Linking multisectoral economic models and consumption surveys for the European Union

Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=cesr20 Economic Systems Research ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/cesr20 Linking multisectoral economic models and consumption surveys for the European Union Ignacio Cazcarro , Antonio F. Amores , Inaki Arto & Kurt Kratena To cite this article: Ignacio Cazcarro , Antonio F. Amores , Inaki Arto & Kurt Kratena (2020): Linking multisectoral economic models and consumption surveys for the European Union, Economic Systems Research, DOI: 10.1080/09535314.2020.1856044 To link to this article: https://doi.org/10.1080/09535314.2020.1856044 © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group View supplementary material Published online: 24 Dec 2020. Submit your article to this journal Article views: 2163 View related articles View Crossmark data ECONOMIC SYSTEMS RESEARCH https://doi.org/10.1080/09535314.2020.1856044 Linking multisectoral economic models and consumption surveys for the European Union Ignacio Cazcarro a,b, Antonio F. Amores c,d, Inaki Arto band Kurt Kratena e aAgencia Aragonesa para la Investigacion y Desarrollo (ARAID), Agrifood Institute of Aragon (IA2). Dept. Economic Analysis, Zaragoza, Spain; bBasque Center for Climate Change, Bilbao, Spain; cEuropean Commission Joint Research Centre, Circular Economy and Industrial Leadership, Seville, Spain; dDept. Economics, Quantitative Methods and Economics History, Pablo de Olavide University, Sevilla, Spain; eWIFO, Vienna, Austria ABSTRACT Multisectoral models usually have a single representative household. However, more diversity of household types is needed to analyse the effects of multiple phenomena (i.e. ageing, gender inequality, distributional income impact, etc.). Household consumption surveys’ microdata is a rich data source for these types of analysis. However, feeding multisectoral models with this type of information is not simple and recent studies show how even slightly inaccurate procedures might result in significantly biased results. This paper presents the full procedure for feeding household consumption microdata into macroeconomic models and for the first time provides in a systematic way an estimation of the bridge matrices needed to link European Union Household Budget Surveys’ microdata with the most popular multi-regional input–output frameworks (e.g. Eurostat, WIOD, EORA, OECD). ARTICLE HISTORY Received 14 January 2019 KEYWORDS Household budget surveys; CPA-COICOP; contingency bridge matrices; valuation matrices; EU28 1. Introduction Most economic models, such as Input–Output (IO) models or Computable General Equilibrium (CGE) models, represent private consumption using a single representative agent. However, this assumption of the representative agent has been widely criticised (Brock & Durlauf, 2001;Hoover,2008;Kirman,1992; Lehtinen & Kuorikoski, 2007;Savard, 2004;Stiglitz,2018) and the inclusion of heterogeneous household profiles in economic modelling is increasingly encouraged. For example, the recommendations of Stiglitz et al. (2009) included ‘2. Emphasising the household perspective’ and ‘4. Give more prominence to the distribution of income, consumption and wealth’. The G-20 ‘Data Gaps Initiative’ also recommended CONTACT Antonio F. Amores antoniof[email protected]opa.eu European Commission Joint Research Centre, Circular Economy and Industrial Leadership, c/ Inca Garcilaso, Ed. EXPO, Seville, 41092 Spain; Economics, Quantitative Methods and Economics History, Pablo de Olavide University, Ctra. Utrera Km.1, Sevilla, 41013 Spain Supplemental data for this article can be accessed here. https://doi.org/10.1080/09535314.2020.1856044 © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2I. CAZCARRO ET AL. establishing a link between National Accounts (NA) data and distributional information as a conceptual/statistical framework. As –among others– Kim et al. (2015) have shown, treating the household aggregate and its consumption structures in a heterogeneous way (differentiating household types) reveals important insights into structural change driven by socio-economic changes. There is a continuum between introducing many (hundreds of) household groups in a model and linking a multisectoral (IO or CGE) model with a micro-simulation model. Recently there has been some development in this area (see, among others, Colombo, 2008)andithas been shown how this approach outperforms the representative agent approach for many policy issues that involve income distribution (Savard, 2004). Nevertheless, a large part of IO and CGE models do not take full advantage of the information available on consumption structures by household types in increasingly publicly available official data. Indeed, several factors contribute to this state of ‘under-research’. One important issue is that, in all cases, the structural information in household surveys (Household Budget Surveys, HBS) needs to be bridged to the