This annual report provides a quality review of the national balance of payments (b.o.p.), international investment position (i.i.p.) and the international reserves template of the Eurosystem (international reserves), as well as the associated euro area aggregates. The report fulfils the formal requirement obliging the ECB Executive Board to inform the Governing Council of the quality of these statistics, as set out in Article 6(1) of Guideline ECB/2011/23 (hereinafter the “ECB Guideline”). Furthermore, the report provides information supporting the Macroeconomic Imbalance Procedure (MIP) data quality assurance process, as laid down in the “Memorandum of Understanding between Eurostat and the ECB/DG‑S on the quality assurance of statistics underlying the MIP” (“the MoU”).
The main principles and elements guiding the production of ECB statistics are set out in the statistics quality framework (SQF) and quality assurance procedures, published on the ECB’s website. This report therefore provides a quality analysis of the statistical output, covering the elements of: (i) methodological soundness, (ii) timeliness, (iii) reliability and stability, (iv) internal consistency (completeness and validation, and net errors and omissions) and (v) external consistency/coherence with other comparable statistical domains (euro area accounts, foreign trade in goods statistics, Monetary Financial Institutions (MFI) balance sheet items, money market funds, investment funds and securities holdings statistics).
The descriptive and quantitative indicators used throughout this report are based on monthly data from September 2015 to June 2018 (unless otherwise indicated) and on quarterly data from the third quarter of 2015 until the second quarter of 2018 (unless otherwise indicated). Data and revisions published up to 20 October 2018 are included. Supporting tables/charts and details of how the indicators are computed can be found in the respective annexes to this report.
Given the specific requirements of the MIP and the responsibilities entrusted to the ECB in the context of the MoU, the box at the end of the report presents some indicators relating to the fitness for purpose of the data for all EU countries. The box draws on annual data up to 2017 and revisions up to 2016 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 2017 and 2018
In general, euro area countries have fully implemented the sixth edition of the International Monetary Fund’s (IMF’s) Balance of Payments and International Investment Position Manual (BPM6) and the respective ECB data requirements. This has enabled national compilers (national central banks (NCBs) and national statistical institutes (NSIs)) to report and make publicly available relevant data 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.
While statistical standards are generally observed, there is still room for improvement in terms of methodological soundness. Luxembourg, the Netherlands, Malta and Cyprus are encouraged to continue working to increase the coverage and quality of data for special purpose entities (SPEs). Greece should start reporting data for financial intermediation services indirectly measured (FISIM). Belgium, Germany, France and Lithuania should improve their reporting of financial derivatives positions/transactions for general government. Regarding foreign direct investment, some countries should make an extra effort to correctly report transactions (and associated positions) in debt securities and trade credits between companies engaged in foreign direct investment relationships in the appropriate functional category. The Netherlands should correctly identify transactions and positions between fellow enterprises, particularly as regards debt instruments. Furthermore, national compilers in general should make 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. Ireland should put in place contingency measures in order to ensure that such restrictive situations do not reoccur. In terms of data availability, Malta should take the necessary steps to start reporting complete datasets for monthly and quarterly other flows as soon as possible.
Regarding accuracy and reliability, most countries record regular revisions that do not fundamentally change the economic assessment of first vintages. However, countries are encouraged to regularly report to the ECB information on major events and revisions (by means of the so-called metadata template) and hence increase transparency and the analytical value of the data for policy use.
Concerning internal consistency, the large majority of countries provide fully consistent data to the ECB. Austria has made improvements; however, further efforts are needed to increase the consistency of monthly and quarterly data for goods and the current account as a whole. Ireland has also improved the consistency of monthly and quarterly data, but at the expense of the reliability of the revised monthly data. Belgium and Finland should ensure that quarterly positions and flows are reconciled for all periods.
With regard to consistency/coherence with other datasets, overall b.o.p./i.i.p. data are in line with other datasets, thus ensuring comparability across statistical domains. However, it is of utmost importance that all countries follow the agreed steps to ensure full consistency vis-à-vis sectoral accounts. Regarding other datasets, the ECB encourages b.o.p./i.i.p. colleagues to interact with their counterparts to structurally reduce discrepancies and/or to reconcile differences between datasets when there are objective methodological differences.
In particular, methodological differences between financial accounts and b.o.p./i.i.p. statistics were removed with the introduction of the new European System of National and Regional Accounts (ESA 2010) and BPM6. It is therefore critical that all countries follow the agreed steps to ensure full consistency.
The European System of Central Banks (ESCB) working groups on Financial Accounts (WG FA) and on External Statistics (WG ES), along with other sub-structures of the Statistics Committee (STC), 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 (OFIs) 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.
On the basis of this report, a list of notable issues affecting certain euro area countries, as well as the scope for improvement, is provided in Table 1 below.
Notable issues and scope for improvement (euro area countries)
1) According to BPM6 standards, margins on buying and selling financial assets should be included in the service account. However, due to the complexity of including this item in the accounts, the WG ES, in cooperation with national compilers, will provide guidance for estimating margins in the EU.
2) The WG ES is committed to provide guidance on the determination of the functional category for hybrid instruments. As a general rule and as specified in BPM6, transactions and positions in debt securities between companies engaged in a direct investment relationship should be recorded in direct investment. On this basis, the WG ES will provide updated and encompassing guidance on the recording of transactions and positions in debt instruments between companies engaged in a direct investment relationship.
3) The implementation of this recommendation is linked to the update of the ECB Guideline, which requires a breakdown of debt instruments in direct investment (including debt securities, loans, trade credits and other).This recommendation also impacts other investment.
4) This also applies to the Central Bank of Malta in terms of completeness and validation checks. Malta has started to report these data, but further efforts are required to achieve a complete and validated dataset.
5) 6) Finland has started to report the data, although only in transmissions more recent than those relevant for this report. Longer time series will be provided according to the revision policy.
7) The coherence between quarterly investment position data and IVF statistics has improved compared to the 2017 report. The remaining discrepancy will be resolved by September 2019.
Statistical issues affecting MIP indicators
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 A11, C3, F1 and G1 in Table 1). Other recommendations, such as those related to functional classification (e.g. A5.1 to A5.3) or to the reconciliation of stocks and flows (C1, E2), do not impact the computation of the main MIP indicators but play a role in the calculation and analysis of auxiliary indicators. However, the particularities of the annual data and of the process, as well as the scope of the ECB’s responsibilities in the context of the MoU on the MIP (all EU NCBs that are responsible for the compilation of b.o.p./i.i.p. statistics), create special analytical needs. In particular, longer time series (up to 15 years) 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 very few exceptions for the goods and services balance). However, further data are needed for the calculation of auxiliary indicators (in particular the new auxiliary indicator, net international investment position excluding non-defaultable instruments as a percentage of GDP) from Bulgaria, Croatia, the Czech Republic, Greece, Malta, Latvia and Romania.
