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International Journal of Arts and Sciences
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Real GDP, Well-being, and Happiness
Maria Cornachione Kula, Roger Williams University, USA
Priniti Panday, Roger Williams University, USA
Keith Mantia, Roger Williams University, USA
Abstract: Criticisms of real gross domestic product (real GDP) as a measure of well-being are
not new. The topic has taken on greater urgency given the September 2009 release of the “Report
of the Commission on the Measurement of Economic Performance and Social Progress”. The
Commission was created by French President Sarkozy and chaired by Nobel Prize winning
economist Joseph Stiglitz and was tasked with enumerating the problems inherent in using real
GDP as a measure of living standards and with proposing a new, better measure. Two areas
highlighted by the Commission as particularly problematic regarding real GDP are the incorrect
valuation of goods and services provided by governments and the omission of subjective
measures of well-being, such as happiness and satisfaction. This paper asks if real GDP can be
considered an adequate measure of living standards in light of these specific criticisms. For a
sample of OECD countries, real GDP will be compared and contrasted with measures of the
government provision of goods and services (e.g., government share of health care) and
measures of life satisfaction and happiness taken from recent research by Stevenson and Wolfers
(“Economic Growth and Happiness: Reassessing the Easterlin Paradox,” Brookings Papers on
Economic Activity, Spring 2008).
Keywords: Well-being, Happiness, Life Satisfaction
Introduction
Real GDP per capita, a measure of a country’s output of goods and services per person, is often
used as a proxy for living standards or well-being. This use has not been uncontroversial.
Common criticisms include: (1) the exclusion of non-material dimensions of well being and
benefits of things such as leisure (2) the exclusion of non-market activities (3) the inclusion of
items which are actually harmful (e.g. negative environmental externalities associated with
increased production) (4) ignorance of the distribution of income within a country.
The limitations of GDP have lead to the development of alternative measures of well being. Most
alternatives begin with GDP and then make adjustments to it in order to get a more refined
measure of consumption. The most influential among the early offerings would be the Measure
of Economic Welfare (MEW) formulated by Nordhaus and Tobin (1973). To quantify things
such as leisure or the quality of the environment, imputations for prices, as well as value
judgments, are used (see, e.g., Morse (2004) for a summary of the major alternatives to GDP and
a more detailed discussion on these points).
Rather than adding to or subtracting from GDP, the United Nations Development Program
(UNDP) takes a different approach with its Human Development Index (HDI). The HDI is a
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composite index which includes measures of life expectancy at birth, the adult literacy rate, and
the extent of school enrollment in addition to a modified output measure (where real GDP is
capped such that beyond a certain amount it does not contribute to the HDI). This measure is
supposed to capture a “bigger picture” which is closer to human well being than ever more
refined measures of “consumption” could hope to encompass.
The UNDP also calculates two Human Poverty Indices (the HPI-1 for developing countries and
the HPI-2 for developed economies) which measure adverse outcomes. The HPI-2 is derived
from measures for the probability at birth of not surviving to age sixty, the percentage of the
population lacking functional literacy skills, the percentage of the labor force that is long term
unemployed, and the percentage of the population that is below the poverty line. The HPI-2 is
designed to capture differences among the developed economies obscured by their very similar
HDI scores.
The development of the UN’s measures were heavily influenced by the seminal work of
Armartya Sen (e.g., 1999) which emphasized the importance of the ability to make choices for
oneself on how to live one’s life. However, the UN indices can more accurately be described as
measuring the outcomes of an underlying “enabling environment”. Kula et. al. (2008) focuses on
measuring well being by quantifying the enabling environment first envisioned by Sen. The
HENX is constructed for a sample of advanced economies and is intended to capture well-being
in terms of freedom and the opportunity for choice in broadly defined categories: freedom from
oppressive government and corruption; uninhibited access to information; the level of material
living standards and opportunity to participate in economic life; freedom of association, and
freedom from the harmful actions of others.
