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Jul 5, 2007 - indicators Б Green GDP Б Environmental costs Б Happy life years Б .... as the difference between happiness and life satisfaction elsewhere (Ng.
Soc Indic Res (2008) 85:425–446 DOI 10.1007/s11205-007-9135-1

Environmentally Responsible Happy Nation Index: Towards an Internationally Acceptable National Success Indicator Yew-Kwang Ng

Accepted: 25 April 2007 / Published online: 5 July 2007  Springer Science+Business Media B.V. 2007

Abstract Amidst increasing attention to happiness studies by economists, the New Economics Foundation launched in July 2006 the Happy Planet Index (Marks et al. 2006). This is the ratio of the average happy life years (HLY) to the per capita ecological footprint of the country concerned. HLY is in turn the product of the average happiness (or life satisfaction) index and the life expectancy index. Some essential revisions to this index are proposed to reach an internationally acceptable national success indicator that aims positively at long and happy lives but negatively at the external costs of environmental disruption. Hopefully, this ‘environmentally responsible happy nation index’ will lead to some re-orientation of both the market and national governments towards something more fundamentally valuable. Keywords Happiness  Life satisfaction  Subjective well-being  National success indicators  Green GDP  Environmental costs  Happy life years  Happy planet index

1 Introduction For many decades, some measures of national income (GDP, GNP and its per capita values) have been used as a comprehensive achievement or success indicator of a nation, at least in the economic sphere. The inadequacy of such income measures prompted the development of improvements or alternative measures, including the proposals in the 1970s for taking account of leisure time and pollution, a ‘genuine progress indicator’ (Halstead 1998; Hamilton 1998), a ‘measure of domestic progress’ (Jackson 2004), and the A keynote speech based on this paper was presented at the 4th Biennial Conference of the Hong Kong Economic Association on 15 Dec. 2006. Y.-K. Ng (&) Department of Economics, Monash University, Clayton 3800, Australia e-mail: [email protected]

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launching by the United Nations of the Human Development Report in 1990 (which provides the Human Development Index, the Gender-related Development Index, the Gender Empowerment Measure, and the Human Poverty Index). The Human Development Index combines indices of life expectancy, education, and PPP-adjusted GDP per capita. While such indices are certainly relevant, they are still inadequate. Thus, it is ‘perfectly possible ... to be well-educated, free of illness and rich, but miserable and lonely’ (Marks et al. 2006, p. 6). At least from the time of Aristotle,1 it has been recognized that the ultimate objective of life is happiness.2 A recent reaffirmation of this from a prominent economist is Ian Little who goes as far as saying not only that happiness is the final criterion but also that happiness or ‘welfare can trump justice’ (see the interview of Ian Little by Pattanaik and Salles 2005, p. 364, 368). This may appear excessive by some but I think it is very reasonable and fundamental. In my view, ultimately speaking, injustice is the denial of due happiness (or the inflicting of unhappiness). However, readers do not have to go as far as Little to agree that happiness is an important (if not the most important or the only ultimate) objective in life. Thus, the recent focus on happiness and its relationships with economic and other variables by economists (e.g. Di Tella and MacCulloch 2006; Frey and Stutzer 2002a; Layard 2005; van Praag & Ferrer-i-Carbonell 2004) is to be welcome. Sociologists and other researchers have also devised various measures of quality of life (QOL) indicators (see Hagerty et al. 2001 for a review and a set of criteria for evaluating the indexes). However, as emphasized by Hajiran (2006, pp. 33–34), ‘Improving QOL is just ‘‘a means’’ and not ‘‘an end’’ in itself. The ultimate goal of improving QOL is to maintain and enhance the scope, depth and intensity of human well-being or ‘‘happiness’’. Thus, good QOL indicators should reflect this ultimate goal. More subjective indices have also been proposed, from Andrews and Withey (1976) and Campbell et al. (1976) to Diener (2000), Cummins et al. (2003), and Kahneman et al. (2004). For an individual, happiness or welfare3 is probably the most important ultimate objective. However, at the national and global level and for the welfare of the future, we have to consider two related factors, the possible costs imposed on others and the sustainability of the situation. The most important variable here is the environmental disruption imposed on our life support systems. Probably in this spirit, the New Economics Foundation recently (12 July 2006) launched the (un) Happy Planet Index (Marks et al. 2006; available at http://www.happyplanetindex.org). This index is calculated for each nation and is given by the ratio of the average happy life years (HLY) and the per capita ecological footprint of the nation concerned. HLY is in turn the product of the average (over people in the relevant nation) happiness (or life satisfaction) index and the life 1

In China, similar thinking was recorded around the similar period, i.e. 3–4 centuries B.C.

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It is true that philosophers are not in consensus about whether what Aristotle meant by happiness is the same as the psychological happiness used by virtually all happiness researchers; Aristotle and some later philosophers may require some element of virtue for true happiness (see, e.g. Haybron 2000). However, I strongly believe that (1) virtually 99% of the lay public take happiness to be psychological happiness, and (2) ultimately speaking, virtue is the contribution to future and/or others’ happiness anyway. Elsewhere, I discuss this and related issues such as the difference between happiness and life satisfaction elsewhere (Ng 2007). Here, these issues are abstracted away.

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These two terms are used interchangeably here. In everyday usage, happiness probably refers to current situation while welfare refers to the long-term. For any given time period, the two are the same. If I am very happy over a certain period, my welfare over that period must be high. On the other hand, ‘utility’ which represents ‘preference’ may differ from happiness due to ignorance (including imperfect foresight), a concern for the welfare of others, and imperfect rationality; see Ng (1999).

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expectancy index. Or, Happy Planet Index (HPI) = Life Satisfaction  Life Expectancy/ Footprint = HLY/Footprint. It is thus a ‘new measure that shows the ecological efficiency with which human well-being is delivered’ (Marks et al. 2006, p. 2). We do not really want just a single index. For different purposes, different indices may be relevant. For example, even just for purely economic production, even after we know the GDP figure, we may still want to know the figure for say total output of cars. On the other hand, some indices could certainly be improved. For example, the gross material production index used by the former centrally planned economies that involves double counting over different stages of production (e.g. wheat, flour, and bread all counted at their full values, not just values added) but that excludes services can be seen to be inferior to the modern concept of national income or product. The former index had thus been discarded. It is the purpose of this paper to propose some improvements over certain measures and to advance a measure of national success indicator that takes into account the ultimate objective of life and the external costs imposed on others and on the future. In particular, this paper argues that • Happiness is the ultimate objective of rational individuals and is the acceptable ultimate criterion in judging social states and is a good national success indicator (Sect. 2).4 • Cardinal and interpersonally comparable happiness indices are possible (Sect. 2; aimed at sceptical economists in particular). • The existing method of calculating the measure of HLY should be revised to give a more accurate figure (Sect. 3). • A revised index called the environmentally responsible happy nation index (ERHNI) is proposed as an internationally more acceptable national success indicator (Sect. 4) and calculated for the various countries with the relevant data (Sect. 5). • ERHNI = revised HLY—per capita external costs.