All in all, the impact of revisions on the direction (information) of first assessments is relatively minor. National errors and omissions in general remained stable in the last review period, but they are on average still above 2% of GDP in Malta, Finland, Bulgaria, Hungary and Sweden (see Chart MIP 1). Furthermore, a bias (higher than 2% of national GDP in the period 2015‑17) was identified in the net errors and omissions of Malta, Slovakia, Finland and Denmark. Last but not least, the analysis shows that discrepancies between b.o.p./i.i.p. statistics and sectoral accounts persist for several countries, negatively affecting the analytical combination of the two datasets as well as indicating a lack of reliability or adequacy to the methodology of at least one of the two statistics. Despite this, the situation has improved compared with the previous quality report.
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.
This annual report provides a quality review of statistics on the balance of payments (b.o.p.), international investment position (i.i.p.) and the international reserves template of the Eurosystem (international reserves). It fulfils the formal requirement obliging the ECB Executive Board to inform the Governing Council of the quality of these statistics, as set out in Article 6(1) of Guideline ECB/2011/23 (hereinafter the “ECB Guideline”). Furthermore, the report provides information supporting the Macroeconomic Imbalance Procedure (MIP) data quality assurance process, as laid down in the “Memorandum of Understanding between Eurostat and the ECB/DG‑S on the quality assurance of statistics underlying the MIP”. The report follows the recommendations adopted by the Committee on Monetary, Financial and Balance of Payments Statistics (CMFB).The focus of the report is on national data for euro area countries and euro area aggregates. The data for EU Member States are commented on in the MIP box at the end of the report and are also available in the annexed tables.
Scope of data coverage and structure of the report
This report analyses a number of aspects by which data quality can be measured. These include (following the order they are presented in the report): (i) a review of methodological issues where national compilers diverge from statistical standards; (ii) an assessment of compliance by NCBs with their obligations to transmit data to the ECB, in terms of timeliness and coverage; (iii) the reliability of the statistical data; (iv) the internal consistency of the statistics, particularly as regards consistency over time, across frequencies and between accounts (net errors and omissions); and (v) external consistency/coherence, i.e. consistency vis-à-vis other statistical domains/datasets, namely foreign trade statistics, euro area (sector) accounts, MFI balance sheet statistics, investment fund statistics and securities holdings statistics.
The analysis covers quarterly and (in the case of euro area aggregates) monthly data. Sections 3 (timeliness and punctuality), 4 (data and metadata availability) and 6.1 (validation/integrity rules) focus on one year of observations (July 2017/Q3 2017 to June 2018/Q2 2018). Section 5 (accuracy and reliability) analyses the impact of three years of revisions (April 2015/Q2 2015 to March 2018/Q1 2018), and the remainder of the sections focus on three years of data (Q3 2015 to Q2 2018).
The last data vintage used throughout the report is the one available as of 20 October 2018 and the country coverage is the EU28, although the body of the report only addresses the quality of the data for the 19 countries of the euro area.
Given the specificities of the MIP process, some indicators on the fitness for purpose of the data are presented in a box at the end of the report for all European Union Member States. The need for such a box arises from the fact that annual data display different properties compared with monthly and quarterly data, as well as from the need to assess the quality of data from non-euro area EU countries. The box draws on annual data up to 2017 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. All indicators presented in the MIP box relate to national GDP to facilitate the analysis relating to the actual MIP scoreboard indicators.
2 Methodological soundness and statistical procedures
Methodological soundness means that concepts and definitions used to compile b.o.p./i.i.p. statistics are in broad conformity with the principles and guidelines outlined in BPM6 and take into consideration the agreements of the STC (and respective sub-structures) for the compilation of euro area aggregates.
Since the start of the BPM6 changeover, the focus has been on producing complete and consistent BPM6 data. One of the key elements of compiling consistent data is to adhere to the agreed standards and to transparently describe deviations. A detailed and up-to-date description of the data sources and compilation methods used by all Member States is available on the ECB’s website. Most of 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.
In this quality report, a succinct overview of the methodological soundness of b.o.p. and i.i.p. data is provided for the main dimensions/principles.
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 so-called 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. In 2018, Cyprus and Malta revised the geographical allocation of their foreign direct investment positions from reference period 2016, as reflected in an improvement in the bilateral asymmetry indicators regarding direct investment positions (see Section 8.2). The revisions in the geographical allocation of positions for Malta introduced a series break in Q1 2016; back data are scheduled to be provided gradually. Luxembourg covers in its SPE survey all SPEs with a balance sheet of over €500 million. Grossing up is performed for SPEs with balance sheets of between €300 and €500 million and assets/liabilities vis-à-vis the rest of the world, which results in a final coverage of approximately 90% of total assets/liabilities.
The Netherlands has also improved its coverage of SPEs compared with 2017; in particular, it has clarified the residency and improved the registration of those entities that are registered in two countries and increased the coverage of non-financial corporations (NFCs) in the context of the integration of the b.o.p. and RoW compilation. The geographical allocation of the grossed up figures still has room for improvement.
2.2 Functional classification
Most countries classify b.o.p. and i.i.p. data by function in conformity with BPM6 methodology. However, there is still room for improvement.
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 to the residual approach used to calculate euro area portfolio investment liabilities. Therefore, the ECB will provide updated and encompassing guidance on the recording of transactions and positions in debt instruments between companies engaged in a direct investment relation. Similarly, trade credits and advances between companies in a direct investment relationship are included in other investment by Belgium and Spain. Germany classifies all transactions and positions in loans/deposits as other investment if at least one of the counterparts under a direct investment relation is an MFI.
Transactions and positions between fellow enterprises are not fully recorded under foreign direct investment. In particular, the Netherlands does not yet identify transactions and positions in both equity and debt instruments, while Germany, Greece, Austria, Slovenia and Slovakia do not include transactions and positions in equity. France has started to record transactions and positions between fellow enterprises in equity from Q1 2018 onwards, although both it and Belgium occasionally report negative positions. Moreover, Belgium, Germany, France, Lithuania, Austria, Slovenia, Slovakia and Finland do not identify reverse direct investment in equity, and Malta shows negative values for positions.
Financial intermediation services indirectly measured are not yet classified in the services account in Greece, remaining instead with income. Similarly, service margins on buying and selling financial assets are not recorded by a large number of countries, namely Belgium, Germany, Greece, Spain, France, Luxembourg, Malta and Slovakia. Given the complexity of this issue, the WG ES has started investigating approaches to define best practices and support those countries that have not yet estimated this financial service. Work is ongoing and concrete output results are expected from 2020 onwards.
According to public metadata, Cyprus and Luxembourg do not currently estimate compensation arising from employee stock options.
Greece has included in its official statistics the results of a new estimation method for sea transport services. The model represents a significant improvement relative to the previous method of estimating sea transport services. A task force for the recording and compilation of maritime transactions in national accounts and balance of payments is currently mandated to identify data collection methods and compilation techniques that are feasible for addressing the coverage and consistency of maritime activities in the accounting frameworks.
