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TARPTAUTINIŲ APKLAUSŲ DUOMENŲ ANALIZĖ
TARPTAUTINIŲ APKLAUSŲ DUOMENŲ ANALIZĖ

Tarptautinis specializuotas duomenų analizės seminaras

TARPTAUTINIŲ APKLAUSŲ DUOMENŲ ANALIZĖ (anglų kalba)
2010 m. gruodžio 17 - 18 d., KTU

Autoriai: Atle Jåstad (Norvegijos Socialinių mokslų duomenų tarnybos (NSD), Norvegija)
Saamah Abdallah (Naujosios ekonomikos fondas (NEF), Didžioji Britanija)

 Įvadas  1 diena / 2010 12 17 2 diena / 2010 12 18

2 diena / 2010 12 18
Autorius Saamah Abdallah
Report on day 1 of the Data Confrontation Seminar arranged by Kaunas University of Technology in Kaunas 17th – 18th of December 2010

Seminaro medžiaga

Day 2 of the seminar: INTERNATIONAL DATA SURVEY ANALYSIS: WELL-BEING
By Saamah Abdallah

Introduction

This report is based on day 2 of a 2-day seminar at Kaunas University of Technology on 19th December 2010.  Day 2 was delivered by the centre for well-being, nef (the new economics foundation), a think-and-do tank based in London striving for a new economics which can deliver people’s well-being, sustainability and social justice.  The seminar itself was entitled “International data survey analysis”. The first day was delivered by Dr. Atle Jastad from the  Norwegian Data Archive (NSD), where participants were introduced to the European Social Survey (ESS), the ISSP and Nesstar – an online tool for analyzing data.
Day 2, reported upon here, focused on the measurement of well-being in the context of the search for alternative measures of progress. The day was combination of presentations and activities for participants. The activities that actually took place differed slightly from those planned. What is reported on here is a combination of the original presentation plans, and a short description of the activities that actually took place.
nef gave three presentations as part of the day’s seminar:

  • Why measure well-being?
  • How to measure well-being
  • Beyond well-being?

Between presentation 2 and 3, participants spend time analysing data from the European Social Survey using a combination of SPSS, Nesstar and Excel.
This report covers presentations 1 and 2 in detail, and briefly summarises presentation 3.

About nef

nef is an independent think-and-do tank founded in London in 1986 as the legacy organization of The Other Economic Summit – an alternative meeting set up in parallel to the G7 to discuss economic issues from the perspectives of people’s well-being, sustainability and social justice. nef currently has around 40-50 staff on works on a range of topics including:

  • well-being
  • valuing what matters
  • business and finance
  • natural economies
  • connected economies
  • energy and climate change
  • social justice
  • democracy and participation

nef also co-ordinates the new Great Transition campaign which aims to bring together campaigners working on a wide range of issues and highlight the common purpose and focus efforts on the key changes that need to happen for a better world.
The centre for well-being was founded in 2006, though nef had been working on well-being for several years before that. The centre’s aim is to enhance individual and collective well-being in ways that are environmentally sustainable and socially just.  We work to influence both policy and the public – by changing the way we measure progress, by encouraging a questioning of values, by highlighting the evidence around what does and doesn’t lead to high well-being, and by exploring how policy-making could be different with a focus on well-being. 

Why measure well-being?

The importance of measurement

Focussing on measurement may seem like a strange thing to do with all the grave problems facing the world – climate change, resource limits, economic instability and growing inequality. And yet we believe that how we measure progress may indeed be central to many of these problems. Since the middle of the 20th century we have focused on one particular way to measure progress – GDP (gross domestic product). Policies which are expected to increase GDP are favoured over those that are not likely to, and policies that are expected to increase GDP the most are favoured over those that might only increase it a little – usually disregarding social and environmental impacts, and often with undue caution as to even the long-term economic sustainability of the policies – as has been seen in the crash of 2008.
So, wealthy nations such as Canada exploit hard to reach tar sands so as to maximum growth, with little regard for their greater contribution to climate change, and the harm caused to ecosystems and communities living in the area.
Poor countries welcome multinational companies in export zones where they pay reduced taxes and are able to pay minimum wages and ignore labour laws. The countries compete with one another in cutting regulation so as to ensure companies come to them and bolster their weak economies.
Meanwhile, in places like Nigeria, the government has been accused of being complicit in some of the harshest treatment of local populations, again in the rush for growth. Huge areas of forest are cleared, to allow agricultural land to grow crops to export to wealthy countries to feed excessive meat consumption far beyond what can be considered healthy, and only tolerated because it too contributes to economic growth.
There are more subtle impacts in developed countries too.  Why don’t governments take steps to support reduced working hours when there is plenty of evidence that many people would be willing to take a cut in their income and work less provided they did not feel it would harm their careers? Why don’t governments seek to restrict obesogenic consumption and lifestyles given the huge impacts on health and the huge costs to health care services? Why do governments continue to allow the sale of arms to countries whose human records they simultaneously condemn? Why do local authorities support, indeed encourage, the construction of out-of-town superstores despite them ripping the souls out of communities and fostering car-dependent, fuel-intensive lifestyles? 