consumption structure information in IO statistics, which is not straightforward given the lack of publicly available ‘contingencymatrices’(commonlyknownas‘bridgematrices’ 1) and the different valuationmethodsofthetwodatasetsinvolved.Bothissuescomplicateandhinderanalysesof the implications of household heterogeneity for the effectiveness of public policies or the distributional effects of policies. Contingency/bridge matrices are part of the national accounts (NA) but national statistical institutes (NSI) do not usually publish them. In consequence, most of bridge matrices used in the literature are ad-hoc estimates and the methods used for their construction are not always explained in depth. Among other works, some examples in the past were Kehoe et al. (1988a,1988b) for the construction of social accounting matrices (SAMs) and CGE models;orWieretal.(2001), Flores & Mainar (2009), Steen-Olsen et al. (2016), to analyse environmental impacts. Serrano and Fernandez-Vázquez (2017)recentlyemphasised the importance of using accurate contingency matrices by showing how significantly consistency can affect the results in terms of impact analysis. Notice that in demand-driven models, the size of the impact is often more due to the size of the shock than to the technological structure (see for example Arto & Dietzenbacher, 2014). Therefore, an error in the size of the shock is especially relevant. Ontheotherhand,thereare someaccounting differences betweenthehousehold expenditures reported in surveys and the consumption data in the IO framework. For example, expenditure data from surveys is reported in purchasers’ prices while consumption data in IO tables is reported in basic prices. Also, the data have to be adjusted due to differences in the geographical scope (expenditure data follows the residence principle while IO data follows the territorial principle) and, when the IO tables are industry by industry, expenditure data have to be transformed from product to industry. Thus, the linkages between the two datasets requires a number of transformations that are not always known by modellers 1Bridge matrices are made of transformation coefficients or ratios. Contingency matrices are the matrices with values that underlie the coefficients of the bridge matrices. The advantage of publishing contingency matrices is that the user can aggregate rows and columns to adapt them to the aggregation needed. ECONOMIC SYSTEMS RESEARCH 3 and practitioners. Relevant steps to follow when linking consumption (e.g. household) surveys to multisectoral models were explained in Mongelli et al. (2010) and Min and Rao (2017). In this context, this article introduces a systematic method for linking consumption data from the IO framework and expenditure surveys and provides estimates of the bridge matrices for the 28 countries that made up the European Union (EU-28) in 2016 (when the consumption survey microdata currently available was released). The remainder of the article is organised as follows. Section 2presents the data and methods for bridging information from consumption surveys and macroeconomic models, including a brief description of the different steps for linking the two datasets and the method for estimating the bridge matrices, which are a central element of the procedure. Section 3shows the results of the estimation of contingency matrices for the EU-28 and tests the main assumptions. Section 4concludes and discusses the results. 2. Methods Atfirstsight,linkingexpendituredatafromsurveysandconsumptiondatafromtheIO framework could be seen as a simple problem that can be solved with a direct conversionofclassificationsandanadjustmentmethodsuchasaRAS(thisisrepresentedbythe dashed arrow in Figure 1). However, as we show in Section 2.1,thisapproachiscompletely wrong and results in biased outcomes. Producing results at the household level sounds very appealing; however, before starting the calculations, it is important to understand and apply the right method in order to produce robust results. As shown in the lower panel of Figure 1, the procedure involves a number of steps and relevant concepts of the National Accounts (NA). Figure 1. Linking microdata from consumption surveys and multisectoral models. Source: Own elaboration. 