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Applied Spatial Analysis and Policy

, Volume 12, Issue 4, pp 797–829 | Cite as

Evaluation of Sub-National Population Projections: a Case Study for London and the Thames Valley

  • P. ReesEmail author
  • S. Clark
  • P. Wohland
  • M. Kalamandeen
Open Access
Article

Abstract

Sub-national population projections help allocate national funding to local areas for planning local services. For example, water utilities prepare plans to meet future water demand over long-term horizons. Future demand depends on projected populations and households and forecasts of per household and per capita domestic water consumption in supply zones. This paper reports on population projections prepared for a water utility, Thames Water, which supplies water to over nine million people in London and the Thames Valley. Thames Water required an evaluation of the accuracy of the delivered projections against alternatives and estimates of uncertainty. The paper reviews how such evaluations have been made by researchers. The factors leading to variation in sub-national projections are identified. The methods, assumptions and results for English sub-national areas, used in five sets of projections, are compared. There is a consensus across projections about the future fertility and mortality but varying views about the future impact of internal and international migration flows. However, the greatest differences were between projections using ethnic populations and those using homogeneous populations. Areas with high populations of ethnic minorities were projected to grow faster when an ethnic-specific model was used. This result is important for assessing projections for countries housing diverse populations with different demographic profiles. Historic empirical prediction intervals are used to assess the uncertainty of the London and the Thames Valley projections. By 2101 the preferred projection suggests that the population of the Thames Water region will have grown by 85% within an 80% empirical prediction interval between 45 and 125%.

Keywords

Evaluation of sub-National Populations Projection methods Projection assumptions Projection variants Projection uncertainty Empirical prediction intervals 

Introduction

Projections of future sub-national populations are needed for public and private sector planning. Sub-national population projections are used in grant allocation from central to local government departments and agencies and are employed in service planning by local governments, police authorities, fire and rescue services and health agencies. Projected populations are important in planning provision of utility services, such as electricity, gas, water and sewage disposal.

The future horizon for which projected populations are needed varies from one to five years for budget planning, to short-term intervals of 25 years in UK official sub-national projections through medium horizons of 30 to 50 years in local authority (GLA 2014) or academic work (Rees et al. 2016a) to long-term periods of 100 years in pension planning (Pensions Commission 2005).

Thames Water Utilities Limited (Thames Water or TWUL) commissioned the University of Leeds (LEEDS) to carry out long-term population and household projections to 2101 as an input to forecasts of domestic water demand for Thames Water’s Water Resource Zones (WRZs). Thames Water were interested in the impact of additional consumption by households in selected ethnic groups because water consumption records showed that South Asian headed households consumed, per capita, about 53 l per day more than Other Ethnic headed households (Nawaz et al. 2018). For a geographic context to this study, Fig. 1 shows the Thames Water region, its constituent WRZs and the boundaries all Local Authority Districts (LADs) which contribute populations to the WRZs. An inset map locates the Thames Water region within the UK. We refer to these WRZs collectively as the Thames Water region or TW region. Projections of populations, households and water demand were produced for Thames Water by a team at the University of Leeds, referred to as LEEDS in the rest of the paper (Thames Water 2017, Rees et al. 2018).
Fig. 1

Outline map of the Thames water water resource zones and associated local authority districts. Sources: LAD boundaries from UK Borders, crown copyright. WRZ boundaries from TWUL

For quality assurance, Thames Water asked LEEDS to compare their projections with those of the Greater London Authority (GLA), the Office for National Statistics (ONS) and Edge Analytics Ltd. (EDGE), using local authority projections converted to WRZs. LEEDS was required to explain how and why their population projections differed from other projections. The aims of this paper are (1) to review approaches used to evaluate sub-national population projections, (2) to describe the methods and assumptions used in five sub-national projections for the English LADs that cover the WRZs, (3) to compare the LEEDS projected populations against the other projections, (4) to propose reasons for the differences, (5) to estimate the uncertainty of the LEEDS projection and (6) to produce an overall evaluation of the results. Thames Water asked us to argue the case for adopting the LEEDS results as the basis for their domestic water demand projections.

The paper is organized as follows. The second section reviews approaches used by practitioners to evaluate alternative projections, drawing on a growing literature. The third section describes data and methods in the projections evaluated. A fourth section discusses the assumptions used in the five projections. These two sections constitute a valuable resource for researchers and practitioners in southern England. The fifth section compares the results of the central forecast in the set of projections across WRZs and compares variants produced by LEEDS and the GLA. The sixth section presents uncertainty ranges for the LEEDS projections using empirical prediction intervals. The final section summarizes findings and discusses how comparative evaluations might be improved.

Review of Approaches to Evaluating Population Projections

This paper aims to evaluate a population projection for London and the Thames Valley water supply area against a set of alternative projections. A typology of approaches for such an evaluation is set out in Table 1. It provides a label for each evaluation method in the first column, a description in the second and citations of selected papers that exemplify the method in the third column.
Table 1

Methods for evaluating sub-national population projections

 

Evaluation Method

Description of Evaluation Method

Example Papers

1A

Interpretative comparison

Projections by a variety of producers are compared, differences identified and reasons for differences suggested

KC et al. (2017), ONS (2018a, b), Rees et al. (2001)

1B

Controlled comparison

Using a fixed set of inputs, software developed to implement a variety of projection models

Wilson and Bell (2004), NRS (2018)

1C

Tested comparison

Using either controlled comparison types, projections are calibrated on one part of a time series and tested on a following part

Wilson and Rowe (2011), Wilson (2015), Wilson et al. (2018), Wilson (2018), Raftery et al. 2012, Sevcikova et al. 2018

1D

Plausibility evaluation

Projections are examined against a checklist of tests which identify errors or unrealistic models or inputs

Wilson (2017)

1E

Variant projections & Sensitivity analysis

A range of inputs are assembled and run through the same projection model. Development of elasticities of projected outcomes to changes in component inputs.

NRS (2018), Rees et al. (2013), Caswell and Gassen (2015)

1F

Probabilistic projections

Generation of a large set of projections by sampling from error distributions producing probability distributions of future population

Wilson and Bell (2007), Wilson (2013), Sevcikova et al. (2018), Raymer et al. (2012)

1G

Error analysis

Use of the historical errors from tested comparisons as empirical predictive intervals in projections

Smith et al. (2001), Shaw (2007), Shaw (2008), Rayer et al. (2009), Tayman (2011), Wilson (2012), Smith et al. (2013), Simpson et al. (2018)

1H

Use of projections

Advice on how to use evaluation knowledge, Shelf life

Keilman (2008), Wilson et al. (2018) Wilson (2018), Simpson et al. (2018)

The first type of evaluation (Table 1A), Interpretative Comparison, involves comparing key numbers, identifying differences and then developing plausible reasons for the differences, based on knowledge of the models used, input data and future assumptions. KC et al. (2017) produce a sub-national projection of the populations of India’s provinces, split between urban and rural, using estimates and forecasts of the populations by age, sex and educational attainment. The authors make skilful use of the available data in the India Census of 2001 and Sample Registration Survey to estimate the input rates and proportions needed, while acknowledging deficiencies and potential data errors. Results are compared with projections of the all India population in the United Nation’s World Population Prospects (UN 2015) and in the Wittgenstein Centre’s SSP2 (Shared Socio-economic Pathway 2) projection provided in Lutz et al. (2014). The comparisons are made between national populations and the sum of their more detailed province/urban/rural populations. The authors are surprised at the consistency of the projected total populations of India but their interpretation indicates that differences in the structure of the projections and assumptions across components may cancel out. Other examples of this evaluation type include the comparison of methods used in UK Sub-National Population Projections reported in ONS (2018b) and the comparison of methods, assumptions and results of European Union regional population projections in Rees et al. (2001).

The second evaluation approach (Table 1B), Controlled Comparison, involves using a fixed set of inputs (populations and components) and assumptions when running a suite of projections which differ in model design for just one component. Wilson and Bell (2004) test out ten different models for projecting internal migration, including the net migration flow model, the multi-regional model, a pool model and a gravity-type model. They find major differences between model groups but similarities between multi-regional and bi-regional models (replicating results in a similar evaluation by Rogers 1976). The comparisons are for future populations (unknown at the time of writing), so they adopt the multi-regional model as the gold standard for the comparison. The second example comprises variant projections, frequently generated at national scale, for local areas in Scotland (NRS 2018). The results of adopting low or high assumptions for one component at a time, holding others fixed with principal projection inputs are produced and evaluated.

