unclassified std/cssp/wptgs(2015)24 - oecd

Unclassified STD/CSSP/WPTGS(2015)24 Organisation de Coopération et de Développement Économiques Organisation for Economic Co-operation and Development 23-Mar-2015

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_____________ 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

24-26 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 ([email protected]) or Fabienne Fortanier

([email protected] )

JT03372904

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.

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STD/CSSP/WPTGS(2015)24

2

THE STABILITY OF TIVA ESTIMATES: TESTING DIFFERENT METHODS OF

RECONCILING CONTRADICTORY OFFICIAL DATA

Guannan Miao and Fabienne Fortanier1

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.


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 balanced2 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.


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)

Reporter Partner

Exports Imports Trade Balance

Merchandise

trade TiVA

Merchandise

trade TiVA

Merchandise

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 325 317 3,421 4,666 -3,096 -4,349

Germany 2,183 2,028 8,149 9,020 -5,966 -6,992

Belgium 283 173 2,634 1,938 -2,351 -1,765

France 727 919 3,617 4,367 -2,890 -3,448

USA 935 1,042 1,884 2,547 -949 -1,505

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 involved3. 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.


5

2.1 SUTs and National Accounts

10. Our first example compares Supply and Use tables with data from National Accounts, for

the year 2005. 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 non-

resident 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) 1,084,211

169,662

Household FCE* 815,938 814,964 1.001 134,725 149,143 0.903

General government FCE 268,273 268,088 1.001 34,937 33,225 1.052

Gross capital formation 213,938

41,322

Gross fixed capital formation** 209,689 209,381 1.001 40,020 38,873 1.030

Changes in inventories 4,249 4,472 0.950 1,302 -402 -3.240

Exports of goods and services 340,424 330,794 1.029 44,807 32,861 1.364

Exports of goods 217,476 213,536 1.018 20,490 16,337 1.254

Exports of services 122,948 117,258 1.049 24,317 16,524 1.472

Imports of goods and services 375,862 373,641 1.006 62,741 58,881 1.066

Imports of goods 281,850 293,862 0.959 51,875 50,556 1.026

Imports of services 94,012 79,779 1.178 10,866 8,325 1.305

Gross Domestic Product (GDP) 1,262,710

193,050

Total Value Added*** 1,125,300 1,116,648 1.008 172,595 174,624 0.988

Agriculture, hunting, forestry, fishing (A-B) 7,035 7,530 0.934 8,428 8,403 1.003

Mining, Manufacturing, Utilities (C-E) 184,756 192,249 0.961 21,957 22,494 0.976

Manufacturing (D) 133,390 148,111 0.901 16,428 16,987 0.967

Construction (F) 82,112 69,868 1.175 12,050 10,949 1.101

Wholesale, retail, restaurants and hotels (G-H) 193,664 162,712 1.190 38,094 41,720 0.913

Transport, storage and communication (I) 95,232 80,889 1.177 14,781 17,729 0.834

Other Activities (J-P) 562,501 603,400 0.932 77,284 73,327 1.054

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


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)


CPA

Merchandise

Trade (MT) SUT

Ratio

(MT/SUT)

Merchandise

Trade (MT) SUT

Ratio

(MT/SUT)

