The Annex provides more details on the process of finalising the second edition of the OECD Guidelines on Measuring Subjective Well-being, first, by defining the scope of the update and describing the series of working papers that support the recommendations in this report. The rest of the Annex provides details on each of the modules presented in Chapters 2 and 3, giving additional background context on the importance of each conceptual area to be measured and details on the statistical properties for the measures selected to capture each concept. It then explains the decisions that went into determining the measure composition of each module.
OECD Guidelines on Measuring Subjective Well‑being (2025 Update)
When the OECD Guidelines on Measuring Subjective Well-being were first published in 2013, few OECD countries were measuring aspects of subjective well-being in official statistics. While there had been growing recognition of the concept’s importance to societal well-being, in particular following the Stiglitz-Sen-Fitoussi report, which specifically noted that “it is possible to collect meaningful and reliable data on subjective well-being” (Stiglitz, Sen and Fitoussi, 2009[1]), in practice this was not done with a great deal of frequency, and even less so in a standardised way – in part because it was not clear how data producers should go about operationalising this advice. The OECD Guidelines, then, sought to bring all of the existing evidence together in one place, to clarify the conceptual framing and definition of subjective well-being, to evaluate what is known about the validity and reliability of existing measures, to analyse the effects of different question phrasing and answer scales on response patterns, to understand methodological considerations specific to subjective well-being measures, to clarify how subjective well-being data can (and should) be analysed and used once collected, and, finally, to introduce a core module of standardised measures. Extended modules were also presented, to provide interested data producers with additional measurement options.
The resulting publication not only provided national statistical offices with a wealth of knowledge on the rigour and robustness of subjective well-being data, but by providing a core module with five measures, it also gave interested data producers a set of clear and practical steps to integrate subjective well-being into existing measurement practice. The 2013 edition of the Guidelines was intended to be the first step in advancing the subjective well-being measurement agenda, and indeed, as it stated (OECD, 2013, p. 20[2]):
These guidelines do not aim to provide the “final word’ on the measurement of subjective well-being ... It is envisaged that these guidelines will be followed up by a review of progress on the measurement of subjective well-being over the next few years, with a view to deciding whether the guidelines need revising and whether it is possible and desirable to move towards a greater degree of international standardisation. The intent is that this review will build on information collected by national statistical agencies.
The review process that resulted in these updated guidelines began in 2023 and, in keeping with the intent of the 2013 Guidelines, sought to understand how useful the 2013 recommendations had been to national statistical offices: had more OECD Member countries begun collecting subjective well-being data in official surveys, with greater frequency? Had the recommended measures in the core module been taken up by official data producers? And if the answers to the previous two questions were negative, how could the OECD better refine and clarify its recommendations to make them more useful?
The results of this scoping exercise are outlined in detail in “Subjective well-being measurement: Current practice and new frontiers” (Mahoney, 2023[3]) and can be summarised by four main findings:
As of 2023, a large share of OECD countries not only measure subjective well-being, but consider it an important component of overall well-being, as evidenced by the inclusion of subjective well-being measure(s) in 20 of the 27 (74%) national well-being initiatives developed by Member states.
The definition of subjective well-being and the conceptual framework proposed by the guidelines – identifying three components of subjective well-being, covering life evaluation, affect and eudaimonia – have also been widely adopted (Exton, Mahoney and Smith, 2024[4]).
Measurement of life satisfaction – the primary measure recommended in the original guidelines – has been largely harmonised, with 89% of OECD countries collecting that data in official statistics.
Measurement practices for affect and eudaimonia are more mixed. Almost all countries measure the affective states that had been recommended in the original guidelines; however, they used a variety of different tools and measurement approaches (including tools designed to assess mental health). Fewer countries measure the eudaimonic concept recommended in the original guidelines (feeling that the things one does in life are worthwhile); however, of those that did measure the concept, most all did so using the OECD recommended question.
The review process also took stock of advances in the academic and grey literature to understand how the subjective well-being evidence base has evolved, given the large number of methodological and empirical publications written in the decade since the original guidelines were published. The review touched on many topics but found that much of the evidence corroborated both the methodological guidance set by the OECD (see Chapter 1 of this report) and the rigour of the core module measures. Readers interested in the below topics can refer to (Mahoney, 2023[3]) for a longer discussion, as these additional topics are not addressed further in this publication:
New data sources, including biometric, social media and (linking subjective well-being outcomes to) administrative data.
Methodological issues, covering validity, response scales, question placement, composite indices and mode effects. The working paper discusses a prominent recent critique of the validity of subjective survey data (Bond and Lang, 2019[5]), describing the specifics of the critique as well as a series of rebuttals from academics in the field (Liu and Netzer, 2023[6]) to demonstrate both the external validity of these measures (Kaiser and Vendrik, 2020[7]; Kaiser and Oswald, 2022[8]; Lindqvist, Östling and Cesarini, 2020[9]) and analytical approaches to address scale use heterogeneity without dismissing the data outright (Benjamin et al., 2023[10]). The consensus from the past decade plus of research is that subjective well-being data are meaningful for policy-relevant outcomes.
“Subjective well-being measurement: Current practice and new frontiers” did, however, identify four thematic areas that a future revision of the guidelines should focus on. Three of these research priorities – clarifying measurement recommendations first, for affect and second, for eudaimonia, and exploring globally inclusive measurement approaches – set the parameters for what was in scope, and out of scope, in the process of drafting and shaping the OECD Guidelines on Measuring Subjective Well-being (2025 Update). The fourth and final thematic area identified – to develop subjective well-being measurement guidelines specific to children and young people – will be covered in a forthcoming, second publication. To address the first three topics in detail, the Secretariat worked with expert consultants to draft detailed working papers (Table A A.1). The recommendations in the resulting papers reflect the views and opinions of the external consultants, but the evidence included in each is referenced throughout this publication and helped to shape the final recommendations.
As with the original edition, the full process of updating the guidelines was a consultative one that involved the contributions of many stakeholders. The project commenced with a public conference on subjective well-being measurement, gathering a broad group of stakeholders to discuss advances in measurement since the publication of the 2013 Guidelines and to outline priorities for future measurement work. The OECD Secretariat also convened an informal advisory group of more than twenty experts in the field of subjective well-being, who in aggregate provide a diversity of viewpoints across policy, statistics, academia (including academic disciplines, such as economics, psychology and sociology, among others) and geographic regions. The advisory group met in person on two occasions, once in 2024 and once in 2025 – first to clarify the research agenda and questions to be addressed in the associated working papers, and subsequently to react to an early draft of the updated guidelines publication itself. The work has been conducted under the oversight of the OECD Committee on Statistics and Statistical Policy.
The core module of subjective well-being measures (Box 2.2) differs from the one in the 2013 Guidelines in two ways: it includes a new measure on pain, and it removes three questions on affect. The primary subjective well-being measure, which asks respondents how satisfied with their life they feel on average (Q1), remains the same, as does a eudaimonic question on measuring the extent to which respondents feel that the things they do in their life are worthwhile (Q2). Shortening the core module by two questions reduces both respondent burden and implementation cost, and in doing so can increase the likelihood of its wider adoption and higher frequency of its implementation.
Addition of pain
The key change made to the core module is the removal of three affect questions with a “yesterday” recall period, asking respondents how much they experienced being happy, worried or depressed on the day prior. It is important to note that these affect questions remain recommended by the OECD as being valid and reliable and yielding policy-relevant insights, and interested survey designers can find them in the affect extended module (Box 2.5). Similarly, national statistical agencies who currently field affect yesterday questions as a part of their subjective well-being module can and should continue to do so.
These questions were removed from the core module for two primary reasons. First, because yesterday recall periods require sufficiently large samples sizes to ensure that the aggregate of experiences the day prior yield a representative portrait of affect, regardless of how (a)typical the day had been for each individual respondent. Without sufficiently large samples, the data do not yield valid and reliable estimates. Not all data producers have the resources required to field these large-scale surveys on a regular basis, limiting the applicability of “yesterday” recall period questions to a smaller range of surveys. The core module is designed to be integrated into the widest range of household surveys possible, therefore the measures included in it should be adaptable to a wide range of survey types. Additionally, findings from the OECD scoping work into measurement practice revealed that data producers prefer mental health measures with longer recall periods (Mahoney, 2023[3]). In removing “yesterday” recall items from the core module, the hope is that a larger number of national statistical offices in OECD countries will adopt the full set of measures in the core and capture subjective well-being outcomes more frequently, and that those who are interested will be able to supplement with additional measures from the extended (and experimental) modules.
