Abstract
Subnational input–output (IO) tables capture industry- and region-specific production, consumption, and trade of commodities and serve as a common basis for regional and multi-regional economic impact analysis. However, subnational IO tables are not made available by national statistical offices, especially in the United States (US), nor have they been estimated with transparent methods for reproducibility or updated regularly for public availability. In this article, we describe a robust StateIO modeling framework to develop state and two-region IO models for all 50 states in the US using national IO tables and state industry and trade data from reliable public sources such as the US Bureau of Economic Analysis. We develop 2012-2017 state IO models and two-region IO models at the BEA summary level. The two regions are state of interest and rest of the US. All models are validated by a series of rigorous checks to ensure the results are balanced at state and national levels. We then use these models to calculate a 2012–2017 time series of macro economic indicators and highlight results for 11 states that have distinct economies with respect to size, geography, and industry structure. We also compare selected indicators to state IO models created by popular licensed and open-source software. Our StateIO modeling framework is consolidated in an open-source R package, stateior, to ensure transparency and reproducibility. Our StateIO models are US-focused, which may not be transferrable to international accounts, and form the economic base of state versions of the US environmentally-extended IO models.
Keywords
Introduction
The United States (US) Environmental Protection Agency (EPA) developed and maintains the United States Environmentally-Extended Input-Output (USEEIO) family of models, which are nationally-representative, single-region, environmentally-extended input-output (EEIO) models based on national input-output (IO) tables along with a diverse set of environmental data compiled from public data sources (US Environmental Protection Agency 2020; Yang et al. 2017). These models were initially developed to serve the EPA’s Sustainable Materials Management (SMM) program. This program first used a national EEIO model to form the basis of an analytical framework to prioritize goods and services for policy and non-policy actions to reduce their life cycle impacts in the “Road Ahead” report (US Environmental Protection Agency, Office of Land, and Emergency Management 2009). The USEEIO model now serves as the basis for this SMM analytical framework at a national level, and it is intentionally composed of only publicly available data. Furthermore, all source code for preparing inputs and creating USEEIO models are publicly available under open source licenses by design, to promote transparency and reusability (Li, Ingwersen, Young, et al. 2022b). The “Road Ahead” report included a recommendation that the US state agencies harmonize the quantitative frameworks they are using for materials management, and that policies and programs be developed to target materials and related industries and supply chains with the greatest opportunities to reduce life cycle impacts. National EEIO models like USEEIO are inadequate to use for prioritization of goods and services at the state level due to the stark differences in the composition of economies and industry-level environmental performance among the 50 US states and District of Columbia (DC). Unlike the national IO tables underlying the USEEIO framework that are provided and regularly updated by the US Bureau of Economic Analysis (BEA) without license constraints, state IO tables are not currently provided or maintained by any public entities. Those provided by private entities are closed source and come with license requirements that limit their use. To provide a framework that can be adopted by any state or potential stakeholder interested in evaluating production and consumption in a state context that is fully transparent and freely available, state IO tables constructed with publicly provided and maintained data are needed. In addition, because the US states are highly integrated and economically interdependent, multi-regional EEIO models built with multi-regional IO (MRIO) tables are more suitable for characterizing impact reduction opportunities at the state level.
The MRIO modeling framework overcomes an important limitation of single-region IO models, which is the failure to include an explicit representation of interregional trade. In contrast to single-region IO models, MRIO models highlight the relevance of bidirectional trade linkages between regions. For example, a region that imports from another region that itself in turn imports from ultimately results in deep interregional economic dependencies, in which changes in demand for imports can have feedback effects on supply in the same region. Capturing these regional feedback effects is one justification for a MRIO model in place of single-region models (Miller and Blair 2009). As MRIO models can describe interactions between industries and households located in distinct regional contexts, they are usually used for two main purposes: (1) describing the sectoral structure of a given region and how it interacts with other regions through interregional trade; and (2), estimating Leontief multipliers that quantify how changes in final demand affect the regional industry output beyond the region experiencing the direct shock.
Previous research demonstrated that a two-region (state of interest [SoI] and rest of the US [RoUS]) MRIO-EEIO model was adequate for estimating environmental profiles of goods and services produced and consumed in a US state when compared to a 51-region model for most cases (Yang, Ingwersen, and Meyer 2018). In support of creating regionalized versions of the USEEIO models for states, the authors are proposing an original method for constructing two-region (SoI-RoUS) economic IO tables at the level of summary resolution provided by the BEA (US Bureau of Economic Analysis (BEA) 2019b). The method and model building approach detailed here are consolidated in a publicly available and open source R software package, stateior. Similar to the manner in which USEEIO is openly constructed and maintained, complete transparency in methods and procedures and use of exclusively public data have been paramount in the design of these models.
Literature Review
Both regional and multi-regional IO accounts are constructed by adjusting pertinent national IO tables and their derivatives (e.g., national direct requirements matrix) for regional trade and productivity differences (Lahr, Ferreira, and Többen 2020; Sargento 2009). Given the extent to which interindustry interactions and trade have increased over time and to which economies have become more open, multi-regional models are becoming increasingly popular among regional economists and other researchers for performing economic impact or economic contribution analyses. Although numerous contributions have been made to this formulation (Hewings 2020), the model developed by Leontief and Strout (Leontief and Strout 1963) and then modified by Polenske (Polenske 1980) is the most widely known. Nonsurvey approaches enhanced with superior data through the so-called “hybrid approach” (Lahr 1993, 2001) remain the primary means of developing MRIO models. Regionalization at the subnational level using nested MRIO databases (Faturay, Lenzen, and Nugraha 2017) has been applied to multiple countries, including China (Wang et al. 2015), Portugal (Ramos et al. 2015) and Canada (Bachmann, Roorda, and Kennedy 2015). MRIO models have been applied to high-level policy making, such as carbon footprinting in the United Kingdom (Barrett et al. 2013) and global material resource efficiency and decoupling (Wiedmann et al. 2015).
Besides the MRIO models in the US context, there are other experiences developed for other countries or economic regions. In the context of South America, there are several examples of the development of regional and multi-regional economic models for Brazilian regions (Guilhoto 1998; Guilhoto et al. 2010; Haddad, Gonçalves Júnior, and Nascimento 2017) and more recently for Colombia (Haddad et al. 2016). Other models have also been developed for countries like China (Zhang et al. 2013; Mi et al. 2018), Portugal (Ramos et al. 2015), Spain (Llano Verduras 2004; Pérez-Balsalobre, Verduras, and Díaz-Lanchas 2019), and the United Kingdom (Wiedmann et al. 2010).
