Official statistics

• Mr. Arora was first appointed Chief Statistician of Canada in September 2016, with his term renewed in June 2018 for another five years.
• Mr. Arora will continue in this role until the end of March 2024, to enable the Government of Canada to identify and appoint a new Chief Statistician of Canada and support a seamless transition.
• – The Honourable François-Philippe Champagne, Minister of Innovation, Science and Industry
  "I am pleased to continue to serve as the Chief Statistician of Canada until March 2024, marking more than 35 years of public service.
• The Statistics Act was amended in 2017 to give the Chief Statistician of Canada authority for decisions on statistical matters and to increase government transparency.

• Smart Data Foundry data science team members Paola Arce, Victor Alfonzo Diaz and Euan Gardner represented the organization under its previous Global Open Finance Centre of Excellence name.
• The week-long synthetic data challenge was put into place to help shape the HLG-MOS's Synthetic Data for National Statistical Offices: A Starter Guide, being published by the UNECE later in 2022.
• Smart Data Foundry is part of the University of Edinburgh and a collaboration with the Financial Data and Technology Association (FDATA) and FinTech Scotland.
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Executive summary

• This annual report provides a quality review of the national balance of payments (b.o.p.
• and international reserves template of the Eurosystem (international reserves), as well as the associated euro area aggregates.
• [1] The report fulfils the formal requirement for the Executive Board of the European Central Bank (ECB) to inform its Governing Council of the quality of these statistics, as set out in Article6(1) of Guideline ECB/2011/23 (hereinafter the ECB Guideline).
• The main principles and elements guiding the production of ECB statistics are set out in the ECB Statistics Quality Framework (SQF)[3] and quality assurance procedures, which are published on the ECBs website.
• Supporting tables/charts and details of how the indicators are computed can be found in the respective annexes to this report.
• The box draws on annual data up to 2018 and revisions up to 2017 and focuses on the following quality dimensions: (i)data availability; (ii)revisions; (iii)errors and omissions; and (iv)external consistency with sector accounts.

Statistical developments between 2018 and 2019

• Within the European System of Central Banks (ESCB), Working Groups on Financial Accounts (WG FA) and the Working Group on External Statistics (WG ES), along with other sub-structures of the Statistics Committee (STC), e.g. the Working Group on Monetary and Financial Statistics (WG MFS), are working closely together on the following common issues:
  • securities held with non-resident custodians that are not covered by national securities holdings statistics;
  • coverage of the other financial institutions (OFI) sector and, in particular, the timely coverage of SPEs, given the lack of primary statistics;
  • coverage of financial derivatives for all sectors, owing to missing data sources and/or counterpart sector details.
  • In addition to the collaborative work listed above, the WG ES and WG FA established a joint group on the valuation of unlisted shares and other equity in January 2020.
  • This has enabled national compilers (national central banks (NCBs) and national statistical institutes (NSIs)) to both report relevant data and make them publicly available with sufficient accuracy and within the agreed deadlines.
  • Nevertheless, some additional efforts are still needed to disseminate more quality data and improve comparability and consistency with other datasets.
  • While statistical standards are generally observed, there is still room for improvement in terms of methodological soundness.
  • Luxembourg, the Netherlands and Malta[4] are encouraged to continue working to increase the coverage and quality of data on special purpose entities (SPEs).
  • While major progress was observed in 2019, Cyprus is still invited to closely monitor the SPE sector and continue improving the counterpart geographical detail.
  • Furthermore, national compilers should in general continue their efforts to improve the coverage of assets held abroad by resident households.
  • The majority of countries have complied on a continuous basis with the deadlines for data transmission, with a few exceptions.
  • benchmark revisions in 2019, which supported the alignment of national accounts (ESA2010) with b.o.p./i.i.p.
  • data.
  • data are in line with other datasets, thus ensuring comparability across statistical domains.

Statistical issues affecting MIP indicators

• The ECB, in collaboration with Eurostat, has continued to monitor specific quality aspects of the statistical outputs, as required under the MoU.
• In fact, some of the quality dimensions addressed in the report are also relevant for assessing the quality of data for MIP purposes (e.g.methodological issues A1 to A10, C3, F1 and G1 in Table1).
• Some recommendations, such as those related to the functional classification (e.g.A5.1 to A5.3) or to the reconciliation of stocks and flows (C1), do not impact the computation of the main MIP indicators, but do play a role in the calculation and analysis of auxiliary indicators.
• However, the particularities of the annual data and of the MIP process, as well as the scope of the ECBs responsibilities in the context of the MoU on the MIP (for those EU28 Member States that have designated the respective NCB to produce the b.o.p./i.i.p.
• In particular, longer time series (up to 15years) are necessary for an accurate construction and analysis of the main MIP scoreboard indicators.
• All necessary data are available for the calculation of the main indicators (with a few exceptions for the goods and services balance).
• Despite this, the situation has improved compared to the previous quality report, in part due to the benchmark revisions.
• For more information on the assessment of data quality for MIP purposes, please see the MIP box at the end of the main body of the report.