Interestingly, current criticisms of real GDP per capita as a measure of well being have not
focused on the substitution of it with one of the existing alternative measures. In particular, in
February 2008, French President Nicolas Sarkozy formed a commission, chaired by Nobel Prize
winning economist Joseph Stiglitz, to study and present a report on the limitations of GDP as an
indicator of economic performance and social progress and to suggest alternatives. The resulting
“Report by the Commission on the Measurement of Economic Performance and Social
Progress 1” was completed in September, 2009. Among other suggestions, the report recommends
that subjective measures of well-being should be included in a measure of living standards,
including people’s self reports of their “… happiness, satisfaction, positive emotions such as joy
and pride, and negative emotions such as pain and worry 2”.
The renewed focus on the inadequacies of real GDP per capita as a proxy for well-being have
occurred at the same time that new work has advanced the understanding of the relationship
between happiness and real GDP per capita. Stevenson and Wolfers’s (2008) exhaustive
1
http://www.stiglitz-sen-fitoussi.fr/en/index.htm
Critics have suggested that the French are interested in alternatives to GDP only because France’s GDP growth has
lagged other developed countries’ growth rates: e.g., from 1982 to 2007, France grew at 2.1% per year while the US
grew at 3.3%. In 2007, using GDP as the measuring stick, Americans were 33% richer than the French.
2
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empirical work using multiple survey data sources covering a large number of countries has
overturned long-held beliefs on happiness.
The conventional wisdom on the relationship between happiness and real GDP per capita was
formed by Easterlin (1974) and several subsequent papers (Easterlin (1995, 2005a, 2005b)),
which found that within countries, higher income individuals are happier, but that people in rich
countries were not happier than those in poor countries, giving rise to the paradox. This paradox
has been explained by focusing on relative differences in income as driving happiness within
countries: Layard (2005) summarizes this nicely with his statement that “people are concerned
about their relative income and not simply about its absolute level. They want to keep up with
the Joneses or if possible to outdo them.” The further argument has been made that past a certain
(very low) income, increases in income do not matter for happiness. To cite one example, Layard
(2003), e.g., states that “once a country has over $15,000 per head, its level of happiness appears
to be independent of its income per head.”
The Easterlin paradox has many policy implications. Since being relatively rich compared to
others is what matters and absolute wealth does not matter, more rapid rates of economic growth
do not contribute to happiness or life satisfaction. The goal of public policy should not be
maximizing economic growth as lower levels of national income do not mean less happiness. In
fact, the focus should be on income inequality reduction; the more equal people are, the happier
they will be. Furthermore, it is desirable to impose taxes to gain a more equal distribution of
income even given efficiency/deadweight loss issues.
Stevenson and Wolfers (2008) marshal strong evidence against the Easterlin Paradox. They find
that within a country, the richer you are, the happier you are, and that people in rich countries are
happier than those in poor countries. They also find no evidence of a satiation point; on the
contrary, they find that increases in real GDP per capita increase happiness. Unlike Easterlin
(1974) which used two international datasets of countries with similar attributes, Stevenson and
Wolfers (2008) use data on a large sample of countries, both rich and poor, and use several
survey sources for happiness and life satisfaction data.
Analysis
The three surveys used by Stevenson and Wolfers (2008) are the “Pew Global Attitudes Survey”
and both the “life satisfaction” section and “happiness” section of the “World Values Survey”.
The Pew Global attitudes survey was conducted in forty-four countries, both developed and
developing, where participants were shown a picture of a ladder with ten steps and asked, “Here
is a ladder representing the ‘ladder of life.’ Let’s suppose the top of the ladder represents the best
possible life for you; and the bottom, the worst possible life for you. On which step of the ladder
do you feel you personally stand at the present time?” Stevenson and Wolfers (2008) map
responses from participants into an ordered point index where 1.5 was the most satisfied and -1.5
was the least satisfied. Figure 3 below is from Stevenson and Wolfers (2008).