01 1,422 1,270 1.120 1,219 1,380 0.883

02 52 52 1.000 6 8 0.750

05 369 400 0.923 293 346 0.847

10 52 44 1.182 5 8 0.625

11 12,015 11,830 1.016 - - -

13 17 -

32 32 1.000

14 7,156 5,088 1.406 116 117 0.991

15 9,325 10,234 0.911 1,841 2,040 0.902

16 647 684 0.946 142 142 1.000

17 2,867 3,183 0.901 624 651 0.959

18 2,306 3,843 0.600 1,276 1,744 0.732

19 897 1,441 0.622 59 65 0.908

20 338 354 0.955 66 67 0.985

21 2,206 2,425 0.910 112 129 0.868

22 4,220 3,379 1.249 128 101 1.267

23 8,718 10,463 0.833 1,345 2,363 0.569

24 35,023 33,574 1.043 1,972 2,036 0.969

25 5,578 5,029 1.109 460 465 0.989

26 2,071 1,983 1.044 357 374 0.955

27 9,749 10,936 0.891 1,440 1,661 0.867

28 4,277 4,202 1.018 382 378 1.011

29 19,546 18,630 1.049 569 575 0.990

30 10,091 9,808 1.029 60 19 3.158

31 6,982 6,739 1.036 391 454 0.861

32 11,814 19,010 0.621 252 306 0.824

33 8,223 8,537 0.963 129 138 0.935

34 23,539 21,736 1.083 199 211 0.943

35 13,380 13,773 0.971 336 361 0.931

36 4,536 4,717 0.962 140 154 0.909

37 3 -

1 - -

40 101 166 0.608 8 15 0.533

Total 207,521 213,530 0.972 13,959 16,337 0.854

Data sources: Merchandise trade data as reported to UN Comtrade, SUT as reported to Eurostat.


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

CPA

code

Merchandise

Trade

End use category as % of total

merchandise imports

SUT

End use category as % of

total SUT imports

Intermediate

Final

demand Dual*

Intermediate

Final

demand

01 6,526 24% 76% - 6,767 17% 83%

02 149 100% - - 135 43% 57%

05 287 4% 96% - 280 50% 50%

10 1,909 100% - - 1,624 98% 2%

11 13,683 100% - - 13,136 98% 2%

13 932 100% - - - - -

14 4,707 9% - 91% 1,449 99% 1%

15 19,815 15% 85% - 23,584 36% 64%

16 239 3% 97% - 1,613 - 100%

17 6,225 36% 64% - 6,275 27% 73%

18 9,990 - 100% - 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 8,165 100% - - 12,085 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 9,974 100% - - 7,245 100% -

28 6,115 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%

37 5 100% - - - - -

40 442 100% - - 466 69% 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.


8

Table 4b. Differences between merchandise trade statistics and SUTs for imports by product and end-

use category*, in millions national currency, 2005, Greece

CPA

code

Merchandise

Trade

End use category as % of total

merchandise imports

SUT

End use category as % of

total SUT imports

Intermediate

Final

demand Dual*

Intermediate

Final

demand

01 1,228 63% 37% - 1,378 54% 46%

02 30 100% 0% - -98 65% 35%

05 82 1% 99% - 92 13% 87%

10 43 100% 0% - 48 100% 0%

13 15 100% 0% - 15 100% 0%

14 87 97% 0% 3% 159 100% 0%

15 3,913 20% 80% - 3,271 23% 77%

16 193 13% 87% - 414 1% 99%

17 1,056 57% 43% - 1,533 50% 50%

18 1,408 8% 92% - 1,688 4% 96%

19 634 5% 95% - 1,103 5% 95%

20 469 94% 6% - 509 94% 6%

21 981 77% 23% - 1,488 75% 25%

22 338 60% 40% - 292 21% 79%

23 1,740 100% 0% - 1,824 63% 37%

24 6,975 51% 12% 36% 6,567 64% 36%

25 1,002 81% 19% - 981 55% 45%

26 676 81% 19% - 762 71% 29%

27 2,365 100% 0% - 2,504 100% 0%

28 869 58% 42% - 896 61% 39%

29 3,062 27% 73% - 4,537 40% 60%

30 923 36% 8% 56% 882 8% 92%

31 918 61% 39% - 904 82% 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%

37 0 100% 0% - - - -

40 131 100% 0% - 132 70% 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


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) SUT

Ratio

(BOP/SUT)

BOP total

services (S200) SUT

Ratio

(BOP/SUT)

Export

s 114,219 117,258 0.97

31,095 16,524 1.88

Import

s 89,622 79,779 1.12

13,381 8,325 1.61

3. 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.