Each of these cases is of course complex and cannot be dealt with in a single paragraph.  However, it is clear that the quest for economic growth is a key factor in these decisions.  GDP growth is the trump card which can defeat other costs and benefits in policy decisions. Governments strive for green growth, smart growth and socially just growth, but in few cases do they ever consider letting go of that second word. So, for example, the EU’s 2004 Lisbon strategy stresses that:

“the promotion of growth and employment in Europe is the next great European project"

Nowhere is this trump card played in a more deadly fashion than in relation to climate change. Perhaps the single greatest reason that governments around the world have not effectively dealt with climate change by limiting emissions has been that limiting emissions enough will inevitably mean limiting growth and indeed perhaps even reversing it. For all the talk of ‘green growth’ and ‘green business’ the reality is that there is a conflict between continuing economic growth and avoiding the worst of climate change, as is argued by the UK Sustainable Development Commission report Prosperity without growth.  And continuing economic growth is still winning the battle.

Perhaps this is not surprising.  As economist E.J. Mishan stated in 1967, amongst most economists and politicians:

“…any doubt that, say, a four per cent growth rate is better for the nation than a three per cent growth rate is near-heresy; is tantamount to a doubt that four is greater than three”

GDP has also captured the attention of the general public. Figure 1 shows the number of times each keyword was cited in major world newspapers in two two-month time periods, according to the online media search tool Nexis. GDP ranks top in both periods.  One should not be deceived by the apparently high number of articles citing well-being.  Most of these are referring to well-being in a very general sense – from discussions of woman’s health to yoga to tourism. Few are talking about well-being as an indicator. Indeed most worrying is that the Human Development Index, developed as a direct alternative to GDP by the UN, theoretically the most important global institution, can barely be seen on this graph. In some ways it appears that, as suggested by Jeroen van den Bergh, we are in ‘conceptual lock-in’


Figure 1: Number of citations of keywords related to measurement of progress in major world newspapers, according to Nexis


Figure 2: Importance of seven concepts rated by students in 47 countries around the world. Source: Diener & Scollon (2003)

More than economics

Having said that, at least some part of us recognises that growth is not the answer to everything. In an international survey 75% reported believing that environmental, health and social indicators should be given as much weight as economic ones. Meanwhile, a poll conducted for the BBC in Britain found that 81% of respondents thought government’s prime objective should be to work for ‘greatest happiness’ rather than ‘greatest wealth’. In a 2003 study, Diener and Scollon asked people (admittedly all students) around the world in 27 nations from the USA to Uganda what they most felt to be important to them (see Figure 2). Happiness ranked top, followed by health, love and meaning. Wealth is only in fifth place out of the seven possibilities given in the study.
For most people, therefore, there is something that is more important than wealth as purportedly measured by GDP (of course even the link between GDP and the wealth of most individuals is tenuous, as has been highlighted by the Stiglitz Commission amongst others). As the early psychologist William James wrote:

“How to gain, how to keep, how to recover happiness, is for most people, at all times ... the secret motive of all they do, and of all they are willing to endure”

Given that this happiness is more important, isn’t it useful to know how each country is doing in terms of achieving it? Might we not learn from such information how to better achieve it?