4I. CAZCARRO ET AL. Therefore, the following steps should be considered before using consumption-surveybased profiles in macroeconomic models: (1) Align consumption microdata to NA principles. On the one hand, survey data does not follow the accounting principles of NA which are a compendium of multiple data sources. On the other hand, multisectoral models are mainly based on NA; thus, it is necessary to adapt the data from surveys to the national accounting principles to be able to use it correctly in macroeconomic modelling (more details of the reasons for this adaptation and the procedure are given in Section 2.1). (2) Convert consumption microdata aligned to NA principles to production-based classifications. Consumption data is usually available in classifications of expenditure according to purpose such as COICOP (Classification of Individual Consumption by Purpose). However, multisectoral multi-industry models follow a classification of products aligned with an industry classification such as CPA (Classification of Products by Activity, Eurostat, 2019a). Thus, it is necessary to bridge the two. For such bridging, estimations of bridge matrices based on public official data of COICOP vs CPA are used. The sub-steps of this central step are as follows: (a) Prepare the available official contingency tables to be used as priors: they should have the proper aggregation and territorial to residential adjustment. (b) Prepare the consumption data from the use tables to be used as input for the estimation process: this data should be in purchasers’ prices and have the proper aggregation. (c) Identify similar countries to select the most suitable proxies for the countries for which official contingency tables are not available, to be used as priors. Estimate the bridge tables consistently for each database, linking the consumption in the use tables and in the final consumption expenditure of households by consumption purpose (COICOP 3-digit level) (part of the National Accounts). In Section 2.2we will introduce further details of this step and in Section 3we will show the results of the estimation procedure. (3) Change the valuation of consumption microdata in NA principles and productionbased classifications to basic prices (bp). Consumption microdata and Household Final Consumption Expenditure (HFCE) in NA are in purchasers’ prices (pp). However, multisectoral models usually work at basic prices (producer prices). The difference is the net taxes (i.e. taxes less subsides) paid by theuserbutnotperceivedbytheproducerandthetradeandtransportationmargins thatneedto be relocatedtothe ‘margin’ industries(e.g. trade and transportindustries). In Section 2.3,weexplainhowtoconvertthepricesfrom‘pp’to‘bp’. (4) Adjust the data from the product classification (i.e. CPA, see EC, 2008)totheindustry classification (i.e. the Statistical Classification of Economic Activities NACE, see EC, 2008,2010), if the model is based on an industry classification. Finally, if the IO table is product by product, the Household Final Consumption in basic prices (HHFC) resulting from the previous step can be connected to the IO table. However, with an industry-by-industry table like the OECD or WIOD, the vector of HHFC must be converted into an industry classification (see Section 2.4). ECONOMIC SYSTEMS RESEARCH 5 The remainder of this section explains the method. 2.1. Alignment of consumption microdata to NA principles MultisectoralmodelssuchasIOandCGEmodelsarebasedonSupplyandUsetables and IO tables which are a core element of the NA (see Eurostat, 2019b). Therefore, the accounting principles of NA are reflected in the data and consequently in the model. However, this is not the case of consumption surveys. Thus, it is crucial to align the accounting principles of all the datasets involved. Aggregate HFCE data of the whole economy is often reported by NSIs using COICOP. This data are part of the NA and their totals are consistent (excluding vintages issues) with thetotalHHFCintheCPAreportedintheSupplyandUsetablesandIOtablesandother mainaggregatesoftheNA,suchasthesplitoftheGDPfromtheexpenditureside.However, the total HFCE resulting from summing up the HFCE of individual households reported in consumption surveys does not match the aggregate HFCE of NA, with differences ranging from 50% to 97% for EU Member States in 2010 (Eurostat, 2018a). Furthermore, not only the aggregates, but, more importantly, the structures of the consumption surveys are inconsistent with the HFCE figures in NA (see Table 16 in Eurostat, 2015). The coverage rate of the consumption surveys with respect to the NA consumption varied across the different COICOP categories from 6% to 119% for EU member states in 2010 (Eurostat, 2018a). This is due to the fact that in the compilation of HFCE of NA, consumption surveys are a major data source but not the only one. Data from survey results is complemented with additional information such as tax statistics or transportation surveys to produce the HFCE of the whole economy. Amores (2018) details some of the sources of the differences such as conceptual and classification differences, measurement errors (e.g. under/over-estimation of some categories like alcohol, tobacco, housing, water, energy or food) and estimation errors (e.g. high-income households being underrepresented). To make the microdata of the consumption surveys consistent with the HFCE in NA, we suggest the following procedure. First, for every COICOP category, we calculate the ratio between the HFCE in NA and the total for the whole population in the consumption survey. These ratios can be interpreted as scaling coefficients of the average representative household of the NA. Second, we use these coefficients to align the consumption profile extracted from the survey to the NA accounting standard. To do so, each category of consumer profiles from the survey is uprated/downrated by multiplying it category-wise against such coefficients. The existence of unmatched COICOP categories between HFCE in NA and consumption surveys makes a stepwise procedure necessary. Further details on such procedure can be found in Section A3.1 in the Supplementary Material. 