Estimating regional social accounting matrices to analyse rural development

SORT 41 (2) July-December 2017, 319-346 DOI: 10.2436/20.8080.02.62 Estimating regional social accounting matrices to analyse rural development Alfredo Mainar-Causap´e1,Jos´e Manuel Rueda-Cantuche2,∗, M. Alejandro Cardenete3,4, Patricia Fuentes-Saguar2,M.CarmenDelgado 3, Fabien Santini4,S´ebastien Mary5and Sergio G´omez y Paloma4 Abstract This paper has two complementary objectives: on the one hand, it introduces the EURO method for the estimation of (regional) Social Accounting Matrices. This method is widely used by Eurostat for the estimation of missing national Supply, Use and Input-output tables but it has not been used before within the context of social accounting matrices or of regional statistics and/or regional impact analyses. On the other hand, this work discusses the possibility of producing non-survey- based regional Social Accounting Matrices that may eventually allow the user to carry out impact analyses such as those of rural development policies, among others. The analysis is carried out for 12 selected European regions based on clusters. MSC: 97K80. Keywords: Social accounting matrices, rural development, European regions, impact analysis. 1. Introduction Social Accounting Matrices (SAMs) are datasets comprising economic transactions that allow the extraction of information on the different economic agents such as producers, consumers, the government and the foreign sector, as well as on the behaviour of productive factors and institutions. They complete the information provided by inputoutput tables. A Social Accounting Matrix can be defined (in a simplified form) as an extension of an input-output table with a more disaggregated structure of expenditures ∗Corresponding author. European Commission, DG Joint Research Centre. Edif. Expo, C/Inca Garcilaso, 3. 41092 Seville (Spain). [email protected] 1Department of Applied Economy III, University of Seville (Spain). 22 Department of Economics, Quantitative Methods and Economic History, University Pablo de Olavide, Seville (Spain). 3Department of Economics, Universidad Loyola Andaluc´ıa, (Spain). 4School of Management and Business, Universidad Aut´onoma de Chile, (Chile). 5Department of Economics, Driehaus College of Business DePaul University, Loop Campus, Chicago IL 60604, USA. Note: The views expressed are purely those of the author and may not in any circumstances be regarded as stating an official position of the European Commission. Received: March 2017 Accepted: July 2017 320 Estimating regional social accounting matrices to analyse rural development and income, integrating the relationships between institutional sectors, estimated with information from national accounting systems. In this way, the objective of closing the full economic flow is achieved. Thus, a SAM is a consistent framework for gathering national income data, product accounts, input-output tables, reflecting the monetary flows among institutions. Therefore, a SAM is a matrix representing in a comprehensive, flexible and disaggregated way all the transactions of a socio-economic system. It reflects the process of income generation by activities, of production, and the distribution and redistribution of income between institutional groups (Pyatt and Round, 1985; Pyatt and Thorbecke, 1976). Figure A1 in Annex 1 shows the standard structure of a SAM. The interest in SAMs is based on the fact that they illustrate the production relationships between the economic sectors as well as the transactions that take place among the different institutions of a certain economic system in terms of revenues or expenses. Besides their statistical interest, which enables us to close the circular flow of income, SAMs have become a useful tool for evaluation of policy interventions in national or regional frameworks. In this sense, it is interesting to have regional SAMs to be able to analyse the effect and impact of regional development policies, especially in rural areas. But the difficulty of obtaining databases for this purpose is an important obstacle that we attempt to overcome with the methodology presented here. Moreover, it is possible to carry out a complete analysis of the productive structure of the economy and to obtain a general perspective of changes that might occur in the event of any shock (e.g. key sectors). Below, we present the approach used for obtaining 12 NUTS 3 level1regional SAMs. The estimates of the NUTS 3 SAMs are obtained using a two-step process: 1. Input-output frameworks are regionalised (i.e. Supply, Use and Symmetric tables) from the NUTS 1 regions or countries concerned, using the EURO method (Beutel, 2002, 2008; Eurostat, 2008; Temurshoev and Timmer, 2011; Valderas et al., 2016). 