Second, the core module is designed to be as concise as possible, to enable its wide integration into the largest number of surveys possible and to field subjective well-being questions on at least an annual basis. Efforts were made in this edition of the Guidelines to streamline recommendations wherever possible. The selected measures for life evaluation (Q1 in Box 2.2) and eudaimonia (Q2 in Box 2.2) represent a single question that best summarises that specific component of subjective well-being. Affect, on the other hand, is a multidimensional construct made up of various positive and negative states: no single measure can encompass “affect” in its entirety. This resulted in three affect questions in the first edition of the Guidelines, to cover both positive and negative states. The approach taken this time is different: rather than include a summary set of measures for affect, a single, well-performing example of affect has been selected. That is, the inclusion of pain is not meant to suggest that pain is a stand-in for the overarching construct of affect. Rather, pain is an example of an affective state – one that works particularly well as a stand-alone measure based on its performance across the four guiding criteria (Box 2.1): statistical properties, brevity, unique policy relevance and consistency.
Affect encompasses feelings, emotions and states – a broad range of outcomes that includes sensations such as pain. The International Association for the Study of Pain (IASP) defines pain as “An unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage” (Raja et al., 2020[14]). This makes clear that the emotional experience of pain is a key component of the overall sensation. Some evidence has found that feelings of pain stemming from social exclusion, rejection or loss rely on the same neural regions that process physical pain (Eisenberger, 2015[15]). For example, evidence from pharmacology highlights the role opioids play in social bonding, beyond pain relief (Panksepp et al., 1978[16]), and neuroimaging studies of pain have found that feelings of social exclusion or social distress activate physical pain-related regions in the brain (Eisenberger, Lieberman and Williams, 2003[17]; Takahashi et al., 2009[18]). The extent of the overlap between social and physical pain is not fully settled and continues to be debated; however, there is greater agreement that both physical and social pain share “a common experiential element … and that is the affective component of pain” (Eisenberger, 2015, p. 621[15]).
Indeed, literature on (subjective) well-being has historically highlighted the relevance and importance of measuring pain, with increasing prominence in recent years. In its call for the collection of subjective well-being data, the Stiglitz-Sen-Fitoussi Commission specifically noted: “Subjective well-being encompasses three different aspects: cognitive evaluations of one’s life, positive emotions (joy, pride), and negative ones (pain, anger, worry)” [bold/italics added] (Stiglitz, Sen and Fitoussi, 2009, p. 16[1]). An authoritative report on subjective well-being measurement, with a particular focus on experienced well-being (momentary measures of affect), includes pain within the states and sensations considered: experienced well-being “… is often further divided into positive experiences, which may be characterized by terms such as joy, contentment, and happiness, and negative experiences, which may be characterized by sadness, stress, worry, pain, or suffering” [bold/italics added] (p. 3[19]), and the report concludes with an explicit recommendation that pain be measured (“pain questions should be included in ExWB [experienced well-being] questionnaires, particularly in domains such as health or housing where this information is particularly germane to research and policy questions” (National Research Council, 2013, p. 44[19]). Pain was indeed already included in the first edition of the Guidelines, although not as a primary recommended measure. More recently, research stemming from the Harvard-based Global Flourishing Study has explicitly called for the inclusion of pain as a component of subjective well-being measurement (Macchia et al., 2025[20]).
Pain is associated with a range of other policy-relevant outcomes (Kudrna et al., 2024[11]). A high prevalence of pain in the population can lead to a greater strain on health care systems, as pain is associated with worse physical and mental health outcomes, alcoholism, over-prescription of opioids, suicide and premature mortality (Case, Deaton and Stone, 2020[21]; Glei, Stokes and Weinstein, 2020[22]). Pain is also associated with worse labour market outcomes, including declining productivity (Wenig et al., 2009[23]; Gaskin and Richard, 2012[24]). Concerningly, some research has hypothesised that economic worry can lead to (physical) pain (Wiech and Tracey, 2009[25]; Chou, Parmar and Galinsky, 2016[26]), and empirical applications have found evidence to support this theory (Macchia and Oswald, 2021[27]). Given the wide-reaching impacts of pain, there have been calls for governments to monitor it when making assessments about overall societal well-being (Macchia, 2023[28]).
Pain has been assessed in clinical settings for many years using many different instruments whose psychometric properties have been extensively studied (Jensen et al., 1999[29]; Hawker et al., 2011[30]; Jensen and Karoly, 2011[31]). The item included in the core module of these guidelines (Q3) is an adapted form of the Numeric Rating Scale (NRS), for which many iterations exist, including both multi-item scales as well as single-item measures (Correll, 2011[32]). The NRS has been shown to be statistically reliable and valid (Jensen et al., 1999[29]; Jensen and Karoly, 2011[31]), including across various cultural and linguistic groups (Karcioglu et al., 2018[33]).
All iterations of the NRS, including single-item measures, have two key features: the question asks about “pain” rather than adding a qualifier (such as “physical”, “bodily” or “psychological”), and all use a 0-10 answer scale, where zero represents no pain and ten is the worst pain imaginable. The first point is important, in that this measure does not delineate between “physical” and “mental” pain, since – as the above quoted IASP definition of pain suggests – the experience of pain is both sensory and emotional, and the two are not easily distinguished (Lumley et al., 2011[34]; Gilam et al., 2020[35]; Eisenberger, 2015[15]). In terms of the answer scale, the 0-10 response format aligns with existing OECD recommendations and with other measures in the core module. Additionally, the numeric scale allows data producers to use thresholds in order to group respondents into the categories of “mild”, “moderate” and “severe” pain, which can be useful for official data producers when monitoring outcomes over time and comparing across population groups. A lot of literature is devoted to identifying appropriate cut points in the NRS scale, with some small disagreements depending on the desired sensitivity or specificity of the thresholds (Hirschfeld and Zernikow, 2013[36]; Oldenmenger et al., 2013[37]; Alschuler, Jensen and Ehde, 2012[38]). The OECD recommended cut points (“no pain” for a score of 0, “mild” is a score ≥ 1 and ≤ 4; “moderate” (5-6); and “severe” ≥ 7) align with findings from a seminal paper in the pain literature from Serlin et al., (1995[39]), based on a sample of cancer patients. Subsequent studies have extended the cut points to other patient populations, with disagreements over the exact placement of the mild and severe cut-offs. When used in a clinical environment to determine an effective treatment plan, clinicians may want to adapt thresholds to the specific patient population. When monitoring societal pain in the aggregate, on the other hand, it is most important that national statistical offices use a consistent approach to cut-offs – such as the one recommended in the core module reporting instructions.
The single-item NRS pain question used in the core module is a measure that appears on the PROMIS (patient-reported outcomes measurement information system) Adult Short Form survey (HealthMeasures, 2021[40]; Cella et al., 2019[41]). There are many studies assessing the validity and cross-cultural comparability of PROMIS survey measures, including pain, albeit most in clinical settings (Sharma et al., 2021[42]; Rawang et al., 2020[43]; Mahmoud, Rady and Mostafa, 2019[44]). This question is also used in the OECD Patient Reported Indicator Survey (PaRIS), which assesses patients’ self-reported views of their experiences with the medical system. The PaRIS survey, and the corresponding pain question, have been successfully fielded in twenty OECD Member states (van den Berg et al., 2024[45]). As official data producers adopt this recommendation and begin fielding the NRS question in non-clinical settings, it is recommended to publish findings on cognitive field testing of the item to show the extent to which respondents understand the question (and its associated prompt) to refer to both physical and mental aspects of pain.
Pain, measured as an affect question – that is, with a short recall period and not phrased as asking about the ways in which pain limits daily activities, an approach used more frequently in health surveys – has been fielded in the annual Gallup World Poll for years (Gallup, n.d.[46]), which has supported academic analysis of the dynamics of pain over time and its broader implications for well-being (Deaton, 2012[47]; Case, Deaton and Stone, 2020[21]; Macchia and Oswald, 2021[27]; Blanchflower and Oswald, 2019[48]). Pain has also been measured in the American Time Use Survey’s well-being module, most recently fielded in 2021 (U.S. Bureau of Labor Statistics, 2022[49]), as a part of the UK Office of National Statistics extended experimental experienced well-being questions (ONS, 2011[50]), and is included in the Mexican national statistical office 2025 ENBIARE well-being survey, as a part of the battery of affect items.
