Meeting the Sustainable Development Goals leads to lower world population growth

A couple of months ago, a paper I worked on with co-authors from the Wittgenstein Centre was published in the Proceedings for the National Academy of Science of the United States of America. The significance blurb and abstract are below.

Abel, G.J., Bakarat, B., K.C., S. and Lutz, W. (2016). Meeting the Sustainable Development Goals leads to lower world population growth. Proceedings of the National Academy of Sciences. 113 (50)  14294–14299

Forecasting Environmental Immigration to the UK

A couple of months ago, a paper I worked on with co-authors from the Centre of Population Change was published in Population and Environment. It summarised work we did as part of the UK Government Office for Science Foresight project on Migration and Global Environmental Change. Our aim was to build expert based forecasts of environmental immigrants to the UK. We conducted a Delphi survey of nearly 30 migration experts from academia, the civil service and non-governmental organisations to obtain estimates on the future levels of immigration to the UK in 2030 and 2060 with uncertainty. We also asked them what proportion of current and future immigration are/will be environmental migrants. The results were incorporated into a set of model averaged Bayesian time series models through prior distributions on the mean and variance terms.

The plots in the journal article got somewhat butchered during the publication process. Below is the non-butchered version for the future immigration to the UK alongside the past immigration data from the Office of National Statistics.

At first, I was a bit taken aback with this plot. A few experts thought there were going to be some very high levels of future immigration which cause the rather striking large upper tail. However, at a second glance, the central percentiles show a gentle decrease where these is only (approximately) a 30% chance of an increase in future migration from the 2010 level throughout the forecast period.

The expert based forecast for total immigration was combined with the responses to questions on the proportion of environmental migrants, to obtain an estimate on both the current level of environmental migration (which is not currently measured) and future levels:

As is the way with these things, we came across some problems in our project. The first, was with the definition of an environmental migrant, which is not completely nailed on in the migration literature. As a result the part of the uncertainty in the expert based forecasts are reflective of not only the future level but also of the measure itself. The second was with the elicitation of uncertainty. We used a Likert type scale, which caused some difficulties even during the later round of the Delphi survey. If I was to do over, then this I reckon problem could be much better addressed by getting experts to visualise their forecast fans in an interactive website, perhaps creating a shiny app with the fanplot package. Such an approach would result in smoother fans than those in the plots above, which were based on interpolations from expert answers at only two points of time in the future (2030 and 2060).

Publication Details:

Abel, G.J., Bijak, J., Findlay, A.M., McCollum, D. and Wiśniowski, A. (2013). Forecasting environmental migration to the United Kingdom: An exploration using Bayesian models. Population and Environment. 35 (2), 183–203

Over the next 50 years, the potential impact of environmental change on human livelihoods could be considerable, with one possible consequence being increased levels of human mobility. This paper explores how uncertainty about the level of immigration to the United Kingdom as a consequence of environmental factors elsewhere may be forecast using a methodology involving Bayesian models. The conceptual understanding of forecasting is advanced in three ways. First, the analysis is believed to be the first time that the Bayesian modelling approach has been attempted in relation to environmental mobility. Second, the paper considers the expediency of this approach by comparing the responses to a Delphi survey with conventional expectations about environmental mobility in the research literature. Finally, the values and assumptions of the expert evidence provided in the Delphi survey are interrogated to illustrate the limited set of conditions under which forecasts of environmental mobility, as set out in this paper, are likely to hold.

Does specification matter? Experiments with simple multiregional probabilistic population projections.

A paper that I am a co-author on, looking at uncertainty in population forecasting generated by different measures of migration, came out this week in Environment and Planning A. Basically, try and avoid using net migration measures. Not only do they tend to give some dodgy projections, we also found out that they give you more uncertainty. Using in and out measures of migration in a projection model give a big reduction in uncertainty over a net measure. They also are a fairly good approximation to the uncertainty from a full multiregional projection model. Plots in the paper were done by my good-self using the fanplot package.

Publication Details:

Raymer J., Abel, G.J. and Rogers, A. (2012). Does Specication Matter? Experiments with Simple Multiregional Probabilistic Population Projections. Environment and Planning A 44 (11), 2664–2686.

Population projection models that introduce uncertainty are a growing subset of projection models in general. In this paper we focus on the importance of decisions made with regard to the model specifications adopted. We compare the forecasts and prediction intervals associated with four simple regional population projection models: an overall growth rate model, a component model with net migration, a component model with in-migration and out-migration rates, and a multiregional model with destination-specific out-migration rates. Vector autoregressive models are used to forecast future rates of growth, birth, death, net migration, in-migration and out-migration, and destination-specific out-migration for the North, Midlands, and South regions in England. They are also used to forecast different international migration measures. The base data represent a time series of annual data provided by the Office for National Statistics from 1976 to 2008. The results illustrate how both the forecasted subpopulation totals and the corresponding prediction intervals differ for the multiregional model in comparison to other simpler models, as well as for different assumptions about international migration. The paper ends with a discussion of our results and possible directions for future research.

The fanplot package for R

Post moved to the fanplot pkgdown site

A comparison of official population projections with Bayesian time series forecasts for England and Wales

A paper based on my some work I did with colleagues in the ESRC Centre for Population Change was published in the the Population Trends. We fitted a range of time series models (including some volatility models) to population change data for England and Wales, calculated the posterior model probabilities and then projected from the model averaged posterior predictive distributions. We found our volatility models were heavily supported. Our median matches very closely the Office of National Statistics mid scenario. It’s a tad surprising that projections based on forecasts of a single annual growth rate per year give a similar forecast to the ONS cohort component projection which are based on hundreds of future age-sex specific fertility, mortality and net migration rates. The ONS do not provide any form probabilistic uncertainty, instead the give a expert based high and low scenario, which roughly calibrated to our 50% prediction interval in 2033. I ran all the models in BUGS and did the fan chart plots in R.

Publication Details:

Abel, G.J., Bijak, J. and Raymer J. (2010). A comparison of official population projections with Bayesian time series forecasts for England and Wales. Population Trends, 141, 95–114.

We compare official population projections with Bayesian time series forecasts for England and Wales. The Bayesian approach allows the integration of uncertainty in the data, models and model parameters in a coherent and consistent manner. Bayesian methodology for time-series forecasting is introduced, including autoregressive (AR) and stochastic volatility (SV) models. These models are then fitted to a historical time series of data from 1841 to 2007 and used to predict future population totals to 2033. These results are compared to the most recent projections produced by the Office for National Statistics. Sensitivity analyses are then performed to test the effect of changes in the prior uncertainty for a single parameter. Finally, in-sample forecasts are compared with actual population and previous official projections. The article ends with some conclusions and recommendations for future work.