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The World Values Survey asked citizens of participating countries questions with responses
limited to their levels of happiness and satisfaction. Some of the questions asked by the World
Values Survey (1999-2004 wave) included, for life satisfaction: “All things considered, how
satisfied are you with your life as a whole these days?” participants responses were limited to a
numerical response between the numbers 1, being the most dissatisfied, and 10, being the most
satisfied. A sample question from The World Values Survey’s happiness section was: “taking all
things together would you say that you are, ‘very happy,’ ‘quite happy,’ ‘not very happy,’ [or]
‘not at all happy?’” Stevenson and Wolfers (2008) map responses from participants into an
ordered point index where 1.5 was the most satisfied/happy and -1.5 was the least
satisfied/happy, and then graph the order point index against real GDP per capita (thousands of
dollars, log scale)
Figure 5 below is from Stevenson and Wolfers (2008). In both instances the subjective well
being measures were normalized using ordered probit regressions of the “happiness” variable on
a series of country fixed effects (see Stevenson and Wolfers (2008) for a complete description of
their methods; also Appendix A of Stevenson and Wolfers (2008) for a description of how their
index compares to four alternative measures.)
Given the criticisms of real GDP per capita as a measure of well being and new results in the
happiness literature, this paper asks: How does happiness correlate with other measures critics
deem important?
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Our sample consists of members of the Organization for Economic Co-operation and
Development (OECD), including: Australia, Austria, Belgium, Canada, Czech Republic,
Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea,
Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic,
Spain, Sweden, Switzerland, Turkey, United Kingdom, and the United States
The following chart shows the correlations of the Stevenson and Wolfers (2008) happiness
indicator with several variables, explained below.
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Data correlations
1
Series1
Series2
0.8
Series3
Series4
0.6
Series5
Series6
0.4
Series7
Series8
Correlation
0.2
Series9
Series10
0
1
Series11
Series12
-0.2
Series13
Series14
-0.4
Series15
Series16
-0.6
Series17
Series18
-0.8
Series19
Series20
-1
Series21
Collected data
Series22
Further explanation of the Legend to the right of the graph:
Series 1: Stevenson and Wolfers (2008) Satisfaction & World Database of Happiness
Happy life years. The life satisfaction indicator from the Wolfers paper was also from the World
Values survey satisfaction section. This section as previously discussed asked citizens of
participating countries questions with responses limited to their levels of happiness and
satisfaction (only satisfaction section used in this correlation
The world database of happiness survey that we used was the happy life years measure
from the world database of happiness in which ['Happy Life Years' is an estimate of how long
and happy the average citizen will live in that nation in this era. Computation: 0-1 enjoyment of
life multiplied by expected length of life] directly from (http://worlddatabaseofhappiness.eur.nl/)
Exclusions: None
Series 2: Stevenson and Wolfers (2008) Happiness & World Database of Happiness Happy
life years. The Happiness indicator from Wolfers’ paper was from the World values survey
happiness section. This section as previously discussed asked citizens of participating countries
questions with responses limited to their levels of happiness and satisfaction (only happiness
section used in this correlation).
The world database of happiness survey that we used was the happy life years measure
from the world database of happiness in which ['Happy Life Years' is an estimate of how long
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and happy the average citizen will live in that nation in this era. Computation: 0-1 enjoyment of
life multiplied by expected length of life] directly from (http://worlddatabaseofhappiness.eur.nl/)
Exclusions: None
Series 3: Stevenson and Wolfers (2008) Happiness & Stevenson and Wolfers (2008)
Satisfaction. The Happiness indicator from Wolfers’ paper was from the World values survey
happiness section. This section as previously discussed asked citizens of participating countries
questions with responses limited to their levels of happiness and satisfaction (only happiness
section used in this correlation)
The Happiness indicator from Wolfers’ paper was from the World values survey
happiness section. This section as previously discussed asked citizens of participating countries
questions with responses limited to their levels of happiness and satisfaction (only happiness
section used in this correlation)
Exclusions: None
Series 4: Corruption Perception Index (CPI) measures the perceived levels of public sector
corruption. The CPI is based on 13 different expert and business surveys. The CPI is on a scale
from 0 to 10 with 0 being completely corrupt and 10 being no corruption at all. (from
transparency.org)
Exclusions: None
Series 5: Real GDP per capita 2004. The GDP of countries adjusted for changes in prices for
the year 2004.
