EUROPEAN COMMISSIONEUROSTAT

Directorate C: National accounts, prices and key indicatorsUnit C5: Integrated Global Accounts and Balance of Payments

17 March 2018

BP/18/04

BALANCE OF PAYMENTS

WORKING GROUP

APRIL 2018

Bech Building: Quetelet

Kirchberg, Luxembourg

Starting: Wednesday, 25 April 2018 at 09.30

Ending: Thursday, 26 April 2018 at 14.00

Consistency issues in b.o.p. and national accounts (ROW)

Agenda item 4

Documents are available on CIRCABC at the following address: https://circabc.europa.eu

I. Introduction

With the introduction of the Balance of Payments and International Investment Position Manual in its 6th edition (BPM6) the methodological standards for the compilation of the European balance of payments statistics (b.o.p.) and the national accounts became harmonised, thus a high degree of comparability and consistency between b.o.p. and the rest of the world account of national accounts (ROW) can be expected1. Eurostat measures the degree of consistency in the quarterly non-financial accounts of the EU-28 Member States since October 2015, and discovered that the consistency requirement does not fully materialise in all Member States equally. The profile of inconsistencies appears highly concentrated around a group of 6 Member States (covering currently almost 80% of total mean discrepancies 2013-2016), while on the other hand another 10 Member States established no or very low inconsistencies (covering currently only ca. 1% of total mean discrepancies altogether). The biannual surveys launched by Eurostat in 2015 and 20172 provided useful insights into the nature of the measured inconsistencies in the non-financial accounts, and revealed at least three patterns (based on the quantitatively most relevant discrepancies), although always applying in a country-specific context – 1. The use of different compilation practices and data sources is considered a predominant pattern in order to explain the current state of inconsistencies, emphasising the need for more coordination of statistical production processes; 2. A remarkable revision and vintage bias was identified; 3. Methodological differences occur only to a smaller extent in the non-financial accounts and cannot be generally claimed to be the major explanation for inconsistencies. The majority of issues detected referred rather to interpreting the standards than “real” methodological deviations. However, in some cases the statistics of national accounts are subject to other, conflicting consistency requirements (e.g. Input-Output tables) which put the compiler into a conceptual dilemma.In the light of the ensuing consistency debate in January 2016 the Committee on Monetary, Financial and Balance of Payments Statistics (CMFB) launched a task force on b.o.p.-ROW consistency under the joint secretariat of Eurostat-ECB. It investigated into the nature of inconsistencies of both non-financial and financial accounts with a focus on methodological issues, explicitly excluding revision and vintage effects. Phase 1 was dedicated to investigations into the goods and services accounts and was conducted during 2016. In February 2017 its summary report presented to the CMFB plenary of relevant issues and recommendations to overcome them3. During 2017 the TF then took up its investigations into inconsistencies of the financial accounts, including a few identified issues of the primary and secondary income accounts, with the summary report being presented to the CMFB plenary in January 20184. 1 BPM6 Appendix 7, ESA2010 Chapter 18

2 Eurostat Statistical Report (2017), Consistency between national accounts and balance of payments statistics – an updated view on the non-financial accounts, 2017 edition (presented as joint Eurostat-ECB analysis of current account inconsistencies to the CFMB plenary in June 2017); http://ec.europa.eu/eurostat/documents/7870049/8244331/KS-FT-17-008-EN-N.pdf/91e05efd-262d-4eb9-90ef-7b58346a0bcb

3 Final report (Phase 1) of the CMFB TF, February 2017; https://circabc.europa.eu/sd/a/2ea49095-bd91-41da-83f1-37cf2369e40d/CMFB%202017-01%20-%20Item%20A.9.2%20-%20CMFB%20BOP-NA%20TF%20-%20Final%20report%20on%20phase%201.pdf

4 Final reports (Phase 2) of the CMFB TF, January 2018; https://circabc.europa.eu/sd/a/89bdc454-c4b4-40df-a8fc-3f9869f7f951/CMFB%202018-01%20-%20Item%20A.8.2%20-%20Final%20Report%20of%20BOP%20NA%20Phase%202.pdf

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This paper would like to give an update on the current state of consistency between b.o.p. and national accounts in the EU-28 with a focus on the recent improvements and based on Eurostat’s regular comparisons of the quarterly non-financial accounts. Then it proceeds to discuss the two major issues stemming from the above mentioned compiler survey on causalities, and finally presents an insight into the recommendations of the CMFB TF wih a particular attention to the current account inconsistencies. This paper intends also to raise awareness of their impact in the national benchmark revisions.