The third evaluation type involves Tested Comparison (Table 1C). The Australian demographer Tom Wilson has improved on the second approach in a suite of papers (Wilson and Rowe 2011; Wilson 2015; Wilson et al. 2018; Wilson 2018) by calibrating models for 5-year inter-census period #1 and then forecasting using the models for period #2. This makes possible assessment of projection outcomes against census results. The method was developed by American demographers for evaluating US census tract and county population projections (Smith et al. 2001; Tayman 2011; Rayer et al. 2009). This approach is more rigorous than Interpretative or Controlled comparison, though authors caution that the best choice of model for a recent time interval may not be the best for the future.

Wilson (2017) points out, in a useful research note, that projection results should be subject to plausibility tests covering projected total population trends, trends across areas, components of change and age-sex structures (Table 1D). These checks are most important for mid/central/principal projections which producers invite users to use as most likely forecasts. He poses 21 questions for which producers should seek answers (Wilson 2017, Table 1). Some are designed to reveal numerical problems. Examples include checking whether all projections by age and sex free of negative values and whether projected net internal migration across the country sums to zero. Others reveal information which helps in deciding further actions, such as whether projected sub-national populations should be adjusted to add up to national projected populations.

A common way of testing the plausibility of principal projections is by running variant projections (Table 1E), in which high and low assumptions for each component are made and the results compared with the main projection. This is standard practice at national scale (ONS 2015) but rarer at sub-national scale (NRS 2018). Developing plausible variants for internal migration is more difficult, with current practice being to use calibration intervals when different migration structures were known to operate (GLA 2014). Reference projections (e.g. no international migration) are also implemented (NRS 2018). Rees et al. (2013) developed system of reference projections based on a design by Bongaarts and Bulateo (1999) which assess the impact of assumptions for each component. This analysis is extended by Caswell and Gassen (2015) to develop a matrix calculus to measure the sensitivity and elasticity of forecast populations to perturbations in assumptions, though the application is for national rather than sub-national projections for Spain.

Variants represent beliefs about alternative futures and are not assigned likelihoods. Three decades ago Nathan Keyfitz emphasized that demographers should be “held responsible for … warning one another and our public what the error of our estimates [of future population] is likely to be” (Keyfitz 1981, p.579). Since then a methodology has been developed for constructing a probability distribution around a preferred projection (Table 1F). Error distributions for future fertility, mortality and migration summary indicators are estimated through one of three approaches: time series analysis (Keilman and Pham 2004), comparison of historical projected populations with later estimated or census populations (e.g. Shaw 2007) and surveys of expert views (Shaw 2008). However, eliciting error distributions from experts when either the number of countries or number of regions within a country is large and can challenge mental capacity. So, Lutz et al. (2014) focus on eliciting the general views of experts about broad trends or scenarios rather than numerical values for parameters to drive probabilistic projections.

Leading indicators are randomly sampled several hundred times from component error distributions and projections generated. The projection outcomes can be described as cumulative probability distributions. Usually the 10 and 90% percentiles are chosen giving an 80% prediction interval. Probabilistic projection distributions are conditional on the chosen principal projection, which trace a path close to the median of the projection set. Probabilistic projections for national populations have been produced in cross-national projects (Alders et al. 2005) for 18 countries in Europe), in projects by international institutes (Lutz et al. 1996 and Lutz et al. 2004) for world regions, by the United Nations (UN 2015; Raftery et al. 2012) incorporating fertility and mortality uncertainties, by academic teams (Azoze et al. 2016) incorporating fertility, mortality and net international migration uncertainties and by national statistics offices. New Zealand’s official demographers construct probabilistic projections using Bayesian methods at both national (Statistics New Zealand 2016a), sub-national (Statistics New Zealand 2016b), national by ethnicity (Statistics New Zealand 2015a) and sub-national by ethnicity (Statistics New Zealand 2015b). Wilson and Bell (2007) present a probabilistic projection for the State of Queensland (Australia), which provides a clear guide to methods. Raymer et al. (2012) experiment with different models for representing internal and international migration in a projection of three English super-regions (North, Midlands and South) using probabilistic methods. Sevcikova et al. (2018) use probabilistic methods to forecast sub-national fertilities across a range of countries.

Table 1G describes a method to use historic projection errors in a simpler way. Recent work has focussed on the development of empirical prediction intervals (EPIs) for small, medium and large regions in countries using analysis of historical errors (Smith et al. 2001, 2013; Rayer et al. 2009; Tayman 2011 and Wilson (2012). Yamauchi et al. (2017) compare the accuracy of Japanese sub-national projections with those in the USA, Australia and England (two sets). In a later section of the paper we develop the Yamauchi comparison further, prior to using EPIs to evaluate our projections of Thames Water WRZ populations.

The final evaluation method (Table 1H) concerns providing advice to users about how far in the future projections can be regarded as reliable. Wilson et al. (2018) introduces the concept of shelf life of a projection, drawing on the use of “best before” and “use by” dates employed widely in the retail grocer sector. The shelf life is the time interval between jump-off year and use-by year, while display period lasts between jump off year and best before date. APE thresholds of 5 and 10% are chosen for “best before” and “use by” dates. Keilman (2008) offers a preliminary description of how risk functions can be used to judge the benefits and costs of using projection outcomes.

This review of evaluation informs our approach to the comparison of alternative projections for the Thames Water study region. Our focus is on “Interpretative Comparison”, on “Variants” and on “Empirical Prediction Intervals”. Most of the checks in Wilson’s plausibility list we used in preparing our projections and they will have been implemented in the official, local government and consultant projections used in the comparison. Ideally, controlled or tested comparisons might have been used but insufficient resource was available to use these methods. Variants were available for two out of the five sets of projections and we examine their results later in the paper. Because a set of empirical prediction intervals based on historic error analysis were available (UKWIR 2015), we use these to gauge uncertainty in our central projections. The shelf life concept is not applied directly but we assess the usefulness of 90-year projections in the discussion section.

Data and Methods Used in the UK Sub-National Projections

Table 2 sets out details of the five sets projections which are compared in this paper. Note that we use one column for the two ONS sub-national projections because they use virtually the same methodology. They differ only in the way in which internal migration between English local authorities and the other home countries is handled. Each projection produces local authority populations for a sequence of years. The columns of Table 2 identify the organization responsible: the LEEDS team (authors of this paper), the GLA (Greater London Authority) Intelligence team led by Ben Corr with Will Tonkiss providing key software expertise, the Office for National Statistics team led by Andrew Nash and the EDGE (Edge Analytics Ltd) team led by Peter Boden, contracted by Thames Water to produce medium-term projections linked closely to the addition of new properties, both occupied and vacant.
Table 2

Data and methods used in sub-national population projections for England

Feature

LEEDS projections

GLA projections

ONS projections

EDGE projections

2A. PROJECTIONS

    

Central

Mid (Ethnic)

Trend

Principal 2014, Principal 2016

Housing-Led & Linked to ONS projections

Variants

High, Low

Short-Term, Long-Term, Housing-Led

None

None

Components varied

International Migration

Internal Migration

NA

NA

2B. GEOGRAPHIES

 Input geography

All LADs in UK (389)

All LADs in England + W, S, N (329)

All LADs in England (326)

All LADs in TW

(85)

 Extracted geography

59 LADs, 1 pair

61 LADs

61 LADs

61 LADs

 Output geography

6 WRZs, GL

6 WRZs, GL

6 WRZs, GL

6 WRZs

 Geo-conversion

LDT from 2011 OAs

LEEDS LDT

LEEDS LDT

LDT based on properties

2C. TIME HORIZON

 Start Year

2011

2015

2014, 2016

2015

 End Year (Length)

2101 (90)

2050 (45)

2039, 2041 (25)

2045 (30)

4D. COMPONENTS

 Base Population

CENSUS2011 Pops & ONS 2011 MYEs

GLA 2015 MYEs for LBs; ONS 2015 MYEs for rest

2014 ONS MYEs,

2016 ONS MYEs

2015 ONS MYEs

 Projection Model

Bi-Regional CCM LADs and RUK in pairs. Out-migration rates

Multi-Regional CCM

O-D Out-migration rates

Multi-Regional CCM

O-D Out-migration rates

Single Region CCM with migration adjusted to housing plans

 Mortality

Ethnic specific rates estimated by GDM

ONS age-sex specific rates

ONS age-sex specific rates

ONS age-sex specific rates

 Fertility

Ethnic fertility rates estimated from births data, CWRs in 2011 Census data & LFS rates

