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Climate change, internal migration, and the future spatial distribution of population: a case study of New Zealand

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Abstract

This paper evaluates the impact of climate change on the spatial distribution of population in New Zealand, focusing on the effects of climate on internal migration dynamics. Specifically, a gravity modelling framework is first used to identify climate variables that have statistically significant associations with internal migration. The gravity model is then embedded within a population projection model to evaluate the effect of climate scenarios on regional populations. Of the climate variables, only surface radiation in the origin exhibits a significant association with internal migration. Including this variable in the population projection model makes a small difference to the regional population distribution, and the difference between different climate scenarios is negligible. Overall, the results suggest that, while statistically significant, climate change in the form of changes in the distribution of the weather will have a negligible effect on the population distribution of New Zealand at the regional level. These null results probably reflect the high capacity for adaptation to climate change available to a developed country.

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Notes

  1. Although see Rees et al. (2010) for an application that incorporates climate change into the migration component of a population projection model for NUTS2 regions in the European Union.

  2. These averages are population-weighted. See later in this paper for details.

  3. While there may be impacts of climate change on mortality (but probably not on fertility), and these impacts may be different for different regions, the direction and magnitude of these impacts are not clear.

  4. The IOM World Migration Report 2015 notes estimates of 232 million international migrants, and 740 million internal migrants.

  5. See Beine and Parsons (2015) for a discussion of four possible channels for the effect of climate on migration.

  6. Area units are the second-smallest geographical area for which Statistics New Zealand produces data, and regions are made up of complete sets of area units. Area units in urban areas are approximately the size of a suburb, with a mean population of about 4500.

  7. Dell et al. (2014) draw the distinction between models that investigate short-run weather variation and those that investigate long-run climate variation. Our model fits somewhere in-between those extremes. For simplicity, we adopt the term climate variation throughout the paper, while noting that some readers may instead prefer to our results as resulting to medium-run variation in the distribution of the weather.

  8. The Census of Population and Dwellings is usually held every 5 years; however, due to the 2010 and 2011 Christchurch earthquakes, the 2011 Census was delayed until 2013. Because the migration data were based on a question that asked for each respondent’s place of residence 5 years previous, and because time series modelling is not employed (see the following section), this break in the five-year frequency of the Census does not pose a serious issue.

  9. The exception is the population for 1991, where an estimated usually resident population was not available due to a change in population definitions at the time, when only the de facto population was reported. In this case, we took the estimated de facto population from the 1991 Census, and scaled it based on the region-specific ratio of de facto to de jure population from the 1996 Census.

  10. Specifically, the population-weighted centroid was calculated from the 2013 estimated usually resident population of each area unit, and the geographic centroid of each area unit.

  11. Similar figures for other climate variables are available from the author on request.

  12. In structural gravity models, it is common to include multilateral resistance terms. Following Anderson and van Wincoop (2003), we use origin and destination fixed effects to capture these effects, as they are able to be included in the population projections model (as described in the following section), and additionally capture other time-invariant differences between the regions. Including origin-year fixed effects or destination-year fixed effects (as in Beine and Parsons 2015), would lead to variables that could not be separately identified from the climate variables, which vary by region and over time. We note that the fixed effects estimator is less efficient than a nonlinear least-squares estimator, but the estimated coefficients are consistent and unbiased (Anderson and van Wincoop 2003). Our specification fails to capture time-variant multilateral resistance (e.g. see Gröschl and Steinwachs 2017). However, given that internal migration flows take place within a common policy environment and there were no significant changes in internal migration costs over the period of our data, we expect any bias resulting from a failure to account for time-variant multilateral resistance to be negligible.

  13. The next most statistically significant variable was relative humidity in the destination (p = 0.124).

  14. To the extent that internal migration leads some migrants to move to areas with higher (lower) fertility rates and lower (higher) mortality rates, this will lead to second (and higher) order effects that increase (decrease) the regional (and total New Zealand) populations.

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Acknowledgements

The author is grateful to Jacques Poot for useful comments and suggestions regarding the population projections model, to Sialupapu Siameja for excellent research assistance, and to the editor and four anonymous reviewers for their helpful comments. The usual disclaimer applies.

Funding

This research was funded by the Ministry of Business, Innovation and Employment as part of the Climate Change Impacts and Implications project (Contract C01X1225), led by Andrew Tait (NIWA) and Daniel Rutledge (Landcare Research).

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  1. Department of Economics & National Institute of Demographic and Economic Analysis, University of Waikato, Private Bag 3105, Hamilton, 3240, New Zealand

    Michael P. Cameron

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Appendix

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Fig. 5
figure 5

Daily average total precipitation, by region

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Fig. 6
figure 6

Daily average surface radiation, by region

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Cameron, M.P. Climate change, internal migration, and the future spatial distribution of population: a case study of New Zealand. Popul Environ 39, 239–260 (2018). https://doi.org/10.1007/s11111-017-0289-8

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