Dissertation

The University of Nottingham
School of Economics

L13500 Undergraduate Dissertation 2015/16

A panel-based investigation into the macroeconomic
determinants of subjective well-being.
Student Name: Tom Collingridge

Student ID Number: 4218093
Individual reference number: 56103788

Supervisor: Robin Cubitt
Word Count: 7485

This Dissertation is presented in part fulﬁlment of the requirement for the completion
of an undergraduate degree in the School of Economics, University of Nottingham.
The work is the sole responsibility of the candidate.
I give permission for my dissertation to be made available to students in future years if
selected as an example of good practice.
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Table Of Contents
1. Introduction……………………………………………………….…….……3

2. Literature Review……………………………………………..…….….….4-6

2.1. Subjective Well-being…………………………………………………………………………….….…..…4

2.2. Macroeconomic patterns………………………………………………………………………..………4-6
3. Data, theory and empirical strategy………………………….……..…7-12

3.1. Well-being data……………………………………………………………………………………….….7-8

3.2. Data sources and statistics……………………………………………………………………………8-11

3.3. Theoretical predictions…………………………………………………………………………….…11-12

3.4. The panel regression model…………………………………………………………………………..…12

4. Empirical Results…………………………………………………….…13-18

4.1. Estimating the macroeconomic determinants of subjective well-being..……………………….13-16

4.2. Comparing some of the results………………………………………………………………………16-17

4.3. Do the determinants of subjective well-being change as a country’s wealth increases?….…17-18
5. Concluding Remarks……………………………………………………….19

