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Modelling Inter-urban Migration in an Open Population Setting: The Case of New Zealand

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Population, Place, and Spatial Interaction

Part of the book series: New Frontiers in Regional Science: Asian Perspectives ((NFRSASIPER,volume 40))

Abstract

In this chapter, we revisit the modelling of gross inter-urban migration flows in New Zealand. As in previous work, we identify a range of geographic, demographic, economic and climatic characteristics of urban areas, which are statistically significant determinants of migration. However, we argue that in a small but open population such as New Zealand (in which one quarter of the resident population is foreign born and one sixth of the New Zealand-born population lives abroad), inter-urban migration should be modelled jointly with rural-urban and international migration. We proceed to estimate a modified gravity model of migration in which the flow matrix is augmented with rural-urban and international migration. Migration data are obtained from four successive population censuses since 1996. We find notable differences in the impact of migration determinants when comparing urban-urban, urban-rural and urban-world migration flows. The estimation of these models is straightforward and does not require collection of data on rural areas or foreign countries. Hence, the method can be easily applied to other case studies in which international and/or rural-urban migration are important components of population churn.

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Notes

  1. 1.

    According to the census, the usually resident population of New Zealand in 2013 was 4.2 million, of whom 75% were New Zealand born. At the time of the 2016 Australian census, there were 518,000 New Zealand-born residents of Australia. The total New Zealand diaspora is, in terms of citizenship, estimated to be around 750,000 (https://en.wikipedia.org/wiki/New_Zealanders), but less in terms of country of birth—perhaps around 600,000. Hence, of the global number of New Zealand born that equals 0.75 × 4,200,000 + 600,000 = 3,750,000, about one in six lives abroad.

  2. 2.

    Regarding such personal characteristics, see, for example, on marital status, Maxwell (1988); on education, Greenwood (1969) and Caldwell (1968); on employment status, Pissarides and McMaster (1990); and on networks, Curran and Rivero-Fuentes (2003), Pedersen et al. (2008) and Michaelides (2011).

  3. 3.

    On income, see, for example: Sjaastad (1962) and Greenwood (1969); on regional unemployment, Westerlund (1998) and Cebula and Alexander (2006); on housing, Jeanty et al. (2010) and Modestino and Dennett (2013); and on climate, Graves (1979); Rappaport (2007) and Poston et al. (2009).

  4. 4.

    See, for example, Frey (1996), Borjas et al. (1996), Card and DiNardo (2000), Borjas (2006) and Glitz (2012).

  5. 5.

    See, for example, Poot (1986), Maré and Timmins (2000), Maré and Choy (2001), Maré et al. (2007), Sloan (2013), Poot et al. (2016) and Cameron and Poot (2019).

  6. 6.

    In the estimation in Stata, we have set these values to 0.

  7. 7.

    The economic and demographic attributes of urban areas are lagged one period to limit the impact of potential endogeneity of the regressors. It is expected that economic and demographic conditions at the time of the previous census are likely to have impacted the migration flow over the subsequent intercensal period. At the same time, a shock to migration impacts on current and future population, but not on past population. Lagging the explanatory variables is even more appropriate when we consider that the migration data are arrived at by checking whose area of residence is different from where they were 5 years ago. Given that expectations are often based on extrapolations from the past, a person whose current residence in the 2013 census is reported as Wellington but who resided in Auckland at the time of the 2006 census is more likely to have changed residence because of conditions in Auckland and Wellington prior to 2006, and including 2006, than subsequently.

  8. 8.

    Because migration flows in gravity models are specified in logarithms, zero flows present a challenge as the logarithm of zero is not defined. See, e.g. Ramos (2016) for alternative approaches.

  9. 9.

    To preserve confidentiality, New Zealand census counts are rounded to multiples of 3: an actual count of 0 is reported as such, but an actual count of 1 is rounded down to 0 with probability 2/3 and rounded up to 3 with probability 1/3, with the reverse probabilities for rounding a count of 2. If the low frequencies were uniformly distributed, a rounded value of 0 is therefore reported in 2/3 of the cases rather than 1/3. However, the distribution of low frequencies is unlikely to be uniform, with 0 likely to be much more common than 1 or 2, particularly in migration matrices referring to small areas or relatively small groups.

  10. 10.

    Estimations in which zero migration flows are excluded are available from the authors upon request.

  11. 11.