The pain measure recommended in the core module here uses a past week recall period (as opposed to yesterday). In general, measures of affect are designed to capture how a respondent is feeling in the moment – or close to it – meaning recall periods should be as short as possible, in part to distinguish these measures from mental health tools (Box A A.1). A past week recall period is used here to enable wider use of this measure, as a part of the core module, including in surveys that do not have sufficiently large sample sizes to ensure valid and reliable measurement of affect yesterday. Unlike the emotional states included in the affect extended module, pain does not appear in the most commonly used mental health screening tools, making it less likely for the longer recall period to conflate this measure with mental health outcomes. Furthermore, this framing aligns with its use in other OECD surveys (van den Berg et al., 2024[45]), providing consistency across OECD recommendations and enabling the benchmarking of outcomes for patient populations participating in the PaRIS survey with population-level outcomes. Some national statistical offices and health agencies may already collect data on pain in health surveys, but it is still recommended to measure pain using the additional question in the subjective well-being core module. Health surveys frequently include questions on pain – often further disaggregating pain by type (back pain, neck pain, etc.) – as well as questions about the duration of pain (chronic pain measures) and the extent to which pain interferes with daily activities. For example, the European Health Interview survey includes a question asking respondents, “how much bodily pain have you had during the past 4 weeks?” These questions provide needed information about physical health, but they are conceptually different from a general question asking about a respondent’s reported pain. At times, health surveys may include general questions on pain that do not specify “physical” pain. Additionally – while specific practice may vary across OECD countries – in general, health surveys are typically not fielded annually, as opposed to general social surveys; to monitor population pain prevalence and make meaningful comparisons between groups (and over time), frequent data collection is needed.
Affect balance vs. mental health screening tools
The first edition of the Guidelines included three affect questions in the core module (feeling happy, worried or depressed). The 2023 review of country uptake of those guidelines found that countries were collecting data on each of these states, showing that the construct had traction among data producers, whereas the measures used by data producers varied widely. Data producers often use mental health tools to collect this information – in part, no doubt, because many existing surveys (in particular health surveys) include mental health screening tools.
To mitigate the potential confusion between affect and mental health measures, the second edition of the Guidelines has taken a different approach:
The series of affect questions asking respondents how they felt the day prior are not included in the core module. The sole affect question is a new measure on pain, given its policy relevance and lack of inclusion in leading mental health screening tools.
Greater clarity is provided on what affect measures are capturing, as opposed to mental health measures, and how data producers and policy makers may want to use both (Box A A.1).
The implementation details accompanying the affect extended module (Box 2.5) and population mental health module (Box 2.7) provide data producers with clear instructions on how to capture data for each.
Affect refers to a person’s feelings or emotional states, typically measured with reference to a particular point in time. Affect measures aim to capture feelings and emotional states as they are experienced, and not as they are remembered. For this reason, data on affective states should be measured as closely as possible to the reference period. The first edition of the Guidelines recommended using a “yesterday” framing, to minimise distance from the recall period and thus the associated recall bias. This approach works well for household surveys. However, affect data are also particularly valuable when included in time use surveys (see Box 2.8).
Data collected through affect measures can be reported as the share of the population experiencing each affective state the day prior. Another approach is to create an affect balance measure, which captures the net balance between positive and negative affect. Data producers can then report the share of the population with a net negative affect balance. Affect balance is a useful metric in that it acknowledges that it is normal, and even healthy, to experience negative emotions; what is deemed a negative overall outcome is when the negative emotions outweigh positive emotions, on average, for a population group. Affect balance also smooths out cultural and regional differences in the likelihood of reporting extreme emotions (both positive and negative), allowing for more meaningful cross-country comparisons.
Affect data have been used to help shape a range of policy interventions, including child-custody arrangements, physical health or healthcare needs (e.g. end-of-life care), transitory changes such as the impact of a cultural event or feelings while commuting, and to highlight affective trade-offs such as sacrificing enjoyment now for long-term goals considered worthwhile (Mahoney, 2023[3]). Furthermore, empirical research using data from national statistical offices has shown affect balance to be sensitive to mental health outcomes, social relationships and financial conditions, making it a meaningful and responsive way to explore inequalities (Pérez Amador, 2023[51]; Pérez Amador, 2025[52]).
While some mental health conditions have affective components (such as affective and anxiety disorders), mental health is a distinct concept much broader than the experience of a particular feeling or emotion. Per the WHO definition, mental health is a “state of mental well-being that enables people to cope with the stresses of life, realize their abilities, learn well and work well, and contribute to their community” (WHO, 2022[53]). Mental health comprises both mental ill-health and specific mental health conditions such as major depressive disorder or generalised anxiety disorder, as well as positive mental health. Nevertheless, common measurement instruments for assessing the latter can share several characteristics with positive affect measures. One distinction between measures used to assess mental health and those used to measure affect is the reference period typically adopted. Because affect measurement instruments are designed to measure feelings as they are experienced, reference periods are short and recent. Mental health tools, on the other hand, are designed to pick up on the persistence of emotions and moods over a longer period of time – usually over the past two or four weeks – which can suggest risk for a mental health condition, or in the case of a positive mental health tool, indicate a healthy mental state. Because mental health tools are designed and tested as a multi-item set, it is not recommended to report prevalence rates for individual items in the tool. Instead, mental health tools should be scored and reported on according to validated processes, which are outlined in detail in the implementation details that accompany the module.
Eudaimonic measurement in the core module
The core module retains the same eudaimonic measure that appeared in the first edition of the Guidelines – the extent to which respondents feel that the things they do in their lives are worthwhile. The initial 2023 scoping review (investigating up-take of the 2013 Guidelines) found only a small number of OECD national statistical offices had taken this construct on board in regular (e.g. annual) national surveys, which clarified the need for a better understanding of the policy relevance of this measure (Mahoney, 2023[3]).
A deep review of the literature on eudaimonia suggests that, given limited survey space, a single question on worth, meaning and purpose remains the most suitable single-item eudaimonic question for household surveys (Abdallah and Mahoney, 2024[12]). The reasons are three-fold. First, the construct appears in almost all of the leading theoretical models of eudaimonia (refer to Table 2.2 in (Abdallah and Mahoney, 2024[12])), providing grounds for its inclusion as a core component of what constitutes eudaimonic subjective well-being. The construct, then, is well founded in the theoretical literature. Additionally, measures of worth and meaning show discriminant validity from life satisfaction, suggesting the two capture distinct latent constructs, therefore strengthening the argument for including both in the core module (Kudrna et al., 2024[11]).
Second, a sense of worth, meaning and purpose in one’s life is associated with many policy-relevant outcomes, especially in the health sphere. It is linked to long-term physical and mental health and is associated with reduced all-cause mortality (Joshanloo and Blasco-Belled, 2023[54]; Alimujiang et al., 2019[55]; Boyle et al., 2009[56]; Cohen, Bavishi and Rozanski, 2016[57]; Hill and Turiano, 2014[58]; Martela, Laitinen and Hakulinen, 2024[59]). A lowered sense of meaning is predictive of the onset of chronic conditions four years in the future (Steptoe and Fancourt, 2019[60]) and is associated with heart-related conditions (Cohen, Bavishi and Rozanski, 2016[57]; Kim et al., 2013[61]; Kim et al., 2013[62]), disrupted sleep (Kim, Hershner and Strecher, 2015[63]; Steptoe and Fancourt, 2019[60]), reduced physical functionality in older adult populations (Kim et al., 2017[64]; Steptoe and Fancourt, 2019[60]) and obesity (Steptoe and Fancourt, 2019[60]). Conversely, higher levels of worth, meaning and purpose are linked to a greater likelihood of using preventative healthcare services (Chen et al., 2019[65]; Kim, Strecher and Ryff, 2014[66]), engaging in more physical activity, better eating habits and lowered alcohol consumption (Steptoe and Fancourt, 2019[60]). Beyond health, a sense of meaning in the workplace is associated with higher productivity (Martikainen et al., 2022[67]), and meaning and purpose are related to reduced risk of divorce and closer relationships with friends (Steptoe and Fancourt, 2019[60]) as well as with higher rates of volunteering (Chen et al., 2019[65]; Steptoe and Fancourt, 2019[60]). Meaning in life is also associated more broadly with prosocial behaviours (Steger et al., 2008[68]).
