Universita of Politecnica delle Marche
Faculty of Economy
International Economics and Commerce
“ BEYOND GDP : MEASURING WELL BEING AND PROGRESS OF NATIONS “
A Report
in BUSINESS STATISTICS
submitted to PROF. CHIARA GIGLIARANO
By
H. KUBRA BAYRAM
February, 2015

BEYOND GDP:
MEASURING WELL BEING AND PROGRESS OF NATIONS
By
H. KUBRA BAYRAM
Abstract
Everyone aspires to a good life. But what does a "good" (or better) life mean? In recent
years, concerns have emerged that standard macro-economic statistics, such as GDP, which
for a long time had been used as proxies to measure well-being, failed to give a true account
of people’s current and future living conditions. The ongoing financial and economic crisis
has reinforced this perception and it is now widely recognized that data on GDP provide only
a partial perspective on the broad range of factors that matter to people’s lives.

BEYOND GDP:
MEASURING WELL BEING AND PROGRESS OF NATIONS
CONTENTS
1. INTRODUCTION
2. PROBLEM STATEMENT
3. MOTIVATION: Why is the problem important?
4. WORK’S GUIDELINE
5. DEFINITION OF VARIABLES
6. EVALUATION: Descriptive analysis of the real data (summary indicators,
graphs), output of the statistical analysis, comment of the results.
6.1. Regression Model
6.2. Cluster Analysis
6.3. Factor Analysis
7. CONCLUSION

“ We need to move beyond gross domestic product as our
main measure of progress, and fashion a sustainable
development index that puts people first. “
- Ban Ki-moon, 8th General Secretary – General of the
United Nations
1. INTRODUCTION
In this report, we aim that looking at the most important components of people's living
standards in terms of well-being by explaining how these kinds of components have
significant impact on people's rate of well-being. During our study, we focus on mainly these
components to identify that how people's well-being beyond GDP : housing expenditure,
household net adjusted disposable income, household net financial wealth, employment rate,
job security, quality of support network, educational attainment, air pollution, consultation on
rule-making, voter turnout, self-reported health, life satisfaction, assault rate, time devoted to
leisure and personal care . Through focusing the components, we goal that providing a
comprehensive aspect of well-being in OECD countries and other major developed economies
by looking at people’s material living standards and quality of life across the population.
2. PROBLEM STATEMENT
In recent years, there have been growing confusions related with the proficiency macro-
economic statistics, in terms of GDP, as measures of people's present and future living
standards. Furthermore, we can say that there are wider confusions about the relation of these
figures as measures of national or societal well-being. In terms of micro side, there are also
confusions about the comparability and comprehensiveness of the statistics being produced
when measuring condition of people's well-being. Because of these reasons, there has been
widespread interest in adding to and improving upon, existing measures of household income,
consumption and wealth as part of a process of developing more comprehensive measures of
people's well-being. So, with this direction, we aim to overcome these confusions by
identifying main components for understanding and measuring human's well-being.

3. MOTIVATION: Why is the problem important?
Today, the most commonly-used measure of economic progress is still GDP, which is
simply the total value of all the goods and services that are exchanged for money. GDP
literally does not count some of our greatest sources of wealth - unpaid household labor,
volunteerism, and a clean environment. GDP doesn't distinguish between good things and bad
ones - and it counts the depletion of our natural wealth as economic gain. Hence, economic
indicators such as GDP were never designed to be comprehensive measures of prosperity and
well-being. In this point, we can describe that well-being is a complex phenomenon and many
of its determinants are strongly correlated with each other, evaluating well-being needs a
comprehensive framework that involves a large number of components and measuring how
their interrelations shape people's lives. Because of this reason, we need adequate indicators
to address global challenges of the 21st century such as climate change, poverty, resource
depletion, health and quality of life. So, our motivation in this report is that advancing to the
beyond of GDP when we measure of human's well-being and living standards.
4. WORK’S GUIDELINE
The inspiration of our project is to provide a comprehensive aspect for human's well-
being by analyzing its strong components to get more accurate result in measuring by thinking
on beyond GDP. In this report, we took into account of 2013 year as a relatively short time
and we studied significantly with 14 components of well-being for 33 OECD countries and
other major developed economies.
Ask for the data, we have collected them from OECD archive that is the primary source of
information for better life indicators. Initially, we have downloaded data for all related
countries across the world (33) but due to the lack of data, we eliminated one of them (Korea).
Our strategy in this study is as follows:
First, we did choose a group of 14 components that we think that are relevant to explain of
people's well-being.
Second, we decided to use 3 statistical methods to analyze of these components for well-
being. We can identify these 3 methods as cluster analysis, regression method and finally

