIV/AIDS and other diseases (PIFS, 2012). Improving child health

remains a challenge in both countries, with 26 per cent of children under the age of five in

Vanuatu and 32 per cent of children in the Solomon Islands found to be stunted (AusAID,

2012).

Given the distinctive lifestyles of Melanesian communities, defining poverty in the

region will always be controversial. There is however, a consensus that conventional

measures of poverty based on income or consumption are considered inappropriate. Further,

participatory poverty assessments conducted in these and other Pacific countries found that

households preferred the term hardship over poverty and reported suffering from a lack of

access to basic services, income earning opportunities and good governance.

In recognition that any measure of poverty in the countries is likely to be

contentious, the focus of this paper is on the well-known Multidimensional Poverty Index

devised by Alkire and Foster (2011). The index is arguably well-suited to Melanesian

countries since it uses information on three non-monetary dimensions of well-being: health;

education and living standards. The impact of relative poverty (how rich people feel relative

to the rest of their community) on happiness is also examined.

The remainder of this paper is structured as follows. Section 2 summarises the

existing literature which has sought to explain the determinants of happiness. Section 3

describes the data and methodology employed by the paper. Section 4 presents the findings

from the empirical analysis and finally, Section 5 concludes.

2. LITERATURE REVIEW

There is now widespread recognition of the inadequacy of income as measure of well-

being and a search for more appropriate measures. As noted above, this is particularly true in

Melanesia where semi-subsistence livelihoods and very strong social support networks imply

that the use of income-based measures of well-being is inappropriate.

The Alternative Indicators of Well-being report for Vanuatu emanated from the 2008

Melanesian Spearhead Group (MSG) Trade and Economic Officials Meeting (TEOM) and

the MSG Leaders’ Summit. The leaders agreed that governments in the region need to do

more to account for and measure the non-cash values that contribute to their peoples’ quality

of life (MNCC, 2012).  Findings suggest that people from Torba province are the happiest in

Vanuatu and this is despite them having low incomes and being a long way from a major

market. The report also demonstrated the importance of resource access, culture and

community vitality, necessitating the inclusion of such factors in the investigation carried out

by this paper. The remainder of this section summarises the determinants of happiness that

have been identified by other studies.

(a) Income and happiness

The focus of many happiness studies has been the impact of income. Such

investigations are motivated by the seminal work of Easterlin (1974) which presented a so-

called paradox. He found that: (i) richer individuals in the US are happier than poorer

individuals but; (ii) over time, as the US got richer, average happiness failed to increase. In

fact, despite income levels increasing, levels of happiness in the US, Japan, UK and most

European countries have been static since the late 1950s (Laynard, 2006). A number of

explanations have been purported to explain this paradox.

The first is that individuals’ relative incomes are important for happiness, rather than

actual income levels (Jiang et al., 2012).5 Individuals compare themselves to others and feel

happier if their relative circumstances improve. According to this theory, if all individuals’

incomes increase together, then their relative standing does not change and happiness remains

unaltered. Secondly, as argued by Layard (2006), people adapt to higher incomes quite

quickly, making it hard to secure permanent increases in happiness from increases in income.

While a rise in income might therefore have an impact on happiness initially, this impact

dissipates over the longer term. Thirdly, the World Happiness Report argues that higher

incomes can come at a cost, such as environmental degradation, an increase in insecurity, a

loss of trust and reduced confidence in government. At a country level, it might also be the

case that additional incomes only flow to the rich, doing little to increase the average level of

happiness (Helliwell et al., 2012).

Another finding is that there is diminishing marginal utility of income with respect to

happiness. In other words, the life satisfaction of the poor can be improved with small

amounts of money while richer individuals require a much larger increase in absolute income

to improve their happiness. At low levels of income, additional financial resources can secure

basic needs such food, clothing, housing, health care, water and sanitation. At higher

incomes, such needs have already been met (Helliwell et al, 2012).

(b) Non-income determinants of happiness

There is generally a consensus on the non-income determinants of happiness

(Appleton and Song, 2008; Dolan et al., 2008; MacKerron, 2012). In particular, after basic

needs are met, aspirations, relative income differences and the security of gains in well-being

are all found to be important (Graham, 2005).6

Helliwell et al (2012) categorise the main determinants of happiness into two groups.