Government’s role in well-being

Some have argued that well-being or happiness are personal matters and it should not be government’s role to meddle in it.  But government does seek to improve health, which is also a personal matter. There is good reason for this.  Government should be a tool for the population, helping us to achieve things that are hard to achieve on our own. In relation to well-being this can work in five ways, none of which are new to political science.
Firstly, it can help those with few resources to achieve well-being.  This has an intrinsic value for those being helped, improving their well-being.  But it also has instrumental value, in that it can reduce the risks of social tension that are linked to inequality and dissatisfaction amongst population groups.
Secondly, it can set rules which protect one group or person from harm caused by another group or person.  Our system of justice aims to do this. For example we prohibit theft as we believe that it causes unfair harm.  Might some other actions, which clearly harm well-being, also be subject to law?
Thirdly, it can resolve social action problems. Sometimes, the rational behaviour of an individual can have unintentional negative impacts on the well-being of others. So, for example, an individual’s choice to drive might mean they can travel faster, but the resulting traffic means that, overall, the population travels slower. Some governments, local and national, therefore attempt to manage traffic to reduce such social action problems.
Fourthly, it can think about the future in ways that individuals might not be motivated to do. This has been a particular challenge for most countries, but efforts to curb climate change by reducing emissions at the moment are an attempt to think about the well-being of future generations. Without governments, these efforts would be very difficult.  
Lastly, actions by government to improve well-being can be more efficient than those of an individual. It is more efficient for communities to pool together their resources and build a library, than for each to buy and own all the books they need.  It is more efficient for governments to invest in information campaigns to improve well-being than for this to be done by word of mouth.
In other words, government has a legitimate role in supporting, protecting and sustaining well-being within any political philosophy. With this in mind, it is only natural that it should seek to measure its impacts on well-being. 

  What is well-being?

All the top four concepts in Figure 2 fit within our understanding of well-being. For us, it is important to extend well-being beyond just happiness and/or satisfaction. These sensations are part of well-being, but can be augmented by other experiences which are universally important to people – such as feeling close to others, feeling autonomous and authentic, and feeling able to contribute to something or someone. These experiences are more about ‘doing’ well, than simply feeling good, and they contribute to sustaining and supporting the more general concepts of happiness and satisfaction.
Our conception of well-being is presented in Figure 3, reproduced from the Foresight Mental Capital and Well-Being Project, a government sponsored project commissioned to explore the implications of well-being science and mental health issues. Good feelings are included in this model, in the top oval. These are determined by good functioning and the satisfaction of the needs noted earlier. The list highlighted here (being autonomous, competent, safe and secure, connected to others) comes from self-determination theory and its development by psychologist Tim Kasser. This in turn is determined by the external conditions an individual finds oneself in, and the psychological resources they bring to their situation.


3: Dynamic model of well-being

Well-being is dynamic, each aspect affecting the others – the arrows shown in Figure 3 highlight the major pathways in the model.  This means that, no matter which aspect is seen as most important, all should be considered in terms of promoting well-being.
This understanding was recognised in the first UK government definition of well-being as:

“… a dynamic state, in which the individual is able to develop their potential, work productively and creatively, build strong and positive relationships with others, and contribute to their community. It is enhanced when an individual is able to fulfil their personal and social goals, and achieve a sense of purpose in society”

 Measuring well-being

The following section of the seminar explored how well-being can be measured. First, participants went through a mock questionnaire, based on the European Social Survey well-being module, to get a flavour of how well-being is measured. Then, some of the issues with measuring well-being and with questionnaire design in general were considered.

Decisions in well-being measurement

Our understanding of well-being leads to the following implications in terms of measurement.
Most importantly, given our definition of well-being as being about how well people feel and how well they are meeting psychological needs, it is clear that measuring it must be, at least in part, through asking people how they feel, i.e. subjectively.  Feelings are subjective experiences and they are best assessed subjectively. No one can be a better judge of an individual’s feelings, than that person themself. Of course, we can, based on what we know about psychology and human biology, measure things which we know to be universally important to people in determining their well-being through objective measures – for example calorie intake, and social interaction. But, the well-being itself is something which happens within the individual and therefore cannot be measured accurately through external observation.
Secondly, as well-being is multi-dimensional, no single indicator can fully capture it. At least one indicator is needed for each hypothesised dimension. As such, a key question in measuring well-being is what are the dimensions? Can the dimensions be prised apart such that one aspect of well-being increases whilst another doesn’t?
Thirdly, measurement should consider whether there is any ‘structure’ for well-being.  Are some aspects more important than others? How should this be reflected in measurement – more questions? Unequal weighting? Are some aspects of well-being negotiable, but others not? Measurement options can reflect such decisions.