II. The current situation of consistency between b.o.p. and national accounts

1. Are the non-financial accounts of the EU-28 consistent?

In order to answer this question, we must resort to two aspects – the statistics of the consolidated EU-28 aggregates in b.o.p. and national accounts, and the national statistics released by the EU Member States. The consolidated EU-28 aggregates of b.o.p. and the sector accounts are compiled and released by the respective production teams in Eurostat. Their calculations are based on the data transmissions from the EU Member States and their underlying legal provisions5. On the other hand, the country statistics of the EU-28 are released by the Member States and are published apart from national publication sources in Eurostat’s publication database eurobase6. Consistency of the 5 non-financial account components can be therefore measured both on the consolidated EU-28 statistics and the underlying country data7.

The current statistics of the EU non-financial accounts of b.o.p. and ROW are fully reconciled in the goods, services and capital accounts (Figure 1). Additionally, the data on b.o.p. credits/ROW payables in secondary income are also fully consistent. Primary and secondary income (b.o.p. debits/ROW receivables) show inconsistencies, but with considerable improvements since 2013 in the gross transactions of primary income, and a stable trend in secondary income, respectively. The extend of overall (absolute) discrepancies assumed values around EUR 1.5 billion in their multiannual mean 2010-2016, although their relative impact is insignificant with values between 0 to 0.02% of the EU GDP. Figure 1: Discrepancies by components of the non-financial accounts as per January 2018, consolidated EU-28, 2010-2016 (EUR million)

5 The b.o.p. Vademecum (current update: December 2017) reflects the requirements of b.o.p. statistics as laid down in the European legislation; the ESA2010 Transmission programme of the national accounts refers to Regulation (EU) No. 549/2013.

6 http://ec.europa.eu/eurostat/data/database

7 For a more detailed description of the applied principles of reconciliation and the conceptual issues and limitations of this data comparison, see Obrzut R. (2016), Consistency between national accounts and balance of payments statistics, in EURONA Nr. 1/2016 http://ec.europa.eu/eurostat/documents/3217494/7556543/KS-GP-16-001-EN-N.pdf/70e246de-734c-42ba-bee2-bc0b3dd97faa

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2010 2011 2012 2013 2014 2015 2016 0

500

1 000

1 500

2 000

2 500

Goods (gross) Services (gross) Primary income (gross)Secondary income (gross) Capital account (net)

Source: Eurostat – Absolute differences gross transactions of b.o.p. minus ROW for the EU-28 aggregates. Discrepancies of capital accounts are measured on net transactions. Discrepancies of the secondary income relate only to b.o.p. debits/ROW receivables.

As mentioned, these reconciliatons are conducted by Eurostat (not the Member States). In order to get a conclusive picture about the state of consistency in each Member States statistics, we must therefore compare the equivalent data sets at country level.

Figure 2: Discrepancies by components of the non-financial accounts as per January 2018, sum of EU-28 country data, 2010-2016 (EUR million)

2010 2011 2012 2013 2014 2015 2016 0

20 000

40 000

60 000

80 000

100 000

120 000

Goods Services Primary income Secondary income Capital account

Source: Eurostat – Absolute differences gross transactions of b.o.p. minus ROW for the EU-28 aggregates. Discrepancies of capital accounts are measured on net transactions. Discrepancies are measured as the sum of discrepancies in Member States statistics of the respective component.

The overall state of consistency in the underlying country data appears less satisfying. Total (absolute) discrepancies for all Member States assumed values around 197 billion EUR in their multiannual mean 2010-2016, which is ca. 1.4% of the EU GDP. This value summarises the absolute discrepancies measured in all 28 Member States. The measure gives a more realistic picture, as it refers directly to the reconciliation practices of the compilers in the respective

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Member States. The exposure to discrepancies differs however in the components of the non-financial accounts (Figure 2). While services still are the most exposed component to inconsistencies reaching an extent to ca. 100 billion EUR in 2016, goods appear the second largest. Primary income has experienced dramatic improvements in the recent past and moved from the second largest to number three in rank. Altogether these three components cover already more than 77% of total discrepancies in the EU-28. On the other hand inconsistencies in secondary income and capital account assume generally appear moderate. In principle this picture has not changed over the past years, however we measured considerable overall improvements since the start of our data comparisons in 2015. We can conclude that while the statistics of the EU accounts is highly consistent, in the country statistics there is still room for improvement8.

2. The impact of revisions to the state of consistency

While the overall extent of inconsistencies assumed values around 274 billion EUR on average in our comparison of October 2015, the recent value fell by more than 28% to 197 billion EUR. This evolution helped to reduce the relative exposure of around 3% of GDP to far below 2% in the EU-28 (currently: 1.4% of GDP for 2010-2016). The improvement can be seen as the result of the evolving consistency debate in European macroeconomic statistics, and compilers’ increasing efforts in reconciling their data in both statistics in view of the harmonised methodological standards. Figure 3 illustrates the significant improvements with recent periods (as from 2012) benefiting more than earlier periods from data revisions.

