3.1. On the value of QALYs and DALYs
The finding of Helliwell et al. (2020a) that differences in health only explains 15% of the difference in wellbeing could be applied to the 0.52 QALY/person-year difference between current and potential wellbeing from Table 1, implying that health impacts should only make up around 15%*0.52 = 0.078 QALY/person-year on average, or 0.6 billion QALY when applied to the global population of 7.7 billion. The global burden of disease according to IHME (2021) is 2.5 billion DALY, which would imply a conversion factor of 2.5/7.7 = 0.24 QALY/DALY. Considering that only a part of the global burden of disease is avoidable (Weidema and Fantke 2018) moves this factor slightly upwards to around 0.3 QALY/DALY. It is nevertheless a surprising finding that there is such large a difference between the two concepts that may intuitively be thought to be congruent. By combining the country-level data on avoidable health from IHME (2021) and the subjective wellbeing data from Helliwell et al. (2020a), we confirmed that for many countries, an assumption of 1 QALY/DALY would result in implausible counterfactual levels of wellbeing, exceeding 1 QALY per person-life-year.
Table 1
Global average level of subjective wellbeing (impacts, current, and potential) in QALY/person-life-year
| Instrumental values | Intrinsic activity benefits | Sum of instrumental and intrinsic values | Comments |
Sum value for all impacts | 0.132 | 0.387 | 0.520 | Sum of all current impacts on wellbeing as quantified in Weidema (2022b), see Annex 1 |
Current wellbeing | 0.110 | 0.329 | 0.438 | Cantril scores from Helliwell (2020a) divided by 10 and adjusted for the QALY value of current years of life lost over the maximum expected lifetime*; distributed 0.25/0.75 over instrumental and intrinsic values as in Fig. 2(a) |
Potential wellbeing | 0.242 | 0.716 | 0.958 | Sum of the above rows |
*) The adjustment is made by subtracting 0.3*(LEmax - LEactual)/LEmax, where LE is the current life expectancy at birth in years, LEmax is 94 years, and 0.3 is the QALY loss for a Year of Life Lost (YLL) due to premature death, considering the conversion of YLL = DALY = 0.3 QALY described in the text. |
Table A1
Distribution of global annual impacts, expressed in million QALY, over the impacts that are attributable to specific activities and non-production-specific impacts. Data for year 2019 from Weidema (2022b).
Impacts | Attributable to specific activities | Non-production-specific |
Sub-soil resource use | 2.5 | |
Marine biomass and biodiversity | 8.7 | |
Freshwater biomass and biodiversity | 9.3 | |
Freshwater resources, overexploitation | 0.2 | |
Freshwater resources, untreated wastewater | 0.3 | |
Terrestrial biomass and biodiversity | 35.5 | |
Underinvestment in physical infrastructure | | 8.7 |
Property damage, from disaster, avoidable | | 0.6 |
Property damage, due to global warming | 0.2 | |
Property damage, air pollution | 0.3 | |
Property damage, amenity value | 3.5 | |
Tangible cultural heritage | 4.9 | |
Property theft, burglary, and related anxiety | | 1.3 |
Health impacts, avoidable, global warming | 18.9 | |
Health impacts, avoidable, undernutrition | 83.5 | 19.7 |
Health impacts, avoidable, clean water and sanitation | 17.6 | 31.6 |
Health impacts, work-related psycho-socially caused | 56.7 | |
Health impacts, avoidable, violence and disasters | | 14.8 |
Health impacts, avoidable, drugs misuse and self-harm | | 55.3 |
Health impacts, avoidable, n.e.c. | 289.0 | 143.0 |
No access to contraceptives | | 6.6 |
Unwanted pregnancies | | 78.0 |
Restrictions on civil liberties | | 116.0 |
Inadequate access to social security | 11.0 | |
Insufficient development of skills | 77.1 | 411.5 |
Impacts on cognitive skills, undernutrition | 37.0 | 8.7 |
Impacts on cognitive skills, lead exposure | 29.8 | |
Impacts on cognitive skills, alcohol misuse | | 1.7 |
Impacts on cognitive skills, child maltreatment | | 68.3 |
Unfair commercial practices | 2.7 | |
Tax avoidance | 2.4 | |
Trade barriers | 18.1 | |
Excess profits and distortions from taxation | | 55.0 |
Distortionary subsidies | 14.3 | |
Labour market monopsony | | 31.9 |
Forced labour | 12.7 | |
Inadequate working conditions | 134.1 | |
Foregone benefits of migration | | 7.1 |
Rent-seeking | 45.7 | |
Current conflicts, except health impacts | | 9.7 |
Intimate partner violence, excl. health impacts | | 80.9 |
Violence against children, excl. health impacts | | 20.4 |
Underinvestment in intellectual infrastructure | | 180.9 |
Discrimination | | 606.4 |
Unemployment | | 10.6 |
Inequality in wellbeing | | 461.4 |
Participation restrictions | | 659.0 |
Incarceration | | 2.6 |
Sum (sums to 4’007 mio. QALY) | 894 | 3113 |
Table A2
Sum of non-production specific impacts per country. Data for year 2019 from Weidema (2022a).