Principles of psychometrics

As well as the implications specific to measuring well-being, there are also some considerations which apply to psychometric measurement in general. Many of these are framed here as the ideal situation – in reality measurement cannot always follow these principles.
Firstly, psychometricians stress the importance of using multiple items in questionnaires to measure a single construct. It is important to recognise that questions do not perfectly measure what they purport to measure.  Asking someone how satisfied with life they are on a scale of 0-10, their answer is likely to be influenced by how happy they are, but also whether they like the interviewer, how they answered the last question, what their favourite number is, what particular aspect of their life they have been thinking about recently, whether they are prone to agreeing or disagreeing, complaining or presenting a positive perspective, etc. Each question and each response is therefore affected by a range of factors. By answering a set of questions, one hopes that the idiosyncracies of response to one are smoother over.  Each question will be affected by its own idiosyncracies, but the intention is that the common variance between them represents something fundamental – the construct one is trying to measure. So, for example, Rosenberg’s Self-Esteem scale is calculated from 20 questions on the single construct of self-esteem.
When constructing questions, questionnaire-designers attempt to make sure they are simple and unambiguous.  It is better not to load too many concepts into a single question. For example, asking “Is time spent with your family stressful or enjoyable?” may be problematic because one has to think about two issues – how much of the time is enjoyable, and also how much of the time is stressful.  In the European Social Survey, two separate questions are asked.
It is important to use language that the vast majority of respondents will understand.  It is also important to only ask about things which will be relevant to as many people as possible. For example, quality of healthcare or education services within an area may be important to some people, but not to everyone. As such, it would probably be imprudent to ask too many questions on such a topic.
A more subtle consideration is question order. The response to a given question is affected by the questions that came before it. In an experiment conducted with students, two groups were given subtly different questionnaires. In group A students were asked to rate their satisfaction with life on a scale from 0-10, and then were asked to report on how often they went on dates. In group B, this question order was reversed – first they were asked about how often they went on dates, then they were asked their life satisfaction.
For group A there was no significant correlation between the two questions mentioned. For group B, a significant correlation of r=0.66 was found.  Before the questionnaire, the two groups were identical, so we can assume there is no difference in the importance of dating to individuals in the groups.  Rather, it appears that being asked about their dating experience, students then bore dating in mind more when considering their satisfaction with life. An individual who was not satisfied with their love life might nevertheless report being satisfied with life in general in group A. However, if they were in group B, and reminded of their poor love life first, they might then report not being satisfied with life in general.  This is called a focussing bias. Generally, focussing biases are more problematic for more general questions such as life satisfaction. For example, it does not appear that being asked to judge one’s life satisfaction before the dating question would have affected responses to the latter question. As such, it is recommendable to ask more general questions such as life satisfaction first, and then more specific questions – a recommendation with might at first seem counter-intuitive.
As with all measurement, reliability and validity need also be considered when developing metrics.  Reliability can be tested using test-retest: how stable are responses to questions if one were to ask the same questions twice with an interval in between. Of course, such an approach must be treated with caution. Some questions are more likely to be unstable for good reasons and not because of low reliability. For example, an individual’s true levels of self-esteem are unlikely to change dramatically over a week, unless something life-changing were to take place. On the other hand, asking someone how happy they have felt over the last day, their responses are likely to change greatly from day to day and this need not be considered as error.
Validity has many facets. Firstly measures should have face validity. There should be some reasonable logical interpretation of how they measure what they purport to measure. Secondly, they should have criterion validity.  This means that the measure should correlate highly with other measures that purport to measure the same thing.  For example, in the case of subjective well-being, we know that an individual’s assessment of their happiness correlates with other people’s assessments of their happiness, how often they smile, and neural patterns known to be associated with positive emotions.
Thirdly, measures should correlate with factors believed to have an effect on them or to be affected by them (convergent validity). For example standard measures of life satisfaction correlates with income, marital status, social relations and a wide range of other variables which one would expect to be important in determining how someone feels about their life.
Lastly, measures should not correlate too much with measures that purport to measure other concepts, if those concepts are believed to be distinct.  For example, self-esteem and feeling satisfied with one’s relations may be correlated to some extent, but if measures of the two are intended to be measuring two different things, then they should only correlate to some extent – this is known as discriminant validity.