    Given that censuses are held at the same time every 5 years (but 7 years between the 2006 and 2013 census), cohorts can be followed over time. After accounting for immigration, internal migration and age and sex specific mortality rates, emigration can be calculated as the residual change in the size of a cohort. Of course the resulting numbers are measured with some error, due to census undercounting, etc.

  12. 12.

    Three types of urban areas exist: a main urban area is one with a population of at least 30,000 people, a secondary urban area is one with a population of less than 30,000 people but where more than 20% of the employed population works in a main urban area and a minor urban area is one with a population of less than 30,000 people and where less than 20% of the employed population works in a main urban area.

  13. 13.

    Manukau city centre was the reference point for the South Auckland urban area, Henderson for West Auckland, North Shore Information centre for North Auckland and Auckland city centre for the Central Auckland urban area.

  14. 14.

    Average rainfall and sunshine information was available for only 20 out of the 40 urban areas. For urban areas where data were unavailable, data from the nearest urban area within a 100 km range were used to proxy for the missing information.

  15. 15.

    The slight difference in the coefficient of distance in Column (2) as compared with Column (1) is entirely due to the additional 640 (6880–6240) observations (representing migration between urban areas and overseas or rural areas), which are assumed to be affected by population in an identical way as the interurban flows. Once the population impact is allowed to differ between interurban and other flows, the distance effect is again the same as in column (1), as can be seen in column (3).

  16. 16.

    The six biggest cities considered are Auckland, Christchurch, Wellington, Hamilton, Tauranga and Dunedin.

  17. 17.

    This effect is statistically significant at the 1% level in a regression that excludes the observations with zero gross migration.

  18. 18.

    In a regression that excludes the observations with zero gross migration, the effect is statistically significant at the 1% level, but the coefficient decreases to −2.482.

  19. 19.

    Excluding the zero migration flows (corresponding to predominantly smaller urban areas), the coefficient in origin income growth becomes −6.098 and is statistically significant at the 1% level.

  20. 20.

    Mayda (2010) examined the determinants of migration into 14 OECD countries by country of origin between 1980 and 1995. Liebig and Sousa-Poza (2004) used a dataset that covers over 23 countries, including the countries of the EU, the USA, Canada, Australia and New Zealand.

  21. 21.

    The Theil index is one of the generalised entropy (GE) measures of inequality. See ConceiĂ§Ă£o and Ferreira (2000) for details.

  22. 22.

    This effect is no longer statistically significant when observations with zero migration are excluded. However, in the latter case, emigration is lower from urban areas with high inequality, with a coefficient of −1.949 (compare with −3.196 in column 2(c)) that is statistically significant at the 5% level.

  23. 23.

    See, e.g. for the USA, Blanchard and Katz (1992); Europe, Decressin and FatĂ¡s (1995); Australia, Debelle and Vickery (1999); and New Zealand, Choy et al. (2002).

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Acknowledgements

Research for this study was originally funded by the 2012–2014 Nga Tangata Oho Mairangi project, Ministry of Business Innovation and Employment grant CONT-29661-HASTR-MAU, and more recently by the 2016–2019 National Science Challenge 11: Building Better Homes, Towns and Cities project. Access to the data used in this study was provided by Statistics New Zealand (SNZ) under conditions designed to give effect to the security and confidentiality provisions of the Statistics Act 1975. All frequency counts using census data were subject to base three rounding in accordance with SNZ’s release policy for census data.

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Authors and Affiliations

  1. Waikato Management School, University of Waikato, Hamilton, New Zealand

    Omoniyi B. Alimi

  2. Motu Economic and Public Policy Research, Wellington, New Zealand

    David C. Maré

  3. National Institute of Demographic and Economic Analysis (NIDEA), University of Waikato, Hamilton, New Zealand

    Jacques Poot

  4. Department of Spatial Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jacques Poot

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  1. Omoniyi B. Alimi
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Correspondence to Jacques Poot .

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  1. Center for Urban and Regional Development Studies (CURDS), Newcastle University, Newcastle upon Tyne, UK

    Rachel S. Franklin

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Alimi, O.B., Maré, D.C., Poot, J. (2019). Modelling Inter-urban Migration in an Open Population Setting: The Case of New Zealand. In: Franklin, R. (eds) Population, Place, and Spatial Interaction. New Frontiers in Regional Science: Asian Perspectives, vol 40. Springer, Singapore. https://doi.org/10.1007/978-981-13-9231-3_11

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