Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet, the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper, we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001–2020. The key themes covered by the review are extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socioeconomic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used. Supplementary Information The online version contains supplementary material available at 10.1007/s11113-021-09671-6.

Written and spoken language utilize the same processing system, enabling text to modulate speech processing. We investigated how simultaneously presented text affected speech recall in babble noise using a retrospective recall task. Participants were presented with text-speech sentence pairs in multitalker babble noise and then prompted to recall what they heard or what they read. In Experiment 1, sentence pairs were either congruent or incongruent and they were presented in silence or at 1 of 4 noise levels. Audio and Visual control groups were also tested with sentences presented in only 1 modality. Congruent text facilitated accurate recall of degraded speech; incongruent text had no effect. Text and speech were seldom confused for each other. A consideration of the effects of the language background found that monolingual English speakers outperformed early multilinguals at recalling degraded speech; however the effects of text on speech processing were analogous. Experiment 2 considered if the benefit provided by matching text was maintained when the congruency of the text and speech becomes more ambiguous because of the addition of partially mismatching text-speech sentence pairs that differed only on their final keyword and because of the use of low signal-to-noise ratios. The experiment focused on monolingual English speakers; the results showed that even though participants commonly confused text-for-speech during incongruent text-speech pairings, these confusions could not fully account for the benefit provided by matching text. Thus, we uniquely demonstrate that congruent text benefits the recall of noise-degraded speech. (PsycINFO Database Record

Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001-2020. The key themes covered by the review are: extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socio-economic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used.

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