从企业级生产网络的聚合估算经济可预测性的损失
Estimating the loss of economic predictability from aggregating firm-level production networks
摘要：
为了估计经济体对政治干预或外部干扰的反应，投入产出（IO）表-通过将数据汇总到工业部门-被广泛使用。然而，经济增长，稳健性和弹性关键取决于非聚合企业级生产网络（FPN）的详细结构。由于没有数据，人们对基于部门的总体模型预测和基于企业一级的详细模型预测之间的差异知之甚少。使用一个几乎完整的全国性FPN，包含243,399匈牙利公司与1,104,141制造商-买方关系，我们自洽地比较了聚合的行业级生产网络（IPN）和颗粒FPN的生产损失。为此，我们对IPN和FPN这两个相同大小的冲击的传播进行了建模，后者捕获了行业内的相关异质性。在受COVID-19影响的情景中，我们根据疫情早期的详细公司层面数据对冲击进行建模。我们发现，使用IPNs而不是FPNs会导致经济损失估计的误差高达37%，这表明行业级IO模型在预测经济结果方面存在自然局限性。我们将这种巨大的差异归因于行业内企业的显著异质性：我们发现，一个行业内的企业平均只向同一行业出售23.5%的产品，并从同一行业购买19.3%的产品，强调了工业行业代表它们所包括的企业的强烈局限性。在估计经济增长、CO2排放量和政策干预对行业级IO模型的影响时，预计也会出现类似的误差水平。粒度数据是合理预测动态经济系统的关键。
英文摘要：
To estimate the reaction of economies to political interventions or external disturbances, input-output (IO) tables -- constructed by aggregating data into industrial sectors -- are extensively used. However, economic growth, robustness, and resilience crucially depend on the detailed structure of non-aggregated firm-level production networks (FPNs). Due to non-availability of data little is known about how much aggregated sector-based and detailed firm-level-based model-predictions differ. Using a nearly complete nationwide FPN, containing 243,399 Hungarian firms with 1,104,141 supplier-buyer-relations we self-consistently compare production losses on the aggregated industry-level production network (IPN) and the granular FPN. For this we model the propagation of shocks of the same size on both, the IPN and FPN, where the latter captures relevant heterogeneities within industries. In a COVID-19 inspired scenario we model the shock based on detailed firm-level data during the early pandemic. We find that using IPNs instead of FPNs leads to errors up to 37% in the estimation of economic losses, demonstrating a natural limitation of industry-level IO-models in predicting economic outcomes. We ascribe the large discrepancy to the significant heterogeneity of firms within industries: we find that firms within one sector only sell 23.5% to and buy 19.3% from the same industries on average, emphasizing the strong limitations of industrial sectors for representing the firms they include. Similar error-levels are expected when estimating economic growth, CO2 emissions, and the impact of policy interventions with industry-level IO models. Granular data is key for reasonable predictions of dynamical economic systems.
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