Working Party on International Trade in Goods and Trade in Services Statistics

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1 Unclassified STD/CSSP/WPTGS(2015)24 STD/CSSP/WPTGS(2015)24 Unclassified Organisation de Coopération et de Développement Économiques Organisation for Economic Co-operation and Development 23-Mar-2015 English - Or. English STATISTICS DIRECTORATE COMMITTEE ON STATISTICS AND STATISTICAL POLICY Working Party on International Trade in Goods and Trade in Services Statistics THE STABILITY OF TIVA ESTIMATES: TESTING DIFFERENT METHODS OF RECONCILING CONTRADICTORY OFFICIAL DATA March 2015 Conference Centre, OECD Headquarters, Paris The presence of trade asymmetries and national data discrepancies means that when building an Inter Country Input Output table (ICIO), decisions must be taken on the correct statistical approach to combining and harmonizing the conflicting data. In the current creation of the ICIO, two important steps (among many) are taken that are addressed in this paper. First, the national SUT or IO data are harmonized with the most recent available information from the National Accounts. Secondly, when combining national data into an international IO, the inevitable discrepancies are currently distributed proportionally across countries and industries (using RAS). This paper analyses the robustness of TiVA indicators by exploring the effect of both of these steps on two focal indicators: Vertical Specialization and bilateral exports. We show that the main TiVA results are robust to adjustments to national accounts, and that also when the RAS procedure is adapted in such a way that the ICIO that is created fully reflects the national statistics of one single country, while permitting other countries trade flows to change to respond to the needs of global balancing, the focal indicators generally do not change significantly. Improvements in the symmetry of bilateral trade statistics, and further investigations into the best way of balancing them when discrepancies continue, would however still improve the TiVA estimates. Agenda item 5.4 Contact persons: Guannan Miao (Guannan.miao@oecd.org) or Fabienne Fortanier (fabienne.fortanier@oecd.org ) English - Or. English JT Complete document available on OLIS in its original format This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area.

2 THE STABILITY OF TIVA ESTIMATES: TESTING DIFFERENT METHODS OF RECONCILING CONTRADICTORY OFFICIAL DATA Guannan Miao and Fabienne Fortanier 1 1. Introduction: the comparability of TiVA estimates with national data 1. The release of Trade in Value Added (TiVA) indicators by OECD and WTO in 2013 has been successful in many ways. By decomposing exports into its various value added components (domestic and foreign, direct and indirect) which are directly comparable to GDP, the indicators have helped our collective understanding of Global Value Chains. They have highlighted the importance of imports for export success, of the large (but indirect) role of services in exports, and have changed the way bilateral trade balances are perceived. These conclusions and their implications continue to be widely discussed among even the highest levels of policy makers. However, despite this success (or perhaps even because of it), an often-raised and important concern is that the data presented in TiVA are not well comparable with official national data. In particular, gross trade data in the TiVA database are sometimes quite different from the trade statistics reported by the country, particularly when broken down by partner country. This lack of comparability potentially reduces the usefulness of TiVA indicators for national policy making, but also begs the question if when the national data were fully considered the TiVA indicators might be very different from the ones currently published. 2. This paper aims to address this concern head-on. We start by introducing the two root causes of the differences between the data in TiVA and those published by NSOs. These include first of all, the presence of trade asymmetries, or differences between the national data as reported by one country compared to the mirror flows reported by the trading partner. Secondly, there are substantial discrepancies in the national data sources that are used to create the Inter-Country Input-Output (ICIO) table that underpins TiVA. National Supply and Use Tables (SUTs) or Input-Output (IO) tables, Systems of National Accounts (SNA), and bilateral trade statistics (all official statistics published by NSOs) may give quite different pictures regarding e.g. value added by industry, trade flows, and on how imports are attributed to end use categories. 3. The presence of trade asymmetries and national data discrepancies means that when building an ICIO, decisions must be taken on the correct statistical approach to combining and harmonizing the conflicting data. In the current creation of the ICIO, two important steps (among many) are taken that are addressed in this paper. First, the national SUT or IO data are harmonized with the most recent available information from National Accounts statistics on main aggregates of GDP. The advantage is that the subsequent results can be readily compared with national GDP. 1 The authors thank Nadim Ahmad for his encouragement and insightful comments. 2

3 However, since these main National Accounts aggregates are updated and revised more frequently than the underlying SUTs and/or IOs, discrepancies between both may occur. Secondly, when combining national data into an international IO, the inevitable discrepancies are currently distributed proportionally across countries and industries using a method called RAS, which is traditionally used to compiling Social Accounting Matrices (SAMs) and IO tables. However, this by definition means that the final balanced 2 ICIO is a compromise of 57 national perspectives, and countries may be interested in understanding how this result may differ from their own national statistics. 4. In this paper we test the impact of these two decisions. In our first test, we study the effect of the adjustment of national SUT/IO data to national accounts aggregates. In the second test, we compare the current balanced TiVA results with the results that are obtained when constraining the RAS procedure in such a way that the ICIO that is created fully reflects the national statistics of one single country, while permitting other countries trade flows to change to respond to the needs of global balancing (i.e. essentially creating 57 the number of countries in TiVA different ICIO tables). We focus specifically on the variation in the outcomes from these 57 national perspectives to obtain a better understanding of the robustness of TiVA indicators. 5. The remainder of this paper is organized as follows. First, section 2 explains in more detail why there are differences between the data presented in the OECD-WTO TiVA database compared to national statistics. We also illustrate this with two country studies, for United Kingdom and Greece. Section 3 presents the methodology that we use to for the two tests, and also provides a brief introduction to the RAS procedure, and explains why we use the degree of Vertical Specialization (VS) and bilateral exports as our main target variables for comparing results. The results are subsequently presented in section 4, first focusing on the effect of benchmarking SUTs to National accounts, and subsequently on different national constraints can impact the balancing results. Section 5 concludes. 2. Prevalence of discrepancies in national data and international trade asymmetries 6. One of the key questions that users of TiVA indicators ask is why the data reported in TiVA can be quite different from the data reported by national statistical offices. This problem holds particularly true for the bilateral trade data reported in TiVA. As illustrated in table 1, with case studies for United Kingdom and Greece, the values that are reported by NSOs on their bilateral trade position differs from the final values that are included in TiVA and that used for the calculations of the indicators on for example the import share of exports or domestic value added in final demand. This difference makes it difficult to compare TiVA with other national data and therefore hampers (policy) analysis. But it also raises the question what would be the TiVA indicators if the reported data were taken into account, which comes down to a request for more insights into the robustness and stability of TiVA indicators. 2 Balanced meaning that all matrix cells add up precisely to the row and column totals. 3