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 A0 with row sums u0 and column sums v0, 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 input-

output 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));


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 A0 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 A0 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 A0. 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 A0 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.


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 = 𝐴𝑚[𝐼 − 𝐴𝐷]−1𝐸

25. In this formula, 𝐴𝑚 is the n x n imported coefficient matrix, I is the identity matrix, and 𝐴𝐷

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) value-

added 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.


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-to-

output 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 sums7.

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 𝐴𝑑 matrix involved country’s domestic IO coefficients and 𝐴𝑚 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.


14

partner shares by industry, and aggregated export statistics, whereas other countries’ data are adjusted

in RAS process to come to a balanced ICIO8.

4. 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

Difference

(2)-(1) VA GO VS TEXP (1) VS

share VA GO VS TEXP

(1) VS

share

01T05 13,691 36,614.66 435.55 3,051 0.14 12,791 34,208 555 4,012 0.14 0.00

10T14 49,915 71,866 1,965 22,800 0.09 58,089 83,634 2,768 32,768 0.08 0.00

15T16 40,033 119,653 2,571 16,091 0.16 36,054 107,761 3,112 19,376 0.16 0.00

17T19 7,780 19,953 2,698 10,513 0.26 7,007 17,970 2,950 11,306 0.26 0.00

20T22 40,433 94,146 2,597 11,666 0.22 36,414 84,788 3,290 14,701 0.22 0.00

23T26 58,111 189,573 29,553 73,699 0.40 52,335 170,731 32,753 82,412 0.40 0.00

27T28 29,260 76,775 5,757 18,047 0.32 26,352 69,144 6,945 21,697 0.32 0.00

29 22,264 57,953 8,218 29,060 0.28 20,051 52,193 9,082 31,506 0.29 0.01

30T33 29,986 78,051 11,326 32,846 0.34 27,005 70,294 12,948 36,787 0.35 0.01

34T35 29,484 108,306 27,771 58,210 0.48 26,553 97,541 29,877 61,944 0.48 0.01

36T37 11,944 30,609 1,496 6,178 0.24 10,757 27,567 1,815 7,429 0.24 0.00

40T41 30,336 108,742 88 689 0.13 35,304 126,550 136 1,126 0.12 -0.01

45 127,033 327,205 168 1,581 0.11 149,295 384,546 148 1,510 0.10 -0.01

50T55 295,841 574,240 7,409 60,797 0.12 352,118 683,477 7,600 61,097 0.12 0.00

60T64 147,071 328,012 4,798 35,431 0.14 173,149 386,172 4,725 35,018 0.13 0.00

65T67 144,642 294,943 3,981 53,599 0.07 134,838 274,951 3,461 43,939 0.08 0.00

70T74 472,940 731,615 5,073 75,359 0.07 440,883 682,025 4,413 60,499 0.07 0.01

75T95 479,512 856,777 2,006 21,353 0.09 447,011 798,704 1,583 16,760 0.09 0.00

Total 2,030,275 4,105,032 117,911 530,970 0.22 2,046,006 4,152,255 128,159 543,885 0.24 0.01