2 Is Happiness Measurable and Interpersonally Comparable? We produce in order to be able to consume the products. We consume in order to stay alive and to enjoy life. We want to enjoy life to obtain happiness. We want happiness for its own sake. Being happy may also make us more productive or even healthier. However, being more productive or healthier are ultimately useful to us precisely because they increase our happiness. Thus, happiness is the ultimate objective. It may be thought that some people sacrifice their happiness and their lives to achieve higher objectives like safeguarding their countries or discovering new knowledge and creating arts of distinction. For such people, is happiness not secondary, less important and less ultimate than such other objectives? True, heroic soldiers may sacrifice their lives to safeguard their countries from invasion. However, such sacrifices are admirable and rational precisely because the happiness of their fellow citizens will be increased by more than the sacrificed happiness. Thus, if we reckon in terms of not just the happiness for oneself but possibly also for others (including

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Only some brief statements are made here. A fuller argument on this is in Ng and Ho (2006) where it is also argued that happiness should be preferred over others such as life satisfaction.

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possibly animals), then such sacrifices do not negate the principle that happiness is the ultimate objective. Similarly, the pursuit of knowledge and creativity may entail much hardship and sacrifice. However, such pursuits, if rational, should also bestow more happiness, including: 1. the joy of such pursuits both during the process of doing so and in achieving the desired outcomes; 2. the higher levels of happiness for other people made possible by the success of such pursuits. We cannot deny the possibility that, for some or even many cases, the sacrifices and pursuits mentioned in the previous two paragraphs may result in more unhappiness (or loss of happiness) than the happiness they generates. If the person involved does not know that this will be the case, the welfare-decreasing sacrifice/pursuit is explained by ignorance or imperfect foresight. If he knows it and still insists on doing the welfare-decreasing act, he is being irrational. I define irrational preferences/actions/choices as preferring/doing something that decreases the happiness of oneself due neither to ignorance nor the concern for the (greater) happiness of others. This reasonable definition makes irrationality, together with ignorance and the concern for the welfare of others, exhaustive causes of the divergence between preference and welfare.5 Even if it is agreed that happiness is the ultimately valued thing and is relevant for assessing social states and in serving as a success indicator, it may still be regarded as not measurable. In particular, economists are very sceptical of the accuracy in the measurement of happiness and typically deny the interpersonal comparability of happiness or any other subjective measures. This is mainly due to the following four reasons. First, most economists work with the positive problems of describing, explaining, and predicting objective economic variables and phenomena, less with the evaluation of the desirability of different social states or outcomes. For the positive problems, economists know that we need only to assume that individuals can rank alternative consumption bundles (or other alternatives) consistently. With some additional conditions (either the countability of the relevant set of alternatives, continuity of preferences, or some preference-denseness assumption), the ranking or preference-ordering of an individual can be represented by a utility function. However, only the ordinal aspect of the utility function is needed, or that the function is unique only to a positive/increasing transformation. Whether the same successively higher indifference curves are labelled (or represent) utility numbers of 10, 20, 30, ... or 1, 100, 100.1, ..., or even minus 100, 1000, 1000.001, ... does not matter as the demand curve/function derived from the income-constrained utility maximization of the individual is the same in all these cases. Thus, the individual preferences positive economists work with need not be cardinally measurable, rankings or orderings are sufficient. This makes economists very sceptical of cardinally measurable subjective variables. This scepticism may be misplaced. True, for positive analysis of objective economic variables like production and consumption, no cardinal measurability of utility is needed. However, policy (including economic policy) evaluation often necessitates the comparison of the gains of some individuals with the losses of others. Thus, for making social choices involving Pareto non-comparable alternatives, interpersonal comparisons of individual cardinal utilities are needed (Mueller 2003, ch. 23; Ng 2000, ch. 2). Second, most economists trust what people do rather than what their say (‘cheap talk’). If an individual is willing to pay from her own pocket to actually buy a certain item, economists are willing to accept that she values that item at least at the price paid. If she 5

See Ng (2000, 2007) for a more detailed argument for happiness as the ultimate objective.

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just says that she values certain thing at a certain value, economists are generally sceptical. Since most if not all existing measurements of happiness are based on how happy people say they are in questionnaire surveys, economists are thus sceptical of their reliability. This scepticism has some validity. However, there are persuasive arguments that existing measures, though imperfect, are rather reliable. For example, different measures of happiness correlate well with one another (Fordyce 1988), with recalls of positive versus negative life events (Seidlitz et al. 1997), with reports of friends and family members (Costa and McCrae 1988; Diener 1984, Sandvik et al. 1993), with physical measures like heart rate and blood pressure measures (Shedler et al. 1993), with EEG measures of prefrontal brain activity (Sutton and Davidson 1997), and with more objective measures of well-being like incidence of depression, poor appetite and sleep (Luttmer 2005). Pavot (1991) finds that respondents reporting that they are very happy tend to smile more. Di Tella and MacCulloch (2000) note that psychologists who study and give advice on happiness for a living use happiness data. ‘Presumably, if markets work and there was a better way to study well-being, people who insist on using bad data would be driven out of the market’ (pp. 7–8). Moreover, correlations of happiness show remarkably consistency across countries, including developing and transitional (Graham and Pettinato 2001, 2002; Namazie and Sanfey 2001). Dominitz and Manski 1999 examine the scientific basis underlying economists’ hostility against subjective data and found it to be ‘meager’ and ‘unfounded’. Rather, ‘survey respondents do provide coherent, useful information when queried systematically’; see Manski (2000, p. 132.). Despite remaining problems of happiness measurement (see, e.g. Schwarz and Stracek 1999; Bertrand and Mullainathan 2001), reported happiness indices may be used as good approximations (Frey and Stutzer 2002b) and ‘happiness surveys are capturing something meaningful about true utility’ (Di Tella and MacCulloch 2006, p. 28). For those economists who are still sceptical or even look down upon and deride at the happiness measures, I call upon them to look at their own backyard. Consider the most important economic variable GDP or GNP. Its measurement is subject to all sorts of inaccuracies, as is well-known to all economists. We used the imperfect measure for many decades. Then came the PPP (purchasing power parity) adjustment which overnight increased the Chinese GDP by 4 times and the Indian GDP by 6 times from this single adjustment alone! Most happiness measures may not be very accurate but I doubt that a 4times adjustment will ever be necessary for the average figure of any nation. Third, though researchers working with risk and/or uncertainty mostly accept the weakly cardinal utility indices (unique up to a linear transformation) obtained by the von Neumann and Morgenstern expected utility hypothesis, these utility indices do not have a well defined zero needed for full cardinal measurability.6 Length is a fully cardinal concept, with only the unit of measurement arbitrary. (The English use foot and the French use meter.) As discussed in the next section, some uses of happiness indices require full cardinal measurability. This makes most economists sceptical. Does the level of happiness have a well-defined zero? It is sensible for someone to say, ‘Had I known the sufferings I had to undergo, I would have committed suicide long ago’, or ‘If I had to lead such a miserable life, I would wish not to have been born at all!’ Hence, it makes sense to speak of negative or positive happiness/welfare. Thus, somewhere in the middle, there is something corresponding to a zero amount of happiness. ‘There can be little doubt that an individual, apart from his 6

Some researchers even question the relevance of the VNM indices for subjective utility/welfare. However, the relevance is established in Ng (1984).