Benefits of Multi-Regional Modeling
Multi-regional modeling better accounts for the effects of endogenous consumption expenditures. When modeling the 1970 Dutch economy for indirect impacts only, Oosterhaven et al. (Oosterhaven and Hewings 2021; Oosterhaven 1981), determined underestimations of the effects of regional income on regional final demand to be 1.1% for the relatively isolated rural Northern Netherlands region and 3.4% for the heavily urbanized greater Rotterdam region. However, if the effects of endogenous consumption expenditures are included in the multipliers (i.e., a Type II or SAM multiplier is used instead of a Type I multiplier), the neglect of interregional feedbacks increases the underestimations of regional income impacts to 3.1% and 6.6%, respectively. The larger feedbacks are driven by interregional commuting and shopping effects captured by endogenous household consumption. The same conclusion was observed by Ferreira et al. (Ferreira et al. 2018). More recently, Bouwmeester, Oosterhaven, and Rueda-Cantuche (Bouwmeester, Oosterhaven, and Rueda-Cantuche 2014) studied the European Union (EU) and compared the differences in income effects of EU27 exports to third countries using either a single-region or multi-regional model. The first-round intra-EU income spillover in a basic multi-region model was 7.7% greater than a single-region model. Furthermore, when running the same analysis in the full interregional EU27 model, the spillover effects appeared to be as large as 10.7% of the weighted-average domestic effect. Thus, transitioning to fully interconnected MRIO models might enable analysts to identify effects that might otherwise be underestimated or completely missed.
Data Limitations
A challenge of MRIO models is the larger amount of data relative to single-region models that are required to incorporate the magnitude of intra and interregional interdependency by industries and households, including regional gross outflows and inflows. The lack of interregional trade data and other economic data has also been well documented in the literature. In some circumstances, particularly in the case of goods, commodity transportation data might be available. Nevertheless, Sargento, Ramos, and Hewings (Sargento, Ramos, and Hewings 2012) enumerate four reasons why even these data are subject to inconsistencies: (1) services are not covered; (2) transport flows are sometimes expressed in physical terms; (3) regions with transport platforms might observe an over-estimation of trade flows; and, (4) detailed origin-destination freight transportation matrices often have non-numerical entries such as “unreliable value”.
Data on Gross Trade
The estimation of gross imports and gross exports is the basis of the so-called cross-hauling problem where countries/regions can simultaneously export and import products, such as cars. The right balance of local and imported consumption is not easy to assess. Polenske and Hewings (Polenske and Hewings 2004) draw attention to the need to address the issue of cross-hauling since it will continue to increase (worldwide) for two major reasons: first, regional trade leakages are growing in sophistication as firms look across the whole country (or even internationally) to find the most cost competitive locations to produce; and second, the search for product differentiation by firms is increasing to keep pace with growing consumer interest in products that can be produced locally or in other regions. Even before this, Treyz and Stevens (Treyz and Stevens 1979) suggested cross-hauling be the rule rather than the exception in most subnational regional economies. Even with this attention, the best method for addressing cross-hauling in MRIO models is still the subject of debate (Court and Jackson 2015).
The data required to adequately estimate international trade intensities at a subnational level are not always available despite these intensities being one of the critical steps in subnational MRIO model development. When constructing an MRIO model, the interregional trade must be balanced while obeying the restriction that everything that is imported must be exported interregionally and the sum of net exports must equal zero.
Interregional Trade Data
Direct collection of interregional trade data via survey is still considered to be too expensive (Lahr, Ferreira, and Többen 2020) and time consuming (Miller and Blair 2009), and, therefore, interregional trade is generally estimated. Lahr et al. (Lahr, Ferreira, and Többen 2020) identify four families of estimation techniques: Location Quotients (LQ), Flegg-Location-Quotients (FLQ), Cross-Hauling Adjusted Regionalization Method (CHARM), and econometric approaches. Of these, the LQ has the lowest data requirement, requiring data on employment, labor compensation, or production by industry or commodity and by region of the MRIO model. Stevens and Trainer (Stevens and Trainer 1980) knew that simple regionalization approaches—typically some form of LQ—in raw form were problematic since LQs do not permit cross-hauling. A rather long list of researchers note that ignoring cross-hauling leads to significantly biased estimates of subnational trade (Boero, Edwards, and Rivera 2018; Lehtonen and Tykkyläinen 2014; Mccann and Dewhurst 1998; Round 1983). Despite these criticisms, simple LQs are now used and will continue to be used as the need for a flexible procedure to infer an IO table and perform the economic analysis outweighs the need to accurately specify each sector in the set of interregional interdependencies.
FLQs are parametric LQs that are applied row-wise to a region’s technology matrix. FLQs and related parametric forms of the LQ and have been used in deriving interregional trade models (Flegg, Mastronardi, and Romero 2016; Flegg and Tohmo 2013, 2016; Jahn 2017; Kowalewksi 2015). FLQs require some a priori knowledge of interregional trade relationships for a nation. FLQ approaches also have been criticized (Lahr, Ferreira, and Többen 2020; Fujimoto 2019; Lamonica and Chelli 2018), because: (1) the value of the interregional parameter that is critical to establish the locally produced requirements is only possible to assess if interregional trade data are known; (2) some of the adjustments applied to LQs can be determined mathematically but there is a lack of meaningful economic interpretation of those values.
The third method for estimating interregional trade, CHARM, requires complete information on regional supply and demand. Depending on the data availability, CHARM can be easier or more complex to apply than FLQ (Lahr, Ferreira, and Többen 2020).
Among the econometric methods, those based on gravity theory have gained some popularity. According to Boero et al. (Boero, Edwards, and Rivera 2018) the method departs from a reference period for which observations on trade flows are available. Then, a linear regression is conducted on observed data to calibrate the parameters that allow for explaining trade flows of a certain commodity and a pair of regions while considering the distance between the regions. The estimation is done by industry using all the possible trade flows in the regression sample and thus leading to a single value for each parameter.
Any of the four methods are capable of accurately estimating the interregional trade of distinct sectors and regions, and the debate of selection of these methods is ongoing. A valid rule of thumb Szabo (Szabó 2015) considers is that the smaller a territorial unit, the greater its dependence on external territories through trade as the self-supplying ability of regions shrinks. Lahr et al. (Lahr, Ferreira, and Többen 2020) suggest that the technique used should be grounded in location theory and that particular emphasis should be given to the analysis of the Regional Purchase Coefficient (RPC). Stevens and Trainer (Stevens and Trainer 1980) coined the term RPC when referring to the extent to which regional producers fulfill the same region’s demand for a commodity. The RPC will vary from sector to sector and from region to region, being close to 1 where the local supply is more important than exports and being close to zero when local production tends to satisfy demand generated abroad, independently of the level of local production. Treyz and Stevens (Treyz and Stevens 1985) and Lahr et al. (Lahr, Ferreira, and Többen 2020) converge on the idea that RPC tends to increase if the transportation costs of the manufactured products increase, if the (geographical or economic) size of region increases, or if the production of a certain commodity is spread throughout the regions and if the region is more exposed to international trade and tourism. In this sense, this indicator is instrumental to establishing the relationship between production and trade at the sectoral and regional level.
