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This column uses Japanese firm-level input-output data to fill this gap. The authors find that negative demand shocks only propagated from large exporters to larger suppliers during the global financial crisis, but were transmitted from small customers even to their small suppliers during the COVID-19 pandemic. Identifying the propagation route helps in the design of targeted policies. The propagation of shocks via input-output linkages among firms (i.e. supply chains) has gained attention among economists. The ongoing COVID-19 pandemic and recent geopolitical incidents further necessitate studies on the theme. In this context, recent theoretical analysis (e.g. Acemoglu et al. 2012) shows that microeconomic shocks to hub firms in an input-output network have a large impact on aggregate output. Furthermore, because of the increasing availability of firm-level data, recent empirical studies directly analyse such firm-level input-output networks and find propagation phenomena. For example, Barrot and Sauvagnat (2016) use natural disasters in the US as the source of negative shocks to firms and examine the propagation of these shocks via input-output linkages. Boehm et al. (2019) and Carvalho et al. (2021) use the Tohoku earthquake in Japan in 2011 and show that this negative shock propagates to other customers in unaffected regions. These previous studies focus on propagation driven by supply shocks; that is, shocks propagate ‘downstream’ from supplies to customers. In contrast, ‘upstream’ propagation from customers to suppliers driven by demand shocks is rarely examined in the empirical literature. Two exceptions are Acemoglu et al. (2016) and Kisat and Phan (2020), but their analysis relies on a sector-level input-output network. To the best of our knowledge, there is no empirical study that directly observes the demand-shock propagation through a firm-level input-output network. In a recent paper (Arata and Miyakawa 2022), we aim to fill this gap in the literature using Japanese firm-level input-output data. We focus on demand-shock propagation originating from the sharp drop in exports during the Global Crisis and the change in consumer behaviour during the COVID-19 pandemic. Viewing these events as exogenous to Japanese firms, we examine how sales growth rates are affected by the presence or absence of their transacted customers damaged by the negative shocks. We use the heterogeneous treatment effect model developed by Athey et al. (2019) and Wager and Athey (2018), which enables us to analyse how the propagation effect depends on firm characteristics, such as firm size. By focusing on the heterogeneity of the propagation effect, we examine the route of the demand-shock propagation through the input-output network. Our analysis shows that during the global financial crisis, the propagation effect is substantial for large suppliers but not for small suppliers (see Figure 1). That is, negative shocks to exporting firms are not transmitted to their small suppliers, especially when the exporting firms are large. We find that while the exporting firms that are facing the decline in exports reduce inventories, the sales growth rates of their small suppliers do not respond to the negative growth rates of these exporting firms. Figure 1 Response of the growth rates of supplier firms’ sales to the growth rates of their customer firms’ sales during the global financial crisis [Click to enlarge] Figure 1 Response of the growth rates of supplier firms’ sales to the growth rates of their customer firms’ sales during the global financial crisisNotes: The propagation effect depends on the log sizes of suppliers and their customers. Customer size is categorized into four groups: small (sales ≤ first quantile), middle1 (first quantile ≤ sales ≤ median), middle2 (median ≤ sales ≤ third quantile), and large (sales ≥ third quantile). The left panel and right panel account for the supplier firms in manufacturing industries and wholesale & retail industries, respectively. Notes: The propagation effect depends on the log sizes of suppliers and their customers. Customer size is categorized into four groups: small (sales ≤ first quantile), middle1 (first quantile ≤ sales ≤ median), middle2 (median ≤ sales ≤ third quantile), and large (sales ≥ third quantile). The left panel and right panel account for the supplier firms in manufacturing industries and wholesale & retail industries, respectively. This is because the propagation effect is not homogenous, and in particular, demand shocks propagate from customers to suppliers only when the latter are the main suppliers (see Figure 2). We find that even in cases where the customers experiencing a large decline in exports are viewed by the supplier as the main customers, the negative shocks are not transmitted to the supplier if this supplier is not viewed by the customer as the main supplier. In addition, we find that large suppliers are likely to be chosen as the main suppliers, especially by large customers. Since most exporting firms and their main suppliers are large firms, demand-shock propagation mainly occurs within these large firms during the global financial crisis. Figure 2 Route of the demand-shock propagation Notes: The size of the circle represents the size of the firm. Demand shocks propagate to suppliers only when they are the main suppliers for the customer. In particular, the propagation of demand shocks does not occur between large customers and small suppliers. The finding of the heterogeneity of the propagation effect raises another question. If demand shocks hit firms with small main suppliers, do the negative shocks propagate to the small suppliers? This is what happened during the COVID-19 pandemic (see Figure 3). Most firms in the COVID-affected sectors, such as restaurants and hotels, are small and their main suppliers are also small. We find that there is no significant heterogeneity of the propagation effect across the size of suppliers. That is, negative demand shocks propagate even to small suppliers. This result is consistent with our interpretation that propagation occurs only through the linkages that are relevant to both suppliers and customers. Figure 3 Responses of the growth rates of supplier firms' sales to the growth rates of their customer firms' sales during the COVID-19 pandemic Note: Customer size is categorised into three groups: small, middle, and large. Our