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Statistics Users Forum November 2009 Geoff Mulgan, Director The Young Foundation Young Foundation – combines empirical research, local collaborations, creation of new organisations in education, health, democracy - and now developing global field of social innovation Metrics help governments and societies to see, hear and make judgements – part of the cognitive toolkit of society. 19th century governments measured their success by their military prowess 20th century governments by GDP growth 21st century governments will primarily judge their success in terms of progress in well-being and human flourishing. Long-term evolutionary trends… • from taxable things (buildings, animals, people) for government to measure society, to measures for society to judge itself and what government is doing (such as school exam results, or corruption) • from physical objects (steel, wheat etc) through aggregate concepts (like GDP) to intangibles (such as innovation or creative industries) • from single measures to indices (poverty, HDI, Indices of Civic Health) • from activities to outputs, to true outcomes (QALYs and DALYs, carbon reductions etc) • from material to subjective measures (fear of crime, patient satisfaction &c) Yet many of the most important problems not caught well by the numbers: emissions, isolation, waste, drugs, obesity - and subjective perceptions ... Is what matters measured?: perceptions of ‘what is important’ across 27 EU countries The relationship between democracy and well-being All measures evolve in shifting relationship with the policy tools used by governments… • formal quantitative targets in strategic plans (US states, UK government) – which helps to sharpen up policy, strategy and accountability for results • open coordination methods of the kinds used by the EU • performance management methods used by higher tiers of government on lower ones • metrics used to support quasi-markets (eg enabling incentives for outcomes) • metrics as part of deeper conversations with the public about priorities and policies (eg Oregon Benchmarks and equivalents). Lessons : many advantages from measurement, transparency &c but: • Metrics risk gaming, distortion, inflexibility etc and diminishing impact. Hence the need for constant refreshment. • Metrics risk being technocratic (eg measuring everything in services except what the public care about) • Metrics risk wrong levels of granularity – when increasingly the most valuable information comes from disaggregation rather than aggregation GDP as example • 1930s-1940s: concept and measurement of GDP brought counterintuitive systemic thinking to interactions of consumption, production, investment and savings • 21st century requires another step in systemic thinking seeing economies as dependent on other systems: ecology, family, public goods • GDP adapted to better reflect true value of finance, household activity, transaction costs &c UK distribution of income – average up, stretched at the top , sagging at the bottom UK distribution of psychological well being – most contented but long and thickening tail of unhappiness, loneliness and stress. Source: BHPS 2007/0 Prosperity Self-harm and suicide in children of rich Isolated homeowning older person Need Material wellbeing Homeless offenders with addictions Victims of trafficking Need AB employed good mix of protective factors DE employed good mix of protective factors Basic state pension high social support High social capital NINA strong immigrant family communitie s Prosperity Psychological Wellbeing Dissatisfied with high income Area: ‘Aspiring households’ (2.6) Area: ‘Asian communities’ (2.2) Area: ‘Settled in the city’ (2.0) Couple without dependent children (2.0) Hardly talks to neighbours (1.8) Poor/ v poor health (1.7) Life is full of opportunities – disagrees (1.7) Long term sick and disabled (1.5) Separated from spouse (1.4) Dissatisfied with low income Unemployed (4.0) Area: ‘Public housing’ (3.0) Dislike neighbourhood (3.0) Work is driving/travelling (2.4) Used social worker (2.4) Long term sick/ disabled (2.3) Separated from spouse (2.0) Caring for sick/disabled in household (1.9) Lone parent (1.8) Aged 16 to 20 (1.7) Full time family carer (1.6) Area – ‘Asian community’ (1.6) Rarely involved sports (1.5) Life is full of opportunities - disagrees (1.7) Satisfied with high income Most proportionately represented among available variables Have a degree (1.8) Area :‘Thriving suburbs’ (1.8)‘ Area: ‘Aspiring households’ (1.5) More likely to vote Conservative (1.5) Caring for sick disabled in household – less likely (0.5) Satisfied with low income Unemployed (1.8) Full time family carer (1.6), Single elderly (1.5) No qualifications (1.5) Retired (1.4) Widowed (1.4) Who is at the bottom? 4% No one to comfort me if I’m really upset 11% No one outside household to help if I’m depressed 2% lack all three types of support 3% No one appreciates me as a person Percentage responses to questions on emotional support 2007/08 Source: BHPS analysis Less visible trends: anxiety and depression 14% 12% 10% 8% 6% 4% Anxiety, depression etc Extrapolated trend 2% 0% 1985 1990 1995 2000 2005 2010 2015 2020 2025 Or raising new questions – like correlations between happiness and blood pressure •Headline numbers provide relatively little information - greatest insight comes from disaggregation, by place, class, gender, age etc – seeing the surprises, the patterns •GDP part of a system of causal relationships which required aggregate numbers – well-being is not •Benefits from common architectures , platforms and comparability, but not from replacing GDP with another number •Growing importance of subjective measures healthy and unavoidable, but lack of adequate ones The UK national indicator set 198 national indicators Comprehensive area assessments At local level greatest insights come not from headline figures but from relevant comparison plus disaggregation by domain, by group or service &c Young What else is needed? i) New tools for segmenting populations by lifestyle, culture and attitude as well as socioeconomic status • Key to understanding culture and behavior change, and the efficacy of many policy tools in critical fields such as healthcare and the environment (eg around obesity, recycling, learning). • Yet the science of segmentation remains in its infancy, and the field is dominated by commercial companies not public statisticians. ii) new tools for measuring the potential value of public, social and service innovations at different stages, a critical gap in current measurements and vital for testing policies for well-being. Iii) new means for citizens to mash, mine and adapt data-sets (eg http://mashupaustralia.org/) iv) New tools that assess current levels of wellbeing but also dynamics, resilience and future readiness … self support system self support system current state strengths vulnerability Well-being, income, health Self-efficacy, assets, resilience Mental illness, poverty, risk factors Daily care, Family, social support (time), networks, provision services Isolation, exclusion GDP, job creation, rights, governance Poverty, weak governance, dangerous environments Government capacity, dynamic economy, future readiness • Enables assessment of resilience to shocks at different levels – personal, community, system - and analysis of policy tools to reinforce strengths and address weaknesses . Emerging tools at root about democratizing and humanizing data … • making it fit better with lived experience and public priorities (what matters to citizens); • opening up a broader range of sources and engagement (engagement of citizens); • enriching data, not replacing one aggregate calculator with another (making numbers useable by citizens) •and turning metrics from a tool of power over society to a tool of power with society.