Bibliography……………………………………………………………..…20-23

Appendices…………………………………………………………………….24

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1. Introduction
Subjective wellbeing (SWB) refers to the affective and cognitive evaluation of one’s life; in colloquial
terms it is often labelled as “happiness” (Diener, 2000). The renowned Greek philosopher Aristotle
pioneered the study of human wellbeing, one of his most powerful proverbs read: “happiness is the
meaning and the purpose of life, the whole aim and end of human existence” (Aristotle, 350BC: 7).
Diener and Scollon (2003) concurred with this statement when they established that happiness was
ranked as the most important value to people worldwide.
The primary goal of public policy should be to maximise wellbeing within a population, and until now
gross domestic product (GDP) has been widely adopted to measure if this goal is being achieved.
However, there are numerous studies that suggest that GDP may in fact be a poor indicator of social
welfare (England, 1998; Fleurbaey, 2009). Furthermore, the threats now posed by environmental
change stem from the pursuit of economic growth by the world’s most developed countries. Such
issues include: biodiversity loss and ecosystem collapse, the depletion of scarce resources, and a
failure of adaption to climate change (World Economic Forum, 2015). Oswald (1997) argues that
people have no real innate interest in the rate of growth; “economic things matter only in so far as they
make people happier” (Oswald, 1997:1). Newfound measures of subjective wellbeing provide an
empirical method of assessing social welfare (Fleche et al., 2011). If we are in conclusion that
personal happiness and wellbeing are the fundamental goals of public policy, how should governments
shape macroeconomic policy to best achieve these goals? Is GDP the best indicator of wellbeing, or
are there are alternative, more efficacious approaches to improving human happiness that do not
come at a cost to the natural environment? If there are, then surely these should become the primary
objectives of public policy.
Since 1972, Bhutan has advocated an approach to economic progress that measures prosperity by
gauging its citizens happiness levels. The bicameral parliament has set out to achieve sustainable
development by improving the physical, social and spiritual health of its citizens (Kelly, 2012). In light
of this approach, in 2010, several senior government officials including David Cameron called for the
creation of national measures of wellbeing (Stratton, 2010). A couple of years later, the Office for
National Statistics conducted their first national wellbeing survey (ONS, 2012). However, not enough
attention has been paid to the global SWB surveys that have been established for several years.
These surveys already possess enough evidence for governments to make informed decisions about
how to try and maximise happiness. This study uses the most comprehensive of these world surveys
to try and measure the macroeconomic determinants of subjective wellbeing. The relative size of the
coefficients will account for the “psychic losses that are usually ignored in economic models” (Di tella
et al., 2003: 815). David Cameron and other government officials need not wait until their national
surveys collect enough data for informed decisions to be made. They should instead act on the results
of this and other investigations (Table 1), and immediately start to maximise subjective wellbeing.
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2. Literature Review
Several other studies have attempted to measure the affect that various macroeconomic variables
have on subjective wellbeing (SWB). The results of which are presented in Table 1. This study uses a
larger sample to confirm the direction of each of the coefficients and provides an insight into the
reliability of the results in the literature. The relative size of each coefficient will also be examined.
2.1. Subjective well-being
Despite earlier concerns, a range of studies have now proved that happiness surveys are relatively
robust indicators of an individual’s wellbeing. Estimates of SWB have been tested against a range of
indirect measures and generally show the expected relationship. Diener (1984) declared that happy
respondents to SWB surveys are also independently rated by those around them as happy. This was
later confirmed by Sandvik et al. (1993). Diener also demonstrated that people who rate themselves
as happy have a tendency to smile more (Diener and Suh, 1999). Furthermore, in the field of
physiology, both Sutton and Davidson (1997) and Urry et al. (2004) find a strong positive correlation
between personal wellbeing scores and the level of EEG activity in a region of frontal cortex that is
associated with positive emotion.
In the past, researchers have worried that different cultural values and norms would lead to
incomparable results between countries. However, Fleche et al. (2011) recently examined the degree
to which cultural measurement bias exists and found that relative to the importance of different
determinants of wellbeing, cultural differences are not major drivers of differences in life satisfaction.
Another criticism of wellbeing surveys is that they do not account for time-varying unobservable factors
(Ferrer-i-Carbonell, 2011). Schwarz and Strack (1999) demonstrated that measures of life satisfaction
can be influenced by situational factors such as the respondent’s mood at the time of answering.
However, Diener (2000: 35) argues that “situational factors pale in comparison with long-term
influences on wellbeing measures”. Furthermore, the availability of longitudinal aggregate data used in
this investigation reduces the influence that situational factors may have on the results. Governments
currently evaluate new policy options by estimating their impact on indicators which indirectly affect
welfare (Benjamin et al., 2014). Subjective wellbeing surveys now act as a better proxy of an
individuals utility, and thus should be used as an additional indicator of a policy’s benefit to society.
2.2. Macroeconomic patterns
Di Tella et al. (2001) were the first to use survey scores to measure the psychological impact of
fluctuations in macroeconomic variables (unemployment and inflation). The authors then proceeded to
estimate the affect that additional variables have on SWB in later studies (Di Tella et al., 2003; Di Tella
and MacCulloch, 2008). In each of these cases, ordered probit regressions were used to deal with the
ordinal, categoric nature of the wellbeing data the authors examined. This is in contrast to the
quantitative, interval data used in this investigation. Therefore, unlike in Di Tella et al. (2001, 2003,
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2008) and Wolfers (2003), this panel data model will be estimated using least squares. Economists
that have that applied both methods to life satisfaction data have found little difference in the results
(Ferrer-i-Carbonell and Frijters, 2004; Stevenson and Wolfers, 2008). In Table 1, the names of the
authors that have used ordered probit regressions are italicised. Since the interpretation of ordered
probit estimates is different to the interpretation of coefficients estimated by least squares, these
results are not directly comparable to the results of this paper.
The availability of detailed datasets have allowed several authors to control for the personal loss of
becoming unemployed. Unfortunately, the personal details of the respondents are not available to this
investigation. In order to assess the entire wellbeing cost of a change to the unemployment rate, an
average will be taken from the results of the authors that have used linear regression techniques. The
method that will be applied is presented in part A1 of the appendix.
The results in Table 1 have been categorised by the particular survey used by each of the authors.
The Euro-Barometer Survey is by far the smallest of the three surveys in question. All of the authors
that have used this survey have evaluated data from 12 developed nations in the Eurozone over
various time periods. Whereas the two studies that have used the World Values Survey have
evaluated data from the 32 OECD nations included in this poll. Both of these surveys pale in
comparison to the size of the Gallup World Poll used by this investigation. Gallup’s poll provides
economists with the opportunity to evaluate data from a much larger sample of both developed and
undeveloped countries. A detailed description of this survey is included in the next section. To the best
of the author’s knowledge, the range of variables evaluated by studies that have used Gallup’s poll
has thus far been limited (Table 1).
Despite using different surveys, all of the authors are in agreement that an increase in the
unemployment rate leads to a fall in subjective wellbeing. This can be attributed to both the higher fear
of unemployment for everyone in society as well as the personal cost of losing one’s job. A more
detailed explanation of why this trend occurs is included in the next section. Similarly, the existing
literature is also in agreement that an increase in the inflation rate has a negative impact on SWB. This
is in accordance with the underlying economic theory: inflation reduces purchasing power, the cost of
savings, and ensues uncertainty. However, it does help to prevent the paradox of thrift and can help
out debtors. This may help to explain why the authors estimate inflation to have a relatively weaker
influence on life satisfaction scores than changes to the unemployment rate. This result is found to be
consistent until very high levels of inflation (Blanchflower et al., 2014).
The relationship between GDP per capita and SWB is somewhat less conclusive. Economists that
have used Gallup’s World Poll and evaluated data from a large sample of countries are all in
conclusion that absolute income plays a central role in determining subjective well-being. In contrast,
there is disagreement amongst the authors that have used the Euro-barometer survey. Di tella et al.
(2003, 2008) find that the coefficient for GDP enters positively, but that this effect wears off over time.
Whereas, Welsch (2011), who evaluated the same twelve European countries as Di tella et al. (2003,
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2008) but over a shorter time period, does not find any evidence of a positive correlation between
GDP per capita and life satisfaction. This is consistent with Clark's el al. (2008) explanation of the
Easterlin paradox (1974): a rise in GDP per capita does not affect an individual’s relative income
position. People have strong positional concerns over income and derive utility by comparing
themselves to the average income of others in society. Therefore, due to social comparison, an
increase in the mean level of income for everyone in society does not lead to higher levels of
subjective wellbeing. And lastly, Agan et al. (2009), who used the Worlds Value Survey, find a positive
coefficient for GDP, but its size is negligible: especially in comparison to the coefficients for
unemployment and inflation.
At first glance, there seems to a general trend in the size of the relationship between GDP and SWB.
The effect that GDP has on wellbeing is smaller in a sample of developed countries, and in the same
sample evaluated over a more recent time period. This suggests that the effect that GDP has on
wellbeing diminishes as a country becomes wealthier. This relationship will be closely examined later
on in this investigation. Furthermore, this study will expand on the limited research that has been
carried out on some other possible determinants of subjective wellbeing (Table 1). Theoretical
predictions for the expected relationship between SWB and each of the variables included in the panel
model are laid out in the next section.
Table 1: The results in the existing literature.
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Macroeconomic Variables
GDP per capita (log) Inflation Rate Unemployment Rate Life Expectancy Crime Rate Environment Corruption
Author(s)
Euro-Barometer Survey
Di Tella, MacCulloch and Oswald (2001) -1.200*** -2.800***
Di Tella, MacCulloch and Oswald (2003) 1.408** -0.994** -1.909**
Di Tella and MacCulloch (2008) 0.455** -0.755* -2.797** 0.032 -0.029* -0.003*
Wolfers (2003) -0.626*** -2.306***
Welsch (2011) -0.011*** -0.014*** -0.026***
Blanchflower, Bell, Montagnoli and Moro (2014) -0.0177** -0.0247**
World Values Survey
Agan, Sevinc and Orhan (2009) 0.0191*** -0.033***
Fleche, Smith and Sorsa (2011) -0.030** -0.002** -0.150**
Gallup World Poll
Sacks, Stevenson and Wolfers (2010) 0.342***
Neve et al (2015) 0.295***
Harbi and Grolleau (2010) 0.304*** -0.0122
The authors in italics have used ordered
probit regressions.
* indicates significance at p < 0.10 ** indicates significance at p < 0.05 *** indicates significance at p < 0.01