2. The NUTS 3 SAM estimation is calculated using the regionalised SUT and some additional information to produce the input-output tables. Regarding policies, the Rural Development Policy, often referred to as Pillar 2, has become one of the most significant elements of the Common Agricultural Policy (CAP), representing close to one third of the total CAP budget. Before integration of flexibility between pillars and other adjustments, the amount dedicated to rural development policies over the financial period of 2014-2020 is likely to reach EUR 95 billion out of a total of EUR 348 billion for both pillars of the CAP (27% of the total). In recent years, 1.NUTS: Nomenclature of Territorial Units for Statistics. The NUTS classification is a hierarchical system for dividing up the economic territory of the EU for the purpose of socio-economic analyses of the regions: NUTS 1: major socio-economic regions; NUTS 2: basic regions for the application of regional policies; NUTS 3: small regions for specific diagnoses. The NUTS 2013 classification is valid from 1 January 2015 and lists 98 regions at the NUTS 1 level, 276 regions at the NUTS 2 level and 1,342 regions at the NUTS 3 level. A. Mainar-Causap´ e, J.M. Rueda-Cantuche, M.A. Cardenete, P. Fuentes-Saguar... 321 several research programmes, scientific papers and policy reports have looked at ways to assess the impacts of Pillar 2 at country and regional levels. The European Commission and the Member States carry out periodic ex ante, mid-term and ex post evaluation of the rural development policy and of the Rural Development Programmes. Several FP7 and Horizon 2020 research programmes are dedicated to the evaluation of the impact of rural development policies. However, the diversity of rural situations across Europe has complicated the empirical studies of these impacts of rural development and often makes any comparison between regions rather trivial. Also, rural development policies do not only aim at supporting specific sectors (such as agriculture); indeed several measures are focused on non-farm actors, and others are related to the improvement of quality of life in rural areas. Hence, it is necessary to use multi-sectoral models, requiring a significant amount of data, in order to capture the full economic impact. In this sense, well-known linear multiplier models and computable general equilibrium (CGE) models use SAMs to develop their analysis. Also, given the nature of rural development (regional implementation through Rural Development Programmes and the existence of menus offered to the beneficiaries in each region), the need for modelling at a sub-regional level has led to the application of these models at the NUTS 3 level with models going as deep as modelling the rural area and the urban area of NUTS 3 regions. The challenge of such work is that it requires extensive effort in the construction of NUTS 3 SAMs, especially if the rural-urban split is modelled. In this context, this paper builds NUTS 3 SAMs for 12 regions, following a detailed analysis of the source data rather than using an automatic approach, which would derive regional SAMs directly from superior level tables, using an optimisation method and some regional proxies. It aims to cover all types of NUTS 3 regions with significant participation of rural areas, so that the impact of rural development policies can be studied for most of the types of regions receiving aid, thus allowing the evaluation of their effectiveness. The selection of these NUTS 3 regions uses an empirical classification of NUTS 3 regions (Raggi et al. (2013)), which reflects the heterogeneity of NUTS 3 characteristics in the EU. This multidimensional classification is based on the following set of four criteria: Rural character; Accessibility; Actual economic diversification; and Total gross domestic product per capita. So, this paper has two complementary objectives: it introduces the EURO method for the estimation of (regional) Social Accounting Matrices and illustrates the possibility of producing non-survey-based regional Social Accounting Matrices for rural development policies’ impact analyses. The rest of the paper is structured as follows: Section 2 discusses the methodology used in the regionalised SAM estimations and its application in some European Union regions. Section 3 presents the main results and, finally, Section 4 provides conclusions. Some tables and aggregated versions of the estimated SAMs are included in the Annex. 