The Network Origins of Aggregate Fluctuations

By Daron Acemoglu, Vasco M. Carvalho, Asuman E. Ozdaglar, Alireza Tahbaz-Salehi. 2011

1,962 citations recorded by the source corpus.

This paper argues that in the presence of intersectoral input-output linkages, microeconomic idiosyncratic shocks may lead to aggregate fluctuations. In particular, it shows that, as the economy becomes more disaggregated, the rate at which aggregate volatility decays is determined by the structure of the network capturing such linkages. Our main results provide a characterization of this relationship in terms of the importance of different sectors as suppliers to their immediate customers as well as their role as indirect suppliers to chains of downstream sectors. Such higher-order interconnections capture the possibility of "cascade effects" whereby productivity shocks to a sector propagate not only to its immediate downstream customers, but also indirectly to the rest of the economy. Our results highlight that sizable aggregate volatility is obtained from sectoral idiosyncratic shocks only if there exists significant asymmetry in the roles that sectors play as suppliers to others, and that the "sparseness" of the input-output matrix is unrelated to the nature of aggregate fluctuations.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/bb7fa79aa0ba49a8ff09985c94a5ccdf0743bf66

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Citations in the corpus

A network analysis of the volatility of high dimensional financial series (2017, background): “…evidence that connectivity in the idiosyncratic component, although milder than in the common component, still may be quite non-negligible, even in large dimensional systems (see for example the empirical and theoretical results of Jovanovic (1987), Gabaix (2011) and Acemoglu et al. (2012)).”
The Relationship Between Global Value Chains and Productivity (2017, background): “Evidence for the US suggests that fluctuations in these key sectors are highly correlated with aggregate manufacturing growth since the 1960s (Acemoglu et al., 2012; Carvalho, 2014).”
Networks, Barriers, and Trade (2019, positive): “Our results also relate to work with non-parametric or semi-parametric models of trade like Adao et al. (2017) and Lind and Ramondo (2018) (though our analysis does not rely on the invertibility, or stability, of factor demand systems), as well as Allen et al. (2014), (although we do not impose a gr”
Networks of Value Added Trade (2017, positive): “The structure of the connections between firms (Gabaix, 2011) or sectors (Acemoglu et al., 2012) in a given country plays a fundamental role in the transmission of shocks from individual agents to the whole economy, notably through cascade effects.”
Factor Models with Local Factors — Determining the Number of Relevant Factors (2021, background): “This is also in line with the results in Acemoglu et al. (2012). There, an intersectoral network plays the same role as the firm size distribution in Gabaix (2011) and the distribution of sectoral sizes in our paper – shocks that take more central positions in the network have a disproportionate eff”