34. 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 A0 are fixed


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

Difference

(2)-(1) VA GO VS TEXP (1) VS

share VA GO VS TEXP

(1) VS

share

01T05 10,450 16,773 241 1,623 0.15 10,482 16,823 461 2,989 0.15 0.01

10T14 998 1,863 8 152 0.05

1,001 1,870 15 273 0.05 0.00

15T16 5,741 19,410 455 2,136 0.21 5,552 18,772 843 3,770 0.22 0.01

17T19 2,186 5,187 516 1,808 0.29

2,114 5,016 788 2,639 0.30 0.01

20T22 2,000 4,970 58 228 0.26 1,934 4,806 112 426 0.26 0.01

23T26 5,117 20,699 2,452 4,519 0.54

4,948 20,018 3,946 7,166 0.55 0.01

27T28 2,694 11,039 906 2,241 0.40 2,605 10,676 1,460 3,481 0.42 0.02

29 891 2,192 251 704 0.36

862 2,120 383 1,037 0.37 0.01

30T33 897 2,335 187 564 0.33 867 2,258 291 847 0.34 0.01

34T35 815 2,045 238 706 0.34

788 1,978 358 1,035 0.35 0.01

36T37 786 2,031 42 147 0.29 760 1,964 78 266 0.29 0.01

40T41 5,851 9,215 0 3 0.10

5,874 9,250 1 6 0.10 0.00

45 13,617 31,015 74 286 0.26 14,986 34,132 132 492 0.27 0.01

50T55 51,883 80,896 560 5,373 0.10

47,374 73,866 839 7,420 0.11 0.01

60T64 22,048 37,633 3,418 17,704 0.19 18,382 31,375 4,221 19,023 0.22 0.03

65T67 10,440 14,683 14 357 0.04

11,003 15,475 26 595 0.04 0.01

70T74 30,759 41,004 46 966 0.05 32,418 43,217 86 1,584 0.05 0.01

75T95 49,991 71,714 33 329 0.10

52,688 75,583 58 540 0.11 0.01

Total 217,161 374,703 9,498 39,846 0.24 214,638 369,201 14,098 53,589 0.26 0.02

4.2 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.


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 169,726 325,816 44,482 0.128 0.127 -0.001

