National Ethnic Population Projections 2018-base Update

General methodology is outlined in the National Ethnic Population Projections data collection. ** Projection assumptions for 2018-base national ethnic population projection update ** Base population These projections have as a base the estimated resident population (ERP) of each ethnic group at 30 June 2018. For more information about the ethnicity variable see Ethnicity (information about this variable and its quality). The level 1 ethnic populations (‘European or Other’, Māori, Pacific, Asian, and MELAA) come directly from the 2018-base ERP. See Estimated resident population (2018-base): At 30 June 2018 for more information. For the level 2 ethnic populations (Samoan as a subpopulation of Pacific, and Chinese and Indian as subpopulations of Asian), the separate adjustments are not explicitly estimated. Instead, the level 2 ERPs are based on the ratio of the level 2 ‘census usually resident population count’ to the respective level 1 ‘census usually resident population count’ applied to the level 1 ERP by age-sex. The Estimated resident population (ERP), adjustments to derive ERP at 30 June 2018 (from census usually resident population) table in NZ.Stat provides a summary of the ERP and adjustments to derive ERP at 30 June 2018 for each level 1 ethnic groups. The ERP is the best available measure of the number of people of each ethnic group 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 (e.g. 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 (e.g. 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 (e.g. net census undercount). It also includes uncertainty associated with the post-censal components of population change (e.g. 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'). For each ethnic group, 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 and paternity New birth cohorts are added to the population by applying fertility assumptions to the female population of childbearing age (12–49 years) and paternity assumptions to the male population (15–54 years). The paternity rates allow for births that men of a given ethnic group have with women not of that ethnic group. The assumptions are formulated relative to those in the National Population Projections: 2022(base)–2073 using birth registrations, period fertility rates, and census data on 'number of children born alive' (including rates of childlessness). Total fertility rates (TFRs) and total paternity rates (TPRs) are assumed to vary throughout the projection period. Age-specific fertility rates (ASFRs) and age-specific paternity rates (ASPRs) are assumed to vary throughout the projection period. Under the median assumption, ASFRs decrease at younger ages (for example, below 30 years) but increase at older ages (for example, above 35 years) over time. For men, ASPRs decrease at all ages for most ethnic groups. For each ethnic group, simulations of TFRs and TPRs are produced using a simple random walk with drift model. Random errors for TFRs and TPRs are sampled from a normal distribution with a mean of zero and different standard deviations for each ethnic group: 0.05 and 0.01 for 'European or Other' 0.09 and 0.04 for Māori 0.07 and 0.02 for Asian 0.10 and 0.02 for Chinese 0.09 and 0.04 for Indian 0.11 and 0.05 for Pacific 0.14 and 0.09 for Samoan 0.13 and 0.06 for MELAA. The drift function shifts the median of the TFR and TPR simulations to follow the assumed median TFR and TPR. Median ASFRs and ASPRs are scaled to sum to the simulated TFR and TPR. The projections allow for births to parents of each ethnic group that are not registered as children of that ethnic group. Simulations of this loss factor for each ethnic group and year are produced by drawing a random number sampled from a normal distribution with different means and standard deviations based on historical data for: 'European or Other' – a mean of 1.5 percent and standard deviation 0.3 Māori – a mean of 3.6 percent and standard deviation 0.2 Asian – a mean of 1.9 percent and standard deviation 0.7 Chinese – a mean of 2.5 percent and standard deviation 1.0 Indian – a mean of 1.4 percent and standard deviation 0.5 Pacific – a mean of 2.8 percent and standard deviation 0.3 Samoan – a mean of 2.7 percent and standard deviation 0.3 MELAA – a mean of 9.4 percent and standard deviation 1.5. The projections then allocate births between male and female. Simulations of the sex ratio at birth for each ethnic group and year are produced by drawing a random number sampled from a normal distribution with a mean of 105.5 males per 100 females and different standard deviations for each ethnic group: 1.3 for 'European or Other', 2.1 for Māori, 2.5 for Asian, 4.1 for Chinese, 2.8 for Indian, 2.2 for Pacific, 3.7 for Samoan, and 5.0 for MELAA. The mean and standard deviation are based on historical data. Future fertility trends are uncertain and depend on a range of factors: changes in