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attitude of preference or indifference to a pair of alternatives, may also desire an alternative not in the sense of preferring it to some other alternative, or may have an aversion towards it not in the sense of contra-preferring it to some other alternative. There seem to be pleasant situations that are intrinsically desirable and painful situations that are intrinsically repugnant. It does not seem unreasonable to postulate that welfare is +ve in the former case and ve in the latter’ (Armstrong 1951, p. 269). This is also effectively the conclusion of Kahneman et al. 1997. Hence it seems clear that happiness or welfare as a subjective feeling is in principle measurable in a full cardinal sense. Fourth, the cardinal measurability of happiness does not imply its interpersonal comparability. At least since the influential methodological essay of Robbins (1932), economists are very hostile to the interpersonal comparisons of utility/welfare. In particular, the interpersonal and intertemporal comparability of existing happiness indices is particularly questionable. For one thing, people now may require a larger amount of subjective happiness before describing themselves as ‘very happy’. Thus, despite a possibly substantial increase in happiness, the percentage of people describing themselves as ‘very happy’ may not have increased. This must be accepted as an inadequacy of existing measures of happiness. However, this does not make happiness intrinsically incomparable interpersonally. To overcome the difficulty of incomparability, Ng (1996) develops a method that yields happiness measures that are comparable interpersonally, inter-temporally, and internationally.7 It is based on Edgeworth’s concept of a just perceptible increment of happiness, but developed to be operational and actually used to conduct an actual survey/ measurement. Edgeworth took it as axiomatic, or, in his words ‘a first principle incapable of proof’, that the ‘minimum sensible’ or the just perceivable increments of pleasures for all persons, are equitable (Edgeworth 1881, pp. 7ff., 60 ff.). (Ng 1975) derived this result as well as the utilitarian social welfare function (SWF), that social welfare is the unweighted sum of individual utilities/welfares, from more basic axioms.

3 Revising the Measurement of Happy Life Years Since happiness (or other similar measures like life satisfaction) is measured for a given period (like a week or a year) but an individual may live a short or a long live, the happiness index itself does not give the total amount of happiness enjoyed over the whole lifespan. The concept of HLY (Veenhoven 1996, 2005) is conceived to overcome this problem. Conceptually, HLY is just the product of the average happiness index over the lifespan and the length of the lifespan (of life expectancy). For any given average happiness level (assumed positive), everyone would like to live a longer than a shorter life. Thus, HLY is an important extension of the measurement of happiness. However, an important revision in the actual measurement of HLY is needed. As measured by Veenhoven (2005), happy-life-years = happiness (index in the range of 0–1) times life-expectancy at birth (in years). The happiness index is converted from the normal index in the range 0–100 or 0–10 into 0–1, e.g. an index of 50 (out of 100) is converted into 0.5. This measure has the following problem. It is well known that, for a scale of 0–10, the figure of 5 typically corresponds to the level of neutrality in net happiness. Since the level of overall net happiness can be either positive or negative (an individual can be happy or unhappy), and since the figure of 50% is typically used as the 7

There are of course alternative proposals to deal with the difficulty, e.g. Stone et al. (1999) favour the use of momentary assessment and Larsen and Fredrickson (1999) favour the use of multiple measures.

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bare passing grade in schools, most people also habitually use the figure of 5 out of 10 or 50 out of 100 to represent the level of zero net happiness as is also the case for the curve in Figure 1. (Some surveys explicitly locate the neutrality point at 5.) The average happiness indices of most nations in the world fall within the range of 4–8 (out of 10). Consider the following two situations: • A. An average happiness index of 4 (out of 10) with a lifespan of 90, giving a HLY index of 36 (with the happiness index normalized to the range 0–1 as in Veenhoven’s measure); • B. An average happiness index of 7 (out of 10) with a lifespan of 50, giving a HLY index of 35. Which situation or life experience would you rather have? Since an index of 5 means neutrality (neither happy nor unhappy), the index of 4 really means unhappiness, with negative net happiness, or unhappiness more than happiness. For such a life, it is better to have a shorter than a longer life, as a longer life really mean longer suffering. Thus, most people will definitely choose B over A. However, existing measure of HLY rank A higher than B. This is very misleading. This difficulty can be easily overcome. We should count only the value over neutrality as being valuable. In the above example, the adjusted HLY for the two situations are calculated as: • (4 – 5) times 0.1 (conversion to the scale of 0–1) times 90 = 9. • (7–5) times 0.1 times 50 = 10. The superiority of B over A can then be reflected by the positive index of 10 for B over the negative figure of minus 9 for A. Without the above adjustment, the problem still remains even if no happiness index below the neutrality level of 5 is involved. Consider: • C. An average happiness index of 5.1 with a lifespan of 100, giving an unadjusted HLY index of 51. • D. An average happiness index of 8 with a lifespan of 60, giving an unadjusted HLY index of 48. Again, most people would rather have a very happy life of 60 years rather than a barely worth living life of 100 years. The adjusted HLY gives: • C: (5.1–5) times 0.1 times 100 = 1. • D: (8–5) times 0.1 times 60 = 18. This shows a clear superiority of D over C as consistent with the preferences of most people. As mentioned above, the Happy Planet Index (Marks et al. 2006) of the New Economics Foundation is the ratio of the average HLY and the per capita ecological footprint of the country concerned. The measurement of HLY follows Veenhoven’s method plus certain normalization. The method of normalization is questionable. As explained in Appendix 3 of Marks et al. (2006), a lower goalpost of 25 years and an upper one of 85 years are selected for life expectancy. These two figures are then normalized as 0 and 1, respectively. The intermediate figures are transformed linearly accordingly. For example, the mid point of 55 years corresponds to the figure of 0.5. The idea is to standardize the range of 25–85 years into the interval of 0–1. This is highly unsatisfactory, especially

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when the normalized lifespan is used to multiply with the happiness index to give the measure of HLY. Under this normalization, a person (or nation) with a very high happiness index (say 9) and a lifespan of 25 years is given a HLY of zero. Another person with a much lower happiness index and a lifespan of 25.1 years is given a much higher index. A lifespan of 25 years is 25 years even if it is just the lower goalpost. Thus, it should be a lifespan of 0 year that should be normalized at zero. The upper goalpost of 85 years is also questionable. With medical advance and healthier lifestyle, it may be only a matter of decades when some countries will enjoy an average life expectancy of over 85 years. Thus, if an upper goalpost is needed, a minimum of 100 years should be selected. Better still, no upper goalpost is needed. If both the above adjustments are done, is the adjusted HPI an appropriate national success indicator?

4 Towards an International Acceptable National Success Indicator As a ‘measure that shows the ecological efficiency with which human well-being is delivered’ (Marks et al. 2006, p. 2), HPI after the adjustment of the HLY component seems appropriate, though the measurement of the ecological footprint may be continuously improved. However, if our purpose is to have a national success indicator that encourages lower costs on other nations and the future of the whole world, it is debatable whether the per capita ecological footprint is an appropriate divisor. The measure of Ecological Footprint used for the calculation of HPI was taken from the Living Planet Report (Loh and Wackernagel 2004). It measures people’s natural resource consumption. ‘A country’s footprint is the total area required to produce the food and fibre that it consumes, absorb the waste from its energy consumption, and provide space for its infrastructure. People consume resources and ecological services from all over the world, so their footprint is the sum of these areas, wherever they are on the planet’ (Loh and Wackernagel 2004, p. 4). Thus, if America exports highly priced human skills (with low footprint) in exchange for goods with high footprint from other countries, it will be the consuming America, not the producing countries, that registers high footprint figures. Similarly, if America possesses many resources domestically and use them to produce a lot of food for home consumption, America will register a high footprint figure for using large areas of land for production and consumption, even if no environmental disruption is involved either domestically or globally in this production. Thus, while the existing concept of HPI may serve certain purposes, it may be more relevant to have a different national success indicator that is internationally more acceptable. Since happiness is the ultimate objective in life, having the adjusted HLY as the positive part seems appropriate. For the negative part, instead of using the ecological footprint indices defined and measured in the Living Planet Report, some measure of the per capita external costs (PCEC) imposed on the global community may be more appropriate.8 Moreover, it is debatable whether we should take the ratio of the positive and negative parts (i.e. with the positive part in the numerator and the negative part in the denominator) or just the sum of the two parts (i.e. the positive part minus the absolute value of the negative part). Consider the following two alternatives: 8

As pointed out to me by James Mirrlees, the HPI also ignores the accumulation of capital (we may also add ‘contribution to knowledge’). For our proposed measure discussed below, we could either specifically adjust for this or include capital accumulation and contribution to knowledge as generating (positive) external benefits on others (including the future) which may be subtracted from external costs.