Figure 3: Total discrepancies in the non-financial accounts over time, sum of EU-28 Member States, 2010-2016 (EUR million)

2010 2011 2012 2013 2014 2015 2016100 000

150 000

200 000

250 000

300 000

350 000

Oct-15Jan-16 Oct-16

Jan-17

Oct-17

Jan-18

Oct-15 Jan-16 Oct-16 Jan-17 Oct-17 Jan-18Source: Eurostat – Total absolute discrepancies measured between October 2015 and January 2018 as sum of discrepancies in the EU country statistics.

Since Januar 2016 back data revisions have practically excluded timeseries before 2012. This is supported by the revision practices of b.o.p. statistics which does not foresee the comprehensive revision of historical data series in contrast to the national accounts. As a consequence the measures based on multiannual means appear “contaminated” by such historical effects. The most significant reductions in inconsistency levels were attained between October 2017 and 8 For a more detailed picture in country statistics see the our article on the Eurostat website in Statistics

Explained; http://ec.europa.eu/eurostat/statistics-explained/index.php/Consistency_between_national_accounts_and_balance_of_payments_statistics

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January 2018 on the 2015- and 2016-vintages. The latter proves the ongoing dynamics of revision activities and their continuous potential in improving the situation. However we must also acknowledge that revisions could not eliminate entirely inconsistencies in the EU country statistics so far.

3. Measuring comparability of b.o.p. and national accounts statistics

Both b.o.p. and national accounts statistics refer to a closed accounting framework which allows for the balancing items to be directly comparable. In the non-financial accounts these are the balances of the b.o.p. current plus capital account (CKA) and the net lending/net borrowing (B9) of the ROW sector. A comparison of the two items can give a rough indication about the comparability of both statistics in the country data (abstracting from the potential impact of b.o.p. net errors and omissions)9. In theory both items should be equal, so the extent of deviation can be statistically measured by the mean average percentage deviation (MAPD). In this context the MAPD value of 0.0 would indicate (almost) full identity of the balancing items.

Figure 4: Mean average percentage deviation of balancing items, nonfinancial accounts, EU-28 Member States, 2013-2016

BE

BG CZ

DK

DE

EE IE GR

ES FR HR IT CY LV LT LU HU

MT NL

AT PL

PT

RO SI

SK FI SE

UK

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

1.7

0.6 0.50.0 0.0 0.0 0.1

1.7

0.0

7.0

0.2 0.0 0.00.3

1.50.9

0.00.4

0.0 0.00.6 0.4 0.5

0.1 0.3 0.1 0.1 0.2

Mean absolute percentage deviation

Source: Eurostat – Balancing items: b.o.p. Balance of Current and Capital Account (CKA), ROW balance of net lending/net borrowing (B9); Croatia: QSA data missing for 2014-2016, Malta: QSA data missing for 2016; United Kingdom: QSA data were not updated by the compiler, there is a risk for a vintage effect. – MAPD is calculated as deviation of the ROW value from the b.o.p. value.

12 Member States show MAPDs of around 0.0 or 0.1 over the period of 2013-2016, while 5 Member States show measures of close to 1.0 or even higher, which alert for significant deviations in the balancing items of their statistics. As this is a multiannual mean, historical effects are also likely to blur the picture10. When looking at the more recent data (2016), we take note of at least 14 Member States with MAPDs of around 0.0 or 0.1, indicating slight improvements on the recent data series in some countries. The picture about the comparability of country statistics is finally only complete when looking also at the balances of the component accounts. Generally the country statistics should show the

9 For a broader discussion see Obrzut R. (2016), Chapter 6.3 – Studying measures of comparability, p. 125

10 Such as the case of Belgium, where the non-financial accounts appear fully consistent for 2016.

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same signs in their balances, thus conveying conclusive information to users. However, the 2016-data show 5 cases of opposite signs in some of the components of the non-financial accounts which prevent conclusive economic reading of the statistics. Positive balances represent higher international transactions in exports than in imports, while negative balances mean the opposite. Directional inconsistencies arise when one statistics releases balances with opposite signs to the other. Eurostat has made the concerned Member States aware of this on earlier occasions and trusts that this issue should be treated with high priority in future revisions. Only Belgium has so far implemented the recommendation of the BOPWG of April 2017 and abolished its directional inconsistencies in its statistics.