The first group includes external factors, which policymakers can change such as income,

work, community and governance, and values and religion. Country level data reveals that

happiness is highest in countries with a sense of community, trust and social equity –

Denmark, Finland, Norway, Netherlands. The second group includes person factors such as

mental health, physical health, family experience, education, gender and age.

Unemployment and less job security are found to impact negatively on happiness (Di

Tella et al, 2001; Frey and Stutzer, 2002) while trust, being part of a community, freedom,

equality and religion all have a positive association (Helliwell et al., 2012). Better mental and

physical health are found to be positively associated with happiness although these variables

are often difficult to measure (Helliwell et al., 2012). Marriage is nearly always found to

increase happiness but there is no strong evidence that having children is associated with

happiness (Stutzer and Frey, 2006). The level of education is usually expected to increase an

individuals’ income although there is relatively little evidence of this effect independent of

income (Helliwell et al., 2012). With regard to gender, women are found to be happier than

men in advanced economies but the reverse is sometimes found to be the case in developing

countries (Graham and Felton, 2005; Blanchflower, 2008; Senik, 2004). The relationship

between happiness and age is often found to be U-shaped with happiness declining for

middle-aged individuals before rising in later life (Blanchflower and Oswald, 2004;

Helliwell, 2003). These findings from the literature inform the specification of the empirical

model specified in the proceeding section.

However, while studies have tended to focus on the relationship between income and

poverty, there is little evidence of whether poverty (and non-income poverty in particular) is

related to happiness. It is sometimes argued that the poor are happy possibly due to a focus on

relationships and community vitality rather than on money and materialism with pictures of

smiling people in remote areas of Africa providing testament to this claim. Conversely, it can

be argued that such assertions are naive to the aspirations of the poor and their need for

access to food and basic services for their survival. Amartya Sen notes that the some people

can bear adversity cheerfully (although this does not mean there is no adversity and that we

should ignore the depravity experienced by the poor (see Barford, 2011)). A related issue is

whether there is a relationship between vulnerability and happiness. Vulnerability refers to

the likelihood of being poor or becoming poor in the future. Graham and Pettinato (2002)

find that the self-reported well-being of those who have escaped poverty can be lower than

that of the poor due to an insecurity or risk of falling back into poverty. This paper

contributes to the existing literature by examining these issues in greater detail.

3. DATA AND METHODOLOGY

(a) Data

The paper employs data from a unique household survey conducted in the Solomon

Islands and Vanuatu in 2012-2013. A total of 619 households were surveyed (302 households

in the Solomon Islands and 317 households in Vanuatu). Five locations were targeted in each

country, which were selected based on criteria that sought to reflect diversities in remoteness,

economic activity, and environmental differences.

In the Solomon Islands, communities on the two largest islands were surveyed:

Guadalcanal and Malaita. In the capital, Honiara, two squatter settlements, White River in the

west of the city and Burns Creek in the east, were visited. The former is a multi-ethnic

settlement located in western Honiara while the latter consists mainly of Malaitans that were

displaced during an ethnic conflict from 1999-2003. Two communities on the Weather Coast

of Guadalcanal, Oa and Marauipa, were also surveyed. This is a region renowned for its

geographical remoteness and its exposure to harsh climactic conditions. Finally, households

were surveyed in the densely populated rural centre of Malu’u, which is located about 80 kms

north of Auki, the country’s second largest city, on the island of Malaita.

Comparable communities were visited in Vanuatu. In the capital city, Port Vila,

communities were visited in the squatter settlements of Ohlen in the north of the city and

Blacksands in the south west, both of which are home to migrants from outer islands. Two

migrant communities in the country’s second largest city, Luganville, located on the island of

Espiritu Santo, were also visited (Pepsi and Sarakata). While only separated by the Sarakata

river, these two communities enjoy very different access to government services since, at the

time of the survey, Pepsi community was not considered to be under the auspices of the

Luganville Municipal Council. The final survey location was Hog Harbour, also on Espiritu

Santo, which is a rural village with good access to Luganville following the completion of the

East Santo road, and also to tourism owing to its proximity to the well-renowned Champagne

Beach.

The survey was designed following a literature review of poverty and happiness and

their drivers in developing countries, particularly in the Pacific Islands. In order to capture

potential gender differences, the research teams aimed for a 45 to 55 per cent gender balance

in survey respondents. To measure happiness, household members were asked (in their local

language) ‘On a scale of 1-10 (with 10 being very happy and 1 being not happy at all) how

happy are you?’7 The happiness scores by community for each country are provided in Figure

1 below.

