Li Mo, Ferreira João Pedro, Court Christa D, Meyer David, Li Mengming, Ingwersen Wesley W
General Dynamics Information Technology, Inc, Falls Church, Virginia, USA.
Food and Resource Economics Department, University of Florida Institute of Food and Agricultural Sciences, Gainesville, Florida, USA.
Int Reg Sci Rev. 2022 Dec 23;46(4). doi: 10.1177/01600176221145874.
Subnational input-output (IO) tables capture industry- and region-specific production, consumption, and trade of commodities and serve as a common basis for regional and multi-regional economic impact analysis. However, subnational IO tables are not made available by national statistical offices, especially in the United States (US), nor have they been estimated with transparent methods for reproducibility or updated regularly for public availability. In this article, we describe a robust StateIO modeling framework to develop state and two-region IO models for all 50 states in the US using national IO tables and state industry and trade data from reliable public sources such as the US Bureau of Economic Analysis. We develop 2012-2017 state IO models and two-region IO models at the BEA summary level. The two regions are state of interest and rest of the US. All models are validated by a series of rigorous checks to ensure the results are balanced at state and national levels. We then use these models to calculate a 2012-2017 time series of macro economic indicators and highlight results for I I states that have distinct economies with respect to size, geography, and industry structure. We also compare selected indicators to state IO models created by popular licensed and open-source software. Our StateIO modeling framework is consolidated in an open-source R package, , to ensure transparency and reproducibility. Our StateIO models are US-focused, which may not be transferrable to international accounts, and form the economic base of state versions of the US environmentally-extended IO models.
次国家级投入产出（IO）表记录了特定行业和地区的商品生产、消费及贸易情况，是区域和多区域经济影响分析的共同基础。然而，各国统计机构，尤其是美国的统计机构，并未提供次国家级IO表，也没有采用透明且可重复的方法进行估算，或定期更新以供公众使用。在本文中，我们描述了一个稳健的StateIO建模框架，利用美国经济分析局等可靠公共来源的国家IO表以及州行业和贸易数据，为美国所有50个州开发州级和两区域IO模型。我们在经济分析局的汇总层面开发了2012 - 2017年的州IO模型和两区域IO模型。这两个区域分别是目标州和美国其他地区。所有模型都经过了一系列严格检验以确保结果在州和国家层面保持平衡。然后，我们使用这些模型计算2012 - 2017年的宏观经济指标时间序列，并突出了11个在规模、地理位置和产业结构方面具有独特经济特征的州的结果。我们还将选定的指标与流行的商业软件和开源软件创建的州IO模型进行了比较。我们的StateIO建模框架整合在一个开源R包中，以确保透明度和可重复性。我们的StateIO模型以美国为重点，可能无法直接应用于国际账户，并且构成了美国环境扩展IO模型州版本的经济基础。