2.2. Conversion from COICOP to CPA Once the consumption data in COICOP has been adapted to the NA principles, they must beconvertedintoCPA.Thisisdoneusingtheso-calledcontingencyorbridgematrices.In the case of the European Union, NSIs build these bridge tables to compile NA. However, these matrices are not part of the datasets compulsorily submitted to Eurostat (2014)and NSIs do or do not publish them depending on different considerations (transparency, quality, confidentiality, resources, etc.). In the case of the European Union, only eight countries 6I. CAZCARRO ET AL. (Austria, Czechia, Denmark, Estonia, Finland, Slovakia, Sweden and the United Kingdom) make them available, and it is very unlikely that the majority of NSIs will publish such data in the short run. In this context, we suggest a systematic procedure to estimate bridge matrices that we apply to the estimation of the 20 countries of the European Union for which data is not available.ThestartingpointsarethevectorofconsumptioninCPA(64products)and COICOP (47 categories) from the NA of Eurostat. In the cases in which we found differences between the two vectors, we preserve the vector of CPA, in order to keep it consistent with the IO tables. We combine this information with the set of eight available official contingency matrices for 2010 that will be used as a benchmark to estimate the matrices of the remaining 20 countries. The contingency tables are matrices with a dimension of 64 (CPA products) x 47 (COICOPcategories).The elementxi,jofthematrixrepresentsthetotalquantityofproduct i(e.g. chemical products) that is used for the purpose j(e.g. routine household maintenance);thesumrow-wisegivesthetotalHHFCoftheusetableinpurchasers’prices(CPA) and the sum column-wise gives the total HFCE of the NA (COICOP). One key element for the estimation of the contingency tables is the selection of the benchmark country whose structure will be used as a prior. In our procedure, we try to identify the most suitable proxies for the countries for which official contingency tables are not available by comparing a set of macro indicators with those of the eight potential benchmarks. In particular, we compare the structure of the HHFC from the use table in pp (CPA classification); the structure of the HFCE from the NA (COICOP classification); the GDP per capita as an indicator of development stage, and the sociocultural distance. As Supplementary material, data for the 28 matrices can be found in Annex A1 (to use before Annex A2 with the transformation of data from purchaser’s prices to basic prices), and further details on their estimates in Annex A3. 2.3. Transformation of data from purchaser’s prices to basic prices Once the data are matched with the proper classification (CPA vs COICOP) and with the accounting principles (NA vs surveys), the valuation of the data must be aligned. Definitions of types of prices can be found in the UN Systems of National Accounts 2008 and the European Systems of Accounts 2010 (UN, 2009 and EC, 2013 respectively). As shown in Figure 1,consumptiondata,bothfromsurveysandfromNA,isbasedonpurchasers’ prices. In contrast, the multisectoral models usually work with basic prices. This step is required for two reasons. First, the vector of consumption in basic prices doesnotincludetaxesand,inconsequence,itislowerthanthevectorinpp.Forexample, just looking at the totals of households’ consumption from the Eurostat use tables, the value inpurchasers’pricesisonaveragefortheEU-28around13%higherthantheoneinbasic prices, reaching in some cases more than 20%. Therefore, apart from all issues involving the structure of consumption, in general for each euro of expenditure from a consumption profile from surveys, 13% of the shock tested with an IO table would be an overestimation. Second, the vector of consumption in basic prices records all trade and transportation margins associated with final consumption in the so-called margin industries while in the vector of consumption in purchasers’ prices (i.e. coming from consumption surveys) these margins are reported as part of the value of the product consumed. Thus, the direct link of ECONOMIC SYSTEMS RESEARCH 7 consumption profiles in purchasers’ prices to an IO table in basic prices would result in an underestimation of the impact linked to the demand for trade and transportation services (hidden in the purchasers’ prices) and an overestimation in the rest of all other industries. Data in purchasers’ prices is transformed into basic prices using the following information: the vectors of HHFC in basic prices and purchasers’ prices, the vectors of margins associated with HHFC from the table of trade and transportation margins, and the net taxes associated with HHFC from the table of taxes less subsidies on products. We use this informationtocalculatethe implicitnettax ratesandmarginratesperproductasdescribed by Mongelli et al. (2010) who also explain how to use them, as well as the limitations of the approach. The Supplementary material includes a tool to convert consumption profiles from HBS in purchasers’ prices (already in CPA and aligned to NA) into basic prices (Annex A2) and further details (Annex A3.3). 