1 Introduction

• This annual report provides a quality review of statistics on the balance of payments (b.o.p.
• ), international investment position (i.i.p.)
• and international reserves template of the Eurosystem (international reserves).
• [9] Furthermore, the report provides information supporting the MIP data quality assurance process, as laid down in the MoU.
• The data for EU Member States (EU28) are commented on in the MIP box at the end of the report and are also available in the annexed tables[10].

Scope of data coverage and structure of the report

• This report analyses a number of aspects by which data quality can be measured.
• The analysis covers quarterly and, in the case of euro area aggregates, monthly data.
• Section3 (timeliness and punctuality), Section 4 (data and metadata availability) and Section 6.1 (validation/integrity rules) focus on one year of observations (July 2018/Q3 2018 to June 2019/Q2 2019).
• The last data vintage used throughout the report is the one available as of 23October 2019 and the country coverage is mostly the euro area, although the annexed tables provide information on the quality of the data for the EU28.
• The box draws on annual data up to 2018 and focuses on: (i)data availability, (ii)revisions; (iii)errors and omissions; and (iv)external consistency with sector accounts, i.e.MIP-relevant data quality dimensions.

2 Methodological soundness and statistical procedures

• Methodological soundness means that concepts and definitions used to compile b.o.p./i.i.p.
• statistics broadly conform with the principles and guidelines outlined in BPM6 and take into consideration the agreements of the STC (and respective sub-structures) on the compilation of euro area aggregates.
• One of the key elements of compiling consistent data is to adhere to the agreed standards and to transparently describe deviations.
• A detailed description of the data sources and compilation methods used by all Member States is available on the ECBs website[11].
• The assessment included in this section is based on this ECB publication, as well as on the regular ECB contacts with national compilers regarding general data quality issues.
• [12] This quality report provides a succinct overview of the methodological soundness of b.o.p.

2.1 Residency

• The residency of institutional units should be defined in conformity with BPM6, particularly taking into account whether they have a predominant centre of economic interest in the country.
• This applies in particular to Special Purpose Entities (SPEs), which are considered to be resident in the economy where they are incorporated.
• Most countries correctly apply the residency concept.
• In the euro area, several countries host a large population of SPEs and therefore face certain challenges in achieving full coverage, and sometimes even in defining the residency of a certain entity.
• [13] The 2018 revisions in the geographical allocation of positions for Malta introduced a series break in Q1 2016 that has not yet been solved.
• However, some limitations still apply to the geographical details, especially for debt instruments in FDI and other investment transactions and positions.
• The Netherlands has also improved the accuracy of SPE data compared with 2018 through its efforts to integrate the compilation of b.o.p.

2.2 Functional classification

• Regarding foreign direct investment (FDI), a number of countries, including Germany, Greece, France, Luxembourg and the Netherlands, classify transactions and related positions in debt securities between companies in a direct investment relationship as portfolio investment.
• This deviation creates internal inconsistencies at the euro area level, owing particularly to the residual approach used to calculate euro area portfolio investment liabilities.
• In the context of the implementation of the ECB Guideline, these misclassifications will become more transparent.
• Therefore the compiler should assess the potential relevance of the issue and implement a plan to address it.
• Malta includes most of the securities assets of the SPEs under portfolio investment, as no information is available regarding the relationship with the debtor.
• Moreover, Belgium, Germany, Estonia[15], France, Cyprus, Lithuania[16], Austria, Slovenia, Slovakia[17] and Finland do not identify reverse direct investment in equity, and Malta shows negative values for liability positions.

2.3 Coverage

• According to public metadata, Cyprus[18] and Luxembourg do not currently estimate employee stock options.
• Germany should improve the data quality for transactions in financial derivatives for the government sector, as they are currently reported as zero, while positions are non-negligible.
• In addition, France does not record any transactions and positions in financial derivatives by the government sector.
• Irish financial derivatives data is quite implausible for investment funds as most of the changes in stocks are reported as other volume changes.
• In April 2015, the STC approved a new treatment for the recording of transactions and positions in euro currency in b.o.p./i.i.p.
• Finland[22] does not report insurance, pension schemes and standardised guarantee schemes before reference periods 2016 Q1 on the asset side.
• Furthermore, Malta does not report the breakdown of equity (into listed and unlisted shares, other equity and investment fund shares).
• [25] Many countries also have difficulties in accounting for real estate holdings, in particular those of resident households abroad.
• The valuation of unlisted shares and other equity should also in general be improved and be carried out in a harmonised way.
• For this purpose a joint WG ES and WG FA[26] group on unlisted shares and other equity was established in January 2020.

