New Zealand as a Social Laboratory [A James Cook Fellowship - PowerPoint PPT Presentation

New Zealand as a “Social Laboratory” [A James Cook Fellowship Proposal] COMPASS Seminar Series Monday, 3 August 2015, Fale Pacifika Professor Peter Davis Department of Sociology, COMPASS Research Centre

New Zealand as a “Social Laboratory” [A James Cook Fellowship Proposal] Preamble - Making Knowledge Claims • The Year of Evaluation – RCTs • Impact of societal inequality • Improving inference with better design • The simulation approach

Pickett and Wilkinson, Soc Sci Med 2015 New Zealand The University of Auckland 5

Pickett and Wilkinson, Soc Sci Med 2015 New Zealand The University of Auckland 6

Avendano article, Soc Sci Med 2012 New Zealand The University of Auckland 7

Pickett and Wilkinson, Soc Sci Med 2015 New Zealand The University of Auckland 8

Avendano 2012 9

New Zealand Correlation = -0.92 Source: Avendano data

France Correlation = +0.96 Source: Avendano data 11

New Zealand as a “Social Laboratory” [A James Cook Fellowship Proposal] Preamble - Making Knowledge Claims • The Year of Evaluation – RCTs • Impact of societal inequality • Improving inference with better design • The simulation approach

New Zealand Causal Inference in Observational Settings 7 th Wellington Colloquium Statistics NZ The University of Auckland 30 August 2013 Professor Peter Davis University of Auckland, New Zealand and COMPASS Research Centre www.compass.auckland.ac.nz

New Zealand as a “Social Laboratory” [A James Cook Fellowship Proposal]

New Zealand Assessing policy counterfactuals with a simulation-based inquiry system. Peter Davis and Colleagues The University of Auckland COMPASS Research Centre University of Auckland New Zealand www.compass.auckland.ac.nz DISCLAIMER: Access to the data used in this study was provided by Statistics New Zealand under conditions designed to 17 give effect to the security and confidentiality provisions of the Statistics Act 1975. The results presented in this study are the work of the author, not Statistics New Zealand.

Assessing counterfactuals Counterfactual paradigm of causal reasoning If the putative causal factor had not been present, we would not have observed the recorded outcome. • Randomised Controlled Trials (RCTs) New Zealand • Experimental and quasi-experimental methods • Observational designs and statistical analysis The University of Auckland Simulation techniques 18

Simulation techniques Simulation – in our case, social simulation Use of computer models (computational techniques) to “mimic” social phenomena (e.g. social processes). • Understand phenomena better in constructing the models New Zealand • Once understood and validated, one can alter features • Particularly useful for sub-groups and future projections The University of Auckland • Ability to combine different data sources in a single model • Overcome privacy and confidentiality issues 19

Simulation at COMPASS Model Year Locality Type Life stage Domain Software Data Funder Collaborators End-users MOSC 2005-8 NZ ABM/MSM Adults Marriage market, NetLogo Census Marsden UOA residential Repast segregation Java PCASO 2005-8 NZ Static Older people Health care SAS NATMEDCA HRC UOA discrete- NZHS NatSem time MSM ANHS BCASO 2009-12 NZ Older people Health & social R NZHS HRC UOA care NZDS NatSem Census MEL-C 2009-13 NZ Children Health, Java CHDS MBIE UOA MOE Dynamic education, R DMDHS NatSem MOH discrete- conduct PIFS StatCan MOJ New Zealand time MSM THNR MSD Census2006 Te Puni Kokiri KNOW- 2013-16 World Children & Health, Published MBIE UOA SUPERU LAB young people education, literature StatCan Children’s Commissioner conduct, etc. SOCLAB 2015-17 NZ Whole Social Modgen/ NZLC Royal UOA Open-source modelling society OpenM++ Society community TPM 2015-20 NZ Whole Social Census TEC UOA society MOTU The University of Auckland LEGEND Model Data Funder MOSC: Modelling social change NATMEDCA: National Primary Medical Care HRC: Health Research Council PCASO: Primary care in an ageing society NZHS: NZ Health Survey MBIE: Ministry of Business, Innovation & Employment BCASO: Balance of care in an ageing society NZDS: NZ Disability Survey Royal Society: James Cook Fellowship MEL-C: Modelling the early life course ANHS: Australian National Health Survey TEC: Tertiary Education Commission KNOW-LAB: Knowledge laboratory CHDS: Christchurch Health & Development Study Collaborators SOC-LAB: NZ social laboratory DMHDS: Dunedin Multidisciplinary Health & Development Study UOA: University of Auckland TPM CORE: Te Punaha Matatini PIFS: Pacific Island Families Study NatSem: National Centre for Social & Economic Modelling, University of Canberra THNR: Te Hoe Nuku Roa StatCan: Statistics Canada NZLC: NZ Longitudinal Census MOTU: Motu Economic and Public Policy Research Type End-users ABM: Agent based model MOE: Ministry of Education, MSM: Micro-simulation model MOH: Ministry of Health 20 MOJ: Ministry of Justice, MSD: Ministry of Social Development