322 Estimating regional social accounting matrices to analyse rural development 2. Methodology and data 2.1. The EURO method for estimating supply and use tables The general balancing problem of matrices basically consists of only knowing one single base table (be it a Supply and Use Table (SUT), Symmetric Input-Output Table (SIOT) and/or Social Accounting Matrix (SAM)) and at least the row and column totals for the unknown table that has to be estimated2. There are different ways to approach this underdetermined problem where unknowns (e.g. elements of the interior tables) outnumber external constraints (e.g. RAS3or bi-proportional scaling methods, Lenzen, Gallego and Wood, 2009, among others). However, none of these methods allows the estimation of SUTs and SIOTs whenever row and column totals are not given and with the minimum amount of information possible. Actually, to the knowledge of the authors, the EURO method is the only existing method that allows the estimation of SUTs and SIOTs without given row and column totals. The EURO method typically aims at updating SIOTs at basic prices from one year to another and is based on a previous version initially developed by Beutel (2002) for input-output tables and further explained by the Eurostat Manual of Supply, Use and Input-Output Tables (2008, Ch. 14). The EURO method is a robust update procedure which is inexpensive and has limited data requirements. It exclusively uses official data and integrates all quadrants of SIOTs. Row and column totals for intermediate consumption and output and the corresponding final demand structure are derived endogenously, not allowing for arbitrary changes of input-output coefficients. The method is fully consistent with supply and demand through the Leontief quantity model (Eurostat, 2008). Therefore, it is sustained on economic grounds rather than on optimisation and/or pure mathematical techniques. Recently, Temurshoev, Webb and Yamano (2011) formalised a SUT variant of the EURO method based on Beutel (2008). Beutel and Rueda-Cantuche (2012) elaborated a more detailed version to be used by Eurostat. And, in line with the pioneering works of Hewings (1969, 1977), we formulate an adapted version of the latter to be used in this project for the regionalisation of supply and use tables. The EURO method is used in this paper as a method for regionalisation for the first time. Below, we present an adapted and more detailed explanation of the EURO method for SUT regionalisation, mostly based on Temurshoev et al.’s (2011) description of the EURO method for updating SUTs. The initial SUTs (typically at the NUTS 1 or NUTS 2 level) consist of the following components all expressed at basic prices: domestic and imported intermediate use matri- 2.M´ınguez, Oosterhaven and Escobedo (2009) and Oosterhaven and Escobedo (2011) consider several known tables as base tables but the lack of information at NUTS3 level makes this analysis inappropriate for our purpose. 3.In the original presentation of this method (i.e. working paper), the vector of row multipliers was designated by r, the table of inter-industry transactions in coefficient form in the base year by Aand the vector of column multipliers by s. Hence the juxtaposition of the notation led to the nomenclature RAS (as originally in Stone, 1961). A. Mainar-Causap´ e, J.M. Rueda-Cantuche, M.A. Cardenete, P. Fuentes-Saguar... 323 ces (commodity ×industry); domestic and imported final demand matrices (commodity ×category of final use); supply matrix (commodity ×industry); vector of total value added of industries (industry ×1); and a vector of total taxes less subsidies on products by industries and final use categories. The projected SUTs require the following macroeconomic statistics for the SUTs at the NUTS 3 level, based on regionalisation rates4of macroeconomic variables: value added by industry; total final demand by use; total taxes less subsidies on products; and total imports. The listed data requirements mean that the vectors of value added per industry, totals of final demand categories and aggregate values of taxes less subsidies on products and imports need to be known at the NUTS 3 level too. Following Thissen, Diodato and van Oort (2010), we have used information on interregional transport flows to estimate regional imports and exports. We have used the Eurostat data on road freight transport loading (exports) and unloading (imports) in physical terms and have calculated a ratio over the whole country (in physical terms). The method uses these official statistics as exogenous inputs, and replicates them in the derived SUTs. This method involves minimum data requirements, which is appropriate given the lack of macroeconomic data at the NUTS 3 level. Each of the iterations