2.4 Other methodological issues

• data prevent the ECB from validating the figures reported for net external debt.
• Only the Maltese net external debt total is available and not its representation by sector, instrument and original maturity.
• The quality of the monthly Irish data frequently has a negative impact on the quality of euro area aggregates for goods.
• France and the Netherlands systematically report zero monthly transactions in assets for money market funds (MMF) shares in their first estimates (intra and extra-euro area).
• In Belgium and Finland from 2008 to 2012 the resident banks classified some of the liabilities as loans.

3 Timeliness and punctuality

• In addition, the following ad hoc cases of non-compliance were recorded:
  • The Central Bank of Ireland failed to transmit the banknote shipments data for the reference period February 2019 within the production window.
  • The Central Bank of Latvia transmitted the monthly reserve assets data for the reference period August 2018 one day of delay. Attempts were made to transmit data prior to the deadline, but technical issues prevented their timely sending.
  • The Bank of Finland transmitted the complete quarterly balance of payment data for the reference period Q2 2019 three days of delay. Furthermore, several data transmissions were necessary to reach a sufficient level of data quality for the revised periods.
  • Non-compliance is defined with regard to (transmission) timeliness/punctuality and quality standards vis--vis the requirements laid down in the ECB Guideline ECB/2011/23 (as amended).
  • [27] In the period under review (reference period July 2018 to June 2019), a persistent non-compliance case was recorded in the case of the Central Bank of Malta for not reporting the complete quarterly other flows detail and for recording delays in some monthly and quarterly transmissions.

4 Data and metadata availability

4.1 Completeness

• For the reference period July 2018 to June 2019, the production of b.o.p., i.i.p.
• and international reserves statistics was smooth.
• In terms of completeness, virtually all countries submitted all the mandatory items, albeit sometimes with delays (thus giving rise to cases of non-compliance see Section3 above).
• In some cases this created obstacles to the publication of timely and accurate euro area aggregates.

4.2 Accessibility and clarity

• Accessibility refers to the conditions by which users can obtain, use and interpret data, ultimately reflecting how straightforward these are to access and the extent to which confidentiality constraints hamper the analytical work.
• In line with the ECB legal framework on data confidentiality,[28] all national data must be transmitted with a flag indicating its level of confidentiality.
• The shares are calculated at dataset level for the reference period Q3 2018 to Q2 2019.
• TableA.1.1 in the Annex shows the same indicator for all (mandatory) items transmitted under the ECB Guideline.
• Table 2 Average share of observations marked as free for publication per dataset (main items), for the period Q3 2017 to Q2 2018
• data).
• It should be noted that the percentages are calculated based on the number of observations, without taking into account the relative importance (magnitude) of the data.
• datasets were flagged as non-publishable or confidential by Ireland, Cyprus, the Netherlands and Austria (generally on the basis of national dissemination policies).
• and six euro area countries have done so for the quarterly i.i.p.
• Clarity refers to the information environment of the data, i.e.whether the data are accompanied by relevant and pertinent metadata, illustrations (such as charts), information on their quality and potential limitations as to their use, and background information (sources and methods).
• revaluations and other changes in volume for the euro area as a single economic area.
• book, made available on the ECB website, aims at providing users with an overview of the main features of the b.o.p.
• Furthermore, the ECB has a Statistical Information Request facility to help external users of statistics access and analyse the data.
• A subset of the statistics produced under the ECB Guideline can also be accessed via the Euro area statistics website.
• TableA.1.2 in the Annex presents a summary of the national practices regarding data and metadata accessibility.
• Most euro area countries publish regular press release updates on their websites: on a monthly and/or quarterly basis.
• Last but not least, all countries present extensive information on their institutional environment and statistical processes in the B.o.p.

4.3 Availability of metadata

• Therefore, national compilers are encouraged to make regular and consistent use of the metadata template in all production cycles and publication means.
• In general, in the review period, the metadata transmitted by national compilers has been of sufficiently high quality to allow for the production of the euro area aggregates as well as to explain major developments in the aggregate.
• The ECB welcomes further efforts to improve the accuracy and level of detail in the metadata transmitted to the ECB and also encourages euro national compilers to exchange information with other euro area NCBs within the framework of existing arrangements, for instance in the context of FDI.