Fertility rates based on Census populations aged 0, adjusted to total births by age of mother

ONS estimates of age-specific fertility rates based on registered births & female MYEs

ONS age-sex specific fertility rates

 Internal Migration

Ethnic tables from 2001 & 2011 Censuses, constrained to ONS migration flows to give out-migration (transmission) rates

Out-migration (transmission) rates between all LADs in England and to RUK. In-migration (admission) rates from RUK

Out-migration rates between LADs in England, net flows from RUK

ONS out- & in-migration rates, adjusted for housing plans

 International Migration

Ethnic immigration & emigration flows estimated from IPS/LTIM, controlled to ONS LAD immigration & emigration estimates

Immigration flow assumptions; emigration (transmission) rates from LADs

Net flows from ROW

ONS net flows from ROW

2E. CONSTRAINTS

 Constraint

No constraints

No constraints in TREND projections

LAD projections constrained to NPP for England

OA projections are adjusted to ONS LAD projections

2F. GROUPS

 Ages

SYA, 0 to 100+

0 to 90+

0 to 90+

0 to 90+

 Ethnicity

12 ethnic groups

18 ethnic groups, only for LBs

No ethnicity

No ethnicity

LEEDS: Rees et al. 2016a, Thames water 2017; GLA: GLA 2014, 2016; ONS: ONS 2016, ONS 2018a; EDGE: Thames Water 2017

Table 2A lists the projections to be compared. Each organization produces a central projection while LEEDS and GLA also generate variants. Table 2B specifies the geographical units underpinning the projections. The LEEDS, GLA and ONS projections are for all English LADs plus the other home countries of the UK from which results are extracted for LADs covering the Thames Water region (Fig. 1). EDGE generate projections for 80 LADs that cover the wider TW water supply and sewage disposal region. Results for these LADs were extracted from the larger sets and then converted into populations for the six Thames Water WRZs. A Look Down Table (LDT) based on 2011 Census populations is applied to geo-convert LEEDS, GLA and ONS LAD projected populations to WRZ projected populations. The EDGE projections use an LDT based on geo-referenced individual properties (Thames Water 2017). The time horizon differs between projections (Table 2C). The projections adopt a range of future horizons: 25 years for the ONS SNPP projections, 30 years for the EDGE projection, 35 years for the GLA projections, 90 years for the LEEDS projections though we report mainly on information for 50 years. Jump-off years differ between projections from mid-2011 (LEEDS) to mid-2016 (ONS 2018a).

Table 2D sets out the methods used to represent the components of change in the projection models. The base populations are the ONS mid-year estimates for jump-off years, except that the GLA uses its own modified estimates. The LEEDS projections start at mid-year 2011, when mid-year estimates of population by ethnicity were available. The GLA projections jump-off from mid-2015. The ONS SNPP projections use mid-2014 and mid-2016 baseline populations. The EDGE projections use mid-2015 jump-off populations. All population estimates are specified by sex and single year of age.

All projections employ the cohort-component projection model. Where projections differ is in how internal and international migration are handled. The LEEDS projections use a bi-regional model, in which LAD populations are forecast in pairs, the LAD itself and rest of the United Kingdom. The bi-regional model reduces the number of variables that need estimation compared with the multi-regional model but yields comparable results (Wilson and Bell 2004). In both models, internal migration flows are forecast by multiplying the population of the LAD origin by a forecast rate of out-migration. The GLA and ONS projections both use multi-regional models. The EDGE model uses a cohort-component model, implemented at two levels, COAs and LADs with housing plans changing migration inputs (Thames Water 2017).

All projections base their mortality rate assumptions on a combination of ONS national and sub-national estimates, which are computed from registered deaths by age and sex and the corresponding mid-year populations. The LEEDS projections require ethnic specific mortality rates. These must be estimated indirectly because the ethnicity of the deceased is not recorded in the Register of Deaths. The LEEDS ethnic mortality rates are estimated using the geographical distribution of ethnic populations (Rees and Wohland 2008, Rees et al. 2009, 2016a). The variation across ethnic groups in mortality rates is limited.

All projections either use or adapt age-specific fertility rates for LADs, estimated by ONS, which are based on birth statistics and mid-year population estimates. The LEEDS projections use rates estimated from a combination of birth statistics for LADs, child-woman ratios by ethnicity from the 2011 Census data and ethnic fertility rates by age estimated from the Labour Force Survey (Norman et al. 2014). The GLA ethnic projections use London Borough ethnic census populations of 0-year olds to compute fertility rates, adjusting to total births by mother’s age.

All projections either use or adapt internal migration rates for LADs, estimated by ONS, based on migration origin-destination statistics derived from NHS Register patient records of changes in address. Ethnic specific internal migration rates, required for the LEEDS projections, use commissioned tables from the 2001 and 2011 Censuses and NHS Patient Register migration data for mid-year intervals from 2001 to 02 to 2010–11 (Rees et al. 2018). The GLA and ONS projections make use of estimates of internal migration rates for years after the 2011 Census. The EDGE projections use housing plans for LADs in the Thames Water region to adjust internal migration rates to reflect additional in-migrants occupying new dwellings.

All projections either use or adapt ONS estimates of international migration flows to/from LADs. Immigration estimates use flow statistics from the International Population Survey (IPS)/Long-Term International Migration (LTIM) at national and regional level and proxy variables from administrative data sets at LAD level. To estimate emigration flows at LAD scale, ONS employs a model with co-variates (e.g. previous immigration flows, internal out-migration rates), constrained to national IPS/LTIM emigration tables. The LEEDS projections make use of published and commissioned 2001 and 2011 Census immigration tables by ethnicity based on citizenship information in the IPS data (Lomax et al. 2018). Interpolation methods are used to estimate ethnic international migration for mid-year to mid-year intervals between censuses. These LAD level estimates of immigration and emigration are used differently, depending on projection. The LEEDS projections employ immigration and emigration flow assumptions; the GLA projections use emigration rates and immigration flow assumptions. ONS uses net international migration assumptions in the NPP and SNPP 2014-based projections. Experiments by ONS and by the LEEDS team suggest that the choice of method for modelling future international migration can make a substantial difference in population projections.

Table 2E indicates whether LAD level projections are constrained to higher level populations. The LEEDS projections are unconstrained or “bottom-up”. The GLA Trend projection is unconstrained, so that the forecast for Greater London is the sum of the London Boroughs projections. The GLA Housing-Led projection is constrained to the GLA Trend projection but only at the Greater London scale. The ONS LAD projections for England are adjusted to sum to the totals for England, derived from the ONS National Population Projections (2014-based or 2016-based). The EDGE projections use a top-level, housing led LAD model and a bottom level Census Output Area model that links to property information (Thames Water 2017). The Census OA projections are constrained to the LAD projections.

Table 2F indicates that all the LEEDS projections use LAD ethnic sub-populations. The GLA only implements ethnic group projections for London Boroughs, adjusted to sum to results from the GLA Trend projection, and not for LADs outside London. Neither ONS nor EDGE produce ethnic population projections.

This review of data and methods used in sub-national projections for England finds similar approaches adopted and a largely common database of population and component estimates. However, some crucial differences are apparent. Only the LEEDS projections use ethnic sub-populations which vary greatly in their growth potential and only the LEEDS projections adopt a bottom-up approach. All other projections constrain results to the ONS England projections. There are also differences in the calibration period use to estimate internal migration rates between LEEDS, GLA and ONS/EDGE projections. Methods of projecting international migration differ between GLA and other projections.