JPN 4,541,977 8,616,279 634,222 0.15 0.149 -0.001

AUS 704,453 1,470,719 144,050 0.124 0.123 -0.001

KHM 5,965 13,116 3,831 0.38 0.413 0.033

AUT 272,865 556,281 141,099 0.318 0.32 0.002

KOR 757,619 1,863,670 320,872 0.365 0.358 -0.007

BEL 335,571 783,109 215,356 0.415 0.418 0.003

LTU 23,491 46,109 14,210 0.38 0.365 -0.015

BGR 24,454 63,497 10,819 0.307 0.296 -0.011

LUX 33,668 94,007 54,593 0.56 0.561 0.001

BRA 756,762 1,557,087 133,091 0.129 0.128 -0.001

LVA 14,114 32,155 7,241 0.287 0.299 0.012

BRN 9,341 14,602 6,606 0.067 0.081 0.014

MEX 823,342 1,451,870 218,745 0.304 0.311 0.007

CAN 1,056,764 2,039,851 413,907 0.257 0.261 0.004

MLT 5,161 11,205 4,262 0.394 0.424 0.03

CHE 350,577 693,817 175,001 0.292 0.29 -0.002

MYS 135,195 327,625 156,397 0.421 0.431 0.01

CHL 111,473 232,817 47,267 0.165 0.168 0.003

NLD 567,306 1,196,376 302,615 0.341 0.333 -0.008

CHN* 2,257,006 6,761,571 802,684 0.285 0.285 0

NOR 268,832 491,376 132,813 0.139 0.143 0.004

CYP 15,193 25,485 6,129 0.151 0.172 0.021

NZL 108,881 234,563 29,972 0.191 0.191 0

CZE 111,667 303,564 80,222 0.409 0.411 0.002

PHL 101,010 207,573 45,151 0.457 0.424 -0.033

DEU 2,516,901 5,054,014 973,072 0.268 0.269 0.001

POL 267,759 603,798 107,576 0.304 0.311 0.007

DNK 218,255 443,296 109,374 0.306 0.308 0.002

PRT 165,251 360,513 49,889 0.264 0.271 0.007

ESP 1,012,008 2,199,966 261,389 0.266 0.271 0.005

ROU 87,599 188,418 30,887 0.276 0.282 0.006

EST 12,304 30,393 10,294 0.472 0.48 0.008

RUS 654,694 1,308,870 262,308 0.083 0.084 0.001

FIN 169,950 377,125 79,521 0.331 0.335 0.004

SAU 309,271 442,592 189,229 0.034 0.035 0.001

FRA 1,914,994 3,851,835 539,316 0.248 0.253 0.005

SGP 119,724 340,359 160,827 0.518 0.518 0

GBR 2,030,279 4,104,732 542,935 0.202 0.211 0.009

SVK 42,511 107,255 35,974 0.476 0.483 0.007

GRC 217,161 374,703 54,614 0.217 0.235 0.018

SVN 31,310 71,367 21,209 0.405 0.42 0.015

HKG 174,770 403,894 79,747 0.266 0.259 -0.007

SWE 324,123 692,253 168,971 0.322 0.327 0.005

HUN 94,323 227,332 64,929 0.483 0.485 0.002

THA 158,717 403,514 124,172 0.385 0.385 0

IDN 280,151 569,699 93,694 0.177 0.187 0.01

TUR 425,515 903,308 99,871 0.209 0.212 0.003

IND 781,681 1,602,346 154,264 0.181 0.179 -0.002

TWN 354,569 857,197 217,972 0.422 0.4 -0.022

IRL 177,717 412,321 158,334 0.446 0.447 0.001

USA 11,695,10

0

21,606,56

6

1,203,11

8 0.132 0.134 0.002

ISL 13,518 28,674 4,912 0.362 0.388 0.026

VNM 51,859 120,659 34,742 0.352 0.35 -0.002

ISR 122,407 249,553 54,933 0.373 0.343 -0.03

ZAF 220,317 509,096 64,696 0.165 0.175 0.01

ITA 1,597,329 3,472,846 457,535 0.272 0.271 -0.001

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.


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 3,967 0.118 0.123 0.005

10T14 49,915 71,866 28,282 0.082 0.089 0.007

15T16 40,033 119,655 18,687 0.146 0.151 0.005

17T19 7,780 19,953 11,656 0.216 0.219 0.003

20T22 40,433 94,147 15,281 0.198 0.207 0.009

23T26 58,112 189,576 91,915 0.332 0.354 0.022

27T28 29,261 76,776 23,512 0.264 0.274 0.010

29 22,264 57,954 33,796 0.236 0.245 0.009

30T33 29,986 78,052 38,832 0.275 0.281 0.006

34T35 29,484 108,307 63,260 0.376 0.389 0.013

36T37 11,944 30,610 6,587 0.218 0.225 0.007

40T41 30,337 108,742 944 0.106 0.118 0.012

45 127,033 327,205 1,239 0.096 0.100 0.004

50T55 296,909 576,312 43,055 0.111 0.114 0.003

60T64 146,000 325,623 30,861 0.123 0.129 0.006

65T67 144,643 294,944 45,023 0.061 0.064 0.003

70T74 472,941 731,617 69,106 0.073 0.076 0.003

75T95 479,514 856,781 16,932 0.077 0.079 0.002

Total 2,030,279 4,104,732 542,935 0.202 0.211 0.009


18

Table 7b. Greece, Value-added, Gross outputs, exports and VS share by industry, 2005

ISIC VA GO TEXP

VS share full

RAS (1)

VS country

perspective (2)

Difference (2)-

(1)