of the EURO method consists of two steps (see Figure 1). The first step of the first iteration defines domestic and imported intermediate and final uses, the vector of value added, the vector of taxes less subsidies on products, and the supply matrix of the projected SUTs. This first estimation of the (unbalanced) use table is basically a cell-wise arithmetic average resulting from multiplying the corresponding regionalisation rates by the rows and columns of the initial use table. Subsequently, the total commodity output (from the estimated use table) is allocated row-wise proportionally to the initial supply table (i.e. constant market shares) in order to obtain the first estimation of the supply table at the NUTS 3 level. The total industry outputs and inputs are not equal after this first step (column sums of projected supply and use tables). To make the derived SUTs consistent, it is assumed that the domestic and imported input structures of industries and the totals of commodities’ final uses from the first step are valid. Given this assumption, the so-called fixed commodity sales structure model determines consistent industry output and input levels (Eurostat, 2008, Model D, p. 351). This second step ensures the consistency of the industry outputs and inputs, and commodity supply and demand, but it deviates from macroeconomic statistics, i.e. value added per industry, final uses of categories, total value added and total imports. The regionalisation rates initially used are then adjusted in an iterative procedure in order to make the difference between the actual and projected (in each of the iterations) regionalisation rates minimal (less than 1%). The observed deviations are used to correct these rates in such a way that it should ensure that if the model overestimates (underestimates) the available macroeconomic statistics, the corresponding regionalisation rates 4.They are calculated as regional/national ratios. 324 Estimating regional social accounting matrices to analyse rural development Figure 1: EURO method for regionalising SUTs. Source: Own elaboration based on Beutel and Rueda-Cantuche (2012). A. Mainar-Causap´ e, J.M. Rueda-Cantuche, M.A. Cardenete, P. Fuentes-Saguar... 325 are decreased (increased). This is done through correction factors (see Eurostat, 2008). Then, the first step of the second iteration computes the projected SUT components as in the first iteration, i.e. domestic and imported intermediate and final uses, the vector of value added, the vector of taxes less subsidies on products, and the supply matrix of the projected SUTs. As was the case with the first step of the first iteration, the results do not ensure the equality of industry outputs and inputs. The consistent industry outputs and inputs are again found using the fixed commodity sales structure model, which is then used to derive the consistent SUTs of the second iteration in exactly the same manner as defined earlier for the first iteration. However, note that now the domestic and imported input structure matrices are derived from the outcomes of the first step of the second iteration. As a result, one obtains a new deviation vector, which quantifies the difference between the projected regionalisation rates and the macroeconomic statistics. If the difference between the actual and projected regionalisation rates is acceptable, the resulting SUTs are the final outcome of the EURO projection. Otherwise, the steps of the second iteration are repeated until the projected variables resemble (closely or perfectly) those of the macroeconomic statistics. It is important to note that each such subsequent iteration begins with the computation of new correction factors, which are then used to correct the regionalisation rates from the previous iteration. The convergence in the EURO method can always be found by changing the tolerance level until convergence is reached. The last important point concerning the EURO method is that it requires that the number of industries and commodities are equal. Thus, even though the EURO method distinguishes between products and industries, it does not allow for the estimation of rectangular SUTs5. The data requirements of the EURO method are the following for the NUTS 3 case studies: gross value added by industry; taxes less subsidies on products (total); final demand components (totals), including exports; and total imports. The following sections explain the data sources and methods used in the calculation of the necessary data for the projections. Gross value added by industry. It is not very common or easy to find detailed data on gross value added by industry at the NUTS 3 level. In this paper, we use a breakdown of 6 products/sectors (see below), which will be split up into 13 products/sectors according to the NUTS 1 or NUTS 2 shares, depending on the available information (see Table A2 in Annex 1 for details about the 13 products/sectors). Taxes less subsidies on products (total). Provided that the GDP is available for the NUTS 3 regions, its difference with respect to the total sum of gross value added at basic prices (also available) makes the overall total of taxes less subsidies on products. 