Assumptions Used in the UK Sub-National Projections

Table 3 describes the component assumptions used in the projections. The approach across all projections is to specify long-term assumptions for national leading indicators for each component and then to trend from rates or flows estimated current just prior to the long-term assumption. The factors used to scale leading indicators to local scale are assumed constant at values in the time interval before the mid-year jump-off point. In the UK there has been little investigation of trends in local variation in demographic components. Local areas are assumed to behave in the same way as the national or system population.
Table 3

Component assumptions used for projecting sub-national populations in England

Components

LEEDS projections

GLA projections

ONS 2014 SNPP projections

ONS 2016 SNPP projections

Mortality

ONS 2014 NPP short-term assumptions and long-term assumption 1.2% decline pa

Uses London Borough model, which employs the ONS decline in mortality rate assumptions

Mortality rates decline at 1.2% per annum, as observed between 1914 & 2014. Life expectancies in 2091 are 90.5 (m) & 92.8 (f)

Mortality rate improvements converge on 1.2% pa in 2041 and continue at that rate thereafter. Life expectancies in 2091 are 89.3 (m) & 91.6 (f)

Fertility

TFRs by ethnic group, controlled to ONS national TFR assumptions, remain constant in long-term

ASFRs assumed constant multiples 1% lower than trends in ONS’s SNPP2014 for London Boroughs

Long-term fertility is assumed to decline to 1.89 children per woman and then remain constant

Long-term fertility is assumed to decline to 1.85 children per woman and then remain constant

Cross-Border Migration

All internal migration uses bi-regional out-migration rates

Uses ONS NPP assumptions for cross-border flows

Uses ONS NPP assumptions for cross-border flows

Uses ONS NPP assumptions for cross-border out-migration rates

Internal Migration with England

Assumes internal migration rates are constant, with averaging period differing by variant (Table 6)

Assumes internal migration rates are constant, with averaging period differing by variant (Table 6)

Assumes internal migration rates are constant, using a 5- year period for averaging (Table 6)

Assumes internal migration rates are constant, using a 5- year period for averaging (Table 6)

International Migration

High, Mid and Low Variants for immigration and emigration flows assumptions for UK Home Countries factored to LADs (Table 6)

Assumes constant emigration and immigration rates linked to ONS SNPP2014, factored to LADs (Table 6)

Assumes Long-term constant net international migration flows (NIM = +185 k) factored to LADs (Table 6)

Assumes Long-term constant net international migration flows (NIM = +165 k) factored to LADs (Table 6)

EDGE Assumptions: Housing plans assembled from LAD documents & communications are used to modify total migration flows via a model. Otherwise EDGE projections follow ONS assumptions for fertility, mortality and international migration

See Table 2

Mortality trends adopted in the projections follow the ONS 2014-based assumption of an average decline of 1.2% per annum in age-specific mortality rates, based on the average decline between 1914 to 2014. Since 2013 declines in mortality have stalled (ONS 2015; Hiam et al. 2017). In the 2016-based national and sub-national projections (ONS 2017, 2018a), the decline is modified, recognizing that mortality rates at the oldest ages have stopped falling. Continuing improvement is assumed for younger ages using a 1.2% decline, but mortality rates from age 65 onwards are assumed to decline more slowly to 2040–41 and resume the 1.2% decline thereafter.

Fertility rate assumptions in the LEEDS, ONS 2014 and EDGE projections are based on the ONS NPP 2014 long-term assumption of a total fertility rate (TFR) of 1.90 (children per woman) for England and 1.89 for the UK. The ONS assumption is based on a careful analysis of cohort fertility rates (completed number of children ever born), which has been less volatile over time than the period TFR. It is assumed that the tempo shift of the two previous decades, when women postponed births in their twenties only to later bear children in their thirties, has ended. In the ONS 2016-based projection UK long-term fertility was assumed to be 1.85 children per woman, down from the 2014 assumption of 1.89. The LEEDS projections use fertility rates for ethnic groups. The UK total fertility rates (TFRs) in 2011 for the groups comprising the “South Asian” ethnic grouping, were 2.20 for Indians, 3.20 for Pakistanis and 3.47 for Bangladeshis, compared with a TFR of 1.83 for the “Other Ethnic” grouping. These high rates for South Asians are coupled with a current youthful age structure, leading to substantially higher growth than for the White British and Irish majority and the other minority ethnic groups. After adjustment to the ONS long-term assumption, factoring to LADs and allowance for a short-term trend, the age specific fertility rates are held constant. In the GLA ethnic projections, ethnic specific fertility rates are also used but their effect is suppressed by the adjustment of populations by ethnicity to the total population constraints of the GLA Trend projections.

Internal migration involves both origin and destination regions. To take this into account a different approach to assumption setting is used. Internal migration is a re-distributor of populations whose size is largely determined by the current national population age structure, natural increase components and international migration. Lomax and Stillwell (2017) and Stillwell et al. (2017) showed that the redistribution effected by internal migration differed between the start, middle and end of the 2001 to 2011 decade, especially in the Greater South East. GLA have established through their analyses that the level of out-migration from London and in-migration to the Outer Thames Water region differs considerably over time, depending on the state of the economic cycle. GLA proposed variant projections that averaged internal migration rates over different time periods. The first was over a short-term period, heavily influenced by the 2008–09 Global Financial Crisis, which reduced out-migration from Greater London to the Rest of the South East. The second was a longer-term period which covered the boom of the early 2000s, the recession and the recovery to the present. Table 4 lists the periods over which internal out-migration transition rates were averaged and then introduced in central (ONS, EDGE), variant (GLA, LEEDS) projections. The average rates are assumed constant from the jump-off year for the rest of the forecast period. In the rightmost column of Table 4A, we indicate the likely impact of the exchanges of migrants between Greater London and the South East.
Table 4

Internal and international migration assumptions

5A. Time intervals used for averaging internal migration rates by projection

Projection

Years

Start period

End period

London to South East migration

 LEEDS Mid

5

2006–2007

2010–2011

Middle

 LEEDS High

1

2010–2011

2010–2011

Low

 LEEDS Low

10

2001–2002

2010–2011

High

 GLA Trend

10

2005–2006

2014–2015

Middle

 GLA Short-Term

5

2010–2011

2014–2015

Low

 GLA Long-Term

14

2001–2002

2014–2015

High

 ONS 2014 Principal

5

2009–2010

2013–2014

Low

 ONS 2016 Principal

5

2011–2012

2015–2016

Middle

 EDGE Housing Led

5

2009–2010

2013–2014

Low

5B. International migration assumptions for the UK, annual averages (1000s)

Variants

Flow

2011–2016

2031–2061

2061–2101

Year constant assumed

 ONS NPP 2014

Immigration

(574)

(519)

(519)

2020–2021

 

Emigration

(324)

(334)

(334)

 
 

Net Balance

249

185

185

 

 ONS NPP 2016

Immigration

estimates

(464)

(464)

2022–2023

 

Emigration

estimates

(299)

(299)

 
 

Net Balance

estimates

165

165

 

 LEEDS HIGH

Immigration

577

617

617

2020–2021

 

Emigration

316

364

364

 
 

Net Balance

261

253

253

 

 LEEDS MID

Immigration

577

518

518

2020–2021

 

Emigration

316

333

333

 
 

Net Balance

261

185

185

 

 LEEDS LOW

Immigration

573

341

185

2079–2080

 

Emigration

320

142

74

 
 

Net Balance

253

199

111

 

See Table 2 for sources

1. Years refer to mid-year (30 June/1 July) to mid-year intervals

2. The ONS figures for 2011 to 2016 are based on the latest estimated figures for calendar years from ONS (2017) Migration Statistics Quarterly, May 2017

3. The LEEDS HIGH, LEEDS MID and ONS NPP 2014 projections all assume a short-term decline to 2020–2021, when the constant assumption is adopted. The LEEDS LOW projections model the decline from MY2009 to MY2015 using citizenship data and continue the trend until a limit of 100,000 net balance is reached in 2079–2080

4. Immigration and emigration assumptions for ONS 2014 and 2016-based projections are bracketed to indicate that these author estimates based on the ratios of immigration and emigration to net international migration in 2011–2016. ONS assumptions are specified in net terms only

Table 4B presents a summary of the international migration assumptions used in the ONS and LEEDS projections. GLA and EDGE use the ONS 2014-based National Population Projections assumptions. The ONS sub-national assumptions are based on the 2014-based National Population Principal projections (NPP2014). The long-term assumption was set as a net international migration total of 185 thousand net migrants per year. We estimate that the net balance is associated with flows of 519 thousand immigrants and of 334 thousand emigrants. In the ON 2016-based National Population Projections, the net international balance is assumed to decline to a lower long-term constant of +165 thousand per year, anticipating reduced immigration from the European Union revealed in the mid-2016 to mid-2017 estimates.