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• E. An individual (nation) achieves a HLY of 80 and imposes a (per capita) total external cost of 20 (in comparable units as HLY). • F. An individual (nation) achieves a HLY of 1 and imposes a (per capita) total external cost of 0.1. If we use ratio, situation E will have an index of 4 and situation F an index of 10. If we use summation, situation E will have an index of 60 and situation F an index of 0.9. If all nations can achieve situation E (before considering the negative effects of the per capita external cost of 20), then after counting the external cost of 20, every nation on earth will still have an HLY of 60. On the other hand, if all nations achieve situation F, people on earth will on average end up with an HLY of 0.9. Clearly, situation E is a much superior outcome and is the one to be encouraged in comparison to situation F. Thus, the ratio index is not suitable for this purpose. In fact, the net happiness of an individual is the amount of positive happiness less the amount of negative happiness (Kahneman et al. 1997). To provide an appropriate national success indicator, it seems natural to use the summation (or difference) method.9 We then have, ERHNI = Adjusted HLYper capita external costs where ERHNI = Environmentally Responsible Happy Nation Index. The index for PCEC measures the aggregate costs imposed on the global community by the nation concerned in per capita terms. Note that the ‘per capita’ here is in the sense of per person-within the home nation. Thus, if a nation of 1 billion persons imposes a total environmental costs on the rest of the world (including the future) equivalent to 6 billion adjusted HLY, its per capita external cost is not 0.1 (6 billion/60 billion) but rather 6 (6 billion/1 billion). Thus, ERHNI measures the amount of happy life years a nation achieves for an average person less the per capita costs imposed externally on the global community. A nation that achieves a high HLY and a low PCEC (and hence a high ERHNI) not only entails high HLY for its own residents but also imposes low (per capita) costs externally (on others). Since the main form of external costs is probably environmental disruption, the index is called ‘environmentally responsible happy nation index’ (ERHNI). If ERHNI is accepted as a measure of national success, governments and people in different nations around the world will not only each strive to achieve a high level of HLY but will also strive to lower the costs imposed on others. This will enhance the ability of each nation to achieve a high HLY index and the ability of the world to sustain a high HLY more permanently. If we sum the two terms (on the right hand side of the above equation) to get ERHNI, the two terms have to be in comparable units. Since the relevant external costs are on the whole world including the effects in the future, we cannot expect to have a very accurate estimate. However, starting with some imperfect estimates (or even just guestimates) may have the advantage of leading to more accurate estimates. It is better to be roughly right on 9

A possible issue is whether the equality in happiness should be taken into account. In my view, inequality in income is undesirable both because of the diminishing marginal utility of income and because of the indirect undesirable effects of inequality in reducing happiness through for example reducing social cohesion. However, since happiness is already the ultimate objective, we can neither have diminishing marginal happiness of happiness nor further indirect effects, except in an intertemporal framework where the happiness in the future has not yet been accounted for. (Either accounting for this intertemporal effect or ignoring it, an objective function that is not linear in individual happiness can be shown to violate some compelling axiom, i.e. treating a perceptible increment of happiness as less important than a less than a perceptible one; see Ng 1984.) Moreover, the argument for the utilitarian social welfare function (Ng 2000, ch. 5) supports not taking into account inequality in the ultimate objective. Also, Ott (2005) shows that higher average happiness tends to go with higher equality in happiness.

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important things than to be perfectly accurate on things that are irrelevant. Since environmental disruption is clearly a very important issue that may even threaten our survival, it is imperative that we have some national success indicator that gives sufficient recognition of the negative environmental costs. The concept of green GDP takes some account of this. However, recent happiness studies (see summaries in e.g. Frey and Stutzer 2002a; Kahneman and Krueger 2006; Layard 2005) show that, at the social level (where individual relative competition cancels out) incomes above a relatively low level does not increase happiness (at least not to any significant extent). Thus, income is inferior to happiness as the ultimate national success indicator. Second, depending on the particular method of adjustment (from the traditional GDP), the measure of green GDP may mainly emphasize the environmental effects on the country concerned, while the concept of ERHNI emphasize the costs imposed on others and the future. Also, though we propose to start with environmental costs, the concept of external costs in ERHNI need not be confined to environmental costs. Once we shift from income to happiness, the environmental costs internal to the country concerned is already largely reflected in the measure of HLY of that country, though some effects on the future may still not be fully captured. Thus, for the measure of ERHNI, we focus on the environmental costs external to that nation and imposed on the world. What should be included under external costs could be further discussed. However, we may start with the global environmental costs imposed by a nation. Before a more comprehensive measure of PCEC or its main component per capita global environmental costs has been calculated, we may use the figures for CO2 emissions calculated by the United Nations’ Department of Economic and Social Affairs as the proxy. This is based on the reasoning that the most important global environmental cost is probably that of global warming which is threatening the sustainability of the whole life support system of the whole world. CO2 is the principal greenhouse gas. Thus, using CO2 emissions may be a better proxy for external costs than the full ecological footprint which includes both external and non-external items. In any case, our calculation is mainly illustrative. When a more appropriate figure for the external cost index is available, it could be used instead.

5 Estimating the Environmentally Responsible Happy Nation Index The ERHNI may be estimated for the various nations in the world if sufficient data are available. If we wait until we have perfect data, we will wait forever. Partly for the purpose of illustration and partly to kick start the endeavour, the ERHNI index is calculated for each country based on very rough estimates of the relevant variables. For each individual nation, ERHNI = Adjusted HLYper capita external costs (PCEC).10 The calculation of adjusted HLY is explained in Sect. 3 above. Some of the raw data are from similar sources as the calculation of the Happy Planet Index, i.e. the data for the life expectancy at birth in 2003 are from the UN Human Development Report 2006, and the life satisfaction data are from the Happy Planet Index which is based on a number of sources including Ruut Veenhoven’s World Database of Happiness. (See Marks et al. 2006, Appendix 2.) The details of the method used to calculate the PCEC index is discussed in the appendix. A brief outline is given here. An estimate of the number of premature deaths from air pollution is used as a starting point. (This is why it can be in a 10 This equation does not apply strictly to the global and regional average figures due to slight differences obtained through different routes of averaging.

