National Population Projections 2014-base

General methodology is outlined in the National Population Projections data collection.

Changes to assumptions since the previous 2011-base projections

Deriving the projections involved a review of all projection assumptions. The main changes from the previous 2011-base projections relate to the base population and migration assumptions for 2015–16.

The base population at 30 June 2014 of 4.510 million is 8,000 (0.2 percent) higher than the median of the 2011-base projections, mainly because observed net migration in 2012–14 (43,000) was higher than assumed (median of 4,000). The base population estimate incorporates results from the 2013 Census of Population and Dwellings and 2013 Post-enumeration Survey. This resulted in the previous 30 June 2013 population estimate, from a 2006 Census base, being revised down by 29,000.

The median annual net migration gain is assumed to be 12,000 in the long term, the same as the 2011-base projections. In the short term, the median net migration assumptions are higher at 54,000 and 33,000 in the June years 2015 and 2016, respectively. Simulations of net migration are produced using an ARIMA (0,1,2) model, rather than the ARIMA (1,0,1) model used in the 2011-base projections. The change results in a gradually increasing probability interval.

Stochastic (probabilistic) population projections

The 2014-base national population projections (released November 2014) have as a base the estimated resident population (ERP) of New Zealand at 30 June 2014, and cover the period to 2068 at one-year intervals. They supersede the 2011-base national population projections (released July 2012).

Stochastic (probabilistic) population projections are produced to give estimates of uncertainty, although these estimates are themselves uncertain. The stochastic population projections are produced by combining 2,000 simulations of the assumptions. These simulations can be summarised by percentiles, which indicate the probability that the actual result is lower than the percentile. For example, the 25th percentile indicates an estimated 25 percent chance that the actual value will be lower, and a 75 percent chance that the actual result will be higher, than this percentile.

Seven alternative percentiles of probability distribution (5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles) are available for the 2014-base projections.

At the time of release, the median projection (50th percentile) indicates an estimated 50 percent chance that the actual value will be lower, and a 50 percent chance that the actual value will be higher, than this percentile.

The median projection for 2014-base projections assumes:

• the total fertility rate declines to 1.9 births per woman in 2036 and beyond
• period life expectancy at birth increases to 89.0 years for males and 91.5 years for females in 2068
• annual net migration of 54,000 in 2015, 33,000 in 2016, and 12,000 in 2017 and beyond.

'What if?' scenarios

Five 'what if?' scenarios have been produced to illustrate what happens when different specific levels of fertility, mortality, and migration assumptions are combined.

Very high fertility assumes:

• the total fertility rate increases to 2.5 births per woman in 2036 and beyond
• period life expectancy at birth increases to 89.0 years for males and 91.5 years for females in 2068
• annual net migration of 54,000 in 2015, 33,000 in 2016, and 12,000 in 2017 and beyond.

Very low mortality assumes:

• the total fertility rate declines to 1.9 births per woman in 2036 and beyond
• period life expectancy at birth increases to 96.0 years for both males and females in 2068
• annual net migration of 54,000 in 2015, 33,000 in 2016, and 12,000 in 2017 and beyond.

No migration assumes:

• the total fertility rate declines to 1.9 births per woman in 2036 and beyond
• period life expectancy at birth increases to 89.0 years for males and 91.5 years for females in 2068
• no external migration from 2015 onwards (ie a 'closed' population).

Cyclic migration assumes:

• the total fertility rate declines to 1.9 births per woman in 2036 and beyond
• period life expectancy at birth increases to 89.0 years for males and 91.5 years for females in 2068
• annual net migration of 54,000 in 2015, 33,000 in 2016, and 12,000 in 2017 and 2018, net migration then fluctuates between -10,000 and 35,000 over a 10-year cycle, with an average of 12,000.

Very high migration assumes:

• the total fertility rate declines to 1.9 births per woman in 2036 and beyond
• period life expectancy at birth increases to 89.0 years for males and 91.5 years for females in 2068
• annual net migration of 54,000 in 2015, 33,000 in 2016, and 25,000 in 2017 and beyond.