Methods/metrics for assessing accuracy of estimations
Due to the lack of data, it is practically impossible to compare the values obtained after applying a non-survey method to estimate interregional flows and the real numbers. In fact, researchers have been discussing what might be the best way to estimate interregional flows by comparing with the small number of regional accounts that correspond to official data. Kowalewksi (Kowalewksi 2015) discusses the adequacy of the FLQ method in determining the regional accounts for 1993 in Germany. Flegg and Tohmo (Flegg and Tohmo 2019) apply the FLQ method to study the errors generated in the regional accounts in South Korea. And recently, Lahr et al. (Lahr, Ferreira, and Többen 2020) compare distinct methods to establish interregional trade in the EU. All these studies agree that it is not enough to just estimate net imports. Countries import products that they also export and regional accounts need to reflect the difference between regions. Another thing that is not adequately incorporated by all the methods but is supported by Treyz and Stevens (Treyz and Stevens 1985), Sargento et al. (Sargento, Ramos, and Hewings 2012), and Lahr et al. (Lahr, Ferreira, and Többen 2020) is that there is heterogeneity among products. Manufacturing products will be more tradable than Services and products with high transportation costs will be less tradable than products that are easy to transport. This means that in some cases, like the case of stateior, derived regional accounts are both state-specific but also product-specific as they try to incorporate information for each product.
One of the common indicators in most studies and methods applied to derive interregional trade is the RPC, which measures how local demand is satisfied by local production. This measure excludes the dependence of international trade. The RPC is relevant since it is also a way to measure how local supply will react to a shock on the demand side at the regional level. The RPC formula is given by
IO models can take the form of open or closed models. Open models can be used to create Type I multipliers, which account for the direct and indirect effects on output of changes in final demand. Closed models also account for the effects of income in household consumption in the matrix “core” and can be used to generate Type II multipliers. Katz (Katz 1980) shows how Type II multipliers are always higher than Type I multipliers as they also account for the induced effects of a shock. By induced effects, Katz (Katz 1980) refers to the shock of the income distributed by industries in the household consumption. Emonts-Holley, Ross, and Swales (Emonts-Holley, Ross, and Swales 2015) underline the danger of underestimation in the case of using Type I multipliers and the problem of overestimation in the case of Type II multipliers since not all the household consumption depends on the direct distribution of income by industries.
Existing MRIO Applications
After being derived, MRIO models can be extended with social-accounting matrices or environmental satellite accounts, to extend the analysis of the impacts to other dimensions besides the strictly economic impacts. Towa et al. (Towa, Zeller, and Achten 2020a) underline the role of environmental IO applied at the sub-national level as a form of performing a more accurate consumption-based analysis of carbon footprint impacts. As nations rely on the production of other countries, they in turn also rely on natural resources and products from other regions. Trade also extends the responsibility for the impacts of emissions, wastes, and natural resource extraction beyond political or administrative boundaries (Minx et al. 2009; Towa et al. 2020a, 2020b).
In the context of the US, several subnational MRIO models have been developed, varying both in geographical and temporal scope. IMPLAN is a widely used tool for regional economic impact assessment based on a US subnational MRIO model. IMPLAN was originally developed by the United States Department of Agriculture (USDA) Forest Service and is now maintained by the Minnesota IMPLAN Group, Inc. (Dahal, Henderson, and Munn 2015; Minnesota IMPLAN Group, Inc. (MIG) 2004). IMPLAN now includes MRIO-based datasets at the zip code, county, state, and national level. In IMPLAN, multipliers are used to estimate direct, indirect, and induced impacts resulting from changes in industry activity, employment, income, or other economic activity. According to Lindall et al. (Lindall, Olson, and Alward 2006), the IMPLAN trade model is a doubly-constrained gravity model that uses county level estimates of commodity supply and demand and guarantees that trade is balanced, such that the gross domestic imports are equal to gross domestic exports for all states.
With more specific applications, other multi-regional models have also been quite important in performing specific economic impact or contribution analyses. Besides describing the IMPLAN model, Rickman and Schwer (Rickman and Schwer 1995) also devote some attention to REMI and RIMS II. The REMI model was produced by Regional Economic Models, Inc. of Amherst, Massachusetts (Treyz, Rickman, and Shao 1991) and has the non-survey-based IO component linked to an econometric component. The econometric specifications of REMI are derived from the neoclassical idea of interregional equilibrium. The econometric component in REMI is applied for manufacturing sectors, using the Census County Business Pattern data and the Census of Transportation. For non-manufacturing sectors, supply-demand ratios and subjective intraregional trade coefficients are used to calculate the RPCs (Treyz and Stevens 1985). The RIMS II model, last updated in 2016, was developed by the US Department of Commerce/Bureau of Economic Analysis (Cartwright 1981). This model has been severely criticized (Drake 1976; Rickman and Schwer 1995) since it assumes that local demand is satisfied first, with the remainder of an industry’s output assumed to be exported and not allowing for cross-hauling between regions.
Other examples of MRIO models are the WiNDC model from the University of Wisconsin (Wisconsin National Data Consortium 2021), the Chicago model from the Regional Economic Applications Laboratory (Hewings, Okuyama, and Sonis 2001); the RUBMRIO model for Texas (Kockelman et al. 2005); and the NIEMO model for the 50 states plus Washington D.C. (Park et al. 2007). Unfortunately, these MRIO models either lack trasparency for verifying model outputs are following the reported methods or suffer from their proprietary nature that obstructs the public from automatically updating or reproducing model results. Our economic modeling framework, built with stateior, is developed to overcome these barriers and provide open source, transparent MRIO models in the US context to the public.
Methodology
An original method for constructing economic IO tables in the form of two-region, SoI and RoUS, at the level of summary resolution as defined by the BEA (US Bureau of Economic Analysis (BEA) 2019b) is described. This method is implemented in a publicly available and open-source R software package, stateior. Three main steps are taken to construct StateIO tables: (1) the creation of a state supply model, (2) the creation of a state demand model, and (3) estimation of interregional trade to create a two-region IO model.