3. Data, theory and empirical strategy

This is a panel dataset consisting of a time series for each cross-sectional recording. The panel
variable is each country and the time variable is each year of observation. The number of observed
time periods is the same for each cross-sectional observation (country): this dataset is strongly
balanced. In total, 141 out of a possible 196 countries have been observed biennially between 2006
and 2014; this covers around 98% of the worlds population.
This extensive sample observes a range of nations that vary by cultural, religious and economic
characteristics; its representative nature removes the chance of sampling error influencing the
estimates of the coefficients. Further to this, there is data available for 93% of the possible
observations during this time period. Any missing data is at random, therefore not introducing any
bias; the random sampling assumption, MLR 2, still holds (Wooldridge, 1999). The large sample size
allows the parameters to be estimated more precisely. This reduces the size of the standard errors
relative to the coefficient estimates and increases the chance of attaining statistically significant
results.
3.1. Well-being data

In order to assess how ‘happy’ a country is, this study will be using data from Gallup’s “World Poll”, a
biennial questionnaire that has been conducted since 2006. A typical survey for each country includes
at least one thousand respondents. Each of the randomly selected individuals were asked to:
“Imagine a ladder with steps numbered from zero at the bottom to ten at the top. Suppose the top of
the ladder represents the best possible life for you and the bottom of the ladder the worst possible life.
On which step of the ladder would you say you personally feel you stand at this time?” (Gallup, 2016).
This question has been adapted from the Hadley Cantril’s “Ladder of Life Scale” (1965). As mentioned
earlier, numerous studies have proved this type of survey to be a valid method of evaluating an
individual’s happiness. Furthermore, Diener and Tov (2007) conclude that these measures tend to be
relatively stable over time; they have a high test-retest reliability. This implies that the data is driven by
actual wellbeing amongst individuals, and not by transient influences such as one’s immediate mood
or the weather.
Out of all of the surveys currently available, the Gallup “World Poll” is by far the largest. Prior to its
inception in 2006, the largest poll only covered fifty two nations. It has only been in the past decade
that researchers have had access to a comprehensive database on subjective wellbeing around the
world. Since then only a handful of studies have evaluated this dataset. The results of these and
several other studies are included in Table 1. The table has been categorised by the subjective
wellbeing survey used by each author(s). Since there is a difference in the sample size used by each
of the surveys, care must be taken when directly comparing the results of this global study to some of
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the figures in Table 1. This panel based investigation is set to become the most comprehensive
enquiry into the macroeconomic determinants of subjective wellbeing. It will paint a precise and
accurate picture of the global coefficients for a range of economic indicators.
3.2. Data sources and statistics

A short description of each variable and its respective source is included in Table 2. Whilst in Table 3,
the summary statistics for each of the variables included in the regression model have been stated.
Table 2: The variables included in the model and their respective sources.

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Abbreviation Measure Definition Source
SWBict Subjective wellbeing
Subjective well-being (SWB) is defined as 'a person's
cognitive and affective evaluations of his or her
life’ (Diener, 2002: 63). Subjects are asked to rank
themselves on Cantril’s ladder of life scale.
Gallup World Poll
GDPct GDP per capita
GDP per capita is the gross domestic product of a
nation divided by its midyear population. The data has
been converted to current US dollars.
World Bank
INFct Inflation rate
The inflation rate (measured by the consumer price
index) reflects the annual percentage change in the
cost of acquiring a basket of goods and services. It
measures the change in the purchasing power of a
particular currency.
World Bank
UNEct Unemployment rate
The unemployment rate refers to the share of the
labour force that is without work but available for and
actively seeking employment.
World Bank
LFEct Life expectancy at birth
Life expectancy at birth estimates the number of years
that a newborn is expected to live, if the prevailing
patterns of mortality at the time of birth stay the same
throughout the infants life. (World Bank, 2015)
World Bank
CRIct
Intentional homicides
(per 100,000 people)
These are estimates of unlawful homicides purposely
inflicted as a result of domestic disputes, interpersonal
violence, and various other forms of violent conflicts.
World Bank
ENVct CO2 emissions per
capita
Carbon dioxide emissions are those stemming from
the burning of fossil fuels and the manufacture of
cement. This includes carbon dioxide produced during
the consumption of solid, liquid, and gas fuels. (World
Bank, 2015)
World Bank
CORct
The level of public
sector corruption
Corruption is the abuse of entrusted power for private
gain. It is any form of dishonest or unethical conduct
by a person entrusted with a position of authority.
Transparency
International

Table 3: Summary statistics for aggregate variables, 141 countries: 2006-2014

Table 3 lists the figures for the overall, between and within variances. Each variable’s standard
deviation is decomposed into a between and a within component. The within statistic captures the
variation of a country over time, whereas the between statistic describes how much variation occurs
across the countries. The table also reports the minimum and maximum for each of these variations.
As an example, for SWB; the mean response to Gallup’s “ladder of life” survey is 5.48. These scores
range from the lowest average response of 2.93 in Togo, to the highest average response of 7.74 in
Denmark. The standard deviation for the between variation is greater than the within variation: there is
more variation in SWB across countries than that observed within a country over time. See appendix
A2 for instructions on how to interpret the minimum and maximum for the within variation. Note that
SWB is recorded on a finite scale from zero to ten.

