Industry Accounts - Input Output Analysis

Industrial activity periodically experiences breakthrough innovations in energy efficiency, but the estimated impacts of these innovations on aggregate energy use are highly varied. We develop a general equilibrium model to investigate whether this variation is determined by the structure of the economy’s input-output network. Our results show sector-specific energy efficiency improvements affect aggregate energy use through adjustments in factor markets and commodity markets, and a process of structural transformation that alters the way energy is used and produced in the economy. We link the aggregate impact of these processes with new network centrality concepts that account for the capacity of a sector to transmit and respond to efficiency innovations. In a calibrated simulation, we find variation in these centrality concepts explains between 38 and 92 percent of variation in the aggregate impacts of energy efficiency, which suggests input-output structure is a critical determinant of the aggregate effects of energy efficiency.

This paper provides new benchmark estimates of industry-level price differentials between Japan and the U.S. for 2011 based on a bilateral price accounting model anchored to the Japan-US input-output tables. We apply the model to translate available demand-side data on purchaser’s price PPPs for final uses (e.g. the Eurostat-OECD PPPs) and intermediate uses (e.g. the METI survey) to unmeasured producer’s price PPPs for industry output. These PPPs allow us to produce price level indexes at the industry level, which we use to assess price competitiveness between Japan and the U.S. Under the nominal exchange rate of 110.6 yen per dollar as of the beginning of July 2018, we estimate that producers in Japan have a pricing advantage in 66 of 106 industries in the manufacturing sector, and in 24 of 50 industries in the service sector. We conclude that price competitiveness of Japanese service industries has considerably improved in the more recent time period. However, Japanese producers have a significant price disadvantage in comparison to their U.S. counterparts in electricity and gas supply, and most of the agricultural producing industries.

This paper presents proof-of-concept trade-in-value added (TiVA) statistics estimated from extended supply-use tables for the United States that account for firm heterogeneity. The tables used to estimate the TiVA statistics extend recently-introduced supply-use tables for the United States by disaggregating the components of supply and use by multinational and other firms. Recent research has shown both the advantages of measuring trade on a value added basis when analyzing bilateral trade flows and the dominance of multinational enterprises in U.S. trade in goods and services. Our TiVA statistics for the United States include measures based on traditional supply-use presentations as well as statistics that reflect firm-level heterogeneity for the year 2011. The comparative analysis of the two sets of statistics allows us to understand better how firms within industries engage in global value chains and if the incorporation of firm heterogeneity provides a more accurate measurement of TiVA. We find that domestic value added as a share of the value of exports is similar within large industry groups. However, there is much more variation in the value added share of exports when firm type is accounted for. Also, the additional granularity shows the share of this value added that comes directly from the producing industry varies much more across industries.

As part of the U.S. Bureau of Economic Analysis’ integration initiative (Yuskavage, 2000; Moyer et al., 2004a, 2004b; Lawson et al., 2006), the Industry Accounts Directorate is drawing upon the Stone method (Stone et al., 1942) and Chen (2006) to reconcile the gross operating surplus component of value-added from the 2002 expenditure-based benchmark input-output accounts and the 2002 income-based gross domestic product-by-industry accounts. The objective of the reconciliation is to use information regarding the relative reliabilities of underlying data in both the benchmark input-output use table and the gross domestic product-by-industry accounts in a balanced input-output framework in order to improve intermediate input estimates and gross operating surplus estimates in both accounts. Given a balanced input-output framework, the Stone method also provides a tool for balancing the benchmark use table.

This paper presents work by the Industry Accounts Directorate to develop and implement the reconciliation and balancing model. The paper provides overviews of the benchmark use table and gross domestic product-by-industry accounts, including features of external source data and adjustment methodologies that are relevant for the reconciliation. In addition, the paper presents the empirical model that the Industry Accounts Directorate is building and briefly describes the technology used to solve the model. Preliminary work during development of the model shows that reconciling and balancing a large system with disaggregated data is computationally feasible and efficient in pursuit of an economically accurate and reliable benchmark use table and gross domestic product-by-industry accounts.