Output and analysis of subjective well-being measures

The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

Note by Turkey: The information in this document with reference to “Cyprus” relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall preserve its position concerning the “Cyprus issue”.


This chapter provides guidance regarding the release and use of subjective well-being data. It briefly re-caps the policy and public interest in the data (outlined in Chapter 1, Concept and validity), before covering how information can be reported and analysed. This includes the statistical outputs that maybe released; basic information about the methods of analysis that may be adopted; and a discussion of key interpretive issues, placing particular emphasis on the extent to which levels of subjective well-being can be expected to vary in different circumstances.

The chapter is divided into three main sections, summarised in Table 4.1. The first and largest section focuses on the use of subjective well-being data to complement existing measures of well-being. This includes examination of trends in subjective well-being over time, the distribution of subjective well-being across different groups within society, and its distribution across different countries. The first part of this section outlines approaches to measuring well-being and the value added that subjective well-being brings relative to other measures. The second part of the section then describes how subjective well-being can be reported, including the summary statistics that may be of interest. Finally, issues in the analysis and interpretation of descriptive statistics on subjective well-being are explored. These include the size of change over time, or difference between groups, that can be expected, as well as the risk of cultural “bias” in cross-country comparisons.

The remaining two sections of the chapter deal with more detailed analyses of subjective well-being, which might be conducted by government analysts and others on the basis of micro-data released by statistical agencies. Section 2 addresses analyses of the drivers of subjective well-being. This includes the relationship between subjective well-being and other important well-being outcomes, such as income and health, as well as the use of subjective well-being data to inform the appraisal, design and evaluation of policy options. Section 3 addresses subjective well-being data as an input for other analyses. First, it considers the use of subjective well-being as an explanatory variable for other outcomes, and then focuses on the potential use of subjective well-being data in cost-benefit analysis.

Section 1 will be of most direct interest to large-scale data producers, such as national statistical agencies, as it concerns the kinds of outputs and analyses that they are most likely to report for a wide range of audiences. Sections 2 and 3 provide a sense of the broader uses of subjective well-being data - which are essential to consider when planning its measurement (as set out in Chapter 3, an approach to Measuring subjective well-being). Analyses of drivers, for example, require consideration of the co-variates to be collected alongside subjective well-being data, and ideally call for data from which causal inferences can be drawn. The potential risk of measurement error, and the various biases that may be present in the data, are also major themes throughout the chapter. However, as the relevance of measurement errors depends on the intended usage of the data (Frey and Stutzer, 2002), the chapter is organised around data uses, rather than around these sources of error. Key interpretive issues for each type of analysis are summarised in Table 4.1.

Table 4.1. Summarising possible uses of subjective well-being data

Data use




Key interpretive issues

1) Complementing existing measures of well-being

Core measures/headline indicators used to examine:

i) National trends overtime.

ii) Distribution of outcomes across different groups within society.

iii) Distribution of outcomes across countries.

Includes indicators of central tendency or “level”, as well as distribution, and the relative rate of rise or decline overtime.

To know if the changes affecting society have an impact on subjective well-being.

To identify vulnerable groups and areas of suffering

- highlighting where key drivers of subjective well-being may lie - and where there may be opportunities for policy interventions.

To conduct international benchmarking, assist in the interpretation of national data, and identify where countries may be able to learn from others’ experiences.

Governments (central, regional, local).

Wider public.

Public, private and third sector organisations.

Researchers interested in country-level drivers of national well-being.

Individuals and organisations - e.g. making decisions about where to live and work.

i) What size of difference between groups or over time can be expected?

ii) What alternative explanations should be considered for observed differences?

iii) What is the role of culture and cultural bias

in cross-country comparisons?

2) Betterunderstanding the drivers of subjective well-being

Analyses based on national and international micro-data, with subjective well-being used as the dependent variable, to:

i) Examine the relationship between subjective well-being and

other important life


such as income and health.

ii) Inform policy options appraisal, design and evaluation.

iii) Inform policy trade-offs.

To improve ourunderstanding of well-being overall, by examining the relationship between subjective well-being, life circumstances, and other important well-being outcomes.

To highlight areas of policy with the greatest potential to improve subjective well-being, and the life events/circumstances most likely to put subjective well-being at risk.

To assist in government decision-making processes, including the allocation of resources and the design elements of policies.

To inform the public and employers about the likely drivers of individual subjective well-being, providing better information for individual and organisational decision-making.



Individuals wanting better information to support decision-making. Employers wanting to understand and improve employee well-being.

i) What size of impact can be expected?

ii) How can the impacts of different drivers be compared?

3) Subjective well-being as an input for other analyses, particularly cost-benefit analysis

Micro-data on subjective well-being, used as an input for other analyses, including:

i) As an explanatory variable for other elements

of well-being or behaviour.

ii) Used to estimate the value of non-market goods

and services, for

the purposes of cost-benefit


To better understand how subjective well-being can contribute to other well-being outcomes and shed light on human decision-making processes, including the various biases that may be present.

To provide an alternative to traditional economic approaches to estimating the value of non-market goods, supporting government (and other organisations) in making decisions about complex social choices.



Individuals wanting better information to support decision-making. Employers wanting to understand and improve employee well-being.

i) The sensitivity of subjective well-being data

to non-market goods.

ii) Measurement error and its impact on valuations.

iii) Co-variates to include in regression models.

iv) Time horizons for study.

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