Econometric Analysis of Production Networks with Dominant Units

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. (2012) and considers a production network with unobserved common technological factor and establishes general conditions under which the network structure contributes to aggregate fluctuations. It introduces the notions of strongly and weakly dominant units, and shows that at most a finite number of units in the network can be strongly dominant, while the number of weakly dominant units can rise with N (the cross section dimension). This paper further establishes the equivalence between the highest degree of dominance in a network and the inverse of the shape parameter of the power law. A new extremum estimator for the degree of pervasiveness of individual units in the network is proposed, and is shown to be robust to the choice of the underlying distribution. Using Monte Carlo techniques, the proposed estimator is shown to have satisfactory small sample properties. Empirical applications to US input-output tables suggest the presence of production sectors with a high degree of pervasiveness, but their effects are not sufficiently pervasive to be considered as strongly dominant. Terms of use: Documents inJEL-Codes: C120, C130, C230, C670, E320.

R&D and wholesale trade are critical to the economy: Identifying dominant sectors from economic networks

“…Following Pesaran and Yang (2017), the cross section exponent of the factor loadings δ γ is defined to ensure that…”

“…A price network is dual to the production network and the former is derived from the sectoral equilibrium prices p it = log(P it ) and the equilibrium wage rates ω t = log(W age t ) as in Pesaran and Yang (2017). Solving sector i's problem leads to…”

“…These economy-wide sectoral networks are formally modeled in Acemoglu et al (2012). 1 Using network theory to develop a formal test to determine the most critical sectors in the economy Pesaran and Yang (2017) establish the dominance of the wholesale trade and transport sector in the US economy. (Dominance is a formally defined term for sectors which pass the threshold value for critical importance in the network.)…”

“…We apply the dominant sector detection methodology of Pesaran and Yang (2017) to inputoutput tables for 49 economies and determine that in many of the more developed markets both R&D and wholesale trade are critical. For a substantial number of countries, including the US, the most recent data indicates a shift towards R&D as the dominant sector.…”

Asian Economic Integration Report 2017:

“…The challenge to researchers and policy makers is to include different asset markets and potentially different players to better reflect the complexity of multiple layers of financial interconnectedness between economies. Sovereign bond market networks will, for instance, differ from equity market networks (Dungey et al 2017a), and real economy networks-such as trade networks or inputoutput production networks (Pesaran and Yang 2016)are closely tied to financial networks, but the weights on the nodes can be different and multidimensional.…”