The Bank for the Accounts of Companies Harmonized (BACH) database. No 11, September 2015.
Date: 2015-11-06
Institutions: European Central Bank
Subjects: European company, accounting, data processing, database, standardised accounting system, statistics

BACH Working Group  

Statistics Paper Series 

The Bank for the Accounts 
of Companies Harmonized   
(BACH) database 

No 11 / September 2015 

Note: This Statistics Paper should not be reported as representing the views of the European Central Bank (ECB). 
The views expressed are those of the authors and do not necessarily reflect those of the ECB 

 
 
 
 
Contents 

Abstract 

Executive summary 

1 

2 

Introduction 

The BACH database 

2.1 

2.2 

The contents of the database 

The main potentialities and limitations of the database 

Box 1 Variable samples and sliding samples 

Box 2 Variable samples versus sliding samples; a real example for 

French trade SMEs 

2.3  Data providers, samples, sources and accounting practices 

3 

Recent developments and their future impact on BACH 

3.1  Revision of the European Union accounting directives 

Box 3 Transposition of the new Accounting Directive 

3.2 

Implementation of ESA 2010 – The case of holding companies 

4 

Case study: the manufacturing sector 

4.1 

4.2 

The sector and its representativity in BACH 

The Importance and structure of the sector 

2 

3 

5 

7 

7 

12 

13 

15 

16 

23 

23 

25 

26 

28 

28 

29 

4.3  An assessment of the economic and financial situation of the sector  31 

Annex 1 Overview of the information available in the BACH database 

Annex 2 National deviations 

Annex 3 National sampling methods and metadata on data collection 

Annex 4 Task force for holding companies and head offices: recommendations 

Annex 5 The ECCBSO 

Annex 6 BACH’s history and the BACH WG 

References 

Statistics Paper No 11, September 2015 

36 

42 

45 

50 

52 

54 

56 

1 

Abstract 

The Bank for the Accounts of Companies Harmonized (BACH) is a free-of-charge 
database containing the aggregated accounting data of non-financial incorporated 
enterprises for, so far, 11 European countries. While the individual accounts feeding 
the database were originally prepared in line with national accounting standards 
consistent with European Accounting Directives, they have been harmonised with a 
view to preserving, to the greatest extent possible, the cross-country comparability of 
the resulting data.  

This article presents the methodology underpinning BACH, including the content of 
the database. It describes the characteristics of national samples and outlines the 
harmonisation process. BACH is a unique tool for analysing and comparing the 
financial structure and performance of firms across European countries. A simple 
case study is also presented in support. 

Keywords: European non-financial corporations, statistics, accounting standards, 
annual individual accounts 

JEL-codes: C81, M41  

 
Executive summary 

The Bank for the Accounts of Companies Harmonized (BACH) database is a very 
powerful and unique tool for evaluating the economic and financial performance of 
European non-financial corporations. The information it provides is relevant to 
several groups of users and purposes, including researchers, national central banks 
and other institutions (for macroeconomic analysis and to support decision making), 
companies (as a benchmark) and also to credit and financial institutions (in credit 
risk analysis). The importance of the BACH database is well recognised by the 
European Banking Authority (EBA, 2012): “The BACH database is the most reliable 
source of available data containing balance sheet and profit and loss data for 
European companies.” 

BACH is a free-of-charge database containing the aggregated accounting data of 
non-financial incorporated enterprises for, so far, 11 European countries (Austria, 
Belgium, Czech Republic, France, Germany, Italy, the Netherlands, Poland, 
Portugal, Slovakia and Spain). Four more European countries will join the BACH 
database in the coming years (Denmark, Luxembourg, Romania and Turkey).  

The BACH database facilitates the evaluation of the economic and financial 
performance of European non-financial corporations, based on the Nomenclature of 
Economic Activities (NACE1 Revision 2), and using detailed and harmonised 
aggregated data to make cross-country comparisons. The underlying data cover the 
period from 2000 onwards 2, providing a large level of detail by business sector (17 
NACE sections and about 80 NACE divisions) and size class (based on net turnover, 
allowing the user to select small, medium and large firms) at country level. Data are 
available in variable and sliding samples.  