5.In this paper, the EURO method is programmed in the Eviews software and Excel templates are used to adapt the results to the standard Eurostat format. 326 Estimating regional social accounting matrices to analyse rural development Table 1: Example of a final demand estimation using NUTS 2 or NUTS 1 data. Baden-W¨urttemberg Share of GDP final demand components Konstanz Values (million EUR) GDP 100% 7,961.68 Consumption of households 54.4% 4,328.71 Consumption of Public Administration and NPISH 15.3% 1,221.22 Gross capital formation 18.4% 1,463.84 Net exports 11.9% 947.91 Source: Own elaboration. Final demand components and imports. Gross domestic product (GDP) is defined as the sum of: final consumption of households; final consumption of government and non-profit institutions serving households; gross capital formation (investment); and net exports (exports minus imports). Therefore, by using this definition of GDP, we split up the value of GDP for NUTS 3 regions using the shares of GDP components from the NUTS 2 or NUTS 1 regions (wherever available). As an example (see Table 1), the Baden-W¨urttemberg (NUTS 2) shares of GDP components are given below as well as the GDP of Konstanz (NUTS 3) for 2007 and the corresponding calculation of its final demand total by category. However, we are interested in calculating exports and imports separately and not as net exports. In order to do so, we estimate NUTS 3 exports and NUTS 3 imports according to the NUTS 3/NUTS 1 share of the Eurostat data on road freight transport loading (exports) and unloading (imports). As a result, in a second step, net exports are recalculated and the other final demand components adjusted accordingly. 2.2. Estimation and selection of representative regional SAMs For the construction of NUTS 3 SAMs, we initially develop a basic SAM linking the input-output framework previously estimated, closing economic flows between productive sectors, commodities and institutional sectors. To do this, we use additional information, most of it from Eurostat in order to achieve greater uniformity in the estimation of the matrices for all the NUTS 3 analysed. However, when more specific information is necessary, we obtain it from local or national statistical offices. The basic sources6 used are: •allocation of primary and secondary income account of households by NUTS 1 and NUTS 2 regions (e. g. Baden-W¨urttemberg/Freiburg-Konstanz) - Eurostat; 6.All Eurostat data can be found in http://ec.europa.eu/eurostat/data/database. A. Mainar-Causap´ e, J.M. Rueda-Cantuche, M.A. Cardenete, P. Fuentes-Saguar... 327 •income of households by NUTS 2 region (e.g. Freiburg-Konstanz) - Eurostat; •compensation of employees by NUTS 2 region (e.g. Freiburg-Konstanz) - Eurostat; •employment by NUTS 3 regions - Eurostat; •non-financial transactions (e.g. Germany-Konstanz) - Eurostat; •gross domestic product (GDP) at current market prices by NUTS 3 region - Eurostat; •gross value added at basic prices by NUTS 3 regions (NACE R1) - Eurostat; •disposable income of households - national statistical offices (e.g. Konstanz: VGR der L¨ander: Regionaldatenbank Deutschland); •Input-output tables at NUTS 1 or country level (e.g. Germany 2005) - Eurostat and OECD7. This information is incorporated into the input-output framework provided, obtaining a first version of the matrix for each NUTS 3 region. Small discrepancies that may arise in the estimation process are corrected by using a simple technical adjustment through RAS8. The result is a NUTS 3 level basic SAM composed of the accounts presented in Table A3 (see Annex 1). Basic SAMs for each NUTS 3 region can be extended to successively incorporate the accounts and sectors needed to perform the required analysis of the corresponding regions. For this, the basic SAM accounts are disaggregated by block, using new information, almost entirely from Eurostat, to achieve the greatest possible homogeneity: •farmland: number of farms and areas by economic size of farm (ESU) and NUTS 2 region; •agricultural accounts according to EAA 97 Rev.1.1 by NUTS 2 region; •average annual earnings by economic activity, sex, occupation - country level;. •employment by occupation and economic activity - country level; •structure of consumption expenditure by degree of urbanisation (COICOP level 2) (1 000) - country level; •mean consumption expenditure by degree of urbanisation (in PPS) - country level; 7.OECD (2015). 