4 Table 1. Examples of differences between gross merchandise trade values included in TiVA and as reported by countries (by partner country, 2009, in million USD) Exports Imports Trade Balance Merchandise Merchandise Merchandise TiVA TiVA Reporter Partner trade trade trade TiVA UK Germany 38,181 40,721 62,055 55,658-23,874-14,937 Netherlands 27,319 15,812 33,546 28,383-6,226-12,571 China 6,583 7,150 42,759 38,719-36,176-31,569 Canada 5,595 7,975 7,739 5,991-2,144 1,984 Norway 4,111 3,432 23,047 26,122-18,936-22,690 USA 51,893 40,281 45,463 36,955 6,430 3,326 Greece Italy 2,163 2,110 7,569 9,455-5,406-7,346 Russia ,421 4,666-3,096-4,349 Germany 2,183 2,028 8,149 9,020-5,966-6,992 Belgium ,634 1,938-2,351-1,765 France ,617 4,367-2,890-3,448 USA 935 1,042 1,884 2, ,505 Sources: OECD-WTO TiVA and UN Comtrade database. Note that TiVA merchandise trade covers ISIC sectors A to E. 7. There are two main factors that drive these differences. First, there are substantive asymmetries in international merchandise trade and services trade data, meaning that the data published by a reporting country on its bilateral trade flows may be vastly different from the data published by its partner countries on the same trade flows. There are many explanations for these asymmetries; the most important ones include differences across countries in the reporting definitions (e.g. country of consignment versus country of origin), definitions of geographical areas, (mis)classification of products; differences in valuation (FOB for exports and CIF for imports); different ways of reporting confidential data; and for some countries, the sheer size of re-exports. 8. Trade asymmetries are problematic for TiVA because the ICIO can only include one number to describe the exports of one country and industry and imports of its partner country and industry. The larger the asymmetries, the more difficult the choice of the right number becomes and the more the final figure will deviate from what is reported by at least one (if not both) trading partners involved 3. Therefore, even though countries often describe trade asymmetries as when you fix one problem, another ten come up or too tough to chew, dealing with trade asymmetries has been and will continue to be a part of the core work for the national statistical offices. International organisations and relevant task forces can facilitate and drive the reconciliation processes, and thereby help to minimize asymmetries, but a bottom-up approach is clearly preferable (see also STD/CSSP/WPTGS(2014)20). 9. Second, even within an individual country, discrepancies exist between the various national data sources that are used in TiVA. Here we use United Kingdom and Greece s 2005 data to demonstrate the magnitude of these data conflicts, which can be partially explained by the difference in definition, but also be caused by data cleaning processes and imputation methods, the difference between survey and administrative data, or timing of revisions. 3 At the moment, TiVA uses import shares by partner country to describe bilateral trade relationships. 4

5 2.1 SUTs and National Accounts 10. Our first example compares Supply and Use tables with data from National Accounts, for the year Table 2 shows, for United Kingdom, in particular that while the differences are small for final consumption expenditure and gross fixed capital formation, this is not the case for value added by industry, where discrepancies are varying from 4% to almost 20% across sectors. At the aggregate level, SNA provides a total value added 0.8% higher than the equivalent data given in the SUT. Finally, when we check the breakdown of trade by goods and services, we observe bigger gaps between two data sources than at the total trade level. As for Greece, final consumption expenditure reported in SUT is 8% higher than reported in SNA while gross capital formation is 7% lower than SNA. Value added data by sector sees differences of up to 20% (positive or negative). The biggest discrepancy can be seen in the area of trade: for both imports and exports, SNA flows are substantially higher than reported in the Greek SUT. This revision could possibly reflect that nonresident expenditures were initially classified as part of household final consumption expenditure in the SUT, but were subsequently reclassified as exports (and similarly for resident expenditures abroad). Table 2. Data comparison for selected variables of SNA and SUT, 2005, in millions of national currency United Kingdom Greece SNA (1) SUT (2) Ratio (1)/(2) SNA (1) SUT (2) Ratio (1)/(2) Final consumption expenditure (FCE) 1,084, ,662 Household FCE* 815, , , , General government FCE 268, , ,937 33, Gross capital formation 213,938 41,322 Gross fixed capital formation** 209, , ,020 38, Changes in inventories 4,249 4, , Exports of goods and services 340, , ,807 32, Exports of goods 217, , ,490 16, Exports of services 122, , ,317 16, Imports of goods and services 375, , ,741 58, Imports of goods 281, , ,875 50, Imports of services 94,012 79, ,866 8, Gross Domestic Product (GDP) 1,262, ,050 Total Value Added*** 1,125,300 1,116, , , Agriculture, hunting, forestry, fishing (A-B) 7,035 7, ,428 8, Mining, Manufacturing, Utilities (C-E) 184, , ,957 22, Manufacturing (D) 133, , ,428 16, Construction (F) 82,112 69, ,050 10, Wholesale, retail, restaurants and hotels (G-H) 193, , ,094 41, Transport, storage and communication (I) 95,232 80, ,781 17, Other Activities (J-P) 562, , ,284 73, Sources: SNA data are from UN; SUT are from Eurostat. Final consumption expenditure and Gross capital formation are in purchasers price. Exports of goods and services include re-exports. * Household FCE includes non-profit institutions service households and ** Gross fixed capital formation includes acquisitions less disposals of valuables. *** Letters in parenthesis refer to ISIC Rev.3 industry classifications 5