01T05 10,450 16,773 2,192 0.112 0.121 0.009

10T14 998 1,863 - 0.132 0.143 0.011

15T16 5,741 19,410 2,336 0.182 0.191 0.009

17T19 2,186 5,187 1,548 0.217 0.219 0.002

20T22 2,000 4,970 335 0.206 0.214 0.008

23T26 5,117 20,699 5,925 0.439 0.529 0.090

27T28 2,694 11,039 3,329 0.357 0.375 0.018

29 891 2,193 724 0.242 0.254 0.012

30T33 897 2,335 775 0.266 0.276 0.010

34T35 815 2,045 559 0.273 0.300 0.027

36T37 786 2,031 186 0.231 0.236 0.005

40T41 5,852 9,215 43 0.094 0.108 0.014

45 13,617 31,015 563 0.209 0.222 0.013

50T55 51,883 80,896 7,293 0.100 0.103 0.003

60T64 22,048 37,633 24,707 0.214 0.225 0.011

65T67 10,440 14,683 827 0.047 0.047 0.000

70T74 30,759 41,004 2,367 0.055 0.055 0.000

75T95 49,991 71,714 905 0.093 0.095 0.002

TOTAL 217,161 374,703 54,614 0.217 0.235 0.018

38. 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 0.01 71 7%

(2) greater or equal to -0.01 but less than 0 168 16%

(3) equal to 0 96 9%

(4) greater than 0 but less or equal to 0.01 499 49%

(5) greater than 0.01 192 19%

Total. 1026 100%


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”

Stats for exports (excl. GBR and NLD perspectives)*

Mean SD Min Max

01T05 145.0 133.6 109.9 145.8 1.8 145.0 155.1

10T14 2267.0 2105.4 2292.1 2266.8 23.3 2147.0 2374.5

15T16 731.4 692.7 658.4 733.3 3.2 730.7 746.5

17T19 189.2 186.6 127.8 190.4 2.1 189.2 199.5

20T22 584.0 598.5 377.9 587.5 7.5 584.0 621.5

23T26 4855.2 4506.1 3901.2 4877.1 49.0 4855.0 5101.8

27T28 563.7 551.1 612.1 562.9 1.9 553.0 564.2

29 693.9 701.9 640.6 695.0 3.4 692.9 718.1

30T33 1067.1 1055.9 1147.1 1065.8 2.8 1052.4 1068.4

34T35 2533.6 2391.4 1312.7 2557.8 44.6 2533.8 2756.5

36T37 180.0 184.5 230.9 179.0 2.2 169.4 181.3

40T41 46.1 41.0 92.4 45.0 4.2 21.8 47.7

45 88.9 84.5 143.4 87.8 3.6 67.4 90.1

50T55 2770.3 2818.2 1815.9 2783.8 22.2 2764.8 2882.9

60T64 944.9 964.8 1111.3 942.0 5.9 913.9 945.2

65T67 2849.4 2789.6 3455.4 2840.4 26.7 2656.3 2855.6

70T74 3269.1 3383.8 4853.9 3239.6 70.1 2774.9 3268.9

75T95 662.2 639.9 979.0 657.8 6.6 625.8 663.0

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


21

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) (‘partial RAS’). The first and

foremost important conclusion can be drawn from this experiment is that the full RAS procedure as

used in TiVA, and the partial RAS procedures that iteratively holds one country’s data constant,

converge to the same solution. The ratio of Vertical Specialization as a share of exports is stable at

both the country and at the country-industry level. For the majority of the countries (46 out of 57) and

country-industry pairs (74%) in question, the differences of full RAS and partial RAS results is less

than 1%. Also the results for total exports proved to be stable, even if in the context of trade

asymmetries, the estimates could differ from the data reported by countries and partners involved.

Implications for trade statistics

46. Data discrepancies, both nationally and internationally, remain one of the big obstacles for

TiVA to tackle. It has been highlighted many times in this paper: at the aggregate level SUT/SNA

definitions and values differ from those in merchandise trade and trade in service statistics; at bilateral

level, trade asymmetries are the rule rather than the exception. Continued efforts to better understand

and if possible reduce these asymmetries are vital. Also the trade balancing process within TiVA can

be improved. At the moment, the total exports by industry (as given by the national IO table) are used

as constraints in the ICIO. Subsequently, partner breakdowns are produced using import partner

shares. Clearly, in the presence of asymmetries, the summation of imports from all partners will result

in different export figures than observed from the bilateral exports recorded in the merchandise and

services trade statistics. Future work on balancing trade statistics can consider information on both

exports and imports in combination with information from SUTs, which provide insights into the use

of imported products by industry.