2.4. Adaptation of the data to the type of IO table (products vs industries) The final step consists of linking the consumption profiles adjusted to NA, in CPA and basic prices, to the IO tables. If the IO table is product by product, this can be done in a straightforwardway.However,iftheIOtableorCGEmodelisindustrybyindustry(e.g.WIODor OECD-ICIO), an additional transformation is required in order to transform the profiles from products (CPA) to industries (NACE classification). This should be done following the same approach followed for transforming the supply-use table into the symmetrical IO table. In the case of MRIO tables, developers often use the fixed product sales structure assumption (Model D, see Box 12.3 in UN, 2018). Therefore, if consumption profiles are to be applied to an MRIO, the recommendation is to use Model D for transforming the consumer profiles as well. On the other hand, NSIs often apply hybrid models (with extensive internal data) that cannot be exactly reproduced by practitioners. However, these hybrid models are often closer to the commodity technology assumption than to the alternative industry technology assumption. On the other hand, the commodity technology assumption tends to produce negatives that should be solved (i.e. applying Almon, 1970). The omission of this step can also generate important errors. As an example, we compared the vector of domestic demand of households in the International Use tables 2010 from WIOD2(Timmer et al., 2015) with the WIOT 2010 (constructed following Model D, see UN, 2018) domestic households vector (Table 1). This gives us a measure of the size of the potential error if this step is omitted. We calculated the relative differences between the two tables by product for each country, and looked at the median and the maximum across countries for each product (upper part of Table 1). The relative differences in each product can be huge: 9% median across countries of the median across products. However, the median across countries of the maximum across products is 100% and the relative difference can be as high as 3189% (Manufacture of coke and refined petroleum products in Croatia). Then, we took the 2http://www.wiod.org/database/int_suts13 We look at the domestic part only because the imported consumption is fob in the International Use Tables and therefore cannot be directly compared with the WIOT. The share of domestic consumption over total consumption of households’ accounts is between 58% and 97% across countries, with a median of 84%. 8I. CAZCARRO ET AL. Table 1. Relative differences and WAPE between the ‘Final consumption expenditure by households’ vectors of each country in the WIOD International SupplyUse Tables and the WIOT. Median Min. across countries Max. Summary across products Median % difference by product 0% 9% 100% Max. % difference by product 0% 100% 3189% Summary across countries Median % difference by product 0% 10% 86% Max. % difference by product 0% 100% 3189% WAPE 0% 9% 63% Source: Own elaboration based on the WIOD Release 2016. median and maximum across the products of each country independently, which, we summarised across countries (bottom part of Table 1). The differences are above 10% in half of the countries. To understand the implications, we need some context because the share consumed of every product is not the same, but there are products that are much more relevant in the consumption basket than others. That is why we calculated the average weighted deviations for each country (WAPE), which can be very significant as well (9% median across countries, but up to 63% in Croatia and 58% in Luxembourg). 3. Results of estimation of contingency matrices for the EU-28 This section shows the results of the estimation of the contingency matrices for the EU28.Sincewewanttoachieveanappropriatemethod,whichwillbeinlinewithusingan official contingency matrix to approach the desired one, we first examine the similarities across official contingency matrices that are available and depart from this comparison. Then we identify benchmark tables by explaining assumptions of criteria which may lead to good choices of benchmark tables. Finally, we test these assumptions and present the final results. 