factor analysis. To analyze the components with the 3 methods, we used to Rcommander
software as a tool.
Thirdly, for the cluster analysis, we provided dendrograms to see how these components
and countries create groups and how is their distance each other in terms of well-being. Then,
we studied with regression model by choosing three dependent variables ( GDP per capita,
life satisfaction and human development index ) to analyze which variable has significant
impact on components of well-being. And finally, we made factor analysis to combine these
14 variables in lower number of unobserved indexes. In the next stages of the report, we
explain deeply that how we used all these three methods when analyzing of our subject.
5. DEFINITION OF VARIABLES
a. Housing Expenditure:
This indicator considers that housing costs take up a large share of the household budget
and represent the largest single expenditure for many individuals and families, by the time
you add up elements such as rent, gas, electricity, water, furniture or repairs.
b. Household net adjusted disposable income:
We can define of household net adjusted disposable income which is the amount of
money that a household earns, or gains, each year after taxes and transfers. It represents the
money available to a household for spending on goods or services. It also includes income
from economic activity (wages and salaries; profits of self-employed business owners),
property income (dividends, interests, and rents), social benefits in cash (retirement pensions,
unemployment benefits, family allowances, basic income support, etc.), and social transfers in
kind (goods and services, such as health care, education and housing, received either free of
charge or at reduced prices).
c. Household net financial wealth:
Household financial wealth is the total value of a household’s financial worth, or the sum
of their overall financial assets minus liabilities. It takes into account: savings, monetary gold,
currency and deposits, stocks, securities and loans.

d. Employment rate:
It is the number of employed persons aged 15 to 64 over the population of the same age.
Employed people are those aged 15 or more who report that they have worked in gainful
employment for at least one hour in the previous week, as defined by the International Labour
Organization – ILO.
e. Job security:
This indicator presents the probability to become unemployed. It is calculated as the
number of people who were unemployed in 2012, but were employed in 2011 over the total
number of employed in 2011.
f. Quality of support network:
It's a measure of perceived social network support. The indicator is based on the question:
“If you were in trouble, do you have relatives or friends you can count on to help you
whenever you need them, or not?” and it considers the respondents who respond positively.
g. Educational attainment:
Educational attainment considers the number of adults aged 25 to 64 holding at least an
upper secondary degree over the population of the same age, as defined by the OECD-ISCED
classification
h. Air pollution:
The indicator is urban-population weighted average of annual concentrations of
particulate matters less than 10 microns in diameter (PM10) in the air in residential areas of
cities with more than 100,000 residents.
i. Consultation on rule-making:
The Consultation on Rule-making indicator describes the extent to which formal
consultation processes are built-in to the regulatory law-making process. The indicator is a
weighted average of yes/no answers to various questions on the existence of law consultation
by citizens, of formal procedures enabling general public to impact regulation and
governmental actions.

j. Voter turnout:
Voter turnout is defined as the ratio between the number of individuals that cast a ballot
during an election (whether this vote is valid or not) to the population registered to vote. As
institutional features of voting systems vary a lot across countries and across types of
elections, the indicator refers to the elections (parliamentary or presidential) that have
attracted the largest number of voters in each country.
k. Self-reported health:
This indicator refers to the percentage of the population aged 15 years old and over who
report “good” or better health.
l. Life satisfaction:
Life satisfaction measures how people evaluate their life as a whole rather than their
current feelings. It captures a reflective assessment of which life circumstances and conditions
are important for subjective well-being. The indicator considers people's evaluation of their
life as a whole. It is a weighted-sum of different response categories based on people's rates of
their current life relative to the best and worst possible lives for them on a scale from 0 to 10,
using the Cantril Ladder (known also as the "Self-Anchoring Striving Scale").
m. Assault rate:
The indicator is based on the question: "Within the past 12 months: have you been
assaulted or mugged?" and it considers people declaring having been assaulted or mugged.
n. Time devoted to leisure and personal care:
This indicator measures the amount of minutes (or hours) per day that, on average, full-
time employed people spend on leisure and on personal care activities.