5 Accuracy and reliability (including stability)

• This section reviews the stability of the data in terms of revisions to the first assessment or first vintage.
• In general, revisions are necessary to improve the accuracy of the data as first assessments may be based on incomplete, late or erroneous responses by reporting agents.
• However, large recurrent (biased) revisions may indicate low quality of data sources and/or methods that need to be addressed.
• Conversely, minimal or no revisions does not necessarily mean that the first assessment was of high quality; it may simply indicate a national preference for not revising the data.
• Different indicators are applied depending on the features of the time series in question.
• Directional stability/reliability indicators measure how frequently first assessments are revised in the same direction (the upward revisions ratio and the directional reliability indicator).
• However, since the last version of this report was published, 19 countries have implemented major national accounts and b.o.p./i.i.p.
• While increasing the accuracy, generally these revisions have not fundamentally altered the analytical interpretation of the first assessments.

5.1 Current account

• In general, revisions to the euro area current account credits and debits were comparable for monthly and quarterly data as can be seen in Chart1 below.
• The euro area aggregates recorded revisions comparable to the euro area country median (1% for the quarterly current account credits and debits), with the monthly data recording slightly higher revisions.
• Cyprus[32] and Malta had the highest revisions among euro area countries for current account credits and debits.
• In terms of current account sub-items, in particular for monthly data, Ireland displayed a higher number of monthly revisions with weaker directional reliability compared with the quarterly data.
• Concerning revisions to the quarterly current account balance (see Chart2 below), the euro area as a whole recorded comparable revisions to the median of the euro area countries (1%).
• For the current account balance, the most sizable revisions were recorded by Ireland.
• Chart2 Revisions to the current account balance (net relative revisions NRR)


Detailed information on SMAPE, upward revisions and directional reliability indicators is available in Tables A.2.1 to A.6.2 in the Annex.

5.2 Financial account transactions

• To overcome the fact that transactions in financial assets and liabilities can be either positive or negative, revisions to financial assets and liabilities are related to the respective i.i.p.
• MACE is therefore used to assess revisions to the financial account.
• For the quarterly euro area aggregates, recorded revisions amounted to 0.2% of the underlying positions for total transactions in financial assets and liabilities, which is slightly lower than the median of euro area countries.
• Revisions to monthly euro area aggregates were considerably higher, as can be seen in Chart3 below.
• All euro area countries recorded revisions of less than 1% of the underlying positions for quarterly financial transactions.
• Concerning revisions to net quarterly financial transactions, the euro area as a whole recorded NRR comparable with the median of euro area countries (0.1%), while revisions to the monthly series were substantially higher (across all functional categories).
• In terms of net financial account transactions for individual countries, Lithuania[36] and Finland recorded the highest level of revisions among euro area countries (see Chart4 below).
• Chart4 Revisions to net financial account transactions (net relative revisions - NRR)


Detailed information on MACE, upward revisions and directional reliability indicators is available in Tables A.2.1 to A.6.2 in the Annex.

5.3 International investment position

• The euro area as a whole recorded revisions (as measured by SMAPE) of approximately 2% for both assets and liabilities, double the median for euro area countries.
• At country level, revisions for assets and liabilities were generally comparable (with the exception of Slovenia).
• Lithuania and Slovenia recorded the highest revisions in the euro area.
• However, with the exception of revisions to Slovenian assets, this level of revision was comparable to other euro area countries.
• Chart5 Revisions to the international investment position (symmetric mean absolute percentage error -SMAPE)
• As regards revisions to net i.i.p., the euro area as a whole recorded revisions totalling 1.1% of the underlying average positions during the period under review (comparable to the median level of revisions for euro area countries).
• Slightly higher revisions (between 1.9% and 3.1%) were recorded in net positions for the various functional categories (direct, portfolio and other investment).
• At the level of individual countries, the highest NRR for net i.i.p.
• Even if by a considerable distance, Slovenia was next, owing in particular to its revisions to assets.
• Chart6 Revisions to the net international investment position (net relative revisions (NRR))


Detailed information on SMAPE, NRR, upward revisions and directional reliability indicators is available in Tables A.2.1 to A.6.2 in the Annex.

6 Internal consistency

This section comprises two parts, assessing the reported national b.o.p. and i.i.p. data for internal coherence and consistency respectively. This comprises consistency over time (i.e. potential breaks in series), reconciliation across different frequencies (monthly and quarterly data) and an assessment of the arithmetic and accounting identities (including net errors and omissions).