Finally, OECD scoping work in 2023 found that seven OECD countries were actively and regularly measuring a sense of meaning and purpose using the recommended measure (those countries are Canada, France, Korea, Mexico, New Zealand, Norway and the United Kingdom) (Mahoney, 2023[3]). Any changes to the wording of this question would disrupt time series in the countries that have been most active in measuring this construct (and in some cases, where those data have been contributing to the evidence base for the policy relevance of meaning (What Works Wellbeing, 2021[69])). Thus, there would need to be a compelling evidence-based rationale for changing the wording of the recommended question.
For these reasons, the original eudaimonia question is retained in this second edition of the Guidelines. It is understood that some official data producers had difficulty in translating this question into languages other than English (Abdallah and Mahoney, 2024[12]), as is evidenced by the findings of a 2013 Eurostat report on that year’s European Union Statistics on Income and Living Conditions (EU-SILC) survey, which contained this question on things in life being worthwhile in its ad hoc well-being module (Eurostat, 2013[70]). In certain languages, the translation gave respondents the impression that the question was asking about accomplishments in their life (which can be associated with financial or labour market achievements) or the perceived futility of actions in life. Additional contextual information has been provided in the implementation details accompanying the core module, both to provide enumerators with the needed context to understand what this question is aiming to capture and to assist effective translations. The Mexican national statistical office provides an example of how translations of this question into the target language can effectively capture the intended meaning, even if the exact language used needs to be slightly amended: INEGI’s translation to Spanish specifies “Por lo general, siento que lo que hago en mi vida vale la pena,” a formulation that maintains the original meaning (the things one does in life are worthwhile) while using accessible and widely understood language in the Mexican context.
Information supporting the creation of extended modules is provided in the sections below. The largest change between the first and second edition of the Guidelines is the refinement of the composition of the extended modules and of the measures contained therein. While in the first edition of the Guidelines these modules provided a range of different tools that could be used to measure the same construct, in this edition the modules present a set of the most important constructs to measure within each component of subjective well-being, and a single measure is recommended for each construct. Care was also taken to understand whether the constructs are truly components of subjective well-being, or whether they are separate constructs measured elsewhere in the OECD Well-being Framework (Box A A.2). Whenever an extended module includes a measure that also appears in the core module, that measure is bolded to alert data producers to avoid double-collection.
Relational measures are closely intertwined with subjective well-being measures, and measures of perceived isolation, loneliness and connection to others often appear in multi-item scales designed to capture psychological flourishing. Indeed, background research commissioned as part of the Guidelines update on how to measure affect and eudaimonia identified social and relational measures as being important constructs underpinning both (Kudrna et al., 2024[11]; Abdallah and Mahoney, 2024[12]). Additionally, two of the most widely used approaches to eudaimonic well-being measurement place relational well-being as a core component (Ryan and Deci, 2000[71]; Ryff, 1989[72]). Separately, the review of subjective well-being measurement tools from culturally and regionally diverse population groups – with a focus on Indigenous measurement approaches – also pulled up many examples of communal, social and relational measures (Smith et al., 2025[13]). Lastly, research led by OECD country national statistical offices highlights the strong interplay between social connections and subjective well-being. The Mexican ENBIARE 2025 well-being survey included a set of three items to assess the emotional reciprocity, respect and collaborative aspects of intimate relationships – covering the extent to which respondents (1) admire their partner's qualities, (2) feel their opinions are taken into account in decision-making, and (3) consider their relationship to function as a team. Outcomes from subsequent analysis of the survey by INEGI (the Mexican statistical office) revealed these measures of relationship quality to significantly enhance the explanatory power of models of subjective well-being.
It is clear that social interactions and close relationships with others are deeply important for overall well-being, and indeed the OECD Well-being Framework includes a dimension of social capital (containing measures of time spent with others, perceived social support, satisfaction with relationships and loneliness). However, what is less clear is the extent to which these measures enter into the previously established subjective well-being framework (Box 1.2) – are these constructs subjective well-being outcomes, or determinants of these outcomes?
Upcoming work at the OECD will produce separate measurement guidelines for social connections. That future activity will include developing a theoretical framework of the construct, identifying the components that should be measured – both objective aspects (e.g. time spent with others vs. time spent alone, network size and composition) and subjective (e.g. the perception that social needs are met, and measures of the quality of relationships and social interactions) – and propose a core module of recommended measures, identifying those with the strongest statistical properties to capture each policy-relevant construct. This work is on-going at the time of this publication.
While not synonymous with subjective well-being, aspects of social connections are deeply relevant to subjective well-being outcomes, and there are overlapping areas in the conceptual framing of both. This is in many ways not dissimilar to the relationship between subjective well-being and mental health measures: mental health is not synonymous with subjective well-being. However, mental health measures include questions assessing the affective states of respondents and in this way often overlap with some measures of subjective well-being. This edition of the Guidelines provides clarifying information on how to use and interpret affect vs. mental health measures to help data producers better understand the relationship between the two (Box A A.1).
When explicit OECD recommendations on social connections measurement are available, the OECD will provide data producers with similar guidance as to how they relate to existing subjective well-being measurement guidelines and how to incorporate the new social connections core module alongside subjective well-being modules. In the interim, data producers already collecting subjective data on social connections – including, but not limited to, measures capturing perceived social support, loneliness and satisfaction with relationships – can include these measures at the end of their subjective well-being survey module or position them immediately following the subjective well-being portion of the survey.
Life evaluation extended module
Life evaluation measures capture respondents’ reflective assessments of their life overall, or of specific aspects of it (Diener, Lucas and Oishi, 2002[73]). Evaluative measures are subjective appraisals, rather than a description of an emotional state – the latter being a feature of affect measures. The process of making an evaluation may involve the respondent envisioning a standard outcome that they view as appropriate and then comparing their circumstances to that standard (Pavot et al., 1991[74]) – whether consciously or unconsciously.
The life evaluation extended module (Box 2.3) includes the primary measure of subjective well-being, the question on life satisfaction (Q1). Alternative approaches to measuring satisfaction with life, which had been included in this module in the first edition, have been removed in order to reduce redundancies and strengthen the OECD’s recommended approach for measuring this construct. A question about perceived satisfaction with life in the past (Q2) has also been retained; however, it has been adapted to ask respondents how satisfied they were one year ago (instead of five years ago); having a more recent reference period decreases the cognitive burden on respondents. Such recalled measures of life satisfaction have value-add in that they are informative in understanding how to interpret current life satisfaction measures and the dynamics of life satisfaction in general (Prati and Senik, 2022[75]).
A new question on hope (Q3) has been added to the life evaluation module. Hope implies both a positive outlook towards the future as well as a sense of agency to achieve good outcomes. This is often differentiated from optimism, a personality trait associated with assuming the best – without an inherent sense of one’s role in achieving good outcomes. This makes hope a more relevant outcome to capture, from a subjective well-being perspective (Abdallah and Mahoney, 2024[12]). Hope is associated with many other economic, relational and civic well-being outcomes. Lack of hope is associated with premature mortality (Graham and Pinto, 2019[76]; O’Connor and Graham, 2019[77]), while more hope can have health benefits in patient populations (Tremolada et al., 2020[78]; Snyder, 2000[79]). Hope is also associated with voting patterns (Ward, 2019[80]; Ward et al., 2021[81]), and lack of hope is possibly correlated with higher susceptibility to misinformation (Graham, 2024[82]). People who report higher levels of hope are more likely to invest in their future (Lybbert and Wydick, 2018[83]), including in their education (Graham and Pozuelo, 2023[84]), and they are more likely to engage in political activism and climate action (Leshem, 2019[85]; Cohen-Chen and Van Zomeren, 2018[86]; Klar and Kasser, 2009[87]; Geiger, Dwyer and Swim, 2023[88]). The psychometric properties of multi-item hope scales have been tested (Edwards et al., 2007[89]; Pleeging, 2022[90]); however, single-item measures are relatively new. The question introduced in the life evaluation extended module is used by the United Kingdom’s Office for National Statistics (ONS) (ONS, 2024[91]) and underwent cognitive testing prior to its addition to official surveys and to the ONS’s Well-being Database.
The domain evaluation extended module remains unchanged from the first edition of the Guidelines. Given the short length of the preceding life evaluation extended module, data producers who value domain satisfaction questions can easily combine elements of the two modules. It is acknowledged that there are domains of life not currently well represented by the extended module, and future iterations may explore these topics in greater depth once more evidence is available. As one example, rather than asking a question about job satisfaction, the Mexican statistical office (INEGI) includes a question in its 2025 ENBIARE well-being survey asking respondents, “How satisfied are you with your main activity (work, housework, studying, caring for or assisting a family member).” This formulation – using the term “main activity” rather than “job” – allows respondents to reflect on both paid and unpaid work, making the question applicable to a broader share of the population.
