This month's featured article
The size of micro-originated fluctuations in the macroeconomy
ARATA YoshiyukiFellow, RIETI
MIYAKAWA DaisukeAssociate Professor of Economics at Hitotsubashi University Business School
The micro-origin of aggregate fluctuations has been widely accepted, but the empirical evidence on how microeconomic shocks contribute to the tail probability of the GDP growth rate is rare. This column fills the gap using Japanese firm-level data. Results show that microeconomic shocks contribute largely to the variance of the GDP growth but almost nothing to its tail probability. The authors conclude that the role of microeconomic shocks in explaining aggregate fluctuations seems to be overestimated in the literature.
What drives aggregate fluctuations? Given recent unpredictable events such as the COVID-19 pandemic and the Ukraine-Russia war, it is obvious that exogenous aggregate shocks drive substantial aggregate fluctuations. However, recent macroeconomic studies (e.g. Acemoglu et al. 2012) argued that microeconomic shocks at the firm level are also another important source of aggregate fluctuations. That is, unlike the classical diversification argument (e.g. Lucas 1977) that microeconomic shocks are averaged out at the aggregate level, the recent literature focuses on the propagation of microeconomic shocks through input-output linkages. It shows the possibility that microeconomic shocks could drive substantial aggregate fluctuations. This idea, called the micro-origin of aggregate fluctuations, has been widely accepted and created a new field of macroeconomics.
A number of theoretical papers analyse how aggregate fluctuations, especially the distribution properties of the GDP growth rate, are related to microeconomic shocks. Acemoglu et al. (2012) show that the variance of the GDP growth rate due to microeconomic shocks remains substantial when the structure of an input-output network is highly heterogeneous across firms. That is, when firms have many transactional relationships with other firms and lie at the heart of the network, microeconomic shocks to these hub firms cannot be cancelled out and are not negligible at the aggregate level. In addition, while Acemoglu et al. (2012) consider the variance (i.e. the second moment of the GDP growth rate), Acemoglu et al. (2017) study the tail probability driven by microeconomic shocks (i.e. the probability of the large deviation of the GDP growth rate) and argue that because of the heterogeneity of the network structure, microeconomic shocks contribute to the tail probability of the GDP growth rate. Furthermore, there are empirical studies that support the relevance of microeconomic shocks on aggregate fluctuations. For example, Miranda-Pinto (2021) uses OECD data at the sector level and shows that the change in input-output networks is the key to the decrease in the variance of the GDP growth rate over time. Magerman et al. (2016) analyse a firm-level input-output network based on Belgian tax data and quantify the variance of the GDP growth rate driven by microeconomic shocks.
To read the full text:
“How demand shocks propagate through an input-output network: The cases of the global financial crisis and the COVID-19 pandemic”
ARATA Yoshiyuki (Fellow, RIETI) / MIYAKAWA Daisuke (Associate Professor, Hitotsubashi University)
“The Size of Micro-originated Aggregate Fluctuations: An analysis of firm-level input-output linkages in Japan”
ARATA Yoshiyuki (Fellow, RIETI) / MIYAKAWA Daisuke (Hitotsubashi University)
“Is Empirical Granularity High Enough to Cause Aggregate Fluctuations? The closeness to Gaussian”
ARATA Yoshiyuki (Fellow, RIETI)
“The Role of Granularity in the Variance and Tail Probability of Aggregate Output”
ARATA Yoshiyuki (Fellow, RIETI)
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