6.1 Validation/integrity rules

• This section reviews to what extent the transmitted national datasets were complete and met all basic accounting validation rules.
• Furthermore, it is strongly encouraged that datasets for different frequencies (i.e.monthly and quarterly) or data recorded in different datasets (e.g.reserve assets transmitted in the i.i.p.
• statement and in the reserve assets template) are kept consistent at all times.
• In order to summarise compliance with validation rules, the average share of satisfied validations is used as an indicator (see section Methodological documentation for quality indicators for more details).
• The share of satisfied integrity rules was also below 95% for Belgium (owing to reconciliation issues) and France (owing to issues in the functional detail, the geographical breakdown, and the resident and counterpart sector breakdowns).
• In terms of time consistency, the vast majority of countries exhibit full consistency between monthly and quarterly data, with only a few exceptions.
• Major breaks are also present in Q4 2015 due to different estimation methods applied to portfolio investment debt securities liabilities.
• Austria: certain breaks apply in primary income credits and debits (from Q1 2013 to Q1 2016) as explained by SPE activity.
• It should be noted, however, that countries are making continuous efforts to improve their data.
• Data transmissions submitted after the review period have already resulted in improved data quality.
• Values for the validation indicators are available in Tables A.7.1 to A.7.3 in the Annex.

6.2 Net errors and omissions

• Net errors and omissions (n.e.o.)
• (the difference between net lending/borrowing as compiled from the current plus capital accounts and the financial account) provide an indication of the internal consistency of the b.o.p.
• However, if these imbalances are large and/or persistent, they indicate problems in sources and/or methods.
• compilation practices, it is not uncommon that statistical modelling and/or expert judgements are applied with the intent of imposing certain properties on net errors and omissions.
• This involves using statistical techniques to account for a lack of source data coverage or uncertainty about certain pre-identified items.
• At euro area level, a correction mechanism that minimises net errors and omissions is also in place.
• The average relative error for current account provides a measure of the magnitude of net errors and omissions in relation to average gross current account flows.
• Monthly errors and omissions were substantially higher than quarterly ones.
• relative to average gross current account flows was 6% for monthly data and less than 2% for quarterly data.)
• as a percentage of average current account gross flows at 14% (Ireland, which had the second highest n.e.o.
• Chart7 Relative net errors and omissions[40] (average absolute net errors and omissions relative to average gross current account flows)


The persistence of the sign of errors and omissions is also relevant as a quality measure as it helps to identify biases in the accounts. Chart 8 below shows the cumulative n.e.o. in relation to current account gross flows. Chart 8 Bias in net errors and omissions (cumulative net errors and omissions relative to average gross current account flows)
Neither the euro area as a whole nor the vast majority of euro area countries display a significant statistical bias in their net errors and omissions. Values for the validation indicators (including n.e.o.) are available in Tables A.7.1 to A.7.7 in the Annex.

7 External consistency/coherence

External consistency is defined as the coherence of b.o.p. and i.i.p. data with other related statistical domains. In this report, the external consistency/coherence of the b.o.p. and i.i.p. is assessed against foreign trade statistics, euro area (sector) accounts, MFI balance sheet statistics (including money market funds), investment fund statistics and securities holdings statistics.

7.1 Coherence with foreign trade statistics

• International trade in goods statistics (ITGS) is typically the main data source used to compile the b.o.p.
• However, when comparing the two datasets, important conceptual differences should be taken into account.
• Differences in concepts and definitions are linked primarily to the fact that b.o.p.
• Given the methodological differences between the two datasets, a direct comparison would not convey an accurate picture.
• and ITGS data exhibit consistent developments and can hence be used as complementary analytical data sources.
• For the euro area as a whole, there was full directional reliability for both imports and exports.
• Four euro area countries displayed full directional reliability for both exports and imports for the two counterpart areas analysed.

7.2 Consistency with euro area sector accounts

• Euro area b.o.p.
• data constitute one of the building blocks of the euro area accounts (EAA) and are widely used at national level for the compilation of the rest of the world (RoW) financial and non-financial accounts as part of the system of national accounts.
• and the RoW account (national accounts) were removed with the introduction of ESA 2010 and the BPM6, albeit some challenges still remain when it comes to interpretation.
• [44] analysis showed that inconsistencies between the two statistical domains persisted in many countries, negatively affecting the combined use of the two datasets and their reliability.
• Acknowledging this, the ESCB worked to precisely identify the differences and to develop national medium-term work plans to be generally observed by September 2019.
• Such issues are tackled in the context of the MIP quality assurance framework.