Figure 1: Happiness Scores by community in the Solomon Islands and Vanuatu

02

46

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Solomon Islands

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(b) Methodology

As outlined in Graham (2005), empirical models of happiness are usually specified in the

same way. We follow this specification:

W i=α+βx i+ε i, (1)

where W i is self-reported happiness of household member i, x i is a vector of explanatory

variables including socio-demographic and socio-economic characteristics. α is a constant

and β is a vector of coefficients. Unobserved characteristics and measurement errors are

captured in the error term (ε).

The inclusion of the variables in vector x i is motivated by the literature review

undertaken in Section II as well as by the characteristics, culture and livelihoods of

households in the Solomon Islands and Vanuatu, discussed in the introduction. The variables

includes gender, age, education, employment, wealth, poverty, vulnerability, the change in

earnings during the past two years, self-reported inequality, household size, whether the

household lives on communally owned (custom) land, whether the household has access to a

garden, and whether the household worried that their food would run out before they got

money to purchase more (as a measure of food security).8

Self-reported inequality was obtained by asking households about their relative

financial situation: ‘Compared with the rest of your country do you feel poor or rich?’ and

respondents were provided with a five point Likert scale from 1 (for very poor) to 5 (for very

rich). The same question was asked in relation to their village or community.

Wealth is measured using an index based on the approach pioneered in Filmer and

Pritchett (2001). It uses a principal components approach to construct a score of

socioeconomic status (the authors refer to this as “wealth”) using indicators of durable assets,

indicators of the quality of water and sanitation access, electricity access and dwelling

characteristics.

Careful consideration was given to the measure of poverty used in the study. For the

reasons discussed above, this paper replicates the Multidimensional Poverty Index (MPI)

developed by the Oxford Poverty and Human Development Initiative. The index has become

a widely accepted and used measure of poverty and is currently reported in the annual

United Nations Development Program’s Human Development Reports. A case is also being

made for reductions in the MPI to be used as a headline goal in the new round of

international development targets in the post-2015 development agenda (Alkire and Sumner,

2013).

The MPI considers three equally weighted dimensions of poverty (health, education

and living standards) captured by ten indicators. The MPI therefore conveys additional

information not captured in single-dimensional measures. It identifies those who are poor

through a two-step process involving identifying cut-offs of deprivation. ‘The first is the

traditional dimension-specific cut-off, which identifies whether a person is deprived with

respect to that dimension. The second delineates how widely deprived a person must be in

order to be considered poor’ (Alkire and Foster, 2011, p.477). In this way, the MPI

simultaneously concerns itself with how many people are experiencing poverty as well as

how much (or the depth of) the deprivation is being experienced.

The MPI is calculated using the following formula:

MPI=H × A, (2)

where H is the headcount or the percentage of people who are identified as

multidimensionally poor and A (intensity) is the percentage of dimensions in which the

average poor person is deprived. A household is deemed poor if it is deprived in at least 33

per cent of the weighted indicators. A measure of vulnerability is also included in the model.

Following Alkire and Foster (2011), vulnerable households are those with a weighted average

of deprivations somewhere between 0.20 and 0.33. Table A1 in the appendix provides the

dimensions, indicators, deprivation thresholds and weights for the MPI. Table A2 provides

variable definitions and summary statistics are provided in Table A3.

4. RESULTS AND INTERPRETATION

This section presents the results from the empirical model explaining the variation in

happiness across the communities in the Solomon Islands and Vanuatu. Our dependent

variable, happiness, is an ordinal variable taking values of 1 to 10. Higher values correspond

to a higher level of happiness. Given the ordinal nature of the dependent variable, OLS would

yield biased and inefficient parameter estimates and an ordered probit model is therefore

estimated. All models include community fixed effects to account for unobserved

characteristics that vary between communities and could potentially affect individual levels

of happiness, such as the proximity to urban centres or the coast.

Column (1) of Table 1 presents results from the model with the wealth variable

included but the poverty variable excluded. Since there is a high degree of correlation

between these two variables they cannot be employed together in the model. The same

applies to the personal perceptions of inequality variables (relative to country and

community).9 Findings from this model are consistent with a priori expectations. First,

people in wealthier households are found to be happier. Second, the variation in happiness in

these two Melanesian countries is partially explained by relative earnings. Specifically, an

increase in earnings over the last two years is found to significantly increase happiness.