Real world compromises in measuring well-being

The principles of psychometrics have been developed by academics rightly intent on identifying the best possible ways to measure psychological phenomena. Often they are most appropriate for academic studies, where the academic has the most control over the measures being deployed.
Proponents of well-being measurement (such as Professor Ed Diener, Lord Richard Layard, Professor Ruut Veenhoven and ourselves at nef) have argued that government should collect data on well-being from the general population to inform policy-maklng. Generally, it is suggested that measures of well-being are included in official national statistics collection processes.
Of course, in such a context, well-being measurement experts must co-operate with and compromise with policy makers and, most importantly, official statisticians. These partners will have very valid concerns about adding too many questions to surveys – they increase costs and respondent-fatigue, and may not be seen as appropriate by respondents. These concerns mean that, for example, the aim of including multiple-item scales to measure each aspect of well-being may be a little optimistic. Indeed, it is even a challenge to convince statisticians to consider the multi-dimensionality of a concept which, until recently, was unknown to them (at least in a professional context!).
Indeed legitimate concerns about space often go hand-in-hand with concerns that we would argue are less-justified with regards to the validity, reliability and usefulness of well-being measurement. Statisticians that have focussed on asking more ‘factual’ questions regarding individual income and habits, have little training in psychometrics, the recommendations it makes with regarding measuring psychological constructs and the assurance it provides as to their validity and reliability if done well.
Other problems of inertia are also encountered. National statisticians prefer to use questions with a proven track record – but of course it is hard for such a track record to develop without financial support from somewhere. Those in charge of specific surveys can also be rather protective about changes that are to be made – not wishing to remove questions that have been used before, or change question order.  These factors can be problematic given what we have already discussed about psychometric principles.
(life sat)

National Accounts of Well-Being

In this section we present a first attempt at an approach that could be used by national, regional or local governments to measure well-being in their jurisdiction. It is an approach that builds upon theories of well-being, and principles of psychometrics, attempts to give consideration to the realities of policy-making, and also the realities of national statistics collection.  As such it represents a compromise to some extent.  It is also worth noting that our theories of well-being have come a long way since the approach began to be developed in 2004 and, in years to come, we expect to be able to update it.
The National Accounts of Well-Being were released as a report and a website (www.nationalaccountsofwellbeing.org) by nef in 2009. It was based on data from a module on well-being included in the third round of the European Social Survey (ESS) in 2006. The 55 item module was developed by a team lead by Felicia Huppert at the University of Cambridge, and including Andrew Clark at the ENS in Paris, Bruno Frey at the University of Zurich, Johannes Siegrist at Dusseldorf University, and Nic Marks at nef. The ESS is a biannual survey and is designed such that a core set of questions is asked each wave, accompanied by two ‘rotating’ modules that are developed by external academics.
In 2010, a new team including Felicia Huppert and ourselves won the opportunity to develop a repeat well-being module for the sixth round of the ESS in 2012, which is work we are now in progress with. Our aim is to improve the module we developed for ESS3, though of course, as with any pre-existing survey vehicle, we must consider constraints such as preserving 60% of the questions from round 3, and, of course, having no say about questions included in the core of the survey.
In 2006, the ESS reached around 40,000 respondents in 22 European countries. More recent waves have managed to reach more countries within Europe.

Structure for measuring well-being     

One of the key innovations of the National Accounts of Well-Being was to develop a structure for measuring well-being. Over 50-items were available for assessing well-being. How would these be brought together to produce a coherent and useful approach?
Figure 4, reproduced from the report, is our solution. The questions were brought together in a set of constructs such as positive feelings, satisfying life or resilience. Many of these concepts, in turn, formed larger constructs such as functioning. All together, the questions were brought together as either personal well-being and social well-being. In reality, in the report, scores are also calculated combining these two high-level concepts.
The idea is that one can look at well-being at various levels of aggregation.  A headline figure can be produced to assess well-being in general  But one can also look at specific aspects of well-being, and explore how a given population group or country is doing in terms of self-esteem, for example, or social relations – allowing priorities and problem areas to be identified.


Figure 4: Indicator structure within the example national accounts framework    

Figures 5 to 8 show how the data from the National Accounts can be used. Figures 5 and 6 show the overall personal and social well-being scores respectively for all the countries in the report, as presented on the website. Shades of green represent high well-being and shades of red represent low well-being.

