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May 18 -- Global Supply Chain Pressure Index: May 2022 Update
Gianluca Benigno, Julian di Giovanni, Jan J.J. Groen, and Adam Noble

Supply chain disruptions continue to be a major challenge as the world economy recovers from the COVID-19 pandemic. Furthermore, recent developments related to geopolitics and the pandemic (particularly in China) could put further strains on global supply chains. In a January post, we first presented the Global Supply Chain Pressure Index (GSCPI), a parsimonious global measure designed to capture supply chain disruptions using a range of indicators. We revisited our index in March, and today we are launching the GSCPI as a standalone product, with new readings to be published each month. In this post, we review GSCPI readings through April 2022 and briefly discuss the drivers of recent moves in the index.

Blog: https://libertystreeteconomics.newyorkfed.org/2022/05/global-supply-chain-pressure-index-may-2022-update/
GSPCI webpage: https://www.newyorkfed.org/research/policy/gscpi#/overview

Mar 3 -- Global Supply Chain Pressure Index: March 2022 Update
Gianluca Benigno, Julian Di Giovanni, Jan Groen, and Adam Noble
Supply chain disruptions continue to be a major challenge as the world economy recovers from the COVID-19 pandemic. In a January post, we presented the Global Supply Chain Pressure Index (GSCPI) as a parsimonious global measure that encompasses several indicators used to capture supply chain disruptions. The main purpose of this post is to provide an update of the GSCPI through February 2022. In addition, we use the index’s underlying data to discuss the drivers of recent moves in the GSCPI. Finally, these data are used to create country-specific supply chain pressures indices.
Jan 4 -- "A New Barometer of Global Supply Chain Pressures," Gianluca Benigno, Julian di Giovanni, Jan J. J. Groen, and Adam I. Noble
Supply chain disruptions have become a major challenge for the global economy since the start of the COVID-19 pandemic. Factory shutdowns (particularly in Asia) and widespread lockdowns and mobility restrictions have resulted in disruptions across logistics networks, increases in shipping costs, and longer delivery times. Several measures have been used to gauge these disruptions, although those measures tend to focus on selected dimensions of global supply chains. In this post, we propose a new gauge, the Global Supply Chain Pressure Index (GSCPI), which integrates a number of commonly used metrics with an aim to provide a more comprehensive summary of potential disruptions affecting global supply chains. . . .
The first set of indicators we draw from focus on cross-border transportation costs. First, we use data on the Baltic Dry Index (BDI), which tracks the cost of shipping raw materials, such as coal or steel. Second, we exploit the Harpex index, which tracks container shipping rate changes in the charter market for eight classes of all-container ships. Finally, the U.S. Bureau of Labor Statistics (BLS) constructs price indices that measure the cost of air transportation of freight to and from the U.S., and we use the outbound and inbound airfreight price indices for air transport to and from Asia and Europe. . . .  
The second set of indicators rely on country-level manufacturing data from the Purchase Manager Index (PMI) surveys. In terms of country coverage, we focus on economies that have both a significant sample length and are substantially interlinked through global supply chains: the euro area, China, Japan, South Korea, Taiwan, the U.K., and the U.S. Note that in case of the U.S., the PMI data start only in 2007, so we combine the PMI data with those from the manufacturing survey of the Institute for Supply Management (ISM). From these PMI surveys, we use the following subcomponents of the country-specific manufacturing PMIs: “delivery time,” which captures the extent to which supply chain delays in the economy impact producers—a variable that may be viewed as identifying a purely supply-side constraint; ”backlogs,” which quantifies the volume of orders that firms have received but have yet to either start working on or complete; and, finally, ”purchased stocks,” which measures the extent of inventory accumulation by firms in the economy. . . .
To estimate our GSCPI measure, we thus have available a data set of twenty-seven variables: the three country-specific supply chain variables for each of the economies in our sample (the euro area, China, Japan, South Korea, Taiwan, the U.K., and the U.S.), the two global shipping rates, and the four price indices summarizing airfreight costs between the U.S., Asia, and Europe. All these variables are corrected for demand effects to the greatest possible extent, as described previously. This data set is made up of monthly time series of uneven length: the advanced economies’ supply chain variables all start in 1997, for Japan they start in 2001 and for the other Asian economies 2004, the Harpex index starts in 2001, the BDI goes back to 1985 and the BLS airfreight price indices go back to 2005 on a monthly frequency and are quarterly from 2005 to 1997. Our aim is to estimate a common, or “global,” component from these time series. To be able to do that while also dealing with data gaps, we follow Stock and Watson (2002) and extract this common component for the 1997-2021 period through a principal component analysis while simultaneously filling the data gaps using this estimated common component. . . .
Our goal in constructing the GSCPI was to construct a parsimonious measure of global supply chain pressures that could be used to gauge the importance of supply constraints with respect to economic outcomes. In a follow-up post, we will quantify the impact of shocks to the GSCPI on recent movements in producer and consumer price inflation.
Also see "The Dynamics of International Shipping Costs," Fernando Leibovici and Jason Dunn, Federal Reserve Bank of St. Louis, January 3, 2022 https://www.stlouisfed.org/on-the-economy/2022/january/dynamics-international-shipping-costs

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In their post at the St Louis fed, the authors elaborate:
"More specifically, we collect data on the 'new orders' PMI subcomponent, which captures the extent of customer demand for firms' products and regress the three country-specific supply chain PMI measures (delivery time, backlogs, and purchased stocks) on the contemporaneous value and two lags of this new orders component in an attempt to purge demand factors from these three supply chain PMI subcomponents. The residuals from these regressions for each country will then be used as inputs in constructing our global supply chain pressure index."

While it makes sense to me why the residuals from such regressions should in theory form the basis for the GSCP, I am afraid the underlying logic only applies if this data exercise is carried out at the firm level. If the "new orders" component of the PMI is aggregated from the firm level to the country level however, the resulting new orders sub-index also comprises orders for inputs and intermediate consumption by all firms along the value chain.

As a consequence, I see an endogeneity problem in this regression because of reverse causality: new orders received by all firms in the aggregate can be explained to a significant extent by a combination of backlog and inventories by all firms in the aggregate. Accordingly, the estimators would be biased and the estimated residuals would be incorrect (and the index based on them would therefore not be reliable).

Does anybody else see a problem here? I would be happy to discuss.