Table 1: Directional inconsistencies, Balances of components, non-financial accounts, 2016 (EUR million)

BOP QSA BOP QSA BOP QSA BOP QSA BOP QSABelgium 1 131 1 130 4 493 4 494 2 691 2 693 -7 909 -7 910 470 468Bulgaria - 985 - 984 3 092 3 060 -1 131 - 125 1 585 : 1 070 973Czech Republic 9 270 9 168 3 738 4 044 -10 053 -11 298 -1 010 -2 148 1 979 1 022Denmark 15 601 15 602 1 588 1 587 7 013 7 014 -3 880 -3 883 18 17Germany 268 368 272 063 -21 219 -21 444 52 137 53 142 -40 002 -35 879 1 111 -1 112Estonia - 790 - 791 1 604 1 603 - 412 - 411 - 2 - 3 225 226Ireland 105 762 105 852 -45 136 -45 208 -46 368 -47 822 -3 442 -3 628 -4 344 -5 062Greece -16 584 -16 473 15 313 15 215 - 4 921 - 597 - 854 1 036 3 050Spain -17 424 -17 424 51 096 51 172 - 176 - 254 -12 011 -12 347 2 682 2 682France -27 321 -28 755 871 -14 658 49 262 35 465 -43 186 -49 880 1 111 1 716Croatia -7 337 : 8 676 : -1 541 : 1 353 : 521 :Italy 59 764 59 762 -2 760 -2 836 5 030 3 771 -16 458 -16 462 -2 566 -2 906Cyprus -3 866 -3 867 3 726 3 724 - 284 - 285 - 463 - 465 36 36Latvia -1 919 -1 919 2 142 2 142 - 63 - 62 181 184 248 249Lithuania -1 769 -1 769 2 246 2 267 -1 561 -1 523 650 787 569 568Luxembourg -3 403 2 102 21 419 17 631 -15 779 -16 956 324 188 - 213 -1 587Hungary 4 701 4 699 6 767 6 758 -2 711 -2 813 -1 830 -1 716 86 - 141Malta -1 909 -1 816 3 028 2 956 - 686 - 584 225 : 79 77Netherlands 83 717 83 717 -6 370 -6 369 -10 248 -8 410 -7 635 -7 690 -1 595 -1 638Austria 304 359 11 073 11 501 - 180 - 181 -3 686 -3 688 - 593 - 593Poland 2 933 2 935 14 077 14 296 -16 863 -15 979 -1 397 2 515 4 470 3 536Portugal -9 256 -8 076 13 290 9 803 -4 259 -4 006 1 546 2 456 1 864 1 687Romania -9 248 -9 247 7 722 7 659 -4 470 -4 007 2 500 2 725 4 257 4 230Slovenia 1 537 1 537 2 174 2 164 -1 294 -1 128 - 309 - 423 - 302 - 322Slovakia 1 663 2 348 407 497 -1 906 -1 145 -1 369 -1 379 1 631 - 154Finland 244 243 -2 768 -2 768 2 044 2 179 -2 503 -2 483 85 77Sweden 10 039 12 610 9 457 9 879 7 223 7 312 -6 220 -5 949 - 373 - 289United Kingdom -165 252 -163 624 115 624 118 437 -62 436 -29 024 -27 539 -29 807 -1 964 -2 224

Goods Services Primary income Secondary income Capital account

Source: Eurostat

III. Myths and facts about the main causes for b.o.p./ROW discrepancies

Eurostat’s compiler survey about the causes for discrepancies in the non-financial accounts revealed insights into their nature (Figure 5)11. We would like to reflect about the main causes identified by the survey, in order to foster an informed discussion about the nature of discrepancies and promote a way forward. The survey identified as main causes:

(1) Considerable revision and vintage differences in the data sets;(2) The use of different data sources and estimation methods in each statistics;(3) Differences due to deviating methodological standards.

Figure 5: Reasons for b.o.p./ROW discrepancies in the non-financial accounts, 2010-2015 (% of total discrepancies)

11 The results of the survey had been presented to the BOPWG in April 2017 and consequently were published as Eurostat Statistical Report (2017) http://ec.europa.eu/eurostat/documents/7870049/8244331/KS-FT-17-008-EN-N.pdf/91e05efd-262d-4eb9-90ef-7b58346a0bcb

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Revision and vin-tage differences,

38.1%

Different data sources, 31.8%

Methodological dif-ferences+

interpretations, 11.8%

Different estimation methods,

6.8%

Different balancing and reconciliation

practices, 5.1%

Reclassifications Services and Goods,

4.4%

Errors identified, 1.6%Different coverage+Other, 0.4%

Source: Eurostat, b.o.p./ROW survey 2017

1. Do discrepancies mostly occur due to vintage and revision effects?

It has been shown that the survey dealt with a considerable revision and vintage bias (38% of total discrepancies), where some compilers felt that the available vintages of the data confrontion were not comparable, although the underlying data sets had been taken from a homogenous and comparable period (October 2016). However, not in all Member States these vintages appeared indeed comparable. As a consequence we tend to assume that instead of consistency concerns with b.o.p. statistics additional quality concerns apply in the QSA, which are related to the upcoming debate on cross-domain consistency in the tables of the ESA transmission programme (Box 1). This resulted in the Eurostat production team withholding data transmissions for the QSA from publication that exceed a tolerance band of deviation across the domains of the ESA transmission programme (0.3% of GDP). The missing data sets in the publication of the QSA statistics effectively jeopardise however the conclusive analysis about the state of consistency between b.o.p. and QSA of country statistics throughout the EU-28, and import vintage effects into the analysis, when using the previous vintage of QSA for comparions with b.o.p. statistics.

