Demographic challengesand 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

OIDBPP t1t

Pt+1 Population at end of time period

Pt Population at start of time period (base population)

B Births during time period

D Deaths during time period

I In-migration (arrivals) during time period

O Out-migration (departures) during time period

Natural increase Net migration

A very mobile population4.9 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 populationsEstablished 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 sourcesHigh 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 ERPNew Zealand, mid-2011

Alternative data sourcesHigh 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 ERPNew 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 modelComponent Description Observed 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+ population2009-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