Projection assumptions

Projection assumptions are formulated after analysing short-term and long-term historical trends, recent trends and patterns observed in other countries, and government policy.

Base population

These projections have as a base the estimated resident population (ERP) of New Zealand at 30 June 2014. This population (4.510 million) was derived from the ERP of New Zealand at 30 June 2013 (4.442 million), updated for births, deaths, and net migration between 30 June 2013 and 30 June 2014 (+68,000). The ERP of New Zealand at 30 June 2013 was derived from the census usually resident population count at 5 March 2013 (4.242 million) with adjustments for:

• net census undercount (+104,000)
• residents temporarily overseas on census night (+82,000)
• births, deaths and net migration between census night and 30 June 2013 (+9,000)
• reconciliation with demographic estimates at ages 0–9 years (+5,000).

Fertility

Fertility assumptions are formulated using birth registrations, period and cohort fertility rates, census data on children ever born (including rates of childlessness), and international comparisons.

Fertility rates are assumed to vary throughout the projection period. The median period total fertility rate (TFR) declines gradually from 1.99 births per woman in 2014 to 1.91 in 2025, and to 1.90 in 2036 and beyond.

• In the 38 years from 1977 to 2014, the period TFR was generally in the range of 1.9–2.2 births per woman.
• The cohort TFR indicates a progressive decline in completed family size. Women born in the early 1970s averaged 2.2 births each, compared with 2.5 for those born in the early 1950s.
• Census data (1981, 1996, 2006, 2013) on the number of children ever born also indicate progressive declines in completed family size and progressive increases in childlessness.
• Internationally, TFRs are generally declining, or are already lower than in New Zealand. New Zealand's TFR is one of the highest among Organisation for Economic Co-operation and Development (OECD) countries.

Age-specific fertility rates (ASFRs) are assumed to vary throughout the projection period. The median ASFRs decline for women aged under 32 years, and increase for women aged 32 years and over.

Note: 'Period fertility rates by age' graph shows the 5th, 25th, 50th, 75th, and 95th percentiles.

Future fertility trends are uncertain and depend on a range of factors.

• Changes in population composition and different trends in population subgroups (including ethnic groups).
• Trends in ideal family size and the strength of individual desires for children.
• Trends in the patterns of education and work, including the timing, duration, and proportion of time dedicated to those activities.
• Changing macro-level conditions (eg government policies, childcare facilities, and housing) that influence the cost of children in a broad sense.
• Changing nature and stability of partnerships, including rates of partnership formation (including re-partnering) and dissolution.
• Changing biomedical conditions (eg female fecundity, new methods for assisted conception).

Simulations of TFR are produced using a simple random walk with drift model. Random errors are sampled from a normal distribution with a mean of zero and a standard deviation of 0.0555. The standard deviation is derived by fitting an autoregressive integrated moving average or ARIMA (0,1,0) model to annual TFR for June years 1977–2014. The drift function shifts the median of the TFR simulations to follow the assumed median TFR. Median ASFRs are scaled to sum to the simulated TFR.

Simulations of the sex ratio at birth for each year are produced by drawing a random number sampled from a normal distribution with a mean of 105.5 males per 100 females and a standard deviation of 1.0. The mean and standard deviation are calculated from historical data for December years 1900–2013.

Mortality

Mortality/survival assumptions are formulated using death registrations, period and cohort mortality rates, and international comparisons.

Death rates are assumed to vary throughout the projection period. The assumptions are driven by trends in age-sex death rates. Life expectancy assumptions are not explicitly formulated but are derived from the assumed death rates.

Male and female age-specific death rate assumptions are formulated using a coherent functional demographic method (FDM) developed by Hyndman, Booth, and Yasmeen (Coherent mortality forecasting: the product-​​​​ratio method with functional time series models, 2012). This method builds on the FDM of Hyndman and Ullah (Robust forecasting of mortality and fertility rates: A functional data approach, 2007), which is itself an extension of the Lee-Carter method widely used in mortality forecasting. The research of the authors and Booth, Hyndman, Tickle, and de Jong (Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions, 2006) indicates that FDM forecasts are more accurate than the original Lee-Carter method and at least as accurate as several other Lee-Carter variants. The advantage of the coherent FDM is that it ensures male and female assumptions do not diverge over time.