population composition and different trends in subpopulations (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 (for example, 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 (for example, female fecundity, new methods for assisted conception). Mortality Mortality assumptions are applied to each age-sex group to allow for deaths. The assumptions are formulated relative to those in National Population Projections: 2022(base)–2073 using death registrations and period life tables, which are driven by historic trends in age-sex-specific death rates. Under the median assumption, life expectancy at birth (e0) increases between 2019 and 2043 for both males and females for all ethnic groups. As with the national population projections, death rates change at different rates at different ages, and age-specific survivorship rates (ASSRs) are assumed to vary throughout the projection period. For each ethnic group, simulations of e0 are produced using a simple random walk with drift model. Random errors are sampled from a normal distribution with a mean of zero and the standard deviations are based on ethnic period life tables for males and females in each year. The drift function shifts the median of the e0 simulations to follow the assumed median e0. Median ASSRs are scaled to sum to the simulated e0. 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 subpopulations (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 applied to each age-sex group to allow for net migration (migrant arrivals minus migrant departures). Ethnicity is not collected directly in external migration data, but the migration assumptions are based on the ethnicity of migrants derived from other government data (linked administrative sources); an assessment of recent and expected trends of arrivals and departures of New Zealand citizens and non-New Zealand citizens by birthplace; and observed intercensal ethnic population change. The 2019–2020 years saw high net migration gains. The impact of COVID-19 and the resulting New Zealand and international border closures, significantly reduced migration flows in 2021–2022. The impact of this differs across different ethnicities. Under the median assumptions, net migration levels are assumed to increase from the 2022 low to their long-term levels (2026–2043). However, future net migration is uncertain and is assumed to fluctuate around the median. For each ethnic group, simulations of net migration are produced using an autoregressive integrated moving average or ARIMA (1,0,1) with drift model. Random errors are sampled from a normal distribution with mean of zero and different parameters for each ethnic group. These simulations reflect the uncertainty in future migration trends. This uncertainty comes from a range of factors in New Zealand and other countries: changes in immigration policy changes in the motives for migration (for example, work, family reunification, education, asylum, retirement) changes in migration pressure in source countries (for example, population growth, economic growth) changes in the attractiveness of New Zealand as a place to live (for example, work opportunities, economic conditions, wages relative to costs and other countries, settlement and integration practices) costs of migration (for example, cost of travel, existence of networks and pathways that facilitate migration) environmental changes, disasters, pandemics, and wars. Inter-ethnic mobility Inter-ethnic mobility (IEM) assumptions are applied to each age-sex group to allow for the net effect of people changing their ethnic identification over time. The IEM assumptions have been developed using rates derived from the New Zealand Longitudinal Census (NZLC), specifically the four most-recent linked census pairs (1996–2001, 2001–06, 2006–13, and 2013–18). IEM rates represent the net propensity for individuals to enter or leave an ethnic group during the following year, relative to the ethnic population at the start of the year. The rates are based on linked records where ethnicity was specified in both census years. In addition to smoothing of the data, it was found that the male and female rates were broadly similar. Under the median assumption, there is an average net change to the population in 2019–2043 due to people changing their ethnic identification for: 'European or Other' – 0.2 percent a year Māori – 0.39 percent a year Asian – 0.16 percent a year Chinese – 0.18 percent a year Indian – 0.13 percent a year Pacific – 0.08 percent a year Samoan – 0.09 percent a year MELAA – 0.06 percent a year. For each ethnic group, simulations of IEM rates by age are produced using an autoregressive integrated moving average or ARIMA (1,0,1) with drift model. Random errors are sampled from a normal distribution with mean of zero and different parameters for each ethnic group. The drift function shifts the median of the IEM simulations to follow the assumed median IEM. en-NZ