Country name | Country code (ISO3) | Non-production-specific impacts [million QALY] | Non-production-specific share of all impacts in country |
World, total | | 3112.24 | 0.78 |
Afghanistan | AFG | 24.49 | 0.79 |
Algeria | DZA | 19.40 | 0.84 |
Angola | AGO | 17.34 | 0.81 |
Argentina | ARG | 14.53 | 0.80 |
Bangladesh | BGD | 64.35 | 0.78 |
Brazil | BRA | 59.76 | 0.77 |
Burkina Faso | BFA | 7.01 | 0.59 |
Cambodia | KHM | 6.76 | 0.77 |
Cameroon | CMR | 10.28 | 0.71 |
Canada | CAN | 7.56 | 0.72 |
Chile | CHL | 6.18 | 0.80 |
China | CHN | 572.77 | 0.81 |
Colombia | COL | 14.76 | 0.80 |
Congo, Dem. Rep. | COD | 41.59 | 0.78 |
Cote d'Ivoire | CIV | 9.17 | 0.70 |
Egypt, Arab Rep. | EGY | 49.53 | 0.83 |
Ethiopia | ETH | 55.48 | 0.79 |
France | FRA | 15.31 | 0.73 |
Germany | DEU | 16.77 | 0.68 |
Ghana | GHA | 13.18 | 0.79 |
India | IND | 782.48 | 0.81 |
Indonesia | IDN | 107.73 | 0.81 |
Iran, Islamic Rep. | IRN | 34.30 | 0.77 |
Iraq | IRQ | 16.84 | 0.81 |
Italy | ITA | 15.42 | 0.74 |
Japan | JPN | 38.81 | 0.77 |
Kenya | KEN | 25.39 | 0.82 |
Madagascar | MDG | 12.54 | 0.76 |
Malawi | MWI | 9.98 | 0.80 |
Malaysia | MYS | 12.57 | 0.83 |
Mexico | MEX | 35.08 | 0.74 |
Morocco | MAR | 15.23 | 0.81 |
Mozambique | MOZ | 13.07 | 0.75 |
Myanmar | MMR | 25.37 | 0.79 |
Nepal | NPL | 10.19 | 0.74 |
Niger | NER | 6.81 | 0.53 |
Nigeria | NGA | 83.73 | 0.68 |
Pakistan | PAK | 71.70 | 0.66 |
Peru | PER | 10.40 | 0.80 |
Philippines | PHL | 32.51 | 0.75 |
Poland | POL | 10.86 | 0.75 |
Russia | RUS | 54.33 | 0.79 |
Rwanda | RWA | 7.48 | 0.82 |
Saudi Arabia | SAU | 8.86 | 0.71 |
South Africa | ZAF | 26.14 | 0.82 |
South Korea | KOR | 16.26 | 0.79 |
South Sudan | SSD | 6.64 | 0.78 |
Spain | ESP | 11.88 | 0.73 |
Sri Lanka | LKA | 10.79 | 0.86 |
Sudan | SDN | 21.16 | 0.80 |
Syrian Arab Republic | SYR | 10.03 | 0.87 |
Tanzania | TZA | 31.78 | 0.78 |
Thailand | THA | 21.99 | 0.78 |
Turkey | TUR | 36.74 | 0.85 |
Uganda | UGA | 19.10 | 0.74 |
Ukraine | UKR | 19.16 | 0.79 |
United Kingdom | GBR | 11.94 | 0.63 |
United States | USA | 68.06 | 0.66 |
Uzbekistan | UZB | 10.52 | 0.76 |
Venezuela, RB | VEN | 11.95 | 0.82 |
Vietnam | VNM | 37.17 | 0.82 |
Yemen, Rep. | YEM | 13.51 | 0.75 |
Zambia | ZMB | 10.70 | 0.82 |
Zimbabwe | ZWE | 9.43 | 0.81 |
Rest of World | | 239.39 | |