Source: Penn World Tables (PWT) (this is the source listed on the lab exercise website I used the
information from this excel file and correlated it with the already obtained Wolfers information) I am
unsure as how to cite the lab file itself the link to the data is
(http://wps.aw.com/aw_weil_econgrowth_2/83/21284/5448761.cw/index.html)
Exclusions: None
Series 6: Human Enabling Index or HENX (Kula et. al. (2008) is intended to capture wellbeing in terms of freedom and the opportunity for choice in broadly defined categories: freedom
from oppressive government and corruption; uninhibited access to information; the level of
material living standards and opportunity to participate in economic life; freedom of association,
and freedom from the harmful actions of others.
Exclusions: Austria, Belgium, Czech Republic, Denmark, Germany, Greece, Hungary, Iceland,
Ireland, Italy, Korea, Luxembourg, Mexico, New Zealand, Poland, Slovak Republic, Spain,
Sweden, Switzerland, Turkey
Series 7: Total expenditure on health as a percentage of GDP is defined as the sum of
expenditure on activities that have the goals of:
- Promoting health and preventing disease, curing illness and reducing premature mortality,
caring for persons affected by chronic illness who require nursing care, caring for persons with
health-related impairments, disability, and handicaps who require nursing care, assisting patients
to die with dignity, providing and administering public health, providing and administering
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health programs, health insurance and other funding arrangements.(does not include general
safety measures such as monitoring road safety or hygiene control)
Exclusions: None
Citation: OECD statistics
Series 8: Trade Union Density corresponds to the ratio of wage and salary earners that are trade
union members, divided by the total number of wage and salary earners. (This is in a percentage)
Exclusions: Iceland
Citation: OECD statistics
Series 9: Total government expenditure as a percentage of GDP. This was done by dividing
total government expenditure (national currency, current prices, millions) by GDP expenditure
approach (national currency, current prices, millions).
Exclusions: Australia, Switzerland, Turkey
Citation: OECD statistics page ( I combined 2 OECD statistics the GDP and government
expenditure to get the % to correlate
Series 10: Public expenditure on health an as percentage total expenditure on health. Health
expenditure incurred by public funds. Public funds are state, regional and local Government
bodies and social security schemes. Public capital formation on health includes publicly financed
investment in health facilities plus capital transfers to the private sector for hospital construction
and equipment.
Exclusions: Belgium, Netherlands
Citation: OECD statistics page
Series 11: Total tax revenue as a percentage of GDP for the federal government. Total tax
revenue for federal or central government as a percentage of GDP
Exclusions: None
Citation: OECD statistics page
Series 12: Involuntary part-time employment. Involuntary part-time workers are part-time
workers who are working less than 30-usual hours per week because they could not find a fulltime job. Represented in thousands.
Exclusions: Finland, Iceland, Korea, Mexico, United States
Citation: OECD statistics page
Series 13: Unemployment by duration average (all durations averaged out into one
correlation). The different unemployment ranges were less than 1 month, 3 months to 6 months,
6 months to 1 year, over one year, total, total declared, and unknown. The correlations for each
duration were then averaged. This is important because the longer the duration of unemployment
the more effect it has on the natural rate of unemployment due to diminishing relevant skills.
Exclusions: Korea, Turkey (<1 month duration) and Australia, Austria, Belgium, Greece,
Hungary, Japan, Korea, Luxembourg, Poland, Slovak Republic, United States (Unknown
duration). Citation: OECD statistics page
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Series 14: Military expenditure as a percentage of GDP. This entry gives spending on defense
programs for the most recent year available as a percent of gross domestic product (GDP); the
GDP is calculated on an exchange rate basis, i.e., not in terms of purchasing power parity (PPP).