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comparable unit as that for HLY.) From this, the total costs (including premature death and the lowering of the QOL while still living) from both air pollution and other environmental disruption are estimated. Then only a portion is taken as external (i.e. to other nations). Some adjustments are also made to make the index comparable to the Adjusted HLY. The per capita contribution of each nation is taken to be proportional to its per capita CO2 emission. The emission data are taken from the Millennium Development Goals Indicators by the United Nations’ Department of Economic and Social Affairs. The results of the estimation are reported in Table 1. (Readers who want to compare with the Happy Planet Index may access the latter at http://www.happyplanetindex.org). Adjusted HLY = (Life Sat 5) times 0.1 (to convert into a scale of 0–1) times Life Exp. The external cost ratio of a nation is the per capita CO2 emission of this nation to that of the world average. The ERHNI equals Adjusted HLY minus the per capita external cost (PCEC; measured in comparable units to HLY; see the appendix for details). The current estimation of this includes only the global environmental disruption imposed on other nations (including effects in the future). For a minority of nations (mainly African and former communist countries) which have a life satisfaction of less than 5 (out of a range between 0 and 10), the adjusted HLY itself is already negative (i.e. people are on average unhappy). Their ERHNI values are even more negative. A nation with a positive but low adjusted HLY and high PCEC may still have a negative ERHNI which means that the happiness level of its people on average is lower than the PCEC imposed externally on other nations, calculated in a comparable unit as adjusted HLY. However, for most nations, the ERHNI values are positive and average (weighted by population) to a global value (only for nations included in Table 1; this applies to all averages reported in Table 1) of nearly 4.7.11 Since life satisfaction is converted into a range between 0 and 10, and since only values above the mid point of neutrality is counted, one unit of ERHNI value is equivalent to two years of perfect happiness (10 times 0.1 times 2 = 1), or 10 years of an unadjusted life satisfaction value of 6 [(6–5) times 0.1 times 10 = 1]. A representative individual in the world (really only nations included in Table 1) lives for about 67– 68 years, and enjoys an unadjusted life satisfaction value of about 6 and imposes an external environmental costs on other nations (including the future) equivalent to about 1/3 of his own (net) happiness. Our estimates show that nations with low ERHNI indices are mainly African and former communist countries (with their poverty and difficulties of transition, respectively), due more to their low life satisfaction figures than their high external costs. In Asia, only Pakistan has a negative figure and no nation in Western Europe and (North and Latin) Americas has a negative index. This is partly because our estimate of PCEC is conservative or has a significantly downward bias. However, although our conservative estimate of external costs does not turn the ERHNI of these nations into a negative figure, it nevertheless gives a different picture than just the figures for HLY. (It also differs significantly from the Happy Planet Index.) For example, in North America, Canada and the USA have very similar values in terms of life satisfaction, but Canada has an ERHNI value (11.3) significantly higher that of the USA (8.064) due to a lower per capita total CO2 emission (and hence our estimate of PCEC) of Canada than USA. Nations in Western Europe which mostly have emission figures even lower than Canada, register high ERHNI figures, taking 11 This average is calculated from the weighted average of the ERHNI of the various nations. If we calculate the ERHNI of the world based on the average figures of the global average values of Life Satisfaction, etc., the figure will be somewhat different due to the well-known difference of different routes of averaging.

123

436

Y.-K. Ng

Table 1 Calculation of ERHNI (Using CO2 emissions as proxies for external costs) Country

Life Sat

Life Exp

Adjusted HLY

External cost ratio

PCEC

ERHNI

World Weighted average Africa Weighted average

5.93

67.47

6.816

0.9109

2.111

4.705

4.99

50.90

0.119

0.2568

0.595

0.477

1

Algeria

5.20

71.10

1.422

1.1776

2.729

1.307

2

Angola

4.80

40.80

0.816

0.1313

0.304

1.120

3

Benin

5.40

54.00

2.160

0.0591

0.137

2.023

4

Botswana

5.40

36.30

1.452

0.5327

1.234

0.218

5

Burkina Faso

4.70

47.50

1.425

0.0192

0.044

1.469

6

Burundi

3.00

43.60

8.720

0.0077

0.018

8.738

7

Cameroon

5.10

45.80

0.458

0.0515

0.119

0.339

8

Central African Rep.

4.90

39.30

0.393

0.0146

0.034

0.427 2.187

9

Chad

4.50

43.60

2.180

0.0029

0.007

10

Congo

5.70

52.00

3.640

0.0838

0.194

3.446

11

Congo, Dem. Rep.

3.30

43.10

7.327

0.0076

0.018

7.345

12

Cote d’Ivoire

4.50

45.90

2.295

0.0744

0.172

2.467

13

Egypt

4.80

69.80

1.396

0.4493

1.041

2.437

14

Eritrea

4.40

53.80

3.228

0.0396

0.092

3.320

15

Ethiopia

4.70

47.60

1.428

0.0228

0.053

1.481

16

Gabon

6.20

54.50

6.540

0.2090

0.484

6.056

17

Gambia, The

5.70

55.70

3.899

0.0450

0.104

3.795

18

Ghana

6.20

56.80

6.816

0.0836

0.194

6.622

19

Guinea

5.10

53.70

0.537

0.0341

0.079

0.458

20

Guinea-Bissau

5.40

44.70

1.788

0.0414

0.096

1.692

21

Kenya

5.60

47.20

2.832

0.0615

0.142

2.690

22

Libya

5.70

73.60

5.152

2.0443

4.738

0.414

23

Madagascar

5.80

55.40

4.432

0.0304

0.071

4.361

24

Malawi

4.60

39.70

1.588

0.0164

0.038

1.626

25

Mali

5.30

47.90

1.437

0.0099

0.023

1.414

26

Mauritania

5.30

52.70

1.581

0.1980

0.459

1.122

27

Mauritius

6.50

72.20

10.830

0.5903

1.368

9.462

28

Morocco

5.60

69.70

4.182

0.2843

0.659

3.523

29

Mozambique

5.40

41.90

1.676

0.0189

0.044

1.632

30

Namibia

6.50

48.30

7.245

0.2686

0.623

6.622

31

Niger

4.50

44.40

2.220

0.0212

0.049

2.269

32

Nigeria

5.50

43.40

2.170

0.0950

0.220

1.950

33

Rwanda

4.40

43.90

2.634

0.0157

0.036

2.670

34

Senegal

5.60

55.70

3.342

0.0997

0.231

3.111

35

Sierra Leone

5.00

40.80

0.000

0.0292

0.068

0.068

36

South Africa, Rep.

5.70

48.40

3.388

1.7798

4.125

0.737

37

Sudan

3.60

56.40

7.896

0.0591

0.137

8.033

38

Swaziland

4.20

32.50

2.600

0.2117

0.491

3.091

39

Tanzania, United Rep.