The LEEDS projections adopt three variants for future international migration flows. The LEEDS HIGH variant is the product of logistic models fitted to time series (1991 to 2015) of immigration and emigration flows. The logistic asymptote generates a long-term level of immigration of 617 thousand immigrants per year to the UK and a long-term emigration level of 364 thousand per annum (net 253 thousand per annum). The LEEDS MID variant is based on the ONS assumption made in the NPP 2014-based projections. The LEEDS LOW variant sets assumptions through analysis of a time series of international migration, using the citizenship data in the International Passenger Survey, which classifies international migrants as British, European Union or Non-European Union citizens. It is assumed that the downward trend observed between mid-2009 and mid-2015 by citizenship for non-EU immigrants and emigrants will also apply to EU citizens post-Brexit, from 2019 to 20 onwards. In this variant, the long-term limit is set at immigration and emigration levels equivalent to net international migration of 100,000 per year. However, because emigration declines at the same time as immigration, this level is not reached until 2079–80.

Results: Interpretative Comparisons of the Thames Water Projections

Table 5 assembles, for selected years, the five sets of results for the six Water Resource Zones, plus the Thames Water region, for 2011 to 2039, the period for which all five projected populations are available. Figure 2 graphs populations for all mid-years, extending the comparison to 2061 to demonstrate the longer-term trajectory of the LEEDS central projection. To anchor the comparison, the table provides the populations for 2011. The LEEDS MID, ONS SNPP 2014, ONS SNPP 2016 and EDGE 2011 populations are mid-year 2011 ONS population estimates, converted to WRZs using the LEEDS geo-conversion method. The GLA 2011 estimates are revisions of the ONS estimates geo-converted using the LEEDS method. All projected populations are geo-converted using the LEEDS method except for the EDGE populations which are converted using a finer property-based geo-conversion process (Thames Water 2017). The final column in Table 5 converts the 2039 projected populations into time series indices (2011 = 100) to compare WRZ populations, small and large, using the same metric.
Table 5

Alternative projected populations (1000s) for water resource zones, 2011–2039

Water Resource Zone

Forecast

2011

2021

2031

2039

Time Series 2039

Guildford

LEEDS MID

148

160

169

175

118

GLA TREND

148

160

170

176

119

ONS 2014 SNPP

148

161

171

177

120

ONS 2016 SNPP

148

159

165

168

114

EDGE HOUSING

148

167

187

198

134

Henley

LEEDS MID

47

50

54

56

119

GLA TREND

47

51

54

56

120

ONS 2014 SNPP

47

50

53

55

117

ONS 2016 SNPP

47

51

53

54

115

EDGE HOUSING

47

50

52

53

112

Kennet Valley

LEEDS MID

382

414

444

465

122

GLA TREND

382

410

433

449

118

ONS 2014 SNPP

382

407

425

437

114

ONS 2016 SNPP

382

409

425

435

114

EDGE HOUSING

382

425

453

466

122

London

LEEDS MID

6629

7772

8841

9732

147

GLA TREND

6639

7465

8067

8474

128

ONS 2014 SNPP

6629

7593

8314

8828

133

ONS 2016 SNPP

6629

7403

7.879

8221

124

EDGE HOUSING

6600

7400

7774

8078

122

Slough-Wycombe-Aylesbury

LEEDS MID

501

568

638

696

139

GLA TREND

501

550

591

617

123

ONS 2014 SNPP

501

551

590

617

123

ONS 2016 SNPP

501

547

575

591

118

EDGE HOUSING

499

561

593

622

125

Swindon-Oxfordshire

LEEDS MID

989

1070

1140

1188

120

GLA TREND

989

1065

1134

1179

119

ONS 2014 SNPP

989

1068

1132

1173

119

ONS 2016 SNPP

989

1052

1090

1111

112

EDGE HOUSING

987

1133

1254

1311

133

Thames Water

LEEDS MID

8697

10,035

11,286

12,313

142

GLA TREND

8707

9701

10,448

10,952

126

ONS 2014 SNPP

8697

9831

10,685

11,286

130

ONS 2016 SNPP

8697

9622

10,187

10,581

122

EDGE HOUSING

8664

9735

10,313

10,729

124

Thames Water

Minimum

8664

9622

10,187

10,581

122

Maximum

8707

10,035

11,286

12,313

142

Median

8697

9735

10,448

10,952

126

Mean

8692

9785

10,584

11,172

129

1. The populations are estimates or projections at mid-year (30 June/1 July)

2. The time series is based at mid-year 2011, so that 2011 = 100

3. The mid-2011 population estimates are based on ONS mid-year estimates for the LEEDS MID, ONS SNPP 2014, ONS SNPP 2016 and EDGE HOUSING projections. The GLA TREND values are independent GLA estimates

4. The LEEDS MID, GLA TREND and ONS 2014 and ONS 2016 estimated and projected populations are geo-converted to WRZs using LEEDS conversion table (LADs to WRZs) based on COA populations in the 2011 Census. The EDGE estimate and projected populations are converted from LADs to WRZs using property-based look down and look up tables

Fig. 2

Percentage changes in central projected populations for the water resource zones of Thames Water, 2011-2061

The ordering of the projections differs by WRZ. For the Thames Water region, the ONS 2016-based projected populations are the lowest and 8% below those of the ONS 2014-based projections. These two projections share virtually the same methods, so the differences are due to shifts downwards in assumptions about fertility, survival and net immigration. To determine the relative contribution would need controlled or tested comparisons (Table 1B, C). The LEEDS MID projection produces the highest growth for the Thames Water region, based on high growth for the London and Slough-Wycombe-Aylesbury WRZs. We argue later in the paper that this reflects the growth potential of ethnic minority populations which are highest in the UK capital and the zone including the industrial city of Slough, with a high South Asian population share. For Guildford and Swindon-Oxfordshire the EDGE projections report the highest growth. For Henley and Kennet Valley all projections fall within an 8% range (maximum less minimum); for Guildford, Slough-Wycombe-Aylesbury and Swindon-Oxfordshire the range is 20 or 21; London experiences the greatest range at 25%.

For Guildford WRZ, the main contrast is between the EDGE Housing-Led and the other three projections. The higher projections are the result of housing developments planned in LADs contributing to the Guildford WRZ. For the Henley WRZ, the GLA and LEEDS projected populations were considerably higher than in the ONS or EDGE projections. This is likely to be a result of the different internal migration averaging periods used. Both GLA and LEEDS projections include internal migration rates from years prior to the Global Financial Crisis when out-migration from London was higher than in the recession years included in ONS’s averaging period. In the Kennet Valley WRZ, the EDGE and LEEDS projections move in parallel, while the GLA and ONS projections are lower. The EDGE growth is driven by new housing starts while the LEEDS growth is driven by a combination of favourable internal migration rates and an increasing ethnic minority population, particularly in Reading. For the Slough, Wycombe and Aylesbury WRZ, the LEEDS MID projected populations are much higher than in the GLA, ONS and EDGE projections, paralleling the outcome in the London WRZ (Table 7). The outcome for this WRZ is different from other WRZs outside London because of the high South Asian population share in Slough LAD (Table 6). The Swindon & Oxfordshire WRZ shows the same pattern of population increase across the projections as the Guildford WRZ, where the EDGE projections are higher than the others. This is a highly desirable migration destination, reflected in the housing plans that drive the EDGE projections.
Table 6

Ethnic composition of the projected LEEDS MID populations of WRZs, 2011–2101

WRZ, Ethnicity

Populations (1000 s)

Times Series (2011 = 100)

Composition (% of WRZ)

 