Gross domestic product per capita (GDPct) is recorded in current U.S dollars. In this study, it is being
used to measure the average per-person income in a country. The histogram in Figure 1 depicts the
shape of the GDPct distribution; it is highly skewed. By taking a log transformation of GDPct, the once
skewed distribution tends to normal (Figure 2) and the previously exponential relationship is
linearised. The linear relationship between SWBct and log(GDPct) can now be estimated. Furthermore,
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Variable Units Observations Mean SD Minimum Maximum
Subjective well-being
Between
Within
Personal rating
between 1-10
Total = 755
n = 141
t-bar = 5.35461
5.475374 1.137084
1.100776
0.2883863
2.8
2.93
3.989374
8.01
7.74
6.373707
GDP per capita
Between
Within
GDP (Current US$)
Total = 831
n = 141
t-bar = 5.89362
13769.26 19490.35
19248
2716.624
154.9245
212.0692
-2585.246
116664.3
104688.7
29774.46
GDP per capita (in logs)
Between
Within
logGDP (Current US$)
Total = 831
n = 141
t-bar = 5.89362
8.486401 1.568248
1.55352
0.2023006
5.042938
5.336793
7.716227
11.66706
11.55496
9.078259
Inflation rate
Between
Within
Consumer prices
(annual % change)
Total = 811
n = 141
t-bar = 5.75177
6.432547 11.28044
8.844808
8.779636
-4.479938
0.3690376
-81.83412
266.8
93.36666
179.8659
Unemployment rate
Between
Within
% of total labor force
Total = 840
n = 140
t-bar = 6
8.292976 5.851621
5.606387
1.731244
0.1
0.6
-1.157024
37.6
32.15
21.49297
Life expectancy at birth
Between
Within
Years
Total = 846
n = 141
t-bar = 6
70.35277 9.116499
9.026102
1.456744
42.81071
47.50056
48.85293
83.8
82.97195
82.419
Crime rate
Between
Within
Intentional homicides
(per 100,000 people)
Total = 658
n = 141
t-bar = 4.6667
7.878191 12.69279
11.37827
2.586059
0
0.2602006
-19.98046
91.03942
72.1646
26.75301
CO2 emissions per capita
Between
Within
Metric tons per capita
Total = 704
n = 141
t-bar = 4.99291
4.681865 5.719784
5.71707
0.4440958
0.0205421
0.0214263
2.601359
38.33784
36.62663
7.03208
Level of corruption
Between
Within
Ranking between
1-100
Total = 833
n = 141
t-bar = 5.9078
58.08884 21.31792
21.01293
3.251864
4
7.16667
43.42217
89
84.83333
71.92217

due to the size of the scale; the logarithmic approximation to percent change is more convenient for
the interpretation of the results.
Figure 1: Histogram GDPct Figure 2: Histogram log(GDPct)
The other headline economic indicators: the rate of inflation (INFct) and the rate of unemployment
(UNEct) are recorded in annual percentage change (%). Across the dataset, the mean rate of inflation
is 6.43%. Notable outliers include a period of hyperinflation in both Zimbabwe (2006) and Venezuela
(2012) when the annual inflation rate hit 267% and 62.2% respectively. The unusual episode of
deflation (-4.48%) occurred in Ireland (2009). As a consequence of the financial crisis, a sharp rise in
unemployment and a fall in domestic output led to a period of uncertainty, and deflation ensued
(Bermingham et al., 2012). Unlike the inflation rate, the unemployment rate lies on a scale with an
upper and lower bound: it takes a value between 0 and 100. The global average for this time period is
5.85%; there are no significant outliers to note.
International homicides per 100,000 people (CRIct) is being used to gauge how widespread crime is
within a nation. A report by the UNODC (2011) concluded that the homicide rate acts a reasonable
indicator for both violent crime and crime in general. The report found a direct relationship between
those countries that have higher homicide rates and those that have higher robbery rates. It is an
indicator of how much control the state has over its citizens, and serves as a valid proxy for one’s own
personal safety and security. The global average is 12.7 murders per 100,000 people. Perhaps rather
remarkably, there were no recorded murders in Iceland in both 2006 and 2008. This country has an
admirable record of very low levels of criminal activity. At the other end of the spectrum, Honduras
averaged a homicide rate of 72 during the period evaluated. The government lacks the resources
needed to investigate and prosecute perpetrators, and as a result, crime is rife.
The data from Transparency International’s “Corruption Perceptions Index” is being used to assess the
perceived level of public sector corruption apparent in each country (CORct). Each country is given a
score on a scale of 0 (highly corrupt) to 100 (very clean). For the purpose of this investigation, these
scores have been inverted. This allows the interpretation of CORct to be in line with that of the other
coefficients. The scale now ranges from 0 (very clean) to 100 (highly corrupt). The mean (58.08)
suggests that on average, countries around the world are more ‘corrupt’ that ‘clean’. The most and
least corrupt nations are Sudan and New Zealand respectively.
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Life expectancy at birth (LFEct) is measured in years and indicates the state of a country’s health
conditions. As one might expect, 95% of the observations have a within variation of less than 3 years:
life expectancy does not vastly change over the short time period in question. There are no unusual
observations to report. And lastly, the World Bank’s data on national carbon dioxide emissions, as
measured in metric tons per capita (ENVct), is being used to measure how much pollution is preset
within a country. Hoffmann et al. (2005) agree that CO2 emissions can be used as a valid proxy for
pollution in general; they found there to be a high correlation coefficient between the three major
pollutants (CO2, NO and SO2). It is worth noting that there is no data available for CO2 emissions per
capita in 2014. This is not an issue since the size of the sample is still greater than the minimum
needed to work at a 99% confidence level.
3.3. Theoretical predictions

The national relationship between GDP per capita and subjective wellbeing is hard to foresee. On the
one hand, those with higher incomes have access to a wider range of goods, services and
recreational activities. They are free from the stress of financial trouble. Therefore, increasing earning
capacity should improve life satisfaction and make people intuitively happier. However, on the other
hand, the Easterlin Paradox (1974) argues that due to social comparison, on a national basis,
wealthier countries are no happier than those that are not as wealthy. People care more about having
money relative to some reference point, rather than having money itself (Easterlin, 1974). The existing
literature (Table 1) suggests that the Easterlin Paradox only holds in developed countries; this
apparent relationship will be closely examined.

