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Overcoming missing data bias in water utility indicators by using nested balanced panels
Utilities Policy ( IF 3.8 ) Pub Date : 2020-10-21 , DOI: 10.1016/j.jup.2020.101109
Luis A. Andres , Aroha Bahuguna

This paper demonstrates a methodology for calculating trends in unbalanced panel nonrandom sample datasets, using the International Benchmarking Network for Water and Sanitation Utilities (IBNET) dataset on more than 5000 utilities. The methodology can be used for any dataset and calculates the change, or delta, between the same unit of observation (in this case, a utility) over two consecutive years and nests these deltas to calculate an average trend for a given variable over the longest time horizon possible. We use this method to show trends in water utilities’ performance between 2004 and 2015 at a global level and to reveal differences in performance between groups of utilities. For the sake of comprehensiveness, the representativeness of IBNET is also discussed to provide more context to the dataset used. A probit analysis, conducted to shed light on the representativeness of utilities in the IBNET dataset over time, reveals that the utilities that reported their data in earlier years, in general, have a higher number of connections and perform better than the utilities that reported their data in later years. This implies that over the years, as the number of utilities reporting their data increases, more utilities outside of the bigger (more connections) and better performing utilities start reporting. In other words, in the earlier years it is the bigger and better performing utilities that first report data. In the later years, the smaller and not so well performing utilities also start reporting their data.



中文翻译:

通过使用嵌套的平衡面板来克服自来水公司指标中缺失的数据偏差

本文演示了一种使用国际水和卫生设施基准网络(IBNET)的5000多个公用事业基准数据来计算不平衡面板非随机样本数据集中趋势的方法。该方法可用于任何数据集,并计算连续两年中同一观测单位(在本例中为公用事业)之间的变化或增量,并嵌套这些增量以计算最长时间内给定变量的平均趋势。时间范围可能。我们使用这种方法来显示全球范围内2004年至2015年间水务公司的绩效趋势,并揭示水务集团之间的绩效差异。为了全面起见,还讨论了IBNET的代表性,以便为所使用的数据集提供更多上下文。概率分析 旨在揭示IBNET数据集中的实用程序随时间变化的代表性,它揭示了在较早年份报告其数据的实用程序通常比在较晚年份报告其数据的实用程序具有更高的连接数量和更好的性能。 。这意味着多年来,随着报告数据的实用程序数量的增加,更大(连接更多)和性能更好的实用程序之外的更多实用程序开始进行报告。换句话说,在较早的年份中,更大,性能更好的实用程序会首先报告数据。在后来的几年中,规模较小且性能不佳的实用程序也开始报告其数据。通常,与以后报告数据的实用程序相比,连接数量更多,并且性能更好。这意味着多年来,随着报告数据的实用程序数量的增加,更大(连接更多)和性能更好的实用程序之外的更多实用程序开始进行报告。换句话说,在较早的年份中,更大,性能更好的实用程序会首先报告数据。在后来的几年中,规模较小且性能不佳的实用程序也开始报告其数据。通常,与以后报告数据的实用程序相比,连接数量更多,并且性能更好。这意味着多年来,随着报告数据的实用程序数量的增加,更大(连接更多)和性能更好的实用程序之外的更多实用程序开始进行报告。换句话说,在较早的年份中,更大,性能更好的实用程序会首先报告数据。在后来的几年中,规模较小且性能不佳的实用程序也开始报告其数据。换句话说,在较早的年份中,更大,性能更好的实用程序会首先报告数据。在后来的几年中,规模较小且性能不佳的实用程序也开始报告其数据。换句话说,在较早的年份中,更大,性能更好的实用程序会首先报告数据。在后来的几年中,规模较小且性能不佳的实用程序也开始报告其数据。

更新日期:2020-10-21
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