2011

2041

2071

2101

2041

2071

2101

2011

2041

2071

2101

Guildford WRZ

 South Asian

2

4

6

8

208

317

411

1.3

2.3

3.3

4.2

 Other Ethnic

146

172

185

185

118

127

127

98.7

97.7

96.7

95.8

 Total

148

176

191

194

119

129

131

100.0

100.0

100.0

100.0

Henley WRZ

 South Asian

2

6

9

13

288

486

676

4.1

9.8

14.6

19.0

 Other Ethnic

45

51

54

55

113

121

123

95.9

90.2

85.4

81.0

 Total

47

56

64

68

121

136

145

100.0

100.0

100.0

100.0

Kennet Valley WRZ

 South Asian

20

49

80

113

242

397

558

5.3

10.4

15.0

20.0

 Other Ethnic

361

422

455

453

117

126

125

94.7

89.6

85.0

80.0

 Total

382

471

535

566

123

140

148

100.0

100.0

100.0

100.0

London WRZ

 South Asian

682

1326

2026

2642

195

297

388

10.3

13.3

16.2

20.7

 Other Ethnic

5948

8626

10,485

10,143

145

176

171

89.7

86.7

83.8

79.3

 Total

6629

9952

12,512

12,785

150

189

193

100.0

100.0

100.0

100.0

SWA WRZ

 South Asian

74

189

338

524

256

460

711

14.7

26.5

36.5

46.8

 Other Ethnic

428

522

590

595

122

138

139

85.3

73.5

63.5

53.2

 Total

501

711

928

1119

142

185

223

100.0

100.0

100.0

100.0

SWOX WRZ

 South Asian

28

59

95

130

208

333

459

2.9

4.9

7.2

9.6

 Other Ethnic

961

1140

1222

1230

119

127

128

97.1

95.1

92.8

90.4

 Total

989

1199

1317

1361

121

133

138

100.0

100.0

100.0

100.0

All WRZs

 South Asian

808

1632

2556

3430

202

316

425

9.3

13.0

16.4

21.3

 Other Ethnic

7889

10,933

12,991

12,662

139

165

161

90.7

87.0

83.6

78.7

 Total

8697

12,566

15,547

16,093

144

179

185

100.0

100.0

100.0

100.0

Authors’ computations

To examine the differences between variant projections the results are graphed for Greater London (Figs. 3 and 4). Greater London covers 32 London Boroughs and the City of London; the London WRZ includes 29 London Boroughs and parts of other LADs to the south and north (Fig. 1). The differences between projections for Greater London are substantial. The LEEDS projections generate almost twice as much growth by 2050 than does the GLA projections (Fig. 3). The main reason is that the LEEDS forecast uses London Boroughs and LAD populations disaggregated by ethnicity. Ethnic minority groups are growing much faster than the White British and Irish “host” population. Work by the LEEDS team since 2008 using both 2001-based and 2011-based ethnic projections (Rees et al. 2011, 2013, Rees et al. 2016a, b) has shown that ethnic minority populations are growing very fast. London is one of the most diverse world metropolises. In 2011, many London Boroughs had “minority-majority” populations. While the GLA does produce ethnic population projections for Greater London, the results are constrained to the GLA Trend projections and fail to reflect fully the effect of this heterogeneity on population growth. The share of the Thames Water region population residing in the London WRZ will grow under the LEEDS MID projection from 76% in 2011 to 81% in 2061.
Fig. 3

Comparison of variant projections for greater London, percent population change, 2011–2050

Fig. 4

Percentage changes in projected population, water resource zones, Leeds MID, HIGH and LOW scenarios, with 80% empirical prediction intervals around the MID variant, 2011 to 2101. Note: The % population change axis differs between WRZs

Thames Water asked the LEEDS team to carry out population projections beyond 2061 until the next century (2101) to inform long-term forecasts of domestic water demand. Selected results are assembled in Table 7, reporting on both South Asian and Other Ethnic populations. Against a background of 85% growth in the Thames Water’s population, the highest growths are projected for the Slough-Wycombe-Aylesbury and London WRZs, which will experience increases of 123 and 93% respectively. In all WRZs, the South Asian population is expected to more than triple (a 325% increase), while the Other Ethnic population only increases by 61%. The South Asian population increases more in the WRZs outside London than in the London WRZ, indicating that internal migration redistributes this sub-population outwards. Both the London and Slough-Wycombe-Aylesbury WRZs gain in share of Thames Water region population by 3.2 and 7.0% respectively. These long-term results show the importance of including ethnic heterogeneity in LAD and WRZ projections.
Table 7

LEEDS variant projected populations, Thames water region, 2011–2101

All Water Resource Zones

2011

2041

2071

2101

Populations (millions)

 LEEDS HIGH

8.70

13.18

17.11

18.32

 LEEDS MID

8.70

12.57

15.55

16.09

 LEEDS LOW

8.70

12.70

13.71

11.07

Time Series (2011 = 100)

 LEEDS HIGH

100

152

197

211

 LEEDS MID

100

144

179

185

 LEEDS LOW

100

146

158

127

As % of Leeds MID

 LEEDS HIGH

100

105

110

114

 LEEDS MID

100

100

100

100

 LEEDS LOW

100

101

88

69

How should the user of the projections, Thames Water, cope with this diversity of results? The first coping mechanism would be to plan for all the eventualities embedded in the five competing projections, using the maximum and minimum projected population as set out in the bottom panel of Table 5. However, the sample of projections is very small so perhaps the full range of possible outcomes is not catered for. For Greater London, the set of projections was extended by including variant projections, produced by LEEDS and by GLA (Fig. 3). This extends the range across the projections by adding LEEDS HIGH and LOW variants. Note that despite the Brexit Referendum result, the UK’s international migration balance was still +280 thousand in 2017 with reduced immigration from the European Union compensated by increased immigration from outside the EU (ONS 2018a). The LEEDS High projection adds 12% to total population growth compared with the LEEDS MID forecast, while the LEEDS LOW reduces growth by 3%. The GLA Short Term forecast adds 4% to the GLA Trend, while the GLA Long Term forecast reduces growth by 3%. Contrast these differences with the 34% difference between the LEEDS MID and GLA Trend, due to the ethnic heterogeneity built into the LEEDS projection.

Empirical Prediction Intervals Applied to the Thames Water Projections

The review of evaluation methods earlier in the paper described a growing body of evidence about historical errors in population projections (Table 1G). Those errors show a positive linear relationship with length of projection horizon and a negative exponential relationship (virtually constant for larger populations) with size of population. The original studies either report Absolute Percentage Errors (APE) (Smith et al. 2001 summarised in Tayman 2011, Yamauchi et al. 2017 and Wilson et al. 2018) or Empirical Prediction Intervals (UKWIR 2015) or both (Simpson et al. 2018). In Table 8, we bring together the APE or EPI estimates for a sample of countries which have been studied. The APEs have been converted into EPIs or vice versa, for consistent comparison, assuming the errors are normally distributed. In each country EPIs decrease systematically with size. Most authors state that this decrease applies to the smaller populations and that above a threshold EPIs are constant, although Table 8F suggests a negative exponential function gives a better fit. As Yamauchi et al. (2017) comment, EPIs are highest in the USA, moderate in Australia, lower in England and lowest in Japan. A second set of EPIs is reported for England (Table 8D), from Simpson et al. (2018), which are broadly similar to the first set (UKWIR 2015), though the authors consider variation by LAD type more important than variation by size, with higher EPIs found in London Boroughs. These differences between countries are associated with international differences in internal migration intensity (Bell et al. 2015, 2018; Rees et al. 2016c) and the degree to which population change is driven by international migration. All studies of EPIs find they systematically increase with time horizon. As reported in Table 8C, the UK Water Industry Research (UKWIR) report on empirical prediction intervals, based on an analysis of historical errors in ONS sub-national projections. For the current study, 80% EPIs are interpolated between or extrapolated beyond the LAD, County and Region tables (UKWIR 2015) using WRZ 2011 populations.
Table 8

Historic errors in sub-national population projections, selected countries

8A. USA: Typical average absolute percentage errors (Tayman 2011, Table 1, from Smith et al. 2001, p349)

Horizon

Census Tract

4 k

County

90 k

State

5628 k

    

Years

Mean APE

80% EPI

Mean APE

80% EPI

Mean APE

80% EPI

    

 5

9

17.1

6

11.4

3

5.7

    

 10

18

34.2

12

22.8

6

11.4

    

 15

27

51.3

18

34.2

9

17.1

    

 20

36

68.4

24

45.6

12

22.8

    

 25

45

85.5

30

57.0

15

28.5

    

 30

54

102.6

36

68.4

18

34.2

    

8B. Australia: Median absolute percentage errors, selected local areas (Wilson et al. 2018, Wilson 2018, Table 1)