5.50

46.00

2.300

0.0236

0.055

2.245

40

Togo

4.90

54.30

0.543

0.0863

0.200

0.743

123

Towards an Internationally Acceptable National Success Indicator

437

Table 1 continued Country

Life Sat Life Exp Adjusted HLY External cost ratio PCEC

ERHNI

World Weighted average Africa Weighted average

5.93

67.47

6.816

0.9109

2.111

4.705

4.99

50.90

0.119

0.2568

0.595

0.477

41 Tunisia

6.40

73.30

10.262

0.4840

1.122

9.140

42 Uganda

4.70

47.30

1.419

0.0146

0.034

1.453

43 Zambia

4.90

37.50

0.375

0.0446

0.103

0.478

44 Zimbabwe

3.30

36.90

6.273

0.2044

0.474

6.747

Middle east and Central Asia 5.83

69.17

5.768

1.2025

2.787

2.982

1 Armenia

Weighted average

3.70

71.50

9.295

0.2586

0.599

9.894

2 Azerbaijan

4.90

66.90

0.669

0.8057

1.867

2.536

3 Georgia

4.10

70.50

6.345

0.1871

0.434

6.779

4 Iran

6.00

70.40

7.040

1.2828

2.973

4.067

5 Israel

6.70

79.70

13.549

2.4189

5.606

7.943

6 Jordan

5.10

71.30

0.713

0.7239

1.678

0.965

7 Kuwait

7.20

76.90

16.918

7.1254

16.513

0.405

8 Kyrgyzstan

6.60

66.80

10.688

0.2370

0.549

10.139

9 Lebanon

1.444

5.60

72.00

4.320

1.2408

2.876

10 Saudi Arabia

7.30

71.80

16.514

2.9720

6.888

9.626

11 Syria

5.10

73.30

0.733

0.6191

1.435

0.702

12 Tajikistan

6.10

63.60

6.996

0.1678

0.389

6.607

13 Turkey

5.30

68.70

2.061

0.7080

1.641

0.420

14 Turkmenistan

4.00

62.40

6.240

2.1151

4.902 11.142

15 United Arab Emirates 7.40

78.00

18.720

7.6809

17.801

0.919

16 Uzbekistan

6.40

66.50

9.310

1.0974

2.543

6.767

17 Yemen

6.20

60.60

7.272

0.1984

0.460

6.812 5.158

Asia-Pacific 5.91

68.15

6.440

0.5534

1.283

1 Australia

Weighted average

7.30

80.30

18.469

4.1154

9.538

8.931

2 Bangladesh

5.70

62.80

4.396

0.0581

0.135

4.261

3 Cambodia

5.60

56.20

3.372

0.0090

0.021

3.351

4 China

6.30

71.60

9.308

0.7309

1.694

7.614

5 India

5.40

63.30

2.532

0.2727

0.632

1.900

6 Indonesia

6.60

66.80

10.688

0.3113

0.721

9.967

7 Japan

6.20

82.00

9.840

2.2104

5.123

4.717

8 Korea

5.80

77.00

6.160

0.7975

1.848

4.312

9 Lao PDR

5.40

54.70

2.188

0.0507

0.117

2.071

10 Malaysia

7.40

73.20

17.568

1.4675

3.401

14.167

11 Mongolia

6.70

64.00

10.880

0.7079

1.640

9.240

12 Myanmar (Burma)

5.30

60.20

1.806

0.0438

0.102

1.704

13 Nepal

5.50

61.60

3.080

0.0260

0.060

3.020

14 New Zealand

7.40

79.10

18.984

2.0196

4.680

14.304

15 Pakistan

4.30

63.00

4.410

0.1725

0.400

4.810

123

438

Y.-K. Ng

Table 1 continued Country

Life Sat

Life Exp

Adjusted HLY

External cost ratio

PCEC

ERHNI

World Weighted average Africa Weighted average

5.93

67.47

6.816

0.9109

2.111

4.705

4.99

50.90

0.119

0.2568

0.595

0.477

16

Papua New Guinea

6.30

55.30

7.189

0.1018

0.236

6.953

17

Philippines

6.40

70.40

9.856

0.2201

0.510

9.346

18

Singapore

6.90

78.70

14.953

2.5972

6.019

8.934

19

Sri Lanka

6.10

74.00

8.140

0.1158

0.268

7.872

20

Thailand

6.50

70.00

10.500

0.8930

2.070

8.430

21

Vietnam

6.10

70.50

7.755

0.2128

0.493

7.262

Latin America and the Caribbean Weighted average

6.57

71.94

11.382

0.5516

1.278

10.104

1

Argentina

6.80

74.50

13.410

0.7692

1.783

11.627

2

Belize

6.90

71.90

13.661

0.6896

1.598

12.063

3

Bolivia

5.50

64.10

3.205

0.2049

0.475

2.730

4

Brazil

6.30

70.50

9.165

0.3771

0.874

8.291

5

Chile

6.50

77.90

11.685

0.8407

1.948

9.737

6

Colombia

7.20

72.40

15.928

0.2879

0.667

15.261

7

Costa Rica

7.50

78.20

19.550

0.3475

0.805

18.745

8

Cuba

6.30

77.30

10.049

0.5160

1.196

8.853

9

Dominican Rep.