22

REFERENCES

Allen, R. (1974) ‘Some Experiments with the RAS Method of updating Input-Output Coefficients’,

Oxford Bulletin of Economics and Statistics, 36, 217-228.

Allen, R. and Lecomber, J (1975) Some Tests on a Generalised Version of RAS.

Bacharach, M (1970) Biproportional Matrices and Input-Output Change. Cambridge, UK, Cambridge

University Press.

Battern, D. and Martellato (1985) Classical Versus Modern Approaches to Interregional Input-Output

Analysis

Eurostat (2008) Eurostat Manual of Supply, Use and Input-Output Tables.

Gilchrist, D. and St Louise, L. (1999) ‘Completing Input-Output Tables Using Partial Information,

with an Application to Canadian Data’, Economics Systems Research, 11, 185-193.

Gilchrist, D. and St Louise, L. (2004) ‘An Algorithm for the consistent Inclusion of Partial

Information in the revision of Input-Output tables’, Economic Systems Research, 16, 149-156.

Hammels, D., Ishii, J. and Yi, Kei-Mu (2001) ‘The Nature and Growth of Vertical Specialization in

World Trade’, Journal of International Economics 54(1): 75-96.

Jackson , R. and Comer J. (1993) ‘An alternative to Aggregated Base Tables in Input-Output Table

Regionalisation’, Growth and Change, 24, 191-205.

Junius, T. and Oosterhaven, J. (2003) ‘The Solution of Updating a Matrix with Both Positives and

Negatives Entries’, Economic Systems Research, 15, 87-96.

Lahr, M (2001) A strategy for Producing Hybrid regional Input-Output Tables.

Lahr, M and de Mesnard L. (2004) ‘Biproportional Techniques in Input-Output Analysis: Table

Updating and Structural Analysis’, Economic Systems Research, 16, 115-134.

Lecomber, J (1975) A critique of Methods of Adjusting, Updating and Projecting Matrices, Together

with Some New Proposals.

Lenzen, M. Gallego, B. and Wood, R. (2006) ‘A Flexible Approach to Matrix Balancing under

Partial Information’, Journal of Applied Input-Output Analysis, 11-12, 1:24.

Lenzen, M. Wood, R. and Gallego, B. (2007) ‘Some comments on the GRAS Method’, Economic

Systems Research, 19, 461-465.

Lenzen, M. Gallego, B. and Wood, R. (2009) ‘Matrix Balancing under Conflicting Information’,

Economic Systems Research, 21:1, 23-44.


23

Leontief, W. (1941) The structure of American Economy, 1919-1929: An Empirical Application of

Equilibrium Analysis, Cambridge, UK, Cambridge University Press.

Miller, R. E. and Blair, P. D. (1985, 2009) Input-Output Analysis – Foundations and Extensions. 1ed

and 2ed. Cambridge University Press.

Oosterhaven, J., Piek G. and Stelder, D (1986) ‘Theory and Practice of Updating Regional versus

Interregional Interindustry Tables’, Papers of the Regional Science Association, 59, 57-72

Stone, R (1961) Input-Output and National Accounts, Organization for European Economic

Cooperation, Paris

Stone, R. and Brown, A. (1962) A Computable Model of Economic Growth, London, Chapman and

Hall.