8.The only exception in this initial procedure is the SAM for Huesca (Aragon, Spain), which comes from a previous expert’s version for 2005 (elaborated by the authors) and which has simply been updated to 2007 using basic information from Eurostat and the RAS adjustment. 334 Estimating regional social accounting matrices to analyse rural development In Huesca (Cluster 5), the agri-food activities represent 13% of its regional GVA, with a very small public sector share in its total regional investment (i.e. 3%) and around 40-60% of its GDP traded with other territories. Finally, the agricultural sector and the agri-food industry in Set´ubal (Cluster 6) represent together 4% of its regional GVA, with a public investment close to 20% of their total regional investment. Trade shares over GDP with other regions and countries are similar to Huesca, although slightly higher. Therefore, in the light of the results obtained, the methodology used for estimating the SAMs at NUTS 3 level appears to be adequate and provide a significant contribution as a tool for obtaining such information, which is important for the assessment of regional economic development policies. 4. Conclusions This paper describes a novel methodology for estimating non-survey-based regional Social Accounting Matrices with limited information for a selection of 12 NUTS 3 EU regions. For the first time, a modified version of the EURO method for Supply and Use Tables has been used as a method for regionalisation. The resulting SAMs can be further used for policy analysis, for example for modelling the impacts of rural development policies by using linear multipliers or computable general equilibrium (CGE)- based model approaches. These SAMs used, as far as possible, existing regional/local data from their respective national and/or regional statistical offices, with consideration of the disaggregation of specific institutional sectors by degree of urbanisation (rural vs. urban areas). Given the lack of official survey-based information to build regional (NUTS 2/NUTS 3) SAMs, we conclude that the methodology proposed in this paper can be useful (and replicated) to estimate non-survey-based regional SAMs with (optional) ad hoc specific considerations for certain sectors depending on the purpose of the analysis, i.e. rural/urban split for analysing rural development policies. Notwithstanding the caveats/assumptions made in our approach, we believe that sound impact analyses (e.g. using linear multipliers, CGE models, etc.) can be carried out in the future with regional SAMs estimated in the way we propose in this paper. A. Mainar-Causap´ e, J.M. Rueda-Cantuche, M.A. Cardenete, P. Fuentes-Saguar... 335 Annex 1. Tables and figures Table A1: NUTS 3 regions selected. NUTS 3 Cluster NUTS 2 NUTS 1 Member State 1L¨uneburg (1) L¨uneburg NIEDERSACHSEN Germany 2 Norfolk (1) East Anglia EAST OF ENGLAND United Kingdom 3 Konstanz (1) Freiburg BADEN-W ¨ URTTEMBERG Germany 4 Finist`ere (2) Bretagne OUEST France 5 Gorenjska (2) Zahodna Slovenija SLOVENIJA Slovenia 6 Noord-Drenthe (2) Drenthe NOORD-NEDERLAND Netherlands 7¨ Orebro (2) ¨ Ostra Mellansverige ¨ OSTRA SVERIGE Sweden 8Heves (3) ´ Eszak-Magyarorsz´ag ALF ¨ OLD ´ ES ´ ESZAK Hungary 9L¨a¨ane-Eesti (3) Eesti EESTI Estonia 10 Słupski (3) Pomorskie REGION P ´ OŁNOCNY Poland 11 Huesca (5) Arag´on NORESTE Spain 12 Pen´ınsula de Set´ubal (6) ´ Area Metrop. de Lisboa CONTINENTE Portugal Source: Own elaboration. Table A2: List of products/sectors. Original data source Used in SAMs 1. Agriculture, forestry and fishing 1. Agriculture13 2. Manufacturing industry 2. Forestry 3. Construction 3. Fishing 4. Trade, transport and telecommunications 4. Mining 5. Finance, renting and business services 5. Food and beverages 6. Public services and other services 6. Other manufacturing activities 7. Utilities 8. Construction 9. Trade 10. Hotels and restaurants 11. Transport and telecommunications 12. Other private services 13. Public services Source: Own elaboration. 13.This industry still needs to be broken down further into arable crops, permanent crops and other agricultural products. 