Consistent with the literature, this study anticipates a negative relationship between the inflation rate
and subjective wellbeing; although its exact cause is difficult to isolate. One possible explanation is
the detrimental effects that inflation may have on purchasing power. Salaried and fixed wage earners
will experience a rise in the costs of living. Business owners may also lose out if they experience a
loss in international competitiveness or ‘menu costs’ associated with changing prices. Furthermore,
Schiller (1997) believes that individuals also experience a variety of unconventional costs, such as
damaged national prestige and the loss of morale.

A higher national rate of unemployment should also have a detrimental effect on mean life
satisfaction scores. First and foremost, unemployment results in a lower earning capacity which
hampers one’s ability to derive utility from consumption. It also leads to a loss of economic identity,
an overwhelming sense of insecurity and an inert feeling of personal failure (Winkelmann, 2014).
There may also be a small taxation effect as the bill for unemployment benefits rises. Moreover, the
proportion of unemployed respondents will be higher in countries with increased unemployment
rates; said respondents are known to report lower life satisfaction scores (Di tella et al., 2001).

Unless the discount rate is very high, people care about their health and the number of years that
they are expected to live. Respondents from countries where health conditions and services improve
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the chance of living a better life should therefore report higher scores of subjective wellbeing. On the
other hand, the crime rate is expected to be negatively correlated with happiness. In general, people
fear being a victim of crime; whether that be the chance of personal injury or the event of financial
loss through theft (Gray et al., 2008). This study also anticipates a negative relationship between
emissions and subjective wellbeing. Air pollution is casually linked to a range of adverse health
conditions (Brunekreef and Holgate, 2002). It also leads to acid rain which may indirectly impact
wellbeing. Acid rain damages aquatic life, contaminates drinking water and harms crop yields.
Furthermore, if the air pollution is extreme, it can act as a form of visual pollution.

And finally, in reference to a nation’s political and institutional environment; Rothstein (2010: 18)
concludes: “people that have a pessimistic outlook and a low opinion about the moral standards in
the society are not likely to express feelings of happiness”. Under a corrupt government, voter
preferences are often not satisfied and people lose trust in the political system. Corruption increases
costs to private business owners, leads to the inefficient use of public resources and perpetuates
poverty (OECD, 2014). Therefore the relationship is expected to be negative.

3.4. The panel regression model

The regression model is presented in Figure 3. The subscripts represent individuals (i), countries (c)
and time periods (t). For example, SWBict, is the well being of an individual i, who lives in country c, in
year t. The idiosyncratic error term, εct, represents the unobservable factors that change over time
and affect SWBict. Whereas, uc, captures all of the unobserved, time constant factors that affect
SWBict.

SWBict = β0 + β1logGDPct + β2INFct + β3UNEct + β4LFEct + β5CRIct + β6ENVct + β7CORct + uc + εct
εct ~ N (0, σε
2)
uc ~ N (0, σu
2) or uc are fixed parameters
xct = xc for all t
Figure 3: The panel data regression model.

There are two widely used techniques for estimating unobserved effects panel data models
(Wooldridge, 1999). These are the fixed and random effects methods. The fixed effects method
assumes that a country’s unobservable time-invariant characteristics (uc), such as its culture and
climate are correlated with each explanatory variable. These individual specific characteristics are
then factored out, allowing one to assess the net effect of the explanatory variables on SWBict. On the
other hand, the random effects method assumes that the variation across entities is random and
uncorrelated with independent variables included in the model (Torres-Reyna, 2007). The
unobservable time-invariant factors are retained in the model. A Hausman test will be used to
formally test for any statistically significant differences between the fixed and random effects
estimators. If there is a significant difference, the unique errors (uc) are most likely correlated with the
regressors and the fixed effects method will be used.

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4. Empirical Results

4.1. Estimating the macroeconomic determinants of subjective well-being

To begin with, the Breusch-Pagan LM test (Figure 4) was used to check for the presence of any
individual effects. If both εct and uc are not found to be significantly different from zero, a simple OLS
regression is appropriate. A significant LM test statistic (0.0000) indicates that there are individual
effects present and that the preferred specification is either the fixed or random effects model. A
Hausman test (Figure 5) was then run to distinguish the correct method of estimation. The Hausman

Figure 4: The Breusch and Pagan LM Test. Figure 5: The Hausman Test.

statistic (0.0857) failed to reject the null hypothesis that the difference in coefficients is not systematic
at the 5% level of statistical significance. Therefore the unique errors are not correlated with the
regressors and it is fine to use the results from the random effects regression. The results of both
regression methods are reported in Table 4. Next, a test was used to check for the presence of serial

Table 4: The results of the fixed and random effects main regressions.

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Dependent variable: Subjective well-being
Fixed effects (within) regression Random effects GLS regression
Coefficient Robust Std. Err. P > | t | Coefficient Robust Std. Err. P > | z |
GDP per capita (log) 0.311 0.1134 0.007 0.320 0.0682 0.000
Inflation rate -0.0052 0.0017 0.004 -0.0038 0.0016 0.015
Unemployment rate -0.0182 0.0088 0.040 -0.0239 0.0053 0.000
Life expectancy at birth -0.0004 0.0237 0.986 0.0204 0.0087 0.019
Crime rate 0.0157 0.0073 0.033 0.0201 0.0041 0.000
Level of corruption -0.008 0.0048 0.094 -0.0118 0.0032 0.000
CO2 emissions per capita 0.0567 0.0207 0.007 0.0138 0.0090 0.128
sigma_u 0.6286245 0.5338939
sigma_e 0.26957879 0.26957879
R-squared 0.6453 0.721
rho 0.84466378 0.79684207

correlation in the idiosyncratic error term. If present, autocorrelation results in biased standard errors
and leads to less efficient results. The insignificant F statistic (Figure 6) concludes that this model

Figure 6: The Wooldridge test for autocorrelation in panel data.

does not suffer from serial correlation. Lastly, a likelihood ratio test was used to test for the existence
of panel-level heteroskedasticity. A significant LR test statistic meant that heteroskedasticity was
present in the model. Although this does not lead to biased parameter estimates, heteroskedasticity
can lead to bias in the test statistics and confidence intervals. Therefore, in order to deal with this and
to ensure that the p values are still accurate, heteroskedasticity-robust standard errors were used in
each regression. The results from the random effects GLS regression can now be discussed.