Source: cia.gov
Exclusions: None
Citation: from CIA.gov https://www.cia.gov/library/publications/the-worldfactbook/rankorder/2034rank.html
Series 15: Strictness of Employment Protection – Overall. Covers three different aspects of
employment protection including, individual dismissal of workers with regular contracts,
additional costs for collective dismissals, and regulation of temporary contracts and is on a scale
from 0-6 with 0 being no protection and 6 being the most protection
Exclusions: Iceland, Luxembourg
Citation: OECD statistics page
Series 16: HPI-2 uses indicators of the most basic dimensions of deprivation: a short life, lack of
basic education and lack of access to public and private resources. The HPI-2 concentrates on the
deprivation in the three essential elements of human life already reflected in the HDI-2:
longevity, knowledge and a decent standard of living. The HDI-2 is done for the high income
OECD countries.
Exclusions: Austria, Czech Republic, Greece, Hungary, Iceland, South Korea, Mexico, New
Zealand, Poland, Portugal, Slovak Republic, Turkey
Citation: Human Development report 2007/2008 page 241 table 4 Human and income poverty:
OECD countries, Central and Eastern Europe and the CIS
Series 17: Discouraged workers with a desire to work
This table contains data on discouraged workers who are persons not in the labor force who
believe that there is no work available due to various reasons and who desire to work.
Exclusions: Czech Republic, Iceland, Korea, Mexico, Portugal, Slovak Republic, Turkey
Citation: OECD statistics page
Series 18: Out-of-pocket expenditure on health as a percentage of total expenditure on
health. Household out-of-pocket expenditure comprised of cost-sharing, self-medication and
other expenditure paid directly by private households. Self-medication includes informal
payments extracted by medical care providers above the conventional fees, to over-the-counter
prescriptions and to medical services not included in a third-party payer formulary or
nomenclature of reimbursable services.
Exclusions: Greece
Citation: OECD statistics page
Series 19: Discouraged workers without a desire to work. This table contains data on
discouraged workers who are persons not in the labor force who believe that there is no work
available due to various reasons and who no longer desire to work.
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Exclusions: Belgium, Finland, Iceland, New Zealand, Norway, Poland
Citation: OECD statistics page
Series 20: Average annual hours worked per worker. The concept used is the total number of
hours worked over the year divided by the average number of people in employment. The data
are intended for comparisons of trends over time; they are unsuitable for comparisons of the
level of average annual hours of work for a given year, because of differences in their sources.
Part-time workers are covered as well as full-time workers.
Exclusions: None
Citation: OECD statistics page
Series 21: Economy wide regulation. A questionnaire asking different questions about the
countries regulation including required schooling, licensing, and quotas, also asked questions
about regulation on advertising, prices and fees
Exclusions: None
Citation: http://www.oecd.org/document/24/0,3343,en_2649_34323_35858776_1_1_1_1,00.html
This is the PMR indicators of economy wide regulation
Series 22: Pharmaceutical expenditure as a percentage of total expenditure on health.
Total expenditure on pharmaceuticals and other medical non-durables comprises
pharmaceuticals such as medicinal preparations, branded and generic medicines, drugs, patent
medicines, serums and vaccines, vitamins and minerals and oral contraceptives.
Exclusions: Ireland, Netherlands, Turkey, United Kingdom
Citation: OECD statistics page
Results
Correlations have been categorized into four groups: 1: high positive correlation 2: slight
positive correlation 3: slight negative correlation and 4: high negative correlation. In the text that
follows, the “Happiness” variable and the “Satisfaction” variable refer to the variables in
Stevenson and Wolfers (2008).
Group 1: Satisfaction and World Database of Happiness Happy Life Years, Happiness and
World Database of Happiness Happy life years, Happiness and Satisfaction, Happiness and
Corruption Perception Index, Happiness and Real GDP per capita 2004, Happiness and HENX,
Happiness and Total expenditure on health as a percentage of GDP.