6 2.2 SUTs and merchandise trade statistics 11. A second set of national data sources that can be compared include merchandise trade statistics and SUTs, for the exports (including re-exports) and imports of goods. Table 3 first compares the export figures. At the aggregate level for United Kingdom, both sources provide a rather similar picture for the reference year (2005). Merchandise exports totalled 208 billion pounds, or only 3 percent smaller than what is reported in the SUT. However, the level of discrepancy rises when breaking down the trade statistics by product category (using the CPA classification), with differences up to ± 40% (See Table 3). For Greece, important differences can be observed even at aggregated level, with merchandise trade data that are 15% lower than the data reported in the SUTs. Also at the product level, differences are substantial. Table 3. SNA and Merchandise trade data, exports in millions of national currency (2005) United Kingdom Greece Merchandise Ratio Merchandise Ratio SUT SUT CPA Trade (MT) (MT/SUT) Trade (MT) (MT/SUT) 01 1,422 1, ,219 1, ,015 11, ,156 5, ,325 10, ,841 2, ,867 3, ,306 3, ,276 1, , ,206 2, ,220 3, ,718 10, ,345 2, ,023 33, ,972 2, ,578 5, ,071 1, ,749 10, ,440 1, ,277 4, ,546 18, ,091 9, ,982 6, ,814 19, ,223 8, ,539 21, ,380 13, ,536 4, Total 207, , ,959 16, Data sources: Merchandise trade data as reported to UN Comtrade, SUT as reported to Eurostat. 6

7 12. On the import side, similar discrepancies can be observed. What is even more important, is that since imports are also classified by End-Use categories in SUTs, the merchandise data must be well-aligned with these categories as well. In table 4a (for the UK) and 4b (for Greece), a comparison is made between imports by product and main End-Use category as reported in the SUT with similar data derived from merchandise trade statistics. Table 4a. Differences between merchandise trade statistics and SUTs for imports by product and end-use category, in millions national currency, 2005, United Kingdom Merchandise End use category as % of total merchandise imports End use category as % of total SUT imports CPA Trade Final Final code Intermediate demand Dual* SUT Intermediate demand 01 6,526 24% 76% - 6,767 17% 83% % % 57% % 96% % 50% 10 1, % - - 1,624 98% 2% 11 13, % ,136 98% 2% % ,707 9% - 91% 1,449 99% 1% 15 19,815 15% 85% - 23,584 36% 64% % 97% - 1, % 17 6,225 36% 64% - 6,275 27% 73% 18 9, % - 11,280 13% 87% 19 3,886 3% 97% - 4,094 39% 61% 20 3,086 95% 5% - 2,807 73% 27% 21 5,700 88% 12% - 5,420 85% 15% 22 3,131 47% 53% - 2,330 23% 77% 23 8, % ,085 42% 58% 24 32,272 67% 10% 23% 29,548 84% 16% 25 7,429 81% 19% - 6,874 61% 39% 26 3,035 85% 15% - 2,916 64% 36% 27 9, % - - 7, % ,115 70% 30% - 5,834 61% 39% 29 20,394 38% 61% 2% 20,106 37% 63% 30 15,769 34% 3% 63% 9,850 13% 87% 31 8,518 65% 35% - 8,197 57% 43% 32 17,558 29% 47% 24% 15,082 50% 50% 33 8,228 28% 72% - 8,694 64% 36% 34 37,275 31% 10% 60% 31,429 43% 57% 35 10,986 28% 12% 59% 13,210 60% 40% 36 10,133 12% 80% 8% 8,914 8% 92% % % % 31% Total 276,562 46% 33% 20% 261,247 51% 49% Source: Merchandise trade data as reported to UN Comtrade converted to End-Use using an OECD-adapted version of the UN conversion table (cf BTDIxE). SUT data are sourced from Eurostat. * Dual: certain products have not been converted to an individual end use category but have been treated separately as dual use products, including e.g. personal computers, cell phones, and cars. 7