The coefficient estimate for GDP per capita (GDPct) is positive and significant at all conventional
levels. Due to the logarithmic nature of GDPct, β1 divided by 100 is the change in SWBict resulting
from a 1% change in GDPct. The result states that if we increase a given country’s GDP per capita by
1%, SWB in that particular country is expected to increase by 0.00320 units. This estimate is almost
identical to the results found by Sacks et al. (2010), Neve et al. (2015) and Harbi and Grolleau (2010);
who all also used least squares to evaluate data from Gallup’s World Poll. It provides significant
evidence that absolute income plays a central role in determining subjective wellbeing and casts
doubt on the Easterlin Paradox. On a global scale, a country’s mean level of happiness is shown to
increase as average absolute incomes rise over time. Although not directly comparable, this result is
consistent with the work of Di Tella et al. (2003, 2008). These authors also showed that GDP per
capita enters positively in a cross country panel with unobservable effects. Interestingly, they found
that this effect wears off over time. An investigation into this apparent trend is carried out in part 4.3.

The coefficient estimates for all of the other regressors take on a ‘level-level’ interpretation; each
states the change in SWBict resulting from a unit change in the explanatory variable. Again in
accordance with the economic theory and in agreement with the literature, the coefficients for both
the rate of inflation and the rate of unemployment are negative and statistically significant. A one
percentage point increase in either the unemployment or inflation rate is estimated to decrease
SWBict by 0.0239 units and 0.0038 units respectively. The adverse affect of a marginal increase in the
unemployment rate is approximately six times the magnitude of a one unit increase in the inflation
rate. This result is consistent with the work of Blanchflower et al. (2014), who estimated the marginal
rate of substitution between inflation and unemployment to be 5.6. Furthermore, it confirms that the
relationship still holds for a much larger sample of countries from around the world. Blanchflower et
al. (2014) argue that this relationship is due to the two-part consequences of unemployment. An
increase in the rate of unemployment not only indirectly stimulates fear in society, but also directly
impacts those individuals that actually lose their jobs. These people also experience an array of
Page of14 24

monetary and personal costs that directly affect their wellbeing. On the other hand, the welfare losses
associated with changes to the price level are indirect and not necessarily a detriment to everyone in
society. They may be partially offset by the benefits that accrue to debtors. Instructions on how to
work out the exact coefficient for UNEct in a specific country are included in Appendix A1.

As expected, the coefficient estimate for LFEct (which acts as a proxy for the state of a country’s
health conditions) is positive and statistically significant at the 5% level. An increase in a country’s life
expectancy by one year is estimated to improve subjective wellbeing by 0.0204 units. This is
significant evidence that people derive utility from good health, which intuitively makes sense. The
symptoms of bad health (e.g. pain, functional limitations, disease) are a source of disutility per se.
They limit the utilities that can be obtained whilst undertaking everyday activities. Furthermore, those
in poor health are more susceptible to mental illnesses (e.g. stress, anxiety, hostility) which in turn
lead to negative feelings of emotion (Keyes, 2006). Health is a desirable good which individuals aim
to invest in (Grossman, 1972). It not only acts as a form of utility in itself, but also increases the
utilities that one can be expected to gain over a lifetime. This investigation concludes that the state of
a country’s health conditions (which in turn affect longevity) have a profound impact on subjective
wellbeing.

Rather surprisingly, the crime rate is estimated to be positively correlated with subjective wellbeing.
Logically this does not make sense. Why would the increased prevalence of crime lead to higher
levels of subjective well-being? To explain this significant relationship there may be an underlying
issue with the data or the model specification. The correlation coefficients were first checked for the
existence of multicollinearity. If present, high levels of collinearity between the regressors can cause
the coefficients to switch signs (Wooldridge, 2000). Figure 7 shows that multicollinearity was not an
issue here. Perhaps CRMct is acting as a proxy for an omitted variable? If the confounding excluded
variable is positively associated with SWB, omitting this variable may be biasing the estimated effect
upward. That being said, the author of this study can not conceive an omitted variable which has a
strong positive correlation with both SWB and the crime rate. It is more likely that there are some
unusual observations influencing the results. Unusual observations receive a lot of weight in the least
squares minimisation problem and can greatly affect the OLS estimates (Wooldridge, 2000). A scatter
of CRMct against SWBict was examined for the presence of outliers (Figure 8). There are a handful of

Figure 7: Correlation matrix for CRM Figure 8: Scatterplot for (CRM, SWB).

Page of15 24
345678
swb
0 20 40 60 80 100
crm

countries which have very high homicide rates relative to the rest of the dataset. Several of these
observations also have relatively high levels of subjective wellbeing. After excluding these outliers (to
the right of the red line), the coefficient for the crime rate became statistically insignificant. It is most
likely that these outliers were the initial cause of the significant association. In summary, this study
can not conclude that the national crime rate has an impact on subjective wellbeing.

