Urban Place Analyses

The data to 2013 are drawn from the Royal Society Marsden project The ebbing of the human tide. What will it mean for the people? (also known as 'Tai timu tangata. Taihoa e?''). The project was undertaken by researchers from the University of Waikato, Massey University and Motu between 2014 and 2016. Data for the period 2013-2018 have been generated for the CaDDANZ project by the same researchers using the same methodology.

For these maps and graphs, net migration, natural change (births minus deaths/change in cohort size), and total (net) population change were calculated for each urban place, for five- and ten-year periods, back to 1976.1 The methodological notes appended below summarise how this data set was constructed. The CaDDANZ project explores how these data can be visualised and made more accessible and useful to end users.

Population Change (Urban Places)

These graphs (and related maps) provide underlying detail on population change for each urban area and rural centre as defined by Statistics New Zealand prior to their recent shift to SA2 geographic classification; the data thus provide a unique and comprehensive historical picture of urban area change across the period 1976-2018.

The separation of net migration and natural change provides a clear picture of how these components interact to determine total population change. For most urban places, overall natural increase (births minus deaths) is tracking downwards as birth numbers fall and death numbers rise (the result of population ageing); however, this situation differs by age group. For most towns and rural centres, natural change below age 54 has been diminishing, as smaller, later-born (Gen X, Y and millennial) cohorts replace larger earlier-born ones (the baby boomers), while that above 55 years of age is increasing, as baby boom cohorts replace—and in many cases join—their smaller cohort parents. By contrast, the net migration trends for each age group vary considerably by urban area, although net loss at 15-24 years is historically widespread, while areas gaining people aged 25-39 and 40-54 years invariably see an accompanying net migration gain at 0-14 years, their children. This information can be used to determine what age groups have been/are attracted to a town or conversely what age groups have moved/are moving away. Towns can use this information to plan and strategize accordingly.

Urban Places Methodology

The data were generated using four main sets of data originally sourced from Statistics New Zealand:

  • Census usually resident population by age, sex and meshblock-to urban area for the census years 1976, 1981, 1986, 1991, 1996, 2001, 2006, 2013 and 2018 (Database 1);
  • Estimated resident population by age, sex and territorial authority area for the census years 1996, 2001, 2006, 2013 and 2018;
  • Births by age and territorial authority area of residence of mother for the years 1997, 2001, and 2006-2018 inclusive; and
  • Lifetabletalb13 which was used to calculate survivorship ratios by age, sex, and territorial authority area for the life table periods 2005-2007 and 2012-2014.

All data including that for 2018 were re-based to 2013 geographic boundaries.

Population data: Mesh-block level counts of the usually resident population by sex and 5-year age group (to 80+ years) for all census years 1976-2013 were sourced from Statistics New Zealand and aggregated to the 2013 geographic area boundaries at urban area (UA) level, based on their urban area and rural centre status in 1976. The allocation of data for the period 1976-2013 to 2013 geographic areas was based on a ‘user-derived correspondence’. The 2018 data to update the study were purchased directly from Statistics New Zealand, aggregated to the same specifications (Statistics New Zealand 2020). The counts are not official statistics but should be thought of experimental estimates intended for use in research.2 This exercise resulted in data for 143 urban areas and 132 rural centres.

Birth and survivorship rates for all years for which these data were required (i.e., back to 1976) are not available at urban area or rural centre level and were instead constructed using indirect standardisation. In order to construct birth rates, two customised datasets of births by 5-year age group and territorial authority area of residence of mother were purchased from Statistics New Zealand (2016 and 2019), covering the periods 1997-2013 and 2013-2018 (June years) at 2013 geographic boundaries. Survivorship (Lx)1 rates by age and sex for each territorial authority area were accessed for the years 2005-07 and 2012-14 (the only years for which these data are available) (Statistics New Zealand 2015a).

Calculating birth rates for missing years via indirect standardisation was done in two main steps. First, age-specific fertility rates were constructed for each of New Zealand’s 67 territorial authority areas (TA) for the June years 1996-97, 2001-02, 2006-13 and 2013-18 inclusive, using number of births by age of mother as sourced above, and female estimated resident population counts for corresponding 5-year age groups 15-49 years sourced from Statistics New Zealand (2015b and 2019b). The age-specific fertility rates for 1996 and 2001 were then summed and averaged (for each age group and each TA), and their ratio to the equivalent rates for total New Zealand constructed (drawing on Statistics New Zealand 2015c). These relative age-specific fertility ratios for each TA were then held constant and multiplied by the equivalent rates for total New Zealand for the missing years, 1976, 1981, 1986, and 1991. That is, the national values were retrospectively inflated or deflated by the relevant ratio, for each of the four observations 1976-1991, to generate approximate TA level age-specific rates for those years. For the period 2006-2018, rates were directly constructed for each TA.

The second step involved constructing age-specific fertility rates for each town and rural centre, by applying the age-specific rates for the TA in which each is located to the number of women in each 5-year age group 15-49 years, in each town and rural centre (from the population database).

The resulting birth rates and numbers at TA level differ slightly from those published by Statistics New Zealand (2015d) because they are constructed experimentally. As with the underlying population counts, the fertility rates and resulting birth numbers should be thought of as best approximations extracted for these research purposes.