6. EVALUATION: Descriptive analysis of the real data (summary
indicators, graphs), output of the statistical analysis, comment of the
results.
6.1. Regression Model
We have chosen Linear regression models as one of our statistical techniques to do our
observations in our assignment since it can tell us something about the linkages between a
large number of indicators and a single output measure that represents the objective to be
attained.
There are a number of different approaches to monitoring well-being through dedicated
reports using Regression model.
We set three multiple regression models with three different dependent variables keeping
the same explanatory variables representing well-being indicators mentioned above. Multiple
regressions enable us to determine the simultaneous effect of all those indicators on dependent
variables using the least squares principles.
In the first model we assign GDP per capita1
as dependent variables in order to make sure
that it is an economic measure not associated with increasing national well-being. Our result
confirm in part this claim and we can conclude that most of the predictor well-being variables
do not have significant effect on GDP per capita since their p-values are higher than level of α
equal to 0,05 although the model is significant as a whole (F-distribution > threshold Fp,n-p-
1,α). The only variable which has significant impact on the response variable is Household net
adjusted disposable income (with p-value = 0,014). We can comment the impact of this
regressor as follows:
- If the Household net adjusted disposable income increases by one dollar, then GDP
per capita increases by 1,781e+00 dollars, ceteris paribus.
While, not suggesting GDP to be used as a proxy measure for the overall well-being of
countries, since it is simply measures what is sets out to measure the value of a country’s
economic activity in terms of its productive sectors. Other commonly integrated-cited
indicators which truly account for the progress and well-being of a county and there is worth
of other more suitable that can be used as very inclusive, measures such as Human
1
GDP per capita, PPP (constant 2011 international $)

Development Index, Income and population inequality and Life satisfaction. These provide
information on outcomes that may not be accurately captured by GDP per capita, but which
are important to well-being.
In the second Regression model we remove predictor variable Life satisfaction and assign
it as dependent variable since it is a subjective measure of well-being using to complement
existing measures of national progress that emphasizes the views of individuals. It thus
presents an overall picture of well-being that is grounded in people's preferences, rather than
in a priori judgments about what should be the most important aspects of well-being.
Subjective well-being measures reflect the unique mix of factors that influence an individual's
feelings and assessments. This is not to say that subjective well-being should replace other
important economic, social and environmental indicators, but it does provide a useful and
easy-to-understand complement to existing measures, because it can indicate the combined
impact of life circumstances on subjective perceptions and emotions.
This fact we certify with the result of our Regression model, where most of predictor
variables have significant impact on dependent variable and the model is significant as a
whole (F-statistics > 2,31). We can comment the impact of these significant regressors as
follows:
- If the Air pollution increases by one microgram per cubic meter, then the Life
satisfaction increases by 0.030174 mean value (Cantril Ladder), ceteris paribus.
- If the Assault rate increases by one percentage of people aged 15 and over, then the
Life satisfaction increases by 3.59e-05 mean value, ceteris paribus.
- If the Education attainment increases by one percentage of the adult population (aged
25 to 64), then the Life satisfaction increases by 0.036927 mean value, ceteris paribus.
- If the Employment rate increases by one percentage of the working-age population
(aged 15-64), then the Life satisfaction increases by 0.007910 mean value, ceteris paribus.
- If the Household net adjusted disposable income increases by one US dollars at
current PPPs per capita, then the Life satisfaction increases by 0.005830 mean value, ceteris
paribus.
- If the Job security increases by one percentage of the dependent employed, then the
Life satisfaction increases by 0.017394 mean value, ceteris paribus.