Box 1: Quality concerns in QSA statisticsIn order to conduct conclusive analyses about the state of consistency in both statistics, it appears instrumental to the user to have complete and timely data sets at hand for comparison. Although data confrontations could be hampered additionally by flagged data (non-publishable, confidential), the major obstacle to the analysis of b.o.p./ROW inconsistencies indeed is the absence of comparable data sets in the QSA statistics of a few Member States. Due to this, there are vintage effects imported into the analysis that blur the results, because the analysis has to resort to the latest available data versions. The data sets of the current analysis refer to the release calendars of January 2018 in both statistics, which are supported by the respective legal provisions of each statistics. However, by release date the QSA data sets of 4 Member States had not been published by Eurostat, either due to quality concerns or sizable discrepancies across the domains of national accounts. In one Member States data on secondary income

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were published by the b.o.p. compiler, while the QSA equivalent data set was flagged as non-publishable, here a coordination issue is summoned.

– Bulgaria: data for 2016Q4-2017Q3 were sent, but not validated due to internal inconsistencies in the transmitted data set.– Luxembourg: quarterly time-series for primary/secondary income and capital account missing, no updates of time series will be received until September 2018. – Croatia: data transmission received, but not published due to quality concerns. Due to this data are currently missing for period 2014-2016.

– Malta: updates of time series were received, but secondary income is flagged as non-publishable, while the corresponding b.o.p. data sets are available without restrictions.– Romania: data not validated due to sizable discrepancies with QNA and GFS.– United Kingdom: data not validated due to sizable discrepancies with GFS, no updates

of time series will be received until June 2018. In the domain of b.o.p. statistics quarterly data releases occur during the months of January, April, July and October, generally respecting the offical timetable of t+82 days (euro area)/t+85 days (non-euro area). In QSA comparable data releases occur during the same months, generally respecting the official timetables of the ESA transmission programme of t+85 days (euro area)/t+3 months (non-euro area). This heterogeneous setting results in data transmission from b.o.p. arriving during the beginning of the release month, while data from QSA arrive towards the end of the release month (plus allowing some time for the processing and validation). With roughly 3 weeks in between, it can be however reasonably assumed that the releases of both statistics are comparable and the risk for vintage effects is limited. The revision calendars of both statistics differ however significantly. While the QSA could foresee complete updates of the entire time-series thus including historical data, the b.o.p. revision calendars usually refer to 3 years for back data revisions12. As a consequence historical data become uncomparable between b.o.p. and QSA due to revision effects. As vintage effects are summoned to be limited due to the same release month of data sets, and revision effects occur mostly on back data (before 2014), we would expect vintage and revision effects to be rather small. This is in contradiction with the explanations received from the survey. It must therefore be assumed that other aspects than purely data-driven issues had been blended into the analysis, and that the extent of the vintage and revision effects has been overestimated by compilers.

Box 2: Defining vintage and revision effectsVintage and revision effects have been prominently used to explain differences between statistics in data comparisons. Indeed the set up of data comparisons across countries proves difficult, where country-specific production and dissemination practices apply. This triggers a fundamental risk for such a bias in the analysis. However, a clear definition should help to avoid misconceptions and to widely attributing differences in statistics to revision or vintage effects. There is a difference between a vintage and a revision effect, but usually they apply simultaneously.Vintage effect:The concept of vintage effects is related to release calendars. Each statistics consists of time series which are continuously updated in the light of new information and according to agreed release calendars. Each release creates a new vintage of these time series, and thus determines the comparability with other statistics. This is particularly relevant in data comparisons, where the release calendars of the underlying statistics differ from each other. The vintage effect is therefore determined by the point-in-time when a data comparison is launched.12 In the current quarterly revision calendar of 2018 this will cover data revisions back to 2014.

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(1) There are three observation points-in-time over a period: t0 < t1 < t2

(2) At t1: the data sets of statistics A are released, creating a vintage of A(t1); while, the data sets of statistics B are not released yet, keeping the previous vintage of B(t0).At t2: the data sets of statistics B are released, creating now the vintage of B(t2).