Group 2: Happiness and Trade Union Density, Happiness and total government expenditure as a
percentage of GDP, Happiness and Public expenditure on health an as percentage total
expenditure on health, Happiness and Total tax revenue as a percentage of GDP for the federal
government
Group 3: Happiness and Involuntary part-time employment, Happiness and Unemployment by
duration average (all durations averaged out into one correlation), Happiness and Military
expenditure as a percentage of GDP, Happiness and Strictness of Employment Protection –
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Overall, Happiness and HPI-2, Happiness and Discouraged workers with a desire to work,
Happiness and Out-of-pocket expenditure on health as a percentage of total expenditure on
health
Group 4: Happiness and Discouraged workers without a desire to work, Happiness and Average
annual hours worked per worker, Happiness and Economy wide regulation, Happiness and
Pharmaceutical expenditure as a percentage of total expenditure on health
Discussion
Not surprisingly, we see strong positive correlations between the Stevenson and Wolfers (2008)
measure of happiness, the Stevenson and Wolfers (2008) measure of satisfaction, and a measure
of happiness from the World Database of Happiness. Any other result regarding these would
raise strong questions about the indices themselves.
More interestingly, we see a strong positive correlation with the HENX, which was specifically
designed to capture well-being in terms of freedom and the opportunity for choice, and
between happiness and the lack of corruption within a country (note that the corruptions
perception index is also a component of the HENX).
We also see a strong positive association between happiness and total expenditure on heath care
as percentage of GDP.
The second group, with a positive correlation but not as strong as with group one, presents an
interesting counterpoint with group one. The HENX result suggests freedom is important; here
we find happiness also positively correlated with what one could term an “involved”
government, as there is a positive correlation between happiness and total government
expenditure as a percentage of GDP, and also a positive correlation between happiness and total
tax revenue as a percentage of GDP. There is also a positive correlation between happiness and
trade union density.
The positive correlation here between public expenditure on health and in group one with total
expenditure on health are not contradictory and suggest the overall importance of health to
people. Government provision, given our data, is not more important than total expenditure.
The negative correlations in group three and four are not unexpected. The unemployment results
match research on the human toll of unemployment shown in both cross-sectional studies (e.g.,
Clark and Oswald (1994) and when following the same person over time (Winkelman and
Winkelman, 1998)). Other research has shown the unemployed feel sadder and more pained than
the employed (Krueger and Mueller, 2008).
What is surprising is the highly negative correlation between happiness and pharmaceutical
expenditure as a percentage of total expenditure on health. Groups one and two clearly indicate
health is positively correlated with happiness. It is not obvious why pharmaceutical spending
would have a negative correlation; advances in pharmaceuticals in the last decades (e.g.,
cholesterol lowering drugs) are believed to be responsible for many improvements in health. This
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may be capturing the dislike of out-of-pocket expenses related to health, but if this is the case, it
does run counter to the finding that total expenditure on health as a percentage of GDP.
When taken as a whole, results suggest a corruption-free, involved government and high
spending on health care are the keys to happiness. Further research using a wider sample of
countries would be instructive as to whether this result is sample driven. Importantly, for our
sample, every index analyzed (other than various measures of “happiness”) was less correlated to
happiness than real GDP per capita. If the goal of public policy were to change from maximizing
output (or the growth of output) to one of maximizing happiness, basic correlations suggest this
can be achieved by maximizing real GDP and its growth!
Conclusion
Many have criticized the use of real GDP per capita as a measure of well being. A recent
important criticism suggests using happiness and other subjective measures in a well being
metric. At the same time, recent research on happiness has called into question the long held
belief that only relative differences in income matter for happiness. This leads to two questions:
How does happiness correlate with other measures critics deem important? Is real gdp per capita
an adequate measure of well being?
Through our correlation research we have found that every index we analyzed (other than
various measures of “happiness”) was less correlated to happiness than real gdp per capita, with
the exception of the corruption perception index. This clearly suggests that real gdp can be
considered an adequate measure of living standards and well-being.
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