估计从聚合企业级生产网络中产生的经济可预测性损失
Christian Diem1,2, András Borsos1,3,4, Tobias Reisch1,5
1Complexity Science Hub Vienna, Vienna A-1080, Austria.
PNAS nexus
|March 27, 2024
概括
与企业级生产网络相比,行业级经济模型低估了高达37%的损失. 细粒度的公司数据揭示了重要的经济异质性,这对于准确预测经济动态至关重要.
科学领域:
- 经济学 经济学 经济学
- 网络科学 网络科学
- 经济建模经济建模
背景情况:
- 输入-输出 (IO) 表汇总数据到工业部门进行经济分析.
- 企业级生产网络 (FPN) 为经济增长,稳健性和弹性提供了更详细的视图.
- 由于数据限制,聚合模型和颗粒模型之间的差异在很大程度上是未知的.
研究的目的:
- 为了比较综合行业级生产网络 (IPN) 和详细的FPN之间的生产损失预测.
- 用两种模型类型量化经济冲击传播的差异.
- 突出IO模型在捕捉企业级异质性的局限性.
主要方法:
- 利用了全国范围内的243,399家匈牙利企业和1,104,141个供应商-购买者关系的FPN.
- 在IPN和FPN上模拟冲击传播,以比较生产损失.
- 使用公司级数据模拟了COVID-19经济冲击场景.
主要成果:
- 与FPN相比,行业级模型低估了高达37%的经济损失.
- 在行业内观察到显著的异质性:平均而言,企业在自己的行业内仅销售23.5%并购买19.3%.
- 这种异质性解释了IPN和FPN预测之间的巨大差异.
结论:
- 产业级IO模型在预测经济结果方面存在自然限制,原因是未捕捉到的企业级异质性.
- 精细的企业级数据对于准确预测经济增长,二氧化碳排放和政策影响至关重要.
- FPN提供了比聚合的IPN更现实的经济系统的表现.
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