Standard matrix algebra notation is used to delineate the steps of creating the state Supply and Use models, using conventions for variable names commonly used in the IO literature and the existing USEEIO model documentation when possible. Capital letters indicate matrices and lowercase letters denote vectors. A “^” over a variable represents the diagonalization of a vector as a matrix. An exponent of “-1” represents an inverse. A “
The model was designed to have the structure and components described in Figure 1. The structure reflects an IO model that is open with respect to households and other institutions. Bi-regional IO model for StateIO.
Notation Within the StateIO Framework.
Final Demand Components and Relative Weights in 2017.
Since our goal is to establish 51 state models (the 50 states and DC) that are coherent with the national IO tables, the sum of flows between an SoI and its corresponding RoUS must equal the national totals. Therefore, all transactions in the national territory must be allocated to the regions. However, for some economic indicators, the sum of the 51 regional values is close but not exactly equal to the national total. The cause of this divergence is the overseas adjustment. 1 To keep the consistency between state and national flows, a 52nd region is created, defined as Overseas, that is only used for closing the balance with the transactions not allocated to one of the 51 geographic regions.
State Supply Model
Supply (or Make) tables describe commodity production by industries in a given region. The US Make table published by BEA is not available at any sub-national geographical level. The state Make tables constructed here have industries as rows and commodities as columns just like the BEA US Make table allowing for byproduct production, so each cell contains the dollar value of each commodity produced by a given industry. In each state Make table, the sum of each row represents state industry output, while the sum of each column represents state commodity output.
The BEA regional accounts do not publish state industry (
In equation (2),
Now that the total state industry output,
The primary source of commodity output is the national Make table. For commodities where data are available that can serve as proxy values for commodity output, initial state commodity output is estimated independently. State agricultural commodity output are obtained from USDA Census of Agriculture; state fisheries commodity output is obtained as values of state-level fishery landings from National Oceanic and Atmospheric Administration (NOAA) Fisheries; state forestry commodity output is obtained as cut values of forestry from the United States Forest Service (USFS) Forestry Inventory. State output of manufactured and transported commodities are obtained from the Freight Analysis Framework (FAF) developed by the Oak Ridge National Laboratory (ORNL) (Oak Ridge National Laboratory (ORNL) 2020). The alternative data sources are used to create SoI-RoUS commodity output ratios as shown in equation (3)
To estimate the final value of each cell in the state Make table,
The approach of constructing the state supply model is illustrated in Figure 2 with a more detailed version in the Appendix in the Detailed Approach of Constructing State Supply Model section (Figures A1–A2). At this point, a state supply model contains one Make table (industry by commodity) and the sum of its rows and columns corresponds to the sum of state industry output and the sum of state commodity output, respectively. Approach of constructing state supply model.
State Demand Model
With industry and commodity production at the state level determined, consumption of commodities by industry is estimated, also called intermediate consumption
Before deriving the state Use table, a vital adjustment is applied to solve the discrepancy between the US import matrix and the imports of commodity in the BEA national Use table before redefinition (import column, F050), referred to as international trade adjustment (ITA) hereafter. The ITA values are added as a column with a component code of F051 3 in the domestic Use table in order to balance national and state production and consumption. Details about ITA are described in the Appendix in the Adjustments in the National Matrix section.
State Intermediate Consumption
For StateIO models, it is recommended by BEA to use the hypothesis of a homogeneous production function to derive state intermediate consumption,
As Miller and Blair (Miller and Blair 2009) recognize: “Soft drinks of a particular brand that are bottled in Boston probably incorporate basically the same ingredients in the same proportions as are present in that brand of soft drink produced in Kansas City or Atlanta or in any other bottling plant in the US. But, electricity produced in eastern Washington by water power represents quite a different mix of inputs from electricity that is produced from coal in Pennsylvania or by means of nuclear power or wind farms elsewhere.” The problem of industry specificities led Lahr and Stevens (Lahr and Stevens 2002) to prove that one can obtain a more accurate representation of industries’ input consumption structure if a more disaggregated model is available for further distinguishing between close, yet different, sectors.
The application of the rationale explained above means that, in mathematical terms (equation (4)), elements of state industry intermediate consumption,
Thus, theoretically every industry in every state can have a unique input consumption structure. However, data limitations make it difficult to describe even one sector in one state. Survey methods for subregions are too costly and expensive (Miller and Blair 2009; Boero, Edwards, and Rivera 2018). At this point, it is important that the users less familiar with IO can understand the implications of such limitation. According to Lahr (Lahr 1998), if the regional modeler’s goal is to describe one sector in one given state in detail, the identification of critical sectors and uses and the collection and application of superior data will significantly improve model accuracy.
State Final Demand
Final demand is composed of ten distinct components. Household consumption, designated as PCE, makes up the largest share of final consumption. According to Kim, Kratena and Hewings (Kim, Kratena, and Hewings 2015), this component accounted for 70% of gross domestic product in the US as of 2011, compared to 56% on average for OECD (Organisation for Economic Co-operation and Development) countries. The other components are private fixed investment, change in private inventories, international exports, federal, state and local government expenditures and investments. In this section, the distinct methods and data used to regionalize each US final demand component to the corresponding state final demand component is described.
Personal Consumption Expenditures
Personal consumption is not assumed to be proportional to the population in every state. Different states might exhibit distinct consumption patterns as the population can be very heterogeneous across states. It is well documented that consumption patterns can change according to socio-economic characteristics of the population: age, educational background, urban, suburban or rural contexts, among others (Druckman and Jackson 2009; Kim and Hewings 2015; Ferreira et al. 2018). More importantly, the level of consumption is highly correlated with the level of income that households attain in a certain geographical area (Varlamova and Larionova 2015). Personal consumption expenditures data by state are used to distribute the consumption among the 50 states plus DC.
International Exports
States exports are not assumed to be directly proportional to output, as states can have a smaller or larger propensity to export their goods to foreign markets. This can depend, for instance, on a state industry’s position in the supply chain, the distance to the border, or the existence of transportation infrastructure such as a cargo airport or a port. Farole and Winkler (Farole and Winkler 2014) explore the differences in state exports and show how the distance to national agglomerations can affect the openness to the global market. From the StateIO perspective, this means that to deal with such an important level of heterogeneity, the estimation of exports can largely benefit from the inclusion of region specific data. Foreign trade data from the US Census Bureau (US Census Bureau 2020b) is used to estimate the state commodity export ratios. The state trade data are created in part by models of the exports from the region where the commodities were produced, and the models do not always result in reliable estimates.