International accounting harmonisation is not an easy task – the main concern in 
defining BACH indicators is the minimisation of the differences in accounting 
practices among countries. For that purpose, an extensive stock-taking exercise 
covering each national database/accounting item was conducted. In this sense, the 
BACH database currently represents a significant data harmonisation effort both 
within and across countries, with a view to minimising differences in accounting 
practices among countries, minimising gaps/breaks in time series/limitations of 
national samples, providing complete accounting statements, and fulfilling user 
needs, while adjusting to the requirements of modern analysis.  

BACH data are based on accounting figures, in accordance with national accounting 
standards, and include a set of indicators related to the balance sheet (22), the 
income statement (41), the notes on investment behaviour (3) as well as economic 

1   NACE is a common statistical classification of economic activities in the European Community. 

Regulation (EC) No 1893/2006 of the European Parliament and of the Council of 20 December 2006 
establishes the statistical classification of economic activities NACE Revision 2, and amends Council 
Regulation (EEC) No 3037/90 as well as certain EC regulations on specific statistical domains. 

2   For some countries, data start in a different year (after 2000). 

Statistics Paper No 11, September 2015 

3 

                                                                    
and financial ratios (29), which have previously been subjected to several tests, in 
each country and across countries, in order to guarantee the quality of the underlying 
data.  

Statistics Paper No 11, September 2015 

4 

1 

Introduction 

The non-financial corporation (NFC) sector3 is the backbone of the European 
economy. According to Eurostat, the NFC sector provides by far the biggest share of 
nominal gross value added in the euro area. In 2013, 58% of nominal gross value 
added came from the NFC sector, 22% from households, 5% from financial 
corporations and 15% from the general government sector  4. 

This article aims to describe the particularities and contents of the Bank for the 
Accounts of Companies Harmonized (BACH) database, noting the main advantages 
and potentialities of BACH. Reference is made to the national data providers, 
samples, sources and accounting practices (Chapter 2): 

• 

• 

• 

• 

The national institutions that provide BACH data collect the data for different 
uses (statistics, banking supervision and credit rating). This partially explains 
differences in coverage rates as well as variations in the level of detail of 
national samples; 

Some countries have administrative databases that cover the entire population 
of non-financial corporations (NFCs). For other countries, subsets of the total 
population are available, in which large companies are generally 
overrepresented. In either case, trends and cross-country comparability are 
generally reliable;  

Since one of the uses of the data available is cross-country comparison, while 
the BACH database was being built the harmonisation of concepts was always 
a concern. Although the different national accounting standards are based on a 
single European Accounting Directives package, there is a broad spectrum of 
national specificities that hamper cross-borders comparability. Substantial 
harmonisation efforts have been undertaken in recent years; the consistency 
and robustness of the indicators with regard to national accounting practices 
have been taken into consideration; 

The database includes a rich set of indicators for the income statement, the 
balance sheet, investment behaviour and economic and financial ratios. For all 
these variables it is possible, by country, to combine both enterprise size and 
the economic activity sector for a time span larger than a decade; 

3   The NFC sector represents one of the economy’s institutional sectors. The institutional sectorisation of 

economic agents is carried out in accordance with the 2010 European System of National and 
Regional Accounts (ESA 2010), approved by Council Regulation (EU) No 549/2013 of 21 May 2013. 
ESA 2010 is a harmonised reference concerning the compilation methodology and deadline release of 
the national accounts of European Union countries. According to this regulation, sole proprietorships 
are included in the household institutional sector. See also BACH Userguide, chapter 3.2 – Database 
contents. 

4   Eurostat. Contributions of each institutional sector to macroeconomic developments. See 

http://ec.europa.eu/eurostat/web/sector-accounts/data/annual-data. 

Statistics Paper No 11, September 2015 

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• 

Together with the database, complementary BACH products and services are 
available free of charge, allowing the user to gain detailed knowledge of 
European companies. These currently include “Outlook” booklets and “Yearly 
Get Insights”.  