Figure 5: Personal well-being


Figure 6: Social well-being

Figures 7 and 8 show how the data for several dimensions can be combined in one diagram, creating a ‘well-being profile’ for a country or, in the case of Figure 8, a subset of the population in a given country.


Figures 7 & 8: Well-being profiles for Spain and UK

Calculation metrics

How are questions brought together to calculate scores at the various levels? Figures 7 and 8 show scores on a scale of 0-10. Importantly, how can scores in different components of well-being be compared? This is not a simple task.  One cannot simply take an average of answers to each question, because they are based on different scales, some from 0-10, some simply yes or no. Some questions are worded such that a high number means high well-being, for others it is the reverse. In any case, even if the wordings and scales were the same, one could not be sure that a 7 for one question actually represents higher well-being than a 6 on another. Consider the following two statements assessed on the same scale:

“I’m always optimistic about my future”
“There are people in my life who care about me”

The former question is designed to assess optimism, the latter assesses close social relations. Both are answered on a 5-point scale from strongly disagree to strongly agree. In both cases, the highest well-being is to respond ‘strongly agree’. But there the similarities stop.  Far more people agree strongly with the second statement than the former. Does that mean that overall people’s social relations are better than their optimism? Maybe not.  Consider if the first question was changed slightly to “I’m usually optimistic about my future”.  In that case one would expect that many more people would agree with the statement, perhaps even more than the social relations question. The fact that the wordings of the questions can have a big impact on the responses means we cannot compare mean scores for two questions directly and hope for anything meaningful. Indeed, it would be very hard to produce any results that could allow us to say, in absolute terms, that people in Europe have greater optimism, than, positive relations. The best we can hope for is to compare sub-groups within Europe, relative to the mean. We can then say that people in Spain, for example report better supportive relationships than sense of vitality, compared to the European average. What this means is that Spanish people have above average responses in terms of supportive relationships, but only at average for sense of vitality.
To determine these patterns, to produce scores from 0-10, we developed a three stage process.
Firstly, we used a standard statistical technique to standardize results for each question, as shown in the equation below.

The formula results in a z-score for each individual for each question, which is basically the distance away from the mean for that question across all Europe, in units of standard deviation. If someone scores above average on a question, they will have a positive z-score.  If they score below average, they will have a negative z-score.  The units can be meaningfully compared between two questions. If someone scores 2.00 on one question, and 1.00 on another, that means that they are further above the mean on the first one – and so one can conclude they having higher well-being in that aspect.
The next step is to aggregate results to calculate components scores.  This is simply a matter of taking averages,
The last step is more unusual.  Z-scores may be okay for academics, but they are not very meaningful or understandable for anyone else. Importantly, one does not have a sense of what the maximum or minimum is.  To deal with this, we developed an equation to transform z-scores into scores on a scale of 0-10 such that 0 represents the lowest possible score, 10 the highest and 5 the mean (i.e. 0 as a z-score). The function is demonstrated in the graph in Figure 9 for an example question:


Figure 9: Transformation function from z-score to 0-10 scale

The result is neat 0-10 scores which can be calculated at any level of aggregation, be it a single question, or well-being overall for a nation.

Beyond well-being?
After participants spent several hours exploring well-being data, the final presentation explored the measurement of progress beyond well-being. Of course well-being is important but there is more to progress. Figure 10 presents a prototype measurement framework.


Figure 10: Measurement framework

The framework highlights three spheres of measurement – human well-being, the environment and other human systems, particularly the economic system.  It also highlights the interactions between them as being important to study. So, for example, decoupling between economic activity and environmental impact is desirable.  Similarly the efficiency of well-being output per unit of economic activity should be considered – how efficient is our economy in achieving our goals. Most important, based on this framework, however is the relationship between the environment and human well-being.  In a way, this is the ultimate efficiency measure – how much well-being is achieved per unit of environmental impact.

Happy Planet Index

The Happy Planet Index, developed by nef in 2006, is an attempt to operationalise the ecological efficiency. The Happy Planet Index takes life satisfaction (a measure of subjective well-being) and life expectancy (a measure of the key health outcome) and multiplies them together. The resulting figure is called happy life years, based on the original approach developed by Dutch sociologist Ruut Veenhoven. It then divides this figure by the ecological footprint of a country (the land required to produce the resources and sequester the CO2 produced by a nation, based on consumption patterns). A couple of adjustments are made to control for the differences in variation in the different data sources.