Affect extended module
Affect measures capture information about feelings and emotional states as people experience them, or as close to the time as possible, rather than evaluative measures of feelings in general (see Box A A.1 for more details). Affect measures usually are composed of a battery of measures asking respondents their experience of a list of different emotional states. There are different theoretical approaches to affect measurement to structure which emotions are selected for inclusion (see (Kudrna et al., 2024[11]) for a brief overview). The circumplex model of affect captures the multidimensionality of affect by plotting two dimensions of classification: positive vs. negative emotions and low vs. high arousal. This theoretical model provides the underpinning for the selection of the four short-list measures of affect in the extended affect module (Box 2.5).
As is illustrated in Figure A A.1, the four affect measures making up the short list of measures (in case of limited survey space) cover each of the four quadrants: high-arousal negative affect (worried), high-arousal positive affect (happy), low-arousal negative affect (sad) and low-arousal positive affect (calm). Having two positive and two negative affective states allows for the calculation of an evenly balanced affect balance. This recommendation deviates only slightly from the short list of affect measures in the first edition of the Guidelines. First, the new set measures “sad” rather than “depression”, electing to measure mental health states using mental health tools (see Box A A.1 and Box 2.7). The second change is the addition of calm. Based on research suggesting that negative affect is more multidimensional than positive affect, the first edition of the Guidelines elected to include a single positive affect measure (happy) in its short set of affect measures. A recent review of the literature finds that affective measures of calm and peace show greater discriminant validity from life satisfaction than do measures of happiness – illustrating that calm vs. happy are capturing different latent constructs (Kudrna et al., 2024[11]). Separately, a review of globally inclusive approaches to subjective well-being measurement highlights that, despite the importance given to low arousal positive emotions in Eastern philosophy and traditions like Confucianism, Taoism and Buddhism, the value of the emotion is universal (Smith et al., 2025[13]). Findings from the Gallup World Poll support the global relevance of feeling calm, showing that it is both experienced and valued by populations worldwide and not centred on a specific geographic region (Lomas et al., 2022[92]).
Source: Adapted from OECD (2013[2]), which was itself derived from Russell (1980[93]).
The remaining measures in the affect extended module comprise a long list of affect measures, which data producers can field if they have sufficient space. These measures are retained from the first edition, with a few minor adjustments. The question on “enjoyment” has been changed to “joyful”, based on findings from Kudrna et al. (2024[11]).
Eudaimonia extended module
A core research question for updating the guidelines was a better understanding of eudaimonia, by distilling lessons from its vast multi-disciplinary literature. A recent working paper review of the most prominent theoretical frameworks of eudaimonia finds that, in its essence, eudaimonia is about a sense of living well (Abdallah and Mahoney, 2024[12]). This constitutes: (1) motivations (orientations, attitudes or values that determine behaviour), (2) feelings (emotional states) and (3) behaviours (observable actions). Eudaimonia, then, can be defined as an orientation towards feelings that value, and actions that foster, a long-term positive impact on oneself and others. In the context of measuring subjective well-being (which is specifically about the mental states that people experience, rather than their behaviours), it is the feelings component that is of greatest relevance – and eudaimonic feelings can be understood as the set of universally desirable feelings or experiences associated with a sense of living well. Both definitions imply a multidimensional concept, with different constructs that should be captured in an extended eudaimonia measurement module (Abdallah and Mahoney, 2024[12]).
To refine the list of constructs that should be measured, the same working paper applied the following criteria: the construct is included in the existing academic literature; it fits well under the working definition of eudaimonia; it has predictive power for policy-relevant outcomes; it is distinct from life satisfaction and therefore not duplicative; and, lastly, that there are existing measures (preferably in official statistics) to measure the construct (Abdallah and Mahoney, 2024[12]). This yielded a list of the following constructs, all of which are included in the eudaimonia extended module (Box 2.6):
The things one does in life are worthwhile (Q1) – as in the core module
Autonomy (Q2)
Competence, accomplishment or environmental mastery (Q3)
Self-esteem or self-acceptance (Q4)
Personal growth and self-actualisation (Q5).
The working paper highlighted additional constructs that are not included in the extended module for space reasons; only the top-performing constructs were selected for inclusion. The one exception is the construct of relatedness. This construct performed well on the six selection criteria, but it is a measure of social connection and therefore is not included in these recommendations (see Box A A.2 for a discussion on the relationship between subjective well-being and social connections and on forthcoming measurement guidance on social connections).
Of the selected measures, three were consistent with the first edition of the Guidelines: Q1 (worth / meaning), Q3 (competence) and Q4 (self-esteem). For Q2, while the construct of autonomy was retained, the question used has been updated. The new measure was developed by Martela and Ryan as a validated single-item assessment of the construct of autonomy (2024[94]), and it was fielded by the Finnish Prime Minister’s Office via its Citizens’ Pulse survey from 2022-2024 (Statistics Finland, 2024[95]). It will be included in the forthcoming European Social Survey 2025 well-being module, where cognitive testing in advance of fielding the full survey has found that it performs well (Rutherford et al., 2024[96]).
The eudaimonia module contains one new measure, on personal growth (Q5). Unlike other measures of eudaimonia, personal growth is future-oriented and contributes to the sustainability of subjective well-being into the future – this makes it an important conceptual addition to the module. Personal growth has been found to be associated with improved physical health outcomes (e.g. metabolic syndrome) (Ryff, Boylan and Kirsch, 2021[97]). Measures on personal growth have been assessed as a part of longer scales on eudaimonic or psychological well-being (Ryff and Keyes, 1995[98]; Waterman et al., 2010[99]) that use an agree/disagree formulation (often with Likert scale responses). The measure included in the eudaimonia module is a single item taken from Ryff’s Psychological Well-being Scale (Ryff and Keyes, 1995[98]; Ryff, 2014[100]), which itself (as a whole) has been extensively validated, translated into 40+ languages and integrated into a large-scale nationally-representative longitudinal survey (UW Madison, 2025[101]).
Mental health module
A module on population mental health measures is included in this edition of the Guidelines to provide data producers with clarity as to how mental health measurement approaches can complement subjective well-being measures, and more specifically, how mental health measures differ in their implementation and subsequent interpretation from affect measures (see Box A A.1).
The recommendations in the mental health extended module pull from previous OECD work, in particular Measuring Population Mental Health (OECD, 2023[102]). This publication was the first in a series of two: the second, How to Make Societies Thrive? Coordinating Approaches to Promote Well-being and Mental Health (OECD, 2023[103]), looks at the policy implications of the wide-ranging societal impacts of mental health outcomes. Interested readers can reference that original report for greater detail on conceptual frameworks for mental health measurement, an overview of OECD national statistics offices’ current practice, and methodological and measurement considerations. This deep review culminated in the three recommendations included in the extended module: a general self-assessment of one’s mental health; the four-item Patient Health Questionnaire to assess the risk of depression and anxiety; and the five-item WHO-5 Well-being Index to measure positive mental health. The mental ill-health measure suggested also aligns with on-going pilot data collection work undertaken by the OECD Health Division to measure the performance of mental health systems. In that stream of work, the full-length, eight-item Patient Health Questionnaire (PHQ-8) is recommended to measure risk for depression, and the full-length seven-item Generalized Anxiety Disorder (GAD-7) survey is recommended to measure risk for anxiety (OECD, forthcoming[104]).
Time use modules
Time use surveys are especially suitable for the collection of affect measures, because by integrating these measures into time use diaries, it is possible to capture people’s emotional experiences as they happen (or close to it) – tied to specific activities – rather than as they are recalled. Affective states should be measured, because they can be used to assess policy impacts in specific cases where general life evaluation measures may be less pertinent (refer to Box A A.1 for a brief list).
Affect data that capture how people are feeling in the moment is particularly useful when that information is tied to the activities the person is engaging in while feeling that way and to whom they are with. These data have direct policy applications, for example, understanding the full cost associated with sitting in traffic during commuting time (which can then inform congestion pricing policy) (Krekel and MacKerron, 2023[105]); providing new methodologies for valuing the benefits of green spaces (Smith, 2023[106]); or better crafting labour market or workplace well-being interventions (Mylona, 2023[107]; Krueger and Mueller, 2012[108]; Hoang and Knabe, 2021[109]; Wolf et al., 2019[110]). International guidance on time use surveys is now highlighting the important role of such data in understanding (gender) patterns in unpaid work and caregiving (UNSD, 2025[111]; Tchipeva, Miceli and Ninka, 2024[112]). Integrating affect questions into these time use diaries would help more fully capture the cognitive burden of these tasks and provide a comprehensive view of how people value their time (Krekel and MacKerron, 2023[113]). Lastly, the social aspect of time use surveys (asking respondents whom they were with during each activity) provides insight into the dynamics of spending time with others in person and our emotional states, as well as the association between engaging in certain activities – such as the use of digital technologies – and feelings of loneliness and disconnection (OECD, 2025[114]).