finding has an important implication for the literature on the micro-origins of aggregate fluctuations. The studies mentioned above emphasise the importance of the network structure in the context of the aggregate fluctuation driven by microeconomic shocks. However, it is implicitly assumed that the propagation of the shocks is homogeneous across firms. Because of this assumption, the impact of microeconomic shocks is proportional to the number of links (i.e. transaction relationships) that the firm has. Since large firms have transactions with many small suppliers and customers, or a negative degree of assortativity (e.g. Bernard and Moxnes 2018, Bernard et al. 2014, Lim 2018, Bernard et al. 2019), the model predicts that shocks to these large firms propagate across an economy. In contrast, our finding suggests that the role of large firms in demand-shock propagation is limited because demand-shock propagation does not occur through these links between large customers and small suppliers. Demand shocks to large firms are transmitted only to their large suppliers and do not spread further across an economy. An additional important message from our analysis is that links through which demand shocks tend to propagate and links through which demand shocks cease to propagate coexist. In other words, connecting the former links in an input-output network enables us to identify the route of the demand-shock propagation. Identifying the propagation route is also relevant to policymakers because we can assess the effectiveness of policy measures such as subsidies to targeted firms more precisely. Our finding is the first step for this purpose. Editor’s note: The main research on which this column is based (Arata and Miyakawa 2022) first appeared as a Discussion Paper of the Research Institute of Economy, Trade and Industry (RIETI) of Japan. This article first appeared on www.VoxEU.org on June 11, 2022. Reproduced with permission. Reference(s) Acemoglu, D, U Akcigit and W Kerr (2016), “Networks and the macroeconomy: An empirical exploration”, NBER Macroeconomics Annual 30(1): 273–335. Acemoglu, D, V M Carvalho, A E Ozdaglar and A Tahbaz-Salehi (2012), “The Network Origins of Aggregate Fluctuations”, Econometrica 80(5): 1977–2016. Arata, Y and D Miyakawa (2022), “Demand Shock Propagation through an Input-Output Network in Japan,” RIETI Discussion Paper Series 22-E-027. Athey, S, J Tibshirani and S Wager (2019), “Generalized random forests”, The Annals of Statistics 47(2): 1148–1178. Barrot, J and J Sauvagnat (2016), “Input Specificity and the Propagation of Idiosyncratic Shocks in Production Networks”, Quarterly Journal of Economics 131(3): 1543–1592. Bernard, A B, E Dhyne, G Magerman, K Manova and A Moxnes (2019), “The origins of firm heterogeneity: A production network approach”, Technical report, National Bureau of Economic Research. Bernard, A B and A Moxnes (2018), “Networks and trade”, Annual Review of Economics 10: 65–85. Bernard, A B, A Moxnes and Y U Saito (2014), “Geography and firm performance in the Japanese production network”, Technical report, RIETI. Boehm, C E, A Flaaen and N Pandalai-Nayar (2019), “Input Linkages and the Transmission of Shocks: Firm-Level Evidence from the 2011 Tohoku Earthquake”, Review of Economics and Statistics 101(1): 60–75. Carvalho, V M, M Nirei, Y U Saito and A Tahbaz-Salehi (2021), “Supply chain disruptions: Evidence from the Great East Japan Earthquake”, The Quarterly Journal of Economics 136(2): 1255–1321. Kisat, F and M Phan (2020), “Consumer demand shocks & firm linkages: Evidence from demonetization in India”, available at SSRN 3698258. Lim, K (2018), “Endogenous production networks and the business cycle”, Working Paper. Wager, S and S Athey (2018), “Estimation and inference of heterogeneous treatment effects using random forests”, Journal of the American Statistical Association 113(523): 1228–1242. Tweet June 14, 2022 Print Article(s) by this author The size of micro-originated fluctuations in the macroeconomy September 16, 2022［RIETI Report］ How demand shocks propagate through an input-output network: The cases of the global financial crisis and the COVID-19 pandemic June 17, 2022［RIETI Report］ How demand shocks propagate through an input-output network: The cases of the global financial crisis and the COVID-19 pandemic June 14, 2022［VoxEU Column］ Bankruptcy propagation in customer–supplier networks: An empirical analysis in Japan February 28, 2019［RIETI Report］ Bankruptcy propagation in customer–supplier networks: An empirical analysis in Japan February 26, 2019［VoxEU Column］ Articles Column Special Series Newspapers & Magazines Fellows' Works VoxEU Column From IZA Perspectives from Around the World Other Contents RIETI Report Policy Update Keizai Sangyo Journal Communications Newsletter RSS Feed Facebook X YouTube Research Areas Research Programs (FY2024-2028) Research Programs (FY2020-2023) Research Programs (FY2016-2019) Research Programs (FY2011-2015) Policy Research Domains (FY2006-2010) Projects Survey Fellows Research/Policy Papers Discussion Papers (English) Discussion Papers (Japanese) Policy Discussion Papers (English) Policy Discussion Papers (Japanese) Technical Papers (English) Technical Papers (Japanese) Non Technical Summaries List of Articles in Journals Research Digest Discussion Papers Search Publications RIETI Books (English) RIETI Books (Japanese) History of Japan's Trade and Industry Policy Authors' Words Other Publications (English) Other Publications (Japanese) Events Symposiums Workshops BBL Seminars Archived Seminar Series Data JIP Database R-JIP Database CIP Database Industry-Specific Nominal and Real Effective Exchange Rates AMU and AMU Deviation Indicators JSTAR RIETI-TID RIETI FDI Database ICPA Project Links Articles Column Special Series Newspapers & Magazines Fellows' Works VoxEU Column From IZA Perspectives from Around the World Other Contents RIETI Report Policy Update Keizai Sangyo Journal (METI Journal) About RIETI Privacy Policy Site Policy Site Map Help Contact METI Web Site Research Institute of Economy, Trade and Industry, IAA (JCN 6010005005426)JCN: Japan Corporate Number Opinions expressed or implied on this website are solely those of the author, and do not necessarily represent the views of the Research Institute of Economy, Trade and Industry (RIETI).Titles, numbers, specific names, etc. on this website are as of the date of publication. 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