Moreover, if individuals believe themselves to be richer than others in their community or

country, then they are also happier. Our findings, therefore, contradict evidence from other

developing countries, showing a negative relationship between inequality and happiness.10

Economists have formulated a number of theories to explain how the happiness of

an individual can be affected by the happiness of others. For example, classical theorists such

as Bentham and Marshall describe how utility is a function not only of personal consumption,

but also, the desire for distinction from others in society (Barber, 1991). Specifically, they

suggest that utility is a function of factors such as status, prestige and reputation. Moreover,

they argue that consumption of general goods and services, itself, can be driven by our

perceptions of others. It seems, therefore, that in poor communities in Melanesia, status

seems to be playing an important role in determining utility. In other words, being richer than

others or having had improvements in one’s own wealth will increase happiness in these

households.

Table 1 also reveals that individuals living on communally owned land are happier.

Primarily, this may occur because individuals with access to the land may use it to establish

gardens and grow food without having to pay for usage rights. Individuals with access to this

land may also feel happier because of stronger cultural connections to land in these countries

and a sense of belonging stemming from the fact that these lands have been passed from

generation to generation through a variety of traditional tenure systems for millennia

(MNCC, 2012).

Turing to the remaining variables, Table 1 also indicates that large household size is

associated with lower happiness in these communities. To put this into perspective, an

increase in the number of people living in a household by 1 lowers the probability that an

individual will attain a score of 10 in the happiness scale by 1 per cent.11 This finding is

consistent with anecdotal evidence that extended family members are moving to areas with

better employment opportunities and placing stress on the households which host them.

Increasing numbers of mouths to feed with limited employment opportunities and sometimes

a shortage of land for gardens is reducing the happiness of household members. Findings

from Table 1 also indicate, not surprisingly, that households experiencing food insecurity are

found to be less happy.

Column (2) presents results when poverty is introduced into the model (and the

wealth variable is excluded, given that they share a number of the same components). The

negative and statistically significant coefficient attached to this variable indicates that people

living in multidimensionally poor households are less happy. This is a unique and important

finding suggesting a vital link between the two. Interestingly, the MNCC (2012) found that

people in Torba province, the northernmost region of Vanuatu, which includes the Banks

Islands, are on average, the happiest people in Vanuatu. Yet previous research also reveals

that the rate of multidimensional poverty is also quite high in the most geographically distant

regions of the Solomon Islands and Vanuatu, which includes the Banks Islands, suggesting

that Torba is likely to be an exception (Clarke et al., 2013).

Columns (3) and (4) of Table 1 present results from models using the alternative

inequality variable (where respondents are asked how rich they feel relative to the rest of

their community, rather than their country). Results, again, suggest a positive finding between

happiness and inequality while results relating to the other variable remain largely

unchanged.

Table 1: The Determinants of Happiness in the Solomon Islands and Vanuatu

Independent variables (1) (2) (3) (4) (5) (6)Male 0.12 0.15 0.10 0.13 0.16 0.13

[1.28] [1.57] [1.07] [1.35] [1.59] [1.38]Age -0.024 -0.022 -0.026 -0.024 -0.022 -0.024

[-1.34] [-1.21] [-1.47] [-1.34] [-1.24] [-1.36]Age squared 0.00029 0.00027 0.00032* 0.00029 0.00027 0.00029

[1.51] [1.37] [1.65] [1.51] [1.39] [1.53]Wealth 0.15*** 0.15***

[3.19] [3.30]Poverty (MPI) -0.20* -0.21** -0.23* -0.24*

[-1.91] [-1.97] [-1.89] [-1.89]Vulnerability -0.059 -0.052

[-0.51] [-0.45]Change in earnings over two years 0.14*** 0.14*** 0.16*** 0.16*** 0.14*** 0.16***

[2.80] [2.79] [3.21] [3.23] [2.78] [3.22]Inequality (Country) 0.44*** 0.46*** 0.46***

[6.02] [6.43] [6.35]Inequality (Community) 0.44*** 0.46*** 0.46***

[5.52] [5.90] [5.84]Employed 0.37 0.40* 0.22 0.24 0.40* 0.24

[1.56] [1.70] [0.90] [1.01] [1.65] [0.98]Total people in household -

0.042***-0.031** -

0.044***-

0.033**-

0.031**-

0.033**[-2.75] [-2.08] [-2.92] [-2.24] [-2.06] [-2.22]