Experience sampling
Experience sampling methods involve collecting data on affective states in real time. There are different ways to approach data collection, and increasingly smartphone-based surveys are being used. As one example, in 2021 Statistics Canada piloted an experience sampling survey, the Pilot Study on Everyday Well-being. The survey used an app to field questions about affective states up to five times a day, over a thirty-day period. During each check-in, respondents were asked how happy, anxious, relaxed, focused or in control of their emotions they felt (Figure A A.2). The survey was designed in collaboration with the Canada Council for the Arts and Canadian Heritage to focus on the impact of the arts and cultural activities on overall well-being (Kudrna et al., 2024[11]).
Pilot Study on Everyday Well-being (CAN)
Source: Kudrna et al. (2024[11]), “Measuring affective components of subjective well-being: Updated evidence to inform national data collections”, OECD Papers on Well-being and Inequalities, No. 31, OECD Publishing, Paris, https://doi.org/10.1787/6c72da70-en.
Full day time use diary method
The full day time use diary method module (Box 2.8) provides a means of integrating affect data in time use diaries; this approach has been implemented by a number of OECD national statistical offices. For example, this approach has been used by INSEE, the French statistical agency, in its time use survey, Enquête Emploi du temps 2010; the Italian statistical agency, Istat, has also used this approach in its last two full-length time use surveys (see Figure A A.3, Panel A for the 2023 questionnaire). Both the French and Italian measures use the same answer scale (from -3 (very unpleasant) to +3 (very pleasant)), with varying reports of its efficacy. Istat has found the answer scale to be effective and plans to continue using it in 2033, when the next time use survey is fielded. INSEE, on the other hand, will change its approach in the 2025-26 time use survey and will instead field a series of measures on general satisfaction with time use (see below for more details). Statistics Finland has fielded a variation of this approach but uses a visual approach rather than a numeric scale (the faces are coded +2, +1, 0, -1, -2, respectively, when interpreting the data – see Figure A A.3, Panel B). Another answer scale variation is that used by Statistics Canada in its time use surveys: respondents are asked, “On a scale of 1 to 5 where 1 means “Very unpleasant” and 5 means “Very pleasant”, how would you rate the activity you were doing?” (Statistics Canada, 2022[115]).
Source: Panel A: Istat (2023[116]), Como uso il mio tempo: Indagine statistica multiscoppo sulle famiglie, https://siqual.istat.it/SIQual/files/IMF-13%20B%20Anno%202022-23.pdf?ind=0071301&cod=5591&progr=1&tipo=4. Panel B: Statistics Finland.
Random sample of three activities within a time use diary
One alternative to gathering affect data for every activity experienced in a time use diary is to ask respondents a richer set of questions about a random sub-set of just three activities (episodes) only. By reducing the number of activities assessed, it is possible to expand the question set to include multiple positive and negative affective states, for example, how happy, worried, calm, sad, lonely or in pain the respondent felt, or how meaningful the activity was. This approach follows a format similar to the Princeton Affect and Time Survey (Krueger et al., 2009[117]), and it has been adopted by the American Time Use Survey (ATUS), which administered a well-being module in 2010, 2012-13 and most recently in 2021 (Figure A A.4).
This type of data collection may be prioritised over the full day diary where the need for data on several separate affective states (e.g. in a medical study where separate information about pain and tiredness may be paramount) takes priority over the goal of calculating an accurate daily affect measure for each individual respondent. Thus, the data collection method to prioritise depends on the research goal.
This approach is typically administered in an interview shortly following the completion of a time use diary. Because recall is important, it is desirable that the interview take place as soon as possible after the diary has been completed – preferably the day after the day covered by the diary. Three episodes are selected from the time use diary, omitting episodes when the respondent was sleeping or otherwise unconscious. The procedure is designed to select episodes ensuring that, over the sample as a whole, there are an adequate number of responses for each major time use activity. Because of the complexity of the sampling design, it is important to weight responses correctly to ensure that the resulting estimates are representative (refer to (BLS, 2022[118]) for a detailed discussion). The module randomly selects three activities from a time use diary and asks respondents how happy, tired, stressed, sad or in pain they felt during the episode (for example), as well as the extent to which they found the activity meaningful. Figure A A.4 shows an example of what this module looks like using the Web CATI instrument interface.
Source: Kudrna et al. (2024[11]), “Measuring affective components of subjective well-being: Updated evidence to inform national data collections”, OECD Papers on Well-being and Inequalities, No. 31, OECD Publishing, Paris, https://doi.org/10.1787/6c72da70-en.
General assessments of satisfaction with time use
Other general evaluations and assessments of one’s time use have been included in time use surveys fielded by national statistical offices as a means of incorporating subjective assessments of time use. These questions can be implemented alongside the day reconstruction method outlined in Box 2.8, by asking respondents general questions upon completion of the time use diary. Examples include:
Overall, how satisfied are you with how you use your time? [0-10] (Adapted from a question that appears in Statistics Canada’s 2022 Time Use Survey (Statistics Canada, 2022[115]).)
Overall, how satisfied are you with the balance between your job and home life? [0-10] (Adapted from a question that appears in Statistics Canada’s 2022 Time Use Survey (Statistics Canada, 2022[115]).)
Overall, how satisfied are you with the division of the housekeeping burden between you and your partner? [0-10] (Adapted from INSEE’s 2025-26 Time Use Survey.)
Overall, how satisfied are you with the division of family care duties between you and your partner? [0-10] (Adapted from INSEE’s 2025-26 Time Use Survey.)
A series of questions included in Eurostat’s 2020 Harmonised European Time Use Surveys Guidelines (Eurostat, 2020[119]), immediately following the time use diary portion:
What was the most pleasant activity described in the diary?
What was the most unpleasant activity described in the diary?
What was the most stressful activity described in the diary?
Overall, how do you appreciate this day?
(These questions do not directly capture affect, in that they are evaluative and prime the respondent to think in comparative terms rather than focusing on states, moods or emotions that are experienced in the moment. They are therefore not recommended for understanding subjective well-being outcomes but can be used for other purposes.)
The experimental measures and concepts included in Chapter 3’s cross-cutting module reflect findings motivated by one of the three research streams underpinning this guidelines update: exploring globally inclusive approaches to subjective well-being measurement to ensure the broad relevance of OECD recommendations (covered by the resulting modules in Chapter 2) and to identify important concepts that are as-of-yet not included in international measurement (Chapter 3). To address these, the Secretariat commissioned a working paper that, among other issues, explored the diversity of measurement practice across the globe by conducting a literature review focusing on Indigenous measures of subjective well-being (Smith et al., 2025[13]). The themes and concepts uncovered through this exercise form the basis of both the recommendations in the cross-cutting module and the question banks that follow.
Cross-cutting module measures
The cross-cutting module in Chapter 3 includes subjective well-being measures that span the components of life evaluation, affect and eudaimonia. Thematically, the concepts included cover: a subjective appraisal of well-being of an entity or group broader than the individual (Q1 – family; Q3 – spirituality; Q4 – impact on others); low-arousal positive affect (Q2 – mind is at ease); and affective states that relate to interpersonal interactions (Q5 – treated with respect; Q6 – envious; Q7 – compassionate; Q8 – forgiving; Q9 – fearful; Q10 – generous; Q11 – selfish) (Box 3.1).