Horizon

Small N’hoods

<1 k

Large N’hoods

10 k<

12.5 k

Small Districts

50 k- < 75 k

Medium Districts

100 k < 150 k

Large Districts

150 k+

Years

Median APE

80% EPI

Median APE

80% EPI

Median APE

80% EPI

Median APE

80% EPI

Median APE

80% EPI

 5

7.9

15.0

2.5

4.7

2.3

4.4

1.7

3.2

1.8

3.4

 10

13.3

25.3

5.7

10.8

4.7

8.9

3.9

7.4

3.4

6.5

 15

15.3

29.1

10.1

19.2

6.8

12.9

6.7

12.7

4.5

8.5

 20

25.1

47.7

9.9

18.8

7.8

14.8

10.4

19.8

7.6

14.4

8C. England (1): 80% EPI errors, based on ONS SNPPs (UKWIR 2015, Tables 8, 9 and 10)

Horizon

Local Authorities

100 k

Counties

500 k

Regions

5000 k

    

Years

Median APE

80% EPI

Median APE

80% EPI

Median APE

80% EPI

    

 5

3.10

5.88

2.09

3.98

1.99

3.79

    

 10

4.37

8.30

3.00

5.69

2.94

5.58

    

 15

5.20

9.88

3.60

6.84

3.55

6.74

    

 20

5.90

11.21

4.09

7.77

4.04

7.67

    

 25

6.50

12.34

4.52

8.58

4.46

8.48

    

 29

6.96

13.22

4.85

9.22

4.77

9.06

    

8D. England (2): 80% EPI errors, based on OPCS and ONS SNPPs (Simpson et al. 2018, Tables 3 and A3, ONS 2018b)

Horizon

Shire District

110 k

 

Unitary Authority

226 k

 

London Borough

267 k

  

Years

Median APE

80% EPI

80% EPI

Median APE

80% EPI

80% EPI

Median APE

80% EPI

80% EPI

 

 5

2.0

4.1

3.8

2.2

4.9

4.2

3.9

9.0

7.4

 

 10

3.1

6.1

5.9

3.4

7.2

6.5

6.6

13.7

12.5

 

 15

4.6

8.0

8.7

3.8

8.4

7.2

8.5

16.5

16.1

 

 20

6.0

9.8

11.4

4.8

10.6

9.1

12.3

20.9

23.4

 

Horizon

Metro District

332 k

 

Shire County

823 k

     

Years

Median APE

80% EPI

80% EPI

Median APE

80% EPI

80% EPI

    

 5

1.6

3.2

3.0

1.4

2.5

2.7

    

 10

2.3

4.9

4.4

1.8

3.6

3.4

    

 15

3.5

6.3

6.6

2.4

4.5

4.6

    

 20

4.3

8.2

8.2

2.2

5.0

4.2

    

8E. Japan: Median absolute percentage errors, municipalities and prefectures (Yamauchi et al. 2017, Table 15.9)

Horizon

Municipalities M1

10.7 k

Municipalities M2

25.1 k

Prefecture

1706 k

  

Years

Median APE

80% EPI

Median APE

80% EPI

Median APE

80% EPI

  

 5

2.3

4.4

1.9

3.6

0.9

1.7

  

 10

 

4.6

8.7

2.0

3.8

  

 15

  

3.4

6.5

  

8F. The relationship between EPI and population size in five studies

Country

80% EPI vs Pop

80% EPI vs Log10Pop

80% EPI vs Log10Pop

    

Correlation

Correlation

Slope

    

 USA

−0.534

−0.610

−12.589

    

 Australia

−0.534

−0.714

−7.709

    

 England (1)

−0.350

−0.492

−1.791

    

 England (2)

−0.406

−0.303

−4.953

    

 Japan

−0.963

−0.992

−1.153

    

1. APE = Absolute Percentage Error = 100 × |forecast population – estimate population|/estimate population

2. EPI = Empirical Prediction Error = historic errors in past projection, used to inform current projections

3. 80% EPI = 80% Empirical Prediction Interval = 100 × 0.5 |90th percentile, error distribution – 10th percentile, error distribution|

4. Bold numbers are APEs reported in the source

5. Bold-Italic numbers are 80% EPIs measured from the observed distribution of historic errors

6. Italic numbers are conversions of APEs to 80% EPIs or conversions of 80% EPIs to APEs

7. Assumping error distributions follow the cumulative normal distribution, the conversion factor from Median APE to 80% EPI is 1.8998 and the conversion factor from 80% EPI to Median APE is the reciprocal, 0.5264

8. For each set of spatial units, an average (mean or median or mid-range) population is provided, in 1000s (k)

The 10th and 90th empirical prediction values, based on the UKWIR (2015) tables are plotted for the WRZs together with the variant projections (Fig. 4). The variant projections, for the most part, fall well within the 80% empirical prediction range. There are some cases, London and Slough-Wycombe-Aylesbury, where the HIGH variant populations are close to the 90% EPI and the LOW variant population pass below the 10% EPI. Under the LOW variant international migration flows decline to a limit of 100,000 in the second half of the projection horizon. These are the two WRZs with the highest growth under the MID scenario, which we have associated with the boost to population growth from ethnically diverse populations. However, the EPIs are based on sub-national population projections that do not include ethnic heterogeneity and for these WRZs the 80% EPI may be under-estimated.

Discussion and Conclusions

During this applied demography project, both Thames Water managers and Water Resource Forum stakeholders challenged our methods and assumptions, in far more depth and detail than usually occurs in academic meetings. It is useful to report here on the questions posed and our responses.

The question was asked: “Why are the LEEDS MID projections higher than the projections by other organizations?” The following explanation was offered. The LEEDS MID projections include LAD ethnic minority populations, which grow much faster than the White British and Irish (WBI) majority population. The reasons for this faster growth differ across ethnic minority groups (Rees et al. 2012). The Indian, Pakistani and Bangladeshi (South Asian) grouping, sub-populations used in the Water Demand projections, are assumed, based on 2001 and 2011 estimates developed by the Leeds team, to have fertility rates higher than the White British and Irish group and to continue to add population through net international migration gains. The South Asian populations are younger than the WBI population. The age distribution for the South Asian grouping is highly concentrated in the family building ages. So, they are the fastest growing groups. Mixed groups grow because of their younger age structure, while, for other groups such as the Chinese or White Other (which includes EU immigrants), the main driver is immigration. Ethnic minority populations are concentrated in the London WRZ and in the Slough-Wycombe-Aylesbury WRZ. Other WRZs have much smaller ethnic minority populations and so grow at a slower rate. Note that this effect only occurs where a bottom-up approach to projection is adopted which assumes that the populations of LADs or WRZs are the sum of their ethnic group populations. GLA adopts a top-down approach and adjusts ethnic group projected populations to sum the all group London Borough forecast populations. ONS and EDGE forecast the total population with no ethnic breakdown and so both projections miss out on the boost from growing ethnic minorities.

There was surprise among manager and stakeholders about the high growth in the population of the Slough-Wycombe-Aylesbury WRZ compared with other WRZs outside London. The SWA WRZ has growth comparable to that of the London WRZ. This WRZ has a high concentration of South Asian ethnic minority groups. In Slough LAD, ethnic minorities constitute a majority of the population. These groups have above average fertility (especially Pakistani families), younger age profiles and continuing immigration through family unification and out-marriage.

We were asked: “Why does growth in population slow down the last thirty years of the forecast?” The slowing down in the growth of population in the thirty years of the twenty-first century (Table 7) is a product of the relationship between constant international migration assumptions and decreases in natural increase. The demographic slowdown derives from the assumptions adopted for natural increase, international migration and internal migration. In the long run (e.g. to 2101), an assumption that the national TFR remains constant at only 1.89 (ONS SNPP 2014) or 1.85 (ONS SNPP 2016) will lead to a decrease in population, because a TFR of 2.07 is required for a population to reproduce itself. Following ONS practice, for the LEEDS MID projection we assume in the long run after the 2020s that the balance of immigration and emigration flows remains constant. In the first decades of the projection, the net immigration gain will more than compensate for the natural decrease associated with the ageing of baby boomers into the high mortality age bands. However, late on in century natural increase will become natural decrease because of ageing of the population: a constant net international migration will no longer compensate for the decrease.

This is, however, an argument about the national population. To explain effects at local scale we must consider internal migration, which redistributes populations and population change between local areas. For internal migration, we assume that the LAD ethnic group out-migration rates remain constant. Out-migration flows from Greater London will therefore increase as Greater London’s population grows, eventually cancelling out the fixed net gains from international migration. On the other hand, WRZs outside London will experience gains through this out-migration from London, which continue to grow, alongside smaller, constant gains from international migration.