Education -0.099 0.00011 -0.12 -0.015 -0.0044 -0.019[-0.75] [0.00084

][-0.87] [-0.12] [-0.034] [-0.14]

Garden -0.094 -0.085 -0.098 -0.089 -0.086 -0.090[-0.74] [-0.68] [-0.78] [-0.72] [-0.68] [-0.72]

Communally owned land 0.26** 0.24** 0.18* 0.15 0.24** 0.15[2.49] [2.26] [1.74] [1.46] [2.27] [1.47]

Food insecure -0.42*** -0.43*** -0.44*** -0.45***

-0.43***

-0.45***

[-4.32] [-4.45] [-4.52] [-4.67] [-4.41] [-4.64]Community FE? Yes Yes Yes Yes Yes YesObservations 619 619 619 619 619 619Pseudo R-squared 0.095 0.093 0.094 0.091 0.093 0.091Chi-squared p-value 0 0 0 0 0 0Notes: Dependent variable is happiness. Robust z-statistics in brackets. *, **, and *** denote statistical significance at the 10, 5 and 1 per cent, respectively.

5. CONCLUSION

Using statistical modelling of unique household survey data, this paper identifies

the main determinants of happiness in both the Solomon Islands and Vanuatu. An import-

ant finding is that the level of income does not explain the variation in happiness in these

two countries. The quest for alternative indicators of well-being in the region is therefore

well justified. So too is a focus on reducing multidimensional poverty. Findings indicate

that the poor in these countries are, on average, less happy. This has important implica-

tions. Improving the health, education and living standards indicators of the MPI will im-

prove happiness in the region, as well as being worthwhile in their own right.

Other findings reveal that wealth, increases in income and relative income are all

positively associated with happiness. In contrast, household size has a negative associ-

ation. These findings are generally consistent with many previous studies explaining the

variation in happiness (usually across country).

Two other findings from the analysis are unique to this study, with further implic-

ations for policymakers in Melanesia. The first is that people on communally owned land

are found to be happier. This is likely to reflect the close ties that communities in these

countries have to their land as well as the importance of land for gardens. Secondly, food

security is also found to be important for happiness. Policymakers should therefore find

ways to strengthen access to land and improve food security. In urban areas this might in-

volve support for urban gardens and land segregation schemes, as well as programs that en-

courage food cultivation in the available space around homes, such as potted and hanging

gardens. Improving agricultural productivity through education, access to finance and the

provision of tools will also improve food security

18

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Appendix

Table A1: Dimensions, Indicators, Deprivation Thresholds and Weights for the Multidimensional Poverty Index

Dimension (weight)

Indicator(weight) Deprived if…..

Health(1/3)

Mortality(1/6) Any child has died in the family

Nutrition(1/6)

Any adult or child for whom there is nutritional information is malnourished*

Education(1/3)

Years of Schooling(1/6)

No household member has completed five years of schooling

School Attendance(1/6)

Any school-aged child is not attending school in years 1 to 8

Standard of Living(1/3)

Electricity(1/18)

The household has no electricity

Sanitation(1/18)

The household´s sanitation facility is not improved (according to the MDG guidelines), or it is improved but shared with other households

Water(1/18)

The household does not have access to clean drinking water (according to the MDG guidelines) or clean water is more than 30 minutes walking from home.

Floor(1/18) The household has dirt, sand or dung floor

Cooking Fuel(1/18) The household cooks with dung, wood or charcoal.

Assets(1/18)

The household does not own more than one of: radio, TV,telephone, bike, motorbike or refrigerator, and does not owna car or truck

Notes: A proxy measure was used for this indicator. A households is deprived if they answered in the affirmative to the question “Did you or any other adults in the house not eat food for an entire day because there wasn’t enough money to buy food” taken from the US Food Security module. The Table is based on Alkire (2011)

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Table A2: Variable Definitions

Variable DefinitionHappiness Measured using a Likert scale (1 to 10)Male Dummy variable taking the value of 1 for male respondentsAge Age of respondent in yearsWealth Index of a households assets and access to basic services. Based on

Filmer and Pritchett (2001)Poverty Binary dummy variable taking the value of 1 for poor households.