The coherent FDM uses smoothed historical data to fit the model, which is then forecast using ARIMA and autoregressive fractionally integrated moving average (ARFIMA) time-series models. The historical data is derived from Statistics NZ's cohort mortality series, transposed to give period death rates for each age for June years 1977–2013. Simulations of death rates are produced using an ARIMA (0,2,2) model to give plausible uncertainty bounds. Forecasting mortality in New Zealand has more detail about the coherent FDM.

The median assumption has male period life expectancy at birth increasing to 85.2 years in 2041 and 89.0 years in 2068. The corresponding female period life expectancy at birth increases to 88.4 years in 2041 and 91.5 years in 2068.

As death rates decline, a given percentage reduction in death rates does not produce the same increase in life expectancy as the same percentage reduction when the death rates were higher.

The median assumption has male cohort life expectancy at birth increasing to 80.8 years for those born in 1964 and 90.5 years for those born in 2014. The corresponding female cohort life expectancy at birth increases to 85.5 years for those born in 1964 and 92.9 years for those born in 2014.

Despite differences in methods, the New Zealand life expectancy assumptions are broadly consistent with those in other countries.

Although mortality reductions are expected to continue in the future, the extent of the trends is uncertain and depends on a range of factors.

• Changes in population composition and different trends in population subgroups (including ethnic groups).
• Changes in biomedical technology, regenerative medicine, and preventative methods including monitoring, treatment, and early intervention.
• Changes in health care systems including effectiveness of public health.
• Changes in behaviour and lifestyle (eg smoking, exercise, diet).
• Changes in infectious diseases and resistance to antibiotics.
• Environmental change, disasters, and wars.

Migration

Migration assumptions are formulated using international travel and migration data (including arrivals and departures by country of citizenship and age), immigration applications and approvals, census data on people born overseas (including years since arrival in New Zealand), and consideration of immigration policies (in New Zealand and other countries).

Migration is assumed to vary throughout the projection period. The median net migration (arrivals less departures) decreases from 54,000 in 2015 to 33,000 in 2016, and to 12,000 in 2017 and beyond. The assumed long-run annual net migration of 12,000 reflects the average annual gain of 10–15,000 since the late 1980s and the influence of current immigration policy.

Net migration by age-sex reflects recent observed trends, with the largest movements at ages 15–38 years.

Note: Percentiles shown are 5th, 25th, 50th, 75th, and 95th.

Future migration trends are uncertain and depend on a range of factors in source and destination countries.

• Changes in immigration policy (in New Zealand and other countries).
• Changes in the main motives for migration (eg work, family reunification, education, asylum, retirement).
• Changes in migration pressure in source countries (eg population growth, economic growth).
• Changes in the attractiveness of New Zealand as a place to live (eg work opportunities, economic conditions, wages relative to costs and other countries, settlement and integration practices).
• Costs of migration, including cost of travel and existence of networks and pathways that facilitate migration.
• Environmental change, disasters, and wars.

Simulations of net migration are produced using an ARIMA (0,1,2) with drift model. Random errors are sampled from a normal distribution with mean of zero and calculated standard deviation that increases gradually over the projection period. The standard deviation, autoregressive parameter, and moving average parameter are derived by fitting an ARIMA (0,1,2) model to annual 'permanent and long-term' migration for June years 1988–2014. The drift function shifts the median of the net migration simulations to follow the assumed median net migration. Net migration by age-sex is interpolated between a high and low pattern, to sum to the simulated net migration level.

Accuracy of projections

The accuracy of these projections is unknown at the time of release. While the assumptions are formulated from an assessment of short-term and long-term demographic trends, there is no certainty that any of the assumptions will be realised. The projections do not take into account non-demographic factors (eg war, catastrophes, major government and business decisions) which may invalidate the projections.

See How accurate are population estimates and projections? An evaluation of Statistics New Zealand population estimates and projections, 1996–2013 for an evaluation of previous Statistics NZ national and subnational population estimates and projections.