- If the Quality of support network increases by one percentage of people, then the Life
satisfaction increases by 0.000974 mean value, ceteris paribus.
- If the Self-reported health increases by one percentage of the population, then the Life
satisfaction increases by 0.005461 mean value, ceteris paribus.
The Coefficient of determination R2 (equal to 0.9271) shows that the model is very good
as well. From this result we find that for this sample 93% of variability in the Life satisfaction
is explained by the linear relationship with observed well-being indicators.
In the third Regression analysis we determine Human Development Index 2
as predictor
variable since it is considered as a worth, suitable measure of better life indicator, and keep all
14 well-being indexes as explanatory variables.
The result plots that the model is significant as a whole (F-statistics equal to 13,36 is
higher than 2,31) though most of explanatory variables do not have significant impact on the
HDI excepting Employment rate. The impact of these significant regressor can be explained
as follows:
- If the Employment rate increases by one percentage of the working-age population
(aged 15-64), then the Human Development Index increases by 0.03183 point, ceteris
paribus.
As a consequences of our analysis we can conclude that to build a sustainable economy,
we need tools of analysis that properly value social, economic and environmental assets, tools
that carefully appraise both costs and benefits, and balance them against one another. That's
what's known as "full-cost accounting" and it is not to say that well-being should replace other
important economic, social and environmental indicators. They can go together in order to
assess and provide a correct measurement of population and economic progress.
2
The Human Development Index was created to emphasize that people and their capabilities should be criteria
for assessing the development of a country, not economic growth alone. The HDI is also used to question
national policy choices, asking how two countries with the same level of GNI per capita can end up with
different human development outcomes. It is a summary measure of average achievement in key dimensions of
human development: a long and healthy life, being knowledgeable and have a decent standard of living. The
HDI is the geometric mean of normalized indices for each of the three dimensions.

6.2. Cluster Analysis
Another statistical method that we focused on our project is Cluster analysis. This
approach has the goal to compute the similarity/dissimilarity between the countries analysed
by us.
Better life index – Cluster analysis
For the first cluster analysis we used all the 14 variables, taking into account the economic
elements, but also the social and human assets. The result grouped the all 33 countries in 3
main clusters. Two of them join at the distance more than 2e+05 and one of them at the
distance 1e+05. The first one is composed by two smaller clusters: one includes Switzerland
and United States, which have almost the same value for: quality of support network,
educational attainment and consultation on rule-making, but a high difference between the
values of assault rate, Switzerland with more than 300% higher than United State. The second
small cluster includes: Belgium, Japan, Luxembourg, Netherlands, Canada, United Kingdom,
which are similar due to economic and non-economic elements.
We can notice this cluster grouped the countries with large economy, having a high level
of well-being. The second main cluster has two subgroups and each of them is composed by
two small sub-groups. The cluster analysis grouped countries as: Poland, Slovak Republic,
Turkey, Hungary, Estonia and Mexico in one cluster, that is similar with the cluster: Norway,

Finland, Spain, Chile, Czech Republic, Slovenia and Greece. The result shows these countries
are similar at the distance 0,5e +0,5, having almost the same level of better life index. This
means the well-being in these countries is due to the same activity of people’s efforts.
Australia, Iceland, Denmark, New Zealand are similar with Ireland and Portugal. The main
variables that made these countries to have a similar level of better life index are economic
assets: the household net adjusted disposable income and net financial wealth, the housing
expenditure and the employment rate. The indicators as consultation on rule making, assault
rate, educational attainment created the low dissimilarity between the countries. Portugal is
the individual with the highest level of assault rate and the lowest rate of educational
attainment.
This group of individuals join at the distance of 1,5e + 0,5 the group composed by the
following countries: Italy, Israel, France, Austria, Germany and Sweden. The housing
expenditure, the time devoted to leisure and personal care and the quality of life are the
indicators, which have the highest level of similarity from all the 14 variables for these groups
of countries. In the same time, the consultation on rule-making is the variable that shows the
highest level of dissimilarity between the states.
The cluster analysis points out that the main indicators, having a significant impact on the
way in which the countries joined are the economic elements. The similarity between
countries has a strong correlation with the household’s material conditions. To see what
impact the economic elements have on our result, we divided the variables in two groups. One
that includes the material conditions (housing expenditure, household net adjusted disposable
income, household net financial wealth and employment rate) and the second, which
represents the quality life indicated by the following indicators: job security, quality of
support network, educational attainment, air pollution, consultation on rule-making, voter
turnout, self-reported health, life satisfaction, assault rate, time devoted to leisure and personal
care.