8 Table 4b. Differences between merchandise trade statistics and SUTs for imports by product and enduse category*, in millions national currency, 2005, Greece Merchandise End use category as % of total merchandise imports End use category as % of total SUT imports CPA Trade Final Final code Intermediate demand Dual* SUT Intermediate demand 01 1,228 63% 37% - 1,378 54% 46% % 0% % 35% % 99% % 87% % 0% % 0% % 0% % 0% % 0% 3% % 0% 15 3,913 20% 80% - 3,271 23% 77% % 87% % 99% 17 1,056 57% 43% - 1,533 50% 50% 18 1,408 8% 92% - 1,688 4% 96% % 95% - 1,103 5% 95% % 6% % 6% % 23% - 1,488 75% 25% % 40% % 79% 23 1, % 0% - 1,824 63% 37% 24 6,975 51% 12% 36% 6,567 64% 36% 25 1,002 81% 19% % 45% % 19% % 29% 27 2, % 0% - 2, % 0% % 42% % 39% 29 3,062 27% 73% - 4,537 40% 60% % 8% 56% 882 8% 92% % 39% % 18% 32 1,481 15% 54% 31% 1,467 22% 78% 33 1,076 19% 81% - 1,271 55% 45% 34 4,176 17% 16% 67% 4,853 16% 84% 35 2,642 8% 92% - 2,653 4% 96% 36 1,081 13% 86% 1% 1,708 11% 89% % 0% % 0% % 30% Total 39,592 42% 42% 16% 43,831 42% 58% Source: Merchandise trade data as reported to UN Comtrade converted to End-Use using an OECD-adapted version of the UN conversion table (cf BTDIxE). SUT data are sourced from Eurostat. * Dual: certain products have not been converted to an individual end use category but have been treated separately as dual use products, including e.g. personal computers, cell phones, and cars. 13. To link merchandise import data to SUTs, they have to be converted from their reported HS product category, into to Industries and End-Use classifications. This was achieved using the approach of OECD s BTDIxE database, which involves applying a conversion key (HS-ISIC-End Use) to all trade flows at the HS 6-digit level. An End-Use category for each HS product is also assigned at the 6-digit level, distinguishing between Intermediate goods and Final demand. 4 Confidential data and 4 We report the results for Final Demand after combining consumptions and capital goods as defined in BTDIxE. When separating ICIO, however, the imports shares are calculated independently for these two categories. The artificial boundaries between Consumptions and Capitals sometimes can be 8

9 goods with multiple uses, such as cars and medicines related products are kept as a separate End Use category (Dual). When comparing the data as presented in tables 4a and 4b, important differences can be observed for both total merchandise imports (in 2005, merchandise imports were 6% higher for the United Kingdom, and 11 % lower for Greece, as compared to SUTs). These differences become even larger when looking at some of the breakdowns by CPA and End-Use. 2.3 SUTs and services trade statistics 14. So far we have only discussed discrepancies in NSO data with respect to merchandise trade. Similar or even larger differences exist in the area of trade in services. This can be explained by the fact that trade in services data are more difficult to collect (for example they are compiled from a variety of different data sources, and are very often based on (small) survey samples rather than a complete observation of all in and outgoing flows as in merchandise trade), and that they are even more difficult to attribute to individual industries. We compare SUT data on total trade in services for 2005 of Greece and the UK with their official trade in services statistics from the Balance of Payments (BOP) in table 5. This comparison shows that for the UK, the figures for exports and imports are rather well-aligned across the two sources. However, this is clearly much less so the case of Greece, where Trade in Services data record flows that are almost twice as high as the Greek SUT table. Table 5. Trade in Services, SUT and EBOPS S200, National Currency 2005 BOP total services (S200) United Kingdom Ratio (BOP/SUT) BOP total services (S200) Greece Ratio (BOP/SUT) SUT SUT Export s 114, , ,095 16, Import s 89,622 79, ,381 8, Methodology 15. The question of how to reconcile various data sources has long been addressed in the context of the construction of national SUTs and IOs, which are built by combining information from customs, various business surveys and other administrative sources. But with the increased attention for TiVA and the underlying ICIO, the issue of reconciling not only national but also international data sources has become even more prominent. As illustrated in section 2, official data may present different values for the same variables, which means that in order to reconcile national and international information, many methodological choices have to be made. It is exactly some of these steps that this paper aims to address in more detail and to test for their impacts on the final results. This section describes our methodology for testing the stability of TiVA estimates under various data reconciliation options. very blurry in both datasets; forcing data to separate introduced more variances than what is reported in Table 4. 9

10 16. The first test deals with the national reconciliation: we compare the effect of reconciling SUT data to the most recent national account aggregates on the main TiVA indicators, as compared to not benchmarking the data to national accounts statistics. The second test deals with the international reconciliation, and compares the results of the balancing the ICIO as currently done in TiVA (i.e., to adjust the national trade data of all countries in roughly equal ways to come to a global picture) with the balanced matrices from a single country s perspective; thereby essentially creating as many versions of the TiVA indicators as there are countries in the dataset. But before explaining the set-up of our tests in more detail, we first explain the RAS technique in more detail (in section 3.1) and subsequently introduce the central variable of interest that we selected for this study to comparing the results: Vertical Specialisation (VS) (section 3.2). 3.1 What is RAS? 17. In compiling and updating national SAM and IO tables, RAS has become a well-established technique. During its history of more than 50 years, it has not lost any of its popularity compared to other alternatives such as optimisation models. RAS offers an algorithm that is easy to program and understand, and this method has been adopted in the OECD-WTO TiVA project to balance national IO tables and the ICIO in an international context. 18. Generally speaking, RAS is a bi-proportional scaling method which iteratively adjusts an old matrix A 0 with row sums u 0 and column sums v 0, to a new matrix A that satisfies a new set of given row sums and column sums, u and v respectively. The adjustments are made by simply applying the proportional distribution of the rows (columns) to the new row (column) total, and repeating this process until the adjustment is complete. Annex 2 gives a simple numerical example of RAS. 19. The RAS method was set in concrete for updating IO tables by Stone in the early 1960s (Stone, 1961; Stone and Brown, 1962). Theoretically, a SAM or IO should always balance, meaning that the row sum should equal the column sum, but empirically they never do so in the first instance. This is due to the fact that creating an SAM or IO means that different data sources (different surveys and administrative data) have to be combined which are never fully consistent. Thus, the RAS technique is useful when for example one data source is preferred to describes the detailed inputoutput industry structure of an economy, but another data source is considered better (or more timely) with respect to the total output (or input) that is produced by industry. Similarly, RAS can be used to harmonize SUTs (with structural information) to the most recent National Accounts data ( new constraints for the row and column totals). And in an international IO setting, to rebalance after splitting import data by partners, RAS also play a critical role. 20. Academics have continued to expand the usability of RAS procedure by developing a number of additional features and successfully tackling some of the challenges. RAS has thereby developed into a family of related procedures, and is now capable to: Incorporate constrains with row and column sums (as explained above, in RAS); Some elements of the matrix should not be changed even if the columns and rows sums do and to incorporate sub-constrains on subsets of matrix elements (as in Modified RAS (MRAS) or Three Stage RAS (TRAS)); 10