Chapter 3 explores progress made in some areas that could have potential future 
impact on BACH aggregates, focusing on the recent revision of the European 
accounting framework: 

• 

• 

Directive 2012/6/EU of 14 March 2012 on micro-entities offers European Union 
Member States the option of creating a specific regulation for these enterprises. 
Member States are allowed to exempt their micro-undertakings from different 
obligations, allowing them, for example, to publish a very simplified balance 
sheet and income statement; 

Similarly, the new Accounting Directive 2013/34/EU of 29 June 2013 diminishes 
the administrative burden on small-sized enterprises, at a cost of demanding 
less accounting information, particularly for smaller companies. 

It is worth noting that this chapter also refers to the implementation of the new 
European System of National and Regional Accounts (ESA 2010). 

Chapter 4 provides a summary of a simple case study in the European 
manufacturing sector. This is intended to serve as a “use-standard” for a BACH-
based analysis and as a reference guide for the user since it highlights some 
important features of the database. The chapter is not intended to restrict the user’s 
analysis - there are several other options based on BACH data. The chapter 
highlights are:  

• 

• 

• 

• 

A BACH database user guide (“Userguide“) is available. This covers the 
methodology underpinning the data following a user-friendly approach. It 
supports the process of observing variables to obtain the extra information 
needed to compare data; 

For each indicator, the weighted mean, the median as well as dispersion 
measures (first and third quartile) are made available, enabling the user to 
better understand the underlying distribution of the indicator; 

Additionally, absolute values are also available, allowing users to construct their 
own indicators, such as the average of a group of countries or sectors; 

Depending on the analysis and on the coverage rate available, in some cases it 
is preferable to use sliding samples instead of variable samples.  

Statistics Paper No 11, September 2015 

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2 

The BACH database  

2.1 

The contents of the database 

The BACH database is a very powerful and unique tool for evaluating the economic 
and financial performance of European non-financial corporations, providing 
sufficient detailed and harmonised aggregated data to make cross-border 
comparisons.  

The information provided is relevant to several users and purposes, including 
researchers, national central banks and other institutions (for macroeconomic 
analysis and for supporting decision making), companies, and also to credit and 
financial institutions (in credit risk analysis). In the words of the European Banking 
Authority (EBA, 2012) in the Assessment of SME proposal for CRD IV/ CRR: “The 
BACH database is the most reliable source of available data containing balance 
sheet and profit and loss data for European companies.”   

BACH is a free-of-charge database containing aggregated accounting data from 
non-financial incorporated enterprises, from, so far, 11 European countries (Austria, 
Belgium, Czech Republic, France, Germany, Italy, the Netherlands, Poland, 
Portugal, Slovakia and Spain). Four more European countries will soon be ready to 
join the BACH database in the coming years (Denmark, Luxembourg, Romania and 
Turkey). The BACH database is hosted by Banque de France 
(http://www.bach.banque-france.fr/?lang=en).  

Under the auspices of the European Committee of Central Balance Sheet Data 
Offices (ECCBSO) 5, the BACH Working Group (WG)6 is responsible for maintaining 
and developing the BACH database. 

The BACH database allows the economic and financial performance of European 
non-financial corporations to be evaluated, based on the Nomenclature of Economic 
Activities (NACE Revision 2), using detailed and harmonised aggregated data to 
make cross-country comparisons. The underlying data come from the non-
consolidated accounting statements of incorporated enterprises in several European 
countries, aggregated in each country by business sector and enterprise size. The 
data cover the period from 2000 onwards 7, providing a high level of detail by 
business sector (17 NACE sections and about 80 NACE divisions) and size class 
(based on net turnover, allowing the user to select small, medium and large firms) at 
country level, available in variable and sliding samples.  

5   The ECCBSO is an informal body whose members consist of experts either from Central Balance 

Sheet Data Offices (CBSOs) belonging or associated with the national central banks (NCB) or from 
national statistical institutes (NSI) of the European Union. More details on the ECCBSO may be found 
in Annex 5: The ECCBSO. 

6   Annex 6 presents information on history of BACH and the BACH WG, including its current composition. 
7   For some countries, data start in a different year (after 2000).     