7.2.1 Current account

• and RoW current accounts.
• As an indicative benchmark, relative differences should ideally be no higher than 0.5% of the underlying average b.o.p.
• At country level, however, differences above 0.5% were recorded for several countries (Belgium,[48] Germany (only for debits), Ireland, Greece, France and Luxembourg).
• Ireland, Greece (only for debits), France (only for credits) and Luxembourg recorded notable discrepancies (above 6%) for their current accounts, with sizeable discrepancies for services (Greece, France and Luxembourg) and primary income (Ireland, France and Luxembourg).
• In addition, differences above the threshold were also observed for a few other countries (Austria, Portugal, and Slovakia), without affecting consistency between the two datasets.
• Chart9 Current account discrepancies between the b.o.p.
• and RoW account (average absolute and relative difference (as a percentage of respective quarterly b.o.p.

7.2.2 Financial transactions

• and the RoW account for financial transactions.
• In this case, discrepancies may be accounted for by time of recording differences, as well as by the reconciliation of national sectoral accounts.
• Both vertical reconciliation (a correction for errors and omissions) and horizontal reconciliation (asset/liability equality across sectors) may entail larger adjustments to the financial transactions in the RoW account.
• Nonetheless, as an indicative benchmark, the relative differences should ideally not exceed 0.3% of the average value of the underlying positions.
• Greece recorded the highest relative discrepancies, while the largest absolute differences were observed in Germany (only for liabilities) and France.
• Chart10 Financial account transactions discrepancies between the b.o.p.
• and RoW stocks of financial assets/liabilities) for the period Q3 2016 to Q2 2019 (b.o.p.

7.2.3 Financial positions

• and the RoW account for financial assets and liabilities (balance sheets/positions).
• As expected, the differences between the two datasets are larger for positions than for transactions.
• Relative differences should, as an indicative benchmark, be below 0.5% of the average financial assets/liabilities totals in the i.i.p.
• The euro area recorded discrepancies of 4% for both assets and liabilities, similar to last year.
• These discrepancies arose mostly from differences between the compilation and reconciliation processes for the euro area i.i.p.
• Other reasons behind the differences also affecting the remaining instrument types include discrepancies in vintages, data sources and estimation methods.
• Chart11 Financial account position discrepancies between the i.i.p.
• and RoW stocks of financial assets/liabilities) for the period Q3 2016 to Q2 2019 (i.i.p.


Further details of these comparisons are available in Tables A.9.1 to A.9.4 in the Annex.

7.3 Coherence with MFI balance sheet data

• Data on cross-border transactions and positions of the euro area MFI sector are recorded in the euro area b.o.p./i.i.p.
• and collected under the MFI Balance Sheet statistics (BSI)[50].
• data for the MFI sector and transactions in external assets and liabilities derived from the BSI statistics of euro area MFIs is essential for the construction of the monetary presentation of the balance of payments and its use for monetary policy purposes.
• [51] Furthermore, this consistency is also paramount for the compilers of euro area accounts, who use both datasets as building blocks.
• On these grounds, the ECB assesses the consistency between the two datasets in every regular production cycle, taking into account details by sector and instrument.
• Comparability issues were, however, observed for quarterly data on asset positions in equity.
• and BSI positions, were recorded for the euro area and explained by French data.
• In the case of loans and deposits, the highest discrepancy was found in Maltese liabilities, averaging 12% over the reference period.
• and monthly aggregated sources for BSI, which leads in particular to differences in valuation criteria (i.e.the b.o.p./i.i.p.
• are calculated at transaction/market prices, while BSI transactions are derived from positions reported at fair, cost or nominal value, depending on accounting practices).

Eurosystem

• Most of the discrepancies in the data for the Eurosystem as a whole (i.e.euro area aggregates) are related to the inclusion in the b.o.p.
• of estimates for foreign holdings of euro banknotes,[53] while in BSI statistics all holdings of euro banknotes are deemed in circulation in the euro area.
• At country level, the treatment of intra-Eurosystem technical claims is also a source of discrepancies, as these are included under remaining assets and liabilities without geographical breakdown in BSI, and under currency and deposits in the b.o.p./i.i.p.
• Additionally, the b.o.p.
• estimations for foreign holdings of euro banknotes are not included in BSI statistics.
• Further details of these comparisons are available in ChartsA.10.1 to A.10.6 in the Annex.