11 Consider the reliability of the initial estimates and of the external constraints (extensions of MRAS); Handel negative values and preserve the sign of matrix elements (this is called Generalised RAS (GRAS)); And deal conflicting external data (as in Konfliktfreies RAS (KRAS)). 21. Box 1 gives more details on each of these different RAS procedures. 5 In this paper, we use the standard TRAS and MRAS procedure. Negative values in TiVA are very few and generally only exist in inventory adjustments. They are dealt with before the RAS procedure which is common practice in the construction of IOs. Similarly, we do not have conflicting external data constraints are imposed separately and their effects on our TiVA estimates are even the subject of our current investigation. BOX 1. Different RAS techniques MRAS. The modified RAS procedure (MRAS) is useful when some of the matrix elements of A are known in addition to its row and column sums. In other words, MRAS utilises additional information compared to RAS, and holds part of matrix A 0 constant when applying the scaling procedure. It does so by first replacing the known elements by zero and subtracting these values from the (new) row and column sum totals (also called constraints), and subsequently subjecting the new net of A 0 to the standard RAS procedure, adding back the known elements afterwards. This also applies to some aggregates of elements of matrix A. For example, in an inter-regional input-output system, national aggregates may constitute partial information as constraints; and for more disaggregated national table, the known aggregated terms can serve as constraints. These are realised by Oosterhaven (1986) using national constraint to construct interregional and inter-industry tables; Batten (1985) is proposing further constraints for intermediate and final demand data in a national table; Jackson (1993) use partition coefficients for groups of cells of a disaggregated base year matrix to disaggregate cells in an updated but aggregated matrix. TRAS. The concept of three stage RAS (TRAS) is similar to MRAS, and was first proposed by Gilchrist (1999), which was based Bacharach s two stage RAS (1970) and Miller s research (1985). It is an RAS algorithm to account for generalised information on various sub-aggregates of the cells in a target matrix. This information includes, but is not restricted to row and column sums, and the contents of particular cells with Canadian data as an example. A variation of MRAS method takes into account the uncertainty of the preliminary estimates. Allen (1974), Lecomber (1975) and Allen (1975) addressed this concern. It has accomplished by introducing a matrix E contain reliability information about the elements in A 0. Lahr (2001), on the other hand, considers the uncertainties of external constraints in treating the tolerances of the RAS criteria as functions of the varying reliabilities of row and column sums. GRAS and KRAS. Junius (2003) argues when negative entries are present, the stand RAS approach of applying the RAS-algorithm to the matrix A 0 easily leads to a new matrix A with a structure that may strongly deviate from the structure of the old matrix, in particular, in the rows and columns with relatively large negative entries. He also suggests in practice if the negative entries are treated outside the RAS-Procedure (i.e. removing 5 See also Lenzen et al. (2009) and Lahr et al. (2004), who provide very detailed literature reviews on the RAS techniques. 11

12 before RAS and substitute back into to the matrix after) indicates that the negative entries in the adapted RAS procedure do not contribute to the minimization, hence a loss of information. His generalised approach takes both negative and positive numbers into account. Lenzen and his research colleagues (2009) again proposed a generalised iterative scaling method which is a step ahead of GRAS method, Konfliktfreies RAS (KRAS), which is able to balance and reconcile IO tables and Social Accounting Matrixes under conflicting external information and inconsistent constraints. Additional to traditional and earlier RAS procedure described, KRAS can handle constraints on arbitrarily sized and shaped subsets of matrix elements, and include reliability of the initial estimate and the external constraints therefore find a compromised solution between inconsistent constraints. 22. It is important to note that in certain circumstances, RAS may go awry (i.e., not result in a balanced matrix). Miller and Blair (2009) explain that this problem of non-convergence may especially occur when the matrix contains too many zeroes (e.g. in highly disaggregated matrices), because RAS only makes adjustments to non-zero elements. Too few of such non-zero cells may make it impossible to find an adequate solution. 3.2 Selection of target indicators 23. To evaluate the impact of RAS and the different conflict national data on the TiVA results, we do not need to assess the impact on a full set of (currently) 38 different TiVA indicators. Instead, we focus on two indicators that is among the most important and that also drives many of the other indicators. 24. First of all, we select the so-called degree of Vertical Specialization (VS). 6 This indictor was introduced by Hummels et al. (2001), and measures the value of imported inputs used directly and indirectly in production of an exported good. In addition to being one of the central indicators in TiVA, the VS indicator has the advantage that it can be calculated for national IO tables, as well as from an international IO table. This facilitates therefore a direct comparison of the results across the four scenarios that we present below. In matrix notation, it is expressed as VS = A m [I A D ] 1 E 25. In this formula, A m is the n x n imported coefficient matrix, I is the identity matrix, and A D is the n x n domestic coefficient matrix. E is a n x 1 vector of exports. VS is also an n x 1 vector is total exports of a country. The size of the matrices is determined by the number of industries (n) used in the calculations. 26. The second indicator we choose to focus on is bilateral exports, which is especially relevant for the test involving international reconciliation. This indicator is key in addressing concerns regarding discrepancies between the data published in TIVA and gross trade statistics as nationally reported. 6 The VS indicator is currently not published in TiVA but it is very similar to foreign (imported) valueadded content of exports. The difference between the two variables is that VS can be calculated both from national Leontief inverse matrices and the global Leontief inverse matrix. 12