7.00

67.20

13.440

0.5654

1.310

12.130

10

Ecuador

5.60

74.30

4.458

0.4139

0.959

3.499

11

El Salvador

6.60

70.90

11.344

0.2258

0.523

10.821

12

Guatemala

7.00

67.30

13.460

0.2043

0.474

12.986

13

Haiti

5.50

51.60

2.580

0.0481

0.111

2.469

14

Honduras

7.20

67.80

14.916

0.2161

0.501

14.415

15

Jamaica

7.00

70.80

14.160

0.9354

2.168

11.992

16

Mexico

6.90

75.10

14.269

0.9141

2.118

12.151

17

Nicaragua

6.30

69.70

9.061

0.1702

0.394

8.667

18

Panama

7.20

74.80

16.456

0.4428

1.026

15.430

19

Paraguay

6.50

71.00

10.650

0.1613

0.374

10.276

20

Peru

5.60

70.00

4.200

0.2208

0.512

3.688

21

Trinidad and Tobago

6.90

69.90

13.281

5.0634

11.734

1.547

22

Uruguay

6.30

75.40

9.802

0.2935

0.680

9.122

Venezuela

7.40

72.90

17.496

1.2787

2.963

14.533

Weighted average

7.42

77.65

18.792

4.4936

10.414

8.378

Canada

7.60

80.00

20.800

4.0993

9.500

11.300

USA

7.40

77.40

18.576

4.5360

10.512

8.064

23

North America 1 2

Western Europe Weighted average

7.06

79.06

16.276

1.8956

4.393

11.883

1

Austria

7.80

79.00

22.120

1.9781

4.584

17.536

2

Belgium

7.30

78.90

18.147

1.8922

4.385

13.762

123

Towards an Internationally Acceptable National Success Indicator

439

Table 1 continued Country

Life Sat Life Exp Adjusted HLY External cost ratio PCEC

ERHNI

World Weighted average Africa Weighted average

5.93

67.47

6.816

0.9109

2.111

4.705

4.99

50.90

0.119

0.2568

0.595

0.477

3 Denmark

8.20

77.20

24.704

2.3148

5.365

19.339

4 Finland

7.70

78.50

21.195

2.9797

6.905

14.290

5 France

6.60

79.50

12.720

1.4287

3.311

9.409

6 Germany

7.20

78.70

17.314

2.2354

5.181

12.133

7 Greece

6.30

78.30

10.179

1.9923

4.617

5.562

8 Ireland

7.60

77.70

20.202

2.3672

5.486

14.716

9 Italy

11.135

6.90

80.10

15.219

1.7624

4.084

10 Luxembourg

7.60

78.50

20.410

5.0240

11.643

8.767

11 Netherlands

7.50

78.40

19.600

2.0008

4.637

14.963

12 Norway

7.40

79.40

19.056

2.2561

5.229

13.827

13 Portugal

6.10

77.20

8.492

1.2706

2.945

5.547

14 Spain

7.00

79.50

15.900

1.6822

3.899

12.001

15 Sweden

7.70

80.20

21.654

1.3464

3.120

18.534

16 Switzerland

8.20

80.50

25.760

1.2819

2.971

22.789

17 United Kingdom

7.10

78.40

16.464

2.1602

5.006

11.458

Cenral and eastern Europe 4.62

68.47

2.358

1.8696

4.333

6.691

1 Albania

4.60

73.80

2.952

0.2253

0.522

3.474

2 Belarus

Weighted average

4.00

68.10

6.810

1.4536

3.369 10.179

3 Bosnia and Herzegovina 5.10

74.20

0.742

1.1193

2.594

1.852

4 Bulgaria

72.20

5.054

1.2868

2.982

8.036 3.954

4.30

5 Croatia

5.90

75.00

6.750

1.2066

2.796

6 Czech Rep.

6.40

75.60

10.584

2.6058

6.039

4.545

7 Estonia

5.10

71.30

0.713

3.1165

7.222

6.509

8 Hungary

5.70

72.70

5.089

1.3155

3.049

2.040

9 Latvia

4.70

71.60

2.148

0.6607

1.531

3.679

10 Lithuania

4.70

72.30

2.169

0.8411

1.949

4.118

11 Macedonia, FYR

4.90

73.80

0.738

1.1910

2.760

3.498

12 Moldova, Rep.

3.50

67.70

10.155

0.3916

0.908 11.063

13 Poland

5.90

74.30

6.687

1.8094

4.193

2.494

14 Romania

5.20

71.30

1.426

0.9556

2.215

0.789

15 Russian Federation

4.30

65.30

4.571

2.3674

5.487 10.058

16 Slovakia

5.40

74.00

2.960

1.5938

3.694

0.734

17 Slovenia

6.60

76.40

12.224

1.7955

4.161

8.063

18 Ukraine

3.60

66.10

9.254

1.5177

3.517 12.771

123

440

Y.-K. Ng

six out of the top ten nations reported in Table 2, with Switzerland and Denmark heading the list. Leading nations in the Asia-Pacific area are New Zealand, Malaysia, Indonesia, Philippines, and Mongolia, as listed in the lower part of Table 2.

6 Concluding Remarks It is true that the existing happiness or life satisfaction measures are not perfectly accurate and the external costs measures are also very rudimentary and incomplete. However, we did not wait for the measure of GNP to be improved by the green adjustments, the PPP adjustments, etc. before using it. We also did not wait for the measures of happiness and life satisfaction to be perfected before using them. Recent happiness studies show that income is a poor correlate with happiness, especially at the social level. Our ultimate objective is really happiness rather than income. Thus, having a measure of national success in terms of some appropriate measure of happiness is very important. Moreover, just shifting to happiness alone is not sufficient. If each of the 200 or so nations in the world strives to increase the happiness level of its own people without sufficient check on the external costs imposed on the rest of the world, we may still have the tragedy of the commons. The increasing urgency of environmental protection has become clear. Recently (mid September 2006), this was emphasized by a diverse group of distinguished personalities and organizations including former US Vice-President Al Gore (reported to ‘begin training 1,000 Climate Project volunteers to help spread his environmental message around the globe’), British billionaire Richard Branson (who recently put up US$3 billion to combat global warming), and the Royal Society of the UK (which in a recent announcement said that man-made global warming is as certain as gravity and evolution). Thus, each nation Table 2 Top scores in ERHNI

Country

ERHNI

World Weighted average

4.705

1

Switzerland

22.789

2

Denmark

19.339

3

Costa Rica

18.745

4

Sweden

18.534

5

Austria

17.536

6

Panama

15.430

7

Colombia

15.261

8

Netherlands

14.963

9

Ireland

14.716

Venezuela

14.533

10

Asia-Pacific Average

123

5.158

1

New Zealand

14.304

2

Malaysia

14.167

3

Indonesia

9.967

4

Philippines

9.346

5

Mongolia

9.240

Towards an Internationally Acceptable National Success Indicator

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striving to produce more with no adequate regard to environmental protection may spell disaster for the whole of mankind. However, the public are still somewhat skeptical about the reality and seriousness of global warming. A recent survey shows that while 100% of the 10% sample of all scientific papers on the subject are unanimous on global warming, reports in the mass media are divided half and half between the believers and the doubters. This is not surprising since, as reported in the news on 3 January 2007, ExxonMobil Corp. gave US$16 million to 43 ideological groups between 1998 and 2005 in an effort to mislead the public by discrediting the science behind global warming, as disclosed by the Union of Concerned Scientists. A desirable national success indicator should take into account not only the (average) happy live years achieved for its own people, but must also take into (negative) account the external costs (only global environmental disruption is taken into account in this paper, but the concept could be extended) imposed on others (including the future). The ERHNI is proposed to serve this purpose. The calculation of this for the various nations reported in the last section is based on very rough and incomplete estimates. Nevertheless, it is hoped that, with further improvements, it will lead to some re-orientation of both the market and national governments towards something more fundamentally valuable and less damaging to our life support system. Acknowledgements The author has benefited from the comments made after the speech, especially from the lengthy discussion with James Mirrlees. The author is also grateful to Lok-sang Ho, Murray Kemp, Andrew Oswald, Bernard van Praag for commenting on the first draft, and to Cora Ng for assistance in the calculation of figures reported in Tables 1 and 2. The author also wishes to thank the referee of this paper for helpful comments.

Appendix Estimating the Per Capita Global Environmental Costs An accurate calculation of the global environmental costs, including the effects on future generations, is clearly impossible. However, ignoring these costs due to the difficulties of calculation/estimation is more unsatisfactory than trying to estimate or even guestimate at best we could. In this spirit, and mainly as an illustration, this appendix provides a very rough estimate. Due to the lack of information/data and the limitation of the pursuit of a single individual, it is certainly not accurate and exhaustive. As a Chinese proverb says, it is merely an attempt to attract jade by throwing out bricks. It is hoped that improvements will be made and some international organizations may take up the challenge in doing more adequate estimates. There are alternative ways of estimating the per capita global environmental costs in a way comparable to the index of (adjusted) HLY. One way is to estimate the per capita global environmental costs in dollar terms and then convert HLY into comparable dollar terms. This conversion could be based on the dollar values of life estimated in the relevant literature. However, while most economists will be comfortable with this method, most non-economists may find the use of monetary valuation of life objectionable. Thus, the following alternative may be more generally acceptable. This is to estimate the per capita global environmental costs in comparable unit as HLY. This could be done by estimating