The first question in the module asks respondents to reflect on how well they perceive their family to be doing these days. The question comes from the New Zealand General Social Survey (GSS), a biennial population-wide data collection exercise (Statistics New Zealand, 2023[120]). It was adapted for the GSS from Te Kupenga – a survey designed to capture the social, cultural and economic well-being of the Māori from their cultural perspective (Statistics New Zealand, 2014[121]). Respondents are not prompted as to what constitutes “family”; in the original New Zealand questionnaire, a follow-up question asks respondents to clarify which group(s) of people they included in their consideration of family, from the following list:
Parents, spouse / partner, siblings, children, brothers /sisters and/or parents-in-law
Grandparents, grandchildren
Aunts / uncles, cousins, nephews / nieces, other in-laws
Close friends, others
This allows for a broad and non-prescriptive understanding of family. Family well-being is a broadly relevant concept; though the question was initially developed for the Māori population (the original question wording asking how one’s whānau is doing – a Māori term that loosely translates to family but has a broader implication), it has performed well since its inclusion in the population-wide New Zealand General Social Survey. Analytical work on this measure suggests that, while highly correlated with life satisfaction, it is distinct, especially in its drivers: an individual’s own health and material well-being are stronger determinants of their life satisfaction than they are of how well their family is doing (Smith, Peach and Cording, 2019[122]). Beyond New Zealand, a study in the United States found that the well-being of the respondent’s family ranked highly in a discrete choice experiment ranking the most important aspects of one’s well-being (Benjamin et al., 2014[123]). Evaluations of family, communal and interpersonal well-being appear in the vast majority of Indigenous subjective well-being measurement tools (Smith et al., 2025[13]).
The second question is a low-arousal positive affect measure (recall the circumplex model of affect, shown in Figure A A.1) asking respondents how often their mind is at ease. This question comes from the Gallup-WPE Global Wellbeing Initiative, which has been fielding questions in the annual Gallup World Poll since 2020. This specific question was included in the 2022 and 2023 iterations. This provides another dimension of low-arousal positive affect, distinct from calm (Box 2.5). In its field testing, Gallup found that other low-arousal positive affect states were difficult to translate: for example, a question on how “content” respondents felt was poorly understood (Lomas et al., 2022[124]). This question, then, provides an effective alternative.
The third question is a broadly framed, inclusive question about spirituality. Spirituality and the vibrancy of one’s life is an important component of many Indigenous well-being approaches (Smith et al., 2025[13]), but it has broader relevance. There are strands of literature investigating different definitions of spirituality (Peng-Keller, 2019[125]; Hill et al., 2000[126]) and how it relates to subjective well-being (Ryff, 2021[127]; van Dierendonck, 2012[128]), which are developing an understanding of how spirituality fosters resilience and leads to better well-being outcomes broadly defined (Koenig, McCullough and Larson, 2001[129]; Long et al., 2024[130]). A range of different multi-item measurement scales have been developed – for an overview, see (Bohlmeijer et al., 2023[131]) – and the Harvard University Human Flourishing Program has curated a set of resources through its Spirituality & Flourishing working group (Human Flourishing Program, 2024[132]). Some existing subjective well-being measurement approaches include a spiritual dimension – for example, the Personal Wellbeing Index, included in the domain evaluation extended module (Box 2.4), includes an additional, optional question on spirituality: How satisfied are you with your spirituality or religion? (International Wellbeing Group, 2024[133]). The question in the experimental module pulls from the Spiritual Well-Being: The Awe Index scale (Hamby, Grych and Banyard, 2013[134]).
The fourth question, on beneficence, captures outcomes related to pro-social behaviours – improving outcomes not only for oneself, but for others around them. The pro-social behaviours themselves (e.g. volunteering, providing support to others) are measured in other domains of the OECD Well-being Framework, but measures capturing beneficence are not widely measured, and only a few studies have tested the psychometric properties of beneficence scales (Martela and Ryan, 2016[135]; Martela and Ryan, 2020[136]). Longer item scales measuring flourishing or psychological well-being contain measures that capture positive social relationships – which may also capture aspects of beneficence (Ryff and Keyes, 1995[98]; Diener et al., 2009[137]) – even though they have not been designed (nor tested) to do so directly. The measure in the module comes from a single item in a scale developed by Martela and Ryan (Martela and Ryan, 2016[135]), with the answer scale adapted to a 0-10 format to align with the rest of the module.
The final set of questions refer to relational affect questions – these are emotional experiences that are inherently social in that the emotion can be interpreted only in the context of other people or interpersonal interactions (Smith et al., 2025[13]). In case of limited space, three questions are prioritised (treated with respect, envious and compassionate), but the full set can be implemented should time and resources allow. These questions come from the Bhutan Gross National Happiness Survey (Ura et al., 2022[138]), with answer scales adapted to a 0-10 format to align with the rest of the module. Additionally, the questions have been slightly reworded to align with the rest of the module as well as with the affect extended module question framing (Box 2.5); similarly, a yesterday recall period is used. The Bhutan Gross National Happiness Survey collects data on the nine domains and 33 measures that make up the Gross National Happiness (GNH) Index, calculated every five years to assess overall well-being and happiness in the population (CBS, 2024[139]; OECD, 2024[140]).
Q5 is based on an existing question in the Gallup World Poll, asking respondents the extent to which they felt respected the day prior. (A question about feeling respected also appears in the Bhutan Gross National Happiness Survey.) The measure included in Box 3.1 is slightly adapted from the Gallup World Poll to have the answer scale match the rest of the measures in the module. This question has been translated into many languages and fielded in many countries worldwide. Gallup includes “respect” in its annual Global Emotions reports (Gallup World Poll, 2024[141]). Respect and human dignity are fundamental to human rights (UN, 1948[142]; European Union Agency for Fundamental Rights, 2007[143]; UN, 2015[144]), and the effects of their absence has informed OECD work in understanding the detrimental impacts of discrimination (Hardy and Schraepen, 2024[145]).
Question banks
The scoping review pulled up additional themes that are broadly important for populations globally; however, these concepts were not able to be integrated into the cross-cutting experimental module, because there is not yet sufficient evidence to identify a well-performing, single-item measure to adequately capture the underlying construct. Because these concepts are important, and the evidence base should be expanded – which is possible only through the testing of new measures – question banks are provided with established examples from the literature. Data producers are encouraged to select from these options, balancing the needs of their local context and the purpose of the specific survey.
The first of these concepts is balance and harmony, which has been the focus of extensive field testing by the Gallup-WPE Global Wellbeing Initiative. Part of this process has been to unpack the conceptual differences between the two constructs. Lomas et al. (2022, p. 4[146]) put forward the following definitions:
Balance: “the various elements which constitute a phenomenon, and/or the various forces acting upon it, are in proportionality and/or equilibrium, often with an implication of stability, evenness, and poise”.
Harmony: “various elements which constitute a phenomenon, and/or the various forces acting upon it, cohere and complement one another, leading to an overall configuration which is appraised positively”.
The definitions are caveated with a statement that there is a strong connection between the two concepts, but note that harmony is more unambiguously positive and has a “warmer” feel to it (Lomas et al., 2022[146]; Abdallah and Mahoney, 2024[12]). Recent work has further examined this relationship and found that while respondents assessed harmony to be more positively valenced, they self-reported a preference for balance (Lomas et al., 2025[147]). What is clear, however, is that both concepts have been absent from most academic considerations of subjective well-being, in particular those developed in a Western context (Lomas et al., 2022[146]; Abdallah and Mahoney, 2024[12]) – this, despite the fact that an international study found that the most prominent definition of happiness included concepts of “inner harmony”, which itself includes themes of inner peace, contentment and balance (Fave et al., 2016[148]; Smith et al., 2025[13]).
Results from Gallup’s fielding of different measures capturing either balance or harmony have found them to be only somewhat correlated with life evaluation measures, showing value-add in comparison to life satisfaction (Lomas et al., 2022[92]; Kudrna et al., 2024[11]). However, cognitive testing of individual measures has illustrated the difficulties in translating specific question wording across linguistic and cultural groups. For example, here are field testing notes relating to issues with the question, “In general, how often are the various aspects of your life in balance?” (Lomas et al., 2022, p. 7[146]):
In cognitive interviews, respondents generally understood it, though interpreted “in balance” differently. About half of interviewees understood it to mean having enough time to spend on all things in life that are most important to them (including work, social relations, family, health, economy, food, and emotional aspects). Some respondents in Italy specifically said balance means “Everything is in its place and having enough time for oneself and for others,” with one person saying balance means “having moments to relax from work.” However, several respondents mentioned that “in balance” referred to a balance of good things and bad things in a person’s life. Others spoke of resilience and achieving goals as being “in balance.” Somewhat differently, a Japanese respondent said, “'I think ‘in balance’ means no bias. There is no bias in thinking or in daily life.” The broad nature of the phrase “various aspects of your life” was also difficult for some respondents. For instance, for some respondents in Lebanon, issues such as violence, strikes, civil unrest, and instability in their country were considered to be among the “various aspects” of their lives, and thus their answers were influenced strongly by the present situation there.
