Probabilistic projection of subnational total fertility rates

Ševčíková, Hana; Raftery, Adrian E.; Gerland, Patrick

doi:10.4054/demres.2018.38.60

Cited by 14 publications

(10 citation statements)

References 38 publications

(34 reference statements)

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“…For subnational mortality estimation, many researchers have used Bayesian hierarchical frameworks to share information about mortality trends across space and time, in contexts where the available data are both reliable (Congdon, Shouls, and Curtis 1997;Alexander, Zagheni, and Barbieri 2017) and sparse (Schmertmann and Gonzaga 2018). For subnational fertility estimation, Sevcikova, Raftery, and Gerland (2018) propose a Bayesian model that produces estimates and projections of subnational total fertility rates (TFRs) that are consistent with national estimates of TFR produced by the UN. Building from the local level up, Schmertmann et al (2013) propose a method which uses empirical Bayesian methods to smooth volatile fertility data at the regional level, before modeling using a Brass relational model variant.…”

Section: Bayesian Methodsmentioning

confidence: 99%

A Bayesian cohort component projection model to estimate adult populations at the subnational level in data-sparse settings

Alexander,

Alkema

2021

Preprint

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Accurate estimates of subnational populations are important for policy formulation and monitoring population health indicators. For example, estimates of the number of women of reproductive age are important to understand the population at risk to maternal mortality and unmet need for contraception. However, in many low-income countries, data on population counts and components of population change are limited, and so levels and trends subnationally are unclear. We present a Bayesian constrained cohort component model for the estimation and projection of subnational populations. The model builds on a cohort component projection framework, incorporates census data and estimates from the United Nation's World Population Prospects, and uses characteristic mortality schedules to obtain estimates of population counts and the components of population change, including internal migration. The data required as inputs to the model are minimal and available across a wide range of countries, including most low-income countries. The model is applied to estimate and project populations by county in Kenya for 1979-2019, and validated against the 2019 Kenyan census.

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Section: Bayesian Methodsmentioning

confidence: 99%

A Bayesian cohort component projection model to estimate adult populations at the subnational level in data-sparse settings

Alexander,

Alkema

2021

Preprint

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“…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…”

Section: C Tested Comparisonmentioning

confidence: 99%

“…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 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.…”

Section: Variant Projections and Sensitivity Analysismentioning

confidence: 99%

“…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.…”

Section: Variant Projections and Sensitivity Analysismentioning

confidence: 99%

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

Rees

Clark

Wohland

et al. 2018

Appl. Spatial Analysis

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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%.

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“…Like other demographic processes, fertility decline intensified in advanced economies since the 1970s, with distinctive regional trends [1][2][3][4][5][6][7][8][9]. In Europe, the 'first demographic transition' has reflected-at least since the late 1960s-a fertility decrease together with (more or less evident) socioeconomic transformations [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

High-to-Low (Regional) Fertility Transitions in a Peripheral European Country: The Contribution of Exploratory Time Series Analysis

Rodrigo‐Comino

Egidi

Salvati

et al. 2021

Data

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Diachronic variations in demographic rates have frequently reflected social transformations and a (more or less evident) impact of sequential economic downturns. By assessing changes over time in Total Fertility Rate (TFR) at the regional scale in Italy, our study investigates the long-term transition (1952–2019) characteristic of Mediterranean fertility, showing a continuous decline of births since the late 1970s and marked disparities between high- and low-fertility regions along the latitude gradient. Together with a rapid decline in the country TFR, the spatiotemporal evolution of regional fertility in Italy—illustrated through an exploratory time series statistical approach—outlines the marked divide between (wealthier) Northern regions and (economically disadvantaged) Southern regions. Non-linear fertility trends and increasing spatial heterogeneity in more recent times indicate the role of individual behaviors leveraging a generalized decline in marriage and childbearing propensity. Assuming differential responses of regional fertility to changing socioeconomic contexts, these trends are more evident in Southern Italy than in Northern Italy. Reasons at the base of such fertility patterns were extensively discussed focusing—among others—on the distinctive contribution of internal and international migrations to regional fertility rates. Based on these findings, Southern Italy, an economically disadvantaged, peripheral region in Mediterranean Europe, is taken as a paradigmatic case of demographic shrinkage—whose causes and consequences can be generalized to wider contexts in (and outside) Europe.

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Probabilistic projection of subnational total fertility rates

Cited by 14 publications

References 38 publications

A Bayesian cohort component projection model to estimate adult populations at the subnational level in data-sparse settings

A Bayesian cohort component projection model to estimate adult populations at the subnational level in data-sparse settings

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

High-to-Low (Regional) Fertility Transitions in a Peripheral European Country: The Contribution of Exploratory Time Series Analysis

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