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Aggregation underestimates growth: Case study in Population Modelling for India

机译:聚集低估增长:印度人口建模案例研究

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Jensen's inequality implies that aggregation underestimates growth and overestimates decay compared to an uncoupled disaggregated approach. As a study in disaggregation, this paper extends previous work in India's top down population modelling. Top down estimates of vital parameters used in country level population modelling hide the wide dispersion and interplay between these parameters that sub-national population cohorts have. Differences in total fertility rates, mortality rates and the demographics between national level averages and sub-national actuals, for large and diverse countries such as India, lead to a significant gap in population growth forecasts at the two levels. Population projections from International agencies, that are based on top down estimates, should be read in the context of these limitations. Higher, country level, starting data points conceal population groupings that demand more policy attention. Disaggregated modelling, used in this paper provides a plug and play alternative to construct sub national population projections that are used to derive country level estimates. As an example, state level population data, based on India's latest, 2011 census, have been used to create a population projection for India. Disaggregated population modelling provides another insight: geographically focused policy planning for fertility programs.
机译:与未耦合的分解方法相比,Jensen的不平等意味着聚集低估了增长和高估衰减。作为分类的研究,本文在印度的顶级人口模型中延伸了以前的工作。在国家一级人口模型中使用的重要参数的顶级估计隐藏了亚民族人口队列的这些参数之间的广泛分散和相互作用。在印度等大国和多元化国家的国家级平均水平和亚国家实际之间总生育率,死亡率和人口统计学之间的差异导致人口增长预测的大幅差距。基于顶级估计的国际机构的人口预测应在这些限制的背景下阅读。更高,国家级,起始数据点隐瞒需要更多政策关注的人口分组。分解建模,本文使用的使用提供了一种插头和替代方案,以构建用于导出国家一级估计的子国家人口预测。作为一个例子,基于印度最新的2011年人口普查的国家级人口数据已被用来为印度创造一个人口投票。分类的人口建模提供了另一种洞察力:对生育计划的地理上重点的政策规划。

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