Belgium and Germany should improve the quality of transactions in financial derivatives for the government sector, as they are either directly derived as the difference of positions or are zero. In addition, France and Lithuania do not record any transactions and positions in financial derivatives by the government sector. There is also scope for increasing the quality of financial derivatives data in general. The WG ES, in cooperation with the WG FA, has mandated a task force to issue recommendations on data sources and data collection and compilation methods. The task force is scheduled to provide guidance in 2020.
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. statistics. Most euro area countries follow the new guidance in a timely and accurate manner, at least from reference period January 2014 onwards – with the exception of Ireland, Malta and Finland.
Furthermore, Ireland does not correctly report monthly intra-Eurosystem technical claims and liabilities, while Finland does not report correct values for either monthly or quarterly data.
Ireland (for financial corporations other than the MFI sector) does not cover assets related to insurance, pension schemes and standardised guarantee schemes, while Finland and Malta do not cover either assets or liabilities.
Furthermore, Malta does not report the breakdown of equity (listed and unlisted shares, other equity and investment fund shares).
In general, most countries have difficulties in producing an accurate estimation of b.o.p. transactions and i.i.p. for the household sector; this under-coverage is believed to be relevant in particular for assets held (including with custodians) outside the euro area. Most euro area countries use mirror data from (i) the locational banking statistics of the Bank for International Settlements (BIS) to cover deposits and loans vis-à-vis non-resident banks, and (ii) so-called third-party holdings collected in the context of securities holdings statistics. Finland does not include any adjustment for household assets held abroad. The Netherlands does not observe securities held by households with custodians outside the Netherlands. Additionally, Germany does not collect information on securities held in custody abroad by non-bank enterprises and does not include an estimation. Many countries also have difficulties in accounting for real estate holdings, both of resident households abroad and non-resident households in the respective euro area countries.
The majority of euro area countries estimate to varying degrees the impact of illegal economic activities. According to the b.o.p./i.i.p. book and bilateral communication, Portugal does not currently include an estimation regarding illegal trade in goods activities requested at EU level (smuggling, trafficking, illegal drugs).
Last but not least, national compilers in general should improve the measurement of reinvested earnings on foreign direct investment and the valuation of unlisted shares and other equity.
2.4 Other methodological issues
Horizontal and vertical inconsistencies in the i.i.p. data of Malta prevent the ECB from validating the figure reported for net external debt. Only the Maltese net external debt total is available and not its presentation by sector, instrument and original maturity.
France and Germany estimate accrued interest for debt securities under portfolio investment income on a security-by-security (s-b-s) basis; however, no equivalent entry is recorded in the underlying instrument in the financial account.
For Ireland, other volume changes owing to changes in methodology or coverage are in some cases reported together with (exchange rate and price) revaluations, impacting the reliability of its position/flow reconciliation. However, there have been improvements in portfolio and other investment in terms of plausibility between sectors and instruments.
With its publication of i.i.p. data for 2017, Germany implemented a change in the way debt securities liabilities are compiled (full residual approach). Previously, debt securities positions were calculated as cumulated b.o.p. transactions. As a consequence, portfolio investment liabilities are higher since reference period 2015.
Ireland does not value monthly reserve assets at end-month market prices (including for exchange rate changes), thereby not reporting revaluation changes for inter-quarter months.
In the case of Ireland, the monthly estimate for goods (according to the community concept) does not display the expected seasonal pattern and often generates negative values for both exports and imports for the community and national concepts. The quality of monthly Irish data has an impact on the quality of euro area aggregates for goods. Furthermore, the revisions for the monthly estimate for goods (see Section 5) display erratic patterns.
3 Timeliness and punctuality
Non-compliance is defined with regard to (transmission) timeliness/punctuality and quality standards vis-à-vis the requirements laid down in the ECB Guideline ECB/2011/23 (as amended).
In the period under review (reference period July 2017 to June 2018), a persistent non-compliance case was recorded in the case of the Central Bank of Malta for not reporting data on the breakdown of quarterly “other flows”. In addition, the following ad hoc cases of non-compliance were recorded:
The Central Bank of Ireland transmitted data for reference period August 2017 with a 24‑hour delay due to severe weather conditions in the country.
The Central Bank of Malta did not report mandatory series for reference periods Q4 2017 and Q1 2018. 24 mandatory series were missing for quarterly b.o.p. in both periods, while 128 and 98 series were missing for quarterly i.i.p., respectively.
4 Data and metadata availability
For the reference period July 2017 to June 2018, the production of b.o.p., i.i.p. and international reserves statistics was smooth.
In terms of completeness, the large majority of countries submit all the mandatory items, albeit sometimes with delays (thus giving rise to cases of non-compliance – see Section 3 above). While complete datasets were eventually transmitted to the ECB, some delays in the correction of data quality issues detected during the data validation phase adversely influenced the production process and in some cases 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, all national data must be transmitted with a flag indicating its level of confidentiality. The ECB encourages national compilers to make as much data available as possible to final users (i.e. by marking observations as “free for publication”) and to ensure that statistical confidentiality flags are appropriately used.
Table 2 below summarises the share of observations marked as “free for publication” for the data requested under Tables 2A and 4A of Annex II of the ECB Guideline (i.e. “main items”). The shares are calculated at dataset level for the reference period Q3 2017 to Q2 2018. Table A.1.1 in the Annex shows the same indicator for “all (mandatory) items” transmitted under the ECB Guideline.
Average share of observations marked as “free for publication” per dataset (main items), for the period Q3 2017 to Q2 2018
The majority of euro area countries (Belgium, Germany, Estonia, Greece, France, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Austria, Slovenia and Slovakia) release all “main items” to the general public. The remaining countries (Spain, Cyprus and Portugal) release more than 90% of this dataset, while only Ireland and Malta release less than 90% of the observations in Tables 2A and 4A of Annex II of the ECB Guideline. With respect to last year’s assessment, Finland considerably increased its share of main items available for publication (from around 60% to 95%), while Malta recorded a small deterioration for quarterly i.i.p. It should be noted that the percentages are calculated based on the number of observations without taking into account their magnitudes.
Full monthly b.o.p. datasets are flagged as “non-publishable” or “confidential” by Ireland, Cyprus, the Netherlands and Austria (generally on the basis of national dissemination policies). Concerning the full extent of quarterly data transmitted to the ECB (Tables 2A and 4A being only a small subset), six euro area countries for quarterly b.o.p. and nine euro area countries for quarterly i.i.p. have made all the data required in the legal act available to final users, in line with last year’s results (see Table A.1.1).
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).
The ECB publishes monthly and quarterly b.o.p. and quarterly i.i.p., price revaluations and other changes in volume for the euro area as a single economic area. Twelve monthly and four quarterly press releases, outlining the latest data and relevant economic developments, are published through wire services and on the ECB’s website. Furthermore, dissemination dates for all press releases are announced at the beginning of each calendar year in the Statistical Calendars of the ECB.