7.4 Coherence with money market fund statistics

• Data on cross-border investment in euro area money market fund (MMF) shares are recorded within the portfolio investment account of the euro area b.o.p./i.i.p.
• Data on assets and liabilities of euro area MMFs are also collected under BSI statistics[54], as MMFs is a sub-sector of MFIs.
• At the euro area level, the i.i.p.
• consistently exceeded the BSI outstanding amounts of MMF shares issued by euro area residents and held by non-euro area residents.
• At country level, small discrepancies were recorded in the period under review for Ireland, France and Luxembourg (the only countries in the euro area with relevant MMF activity).
• The discrepancies between the two sets of statistics were related to the use of different compilation methods in b.o.p., i.i.p.
• portfolio investment liabilities,[55] MMF liabilities are allocated geographically by respondents in BSI statistics.
• In such cases, the first counterpart the custodian or other intermediary may be known, but the final investor often is not.
• Further details of these comparisons are available in ChartsA.11.1 and A.11.2 in the Annex.

7.5 Coherence with investment fund statistics

• Details on cross-border investments in non-MMF investment fund (IF) shares are recorded in the b.o.p.
• statistics within portfolio investment.
• Data on IF assets and liabilities are collected under the Regulation on Investment Funds[56] (IF dataset).
• consistently exceeds the IF dataset in terms of euro area investment fund liabilities.
• The average absolute discrepancy reached a value close to 170billion for positions and 3billion for transactions throughout the period under analysis.
• The discrepancies at the euro area level are partly explained by the use of the residual approach to calculate portfolio investment liabilities (see Section7.4 above).
• In addition, while France displays a relative discrepancy of 5%, the average absolute discrepancy stands at 7.3billion for stocks.

7.6 Coherence with securities holdings statistics

• The ECB Guideline stipulates that portfolio investment collection systems of euro area countries shall as much as possible rely on s-b-s information (see AnnexVI of the ECB Guideline).
• In particular, it is stated that the target coverage is defined as follows: stocks of securities reported to the national compiler on an aggregate basis, i.e.not using standard (ISIN or similar) codes, should not exceed 15% of the total portfolio investment stocks of assets or liabilities.
• statistics and SHSS[57] provide consistent results, mainly because national portfolio investment assets and SHSS should rely on the same s-b-s sources of information.
• [58] This section compares the positions at market value of (i)debt securities and (ii)listed shares and investment fund shares/units as available in the SHSS dataset.
• [59] This analysis considers, on the SHSS side, the cross-border holdings by residents of each euro area country as collected by the respective country, as well as holdings by non-financial investors of each euro area country held in custody in other euro area countries (i.e.the so-called third-party holdings).

7.6.1 Debt securities

• For the euro area as a whole, the level of discrepancies for debt securities was 7% of the underlying i.i.p., which signals a good degree of consistency with SHSS.
• At the level of individual countries, there were, for the first time, no cases of relative discrepancies above 15% owing to SHSS under-coverage.
• Conversely, Cyprus recorded a difference slightly above 15% owing to over-coverage of SHSS amounts.
• This reflects the inclusion of third-party holdings data in SHSS in relation to long-term debt securities held by non-financial investors.
• [60] The decline in SHSS holdings by financial corporations other than MFIs of long-term debt securities issued by non-euro area countries explains, to a large extent, the (positive) b.o.p.-SHS gap.
• The lack of comprehensive coverage of non-ISIN securities data in SHSS,[61] the different revision policies for SHSS and the i.i.p., and the i.i.p.s attempts to cover securities held with custodians outside the euro area explain a significant part of this discrepancy.
• Further details of these comparisons are available in ChartA.13.1 in the Annex.

7.6.2 Listed shares and investment funds shares/units

• For the euro area as a whole, the total discrepancy as a percentage of the underlying i.i.p.
• At country level, discrepancies above the 15% threshold owing to SHSS under-coverage[62] were recorded in Italy, Portugal and Finland.
• Finally, Malta continued to report zero holdings of listed shares and investment fund shares within its b.o.p.
• statistics, meaning that indicators were not calculated for this country despite relevant amounts being reported in the context of SHSS for these instruments.
• To a large extent, the decline in SHSS holdings by financial corporations other than MFIs of listed shares and investment fund shares issued by non-euro area countries explains the positive b.o.p.-SHSS gap.
• The caveats mentioned for debt securities also hold when it comes to explaining this discrepancy.Further details of these comparisons are available in ChartA.13.2 in the Annex.