336 Estimating regional social accounting matrices to analyse rural development Table A3: NUTS 3 basic SAM accounts. A.0-1 Agriculture, hunting and related services C.0-6 Other manufacturing A.0-2 Forestry, logging and related services C.0-7 Utilities A.0-3 Fish C.0-8 Construction A.0-4 Mining C.0-9 Trade A.0-5 Food industries C.0-10 Hotels and restaurants A.0-6 Other manufacturing C.0-11 Transport and communication A.0-7 Utilities C.0-12 Other private services A.0-8 Construction C.0-13 Public services A.0-9 Trade L Labour A.0-10 Hotels and restaurants K Capital A.0-11 Transport and communication ANT Activity net taxes A.0-12 Other private services CNT Commodity net taxes A.0-13 Public services INT Income net taxes C.0-1 Prod. of agric., hunting and related services H Households C.0-2 Prod. of forestry, logging and related services E Enterprises C.0-3 Fish G Government C.0-4 Mining IS I-S C.0-5 Food industries ROW Rest of the world Source: Own elaboration. Table A4: Structure of the NUTS 3 SAM for 2007. Rural activities A.0-1 1R Small arable crops farms Rural A.0-1 2R Large arable crops farms Rural A.0-1 3R Small permanent crops farms Rural A.0-1 4R Large permanent crops farms Rural A.0-1 5R Small other farms Rural A.0-1 6R Large other farms Rural A.0-2 R Products of forestry, logging and related services Rural A.0-3 RFishRural A.0-4 R Mining Rural A.0-5 R Food industries Rural A.0-6 R Other manufacturing Rural A.0-7 R Utilities Rural A.0-8 R Construction Rural A.0-9 RTradeRural A.0-10 R Hotels and restaurants Rural A.0-11 R Transport and communication Rural A.0-12 R Other private services Rural A.0-13 R Public services Rural A. Mainar-Causap´ e, J.M. Rueda-Cantuche, M.A. Cardenete, P. Fuentes-Saguar... 337 Table A4 (cont.) Urban activities A.0-1 1U Small arable crops farms Urban A.0-1 2U Large arable crops farms Urban A.0-1 3U Small permanent crops farms Urban A.0-1 4U Large permanent crops farms Urban A.0-1 5U Small other farms Urban A.0-1 6U Large other farms Urban A.0-2 U Products of forestry, logging and related services Urban A.0-3 UFishUrban A.0-4 U Mining Urban A.0-5 U Food industries Urban A.0-6 U Other manufacturing Urban A.0-7 U Utilities Urban A.0-8 U Construction Urban A.0-9 UTradeUrban A.0-10 U Hotels and restaurants Urban A.0-11 U Transport and communication Urban A.0-12 U Other private services Urban A.0-13 U Public services Urban Commodities C.0-1 1 Arable crops products C.0-1 2 Permanent crops products C.0-1 3 Other agricultural products C.0-2 Products of forestry, logging and related services C.0-3 Fish C.0-4 Mining C.0-5 Food industries C.0-6 Other manufacturing C.0-7 Utilities C.0-8 Construction C.0-9 Trade C.0-10 Hotels and restaurants C.0-11 Transport and communication C.0-12 Other private services C.0-13 Public services Factors SL Skilled labour UL Unskilled labour K Capital Taxes (net) ANT Activity net taxes CNT Commodity net taxes INT Income net taxes Institutional sectors RH Rural households UH Urban households E Enterprises G Government Investment/Save IS I-S Rest of the world ROW Rest of the world Source: Own elaboration. 338 Estimating regional social accounting matrices to analyse rural development Commodi ti e s Acti vi ti es Fac tor s Hous ehol ds Enterpr i s e s Gove rnme nt S avi ng s -Inves tment Re s t of the W or l d Total Commodities Intermediate consumption (inputs) Household consumption Government consumption Fixed capital formation and change in stock (Investment) Exports Total demand Activities Marketed output / Domestic s ales Activity income Factors Facto r in co me from activities Facto r in co me from ROW Factor income Hous eholds Labour and mixed income (Inter Households transfers) Distributed benefits to Households / Other transfers Current transfers to Households Current transfers to Households from ROW Household income Enterprises Operating surplus / Capital income Current transfers to Enterprises Current transfers to Enterprises from ROW Enterprise income Government Net taxes on products Net taxes on production Factor income to Government Direct taxes Surplus to Government / Enterprises taxes Current transfers to Government from ROW Government income S avi ng s -Inves tme nt Household savings Enterprise savings Government savings (Capital accounts transfers) Capital transfers from ROW Savings Rest of the World Imports Factor income to ROW Household transfers to ROW Surplus to ROW Government transfers to ROW Current external balance Foreign exchange outflow Total Total supply Cost of production activities Factor income payments Household expenditures Enterprise expenditures Government expenditures Investment Foreign exchange inflow Figure A.1: Structure of a Social Accounting Matrix. Source: Round (2003) and own elaboration. A. Mainar-Causap´ e, J.M. Rueda-Cantuche, M.A. Cardenete, P. Fuentes-Saguar... 