We were asked the almost impossible question “What about the impact of Brexit on the future populations of the Thames water region?” In LEEDS MID forecast, we adopt the ONS NPP2014 UK assumptions for international migration, which are factored to LADs and ethnic groups which cover the Thames Water region. The ONS 2014-based long-term net international migration assumption at +185 thousand per annum is still below recent levels (+248 thousand in 2016) and so has an element of Brexit effect built in. We have also carried out a forecast (LEEDS LOW) which assumes decline to the net international migration target of 100 thousand immigrants per year for the UK, but this target is only achieved in the last two decades of the projection. In summary, we suggested that the LEEDS HIGH projection might indicate a “soft Brexit”, the LEEDS MID projection as reflecting a “moderate Brexit” and the LEEDS LOW forecast as signalling a “hard Brexit” with lower and lower immigration over time. Similar views were put forward by Werpachowska and Werpachowski (2017) in a projection of England’s population by ethnicity using micro-simulation methods.

In dialogue with Thames Water manager and stakeholders, we were asked to outline the arguments against and for adopting the LEEDS projections as the basis for long-term water demand forecasts and make some final judgements. The arguments against the LEEDS projections include the following. We cannot measure the ethnic specific demographic rates and flows with sufficient accuracy to justify expanding the population groups to include ethnicity. Therefore, we must adopt a conservative approach and not introduce such heterogeneity into the projections. These arguments can be rebutted. The LEEDS projections capture vital heterogeneity due to ethnicity in the demographic dynamics. Substantial effort has been made to improve the quality of estimates of ethnic specific demographic rates between a 2001-based (Rees et al. 2011, 2012) and a 2011-based (Rees et al. 2016a, 2016b) set of ethnic population projections. For example, we changed our approach to estimating ethnic mortality rates taking cognizance of findings by other researchers regarding the health migrant effect. Although there is still uncertainty in many ethnic specific fertility rates, 2011 Census data on child populations indicate that the fertility rates for the South Asian ethnic minority grouping are much higher than the average. To estimate rates of internal migration by ethnicity in the 2011 projections, use was made of special tabulations from two censuses and improved internal migration estimates (Rees et al. 2018).

The youthful age structures of ethnic minority populations were revealed in the results of the 2011 Census. These age structures imply a large demographic momentum effect, which will be largely independent of policy. The published immigration statistics (despite their uncertainty) confirm that the boost to growth through immigration to all minority ethnic groups will be substantial (Rees et al., 2013). Note that the White British and Irish majority loses population through emigration being higher than immigration. Of course, there will be considerable uncertainty about the level of international migration flows because of Brexit, but we argue that, if the UK economy performs well, immigrants from the EU who have had the freedom to move to the UK will be converted into migrants with work permits and indefinite leaves to remain, because of the need for employers to recruit labour from outside the UK to maintain vital production and vital services.

There are many other scenarios or variants that we could have implemented, e.g. HIGH, MID, LOW on the fertility, mortality, internal migration and international migration components, leading to 81 possible variants). This would have been an extension of what ONS do at the national level to the sub-national level. ONS have been lobbied by users of sub-national projections to implement sub-national variants but have not proceeded as yet, citing the level of resource needed. Scotland (NRS 2018) carries out variant projections and New Zealand implements probabilistic projections (Statistics New Zealand 2015a, 2015b. 2016a, 2016b) for both national, sub-national and ethnic populations. Clearly, a full analysis of the sensitivity of future sub-national populations to methods and assumptions is needed to improve their utility. One implication of our evaluation of projected sub-national populations is that future plans to increase supply of utilities to consumers must be flexible in timing. Supply improvements should be timetabled in a sequence of projects that could be brought forward or postponed depending of future forecasts of water demand.

The paper evaluates the authors’ population projections required by a large public utility against alternatives. However, the findings have lessons beyond the case study. There is a need to plan comparisons at the specification stage of a project. Ideally, the plan should include running case study assumptions with the models used in comparators to discover how important the effect of model specification is (Wilson and Bell (2004). Similarly, comparator assumptions should be run on the case study model to discover the impact of differences. Such comparisons are routinely made in implementing variant projections. Variants are usefully organised in a schema (Bongaarts and Bulatao 1999), adapted for sub-national ethnic population projections by Rees et al. (2013) to determine the contributions of component assumptions to population change. Drawing on studies of the errors observed in past projections by component, probabilistic projections can be run to determine whether comparator projections fall within the 80% prediction interval. Or empirical prediction intervals can be projected over space and time and used to ascertain the uncertainty of the projection. This is a formidable list of analyses but one necessary to answer the questions of users. A final lesson learnt in this work is that applied research work for an external client can contribute valuable challenges to academic researchers and push us beyond our comfort zones.

Notes

Acknowledgements

Ben Corr of the Greater London Authority (GLA) advised on methods as an external evaluator for TWUL. Wil Tonkins (GLA) provided access to the GLA 2015-based projections for local authority districts (LADs) outside the GLA before they were published online. Public domain data from the 2011 Census of England and Wales and from the 2014-based and 2016-based Sub-National Population Projections, were produced by the Office for National Statistics (ONS). These data are Crown Copyright and supplied under an Open Data Licence. Peter Boden and Martyna Jasinska of EDGE Analytics Ltd. provided their projected populations for Water Resource Zones (WRZs). Chris Lambert of TWUL critiqued the Leeds results and posed research questions for which TWUL required answers. Ross Henderson of TWUL supplied the WRZ digital boundary data used in geo-converting Local Authority results to WRZs. Tom Wilson, Charles Darwin University, provided valuable advice and guidance to the literature on evaluation of population projections. The two referees of the paper gave robust and constructive advice on the paper, for which we are very grateful.

Author Roles

Philip Rees designed the Leeds projections, assembled the data sets, geo-converted the Leeds, GLA and ONS LAD projections to WRZs and wrote the paper. Stephen Clark revised the NewETHPOP model code and ran the LEEDS MID, HIGH and LOW projections to 2101. Pia Wohland wrote the original model code and advised on the projections to 2101. Michelle Kalamandeen produced the probability matrix for geo-converting the LEEDS, GLA and ONS LAD data to WRZs.

Funding

This study was funded by Thames Water Utilities Limited (TWUL) through award Long-Term Population and Property Forecasts for Thames Water, 2016–2017 (RG.GEOG.107613) to the University of Leeds. The development of methods and software to project local populations by ethnicity were supported by ESRC Award ES/L013878/1, Evaluation and Extension of Ethnic Population Projections – New ETHPOP, 2015–2016 to the University of Leeds.

Compliance with Ethical Standards

Conflict of Interest

Philip Rees has received research grants as Principal Investigator from Thames Water Utilities Limited (TWUL) and the Economic and Social Research Council (ESRC). Stephen Clark worked as a Post-Doctoral Research Assistant on both funded projects. Pia Wohland was a co-investigator on the ESRC funded project and carried out consultancy work on the TWUL project. Michelle Kalamandeen worked as a Research Assistant on the TWUL project. The projections and interpretations in this paper do not reflect the official views of ESRC, TWUL, GLA, ONS or EDGE and are the responsibility of the authors.

Glossary

APE

Absolute Percentage Error

CCM

Cohort-Component Model

COA

Census Output Area

CWR

Child-Woman Ratios

EDGE

Edge Analytics Ltd

EPI

Empirical Prediction Interval

GDM

Geographical Distribution Method

GLA

Greater London Authority

IPS

International Passenger Survey

K

Thousand

LAD

Local Authority District

LB

London Borough

LDT

Look Down Table

LEEDS

University of Leeds

LFS

Labour Force Survey

LTIM

Long-Term International Migration

MYE

Mid-Year Population Estimate

N

Northern Ireland

NPP

National Population Projection

OD

Origin to Destination

ONS

Office for National Statistics

ROW

Rest of the World

RUK

Rest of the UK

S

Scotland

SNPP

Sub-National Population Projection

TW

Thames Water (region)

TWUL

Thames Water Utilities Ltd

W

Wales

WRZ

Water Resource Zone

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© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.School of GeographyUniversity of LeedsLeedsUK
  2. 2.Leeds Institute for Data AnalyticsUniversity of LeedsLeedsUK
  3. 3.School of Earth and Environmental SciencesThe University of QueenslandSt LuciaAustralia

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