Estimating the loss of economic predictability from aggregating firm-level production networks

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, CO2 emissions, and the impact of policy interventions with industry-level IO models. Granular data are key for reasonable predictions of dynamical economic systems.

Active tuning of stroke-induced vibrations by tennis players

This paper investigates how tennis players control stroke-induced vibration. Its aim is to characterise how a tennis player deals with entering vibration waves or how he/she has the ability to finely adjust them. A specific experimental procedure was designed, based on simultaneously collecting sets of kinematic, vibration and electromyographic data during forehand strokes using various commercial rackets and stroke intensities. Using 14 expert players, a wide range of excitations at spectral and temporal levels were investigated. Energetic and spectral descriptors of stroke-induced vibration occurring at the racket handle and at the player's wrist and elbow were computed. Results indicated that vibrational characteristics are strongly governed by grip force and to a lower extent by the racket properties. Grip force management drives the amount of energy, as well as its distribution, into the forearm. Furthermore, hand-grip can be assimilated to an adaptive filter which can significantly modify the spectral parameters propagating into the player's upper limb. A significant outcome is that these spectral characteristics are as much dependent on the player as on the racket. This contribution opens up new perspectives in equipment manufacture by underlining the need to account for player/racket interaction in the design process.