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Using these data in StateIO, there are cases where state commodity exports are greater than state commodity output. This is not possible in economic terms as exports is a component of the commodity production. So, in the rare cases where these data generated this incompatibility, the model automatically corrects this imbalance by using the state commodity output ratio,
Federal, State, and Local Government Expenditures and Investment
Federal government spending data available at USAspending (USAspending.gov 2020) are the main source used to distribute the national flows to the state level. The spending data are first categorized into (1) consumption expenditures or (2) investments in structure, equipment, and intellectual property according to their product or service code (US General Services Administration and Federal Acquisition Services 2020). The investments were further separated into defense and non-defense based on the awarding agency. The data for defense and non-defense investment were obtained separately using data on each particular investment equation (5), while the data on defense and non-defense consumption expenditures are derived based on a combination of intermediate federal government spending, state employment compensation ratios, and national government expenditure weighting factors equation (5).
For the state and local government consumption expenditures and investments, US Census Bureau data (US Census Bureau 2020a) on state and local government expenditures were used to calculate a ratio of state expenditures,
Final Demand Remaining Components
The remaining components of final demand not including imports (residential private fixed investment, non-residential private fixed investment, and change in private inventories) composed about 15% of US final demand in 2017. For lack of state-specific data, state-US commodity output ratios (
International Imports
International imports are reported in BEA tables as final demand because final-use transactions consist of the transactions that make up the final-expenditure components of the GDP and international imports are the most important negative component of GDP (Horowitz and Planting 2009). However, as the IO matrices are commonly used to reflect the impact of changes in the national or regional economy, international imports must be included in a different way. The reason is that international imports represent leakages in the economy; in other words, they have no multiplier effects within the regions. For example, if an industry only used inputs that were produced abroad, changes in their economic activity would not produce changes in the national economy but will instead impact the industries located abroad throughout its intermediate consumption. In this case, the local impacts would be limited to wages and capital transfers. The need to treat international imports for economic impact purposes is well described by Horowitz and Planting (Horowitz and Planting 2009, chaps. 12–5): “IO tables are frequently used to calculate the impact of changes in final uses on domestic output, income, or employment of industries using the total requirements matrix… but before calculating the domestic portion of the requirements, the industry inputs from foreign sources need to be removed. This removal is accomplished using an import matrix—that is, a matrix that shows the use of the imports by industries and final uses.”
Therefore, the first step of estimating state international imports is to expunge all the flows detailed in the IO framework of the part of the demand that are generated abroad. Our model excludes the possibility of reexports
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of a particular commodity, so there are no imports that are directed towards exports of the same product. At the state level, the value of imports is obtained by multiplying the corresponding value on the import matrix,
The same is applied for final demand components.
This procedure estimates international imports at the state level and allows allocation of national international trade adjustment,
The same is applied for final demand components.
When
Since international imports and trade adjustment are part of the final demand, they are then included in two separate rows outside the core of the matrix and will be considered exogenous.
State Value Added
The state VA data set released by BEA has been used in previous sections to derive the state-US VA ratio, which is further used to regionalize US Make or Use table to states. To estimate state VA,
The overall approach of constructing the state demand model is illustrated in Figure 3 with a more detailed version in the Appendix in the Detailed Approach of Constructing State Demand Model section (Figures A3–A4). At this point, the consumption and production in each state are fully derived. However, to have a multi-regional model it is also necessary to estimate the inter-dependencies that occur between commodities, industries, and final demand components at the regional level since not all consumption that happens in one region is supplied by industries in the same region. This will be addressed in the next section. Approach of constructing state demand model.
Interregional Trade Estimation
In the previous section, the processes for estimating the total supply and total demand for each industry and final demand component at the state level was described. However, one important aspect is still missing. In a bi-regional IO model, the multiplier effect of a given shock will be significantly different if one industry is directed towards the internal market (in this case, the state) or if instead is more directed towards external markets (other US states or abroad). With Make and Use tables prepared for each state, the model can produce accurate numbers in terms of net exports. If a state has a large volume of supply and almost no-demand, then in net terms, it is expected that the state will export. However, that does not imply that the volume of imports is zero. Indeed, given the fact that the world is more and more interdependent, states usually import goods that they also export. Additionally, to estimate the economic multipliers data on net exports is not enough. It is essential that the gross imports and gross exports are known and that they are balanced in the sense that everything that is being exported from a region must be imported by another region.
In a two-region framework like the one representing a SoI and the RoUS, the Use table will detail the local consumption (hereafter referred as SoI2SoI and RoUS2RoUS) and the interregional trade between the two areas (SoI2RoUS and RoUS2SoI). For a given pair of SoI and RoUS, the two-region Use table (Figure 4) has a four-quadrant structure overall, where • Red quadrant shows use of commodities by industries in SoI that are produced in SoI; • Pink quadrant shows use of commodities by industries in RoUS that are produced in SoI; • Blue quadrant shows use of commodities by industries in SoI that are produced in RoUS; • Purple quadrant shows use of commodities by industries in RoUS that are produced in RoUS; • Bars on the right represent final demand of commodities in SoI and RoUS with the same color meaning in the quadrants, e.g. pink bars show final demand of commodities in RoUS that are produced in SoI; • Orange boxes at the bottom are VA amounts in the two regions. Two-region Use table.

The goal of the StateIO models is to allow users to estimate a bi-regional model that describes the economic interdependencies between an SoI and its RoUS. This model comprises all the economic activity divided in a group of four distinct economic flows: 1) a SoI2SoI table demonstrating commodities produced and consumed in SoI, 2) a SoI2RoUS table demonstrating commodities produced in SoI but exported to and consumed in RoUS, 3) an RoUS2RoUS table demonstrating commodities produced and consumed in RoUS 4) an RoUS2SoI table demonstrating commodities produced in RoUS but exported to and consumed in SoI.
The models also include the Make tables that describe the production of commodities by industries in the SoI and in the RoUS. Until this point, the model provides the information on what is produced and what is consumed but does not include an estimation of the trade flows between the regions. It is known how much Regions A and B produce, but at this stage how much of Region A consumption comes from its own region or Region B, and vice-versa, must be estimated. This information is essential to perform an accurate economic impact assessment since state economies are interconnected. For example, industries in many states rely on Texas production of oil or Idaho’s production of potatoes.
It is important to understand that this step of the model will work in two stages. First, the products are classified according to their tradability. Some products are considered tradable and others are more locally consumed. To estimate the different degrees of tradability, the model first estimates something designated as the domestic Interregional Commodity Flow (ICF) Ratio. Then, in a second part, this ICF is applied to the model as it is described in the State Demand Model section and the trade is adjusted to balance all consumption, production, and trade flows that are incorporated in a bi-regional model by incorporating a set of economic restrictions that must be present in multi-regional models.