(4) Observation at t1: comparing A(t1) and B(t0). Vintage effect !Observation at t2: comparing A(t1) and B(t2). No vintage effect !

The above illustrates that only when the updates according to both release calendars have been performed a comparability of the data sets is established, otherwise a vintage effect would prevail. Revision effect: Systematic and unsystematic revisions are generally necessary to support statistical accuracy, when new knowledge becomes available to the compiler. Systematic revisions are determined by the official revision calendars, while unsystematic revisions take place discretionarily. Any unilateral revision of statistics will challenge the comparability of data sets, and thus creates revision effects. If one statistics applies a comprehensive revision agenda on its entire back data and the other does not, revision effects naturally are the result (“historical effects”). Differences in compilation/estimation practices:The concept of vintage and revision effects is based on homogeneous production processes in both statistics. This excludes differences in the data sets that arose from autonomous compilation/estimation practices or other organisational/institutional aspects, which challenged the comparability of the data sets. By inaccurately labelling such causes as revision or vintage effect would contribute to its overestimation in the analysis.

2. How relevant is the organisational aspect?

The list of causes identified in the survey which apparently result from a decentralised organisational set-up with little degree of coordination and data sharing in the underlying national production processes has been identified as the main cause for discrepancies. This incorporates at least the use of different data sources (32% of total discrepancies), different estimation methods (7%) and different balancing and reconciliation practices (5%). Hence, this contributes to ca. 44% of total discrepancies, and clearly can be considered the main cause for discrepancies between b.o.p. and QSA statistics. In the consistency debate organisational and institutional aspects were regarded as parameters, at the same time the challenges of economic transactions by residents in a globalised world can hardly be tackled without a (self-)critical view on the national set-up of statistical compilation processes. Eurostat argues for more integrated production processes and a higher degree of cooperation/coordination in the institutional arrangments of Member States13:

Different compilation practices and the use of different data sources are the result from a decentralised and/or uncoordinated organisational set-up (different institutions, different departments, etc.);

Although organisational/institutional aspects in the short-run are a parameter, in the long-run they can be subject to modifications – within one organisation this is certainly easier to achieve than across institutions;

Decentralised compilation systems do not necessarily have to be centralised, as long as they can effectively tackle inconsistencies and are embedded in a cooperative

13 Stapel-Weber K./Verrinder J.: Globalisation at work in statistics – Questions arising from the ‘Irish case’, in: EURONA Nr. 2/2016; https://ec.europa.eu/eurostat/cros/system/files/euronaissue2-2016-art2.pdf

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institutional environment and with a high degree of coordination and task sharing. Centralised solutions may be however more adequate to harvest economies-of-scale in countries where where extreme scarce administrative resources prevail;

The coordination of compilation processes is challenged by the conceptual closeness of the underlying statistics with each other, where production processes are of twinning rather than sequential character14;

Twinning processes require not only coordination but also a high degree of data sharing, as production takes place in parallel;

As long as persistent inconsistencies are measured, we may reasonably assume that the above aspects remain unaddressed and that strategic solutions should also include organisational consequences;

The organisational/institutional aspect is substantially a “structural issue”, of which revision work alone has so far not proven sufficient to eliminate these inconsistencies.

3. Can discrepancies be related to deviating methodological standards?

Our analysis is based on the assumption that the statistical standards have been fully harmonised, and that statistical evidence shall support this. However, as long as inconsistencies are measured, it can be argued that different methodologies limit compilers’ potential to improve the situation, while the focus remains on international institutions to review the standards for the sake of methodological consistency. The Eurostat survey proved this relevant to some degree – 12% of total discrepancies were attributed to methodological differences, 4% to systematic reclassifications between goods and services in order to accommodate conflicting consistency requirements of other macroeconomic statistics. While methodological differences alone cannot entirely explain inconsistencies, they should be investigated and followed up by the international institutions. The major issues in the non-financial accounts have been identified for services (systematic reclassifications to goods), primary income (different estimation methods to reinvested earnings) and secondary income (higher granularity requirements from Excessive Deficit Procedure to the national accounts, accounting of transactions in EU structural funds). The internatonal recommendations15 have drawn so far a way forward in how to address methodological differences in regard to a short-term and long-term perspective.

(1) Where the standards are unspecific: As the standards do not always offer specific compilation instructions, their provisions could appear vague and subject to interpretations across statistics compilers. In such cases a common interpretation should be sought by the compiler community. It has been shown that this effectively paved the way for “quick-wins” in some Member States, where institutional autonomies were practised in a cooperative context, or where one institution had responsibility for both statistics.

