Diem Christian, Borsos András, Reisch Tobias, Kertész János, Thurner Stefan
Complexity Science Hub Vienna, Vienna A-1080, Austria.
Institute for Finance, Banking and Insurance, Vienna University of Economics and Business, Vienna A-1020, Austria.
PNAS Nexus. 2024 Feb 17;3(3):pgae064. doi: 10.1093/pnasnexus/pgae064. eCollection 2024 Mar.
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 nonaggregated firm-level production networks (FPNs). Due to nonavailability 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 an underestimation of economic losses of up to 37%, 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, CO emissions, and the impact of policy interventions with industry-level IO models. Granular data are key for reasonable predictions of dynamical economic systems.
为了评估经济体对政治干预或外部干扰的反应，人们广泛使用通过将数据汇总到工业部门而构建的投入产出（IO）表。然而，经济增长、稳健性和恢复力关键取决于非汇总的企业层面生产网络（FPN）的详细结构。由于数据不可得，对于基于汇总部门的模型预测与基于详细企业层面的模型预测之间的差异程度知之甚少。我们使用一个几乎完整的全国性FPN，其中包含243,399家匈牙利企业以及1,104,141个供应商 - 买家关系，我们自洽地比较了汇总行业层面生产网络（IPN）和粒度FPN上的生产损失。为此，我们对相同规模的冲击在IPN和FPN上的传播进行建模，其中后者捕捉了行业内的相关异质性。在一个受新冠疫情启发的情景中，我们根据疫情早期详细的企业层面数据对冲击进行建模。我们发现，使用IPN而非FPN会导致经济损失低估高达37%，这表明行业层面的IO模型在预测经济结果方面存在天然局限性。我们将这种巨大差异归因于行业内企业的显著异质性：我们发现，一个部门内的企业平均仅向同一行业销售23.5%的产品，并从同一行业购买19.3%的产品，这凸显了工业部门在代表其所包含企业方面的严重局限性。在使用行业层面的IO模型估计经济增长、碳排放和政策干预的影响时，预计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。