The results are quite unique, but makes sense when one considers what is being calculated. As is shown in Figure 11, the desired situation is of course high happy life years and low ecological footprint. But most countries have one or the other. Rich Western nations have happy life years and a large footprint.  Poor African nations have small footprints, but low happy life years. However, the graph also shows the relationship is not generally linear. There appears to be a case of diminishing returns with increasing footprint contributing more to well-being for poorer countries.  Also, of course, there is much variation between countries, with Latin American countries appearing to be more ‘efficient’ in converting their middle-sized footprints into well-being than other countries with similar sized footprints, such as the former communist nations of Central/Eastern Europe and Central Asia.

Figure 12, shows what happens when combines these figures in a single map. Green indicates a greater approximation to ecological efficiency, red less. 


Figure 11: Happy life years and ecological footprint

As can be seen in Figure 12, Latin American countries fare the best, with Costa Rica taking top position in the HPI scoring (see Figure 13)


Figure 12: Happy Planet Index coloured map                                          


HPI rank Countries Region Life Sat Life Exp EF   HPI
1 Costa Rica 1a 8.5 78.5 2.3 = 76.1
2 Dominican Rep 1a 7.6 71.5 1.5 = 71.8
3 Jamaica 1a 6.7 72.2 1.1 = 70.1
4 Guatemala 1a 7.4 69.7 1.5 = 68.4
5 Vietnam 6c 6.5 73.7 1.3 = 66.5
6 Colombia 1b 7.3 72.3 1.8 = 66.1
7 Cuba 1a 6.7 77.7 1.8 = 65.7
8 El Salvador 1a 6.7 71.3 1.6 = 61.5
9 Brazil 1b 7.6 71.7 2.4 = 61.0
10 Honduras 1a 7.0 69.4 1.8 = 61.0
20 China 6a 6.7 72.5 2.1 = 57.1
35 India 5a 5.5 63.7 0.9 = 53.0
51 Germany 2c 7.2 79.1 4.2 = 48.1
74 UK 2c 7.4 79.0 5.3 = 43.3
114 USA 2b 7.9 77.9 9.4 = 30.7
143 Zimbabwe 4a 2.8 40.9 1.1 = 16.6

Figure 13: HPI scores and ranks for selected countries

  Gathering momentum

This final section summarises some of the other developments in the field of measuring progress taking place around the world.
Perhaps most influential has been the so-called Stiglitz Commission (officially the ‘Commission on the Measurement of Economic Performance and Social Progress’) set up by French President Nicholas Sarkozy in January 2008. The Commission, headed by Joseph Stiglitz, Amartya Sen and Jean-Paul Fittousi and consisting of many other leaders and experts in the fields of well-being, environmental assessment and economics, resulted in 21 recommendations divided into three domains: quality of life, the environment and ‘classical GDP issues’ – the latter referring to how to improve or supplement GDP in measuring economic issues. Amongst the recommendations made were to develop and integrate subjective measures of well-being in national statistics.
Before the Stiglitz Commission had been set up, however, the OECD had already been working on measuring progress. Their work kicked off with the Istanbul Declaration in the Autumn of 2007. Later they produced a framework for measuring progress, which, like ours, highlights the importance of well-being and the ecosystem. In 2011, the OECD will be launching a Handbook of Subjective Well-being with clear guidelines of National Statistics Offices on measuring subjective well-being.


Figure 14: OECD Framework for Measuring Progress

The polling company Gallup have also been actively involved. They have been collecting data on well-being in the US from 1000 individuals every day. They have also launched the Gallup World Poll which covers well-being to some degree.
Within the European Commission, the Beyond GDP  process began with a seminal conference in the European Parliament in November 2007. They have a road map for incorporating alternative indicators and in 2008 a feasibility study for measuring well-being for Europe began, a second study beginning in 2011. 
Lastly, many countries are getting actively involved. In the UK, David Cameron recently announced that the UK Office of National Statistics would be collecting well-being data and that it was time to acknowledge that there was more to national progress than GDP.

Abdallah S, Thompson S, Michaelson J, Marks N & Steuer N (2009) The (un)Happy Planet Index 2.0; Why good lives don’t have to cost the Earth (London: nef)

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