The finding of Helliwell et al. (2020a) that log income explains 23% of the difference in average wellbeing, could be used a basis for converting the contribution of income in monetary units to units of wellbeing. This approximate conversion rate can also be confirmed from the episodic (moment-to-moment) affect data collected by Krueger (2007) and Gershuny (2013). A useful insight from these data is the difference in positive affect experienced between leisure and work activities, which is around 0.25 on a 0–1 scale (after conversion from Krueger’s 0–6 scale and the 0–10 scale used by Gershuny). This observed difference in positive affect between leisure and work activities can be combined with the suggestion of Juster et al. (1981) to express SWB as the sum of the instrumental value of income (equal to the value added generated from work) and the intrinsic activity benefit, i.e., the positive affect from performing or taking part in specific activities. In the illustration in Fig. 1, the activity benefits are divided into those experienced during work (i.e., beyond the value of the work outputs) and those experienced from pure leisure.
Figure 1(a) illustrates the division of SWB into intrinsic activity benefits and instrumental benefits. The instrumental benefits are represented by the value added to the work outputs (VA) as assessed by the value they ultimately add to the time spent in their consumption, i.e., VA = VF.; see Fig. 1(b). The trade-off between pure leisure and work implies that the marginal activity benefit from pure leisure (VL ) must equal the sum of income and marginal activity benefit from work:
VL = VA + VW (Eq. 1)
and since subjective wellbeing (Q) is the sum
Q = VL + VA + VW (Eq. 2; note that the sum does not include VF, to avoid double-counting value added)
inserting Eq. 1 in Eq. 2 obtains
Q = 2 VL
VL = Q/2
VW = VL - VA = Q/2 - VA.
Expressed relative to subjective wellbeing Q = 1:
VL = Q/2 = 0.5
VW = 0.5 - VA.
Re-arranging Eq. 1, we see that the contribution from income (VA) must equal the difference between the activity benefit of leisure and work:
VA = VL - VW.
Now VL - VW = 0.25 can be inserted from the above-cited data of Krueger (2007) and Gershuny (2011) to obtain the values in Fig. 2(a).
Figure 2 Distribution of intrinsic and instrumental benefits derived from combining the trade-off between pure leisure and work with episodic positive affect data from Krueger (2007) and Gershuny (2011). In (a) the values of the shaded cells sum to Q = 1, while (b) shows the values relative to the value added from production (VA = 1). Note that the sums do not include VF, since the instrumental benefits shall only be included once (either when earned or when spent).
Figure 2 (b) express the same relationships as in Fig, 2 (a), but relative to VA = 1, with the implication that
Q = 4 * VA (Eq. 3)
signifying that total SWB has an approximate size of four times the value added of production, thus providing a basis for expressing marginal subjective wellbeing (QALYs) in monetary units, and vice versa.