339 Annex 2. Social accounting matrices14-NUTS 3 regions 2007 (mio EUR, current prices) Lüneburg AgR OtR AgU OtU COM FACT RH UH ENT GOV I-S ROW Tot AgR 280 280 OtR 2,866 2,866 AgU 50 50 OtU 3,618 3,618 COM 196 1,596 36 1,530 475 1,385 673 540 1,639 8,069 FACT 93 1,198 15 2,003 23 3,332 RH 714 140 199 2 1,055 UH 1,976 388 552 5 2,920 ENT 620 360 979 GOV -8 73 -1 85 374 1,093 109 0 50 15 3,565 I-S 193 402 50 349 995 ROW 1,254 22 14 41 292 16 405 2,043 Tot 280 2,866 50 3,618 8,069 3,332 1,055 2,920 979 3,565 995 2,043 14.AgR: Agricultural and food activities Rural; OtR: Other activities Rural; AgU: Agricultural and food activities Urban; OtU: Other activities Urban; COM: Commodities; FACT: Factors; RH: Rural households; UH: Urban households; ENT: Enterprises; GOV: Government (incl. taxes); I-S: I-S; ROW: Rest of the world; Tot: Total. 340 Estimating regional social accounting matrices to analyse rural development Norfolk AgR OtR AgU OtU COM FACT RH UH ENT GOV I-S ROW Tot AgR 534 534 OtR 12,019 12,019 AgU 1,017 1,017 OtU 18,735 18,735 COM 317 5,757 728 8,784 4,282 5,082 5,387 3,394 6,842 40,574 FACT 214 5,115 320 8,139 135 13,923 RH 6,233 1,366 4,027 96 11,722 UH 7,201 1,578 4,653 111 13,544 ENT 423 4,554 4,977 GOV 3 1,147 -31 1,812 5,561 6,544 655 0 776 65 32,995 I-S 1,708 1,717 355 2,372 6,151 ROW 8,269 66 171 201 1,023 92 1,982 11,804 Tot 534 12,019 1,017 18,735 40,574 13,923 11,722 13,544 4,977 32,995 6,151 11,804 Konstanz AgR OtR AgU OtU COM FACT RH UH ENT GOV I-S ROW Tot AgR 676 676 OtR 10,104 10,104 AgU 67 67 OtU 3,721 3,721 COM 458 4,891 47 1,828 559 3,615 1,311 1,456 2,968 17,134 FACT 230 4,990 21 1,802 50 7,093 RH 660 288 214 2 1,164 UH 4,046 1,766 1,312 12 7,136 ENT 2,336 768 3,104 GOV -12 222 0 90 398 2,579 233 0 122 52 7,329 I-S 190 840 192 810 2,032 ROW 2,567 51 16 102 625 36 454 3,851 Tot 676 10,104 67 3,721 17,134 7,093 1,164 7,136 3,104 7,329 2,032 3,851 A. Mainar-Causap´ e, J.M. Rueda-Cantuche, M.A. Cardenete, P. Fuentes-Saguar... 341 Finistère AgR OtR AgU OtU COM FACT RH UH ENT GOV I-S ROW Tot AgR 2,621 2,621 OtR 24,450 24,450 AgU 399 399 OtU 11,637 11,637 COM 1,704 12,078 288 5,411 7,601 3,282 5,298 4,452 6,682 46,795 FACT 918 11,476 110 5,793 132 18,429 RH 10,211 1,528 3,137 78 14,954 UH 5,001 748 1,536 38 7,324 ENT 3,193 2,006 5,199 GOV -1 896 2 432 6,018 2,637 616 0 376 61 22,033 I-S 1,063 1,286 530 879 1,070 4,828 ROW 7,689 24 272 119 1,776 187 10,066 Tot 2,621 24,450 399 11,637 46,795 18,429 14,954 7,324 5,199 22,033 4,828 10,066 Gorenjska AgR OtR AgU OtU COM FACT RH UH ENT GOV I-S ROW Tot AgR 396 396 OtR 6,264 6,264 AgU 10 10 OtU 1,722 1,722 COM 294 4,275 7 1,134 1,283 484 660 1,135 6,279 15,550 FACT 115 1,845 4 549 74 2,586 RH 1,649 48 364 29 2,090 UH 615 18 136 11 780 ENT 243 77 320 GOV -13 144 -1 39 651 245 94 0 37 23 2,419 I-S 109 33 10 37 982 1,171 ROW 7,158 78 48 18 150 22 7,475 Tot 396 6,264 10 1,722 15,550 2,586 2,090 780 320 2,419 1,171 7,475 342 Estimating regional social accounting matrices to analyse rural development Noord Drenthe- AgR OtR AgU OtU COM FACT RH UH ENT GOV I-S ROW Tot AgR 168 168 OtR 633 633 AgU 673 673 OtU 9,848 9,848 COM 109 399 497 5,689 1,318 761 1,342 966 5,811 16,893 FACT 58 217 178 3,964 56 4,472 RH 2,116 476 837 26 3,455 UH 1,161 261 459 14 1,895 ENT 1,085 1,952 3,037 GOV 1 16 -2 195 1,497 868 164 0 89 27 5,695 I-S 564 223 318 164 1,269 ROW 5,572 110 76 44 1,817 54 213 7,886 Tot 168 633 673 9,848 16,893 4,472 3,455 1,895 3,037 5,695 1,269 7,886 Örebro AgR OtR AgU OtU COM FACT RH UH ENT GOV I-S ROW Tot AgR 685 685 OtR 10,838 10,838 AgU 79 79 OtU 6,444 6,444 COM 420 6,083 44 3,207 1,704 1,822 2,452 1,701 6,191 23,626 FACT 289 4,388 39 2,946 53 7,715 RH 3,255 350 1,042 29 4,675 UH 3,090 332 989 27 4,438 ENT 1,303 373 1,676 GOV -23 367 -4 290 1,929 2,062 258 0 148 27 10,090 I-S 953 459 184 515 2,111 ROW 5,579 67 89 95 552 55 262 6,700 Tot 685 10,838 79 6,444 23,626 7,715 4,675 4,438 1,676 10,090 2,111 6,700 A. Mainar-Causap´ e, J.M. Rueda-Cantuche, M.A. Cardenete, P. Fuentes-Saguar... 343 Heves AgR OtR AgU OtU COM FACT RH UH ENT GOV I-S ROW Tot AgR 334 334 OtR 2,986 2,986 AgU 198 198 OtU 1,859 1,859 COM 216 1,878 142 1,129 659 303 483 475 2,449 7,734 FACT 132 1,059 59 697 79 2,025 RH 1,260 55 374 19 1,708 UH 515 22 153 8 698 ENT 225 178 403 GOV -13 49 -3 33 703 314 63 0 30 25 2,406 I-S 312 65 60 175 613 ROW 2,357 25 34 15 202 17 107 2,758 Tot 334 2,986 198 1,859 7,734 2,025 1,708 698 403 2,406 613 2,758 Lääne Eesti- AgR OtR AgU OtU COM FACT RH UH ENT GOV I-S ROW Tot AgR 268 268 OtR 1,523 1,523 AgU 51 51 OtU 1,111 1,111 COM 170 905 32 625 449 255 259 546 1,094 4,334 FACT 106 590 21 466 57 1,240 RH 657 55 109 12 832 UH 359 30 59 7 455 ENT 213 77 291 GOV -9 28 -2 19 322 176 22 0 23 12 1,172 I-S 48 17 8 163 333 569 ROW 1,383 11 14 7 176 1 1,592 Tot 268 1,523 51 1,111 4,334 1,240 832 455 291 1,172 569 1,592