An alternative question, “In general, how often are your thoughts and feelings in harmony?” was deemed difficult to understand and answer by members of this project’s informal advisory group. Because the concepts of balance and harmony have demonstrated importance and relevance, it is hoped that the inclusion of multiple measurement tools will encourage data producers to experiment with the best formulation of the question to move towards a recommended measure in future.
The second concept with an associated question bank is connection to future generations. Many OECD countries have introduced legislation or national strategies that emphasise the importance of better managing economic, social and environmental assets to safeguard well-being for future generations (OECD, 2023[149]). Concepts of intergenerational well-being and a sense of connection to one’s ancestors and descendants also feature prominently in many Indigenous worldviews and non-Western literature on subjective well-being, including, for example (Smith et al., 2025[13]):
The concern for the well-being of future generations features in many Indigenous cultures, such as the Seventh Generation Principle in Haudenosaunee philosophy and practices (Clarkson, Morrissette and Regallet, 2001[150]), the Māori notion of being a “good ancestor”, the importance of “Caring for Country” in Australian Aboriginal culture, the principle of “malama aina” in Hawaiian culture, and the concept of “mino-bimaadiziwin” in Anishinaabe culture in North America, which means “the good life” or “continuous rebirth and renewal”.
Despite this, few explicit measures exist to measure the concept. Known existing measures are included in this question box in order to encourage experimentation.
The final concept is connection to nature. There is a vast literature highlighting the importance of the natural world for subjective well-being and good physical and mental health outcomes (OECD, 2023[103]), most of which views the natural environment as a separate outcome from – and distinct driver of – subjective well-being. A review of Indigenous subjective well-being measurement approaches, however, highlights that nature is not always viewed as a distinct and separate entity – measures instead focus on interconnection or harmony with nature (Smith et al., 2025[13]):
In Andean Indigenous cultures (South America), the principle of “sumaq kawsay” (Quechua) or “buen vivir” (Spanish) is a holistic approach to life that emphasises harmony with nature, community well-being and sustainability. This philosophy includes the responsibility to maintain a healthy environment for future generations.
In Bhutan, the term “happiness” as used in the Gross National Happiness framework is seen as a relational construct – emphasising responsibility, harmony with nature and concern for the happiness of others (Ura, Alkire and Zangmo, 2012[151]).
This work has also been done in Western contexts, exploring how connection to nature feeds into concepts of eudaimonia and subjective well-being (Ryff, 2021[127]; Pritchard et al., 2020[152]; Richardson et al., 2021[153]). Indeed, there are a number of multi-item scales that have been designed to measure connection to nature (Salazar, Kunkle and Monroe, 2020[154]) – however, many were developed in the English-language context, and, as of yet, there are few single-item measures that have been validated. Furthermore, many of these scales are designed to be a measure people’s value systems as they relate to nature, rather than a measure of subjective well-being that encompasses nature. The options in the question bank provide data producers with a starting point of options to explore this concept further.
Collecting subjective well-being data in minority populations
The above findings exploring globally inclusive approaches to subjective well-being measurement have centred on what has been learned in terms of broad-based relevance for measuring subjective well-being across populations and contexts. This enables official data producers from all OECD countries, regardless of geographic region or linguistic tradition, to identify important measurement concepts for further exploration.
However, the process of reviewing these concepts entailed surveying measurement tools and well-being frameworks that were developed by, or in conjunction with, Indigenous and minority communities. Data producers in OECD countries have developed surveys to measure outcomes in specific communities: New Zealand’s Te Kupenga survey of the Māori population being just one example (Statistics New Zealand, 2014[121]). To help data producers understand the unique challenges and considerations of collaborating with minority populations when collecting subjective well-being data, a set of four good practices are described in Box A A.3.
When collecting subjective well-being measures for Indigenous and minority populations, process matters. While the terms “minority” and “Indigenous” refer to distinct and diverse groups, both groups are frequently subjected to systemic discrimination (United Nations, 2018[155]) and share common experiences of marginalisation experiences (Kipuri, 2009[156]; McClintock, Mellsop and Kingi, 2011[157]; Tuhiwai Smith, 2021[158]). For Indigenous peoples, these experiences are rooted in historical colonisation, whereas other groups experience oppression due to discrimination based on their ethnicity, religion or language (United Nations, 2023[159]).
It is important to note that the terms “Indigenous” and “minority” encompass diverse populations, each with their own languages, cultures and customs. Both minority and Indigenous peoples have had their data and knowledge exploited in the name of research, creating intergenerational distrust that is still prevalent today. Data collection involving minority or Indigenous peoples must therefore be approached with humility, sensitivity and cultural competence (Ringelheim, 2008[160]; Tuhiwai Smith, 2021[158]).
Guiding principles for collecting subjective well-being measures for minority populations focus on 1) fostering community involvement; 2) taking a strengths-based approach; 3) developing local ethical guidelines; and 4) ensuring data sovereignty.
1. Fostering community involvement
Building relationships and trust with the community involved is critical to collecting data from minority and Indigenous populations. These relationships must be approached with humility and the willingness to collaborate on data collection, analysis and dissemination methods. Participatory research methodologies are often recommended when working with minority and Indigenous groups. These approaches empower and emphasise community voices (Datta, 2023[161]). Prioritising engagement processes, including consultations with trusted community organisations, elders, advisory committees and researchers, can help ensure practices that reflect community values (Fernandez et al., 2017[162]; Griffiths et al., 2021[163]). One way to do this can be to work with community members to co-develop a survey; such was the approach taken in creating the Survey of Living Conditions in the Arctic (Kruse et al., 2008[164]; Wu, 2021[165]).
Effective community engagement can be time-consuming. This is because effective community engagement often requires ongoing interaction beyond the data collection period. Participatory methods can also demand significant time commitments by participants, and a high respondent burden, particularly when there is a lack of trust or rapport between the researcher and the community (Datta, 2023[161]; De Las Nueces et al., 2012[166]). It is important to dedicate sufficient time and resources for this engagement process, which can only move at the speed of trust.
2. Taking a strengths-based approach
Too often, well-being in minority or Indigenous communities has been portrayed in a negative light, using well-being indicators that reflect the values of Western cultures (e.g. focusing on individual achievement). When applied to non-Western communities, these indicators can result in misleading conclusions, because they overlook or devalue aspects of well-being that are important in other cultural contexts, such as communal relationships, connection to land, spirituality and cultural vitality. Instead, a strengths-based approach to conceptualising and measuring well-being focuses on the resilience, capabilities and assets of communities rather than on deficits, problems or vulnerabilities. Taking a strengths-based approach empowers community members to recognise and build on their capacities and strengths to improve well-being. This fosters a sense of ownership and agency, encouraging active participation and engagement, which can lead to more sustainable and meaningful improvements in well-being.
3. Developing local ethical research guidelines
Where specific local ethical research guidelines exist, adherence to them is important to ensure that research practices are not only scientifically sound but also culturally appropriate and equitable, fostering mutual trust between researchers and the community. For example, the Good Spirit, Good Life measurement instrument was developed after approval by the Aboriginal Health Ethics Committee in Australia. In the absence of such guidelines, approval from key community representatives is crucial. This is seen in the development of a well-being questionnaire for On Reserve First Nation Peoples in Ontario and British Columbia, Canada, where approval was granted from the Chief’s representatives of each First Nation community involved alongside the general ethical approval (Kant et al., 2014[167]). What might be considered ethical in one country or community could be seen as intrusive, offensive or harmful in another. For example, attitudes towards privacy, informed consent or the role of community leaders in decision-making can vary greatly. Institutional ethical review typically reflects Western practices and values. Where possible, local ethical guidelines reflecting community values and worldviews should be followed and reviewed by appropriate advisory boards (Griffiths et al., 2021[163]).
4. Ensuring data sovereignty
Data sovereignty plays a central role when collecting data with minority and Indigenous communities. Indigenous Data Sovereignty refers to the right of Indigenous people to have ownership over their data. This supports the self-determination of Indigenous peoples to govern their data and autonomously make decisions about the methods, management of data collection and its dissemination of (McClintock et al., 2021[168]). Although some Indigenous groups have data sovereignty guidelines, this is not currently the case for many minority groups. In these settings, there is a need to engage with the community to consider management principles for the data collected (Griffiths et al., 2021[163]; Ringelheim, 2008[160]).
Source: Taken from Smith, C. et al. (2025[13]), “Globally inclusive measures of subjective wellbeing: Updated evidence to inform national data collections”.
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