Statistics Paper No 11, September 2015 

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The coverage rates in terms of number of firms, net turnover and number of 
employees are disclosed for each combination (financial year/enterprise size 
class/business sector/sample), depending on the availability of these data in each 
country. All indicators are stated in terms of weighted means8 and quartiles (the 
median as well as the first and third quartiles, which indicate the underlying data 
distribution). Some absolute values are also provided, which includes the “total 
assets”, “net turnover”, “gross value added”, “number of firms” and “employees”. 
Additionally, the “number of firms” used for the quartile calculation is also disclosed.  

BACH data are based on accounting figures, following national accounting 
standards, and include a set of indicators for business analysis relating to the 
balance sheet (22), the income statement (41), the notes on investment behaviour 
(3) and economic and financial ratios (29), that have previously been subjected to 
several tests, in each country and across countries, in order to guarantee quality 
underlying the data.  

The balance sheet indicators are expressed in terms of “total assets”, while the 
income statement and the indicators for the notes are expressed in terms of “net 
turnover”. The possibility of determining the values of balance sheet ratios, income 
statement ratios and items of the notes expressed in weighted means should be 
considered, once the absolute values for “total assets” and “net turnover” have been 
obtained.  

Furthermore, these indicators are based on a common template, having been 
harmonised to increase plausible cross-country comparability among BACH 
aggregates, and also widened because the same classification is used for the size 
class and business sector (see Annex 1: Overview of the information available in the 
BACH database).  

While building the BACH samples, some countries (e.g. Germany) exclude unlimited 
liability companies since their inclusion would weaken the comparability of their 
BACH data with other countries to a significant extent. However, for most countries 
which include companies with unlimited liability, the proportion of these companies is 
insignificant (in most cases below 1%). 

For other countries (e.g. Portugal) the BACH samples cover the NFC institutional 
sector, as defined in ESA 2010. Several legal forms for companies coexist in BACH, 
given that this is not taken into account in the definition (for example, partnerships 
can exist in BACH). Nevertheless, more than 98% of the companies in the BACH 
database are limited liability companies. Sole proprietorships are excluded from all 
national samples.  

The following paragraphs present a detailed summary of the database contents: 

8   The weighted mean corresponds to the ratio between the sum of the individual values underlying the 
concept (numerator) and the sum of the individual values (denominator), taking into account in both 
cases the sample values in each combination (financial year/enterprise size class/business sector/ 
sample).  

Statistics Paper No 11, September 2015 

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Time span – Data start in 20009. Data for a new year are normally first added to the 
database in the fourth quarter of the following reference year. 

Countries – Austria, Belgium, Czech Republic, France, Germany, Italy, the 
Netherlands, Poland, Portugal, Slovakia and Spain. Four more European countries 
will be ready to join the BACH database (Denmark, Luxembourg, Romania and 
Turkey) in the coming years. 

Size class – Classification of enterprises by size is based on the Recommendation 
of the European Commission, 6 May 2003 (2003/361/EC); as concerns turnover 10, 
two major enterprise size classes are considered: 

• 

• 

• 

Large companies (BACH code 2) – turnover higher than EUR 50 million; 

Small and Medium Enterprises (SMEs) (BACH code 1) – turnover lower than 
EUR 50 million. SMEs are also split into two details, identified as follows: 

• 

• 

BACH code 1a – Small companies (turnover lower than EUR 10 million); 

BACH code 1b – Medium-sized companies (turnover between EUR 10 
million and EUR 50 million). 

In the BACH database it is also possible to obtain information with no 
classification for size class, allowing the user to construct several types of total 
for a specific selected combination. 

Variable and sliding samples - Two types of data selection are made available: a 
variable sample and a sliding sample. For a given year (“year n”), the variable 
sample includes all enterprises present in the database in that year (using the code 
“0”), while the sliding sample includes all enterprises that are present in two 
consecutive years. In this case the sliding sample uses the code "1" to identify the 
current year ("year n") and the code "-1" to identify the previous year ("year n-1"). For 
instance, the sliding sample for 2009 includes enterprises present in both 2009 
("year n") and 2008 ("year n-1"), the current and previous years respectively, which 
allows the same subset of enterprises to be compared over two consecutive years. 
For 2010 it includes both 2010 ("year n") and 2009 ("year n-1") and so on.  