The concepts and definitions used in the euro area b.o.p. and i.i.p. statistics are in line with international statistical standards. The “B.o.p. and i.i.p. book”, made available on the ECB website, aims at providing users with an overview of the main features of the b.o.p. and i.i.p. methodological framework and of the data sources and compilation methods used by the ECB (for the euro area) and in individual EU Member States.
The data can be accessed via the ECB’s Statistical Data Warehouse or in the External Transactions and Positions section of the Statistics Bulletin. 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. The aim of this dedicated website is to facilitate the understanding, use and comparison of euro area and national statistics by presenting the data in a user-friendly manner. Another feature of the website is the possibility of easily downloading or sharing data by embedding the graphics into other websites, emails or social media.
Table A.1.2 in the Annex presents a summary of the national practices regarding data and metadata accessibility. Similarly to the ECB, all euro area countries provide technical facilities to download data in different formats (Excel tables, CSV files, PDF documents or via interactive statistical databases). Furthermore, the majority of euro area countries have statistical and/or economic bulletins providing a visual representation of the data in the form of charts, graphs and/or tables. Most euro area countries publish regular press release updates on their websites: some on a monthly and quarterly basis and others either quarterly or monthly. Last but not least, all countries present extensive information on the institutional environment and statistical processes in the “B.o.p. and i.i.p. book”, as well as on their national websites.
4.3 Availability of metadata
Last but not least, the ECB Guideline requires that the “data shall be accompanied by readily available information on single major events and on reasons for revisions, when the magnitude of the change to data caused by such single major events or revisions is significant […]”. Therefore, national compilers are encouraged to make regular and consistent use of the metadata template in all production cycles and publication means.
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 needs 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.
In this report, quarterly revisions (for all euro area countries and for the euro area as a whole) and monthly revisions (for the euro area as a whole only) are assessed using indicators based on the comparison between first and “last”/most recent assessments.
Different indicators are applied depending on the features of the time series in question. Two basic types of indicators are used:
Relative size indicators measure the difference between the first and last assessments either in relation to the underlying series (when strictly positive) (symmetric mean absolute percentage error, SMAPE) or otherwise in relation to a reference series (e.g. GDP or the underlying outstanding amounts for b.o.p. financial transactions) (mean absolute comparative error, MACE). In the case of non-strictly positive (net/balance) time series, revisions cannot be properly related to the series value itself because observations may have different signs and, even more importantly, the value of the series may be close to zero. Therefore, for net/balance series the indicator used is the net relative revisions (NRR). The NRR puts the absolute revisions in relation to the average underlying gross flows for current account items and average positions of assets and liabilities for financial account transactions and positions. Owing to the different denominators employed, the SMAPE, MACE and NRR are not directly comparable.
Directional stability/reliability indicators measure how frequently first assessments are revised in the same direction (the upward revisions ratio and the directional reliability indicator).
All charts depict the indicators calculated for a revision window of three years (Q2 2015 to Q1 2018 for national and euro area aggregates – quarterly series – and April 2015 to March 2018 for euro area aggregates – monthly series).
In general, the revisions recorded for the period Q2 2015 to Q1 2018 are not fundamentally different from the equivalent period analysed in last year’s quality report.
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 Chart 1 below. The euro area aggregates recorded revisions comparable to the euro area country median (2% for the total current account), with the monthly data recording slightly higher revisions.
Cyprus and Malta have the highest revisions among euro area countries for current account credits and debits with a SMAPE value of over 10%. Generally, Cyprus displays a random pattern when revising its current account upwards or downwards. On the other hand, Malta revised its current account downwards in the majority of cases (as explained by a significant downward revision of its secondary income credits and debits). Cyprus shows high directional reliability for its revisions, whereas Malta shows a lesser degree of directional reliability.
In terms of current account sub-items, Ireland displays a significant number of monthly revisions with a SMAPE value of almost 50%. Furthermore, the data show a weak degree of directional reliability with revisions often altering the meaning conveyed by the first assessments. These revisions have a negative impact on the quality of the monthly euro area aggregates.
Revisions to current account credits and debits
(symmetric mean absolute percentage error (SMAPE))
Concerning revisions to the current account balance (see Chart 2 below), the euro area as a whole records comparable revisions to the median of euro area countries (1%). Monthly revisions are slightly higher than quarterly revisions as assessed by the NRR indicator.
For the current account balance, the highest number of revisions is recorded by Ireland and Finland.
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. item for assessing their relative size. MACE is therefore used to assess revisions to the financial account.
The quarterly euro area aggregates recorded revisions of 0.3% of the underlying positions for total transactions in financial assets and liabilities, which is comparable with the median of euro area countries. Revisions to monthly euro area aggregates were considerably higher, as can be seen in Chart 3 below. Monthly revisions to euro area data for direct investment were the highest, at over 1% for both assets and liabilities, followed by revisions to other investment and portfolio investment.
Euro area countries record revisions of less than 1% of the underlying positions for quarterly financial transactions. The highest revisions are recorded by Luxembourg, the Netherlands, Austria and Finland. However, this level of revisions is not significantly higher than for most euro area countries.
Revisions to financial account
(mean absolute comparative error (MACE))
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, Slovakia and Finland recorded the highest level of revisions among euro area countries (see Chart 4 below).
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
Revisions to quarterly i.i.p. (financial account positions) are shown below in Charts 5 and 6. The euro area as a whole recorded revisions (as measured by SMAPE) of approximately 2% for both assets and liabilities, double the median of euro area countries.
At country level, revisions are in general comparable for assets and liabilities. Belgium, Cyprus, Ireland, Luxembourg and the Netherlands recorded the highest revisions in the euro area. All of these countries revised upwards their first assessments of total i.i.p. (for both assets and liabilities) by more than 80%. However, the degree of directional reliability is in general higher than 60% in all the above-mentioned cases.
Revisions to the international investment position
(symmetric mean absolute percentage error (SMAPE))
Regarding revisions to net i.i.p., the euro area as a whole recorded revisions of 1.1% of the underlying average positions during the analysed period (equal to the median level of revisions for euro area countries). A slightly higher number of revisions (between 1.8% and 2.7%) was recorded in net positions for the various functional categories (direct, portfolio and other investment). In the case of other investment, the euro area revisions were above the observed median for euro area countries. This is partly explained by the introduction in April 2017 of a model estimating the euro currency in circulation outside the euro area as well as the euro area errors and omissions correction model.
For individual countries, the highest number of NRR for net i.i.p. was recorded by Slovakia, reaching a level of 3.6%.
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. These include linear constraints that apply to the b.o.p., i.i.p. and international reserves template statements, namely whether credits/assets minus debits/liabilities match the respective net flows/positions for each item, and whether sub-items add up to the respective items/totals, etc. 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 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 quarterly data generally have more validation issues compared with monthly data, but the failed validations in both frequencies did not impair the overall quality of the national data or euro area aggregates.
