  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.