Better life index measured with quality life indicators
We noticed that if we take into account only the materials conditions the dendrogram is the
same with the dendrogram done with all the 14 components of well-being. On the other hand
when we used only social and human assets, the dendrogram showed changes in the
individual positions.
When we made the dendrogram only the quality life’s indicators, the countries changed
their positions and joined to other clusters. We obtained four main clusters. The most
important changes are:
- Italy left its group and moved to the cluster composed by Spain, Chile and Greece
(these states were together also in the first our result). To this group join Mexico and Turkey,
which maintain the initial cluster. The group received a new entry: Portugal;
- Estonia, Hungary, Poland and Slovak Republic kept the same group, to which joined
also Japan and Slovenia.
- The third cluster includes: United States, United Kingdom, Switzerland, Canada
(remain together) and Israel, Ireland, New Zealand, which changed their positions.

- The following countries stayed in the same group: Iceland, France, Germany, Austria,
Australia and Sweden. To this cluster joined: Netherlands, Belgium and Luxembourg, which
moved together from the first group. The new entry is Norway.
Hence, we noticed that there are some individuals, which left together the initial group and
joined the new one. For example, Spain, Chile and Greece maintained the similarity even if
we take into account the all components of well-being index or we divide them.
Therefore we can say that this kind of individuals are correlated strongly one to each other.
For the countries, that changed the clusters in the second analysis is obvious that their
similarity is due to the material conditions, and not to quality life.
Even if we didn’t take into account the GDP per capita index for measuring the well-being,
the analysis above stress the idea that “ a better” life is still mainly affected by the economic
elements.
6.3. Factor Analysis
With Factor analysis, the third chosen statistical method in our project, we aim to describe
variability among correlated well-being indicators in terms of a potentially lower number of
unobserved, uncorrelated variables – factors. Through this method we can create indexes with
variables that measure similar aspects.
From our analysis we consider the all 14 well-being indicators and receive a model of 4
Factors at magnitude of eigenvalue ~ 1 and cumulative variance equal to 0,602 (> 0,60) what
means that the model is good as a whole.
- Factor 1 is explained mainly by Quality of support network and Time devoted to
leisure and personal care indexes. We can combine them under a common name Social life
- Factor 2 is explained mainly by Employment rate, Job security, Life satisfaction, Self-
reported health and Voter turnout indicators. We can unify them under a common name
General welfare.
- Factor 3 is explained mainly by Consultation on rule-making, Educational attainment
and Housing Expenditure, unify under - Civil liability

- Factor 4 is explained mainly by Household net adjusted disposable income and
Household net financial wealth. We unify them under the common name Income.
As a conclusion of our analysis we can highlight that well-being is a complex
phenomenon and many of its determinants are strongly correlated with each other, assessing
well-being requires a comprehensive framework that includes a large number of components
and that, ideally, allows assessing how their interrelations shape people’s lives.
7. CONCLUSION
This report responds to the needs of citizens for better information on well-being and of
policy makers to give a more accurate picture of societal progress. According to our study,
we can say that GDP should not be replaced but indices that better reflect the well-being and
progress of our population must continue to be developed. Legislators must be mindful of
what they desire for their societies remembering that well-being is not just growth, it is also
health, environment, spirit, and culture. It is more than just statistics. It is also a way of
thinking and goals we sat. After all, a country should be judged by how it provides for its
most vulnerable.

Referenses:
http://www.oecd.org/statistics/datalab/bli.htm
http://stats.oecd.org/Index.aspx?DataSetCode=BLI
http://hdr.undp.org/en/content/human-development-index-hdi
http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=wo
rld-development-indicators
http://www.keepeek.com/Digital-Asset-Management/oecd/economics/how-s-life/an-overview-
of-headline-well-being-indicators-in-oecd-countries_9789264121164-table2-en#page1
Apendix:
Dataset and results of Rcommander on attached as separate files.