13 27. In additional to VS (as a share of exports) and total exports, we also report value added (VA), gross output (GO), and total exports (TEXP) in each of our results, to provide better insights into what element(s) cause(s) the differences at 18 industry level. 3.3 Testing different options of national and international data reconciliation National data reconciliation 28. In our first test, we explore to what extent our key target variable, Vertical Specialization, is different when it is calculated from a country s national SUT and IO tables directly, versus after the SUT and IOs have been adjusted to the main GDP main aggregates published in the national accounts statistics. 29. The UK and Greece are chosen for this study because their SUT data differ with respect to their alignment with national accounts: the data for the UK are relatively well-aligned, whereas the Greek data show larger differences between these two sources. The comparison is done for the year 2005, because this is the latest benchmark year available in TiVA database as well as in national country s SUTs. This means that the national data are as robust as possible as they are compiled from the most comprehensive survey information. Before performing the calculations, the national published SUTs and IOs (sourced from Eurostat, and available with a breakdown into 59 industries) are first aggregated to the 18 TiVA industries (see also Annex 1). 30. The national accounts constraints that are subsequently introduced include all the variables listed in table 2 (see above), whereas the fundamental structure of the national IO is maintained. The constraints for value added are given at the A7 industry level. Changes are hence proportionally attributed to each of the underlying industries. Output is modified to using a constant value added-tooutput ratio. Import data (with a breakdown in goods and services) are used as additional constraints for the rows, and final demand and exports (again, of goods and services) to adapt the column sums 7. International data reconciliation 31. Our second test assesses what changes are imposed on a country when its data are balanced in an ICIO framework. We compare how the results from the current balancing procedure used in the TiVA ICIO compare with those that iteratively keep one country s data fixed (NB: in both cases, data have been constrained to the National Accounts). Exports and VS shares are then derived from the different balanced ICIOs, where the A d matrix involved country s domestic IO coefficients and A m is calculated by adding up the imports coefficients from all partners. 32. In essence, we run 57 separate RAS procedures, each time with a new starting point by allowing one and only one country to keep its domestic constraints including total imports, import 7 Note that final consumption expenditure and gross capital formation should be included at basic prices, but that benchmarking to national accounts imposes purchasers price. This will introduce a very small bias: upwards for final demand and downwards for intermediate use. 13

14 partner shares by industry, and aggregated export statistics, whereas other countries data are adjusted in RAS process to come to a balanced ICIO Results 4.1 Test 1: the effect of constraining SUTs to national accounts on Vertical Specialisation 33. The results of our first test is reported in tables 5a and 5b, taking again the examples of the UK and Greece. The tables display value-added and gross outputs by industry both as presented in the original SUTs and after adjustments to the National Accounts. Total export data are presented excluding re-exports (and hence are slightly smaller than those data in Table 2 above, after also adjusting for exchange rates). The tables show that for the UK, the alignment with national accounts data resulted in an increase in value-added and gross outputs, whereas also total exports increased (from 531 million to 544 million USD). For Greece, value added and output decreased, while total exports increased 35%, from 40 million to 54 million (32 and 43 million euros). Table 5a Vertical specialisation calculated from original SUT and SUT after aligns with SNA data, United Kingdom, 2005, USD millions Industry Original SUT SUT benchmarked to national accounts VS share (1) VS (1) VS Difference VA GO VS TEXP VA GO VS TEXP share share (2)-(1) 01T05 13,691 36, , ,791 34, , T14 49,915 71,866 1,965 22, ,089 83,634 2,768 32, T16 40, ,653 2,571 16, , ,761 3,112 19, T19 7,780 19,953 2,698 10, ,007 17,970 2,950 11, T22 40,433 94,146 2,597 11, ,414 84,788 3,290 14, T26 58, ,573 29,553 73, , ,731 32,753 82, T28 29,260 76,775 5,757 18, ,352 69,144 6,945 21, ,264 57,953 8,218 29, ,051 52,193 9,082 31, T33 29,986 78,051 11,326 32, ,005 70,294 12,948 36, T35 29, ,306 27,771 58, ,553 97,541 29,877 61, T37 11,944 30,609 1,496 6, ,757 27,567 1,815 7, T41 30, , , , , , , , , , , T55 295, ,240 7,409 60, , ,477 7,600 61, T64 147, ,012 4,798 35, , ,172 4,725 35, T67 144, ,943 3,981 53, , ,951 3,461 43, T74 472, ,615 5,073 75, , ,025 4,413 60, T95 479, ,777 2,006 21, , ,704 1,583 16, Total 2,030,275 4,105, , , ,046,006 4,152, , , When turning to the calculated VS shares, some differences can be observed when comparing the calculations from the NA benchmarked and original SUTs. For United Kingdom, overall VS share for the entire economy increased 1 percent after alignment with National Accounts. 8 I.e., in each run of the RAS procedure, different subsets of matrix A 0 are fixed 14