8 Asymmetries

• Asymmetries are an inherent feature of all statistics for which mirror data are collected, i.e.for which two countries collect the same type of information in relation to each other.
• In reality, however, for a variety of reasons it is rarely the case that two data sources provide exactly the same results, and this leads to the emergence of asymmetries.
• Asymmetries can be observed at the level of the global economy (where total world assets should equal total world liabilities), at the level of geographical aggregates (where total intra-euro area assets should match total intra-euro area liabilities) and at the level of bilateral pairs (where flows and positions between pairs of countries should match perfectly).

8.1 Intra-euro area asymmetries

Charts 12 and 13 provide an overview of intra-euro area asymmetries in the current and capital accounts and the financial account respectively. Chart 12 Intra-euro area current and capital account asymmetries (EUR billions)
• Current and capital account asymmetries (credits minus debits) were always positive over the period under review.
• The main contributors to the overall asymmetries show structural biases: consistently positive asymmetries in goods and services accounts, with negative contributions being made by the primary income account.
• Chart13 Intra-euro area financial account asymmetries (EUR billions)
• In the financial account, asymmetries were mainly recorded in direct and other investment.
• Portfolio investment and related income do not show asymmetries by construction, owing to the residual compilation approach at the euro area level.
• Financial account asymmetries were fairly volatile in the period under review, with periods where asymmetries in direct and other investment offset each other alternating with periods where they both contributed in the same direction to the overall asymmetry.

8.2 Bilateral asymmetries

• Balance of payments (b.o.p.) and international investment position (i.i.p.) data underpin the construction of the following three headline indicators:
  • current account balance (percentage of GDP), three-year backward-moving average (up to 13 years of data required);
  • net international investment position (percentage of GDP) (up to ten years of data required);
  • export market share (percentage of world exports), five-year percentage change (up to 15 years of data required).
• Additionally, b.o.p. and i.i.p. data are also used for five auxiliary indicators:
  • current plus capital account balance (net lending/borrowing) (percentage of GDP) (ten years of data required);
  • net international investment position excluding “non-defaultable” instruments[64] (NENDI) (percentage of GDP) (ten years of data required);
  • foreign direct investment in the reporting economy, flows (percentage of GDP) (ten years of data required);
  • foreign direct investment in the reporting economy, positions (percentage of GDP) (ten years of data required);
  • export performance against advanced economies (percentage of OECD exports), five-year percentage change (15 years of data required).
  • Quarterly bilateral transactions and positions between euro area countries are transmitted to the ECB on a voluntary basis, hence a full bilateral dataset is not yet available.
  • Owing to data availability, the analysis of bilateral asymmetries between euro area countries is performed only for direct investment.
  • The internal and external country geographical quality indicators (ICGQ and XCGQ respectively) are measures that summarise the quality of the geographical breakdown.
  • The ICGQ aims to assess the accuracy of individual countries geographical classification within the sample of countries for which bilateral data are available by aggregating absolute bilateral asymmetries.
  • More information on these indicators can be found in the section on Methodological documentation for quality indicators.
  • The results of the ICGQ indicator for FDI transactions were characterised by significant variability across countries and over time.
  • Several countries consistently recorded high scores across the entire time period, indicating structural problems in matching counterparties transactions.
  • Meanwhile, the majority of countries experienced high volatility in the measures over time, pointing to quarter-specific problems in capturing the geographical detail of transactions, rather than structural issues.
  • Consequently, most of the countries performed relatively well across the entire time period.
  • This finding is obviously welcome from the point of view of the quality of overall euro area data.
  • For both quality measures, the results recorded for FDI positions were better than those observed for transaction data.
  • Further information on summary indicators of bilateral asymmetries is available in TablesA.14.1 to A.14.4 in the Annex.
  • Together, these indicators provide analytical evidence of possible vulnerabilities and risks that would require further investigation at country level.
  • Breaks are also present in Q4 2015 owing to a change to the estimation method applied for portfolio investment debt securities liabilities.
  • Croatia: Breaks are observed for stocks of financial derivatives assets (2014), stocks of direct investment assets (between 2010 and 2014), and secondary income (2013).
  • and the RoW account (national accounts) were removed with the introduction of ESA 2010 and BPM6.
  • The CMFB endorsed a medium-term work plan designed to eliminate most discrepancies by September 2019.
  • The remaining discrepancies will be analysed in depth by ECB and Eurostat, and the most relevant outstanding differences will be addressed.
  • [65] Nonetheless, with one minor exception (Ireland), none of the discrepancies recorded was above 2% of GDP.
  • and the RoW account are more pervasive, totalling more than 10% of GDP in three cases: France (assets only), Malta and Croatia (assets only).
  • MIP Annex Table1 Annual absolute revisions balance/net items for 2017 (percentage of GDP)