24

ANNEX 1. PRODUCT AND INDUSTRY CLASSIFICATIONS

CPA codes and products definition for goods

Code Description

01 Products of agriculture, hunting and related services

02 Products of forestry, logging and related services

05 Fish and other fishing products; services incidental of fishing

10 Coal and lignite; peat

11 Crude petroleum and natural gas; services incidental to oil and gas extraction excluding surveying

12 Uranium and thorium ores

13 Metal ores

14 Other mining and quarrying products

15 Food products and beverages

16 Tobacco products

17 Textiles

18 Wearing apparel; furs

19 Leather and leather products

20 Wood and products of wood and cork (except furniture); articles of straw and plaiting materials

21 Pulp, paper and paper products

22 Printed matter and recorded media

23 Coke, refined petroleum products and nuclear fuels

24 Chemicals, chemical products and man-made fibres

25 Rubber and plastic products

26 Other non-metallic mineral products

27 Basic metals

28 Fabricated metal products, except machinery and equipment

29 Machinery and equipment n.e.c.

30 Office machinery and computers

31 Electrical machinery and apparatus n.e.c.

32 Radio, television and communication equipment and apparatus

33 Medical, precision and optical instruments, watches and clocks

34 Motor vehicles, trailers and semi-trailers

35 Other transport equipment

36 Furniture; other manufactured goods n.e.c.

37 Secondary raw materials

40 Electrical energy, gas, steam and hot water

TiVA May 2013 industry classification

Index ISIC Rev.3 Description

1 01T05 Agriculture, hunting, forestry and fishing

2 10T14 Mining and quarrying

3 15T16 Food products, beverages and tobacco

4 17T19 Textiles, textile products, leather and footwear

5 20T22 Wood, paper, paper products, printing and publishing

6 23T26 Chemicals and non-metallic mineral products

7 27T28 Basic metals and fabricated metal products

8 29 Machinery and equipment, nec

9 30T33 Electrical and optical equipment

10 34T35 Transport equipment

11 36T37 Manufacturing nec; recycling

12 40T41 Electricity, gas and water supply

13 45 Construction

14 50T55 Wholesale and retail trade; Hotels and restaurants

15 60T64 Transport and storage, post and telecommunication

16 65T67 Financial intermediation

17 70T74 Real estate, renting and business activities

18 75T95 Community, social and personal services


25

ANNEX 2. NUMERICAL EXAMPLE OF RAS PROCEDURE

d e f Current sum DESIRED sum

a 5.0 8.0 3.0 16.0 19.0

b 6.0 8.0 9.0 23.0 21.0

c 8.0 5.0 5.0 18.0 20.0

Current sum 19.0 21.0 17.0

DESIRED sum 20.0 24.0 16.0


a 5.9 9.5 3.6 19.0 19.0

b 5.5 7.3 8.2 21.0 21.0

c 8.9 5.6 5.6 20.0 20.0

Current sum 20.3 22.4 17.3

DESIRED sum 20.0 24.0 16.0


a 5.8 10.2 3.3 19.3 19.0

b 5.4 7.8 7.6 20.8 21.0

c 8.8 6.0 5.1 19.8 20.0

Current sum 20.3 22.4 17.3

DESIRED sum 20.0 24.0 16.0


a 5.7 10.0 3.2 19.0 19.0

b 5.4 7.9 7.6 21.0 21.0

c 8.8 6.0 5.2 20.0 20.0

Current sum 20.0 23.9 16.0

DESIRED sum 20.0 24.0 16.0


a 5.7 10.0 3.2 19.0 19.0

b 5.4 7.9 7.6 21.0 21.0

c 8.8 6.0 5.2 20.0 20.0

Current sum 20.0 24.0 16.0

DESIRED sum 20.0 24.0 16.0

iteration 4) Align matrix content (green) with the desired totals of rows using the proportional

method (e.g. for cell a/d in the topleft corner, we calculate (5.7 / 20.0)*20)

=> now, the current sums of the rows and columns in the matrix are equal to the desired sums (at

1 digit). No more iterations are needed.

Original matrix (in green) with its current sums of all columns and sums of all rows, with the new

desired (in blue) sums of all columns and sums of all rows

iteration 1) Align matrix content (green) with the desired totals of columns using the proportional

method (e.g. for cell a/d in the topleft corner, we calculate (5 / 16)*19)

iteration 2) Align matrix content (green) with the desired totals of rows using the proportional


iteration 3) Align matrix content (green) with the desired totals of columns using the proportional


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