According to the recent tradition in modern IO modeling and international and interregional studies, the StateIO model admits the existence of cross-hauling since regions import commodities that they also export. Polenske and Hewings (Polenske and Hewings 2004) discuss why this happens and will happen even more in the future as production and societies become more and more interdependent and the level of product differentiation increases.
Interregional Trade Estimation
To estimate the trade flows between regions, the StateIO model draws on several publicly available data sources to prepare a set of ICF ratios. For each commodity and state a set of 4 ICF ratios, corresponding to the four distinct economic flows described in the previous section, are calculated.
Interregional Trade in Goods
Interregional trade of goods is estimated based on the Freight Analysis Framework (FAF) domestic commodity flow model (Oak Ridge National Laboratory (ORNL) 2020), where FAF goods correspond to StateIO commodities. The FAF model estimates the weight and dollar value of commodities that are transported from an origin state to a destination state. These data are the main source for a first estimation of the proportions of a commodity produced in one region that stays in the same region or is domestically exported to another US state. Dollar values of total shipments by FAF good are used to develop ICFs as shown in equations (10) and (11).
Movements of electricity, water, and natural gas provided by the utility sector are not tracked in the FAF model, and therefore require sector specific data for modeling interregional trade. ICF ratios for sector “22 - Utilities” are calculated at a more detailed subsector level, including ICF ratios for three subsectors: (1) “221100 - Electric power generation, transmission, and distribution”; (2) “221200 - Natural gas distribution”; and (3) “221300 - Water, sewage, and other systems”.
For “221100 - Electric power generation, transmission, and distribution”, state electricity consumption and interstate trade data from the US Energy Information Administration’s State Energy Data System (US Energy Information Administration 2021) are used. Although the electricity is transmitted across state borders, the amount of electricity transmitted from one state to another is not accurately detailed. Therefore, net interstate trade of electricity is used to represent interstate export or import. If a state’s “net interstate flow of electricity and associated losses” is less than zero, it is considered a net exporter of electricity as in equation (12).
If SoI is a net exporter, consequently
For “221200 - Natural gas distribution” and “221300 - Water, sewage and other systems”, all economic activities are assumed to occur locally, in other words, there is no interstate trade, hence
Interregional Trade in Services
Some services are by nature local because provision of these services must occur within the region of interest. For example, construction or housing activities (imputed or rented) are an extreme case of locally consumed services. According to the rules of regional accounts for state GDP (Adams et al. 2021), the place where such activities occur is where the associated income is generated. For the same reason, international trade in these sectors is non-existent or considered residual. In the StateIO models, locally produced services, including construction, housing, transit, and ground passenger transportation, other transportation, and all government sectors, have an
Services that are not local by definition can be classified as local or tradable according to how the services are produced and consumed in the US. These designations were published by the US Cluster Mapping Project (Harvard Business School and US Department of Commerce, Economic Development Administration 2020). Industries were classified as “traded” or “local” by NAICS (North American Industry Classification System) code. For the StateIO model, the “traded” or “local” NAICS classifications are mapped to the StateIO industries and combined with SoI and RoUS commodity output to approximate commodity ICF ratios.
Sector-specific methods are used for transportation, warehousing, and waste management services. Since the FAF model also provides transportation modes of commodity flows, it is used to estimate the ICF ratios for transportation services. The assumption is that if a trade flow happens within a state all the service is directly provided by the corresponding state transportation industry but if the transportation happens between states, then half of the economic activity of the transportation activity will stay in the origin and half is imputed to the state of destination. For these cases, the
Ratios for RoUS are handled in the same way. The same logic is applied to the warehousing and storage sector, assuming all commodities would be stored in warehouses before transported to destination.
A specific approach is used for the ICF ratio estimation for sector “562 - Waste management and remediation services”. This sector includes the following activities: • 562111 - Solid waste collection • 562212 - Solid waste landfilling • 562213 - Solid waste combustors and incinerators • 562910 - Remediation services • 562920 - Material separation/recovery facilities • 562HAZ - Hazardous waste collection treatment and disposal • 562OTH - Other waste collection and treatment services
ICFs associated with “562 - Waste management and remediation services” are calculated with data on net physical waste treatment by state in a method that is the inverse to how the net physical data for electricity trade were used in equation (12). Imports of waste from RoUS to SoI for treatment and disposal in SoI are not considered ICF from RoUS to SoI, instead they are exports of management service from SoI to RoUS. In this case, data from the EPA’s RCRAInfo Biennial Report (US Environmental Protection Agency 2018) are used to calculate ICF ratios for “562HAZ - Hazardous waste collection treatment and disposal” using equation (14)
As for the other waste management services, the commodity output ratio is used as a proxy for their ICF ratios. Then, to determine the ICF ratios of “562 - Waste management and remediation services”, the ratios of hazardous waste management services and non-hazardous waste management services are summed on an equal basis.
Two-Region Domestic Use Tables
With the ICF ratios determined, interregional flows between the two regions in our model, SoI and RoUS, are estimated. Each commodity in each region is multiplied by the respective ICF ratio to create a new table (equations (15) and (16)) that already includes a first estimation for interregional imports and exports in both SoI and RoUS regions.
Interregional imports, or the demand for products in SoI supplied by the RoUS region,
The sum of the row for each commodity provides the total imports of commodity
In other words, all the production from SoI region that is not consumed locally or exported internationally must be exported to the RoUS region. This method is also applied in the RoUS side and produces values for interregional imports by SoI with origin in the RoUS and by the RoUS with origin in SoI and interregional exports from the SoI to the RoUS and vice-versa. Summarizing, four interregional trade indicators are produced.
While all industries are considered to be affected by interregional trade, not all components of final demand were assumed to be interregionally tradable, including nonresidential private fixed investment in structures, residential private fixed investment, change in private inventories, international exports and imports, and government gross investment in structures.
This first estimation produces inconsistencies because the model uses data from distinct sources. The first inconsistency that has be solved is that in mathematical terms the interregional exports estimated can be negative. This happens if the supply for a given commodity is smaller than the local consumption. However, this is impossible according to economic theory. In the case of deficits where local consumption is greater than local supply, the negative interregional export values are proportionally allocated across industries and final demand sectors in that region, according to the weight of the commodity consumption in total state consumption equations (20) and (21).
After this step, all interregional imports and interregional exports are greater than zero. But there is still an important imbalance that needs to be resolved. Imports from the SoI might not be equal to the exports made from RoUS, and vice-versa. This discrepancy,
The method described above to estimate and balance interregional trade is applied to most of the industries. The exceptions are the industries in which all consumption was assumed to be local. In this case, since no interregional trade is assumed, the distribution of a discrepancy between supply and demand affecting other regions would necessarily result in a model with negative interregional imports, which is untenable from the economic point of view. In this case, the difference between supply and demand is included as a new component of final demand that is called the “export residual”. For commodities that have SoI2SoI ICF ratio equal to 1, their “export residual” equals their “interregional exports”.