The results are fundamentally in line with last year’s assessment, with the exception of Ireland, for which a larger number of validation problems were detected in the monthly data (especially in counterpart and resident sector breakdowns, international accounting items and regular negative gross flows in the current account), due to an incorrect methodology to reconcile monthly with quarterly data. Furthermore, Malta recorded validation problems in some “of which” items of the quarterly b.o.p. and small inconsistencies in the intra/extra-EU geographical breakdown. Finally, reconciliation issues affected the data for Malta (due to incomplete other flows) and Belgium (mostly for financial derivatives).
Consistency between datasets is very important to ensure the overall quality of the b.o.p. As a result, the average time consistency (ATC) and the average relative explained changes (AREC) can be used as indicators to summarise consistency problems between frequencies and between positions and flows, respectively.
In terms of time consistency, the vast majority of countries exhibit full consistency between monthly and quarterly data, with only a few exceptions. Austria improved time consistency for most of its current account items but failed a number of validations for the goods item, with the highest discrepancy recorded for extra-euro imports of goods. Moreover, Ireland displays for most of the analysed b.o.p. and i.i.p. series a level of time consistency below the euro area median, with extra-euro area secondary income showing consistent monthly and quarterly values in only 89% of cases (see Table A.8.1 in the Annex for more details).
In terms of average reconciled amounts for main items, all countries achieve a full reconciliation between positions and flows, with the exception of Malta, which does not provide complete information on other flows (see Tables A7.5‑6 in the Annex for more details).
Although transmissions of back data are non-mandatory, greater efforts by national compilers have resulted in the availability of longer time series for analytical use, including in the context of the MIP. While most countries have provided complete and validated datasets for periods before 2013, there are still several cases where these data are either incomplete or pose serious validation problems. In general, despite improvements in data coverage and quality, it is of utmost importance that countries continue their efforts to provide back data of acceptable quality as agreed by the WG ES.
Regarding series breaks, the following issues can be identified:
Belgium: revisions due to changes in sources and methodology have been implemented only back to Q1 2014, creating a break in the direct and portfolio investment series;
Germany: major breaks are observed due to the reclassification of positions between fellow companies from other investment to direct investment in Q4 2012;
France: breaks are observed for secondary income credits of other sectors in Q1 2014, mainly reflecting the inclusion of net non-life insurance premiums and claims, and social benefits;
Luxembourg: relevant series breaks in foreign direct investment positions are observed in Q4 2014 as a result of changes in the coverage of SPEs;
Malta: a relevant break is observed in secondary income in Q1 2016 due to the revised methodology for personal transfers in the gaming industry;
Austria: certain breaks apply in primary income credits and debits (from Q1 2013 to Q1 2016).
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.7.1‑3 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. In fact, the principle of double-entry bookkeeping implies that the sum of all credit and debit transactions should be equal to zero in the b.o.p. statement (i.e. that n.e.o. are zero). Normal random imbalances commonly result from imperfections in source data and compilation practices. However, if these imbalances are large and/or persistent, they indicate problems in sources and/or methods.
In the context of b.o.p. 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. Such mechanisms are typically incorporated in the compilation system and are applicable during each data production round. At euro area level, a correction mechanism that minimises net errors and omissions is also in place. The rationale behind the adjustment is that certain items in portfolio investment and other investment categories are not appropriately captured in the aggregation of national data.
The average absolute error relative to the current account provides a measure of the magnitude of net errors and omissions in relation to average gross current account flows. Chart 7 below provides a graphical representation of the situation in euro area countries and on aggregate (Chart A.7.7 in the Annex shows the average absolute n.e.o. in relation to the i.i.p.).
Overall, the analysis in this year’s quality report is in line with the analysis presented in last year’s quality report.
As expected, the euro area as a whole did not exhibit high n.e.o. compared with individual euro area countries. Monthly errors and omissions were substantially higher than quarterly ones (the average absolute n.e.o. relative to average gross current account flows was 7% for monthly data and less than 2% for quarterly data).
Quarterly n.e.o. for euro area countries generally exceeded 2% of the average current account gross flows. Over the period under analysis (Q3 2015 to Q2 2018), Finland displayed the highest average n.e.o. as a percentage of average current account gross flows at 17% (Ireland, which had the second highest n.e.o. in the euro area, recorded a value three times smaller than Finland). Countries are encouraged to continuously monitor the quantity of and reasons behind their n.e.o. and address structural problems as soon as possible.
Relative net errors and omissions
(average absolute net errors and omissions relative to average gross current account flows)
The persistency 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 displays the cumulative n.e.o. in relation to current account gross flows.
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 statistical bias in their n.e.o. A clear exception is Finland, with a negative bias in its n.e.o. of more than 10% in absolute terms of its average gross current account flows during the period Q3 2015 to Q2 2018.
Values for the validation indicators (including n.e.o.) are available in Tables A.7.1‑7 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, investment fund balance sheet 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. goods account in all euro area countries. 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. follows the so-called change-of-economic-ownership principle, whereas ITGS record physical cross-border movements of goods.
Given the methodological differences between the two datasets, a direct comparison would not convey an accurate picture. Instead, a directional reliability indicator is used to assess whether b.o.p. and ITGS data exhibit consistent developments and can hence be used as complementary analytical data sources. Furthermore, several countries publish reconciliation tables between the two datasets, which are available on the websites of the respective national central bank or national statistical institute.
Table A.8.1 in the Annex illustrates the individual national directional reliability indicators for the period from Q3 2015 to Q2 2018, for counterpart areas “rest of the world” and “extra-euro area”. The results are comparable to the analysis described in last year’s quality report.
For the euro area as a whole, there is full directional reliability for both imports and exports. Six euro area countries displayed full directional reliability for both exports and imports for the two counterpart areas analysed. A limited number of countries, including Malta, showed a lower degree of directional reliability. On average, data for exports/credits were more directionally reliable than for imports/debits.
It should be noted that full directional reliability is not necessarily a sign of quality and that inconsistencies in the developments of the two datasets may be explained by the economic structure of the external trade in goods account of the respective country.
7.2 Consistency with euro area sector accounts
Euro area b.o.p. and i.i.p. data constitute one of the so-called 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.
The methodological differences between b.o.p./i.i.p. and the RoW account (national accounts) were removed with the introduction of ESA 2010 and BPM6, even though some interpretation challenges still remain. However, the analysis shows that inconsistencies between the two statistical domains persist in several Member States, negatively affecting the combined use of the two datasets and their reliability. Acknowledging this, the ESCB has worked to precisely identify the differences and to develop national medium-term work plans to be generally observed by September 2019. In this context, the removal of inconsistencies between the two statistical domains is progressing in the EU and some countries already compile the two sets of statistics in a consistent manner.
