Demographic challenges and statistical developments Kim Dunstan, Senior Demographer

Population topics Patterns and processes: Population size and change Fertility Mortality Movement of people Geographic base (national, regional, and local) Different population types (residents, visitors) Composition – age, sex, ethnicity, etc

Theories and practicalities Demographic theory Demographic transition Epidemiological transition Statistical models Cohort component methods Life tables Statistical standards and classifications

Basic population equation P t+1 Population at end of time period P t Population at start of time period (base population) BBirths during time period DDeaths during time period IIn-migration (arrivals) during time period OOut-migration (departures) during time period Natural increaseNet migration

A very mobile population 4.9 million arrivals into NZ each year 4.9 million departures from NZ each year Up to ¼ million visitors from overseas in NZ on any given day Up to 200,000 NZ residents ‘temporarily’ overseas on any given day Roughly 1 million overseas-born living in NZ At least 600,000 NZ-born living overseas Over half of NZ’s population changes address within 5 years Seasonal and diurnal flows with work, study, leisure and holidays

How to measure local populations? Especially measuring internal migration 67 local councils; 2,000+ area units (‘suburbs’) Traditional periodic census Sample surveys Administrative data sources Data collected for administrative reasons

Estimating local populations Established administrative data sources Birth and death registrations High coverage Lag between birth and registration Some vague, incomplete and temporary addresses International travel and migration Virtually all movements covered Actual length of stay/absence ≠ intended Some vague, incomplete and temporary addresses Residential building consents Demolitions not well covered No information on onset and extent of inhabitation (eg holiday homes, number of occupants)

Estimating local populations (cont.) Established administrative data sources Electoral enrolments High coverage above age 30 years Excludes people under 18 years and those ineligible to vote Includes some people living overseas Usual address ≠ electoral address School rolls High coverage at compulsory school ages (6–16 years) School location ≠ usual address of student Students from overseas may not be residents Territorial authority annual consultation Local insight into factors affecting population Generally qualitative

Alternative data sources High potential usefulness Health service data (PHO enrolments) Covers all ages Stock and flow/transition data available Differential coverage by age/sex/ethnicity Includes some people living overseas Lag between moving and recording change of address

PHO enrolments v ERP New Zealand, mid-2011

Alternative data sources High potential usefulness Linked employer-employee data (LEED) High coverage above age 20 years Stock and flow/transition data available Includes some people living overseas Usual address ≠ LEED address (eg workplace, PO boxes) Lag between moving and recording change of address

LEED v ERP New Zealand, mid-2011

How to model local populations from multiple imperfect data sources Subjective interpretation Simple weights of different data sources by age-sex stock data, or changes in stocks Multiple regression Bayesian modelling

Bayesian population estimation model ComponentDescriptionObserved directly? Demographic accounts Complete description of births, deaths, migration and population stocks, by age, sex, region and year, during the period of interest No Statistical formulae for births, deaths and migration Formulae that describe age patterns, regional variation, and time trends in births, deaths, internal migration and external migration No Data sources All administrative, survey and census data used in population estimation, such as census counts, vital registration, arrivals and departures, school enrolments, housing consents, etc. Yes Statistical formulae linking data sources and demographic accounts Formulae that use values from the demographic accounts to predict values observed in the data sources (eg that use numbers of people aged 5– 10 from the demographic accounts to predict observed primary school enrolments) No

Inference Unknown components derived using Bayesian Markov chain Monte Carlo (MCMC) methods Result is a set of simulated values Summarised by percentiles and measures of uncertainty

Advantages Deals easily with inaccurate input data Deals easily with irregular input data Measures of uncertainty Automation and efficiency Privacy and data management Extension to projections and other estimation problems

Difficulties Theoretical – relatively new application in demography Practical – large volumes of data can affect efficiency and speed of model Conceptual – more complex, less transparent?

NZ’s 65+ population 2009-base official projections and experimental stochastic projections

Checks using electoral enrolment data 19

Benefits of embedding statistical models in demography Managing and utilising multiple large datasets Transparency and replicability Measures of uncertainty

What statistical skills are needed? Data linking and integration Efficient manipulation of large datasets Measuring and conveying uncertainty Data visualisation