3.1. Similarities across official contingency matrices The comparison of available official contingency matrices provides an idea of possible sources and sizes of errors when using the structure of the matrix of one country to estimate the matrix of a second country. In order to compare the similarity or difference among official contingency tables, we compare the structure of coefficients (or shares) of how each of the COICOP categories is distributed across CPA. We use the Weighed Average Percentage Error (WAPE) of the shares of each cell of the contingency matrix in the total of the country objective Owith respect to those of a benchmark country B,WAPE_sO,B(Annex A4 presents alternative measures). The WAPE is defined as: WAPE_sO,B= m  i=1 n  j=1|sO ij −sB ij| m i=1n j=1sB ij ×100 = m  i=1 n  j=1 |sO ij −sB ij|×100 (1) ECONOMIC SYSTEMS RESEARCH 15 As indicated above, Annex A1 provides these contingency tables. The tables are provided at the Eurostat standard of NA for CPA (64 categories) and 3-digit level of COICOP (with 47 categories, from CP011 to CP127). Withsimpleaggregations,onecanobtainthecontingencytablesfromwhichonederives the bridge at the levels of 56 CPA categories of the international WIOD tables Release 2016 and at the levels of 61 categories of EU countries in EORA. OECD aggregation in ISIC 4 (36 industries) can also be obtained by aggregation, given that the only industry that is more disaggregated in OECD IICOIs than in Eurostat tables is CPA_B (mining) which is notdirectlyconsumedbyhouseholds. Similarly, COICOP categories of the contingency tables can be aggregated to accommodate them to the level of detail desired in the model used. 4. Conclusions Using the detailed information contained in consumption surveys that is becoming increasingly publicly available opens up research avenues for better understanding changes in consumption structure, driven by individual behavioural changes and sociodemographic trends. Integrating that into economic modelling constitutes a major step forward in analysing how changes in consumption structures drive aggregate structural change in an economy. The consistent introduction of information from household consumption surveys into the IO framework also opens up a whole area of research on the impact of household characteristics on economic and environmental variables. That includes issues like the various links (in both directions) between the labour market and income distribution, carbon footprints by income/age group and other household characteristics and many more. However, the proper integration of the information of consumption surveys into the structure of economic models is not straightforward. The process requires a number of data manipulations,whicharenotalwayswellknownbymodellersbecauseitrequireshighly specialised expertise in National Accounts and surveys. Furthermore, it also requires additional data in the form of contingency tables to derive the suitable bridge matrices that, in most cases, are not publicly available. Indeed, although NSIs build these bridge tables to compile the National Accounts and the Supply-Use tables of the IO framework, it is very unlikely that the majority of them will publish this kind of data in the near future. We briefly presented a method, which summarises the main tasks for making this link: (1) align survey data with National Accounts, (2) convert survey data from the expenditure/consumption classification (COICOP) to the product classification (CPA), and (3) change the valuation of consumption data from purchasers’ prices to basic prices. All these tasks are required in order to make this link. However, they are often neglected, unknown or non-transparent in many of the works using data from consumption surveys. The method and the set of contingency tables presented in this paper aim to provide a comprehensive toolbox to make the link between consumption surveys and economic models in a rigorous manner. In this regard, we consider that the linking method and contingency tables presented in this work, together with the tools in the Annexes, can facilitate the use of microdata from consumption surveys in IO analysis and economic modelling. 16 I. CAZCARRO ET AL. The main contribution of this article, apart from clarifying the linking process, is the set of contingency tables that practitioners can use to derive the bridge matrices at their desired disaggregation for the 28 countries that made up the European Union in 2016 (when the consumption survey microdata currently available was released). These contingency tables constitute a key element for this data integration. As analysed by Serrano and Fernandez-Vázquez (2017), inaccurate bridges cause important biases in the analytical studies of consumption. In addition, as the proper estimation of these tables is very time-consuming and requires in-depth knowledge and expertise on National Accounts, thispaperprovidesaready-to-usedatabasethatwillbeusefulformanypractitioners. We departed from 8 official publicly available contingency tables, rearranged them consistently to common classifications, and estimated the remaining 20 tables. We also found that while using an official table as a prior, even if randomly selected, it outperforms recent methods such as the count-seed RAS (Cai & Vandyck, 2020 and Cai & Rueda-Cantuche, 2019) and significantly outperforms the naïve prior. We also developed a method for identifying which of the seven selected available tables is the best to use as a prior to estimate the contingency table of each missing country, finally working with all seven of them as priors. We successfully tested the robustness of this benchmark identification method. All in all, as more or less summarised by a reviewer, from this work it emerges that ‘if you want to obtain a table of country ‘D’, make a RAS of the indicated bridge matrix of country ‘A’ and you will get a good approximation.’ The limitation of the test is the concentration of available official tables used as benchmarks in the northern European countries. Obviously, having some benchmark contingency tables available for southern European countries would improve our estimates for this set of countries. Together with the limited of availability of contingency matrices over time, the article has a geographical focus on the European Union since it has microdata on consumption available for research purposes, which is not common worldwide. However, the method is general and valid whenever there is similar data and similarities between targeted and benchmark countries. Furthermore, we found stability in the choice of prior countries according to the structures of the searched total vectors of the contingency matrices. Consequently, our estimates are valid for the European Union countries of the WIOD and OECD and we consider the application of the method to other countries worthy of further research, as well as to relevant databases such as EXIOBASE or GTAP. Finally, we recommend that future updates of all these databases also include information bridging the data on expenditure with classifications like COICOP. This should comprise more than the bridge COICOP / product classification (specific to the database), but also the survey to NA adaptation and the purchasers’ prices to basic prices transformation. It should be stressed that, due to the importance of these contingency tables for economicanalysis,ideallythisexerciseshouldbecarriedoutwithinofficialstatisticalprogrammes of NSIs. This would reduce the number of assumptions and increase the quality ofthedata.Forexample,atthelevelofEurostatortheOECD,beingabletogatherofficial contingency matrices in a consistent manner, with different links to standard classifications andother parts ofthe National Accounts, wouldenormouslyreduce uncertainty,biases and assumptions made by researchers and users. ECONOMIC SYSTEMS RESEARCH 17 Acknowledgements The authors are grateful to the reviewers and to the editor Manfred LENZEN, whose insightful contributions helped to improve the article. The authors are also grateful to Sanjiv MAHAJAN (Office of National Statistics, United Kingdom), Pat KILDUFF (Central Statistics Office, Ireland), Ylva PETERSSON STRID (Statistics Sweden), Isabelle REMOND-TIEDREZ, Pille DEFENSEPALOJARV, Sigita GRUNDIZA, Friderike OEHLER and Erika TAIDRE (Eurostat) for their clarifications regarding official data. Anna ATKINSON (European Commission’s Joint Research Centre) kindly proofread the manuscript. Ignacio Cazcarro and Iñaki Arto thank the Spanish Ministry of Science, Innovation and Universities for the RTI2018-099858-A-I00 grant (project “MALCON”) and for the PID2019-106822RB-I00 grant (in which Ignacio Cazcarro participates) under the programme of R&D Projects Research Challenges; the Spanish State Research Agency through María de Maeztu Excellence Unit accreditation 2018–2022 (Ref. MDM-2017-0714) and the project LOCOMOTION H2020-LC-CLA-2018-2 (No 821105). Disclaimer Theviewsexpressedinthispaperbelongtotheauthorsandshouldnotbeattributedtothe institutions to which the authors are affiliated. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This work was supported by Ministerio de Ciencia, Innovación y Universidades: [grant numbers RTI2018-099858-A-I00 and PID2019-106822RB-I00]; the Spanish State Research Agency through María de Maeztu Excellence Unit accreditation 2018-2022 [grant number MDM-2017-0714] and the project LOCOMOTION H2020-LC-CLA-2018-2 [grant number 821105]. ORCID Ignacio Cazcarro http://orcid.org/0000-0001-7517-0053 Antonio F. Amores http://orcid.org/0000-0002-0216-4098 Inaki Arto http://orcid.org/0000-0001-6427-7437 Kurt Kratena http://orcid.org/0000-0002-0257-4169 References Almon, C. (1970). Investment in input–output models and the treatment of secondary products. In A. P. Carter, & A. Brôdy (Eds.), Applications of Input–Output Analysis (pp. 103–116). NorthHolland. Amores,A.F.(2018, September 27–28). The challenge of using consumption surveys to feed macroeconomic models. 6th SHAIO Workshop, Madrid. Arto, I., & Dietzenbacher, E. (2014). Drivers of the growth in global greenhouse gas emissions. 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