For users who are interested in the algorithm assumptions and calculations that guide WellWorth, you’ll find that information here.
Conversion of wellbeing indicators
Subjective Wellbeing is traditionally measured using a number of indicators and scales, and so do the studies analyzing the link between wellbeing and its co-benefit. It is therefore important to have a mechanism allowing conversion of one indicator into the other, in this way increasing in the potential of our tool. This is obviously the second best solution, as different wellbeing indicators are designed to measure different aspects of wellbeing. Moreover, it has been shown that positive and negative wellbeing indicators are not simply “the opposite ends of a continuum” (Cohen et al. 2003, p. 652) and actually capture different elements of wellbeing. Ideally, we would need the relationship between wellbeing and its co-benefit to be studied using a number of indicators. However, this is seldom the case and most studies focus just on a single or a few wellbeing indicators.
To overcome this problem WellWorth includes a conversion tool, which allows you to translate any wellbeing indicator into life satisfaction points. The conversion tool exploits the results from Mukuria et al. (2015) of the Policy Research Unit in Economic Evaluation of Health and Care Interventions and from the Cross-Cutting Group (2015). These studies take life satisfaction as a reference indicator and, using data drawn from several population surveys in the UK, measure the exchange rate (i.e. correlation) between several wellbeing measures.
|Life satisfaction (0-10)||1||1|
|ONS-4 total (0-40)||0.91||0.25||HIPO|
|GHQ positive (0-18)||-0.40||-0.42||US|
|GHQ negative (0-18)||-0.48||-0.30||US|
|SF-6D (from SF-12) (0-1)||0.51||9.15||MIC|
|SF-6D (from SF-36) (0-1)||0.48||9.22||MIC|
|EQ-5D-5L (-0.6 – 1)||0.63||5.65||HIPO|
Source: Mukuria et al. (2015) and Cross-Cutting Group (2015)
STD indicates correlation coefficient between Life satisfaction and the other wellbeing measures. UNSTD indicates number of units change in Life satisfaction following a unit increase in the selected wellbeing measure (Cross-Cutting Group elaboration).
Data Sources used by Mukuria et al. (2015): HIPO (Health Improvement and Patient Outcomes); MIP (Multi Instrument Comparison); SYC65 (South Yorkshire Cohort over 65); US (Understanding Society).
Core assumptions behind the tool
Time frames of impact
An important assumption made by WellWorth is that the increase in wellbeing following the wellbeing intervention will persist over the entire time frame of the analysis. This might of course sound unrealistic give that the preferences of individuals are formed in response to their circumstances and are dynamic ie change over time according to the external environment. However, taking into account the dynamic evolution of preferences would involve making more arbitrary assumptions and this would increase WellWorth’s complexity beyond a manageable level.
In section x, WellWorth provides default values derived from the empirical evidence for the age profile of your chosen population and the employment rates by age group. If you don’t have the relevant information for the population undergoing the wellbeing intervention, you can use the default values instead. .
All future monetary values are discounted at the default value of 3.5%, as suggested for periods of 0-30 years by the HM Treasury.
Monetary values derived from empirical studies based on market prices of a certain year, are inflated to 2015 prices using inflation rates based on the Consumer Price Index. Income values are updated to 2015 values according to the increase in wages derived from ONS official statistics based on Annual Survey of Hours and Earnings.
|Time frame of analysis in years||Years when income becomes affected by LS||Discounted present value of age effect on income = 0.05^time*1/(1+r)^time|