15 Also at the industry level, the VS shares based on the adjusted SUT are either 1 percent higher or lower, as compared to the original data. As for Greece, the overall impact of correcting SUT to SNA controlled totals is an upward adjustment of 2 percent. But when examining the details by industry, important differences can be observed: the highest VS share increase can be seen in Transport, storage and communication (ISIC 60T64), at 3 percent. This may be explained by the fact that on one hand, the value-added of Transport, storage and communication decreased nearly 20 percent, while on the other hand both imports and exports in services are heavily adjusted to meet the new totals in SNA. Table 5b Vertical specialisation calculated from original SUT and SUT after aligns with SNA data, Greece, 2005, USD millions Industry Original SUT SUT benchmarked to national accounts VS share (1) VS (1) VS Difference VA GO VS TEXP VA GO VS TEXP share share (2)-(1) 01T05 10,450 16, , ,482 16, , T , ,001 1, T16 5,741 19, , ,552 18, , T19 2,186 5, , ,114 5, , T22 2,000 4, ,934 4, T26 5,117 20,699 2,452 4, ,948 20,018 3,946 7, T28 2,694 11, , ,605 10,676 1,460 3, , , , T , , T , , , T , , T41 5,851 9, ,874 9, ,617 31, ,986 34, T55 51,883 80, , ,374 73, , T64 22,048 37,633 3,418 17, ,382 31,375 4,221 19, T67 10,440 14, ,003 15, T74 30,759 41, ,418 43, , T95 49,991 71, ,688 75, Total 217, ,703 9,498 39, , ,201 14,098 53, Test 2: Effect of different national ICIO balancing starting points on VS and bilateral exports 35. The results of our second test are more elaborate to report. We discuss the findings at various levels of aggregation, moving from the country level, to the country industry level, and finally to the country industry partner level. Country level results 36. The results at the country aggregate level are displayed in table 6. Note that in this case, value-added, output and total exports are exactly the same in full RAS procedure as the partial RAS procedure that constraints to only a single country s national perspective. We observe that the core descriptive statistics of globalisation, i.e. the degree of Vertical Specialization, remains very stable at the total economy level, regardless of the RAS procedure that is used. For 44 out of 57 countries, the differences are less than 1 percent. Only Cambodia, Malta, Israel and the Philippines show slightly higher differences. 15

16 Table 6. VS share for total economies, 2005, USD millions and percent COU VA GO TEXP VS Share full RAS (1) VS Share country view (2) Diff. (2)-(1) COU VA GO TEXP VS Share full RAS (1) VS Share countr y view (2) Diff. (2)-(1) ARG 169, ,816 44, JPN 4,541,977 8,616, , AUS 704,453 1,470, , KHM 5,965 13,116 3, AUT 272, , , KOR 757,619 1,863, , BEL 335, , , LTU 23,491 46,109 14, BGR 24,454 63,497 10, LUX 33,668 94,007 54, BRA 756,762 1,557, , LVA 14,114 32,155 7, BRN 9,341 14,602 6, MEX 823,342 1,451, , CAN 1,056,764 2,039, , MLT 5,161 11,205 4, CHE 350, , , MYS 135, , , CHL 111, ,817 47, NLD 567,306 1,196, , CHN* 2,257,006 6,761, , NOR 268, , , CYP 15,193 25,485 6, NZL 108, ,563 29, CZE 111, ,564 80, PHL 101, ,573 45, DEU 2,516,901 5,054, , POL 267, , , DNK 218, , , PRT 165, ,513 49, ESP 1,012,008 2,199, , ROU 87, ,418 30, EST 12,304 30,393 10, RUS 654,694 1,308, , FIN 169, ,125 79, SAU 309, , , FRA 1,914,994 3,851, , SGP 119, , , GBR 2,030,279 4,104, , SVK 42, ,255 35, GRC 217, ,703 54, SVN 31,310 71,367 21, HKG 174, ,894 79, SWE 324, , , HUN 94, ,332 64, THA 158, , , IDN 280, ,699 93, TUR 425, ,308 99, IND 781,681 1,602, , TWN 354, , , IRL 177, , , USA 11,695, ,606,56 6 1,203, ISL 13,518 28,674 4, VNM 51, ,659 34, ISR 122, ,553 54, ZAF 220, ,096 64, ITA 1,597,329 3,472, , Note that the VS share is similar to TiVA Foreign valued-added content as a share of exports. *Chinese VS is 8 percent lower than TiVA foreign value-added content as a share of exports. The main differences come from how the three Chinese production component are aggregated. 16

17 Country-Industry level results 37. When we explore the findings at a more detailed level, by focusing on the results by industry, we continue to find that the VS measure is rather robust to different balancing starting points. We present here again the results for the UK and Greece, but findings for other countries are available upon request. Table 7a, for the UK, shows that the VS shares that are derived from a full (global) balancing (RAS) of the ICIO are not much different from the results we obtain when keeping the UK data fixed in the final balancing. The overall results of VS are 20.2 percent versus 21.1 percent, respectively. When looking at the industry level, we see that the highest differences are associated with the industries with relatively high VS shares, such as Chemicals and minerals (23T26) and Transport equipment (34T35). For Greece, we find that the VS share when using the Greek data fixed equals 24 percent, or only 0.2 percent higher compared to the results of the full (global) RAS procedure (Table 7b). The industry of Transport equipment (34T35) showed the largest difference, with a 3 percent downwards adjustment. Table 7a. United Kingdom, Value-added, Gross outputs, exports and VS share by industry, 2005 Industry VA GO TEXP VS share full RAS (1) VS country perspective (2) Difference (2)- (1) 01T05 13,691 36,615 3, T14 49,915 71,866 28, T16 40, ,655 18, T19 7,780 19,953 11, T22 40,433 94,147 15, T26 58, ,576 91, T28 29,261 76,776 23, ,264 57,954 33, T33 29,986 78,052 38, T35 29, ,307 63, T37 11,944 30,610 6, T41 30, , , ,205 1, T55 296, ,312 43, T64 146, ,623 30, T67 144, ,944 45, T74 472, ,617 69, T95 479, ,781 16, Total 2,030,279 4,104, ,