7.2.1 Current account
Chart 9 shows the differences between the b.o.p. and RoW current account. As an indicative benchmark, the absolute differences should not be higher than 0.5% of the underlying average b.o.p. and RoW values, as agreed by the STC.
For the euro area as a whole, the differences were not significant and lower than last year, with a high level of consistency displayed between the two datasets. At country level, however, differences above 0.5% were recorded for several countries (Belgium, Germany (only for debits), Greece, France, Austria, Slovenia, Slovakia and Finland). As in the previous year, France recorded a discrepancy of 7% for its current account (only for credits), with sizeable discrepancies in particular for services and primary income. Greece also recorded significant differences driven by the goods and services accounts, the latter only for credits.
Current account discrepancies between the b.o.p. and RoW account
(average absolute and relative difference (as a percentage of respective b.o.p. and RoW items), for the period Q3 2015 to Q2 2018 (b.o.p. vs EAA)
7.2.2 Financial transactions
Chart 10 shows the differences between b.o.p. and the RoW account for financial transactions. In this case, discrepancies may be accounted for by timing differences in the recording as well as the reconciliation of the national sectoral accounts; both the “vertical” reconciliation (a correction for errors and omissions) and the “horizontal” reconciliation (asset/liability equality across sectors) may entail larger adjustments to the financial transactions of the RoW account. Nonetheless, as an indicative benchmark, the relative differences should not exceed 0.3% of the average value of the underlying positions.
For the euro area as a whole, the differences were not significant (smaller than last year for assets and broadly unchanged for liabilities) and showed a relatively high level of consistency between the two datasets. At country level differences of above 0.3% were recorded for several countries (Germany (only for liabilities), Greece (only for assets), France (only for liabilities), Slovakia and Finland). Slovakia and Finland recorded the highest relative discrepancies, while the largest absolute differences were observed in Germany, Ireland and France.
Financial account transaction discrepancies between the b.o.p. and RoW account
(average absolute and relative difference (as a percentage of respective b.o.p. and RoW stocks of financial assets/liabilities), for the period Q3 2015 to Q2 2018 (b.o.p. vs EAA)
Chart 11 below presents the differences between the i.i.p. 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. and sectoral accounts.
The euro area as a whole recorded significant discrepancies of 4% for both assets and liabilities. These discrepancies arose mostly from differences between the compilation and reconciliation processes for the euro area i.i.p. and the RoW. At country level, differences of above 0.5% were recorded for all countries except Germany (assets), Estonia, Cyprus and Latvia (assets), Lithuania, Luxembourg, Malta and Austria (liabilities) and Portugal (liabilities). The highest discrepancies were recorded for France (assets) and Slovakia (liabilities), with values exceeding 4%.
A detailed analysis at the instrument level reveals sizeable differences for financial derivatives, mainly reflecting a different interpretation of the international statistical standards with regard to net or gross recording (which may not cause differences in net figures). Other reasons behind the differences affecting equity and debt instruments (i.e. deposits, loans and debt securities) reflect discrepancies in vintages, data sources, estimation methods and valuation methods.
Financial account position discrepancies between the i.i.p. and RoW account
(average absolute and relative difference (as a percentage of respective b.o.p. and RoW stocks of financial assets/liabilities), for the period Q3 2015 to Q2 2018 (b.o.p. vs euro area accounts))
Further details on the comparison are available in Tables A.9.1‑4 in the Annex.
7.3 Coherence with MFI balance sheet data
Data on cross-border transactions and positions in 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).
The consistency between b.o.p. 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. Furthermore, this consistency is also paramount for the compilers of euro area accounts, which integrate 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. However, persisting discrepancies between the two datasets are in general explained by methodological differences (see below).
For the euro area as a whole, the discrepancies in comparable monthly figures between the two datasets were in general not significant for the period under analysis. Comparability issues emerged for asset positions in equity at quarterly frequency. Discrepancies (around €55 billion) represented more than 16% of the average i.i.p. and BSI positions, and were caused by discrepancies in the French data.
Concerning individual euro area countries, monthly transactions were generally consistent across datasets. Discrepancies affected mostly transactions in equity assets reported by Ireland (9.6%) at quarterly frequency. In terms of positions, Ireland, France, Luxembourg and Slovenia recorded discrepancies above 25% of the average positions for equity securities. In the case of loans and deposits, the highest discrepancy was found in the liabilities of Malta, averaging 13.7% over the reference period. The highest discrepancy in debt securities emerged for Portugal (10.2%).
The main reasons for these inconsistencies include: (i) differences in the classification of instruments, e.g. the b.o.p./i.i.p. may classify an instrument as a deposit whereas it is classified as “remaining assets and liabilities” in BSI statistics; (ii) differences in the treatment of short-selling of securities in certain countries (off-balance-sheet treatment instead of a reduction in assets); (iii) reliance on distinct data collection systems, namely s-b-s for b.o.p. and monthly aggregated sources for BSI, which lead in particular to different valuation criteria (i.e. the b.o.p./i.i.p. is calculated at transaction/market prices, while BSI transactions derived from positions are reported at fair, cost or nominal value, depending on accounting practices).
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, 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 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 on the comparison are available in Tables A.10.1‑6 in the Annex.
7.4 Coherence with money market fund statistics
Data on cross-border investments in euro area Money Market Funds (MMFs) 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 collected under BSI statistics, as MMFs are 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 and for the period under analysis, discrepancies were recorded for Ireland, France and Luxembourg (the only countries in the euro area with relevant MMF activity). In general, discrepancies for positions are larger than for transactions, particularly for Ireland.
The discrepancies between the two sets of statistics were related to the use of different compilation methods in b.o.p./i.i.p. and in MFI balance sheet statistics. While the so-called residual approach is applied to calculate b.o.p. and i.i.p. portfolio investment liabilities, MMF liabilities are allocated geographically by respondents in BSI statistics. Although in the case of MMF shares there is in principle no significant trading in secondary markets, the intervention of intermediaries buying, holding and selling shares on behalf of their clients can make it difficult to identify the residency of the actual holders. In such cases, the first counterpart – the custodian or other intermediary – may be known, but the final investor often is not. Identifying residency becomes increasingly complicated as the length of the chain of intermediaries increases, therefore the residual approach of the b.o.p. and i.i.p. may be more accurate.
Further details on the comparison are available in Tables A.11.1‑2 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. and i.i.p. statistics within portfolio investment. Data on IF assets and liabilities are collected under the Regulation on Investment Funds (IF dataset).
At the euro area level, the i.i.p. consistently exceeds the IF dataset in terms of euro area investment fund liabilities. The average absolute discrepancy reached a value close to €51 billion for positions and €20 billion 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 Section 7.4 above).
At country level, Italy (158%, €7.9 billion) and Malta (200%, €6.7 billion) recorded the highest inconsistencies for IF shares held by non-residents in relative terms. The i.i.p. data reported by Malta is zero, while the IF dataset shows positive (although not very sizeable) outstanding amounts. In addition, while France does not display one of the highest relative discrepancies, the absolute average discrepancy reaches €7.1 billion for stocks; the two datasets are fairly consistent regarding transactions.