At this stage, the model is balanced. Interregional imports from one region equal the interregional exports from the other region. Also, all the domestic supply,
By having established the SoI2SoI table and the RoUS2RoUS table, the other two matrices, one representing the commodities produced in SoI and used in RoUS, SoI2RoUS, and the other commodities produced in RoUS and used in SoI, RoUS2SoI, are estimated by subtracting the value of total consumption from the intra-regional accounts. The final assembly of these four tables results in the two-region domestic Use tables Figure 4.
Two-Region Total Use Tables
The methodology described in the previous section offers StateIO model users a domestic Use table that represents domestic flows and that is suitable for analyzing and interpreting the supply and consumption of products that are produced in the US economy. In economic terms, the domestic Use table can yield multipliers that identify the effects of economic shocks to the national or regional economy. For example, if the demand for a good imported from another country suffers a positive shock, the multiplier in the US should be zero as the demand relies on another economy.
In other cases, the goal of the user is to understand the dependency on a commodity independently of where it is produced. Such cases require what are commonly referred to as Total Use tables, where the uses of commodities include international imports. The Use tables for the US provided by BEA are in this form (US Bureau of Economic Analysis (BEA) 2019b, 2019c). Cells in the Total Use table convey the total consumption of a commodity by a given industry, regardless of national origin. The Total Use table was prepared using equation (24).
The value of the total use of commodity
Model Validation
To assure the two-region Use tables are successfully built, a set of validation steps were performed. Validation is applied to assure that all the tables produced follow the economic rationale that a balanced model must observe. Equations for model validation presented in equations (25)–(28) are used to guarantee that errors produced are limited to minor rounding errors.
Finally, the most important validation is when the Use matrix produced is used to estimate the technical coefficient (direct requirements) matrix and then transformed to estimate the Leontief matrix,
This step (equation (29)) was applied successfully in the cases of all the states and all years covered by the StateIO models.
Additional validation is presented in the Appendix in the Model Validation section.
Results and Discussion
Results from the StateIO models are evaluated through examination of macroeconomic indicators for selected US states that produce commodities that satisfy a substantial fraction of US domestic demand. The RPC metric obtained using the StateIO models are compared state by state and year by year with the RPCs obtained from other available US state IO models. The software implementation of the StateIO models is briefly described.
Sectoral Structure and Interregional Trade
In this sub-section, macroeconomic indicators are presented from the two-region models for 11 distinct states for 2017, including total gross value added (GVA) and gross imports and exports. The 11 states were pre-selected to represent different sizes (by gross product), key economic industries, and a representative set of geographic locations (e.g. the East, West, and Midwest regions). Results from the year 2017 are shown in Figure 5. In this figure, the 11 states are ranked by their total GVA in 2017. Their total exports to the rest of US and imports from the rest of US are shown next to the total GVA. State Total GVA and Interregional Trade in 2017.
As Figure 5 highlights, the structure and the size of the economies are very different from state to state, and the related percentage contribution of interregional trade (using the largest of imports or exports) to total GVA ranges from 44% (California) to 76% (in Iowa). This outcome aligns with economic theory and the idea that smaller regions are more open as they tend to depend more on trade with the outside, while larger regions are often less dependent on exports or imports.
StateIO models also reveal signature commodities produced by states. A 2012–2017 time series of interregional exports from the 11 states is shown in Figure 6. For each of the selected 11 states and years, the contribution of the top five exported commodities/services is highlighted. Interregional exports by commodity.
Overall, exports from the selected states to their corresponding RoUS gradually increased from 2012 to 2017, which is consistent with other studies that have shown how trade is growing between countries, regions, and sectors (Timmer et al. 2015; Los, Timmer, and Vries 2015). The 11 states shared some of the top 5 exports despite their varying economic structure. “42 - Wholesale trade” and “5412OP - Miscellaneous professional, scientific, and technical services” were the common top exports, even the largest and second largest, in all the states except Iowa where “5412OP” was not in the top 5. These sectors are highly aggregated and include some tradable activities and firms that operate nationwide. “111CA - Farms,” “311FT - Food and beverage and tobacco products,” and “524 - Insurance carriers and related activities” made up the top 3 exports from Iowa. The first two exports come as little surprise from a state known for its agriculture-oriented economy. In Delaware, “524 - Insurance carriers and related activities,” “521CI - Federal Reserve banks, credit intermediation, and related activities,” and “532RL - Rental and leasing services and lessors of intangible assets” were the top 3 exports from 2012 to 2017. These financial exports likely reflect the high percentage of firms that incorporate in Delaware (Delaware Division of Corporations 2022) and it is the home of a large percentage of US consumer credit card companies (Tsosie 2017). New York had top-ranking exports that are similar to Delaware, but “523 - Securities, commodity contracts, and investments” ranks top 2 or 3, which is explained by New York being a hub for investment firms and securities exchanges (Bajpai 2021).
The 3 states that were the largest interregional exporters for raw and manufactured commodities from 2012 to 2017 are highlighted in Figure 7. Top 3 export states for raw and manufactured commodities.
California was the largest exporter of farm (111CA) and forestry and fishery products (113FF) to the rest of the US. Two states in the Midwest, Iowa and Nebraska, emerged in second and third place in terms of farm product exports, while Florida and Oregon were the second and third largest exporters of forestry and fishery products. It is worth noting that Oregon was also the largest exporter of wood products (321), meaning that besides raw timber, Oregon also exported significant quantities of manufactured wood products. As for other raw products like crude oil and natural gas (211), Texas was the most important supplier and exporter, followed by Oklahoma and Alaska and more recently, Colorado. However, looking at exports of petroleum and coal products (324), which are crude oil-based products that have been further processed in the refinery, Texas was still the most important exporter, but Louisiana and California, and even Ohio, played an important role in the market in 2012–2017. Petroleum was further processed to produce chemical products (325), which were mainly exported from California, Indiana, and Texas, and plastics and rubber products (326) that were mainly exported from Ohio, Illinois, and North Carolina during these years. California was also the largest exporter of computer and electronic products (334), followed by Massachusetts and North Carolina.
Mined products (212) were primarily exported from West Virginia, Wyoming, and Arizona. Nonmetallic mineral products (327) were mainly exported from Ohio, Pennsylvania, Wisconsin, and Texas. The Southeastern states, including Georgia, North Carolina, South Carolina, and Virginia were the top exporters of food (311FT) and textile products (313 TT). Wisconsin, Pennsylvania, and three Southeastern states, including Alabama, South Carolina, and Georgia, were top exporters of paper products (322).