The above findings from SWB research provide an important correction to Weidema (2009), where wellbeing was seen as exclusively related to consumption activities and as inseparably linked to production through the budget constraint, implying that the value of wellbeing was limited to be a mirror of the value of production, actually only describing the relationship between the two aspects of instrumental benefits in Fig. 1(a), i.e., VA = VF. Furthermore, the focus on the budget constraint led to the counter-intuitive result that the same percentage-wise change in instrumental productivity benefits and intrinsic activity benefits would provide the same change in utility (wellbeing). This would mean that, e.g., a 10% reduction in wellbeing could potentially be compensated by a 10% increase in income. In Weidema (2018), it was suggested to solve this problem by applying a different basis for calculation of equity-weights for changes in wellbeing versus changes in productivity and consumption only (i.e., without any concurrent change in wellbeing), resulting in a ratio of a QALY to value added of
Q = 2.36 * VA (compare to Eq. 3)
However, this solution is blemished by the fact that the ratio is dependent on the number of income steps used in its determination (the value 2.36 reflects a normative choice of three income steps between zero and the current income level), and also maintains the fixed relationship – although no longer 1:1 – between intrinsic activity benefits and instrumental productivity benefits, i.e., it implies an assumption that a change in intrinsic activity benefits will always be accompanied by a change in productivity (Weidema 2009). More fundamentally, it implies an unfortunate dependency between the valuation of intrinsic activity benefits and the valuation of inequality. All of these points of critique do not apply to the more comprehensive expression for the value of a QALY using the relationship between the intrinsic activity benefits and the instrumental benefits from value added, as described above and illustrated in Figs. 1 and 2.
Note that the value of a QALY is quite sensitive to changes in the explanatory variable of preference for income (VA). A change from 0.25 to 0.23 increases the conversion factor from 1/0.25 = 4.00 to 1/0.23 = 4.35 times the value added of production. Previous studies have provided both even lower values for the explanatory variable, giving implausibly high values for non-income effects (Fujiwara and Campbell 2011), and much higher values (e.g., Fritjers et al. 2004, Sacks et al. 2010), which are similarly implausible because they would imply an exceedance the already rather large empirically observed difference in activity benefit between leisure and work activities illustrated in Fig. 2. So, while the preferences for income (VA), work benefits (VW), and pure leisure benefits (VL) may change independently over time and between persons, the given relationships expressed in Fig. 1(b) and Eq. 1 provide good reasons to expect that, at the population level, average changes in relative preferences will be moderate.
On the basis of data from the European Social Survey and the Gallup World Poll, as analysed by Helliwell et al. (2020b), inequality of wellbeing is found to explain a difference of 0.35 Cantril scores (0.035 QALY/person-year) in wellbeing between countries, and to provide a more consistent explanation for the average levels of SWB than inequality in income. This is explained by changes in social connections and the quality of social institutions being of larger importance as buffers against wellbeing impacts for people with the lowest levels of well-being, so that happiness increases more for those, thereby reducing inequality.
For an entire population it will obviously never be possible to reach an average level of 10 on the Cantril ladder or 1 QALY per person-life-year, due to unavoidable life-events, such as natural disasters, deaths of close relatives and friends, or unavoidable diseases. Nevertheless, Table 1 shows how the value of the current impacts on SWB is clearly close to and constrained by the theoretical limit of 1 QALY per person-life-year.
[Table 1 here]
3.2. The consequences for the social footprint methodology
This section first summarises the original social footprint methodology from Weidema (2018), and then describes the adjustments required to integrate the findings from SWB research, as described in the previous section.
The original social footprint methodology (Weidema 2018), is a streamlined methodology, in that it allows quantification of impacts based exclusively on:
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Value added per skill-level, industry, and country;
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Workhours per skill-level, industry, and country;
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A country-specific correction factor for purchasing power (PP) applied to the value added;
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An estimated global potential productivity (value added per workhour);
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An estimated global elasticity of marginal utility of income.