Further details on the comparison are available in Tables A.11.1‑2 in the Annex.
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 Annex VI 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”. Therefore, it is expected that b.o.p. and i.i.p. statistics and SHSS provide consistent results, mainly because national portfolio investment assets and SHSS should rely on the same s-b-s sources of information.
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. The 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
Taking into account the scope of the compilation of portfolio investment on an s-b-s basis as mentioned above, the focus should be on discrepancies that are above 15% of the respective position.
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. For individual countries, cases of relative discrepancies above 15% due to SHSS under-coverage were recorded only for Portugal. This is explained to a large extent by the incomplete coverage of euro area long-term debt securities held by financial corporations other than MFIs in SHSS. This may be linked to caveats such as the current collection of data for pension funds from custodians, the lack of comprehensive coverage of non-ISIN securities data in SHSS, and different revision policies between SHSS and i.i.p.
7.6.2 Listed shares and investment funds shares/units
For the euro area as a whole, the total level of discrepancy as a percentage of the underlying i.i.p. was 5%. At country level, discrepancies higher than the 15% threshold due to SHSS under-coverage were recorded for the following countries: Italy, Portugal and Finland. Some countries also record an over-coverage of the SHSS amounts, which were quite relevant in the case of investment fund shares held by German financial corporations other than MFIs and non-financial investors that were issued mainly by other euro area countries (the latter is linked to the inclusion of third-party holdings data in SHSS), as well as euro area and, to a lesser extent, non-euro area listed shares held by Luxembourgish financial corporations other than MFIs. Finally, Malta continues to report zero holdings of listed shares and investment fund shares within its b.o.p. and i.i.p. statistics, meaning the indicators were not calculated for this country despite relevant amounts being reported in the SHSS context for these instruments.
The smaller SHSS holdings by financial corporations other than MFIs of investment fund shares issued by non-euro area countries explains the b.o.p.-SHSS (positive) gap to a large extent. The same caveats mentioned for debt securities would hold in explaining this discrepancy.Further details on the comparison are available in Tables A.12.1‑2 in the Annex.
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. They occur when one country’s data do not correspond to the data for the same transaction reported by its partner country. 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 the total world assets should equal the total world liabilities), at the level of geographical aggregates (where the total intra-euro area assets should match the total intra-euro area liabilities) or at the level of bilateral pairs (where flows and positions between pairs of countries should perfectly match).
8.1 Intra-euro area asymmetries
Charts 12 and 13 provide an overview of intra-euro area asymmetries in current and capital accounts and in the financial account respectively.
Intra-euro area current and capital account asymmetries
Current and capital account asymmetries (credits minus debits) were always positive over the analysed time span and seemed to stabilise at a higher level from Q1 2017. The main contributors to the overall asymmetries show structural biases: consistently positive asymmetries in goods and services accounts, negative contribution from the primary income account. An exception to this behaviour is detected for Q4 2017, where the primary income account also registered a positive contribution. Secondary income and the capital account significantly affected the overall asymmetries only in particular quarters.
Intra-euro area financial account asymmetries
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 rather volatile; periods where the asymmetries in direct and other investment offset each other alternated with periods where they contributed in the same direction to the overall asymmetry. Other investment asymmetries seemed to drive the direction of the overall financial account asymmetries in the period under review.
8.2 Bilateral asymmetries
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. For future quality reports, the analysis will be extended to other items as data becomes available.
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 at assessing the accuracy of the individual geographical classification within the sample of countries for which bilateral data are available, by aggregating the absolute bilateral asymmetries. The XCGQ aims instead at showing how well a country’s reported intra-euro area aggregate matches its mirror data, calculating the difference between the intra-euro area figure reported by the country under consideration and the corresponding figure derived from counterpart data. 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 across time. Several countries consistently obtained high scores for the entire time span, indicating structural problems in matching counterparties’ transactions; the majority of countries experienced high volatility in the measures over time, underlying quarter-specific rather than structural issues in capturing the geographical detail of transactions.
The XCGQ indicator generally showed better results than the ICGQ, as the indicator is less specific about matching up individual country counterparts and merely measures how well the counterparts as a group match a country’s estimate for that group; most of the countries therefore performed relatively well for the entire time span. This finding is obviously welcome from the point of view of the quality of the overall euro area asymmetry. Nonetheless, several countries still showed rather poor results for several quarters.
The results for FDI positions revealed better scores than for transactions data as regards both quality measures.
Overall, it appears that countries characterised by large populations of SPEs and faced with well-known challenges in capturing and measuring the activity of these institutions were found to have structural issues in matching the figures provided by their euro area counterparts.
Further details on summary indicators for bilateral asymmetries are available in Tables A.13.1‑4 in the Annex.
Quality indicators on b.o.p. and i.i.p. statistics underlying the MIP
The MIP scoreboard for the Alert Mechanism Report (AMR) consists of 14 headline indicators with thresholds (complemented by auxiliary indicators with no thresholds). The composition of the MIP indicators is subject to review and evolves over time in order to reflect the latest developments or increased data needs. Most of these indicators are composite, i.e. they make use of at least two data sources.
Balance of payments (b.o.p.) and internal 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 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 instruments1) (percentage of GDP)” (NENDI) (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).
Together, these indicators provide analytical evidence of possible vulnerabilities and risks that would require further investigation at a country level.
The following sections assess the fitness for purpose of b.o.p./i.i.p. data for the MIP and analyse the same data vintage as that used in the 2018 Alert Mechanism Report.
B.o.p. and i.i.p. data are transmitted to the ECB on the basis of Guideline ECB/2011/23 and to Eurostat on the basis of Regulation (EC) No 184/2005. This annual report follows the basic principles of the “Public commitment on European statistics by the ESCB” and is a requirement under Article 6(1) of Guideline ECB/2011/23. This report is fully coordinated with the report produced by the European Commission (Eurostat) on the basis of Article 4(4) of Regulation (EC) No 184/2005. The quality assessment of the Eurostat report is conducted in accordance with the “European Statistics Code of Practice”.
The indicators used for the MIP are provided by Eurostat on the basis of statistics compiled in the Member States either by NSIs or NCBs. A Memorandum of Understanding between Eurostat and the ECB/Directorate General Statistics (DG‑S) on the quality assurance of statistics underlying the MIP (hereinafter “the MoU”) was therefore signed in November 2016. In the MoU (and the letters that were exchanged), the European Commission and the ECB mutually recognise the quality assurance frameworks in place in the European Statistical System (ESS) and the ESCB and establish practical working arrangements for cooperation with regard to the quality assurance of statistics underlying the MIP.