18 Table 7b. Greece, Value-added, Gross outputs, exports and VS share by industry, 2005 VS share full VS country Difference (2)- ISIC VA GO TEXP RAS (1) perspective (2) (1) 01T05 10,450 16,773 2, T , T16 5,741 19,410 2, T19 2,186 5,187 1, T22 2,000 4, T26 5,117 20,699 5, T28 2,694 11,039 3, , T , T , T , T41 5,852 9, ,617 31, T55 51,883 80,896 7, T64 22,048 37,633 24, T67 10,440 14, T74 30,759 41,004 2, T95 49,991 71, TOTAL 217, ,703 54, When summarizing the results for all 57 countries and 18 industries (i.e. for 1026 observations) and comparing the results from the overall RAS procedure as is used in TiVA, and the procedures that is constraint to each individual national country constant, we see that in three quarters of the cases, the difference is less than 1% (positively or negatively), as displayed in Table 8 (rows 2, 3, and 4). This table also shows on average, the full RAS procedure has a pushes down the VS shares (i.e. gives conservative estimates of the importance of imports for exports): in the majority of cases (rows 4 and 5) the country-industry level results are smaller in the full RAS procedure than when keeping than original country values fixed as in the partial RAS procedures. Table 8. VS as a share of exports - the difference between full RAS and national perspectives VS share from RAS with country fixed perspective, minus VS from full RA N Percent (1) less than % (2) greater or equal to but less than % (3) equal to % (4) greater than 0 but less or equal to % (5) greater than % Total % 18

19 Country-Industry-Partner level results 39. As a final step in our analysis, we examine the results at the partner level. It is important to realize that VS shares do not vary beyond the industry level, since industry production functions are not assumed to be dependent upon the export destination. For example, Chinese textiles exports to the US and to South Africa have the same import content, since the same textiles industry produces these garments. Therefore, in this section, we focus on the effects of the different balancing procedures on bilateral export relationships. 40. Table 9 provides an example of the results for the exports of the UK to the Netherlands. (Again, detailed results for other countries are available upon request). In the table, we compare the full RAS (i.e., the global, TiVA perspective) with the UK perspective which is the solution when the UK s data are kept constant when balancing the ICIO, and the Dutch perspective, which gives the solution when the Dutch data are kept fixed during RAS. This shows that the full RAS solution, when redistributing discrepancies proportionally, can come to a value of exports that is higher (or lower) than reported by either the UK or the Netherlands. Take for example Chemicals and minerals (23T26): table 9 shows that from the UK perspective, the exports to the Netherlands are 4.5 billion, while the Netherlands data reports 3.9 billion. The global RAS finds a solution higher than both countries starting points, at 4.9 billion. Table 9. RAS bilateral solutions for UK exports to Netherlands (Netherlands imports from UK) Ind Full RAS GBR Perspective NLD Perspective 19 Stats for exports (excl. GBR and NLD perspectives)* Mean SD Min Max 01T T T T T T T T T T T T T T T T Note: RAS bilateral solutions for other country-partner are available upon request. 41. The second (right-hand) half of the table shows the statistics for the value of UK-NL exports by summarizing all 57 partial RAS solutions (excluding the Dutch and British perspectives). This

20 shows that the mean estimate from those 55 solutions is also around 4.9 billion, with relatively small standard deviations. The minimum and maximum values shows in the table for Chemicals and minerals are achieved when respectively fixing Viet Nam, and France. The interpretation of this is as follows: when fixing the data using a French perspective, the exports to France given by the initial estimate is lower than expected, meaning that the solution for other countries should be higher. Similarly, while the original exports data for Viet Nam are is higher than expected, the solution for other countries should be lower. The degree of the adjustment in the partial RAS in the end depends on the starting position (i.e. sum of exports is greater or smaller than the constraints) and which number we choose to hold. 42. When examining the results for all 57 TiVA countries (and partner countries) and all 18 industries, we find that in nearly all instances (98%), the estimated value of bilateral exports relations subject to RAS with respect to different benchmark countries, the majority of the data (98%) reject the hypothesis that it is significantly different from the mean estimates when the wide definition is used (i.e. including the observations of exports from a country perspective and from a partner perspective). The standard deviations of bilateral trade are also fairly narrow a small tails for the distributions: 69% of the observation has the deviation within ±10% of their means; and an additional 17% within ±20% boundaries 5. Discussion and conclusion 43. The aim of this study was to offer insights into the stability of TiVA indicators that are derived from the ICIO, with a focus on the measure of Vertical Specialization (as introduced by Hummels et al, 2001) and bilateral export relationships. We highlighted that given the prevalence of trade asymmetries and national data asymmetries, the process of constructing an ICIO means that many decisions have to be taken regarding the correct statistical approach to combining and harmonizing the conflicting data, nationally and internationally. We in particular examined two steps in the ICIO construction process in more detail for their consequences for both VS and bilateral export relationships, which is the benchmarking of national SUTs to national accounts, and the final RAS procedure to balance the ICIO. 44. The first comparison involved the question of whether or not to benchmark national SUTs and IOs to national account aggregates. We found that the effects of restricting a country s SUT to SNA data has only small effects at either the country or country-industry level, with the majority of variations being less than 1% of the final VS share in exports. It should be highlighted though that VS shares continue to be sensitive to how the data is converted from SUT to IO tables, including e.g. the way in which the aggregation from products to industries is performed, how adjustments are made to move from purchasers to basic prices, and the treatment of negative values and outliers. Still, the relative robustness of the main indicators to sometimes quite substantive revisions in national accounts data should not only reassure users of TiVA indicators, but also gives a positive signal regarding the feasibility of a related project, which is the nowcasting or projection of the ICIO in order to derive more timely TiVA indicators (see e.g. STD/CSSP/WPTGS(2015)22). 45. The second test involved comparing the balanced TiVA results for VS and bilateral exports ( full RAS ) with the results that are obtained when constraining the RAS procedure in such a way that the ICIO that is created fully reflects the national statistics of one single country, while permitting 20