On the other hand, this investigation can conclude that the level of public sector corruption has a
profound impact on human happiness. If a country’s score on the “Corruption Perceptions Index”
rises by one unit, subjective wellbeing is estimated to fall by 0.0118 units. This result is statistically
significant at all conventional levels and corresponds to the theoretical predictions of the model.
Corruption “undermines the principal–agent relationship on which democratic representation
depends” (Tavits, 2008: 1612): there is strong negative link between efficacy and happiness
(McGregor & Little, 1998). Furthermore, if public policy is influenced by special interests rather than
the interests of the general public it is likely to foster feelings of “exclusion” and “alienation” (Tavits,
2008: 1612). This study finds evidence in support of corruption directly influencing wellbeing through
these and other social and economic channels.

As with the coefficient for CRIct, the estimate for ENVit is also in discordance with the theoretical
predictions. A higher level of CO2 emissions per capita is estimated to have a positive impact on our
wellbeing, although the result is not significantly different from zero. This may be down to a number of
factors. Firstly, CO2 emissions as a proxy for pollution is highly imperfect and captures, at best, only
one aspect of how the environment impacts subjective wellbeing. Furthermore, it is difficult to isolate
the effect of pollution due to its relatively high correlation (0.62) with per capita GDP. There is perhaps
also an issue with using CO2 emissions per capita: the size of the population distorts the results.
Countries such as China and India rank mid table in terms of emissions per capita, even though they
are some of the worlds largest emitters of CO2 and both experience high levels of air pollution. In
sum, like Harbi and Grolleau (2010), this study can not conclude that CO2 emissions per capita have
an impact on subjective wellbeing. However, this should not be taken as conclusive evidence that
pollution does not affect our wellbeing. Di Tella and MacCulloch (2003) find a weak negative
relationship between happiness and sulphur oxide emissions (SOx) per capita in developed countries.
Perhaps SOx emissions act as a better proxy for pollution? Unfortunately, there is currently not
enough data on SOx emissions worldwide to test this proposition. If enough data becomes available
in the future, subsequent analysis should experiment with using SOx and other proxies for pollution.
Data should also be taken in its raw form and not on a per capita basis.

4.2. Comparing some of the results

The results of the main regression suggest that a politician’s traditional interest in economic growth
has not been completely misplaced. On a national level, it does have a positive impact on subjective
wellbeing. However, the results also suggest that there are other, more influential macroeconomic
factors that determine our wellbeing (CORct, LFEct, and UNEct). People care a lot more about their
Page of16 24

personal health than about how wealthy they are. Using these statistics, one is able to place a value
on an additional year of life in a particular country. Another year of life is equivalent to an increase in
the level of GDP per capita by 6.375 percentage points. As an example, in the USA the most recent
estimate of GDP per capita was $54,629 (World Bank, 2014). Therefore in 2014, an extra life year was
valued at $3,482. This closely compares with $2,841 of annual income in exchange calculated by Di
Tella and MacCulloch (2008); these results have been adjusted for inflation.

A one percentage point increase in UNEct has the same effect as a fall in GDPct by 7.4 percentage
points. Comparably, Di tella and MacCulloch (2008) estimate this marginal rate of substitution to be
5.7. Due to its size compared to GDPct, the benefits to happiness from being in employment must
stretch beyond its pure monetary value. Employment is also central to social identity and status, it
structures our lives, improves self esteem and can lead to a sense of personal achievement (Dodu,
2005). All of which also contribute to an individuals sense of wellbeing. Meanwhile, the inflation rate is
the least influential regressor included in the model. Its marginal rate of substitution with the
unemployment rate (6.2) implies that the majority of central banks’ relative weights differ from the
socially preferred weights given to unemployment and inflation (Blanchflower et al., 2014). George
Osborne often reaffirms the two per cent inflation target, when perhaps he should follow in the
footsteps of the Federal Reserve and establish a target rate of unemployment. It is socially beneficial
for the central bank to focus on maintaining low rates of unemployment at the cost of higher rates of
inflation.

4.3. Do the determinants of subjective well-being change as a country’s wealth
increases?

In order to try and answer this question, the dataset was reordered in terms of GDP per capita (2014).

A regression was then run on both the richer ‘top half’ and the poorer ‘bottom half’ of the dataset; the

results of which are displayed in Table 4. Note the significant difference in several of the coefficient

Table 4: The main regression results.

Page of17 24
Dependent variable: Subjective well-being
Random-effects GLS regression (Top half) Random-effects GLS regression (Bottom half)
Coefficient Robust Std. Err. P > | t | Coefficient Robust Std. Err. P > | z |
GDP per capita (log) 0.1586 0.0954 0.097 0.3015 0.0892 0.001
Inflation rate -0.0029 0.0042 0.480 -0.0039 0.0016 0.016
Unemployment rate -0.0311 0.0066 0.000 -0.0148 0.0093 0.109
Life expectancy at birth 0.0493 0.0153 0.001 0.0108 0.0101 0.327
Crime rate 0.0351 0.0058 0.000 0.0159 0.0044 0.000
Level of corruption -0.0148 0.0034 0.000 -0.0027 0.0068 0.692
CO2 emissions per capita 0.0643 0.0286 0.024 0.1552 0.1233 0.208
sigma_u 0.54368387 0.47219722
sigma_e 0.22636495 0.32117548
R-squared 0.6151 0.4735
rho 0.85226016 0.68369787

estimates. Amongst the more developed, wealthier countries, an increase in national GDP per capita
is estimated to have a much weaker effect on SWB. This is consistent with the work of Di Tella et al.
(2003): the affect of GDP on wellbeing wears off as a country’s wealth increases. A final regression on

Table 5: Regression results (top 20) Figure 9: Scatterplot for (GDP, SWB).

the top 20 countries in the dataset provides further confirmation of this trend (Table 5). The coefficient
for GDP turns negative and insignificant. This logarithmic relationship is clearly visible in a scatterplot
of GDP against SWB. There is a satiation point; beyond which there is no direct relationship between
increases in national income and subjective well-being. In sum, the results of this study both question
and support the Easterlin paradox. Easterlin’s (1974) argument that changes to national income do
not correlate with happiness is only seen to hold for the most economically developed countries. It
remains unclear if this trend is down to Easterlin’s relative income hypothesis and the role of social
comparison, or whether it is due to the decline in the marginal utility of consumption. Clark et al.
(2008) figure that it is most likely a combination of the two.