The MoU specifies that Eurostat and the ECB/DG‑S should regularly conduct assessments of the quality of the national datasets. In particular, the ECB/DG‑S should run its quality procedures for the datasets reported by NCBs and provide Eurostat with the quality-assured datasets and/or information on the quality of the data after the regular data transmission in September/October each year. The MoU also envisages visits by the ECB/DG‑S and Eurostat to NCBs and/or NSIs to help assess the output quality of MIP-relevant data. In 2018, two country visits took place and, as a result of the visits, recommendations for improving data quality were included in the relevant sections of the report.
To ensure full transparency with respect to the quality of MIP-related statistics, a three-level quality reporting system was set up over the last few years with the support of the Committee on Monetary, Financial and Balance of Payments Statistics (CMFB). The system consists of national self-assessment reports (Level 3) which, in turn, feed into the domain-specific quality reports (Level 2) – including this report – which are coordinated between ECB and Eurostat. Finally, a joint Eurostat/ECB summary report assessing the quality of all statistics underpinning the MIP (Level 1) is published each year in the CMFB's section on Quality reports concerning statistics underlying the MIP indicators.
Data availability and confidentiality
The relevant ECB and EU Parliament and Council legal acts do not impose back data requirements in compliance with the BPM6 statistical standard. Despite this, the majority of national compilers have provided the thirteen years of current account back data and ten years of net international investment position back data required for the calculation and analysis of the main indicators. Certain coverage limitations remain for the goods and services balance, mainly as a longer span is required for the calculation of the export market share indicator (15 years).
Regarding the auxiliary indicators, relevant coverage limitations concern the calculation of a new indicator (NENDI) that uses positions in equity securities – information that is not available or not of good quality (as it also includes investment fund shares) for Malta, Croatia (from 2015), Czech Republic and Greece (from 2013), Romania (from 2011), and Latvia2), Poland and Bulgaria (from 2010). In general, all available MIP-relevant data are free for publication.
Sources and methods
The introduction of BPM6 provided an opportunity for a large group of countries to move into survey-based systems as an alternative to traditional international transaction reporting (“settlement”) systems. However, b.o.p. and i.i.p. statistics are by nature rather eclectic as regards data sources, relying on micro (e.g. the Centralised Securities Database (CSDB)) and macro datasets, direct reporting and counterpart information, statistical surveys and administrative datasets (e.g. for the general government sector).
While the compilation of b.o.p. and i.i.p. data in EU Member States is deemed methodologically sound, there are challenges in the measurement of some components and in observing all EU recommendations and/or BPM6 standards. In particular: (i) Luxembourg, the Netherlands, Cyprus and Malta would still benefit from improvements in the coverage of resident SPEs; (ii) some EU countries should make an effort to implement estimates for service margins on buying and selling financial assets (financial services); (iii) some countries should follow EU recommendations and include estimates for certain illegal economic trade activities (illegal drugs, prostitution services, and smuggling of tobacco and alcohol); (iv) most countries have difficulties capturing household assets held abroad; and (v) in general, countries should improve the measurement of reinvested earnings on foreign direct investment and the valuation of unlisted shares and other equity. For more detailed information, please see Table 1 in the executive summary and Section 2.
Accuracy and reliability
For the reference period 2016, revisions to the current account were minor, amounting to under 1% of GDP in most countries (see MIP Annex Table 1). For the current account balance, only Ireland (8%) and Bulgaria (3%) recorded revisions above 1% of GDP. For the net i.i.p., revisions were above 5% of GDP in Belgium (6%), Finland (11%), Ireland (7%), Luxembourg (12%), the Netherlands (6%), Hungary (6%) and Sweden (6%).
Revisions of the underlying data have generally not altered the meaning conveyed by the first assessment of the headline MIP indicators or the underlying economic assessment.
For quarterly b.o.p., most countries fulfil all validation (accounting) rules. One notable exception is the data provided by Ireland before 2012, which still displays inconsistencies such as the addition of functional categories not being equal to the total financial account or assets minus liabilities not being equal to the net financial account (owing to the recording of financial derivatives before 2012). Furthermore, one of the most common issues among countries is the reconciliation of positions and flows, the validity of which is very important for confirming the plausibility of the net i.i.p.
Regarding series breaks, other than the issues mentioned in Section 6.1 (validation/integrity rules), the following breaks apply for periods before 2013 (transmission of data for periods before Q1 2013 is not mandatory):
Belgium: revisions to portfolio investment due to changes in sources and methodology have been implemented only back to Q1 2014, creating a break in the series affected.
Ireland: available foreign direct investment positions data before 2008 follow the directional principle;
Italy: breaks in the series for financial derivatives (assets and liabilities) are observed in 2008 and are due to the introduction of a more accurate quarterly i.i.p. data source for financial derivatives held by resident deposit-taking corporations thereafter;
Luxembourg: relevant series breaks in foreign direct investment positions for 2011 are related to improvements in the coverage of SPEs;
Austria: some breaks apply in foreign direct investment positions (in 2005, due to the introduction of data for SPEs) and financial derivatives (before 2006 the reported value for positions in financial derivatives is zero, whereas non-zero values are reported for transactions);
Portugal: positions in financial derivatives assets and liabilities record a break in 2008 as well as certain sectoral and financial instrument breakdowns; the breaks are generated by an increase in coverage in 2008;
Slovakia: a series break is observed for other investment (assets and liabilities) in 2009.
National net errors and omissions in general remained stable in the last review period, however they are still above 2% of GDP in Ireland, Malta, Finland, Bulgaria, Hungary and Sweden (see Chart MIP 1). In this context, it is important to highlight that some euro area countries have formal correction mechanisms to address this problem, naturally leading to reduced levels of errors and omissions.
Chart MIP 1
Average absolute net errors and omissions
(percentage of GDP)
In cumulative terms for the period 2015‑17, a bias (at least 2% of GDP) can be statistically identified in Malta, Slovakia, Finland and Denmark.
The methodological differences between the b.o.p./i.i.p. and RoW account (national accounts) were removed with the introduction of ESA 2010 and BPM6. However, the analysis shows that inconsistencies between the two statistical domains persist in several Member States, negatively affecting the combined use of these two datasets as well as their reliability. Discrepancies above 0.5% of GDP are recorded (for either credits/debits or both) for the current account of over one-third of EU countries (Greece, France, Malta, Slovenia, Slovakia, the Czech Republic, Poland, Romania, Sweden). Nonetheless, none of the discrepancies recorded was higher than 2% of GDP. For financial account positions, the discrepancies between the i.i.p. and RoW account are more pervasive and total more than 20% of GDP in some cases: Ireland, France (assets), the Netherlands and Sweden.
MIP Annex Table 1
Annual absolute revisions – balance/net items for 2016
Percentage of GDP
Note: All indicators are compiled using neither seasonally adjusted nor calendar adjusted data.
1) Instruments that cannot be subject to default: foreign direct investment equity and equity shares and inter-company cross-border-FDI debt.
2) Latvia will provide the missing breakdowns in the course of 2019.
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