Calculating missing survivorship rates via indirect standardisation was similarly done in two steps. First, Lx values (the average number alive in each age group) by 5-year age group and sex for each TA for two Life-Table periods, 2005-07 and 2012-14, were compared to the average number alive in the preceding 5-year age group. This process produced sex- and age-specific survivorship ratios for each five-year age group to 95+ years, for these two observations (for the purposes of this exercise, considered to be 2006 and 2013). The 2006 ratios were then compared with their national equivalents, to generate relative survivorship ratios for each TA for the missing years: 1976, 1981, 1986, 1991, 1996, and 2001. That is, for each of those observations, the national values were retrospectively inflated or deflated by the relevant sex- and age-specific survivorship ratios for each TA in 2005-2007, to generate approximate TA level rates. For the period 2006-13 the average of the ratios for 2005-07 and 2012-14 (separately by age and sex) were used, and for the period 2013-18, ratios for the period 2012-14 only.

The second step involved constructing sex- and age-specific survivorship rates for each town and rural centre, by applying the rates for the TA in which each is located, to the number of males and females in each five year age group, in each town and rural centre (Database 1). In order to survive age groups above 80 years, the 80+ year age group from Database 1 was prorated to 80-84, 85-89, 90-94 and 95+ years according to the New Zealand distribution (by sex) at those ages.

Again, the resulting data are ‘best approximations’ based on calendar year survivorship ratios and census usually resident population counts.

When the resulting data are compared with published birth and death numbers for each TA, which are available for all years 1992-2018, there is strong correspondence, and the model is thus considered sufficiently robust to use for our purposes of calculating the components of change for towns and rural centres. This is done using cohort component analysis and the ‘residual’ method for separating net migration from net change (e.g., Rowland 2003, Chapter 12).

Calculating components of change by the residual method: The resulting fertility and survivorship rates were used in a conventional cohort component analysis to separate out the contributing effects of both net migration and natural increase. First, survivorship rates for each age group were applied to the baseline usually resident population numbers for each individual observation (separately by sex), and fertility rates applied to survived women aged 15-49 years. The resulting births were summed and apportioned male/female according to the standard sex ratio for New Zealand (105 males per 100 females). Births were entered at age 0-4 years, and all other age groups ‘aged’ by five years. The resulting ‘expected’ population by age and sex was then compared to the actual population at the relevant observation (for example, the survived and ‘reproduced’ population from 1976 was compared to the actual population for 1981), and the difference at each age (five-year age group) taken to be a residual measure of net migration by age across the five-year period. Subtracting total estimated net migration from net change in population size between the two observations in turn generates the natural increase component, which in turn is disaggregated into its births and deaths components by summing each individual component generated at each step.

  • 1. [Data for the period 1976-2013 originated from the Royal Society Marsden project: Tai Timu Tangata: Taihoa e? (English trans. The ebbing of the human tide. What will it mean for the people?)(The subnational mechanisms of the ending of population growth: Towards a theory of depopulation) [Contract MAU1308]. For key output from this project using this data set, see: http://igps.victoria.ac.nz/publications/PQ/2017/PQ-Vol-13-Supplementary-2017.pdf.]
  • 2. [Disclaimer: Access to these data was provided by Statistics New Zealand under conditions designed to give effect to the security and confidentiality provisions of the Statistics Act 1975. The results presented in these tables are the work of the author/s, not Statistics New Zealand.]
  • 3. [Lx values are a statistical function of the Life Table, via which life expectancy is calculated.]

Further reading

Brabyn L and NO Jackson (2019) A new look at population change and regional development in Aotearoa New Zealand. New Zealand Geographer. 75: 116-129. DOI: 10.1111/nzg.12234

Brabyn L, NO Jackson, G Stichbury, T McHardie (2019) Visualising and Communication Population Diversity Through Web Maps. New Zealand Population Review, 45, 46–66.

Brabyn L (2017) Declining Towns and Rapidly Growing Cities in New Zealand- developing an empirically-based model that can inform policy. Policy Quarterly. 13, 37-46. http://igps.victoria.ac.nz/publications/PQ/2017/PQ-Vol-13-Supplementary-2017.pdf.

Jackson NO, L Brabyn, D Maré, MP Cameron and I Pool (2019) From ageing-driven growth towards the ending of growth. Subnational population trends in New Zealand, in J Anson, W Bartl, A Kulczycki (Hg.) Studies in the Sociology of Population. International Perspectives. Switzerland: Springer Nature. pp 161-193.

Jackson NO and L Brabyn (2017) The mechanisms of subnational population growth and decline in New Zealand, 1976-2013’ Policy Quarterly Supplement 13: 22-36. http://igps.victoria.ac.nz/publications/PQ/2017/PQ-Vol-13-Supplementary-2017.pdf.

Jackson, NO, L Brabyn and D Maré (2016) New Zealand’s towns and rural centres 1976-2013 – experimental components of growth’. Working Paper No. 7. National Institute of Demographics and Economic Analysis, University of Waikato, Hamilton New Zealand. Now being revised for AJRS.

References

Rowland DT (2003) Demographic Methods and Concepts Oxford: Oxford University Press.

Statistics New Zealand (2020) Customised database. Census usually resident population by age, sex and meshblock-to urban area for the 2018 census, based on 2013 geographic classification.

Statistics New Zealand (2019a) Customised Database. NZ resident live births by territorial authority are residence of mother and age of mother for the 2013-2019 June Years.

Statistics New Zealand (2019b) Subnational population estimates (RC, TA, AU), by age and sex, at 30 June 1996, 2001, 2006-18

Statistics New Zealand (2016) Customised database. Estimated birth occurrence, at 30 June 1997-2013 by age of mother and territorial authority area of usual residence based on 2013 boundaries.

Statistics New Zealand (2015a) Life Table Functions 2005-07 and 2012-14, by Territorial Authority Area, age and sex.

Statistics New Zealand (2015b) Subnational population estimates (RC, TA, AU), by age and sex, at June 1996, 2001, 2006, 2013 (2013 bounndaries).

Statistics New Zealand (2015c) Live Births per 1,000 Women by Age, Maori and Total, Table DFM019AA.