Our results also show that “Rust Belt” states are the main providers of heavy equipment and metal-based products. Indiana, Ohio, and Pennsylvania were the top exporters of primary metals (331); Ohio and Illinois were the top exporters of fabricated metal products (332); and Illinois, Ohio, Iowa, and Wisconsin were the top exporters of machinery (333). Michigan was the main exporter of motor vehicles (3361 MV), followed by Indiana and Ohio and more recently, Tennessee.
Regional Purchase Coefficients
RPCs from the StateIO models for the selected 11 states are compared with RPCs from three other common models: IMPLAN (Minnesota IMPLAN Group, Inc. (MIG) 2004), IO-Snap (IO-Snap 2022), and WiNDC (Wisconsin National Data Consortium 2021). Figure 8 shows the overall RPC values per state between 2012 and 2017 for these models. StateIO and other model RPC comparison.
RPCs from all four models are generally stable over the period of 2012–2017, only fluctuating by 1–3%. IO-Snap RPCs for Delaware fluctuate more, having a difference of 8% between the lowest and highest values over the period. StateIO RPCs are consistently smaller than IMPLAN and IO-Snap RPCs by an average of 10% and 16%, respectively. The RPCs for Iowa are an exception where the RPCs from IMPLAN are nearly identical to those of StateIO. IO-Snap RPCs are the highest among all except for in Delaware, where they are noticeably fluctuating and lower than IMPLAN RPCs, and in New York, where they are only slightly lower than IMPLAN RPCs in 2015–2017. Alternatively, the WiNDC model produces much smaller RPCs that are on average 30% smaller than StateIO. One possible explanation is that WiNDC relies solely on commodity flow data and commodity output
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to model interregional trade flows and therefore it is more suited to the study of material flows (Rutherford and Schreiber 2019). By treating all commodities as tradable, local preference for some commodities (more specifically services) might be underestimated, which likely overestimates the “openness” of the state economies. Variation in RPCs are likely caused by differences in methods related to inter-regional trade, aggregation bias, etc. State supply model - Part 1. State supply model - Part 2. State demand model - Part 1. State demand model - Part 2.



OLS Econometric Model of StateIO RPCs.
State GDP, Interregional Trade, and RPC.
A complete table of 2012–2017 state GDP, trade, and RPCs is available in the Appendix in the Summary of State GDP, Interregional Trade and Overall RPC section (Table A1).
Software Implementation
stateior v0.1.0 was used to generate the StateIO models that are described in this manuscript (Li, Ingwersen, Ferreira, et al. 2022a). The stateior package is maintained and updated in a public git repository. The main data products of stateior are the two-region (SoI-RoUS) Make, Use, and Domestic Use tables along with industry and commodity output vectors. stateior v0.1.0 generates these data products for all states for years 2012–2017 at the BEA summary level of sector resolution. The resulting models are written to R data format (*.RDS) and pushed to the EPA Data Commons (Zhuang and Balassiano 2021). As well as incorporating all the code to build the models from the raw sources, stateior provides convenient functions for users of the two-region tables to load these tables or vectors for year and state of interest. stateior data products will be used by EPA’s useeior R package (Li, Ingwersen, Young, et al. 2022b) in the near future to construct state-level EEIO models in the USEEIO framework. Plans for future releases of stateior can be found on the stateior Wiki pages.
Conclusion
This article describes a robust StateIO modeling framework for building complete state and two-region IO models and calculating inter-regional trade in the US. The resulting StateIO models produce results that align with known data and understandings of state economies and are comparable to other state IO models. The main contribution of this article is to clearly present to potential users and researchers the robust methodology with its underlying assumptions facilitated by open source programming code available in the stateior package. The transparency and reproducibility of the model allows for deeper review of the models’ core data, algorithms, and assumption, such that it can be more easily compared to other state IO models and methods.
The StateIO models can be used as general purpose IO models for US states from which Type I multipliers can be produced for regional economic impact assessment or the full two-region tables can be used for broader regional science applications or research purposes alongside or in place of existing off-the-shelf models where the available years, level of sectoral resolution, and two-region format of the StateIO models are fit for purpose.
Future work will include development of environmental and employment accounts to align with these models and expansion of the useeior package to build state versions of USEEIO models using the StateIO models. The resulting state EEIO models can be used for state-specific applications by EPA (US Environmental Protection Agency, Office of Land, and Emergency Management 2009) or state government agencies, but also for other regional applications where EEIO models are useful. Planned applications include a state-specific prioritization tool to reveal in which sectors opportunities exist to reduce impacts of state production and a consumption-based greenhouse gas inventory for selected US states (Bridge and Ingwersen 2018), and preparing consumption-based greenhouse gas inventories for US states (Ingwersen, Young, and Cashman 2021).
Footnotes
Acknowledgments
The authors would like to thank Catherine Birney and Andrew Schreiber for helpful comments on earlier versions of this article.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the U.S. EPA’s Sustainable and Healthy Communities Research Program. This research is supported through U.S. EPA contract HHSN316201200013 W, Task Order EP-G16H-01256 with General Dynamics IT (GDIT). Bill Michaud (GDIT) and Bhagya Subramanian (EPA) assist with contract and project management.
Disclaimer
The US Environmental Protection Agency, through its Office of Research and Development, funded and conducted the research described herein under an approved Quality Assurance Project Plan (K-LRTD-0030017-QP-1–3). It has been subjected to review by the Office of Research and Development and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.
Notes
Appendix
Acronyms
Acronym
Definition
BEA
Bureau of economic analysis
CHARM
Cross-hauling adjusted regionalization method
D
Defense
DC
District of Columbia
E
Employment
EEIO
Environmentally-extended input-output model
EPA
Environmental protection agency
EU
European union
FAF
Freight analysis framework
FGD
Federal government demand
FLQ
Flegg-location-quotients
GVA
Gross value added
IC
Intermediate consumption
ICF
Interregional commodity flow
IO
Input-output
ITA
International trade adjustment
LQ
Location quotients
MRIO
Multi-regional input-output model
NAICS
North american industry classification system
NIPA
National income and product accounts
ND
Non-defense
NOAA
National oceanic and atmospheric administration
NTU
Net total use
OECD
Organisation for economic Co-operation and development
ORNL
Oak ridge national laboratory
RoUS
Rest of the United States
RPC
Regional purchase coefficient
SMM
Sustainable material management
SoI
State of interest
US
United States
USEEIO
United States environmentally-extended input-output model
USDA
United States department of agriculture
USFS
United States forest service
VA
Value added