The social footprint of a change in value added of a specific activity is then derived by:
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Calculating the country-specific productivity impact as difference between the potential productivity and the country-specific productivity after subtraction of productivity impacts that are attributable to specific activities;
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Calculating the industry-specific productivity impact as the share of the country-specific productivity impact proportional to the industry’s share in the country-specific value added;
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Calculating the group-specific productivity ratio of the population group affected by the change as the ratio of the global average productivity to the population-group specific productivity;
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Equity-weighting the industry-specific productivity impact by multiplying the impact by the group-specific productivity ratios raised to the power of the global elasticity of marginal utility of income, and finally deriving the equity-weighted social footprint by subtracting the likewise equity-weighted value added (representing the impact of monetary re-distribution).
The findings from SWB research described in the previous section implies that:
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The social footprint should be calculated from the country-specific impacts on wellbeing instead of solely from the country-specific and global productivity impacts;
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The use of equity-weighting should be limited to function as a distribution key for overall SWB impacts within the constraints of the findings from SWB research, such as the relatively low preference for income relative to intrinsic activity benefits indicated in Fig. 2 and the relatively low preference for health improvements relative to overall wellbeing improvements.
More concretely, this implies in terms of new data inputs:
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Using the value of 0.958 QALY/person-life-year from Table 1 as the global potential level of wellbeing (replacing the global potential productivity);
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Using the country-specific Cantril scores from the World Happiness Report (divided by 10 to convert to the 0–1 QALY scale, and modified by lost years of life expectancy) as the country-specific levels of wellbeing;
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Using the country-specific proportion between non-production-specific wellbeing impacts and wellbeing impacts that are attributable to specific activities, based on Weidema (2022a); see Annex 1;
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Using a conversion factor of 1 QALY per 388’000 USD2019 based on the updated global potential productivity of 97’000 USD2019 per person-life-year from Weidema (2022b) and the Q = 4 * VA from Eq. 3.
In terms of the calculation routine, the starting point is now Table A2 in Annex 1, where the sum of non-production specific impacts for each country is shown. As described in the original social footprint method (Weidema 2018), a co-responsibility for these impacts exists for local enterprises because they benefit from the current low internal costs of labour. These impacts should thus be distributed over the industries in proportion to their responsibility. A simple distribution relative to the value added of the industries, i.e., mimicking a value added tax, would punish industries that actually do pay a fair wage. The social footprint method therefore makes the distribution in a somewhat more complex way, including the differences in wage levels between and within industries, with the intention to give more weight to those industries that have low-paid employees. From data on wages and numbers of work hours, provided per industry and skill level in databases such as EXIOBASE (Stadler et al. 2018), the wage/work-hour can be calculated. The wage data make up only part of value added. However, in absence of detailed data on tax re-distribution, taxes and subsidies on products and production are ignored, and the assumption is made that the operating surplus will end up with the same population groups as the wages, i.e., proportionally to the wages. With this assumption, the direct sources of income (compensation of employees + operating surplus) per country can now be divided over industries and divided over wage-groups (income-groups) for each country. In the resulting direct income matrix for each country, each cell DIi,g where subscript i indicates industry and subscript g indicates income-group is then weighted with an equity-weight EW, where WH is Work Hours and subscript c is country):
DIi,g*EWi,g = DIi,g*((DIc/WHc)/(DIi,g/WHi,g))^1.24
The justification for the specific equity-weighting is provided in Weidema (2018). However, in Weidema (2018) the World average wage level is used for the equity-weighting, while here, the country average wage level is used, as justified above.
Finally, DIi,g*EWi,g is used as distribution key for the non-production-specific QALYs for each country (from Table A2) to the cells in the matrix, and thus to each industry. The resulting vector is a vector of ‘Non-production-specific impacts’ per industry that does not need any further characterisation of weighting because it is already expressed in QALY, and may be directly applied for the calculation of footprints, using the normal Life Cycle Assessment calculation routines (Heijungs and Suh 2002).
An example of the above calculation for implementation in the year 2011 hybrid version of EXIOBASE can be found in Weidema (2022a).