(2) Where the standards are effectively different: In (only) a few cases the standards give contradictory or deviating instructions. The CMFB task force has done investigative work in identifying such issues, and provide recommendations. It should be however clarified that the majority of “methodological” issues refers rather to their specific interpretations by the respective compilers than “real” deviations in methodologies. 14 In sequential processes the final product of one process can be used as input to the other processes. Twinning

processes require production activities in parallel, becaue the final product of oneprocess is not timely available to the others (parallel processing).

15 Recommendations of the CMFB Task Force on Consistency between BOP and NA

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Where deviations were identified, the international standards have to be reviewed. In the following we will present a few identified issues.

IV. How to deal with methodological issues – recalling the CMFB task force’s methodological recommendations for the current account

The Committee for Monetary, Financial and Balance of Payments statistics (CMFB) – a joint decision-making body of the ESCB and the ESS – launched a task force in 2016 in order to investigate into the reasons for discrepancies between b.o.p. and national accounts, identify issues and provide recommendations on how to overcome them. The work was conducted in two phases so far. While Phase 1 was dedicated to discrepancies in the goods and services accounts, Phase 2 concentrated on the financial accounts, although including aspects of primary and secondary income into its investigations. Each phase ended with a summary report to the CMFB plenary, that produced a list of issues identified and recommendations on how to overcome them. The summary report of Phase 1, which was summarising the investigative work during 2016, was presented to the plenary in February 2017 and identified at least 11 issues related to the goods and services accounts (Box 3).

Box 3: Thematic issues identified in the goods and services accounts(1) CIF/FOB adjustments(2) Illegal trade(3) Merchanting(4) Processing and repair(5) Transit trade(6) Goods acquired by tourists or in business travel(7) Government goods and services n.i.e.(8) Package tour expenditures(9) FISIM(10) Insurance and pension services(11) Treatment of special purpose entities(12) Other issues16

Source: CMFB, Phase 1 summary report, February 2017

The summary report of Phase 2, which summarised the investigations during 2017, was presented to the plenary in February 2018 and identified 10 issues, with 8 relating to the financial accounts, and 2 to the secondary income accounts (Box 4).

Box 4: Thematic issues identified in the financial and secondary income accounts17

(1) Financial derivatives(2) Investment fund shares(3) Other accounts payable/receivable from banking statistics(4) Euro banknotes and coins(5) Loans and deposits: borderline cases and cash/accrual principles(6) EU funds registering

16 Telecommnication, computer and information, support and waste treatment, transports manufacting services in goods owner by others, manufactured prodcuts, goods bought by international organisations

17 4 additional issues (e.g. related to retained earnings in primary income) were reported in December 2017, after the work of the task force was already finalised. They are briefly mentioned in the summary report of Phase 2.

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(7) Equity in the IMF(8) Classification of instruments as loans, deposits and debt securities(9) Social benefits and contributions(10) Other current transfersSource: CMFB, Phase 2 summary report, February 2018

The investigations of the task force concentrated on methodology-related issues and abstracted from issues related to the use of different data sources or compilation practices with no methodological background. As a consequence not all of the above mentioned thematic issues covered methodological aspects.

The task force’s recommendations can be roughly summarised into a short- and long-term perspective. While its short-term recommendations emphasise the need for common interpretations and – where necessary – the publication of metadata, its long-term recommendations focus on the follow-up by the international institutions for full consistency in the standards of b.o.p and national accounts statistics. Although the time line may vary between countries, there is also an explicit reference to the next benchmark revision of b.o.p. and national accounts, in order to achieve consistent statistics. In brief we present the methodological issues at stake that were identified by the task force in regard to the non-financial accounts.

(1) CIF/FOB adjustments:The standards provide different guidance for the coverage of transportation costs and the valuation principles applied for freight cost (transaction prices vs. market prices). As long-term recommendation the compilation guides and manuals are to elaborate better on transport services and freight costs, and to the ensure consistency in price valuation of these categories. In this regard some additional work is to be done to collect and compile information about transport of goods for processing, the recording of freight costs and transport of merchanting goods.

(2) Goods acquired by tourists or in business travel: Travel is not explicitly defined in the manuals. This leads to different interpretations and can result in goods acquired by tourists or in business travel to be classified as goods in national accounts, while as services under travel in b.o.p. The task force recommends in line with the wording of ESA2010 to classify all expenditure made by tourists and business travellers as exports/imports of services in the national accounts, and to provide a breakdown by component products for the purpose of the supply and use and symmetric input-output tables. In the long-term a compatible definition of “travel” should be amended in both standards.

(3) Government goods and services n.i.e.:Due to the lack of definition in ESA/SNA, their concept of “government goods and services” is likely to deviate from the concept of the BPM. This can lead to different recording of personal expenditure made by government staff residing in territorial enclaves abroad. The task force recommend the ESA/SNA manuals to introduce government goods and services as a component of exports/imports of services with the precise concept and definition aligned to the BPM6. Further, taxes collected on behalf of the European Commission should be registered on a gross basis and the collection costs should be included as a service provided by the national government and recorded under government goods and services (referring to the Manual on Government Deficit and Debt, 2016 edition, II.6.2). In the short run national accountants should apply the concept of the BPM6.