Based on Multidimensional Poverty Index of Alkire and Foster (2011)

Vulnerability Binary dummy variable taking the value of 1 for households that are close to the poverty threshold. Based on Alkire and Foster (2011)

Change in earnings Based on the question ‘During the past two years, how have your earnings changed?’ measured using a Likert scale (1 to 5)

Inequality (Country) Responses to ‘Compared with the rest of your country do you feel poor or rich?’ measured using a Likert scale (1 to 5)

Inequality (Community) Responses to ‘Compared with the rest of your community do you feel poor or rich?’ measured using a Likert scale (1 to 5)

Employed Binary dummy variable taking the value of 1 if the respondent works for money

Household size Number of people in the householdEducation Proportion of household members finished primary and secondary

schoolGarden Binary dummy variable taking the value of 1 if the house has access

to a gardenCommunally owned land Binary dummy variable taking the value of 1 if the house is on com-

munally owned landFood insecure Binary dummy variable taking the value of 1 if the household

worried that their food would run out before they got money to purchase more

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Table A3: Summary Statistics

Variable Obs Mean Std. Dev.

Min Max

Happiness (scale from 1 to 10) 619 7.30 2.33 1 10Male 619 0.54 0.50 0 1Age 619 41.14 14.01 17 86Wealth 619 0.04 1.36 -3.09 3.17Multidimensional Poverty Index 619 0.31 0.46 0 1Vulnerability 619 0.24 0.43 0 1Change in earnings 619 3.14 0.98 1 5Inequality (Country) 619 2.77 0.69 1 5Inequality (Community) 619 2.92 0.65 1 5Employed 619 0.96 0.20 0 1Household size 619 5.53 2.79 1 23Education 619 0.47 0.35 0 1Garden 619 0.77 0.42 0 1Communally owned land 619 0.53 0.50 0 1Food insecure 619 0.55 0.50 0 1

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Endnotes

1 The country of Bhutan is often viewed as taking the lead role on this issue by devising its measure of Gross National

Happiness.

2 While the relationship between GDP per capita and happiness is often analysed at a country level (Helliwell et al.,

2013), studies have not specifically examined whether poor people are happy.

3 While Fisk (1971) was referring to livelihoods in Papua New Guinea, the term has also been applied to other

Melanesian countries.

4 Basic needs poverty lines measure the level of income required to meet certain thresholds for housing, food, clothing,

healthcare, education and to meet customary obligations (AusAID, 2009). If the household earns less currency than the

amount deemed necessary to meet these needs, the household is classified as poor. Urban poverty is found to be far

higher than rural poverty in these countries (AusAID, 2009). However, Gibson (2010) and Narsey (2011; 2012) argue

that this is likely to be the result of errors in calculating comparable poverty lines between urban and rural areas.

5 Relative standing or achievement relative to peers and parents can also be important (Bookwalter and Dalenbeg, 2010)

6 Helliwell (2003) finds that six variables can explain 80 per cent of the variance in happiness across 50 countries: the

divorce rate, the unemployment rate, the percentage of people reporting that “most people can be trusted”; membership

in non-religious organisations; the percentage of citizens who “believe in God”; and the quality of government.

7 This measure is line with previous work in the region (MNCC, 2012) which, in turn, uses the Self-Anchoring Striving

Scale of Cantril (1965). Discussions with local researchers and enumerators revealed that people would interpret the

question as happiness in general or happiness with life as a whole rather than happiness today.

8 It is common for studies to use the log of income as an explanatory variable to capture the diminishing marginal utility

of income. However, the income variable used in this study is ordinal, consisting of five income brackets given the

difficulties in accurately recording income in the countries concerned.

9 It has been suggested that wealth is endogenous in model explaining happiness (see Stutzer, 2004). The same might

also apply to the poverty variable. We tested for the endogeneity of these variables using a Durbin-Wu-Hausman test,

using education and the number of children as instruments, noting that they are not significant determinants of

happiness. These tests indicated that both variables are exogenous at the 1 percent level of statistical significance.

10 While Tomes (1986) finds positive relationship between inequality and happiness in Canada, Alesina et al. (2004)

find a negative relationship between inequality and happiness in Europe and the United States. Similarly, Blanchflower

and Oswald (2004) show that a small, albeit statistically significant, effect of inequality on well-being in the same group

of countries. Finally, for developing countries, Graham and Felton (2006) find a negative relationship between these

variables in Latin America. However, Senik (2004) finds that inequality has no statistical significant effect on happiness

in Russia.

11 This figure is calculated by taking the marginal effect of having one more person in the household on scoring 10 on

the happiness scale, holding everything else at its mean.