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the premature death from environmental disruption and add on other costs in proportion to premature death. According to a study in 2003 by the Chinese Academy on Environmental Planning, more than 400,000 people in China die prematurely annually from air pollution alone (Terradaily 2006). This is said by the relevant researcher to be a ‘conservative figure’. In addition, keeping in mind that ‘China’s rapid economic growth and industrialization is posing a major challenge to the environment with air pollution likely to rise 5-fold in 15 years’ (Terradaily 2005), the estimate based on it below is likely to have a very conservative bias. Starting from the figure of 400,000 premature deaths annually in China from air pollution, we may estimate the global external environmental costs by the following extensions. Define a : the average number of life years lost due to one premature death. For example, if the premature deaths mostly ranges from about one to twenty years with distribution concentrated more in the first half of the range, the average number of years lost might be around 6. Define b : the number of annual premature deaths due to environmental disruption (in China) other than air pollution as a proportion of annual premature deaths due to air pollution. Then, the total number of life years lost each year due to all environmental disruption in China is estimated to be 400,000 a(1+b). Presumably, environmental disruption does not only make people die early but also reduces the quality of life of the living. Define c : the welfare costs of this reduction in the QOL as a proportion to the value of premature deaths. We then have the total yearly environmental costs in terms of life years lost = 400,000 a(1+ b)(1+ c). To compare the value of life years lost with the value of the living life years, we may need an adjustment factor (: f). If the level of happiness is about the same throughout the whole life, no such adjustment is needed, or that the adjustment factor should just be equal to one. If the lost years would have been happier (less happy) years than previous living years, f > 1 (f < 1). While most people tend to believe that a person is likely to have less happiness when old than when young and middle aged, some happiness studies show a Ushaped relationship between happiness and age. People are on average happy in childhood but happiness decreases from the time of teen ages, reaching a trough at around thirty before increasing again. Moreover, happiness levels reach much higher levels than childhood even until the 70s. (See, e.g. Lacey et al. 2006.) It is likely that happiness level may decrease during the last few years of life (see Pinquart 2001 on the decline of positive feelings for the old-old). Considering these opposing factors, perhaps we may take the adjustment factor f to be not much different from one. In any case, we have the total yearly environmental costs in terms of adjusted life years lost = 400,000 a(1+b)(1+ c)f. Since this is an annual value, to convert it into a lifetime value (for comparison with HLY) on a per capita basis, we multiply it with the life expectancy (71.6) and divide the resulting figure by the population (1,286,975,468) of China in 2003. This gives the estimated environmental costs in terms of the proportion of the average HLY of a representative person as 0.0222537 times a(1+ b)(1+ c)f. As this is the figure for China and includes both the costs imposed externally from other nations and internally from China itself, we need another two adjustment factors to convert into the average external environmental costs generated by a representative nation on other nations. Defining g : the ratio of the figure for a representative nation to that for China of the per capita environmental costs in terms of the proportion of the average HLY of a representative person, and h : the ratio of the external portion to the full portion of this index of costs. (By definition h < 1, and g is likely to be smaller than one as the environmental disruption situation in China may be taken to be worse than the world average.) This yields

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k ¼ 0:0222537 times að1 þ bÞð1 þ cÞfgh where k : estimated per capita external environmental costs in terms of the proportion of the average HLY of a representative nation. In addition, we need another adjustment factor to take into account the effects on future generation. If we are already in a steady state with respect to environmental disruption, the effects we (i.e. the current generation in some abstract sense, ignoring the overlapping nature of generations) impose on the future equals the effects previous generations imposed on us. Then the relevant adjustment factor n = 1. However, due to increasing production, it is almost certain that we are on a path of increasing environmental damages, making n > 1. Here, n is the proportion of the per capita external environmental costs we impose on ourselves and the future generations to those imposed by previous generation and ourselves on ourselves. This adjustment factor is necessary as k estimated above is the costs on the current generation (from environmental disruption of previous generations and the current generation). However, what we want to measure is the costs generated by the current generation on both the current and the future generations. The effects on the future could be appropriately discounted in view of the uncertainty in the realization of future welfare. However, this only affects the size of n, not the need to multiply k by n. We now have our final index l ¼ kn ¼ 0:222537 times að1 þ bÞð1 þ cÞfghn where l : estimated per capita (of existing population generating the costs, not on affected population) external environmental costs (in terms of the proportion of the average HLY of the current generation) that we impose on ourselves and the future generations. The value of l depends of course on a, b, c, f, g, h, and n. An adequate estimate of these variables is beyond the scope of this paper and probably beyond the capability of any individual researcher. Hopefully, some organizations may take up the challenge some time.12 However, for the purpose of illustration, some likely values of these variables may be proposed/discussed and used for the illustrative calculation of the relevant indices for the various nations, as done in Sect. 5 in the text. As mentioned above, the value of a (average number of life years lost due to premature death from air pollution in China) may be around 6; let us take a conservative figure of 4. As environmental disruption consists of many forms in additional to air pollution, including water pollution, desalination, deforestation, etc., the value of b (the number of annual premature deaths due to environmental disruption in China other than air pollution as a proportion of annual premature deaths due to air pollution) is unlikely to be much less than 1. Let us assume a conservative figure of 0.7. Though the reduction in the QOL due to air pollution is not as costly (in terms of happiness forgone) as death, this reduction applies over one’s whole lifetime. Hence, c (the welfare costs of this reduction in the QOL as a proportion to the value of premature deaths) is again unlikely to be much smaller than one. Again, we assume a conservative figure of 0.8. As discussed above, f may be taken as around one. As mentioned above, g (the ratio of the figure for a representative nation to that for China of the per capita environmental costs) is likely to be less than one. Let us assume a figure of just 0.5. Similarly, take h (the ratio of the external portion to the full portion of the index of costs) to be 0.5. This is again likely to be a very conservative figure since the 12 The estimation of course need not start with China. Then, the various adjustment factors will be correspondingly different.

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value we estimate is for the effects on future generations as well, the external portion is likely to be quite large. Last, the value of n (the proportion of the per capita external environmental costs we impose on ourselves and the future generations to those imposed by previous generation and ourselves on ourselves) is likely to be very large. This is so partly because much of the costs will be imposed on many generations to come and partly because our current disruption could cumulate to threaten the very survival of the whole mankind, implying sacrificing the welfare of many generations. Let us assume a conservative figure of 5. Under these assumed values, we have l ¼ 0:0222537 times 15:3  0:34 In other words, according to the above conservative estimate, on average, a representative nation imposes each year a per capita environmental costs on other nations (including the future) equal to about 0.34 HLY of a representative person. About a third of the happiness achieved is offset by the costs on people in other nations (not including people in her own country but including people in other nations in the future). This proportion of 34% may appear to be very high. However, since it includes the effects on all future generations, it is really a very conservative estimate, given that our environmental disruption now, if not substantially curtailed, may well cumulate into disasters that may destroy the welfare of all future generations. The figure of 34% is the estimate for a representative nation. To estimate the figure for each specific nation, we may use the per capita contribution to external environmental costs of that nation as a proportion of the average per capita contribution for the whole world. We need of course much more information to give the per capita external environmental costs of the various nations. However, as a very rough estimate for illustrative purposes and to get people interested in this very important exercise, we may provisionally use the per capita total CO2 emission for each nation calculated in the Millennium Development Goals Indicators. This differs from the total ecological footprint (also calculated in the Living Planet Report) used in the calculation of the happy planet index discussed in the text. The total ecological footprint is the sum of the total energy footprint and the total food, fibre and timber footprint. I choose to use just the total CO2 emission on the ground that CO2 emission involves more global environmental disruption. The production and consumption of food, etc. need not give rise to significant external costs on other nations unless very environmental unfriendly methods are used. In any case, as our estimation is meant to be no more than an illustration, further explorations on the more appropriate indices to use may be forthcoming. The per capita external cost of a nation is calculated as 0.34 times its per capita total CO2 emission divided by the average per capita total CO2 emission of the world. The resulting estimates of ERHNI for the various nations are reported in Sect. 5 in the text. References Andrews, F. M., & Withey, S. B. (1976). Social indicators of well-being: Americans’ perceptions of life quality. New York: Plenum Press. Armstrong, W. E. (1951). Utility and the theory of welfare. Oxford Economic Papers, 3, 257–271. Bertrand, M., & Mullainathan, S. (2001). Do people mean what they say? Implications for subjective survey data. American Economic Review, 91(2), 67–72. Campbell, A., Converse, P. E., Rodgers, W. L. (1976). The quality of American life: perceptions, evaluations, and satisfactions. New York: Russell Sage Foundation.

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