(4) Package tours expenditure:

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BPM6 and ESA/SNA show methodological differences in the recording of package tour expenditures related to the treatment of commissions by travel agents and international transport services included in a package tour. A more explicit wording in the manuals in the context of a common travel concept would be highly desirable.

(5) FISIM: Methodological differences between BPM6 and SNA/ESA are related to the treatment of interbank loans and deposits and negative FISIM. Different wording of ESA/SNA and BPM6 leads to different interpretations of the manuals. This includes FISIM consumption of the S.125 subsector that is not recorded in national accounts, and FISIM produced and consumed by government owned-banks computed in b.o.p. Further, different methods in calculating the external reference rate estimates apply. The task force requires the alignment of the concepts, definitions and guidance for the compilation of FISIM in SNA, ESA and BPM6, while the definition of financial intermediaries should be fully harmonised. For interbank loans an alignment of the terms of SNA/ESA on those of BPM6 is considered instrumental. Although there is no direct inconsistency, it is noteworthy that ESA/SNA do not provide any guidance on the treatment of negative FISIM, while the BPM6 compilation guide allows “for practical reasons” to assume negative values as zero.

(6) Social benefits and contributions: The recording of health services is seen as a possible issue where different interpretations of the manuals could arise. The treatment of reimbursements by social security funds in the provison of health services is a particular case where national compilers face difficulties in a consistent compilation on both sides. In the short run special attention should be paid by compilers to the recording of cross-border flows of health services in both statistics. Consistent recording should be supported by exchange of relevant information on social security funds’ flows related to cross-border provision of health services, especially between countries were these flows are sizable. In the long run the wording of the manuls should include a clear and fully consistent guidance, including the case of cross-border workers.

At the BOPWG of November 2017 it has been shown that the above list of methodological issues is not exhaustive, and that the investigation work is not concluded. In many of the above cases it appears that the authors of the manuals had the full harmonisation of national accounts and b.o.p. statistics in mind, but gave less consideration to comparable definitions and concepts in the manual. In the light of the above cases identified by the CMFB task force, manuals require a fundamental review of definitions and concepts covering the presented issue. In the meantime for practical reasons and the sake of consistency of the non-financial accounts, b.o.p. compilers are asked to liaise with their counterparts in order to attain a common interpretation of the mentioned issues.

V. Conclusions

In this paper we present an updated view on the state of consistency between the nonfinancial accounts of b.o.p. and QSA. They imply that although improvements have been achieved since October 2015, there is still ample room for further reconciliation in some Member States.

The statistics of the EU aggregates of the non-financial accounts of b.o.p. and ROW appear greatly consistent;

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The country statistics of the EU-28 however show considerable differences in the consistency of the non-financial accounts of b.o.p. and ROW – in some Member States high inconsistencies prevail, while others appear fully consistent;

Directional inconsistencies in the balances of the non-financial accounts hamper conclusive economic reading of the affected statistics, and could pose a serious threat to compilers’ reputation;

There were prominent improvements made through revisions since the harmonisation of the methodological standards, although revision work could not completely eliminate inconsistencies;

“Structural issues” contributed to the persistence of inconsistencies in some Member States, where these issues are tied to organisational aspects of the statistical production processes;

Due to the twinning rather than sequential character of compilation processes coordination must imperatively include interinstitutional data sharing;

Vintage and revision effects appear overrated as cause for inconsistencies, while methodological differences (although important where applying) play a minor overall role in the observed discrepancies of the non-financial accounts;

The CMFB task force on consistency of b.o.p./ROW identified specific cases of methodological differences in both the non-financial and financial accounts, the major part refers to different interpretations due to the lack of definition of concepts and guidance;

In the short run national counterparts shall search for a common view in order to reconcile their statistics. The task force specified cases where the provisions of the BPM6 should prevail. Situations where conflicting consistency requirements apply, should be well documented;

In the long run a review of the manuals for the sake of methodological consistency in identified cases is indispensable.

In the above context we suggest to resort to the recommendations of the CMFB task force and seek communication with the national accounts partner in your Member State, in particular in the context of the identified thematic issues.

Members of the BOPWG are asked:

- to take note of the current situation of consistency in EU country statistics of b.o.p. and national accounts; - to take note of the directional inconsistencies in the balances of the non-financial accounts of some Member States as a severe reputational threat, and –where applying– initiate reconciliation of opposite signs with high priority;- to take note of the conclusions of this report;- to liaise with their national accounts counterparts in the Member States and the NAWG.

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