National Population Projections 2011-base

General methodology is outlined in the National Population Projections data collection. Reference period This release contains 2011-base projections of the population usually living in New Zealand. These supersede the 2009-base projections released in October 2009. The new projections have the estimated resident population at 30 June 2011 as a base, and cover the period 2012–61 at one-year intervals. Extended projections beyond 2061 are available on request. Email: [email protected]. Changes since the previous 2009-base projections Stochastic projections For the first time, Statistics NZ applied a stochastic (probabilistic) approach to producing population projections. Stochastic population projections provide a means of quantifying demographic uncertainty, although it is important to note that the estimates of uncertainty are themselves uncertain. By modelling uncertainty in the projection assumptions and deriving simulations, estimates of probability and uncertainty are available for each projection result. No simulation is more likely, or more unlikely, than any other. However, the simulations provide a probability distribution which can be summarised using percentiles, with the 50th percentile equal to the median. For each assumption, the median is equivalent to the 'medium' assumption used in previous deterministic projections. Similarly, the median stochastic projection is equivalent to the deterministic projection that combined the medium fertility, medium mortality, and medium migration assumptions in previous projections (ie series 5 in the 2009-base projections). More information about stochastic projections is available in the Statistics NZ working paper Experimental stochastic population projections for New Zealand: 2009(base)–2111. Review of assumptions The derivation of the projections involves a review of all projection assumptions. The main changes from the previous 2009-base projections are: The base population at 30 June 2011 of 4.405 million is 20,000 (0.5 percent) lower than that projected from the 2009-base projections (series 5), mainly because observed net migration in 2010–11 (20,000) was lower than assumed (44,000). Note, however, that the 2006–12 population estimates are subject to revision following the 2013 Census of Population and Dwellings. The median annual net migration gain is assumed to be 12,000 in the long term, compared with 10,000 in the 2009-base projections (medium variant). In the short term, the median net migration assumptions are -3,000, 0, and 7,000 in June years 2012, 2013, and 2014, respectively. The median period life expectancy at birth would reach 88.1 and 90.5 years for males and females, respectively, in 2061. This is higher than the corresponding figures of 85.6 and 88.7 years in the 2009-base projections (medium variant). Projection assumptions Projection assumptions are formulated after analysis of 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 2011. This population (4.405 million) was derived from the ERP of New Zealand at 30 June 2006 (4.185 million), updated for births, deaths, and net migration between 30 June 2006 and 30 June 2011 (+221,000). The ERP of New Zealand at 30 June 2006 was derived from the census usually resident population count at 7 March 2006 (4.028 million) with adjustments for: net census undercount (+80,000) residents temporarily overseas on census night (+64,000) births, deaths and net migration between census night and 30 June 2006 (+9,000) reconciliation with demographic estimates at ages 0–4 years (+3,000). For more information about the base population, refer to information about the population estimates. The ERP is the best available measure of the number of people usually living in New Zealand. However, for projection purposes, some uncertainty in the base population has been assumed. This uncertainty is assumed to vary by age and sex, and arise from two broad sources: Census enumeration and processing. Coverage errors may arise from non-enumeration and mis-enumeration (eg residents counted as visitors from overseas, and vice versa), either because of deliberate or inadvertent respondent or collector error. Errors may also arise during census processing (eg scanning, numeric and character recognition, imputation, coding, editing, creation of substitute forms). Adjustments in deriving population estimates. This includes the adjustments applied in deriving the ERP at 30 June of the census year (eg net census undercount). It also includes uncertainty associated with the post-censal components of population change (eg estimates of births occurring in each time period based on birth registrations; changes in classification of external migrants between ‘permanent and long-term’ and 'short-term'). Simulations of the base population are produced by drawing a random number sampled from a normal distribution with a mean of zero. For each simulation, a random number is multiplied by the assumed standard error for each age-sex then added to the base ERP. 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 2.05 births per woman in 2012 to 1.96 in 2021, and to 1.90 in 2036 and beyond. In the 35 years from 1977 to 2011, the period TFR was generally in the range 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) also indicates progressive declines in completed family size and progressive increases in childlessness. Internationally, TFRs are generally declining. New Zealand's TFR is one of the highest among Organisation of Economic Co-operation and Development (OECD) countries (and was lower than only Israel in the OECD in 2007–08). Age-specific fertility rates (ASFRs) are assumed to vary throughout the projection period. The median ASFRs decline for women aged under 36 years, and increase for women aged 36 years and over. 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 mean of zero and calculated standard deviation of 0.0625. The standard deviation is derived by fitting an autoregressive integrated moving average or ARIMA (0,1,0) model to annual TFR for December years 1977–2011. 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 mean of 105.5 males per 100 females and standard deviation of 1.0. The mean and standard deviation are calculated from historical data for 1900–2011. 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 data 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 method 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 1977–2011. A final adjustment is made to the forecast death rates to give male and female deaths at the start of the projection period, which are consistent with the latest death registrations. Simulations of death rates are produced using an ARIMA (0,2,2) model to give plausible uncertainty bounds. The median assumption has male period life expectancy at birth increasing to 84.3 years in 2036 and 88.1 years in 2061. The corresponding female period life expectancy at birth increases to 87.3 years in 2036 and 90.5 years in 2061. The median assumption has male cohort life expectancy at birth increasing to 80.0 years for those born in 1961 and 90.2 years for those born in 2011. The corresponding female cohort life expectancy at birth increases to 84.7 years for those born in 1961 and 92.9 years for those born in 2011. 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) increases from -3,000 in 2012 to zero in 2013, to 7,000 in 2014, and to 12,000 in 2015 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 main net inflows at ages 15–32 years. 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 (1,0,1) with drift model. Random errors are sampled from a normal distribution with mean of zero and calculated standard deviation of 11,861. The standard deviation, autoregressive parameter, and moving average parameter are derived by fitting an ARIMA (1,0,1) model to annual 'permanent and long-term' migration for June years 1980–2011. 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. Which projection should I use? The projections are summarised by percentiles, which indicate the probability distribution for any projected characteristic. Users can make their own judgement as to which projections are most suitable for their purposes. At the time of release, the 50th percentile (or median) indicates an estimated 50 percent chance that the actual result will be lower, and a 50 percent chance that the actual result will be higher, than this percentile. The 25th percentile indicates an estimated 25 percent chance that the actual result will be lower, and a 75 percent chance that the actual result will be higher, than this percentile. It is important to note, however, that the estimates of uncertainty are themselves uncertain. en-NZ