Micro-simulation approach. We start with a sample of individuals Real (studies) / synthetic (derived from Census) We derive statistical rules to create a “virtual cohort” that mimics the “real” one New Zealand Derive rules best able to reproduce real data Apply these rules to the base file to create a synthetic sample of typical biographies through life course The University of Auckland We then simulate what might happen if policy were to change, by altering parameters 21 Using software application to test counterfactuals

Virtual versus real cohort: family doctor visits, reading ability, and conduct problems, by year of age Year Real cohort (CHDS) Virtual cohort (simulated) Absolute error Absolute error / n=1017 n=1017 CHDS mean Family doctor visits (mean (95% CI)) 1 5.82 5.82 - - 2 5.34 - 5.28 0.06 3 3.31 - 3.18 0.13 4 3.13 - 3.15 0.02 5 3.22 - 3.12 0.10 6 3.35 - 3.32 0.03 7 2.43 - 2.41 0.02 8 2.14 - 2.15 0.01 9 1.96 - 1.90 0.06 10 1.65 - 1.68 0.03 All years 3.24 1.2% 3.20 (3.15-3.25) 0.04 Reading ability: BURT score (mean (95% CI)) 8 45.3 45.3 - - 9 54.4 0.3 - 54.7 10 64.1 0.4 - 63.7 11 72.8 0.9 - 71.9 12 79.5 0.6 - 78.9 13 85.2 0.6 - 84.6 All years 66.9 0.4 0.6% 66.5 (65.7-67.4) Conduct problems (mean (95% CI)) 6 10.6 10.6 - - 7 24.6 0.2 - 24.8 8 24.4 0.6 - 25.0 9 24.7 0.6 - 25.3 10 24.9 0.7 - 25.6 All years 21.8 0.5 2.3% 22 22.3 (22.1-22.4)

Inquiry Tool New Zealand The University of Auckland 23

New Zealand as a “Social Laboratory” [A James Cook Fellowship Proposal] ANY QUESTIONS AT THIS POINT!?

New Zealand as a “Social Laboratory” [A James Cook Fellowship Proposal] Assessing Counterfactuals about Society • Background and concept • Central ingredients of project • COMPASS team contribution • Building blocks • Book proposal

Outline • Background • Central ingredients of the James Cook • COMPASS team contribution • Building blocks – New Zealand Longitudinal Census (1981-2013) – Synthetic base file – Estimating equations – Open-source micro-simulation platform • Book proposal

Background – assessing counterfactuals • New Zealand, an early policy pioneer – 1890-1920 seen by observers as “social laboratory” – Social policies tried out by reforming governments – A “natural experiment” in a new, fluid society • How to draw credible inferences about policy? – RCTs, experimental and quasi-experimental designs – Non-experimental work (e.g. case studies) – Virtual “experiments”, using simulation techniques – Any precedents? Think of climate change scenarios

Background – assessing counterfactuals • New Zealand, an early policy pioneer – 1890-1920 seen by observers as “social laboratory” – Social policies tried out by reforming governments – A “natural experiment” in a new, fluid society • How to draw credible inferences about policy? – RCTs, experimental and quasi-experimental designs – Non-experimental work (e.g. case studies) – Virtual “experiments”, using simulation techniques – Any precedents? Think of climate change scenarios

Central Ingredients of James Cook • Three aims – Create model of NZ pop via synthetic cohort – Statistical model from NZLC to generate cohorts – Conduct experiments, “virtual counterfactuals” 1. Constructing smaller, synthetic cohorts Need synthetic starting file for each cohort, 1981 2. Estimating statistical model driving cohorts Method for reproducing biographical trajectories 3. Testing “virtual counterfactuals” Particular interest in impact of social assets