It is also worth noting the difference in the size of the coefficients for both the other headline
economic indicators. A percentage point increase in the unemployment rate is estimated to have a
more profound negative impact on wellbeing in wealthier countries. This implies that as a country’s
wealth increases, citizens place more subjective value on being in employment. In wealthier
countries, a larger proportion of the population work in the tertiary sector. In general, these
professions are less physically demanding and dangerous than careers in the primary or secondary
sector. They are perhaps intuitively more enjoyable. This may explain why on average, individuals
place more subjective value on being in employment in affluent nations. Similarly, the strength of the
negative relationship between the inflation rate and wellbeing decreases as a country’s wealth rises.
This relationship is supported by Wen (2015), who develops an analytically tractable Bewley model of
money demand to show how inflation is especially painful in developing countries. The major form of
household financial wealth in these countries is liquid money (cash and checking accounts). Wen
(2015: 1) states that “when money is a vital form of liquidity to meet uncertain consumption needs,
the welfare costs of inflation can be extremely large”. Inflation erodes the “buffer stock-insurance
value of money” and has a more profound impact on consumption volatility in developing countries
(Wen, 2015: 31).

Page of18 24
345678
swb
0 50000 100000 150000
gdp
Coefficient Robust Std. Err. P > | z |
GDP per capita (log) -0.022 0.155 0.887
Inflation rate -0.002 0.019 0.084
Unemployment rate -0.056 0.014 0.000
Life expectancy at birth 0.061 0.055 0.268
Crime rate 0.113 0.081 0.159
Level of corruption 0.0002 0.004 0.947
CO2 emissions per capita -0.0136 0.011 0.200

5. Concluding Remarks

This paper builds on the existing academic literature and uses a broader list of countries to truly
determine how various macroeconomic variables influence subjective wellbeing around the globe.
The results are largely consistent with the studies that have used the Eurobarometer survey to only
evaluate a handful of affluent nations; thus implying that these relationships hold throughout the
world and not just in the world’s most economically developed countries. There is a clear positive link
between GDP per capita and happiness which wears off as a country’s wealth increases.
Unemployment depresses subjective well-being significantly more than inflation, and we derive a
great deal of wellbeing value from good health and longevity. There is also a significant negative
relationship between corruption and happiness.

The fundamental goal of public policy should be to maximise wellbeing for as many people as
possible, and for decades policy makers have pursued economic growth under the assumption that
there is a direct relationship between growth and wellbeing. However, through the use of SWB
surveys, which have been proven to be reliable and robust indicators of wellbeing, this investigation
has established that changes to GDP may not be the most effective means of improving welfare.
There is significant evidence, supported by the existing literature, that people care more about other
variables beside income. Especially in economically developed countries, where the impact of GDPct
on SWBict is slight, politicians need to instead focus on maintaining very low rates of unemployment,
facilitating good health amongst the population, and abolishing any forms of corruption. Di Tella and
MacCulloch (2008: 23) argue that these act as “more comprehensive and less materialistic measures
of progress than GDP”. Since the threats now posed by global warming stem from the pursuit of
economic growth (Tucker, 1995), there are both social and environmental benefits to be had from
directing attention to these other macroeconomic measures of wellbeing and progress.

In developing counties, policies directed towards increasing GDP do act as effective methods of
increasing overall levels of wellbeing. El Harbi and Grolleau (2010) believe that this may hinder
humanity’s efforts to prevent global warming. Since happiness in these countries is greatly influenced
by absolute income, governments are not willing to forgo economic growth for lower polluting
emissions. A global effort is needed to help these countries to increase levels of aggregate income
without them disobeying the terms of environmental treaties. Encouragingly, the United Nations
recently labelled the promotion of sustainable employment and economic growth as one of its “17
goals to transform our world” in the 2030 Agenda for Sustainable Development (UN, 2015).

In summary, this investigation has come to a number of important conclusions regarding the future of
public policy. There is overwhelming evidence in support of England’s (1998) and Fleurbaey’s (2009)
view that GDP is an incomplete measure of welfare. Regardless of the country, people care about
other variables besides absolute income. This study calls for all governments to develop their own
direct measures of subjective wellbeing so that these trends can be evaluated further at both a local
and national level. Public policy can then be tailored according to a particular country’s exact
determinants of wellbeing.

Page of19 24

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Appendix A. Notes on interpreting results and statistics.

Appendix A.1. How to work out the exact coefficient for UNEct.

SWBict = β3UNEct + (0.01P * γ1)

At a given participation rate (P), an increase in the rate of unemployment by a one percentage point
(0.01) results in a fraction (0.01*P) of the population becoming unemployed. If becoming unemployed
changes an individuals life satisfaction by -0.294 units (average taken from the literature), the total
effect of a one percentage-point increase in the unemployment rate on SWBict is: -0.0239 +
(0.01*P*-0.294).

E.g. In the UK the participation rate is currently 78.2%. Therefore the total effect of a one percentage-
point increase in the unemployment rate is: -0.0239 + (0.01 * 0.782 * -0.294) = -0.0262.

Appendix A.2. How to interpret the within variation (minimum and maximum).

Stata decomposes each variable xct into a between (x̄ c) and within statistic (xct − x̄ c + x̄ , the global
mean x being added back in make results comparable). The minimum and maximum deviation from a
country’s average is stated. In order to work out the exact minimum or maximum statistic, the global
average for the variable must be taken away from each figure. For example, the global average for
subjective wellbeing in the dataset is 5.47. The maximum statistic is 6.37. Therefore, the largest
increase in subjective wellbeing recorded over the time period was 0.9 (6.37 - 5.47). Meanwhile the
greatest fall in wellbeing recorded was 1.48 (3.99 - 5.47).

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