These two samples can produce different results, depending on the real business 
demography and the contents of the database (see Annex 3), particularly for national 
samples based on subsets of corporations with a bias in terms of coverage of the 
population.  

Box 1: Variable samples and sliding samples explains these two concepts and Box 
2: Variable samples versus sliding samples; a real example for the French Trade 

9   For some countries data start in a different year (after 2000). 
10  

In the definition of enterprise size class, the European Commission takes three variables into account: 
turnover, number of employees and total assets. The different types of relationships between 
enterprises are also considered. However, due to limited access to the number of employees and total 
assets variables, as well as the lack of information about groups of corporations, the enterprise size 
class in BACH only considers turnover thresholds.   

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SMEs presents a numerical example. It is important to bear in mind that, for some 
countries, the variable sample corresponds to the second year of the sliding sample. 

Business sector – business sector is based on the Nomenclature of Economic 
Activities (NACE Revision 2), which includes 17 NACE sections and about 80 NACE 
divisions.  

In general, all sections and divisions of NACE Revision 2 relating to the NFC 
institutional sector are available in BACH, which completely excludes sections O – 
Public administration and defence; compulsory social security, T – Activities of 
households as employers; undifferentiated goods and services-producing activities of 
households for own use and U – Activities of extraterritorial organisations and bodies.  

It is worth highlighting the treatment of holding companies and head offices of the 
NFC sector in BACH:  

(i) 

In the case of NACE K – Financial and insurance activities, data are only 
provided for the group NACE K642 – Activities of holding companies (in this 
case, no details for the section and division of NACE Revision 2 are provided).  

(ii)  For NACE M – Professional, scientific and technical activities, data are also 
provided for the groups NACE M701 – Activities of head offices and NACE 
M702 – Management consultancy services. 

(iii)  Data is provided for the NFC total excluding holding companies and head 

offices: BACH code Z0 – Total NACE without K642; BACH code Zc – Total 
NACE without K642 and M701. 

(iv)  For holding companies and head offices (NACE K642 – Activities of holding 

companies; NACE M701 – Activities of head offices), a limited number of ratios 
is provided – in particular, those involving turnover are not made available. 

Outputs and statistical measures – 95 indicators are provided, covering balance 
sheet items (41), income statement items (22), items from the notes (3) and 
economic and financial ratios (29). BACH data offer a variety of outputs for each 
combination of financial year/enterprise size class/business sector/sample (variable 
or sliding): 

• 

Coverage rate: a measure, for a certain indicator value, of the ratio between 
the enterprises of the sample and the population of non-financial corporations. 
In other words, the coverage rate indicates the weight of the enterprises of the 
sample in terms of the population. Coverage rate is calculated in terms of the 
number of firms, net turnover and number of employees and is disclosed for 
each combination (financial year/enterprise size class/business sector/sample), 
depending on the availability of these data for each country; 

• 

Absolute values: for a set of variables previously selected, these are: 

(a)  The number of firms and the number of employees: the number of 

enterprises and the number of employees, integrating each combination, 
respectively; 

Statistics Paper No 11, September 2015 

10 

(b)  Total assets, net turnover and the gross value added: reported in 

thousands of euro. 

The possibility of deriving the values of balance sheet ratios, income statement 
ratios and items of the notes expressed as weighted means should be 
considered, once the absolute values for total assets and turnover have been 
obtained. 

Balance sheet items (41 indicators): based on a harmonised template and 
expressed as percentage of total assets;  

Income statement items (22 indicators) and items from the notes (three 
indicators): based on a harmonised template and expressed as percentage of 
net turnover;  

Economic and financial ratios (29 indicators): separately presented as 
“Financial structure ratios” (6 indicators), “Financial and debt service ratios” (7 
indicators), “Profitability ratios” (10 indicators), “Activity and technical ratios” (2 
indicators) and “Working capital ratios” (4 indicators).  

Statistical measures: the following statistical measures are available: 

• 

• 

• 

• 

(a)  Weighted mean11 (WM); 

(b)  Quartiles12 (Q1, Q2, Q3); 

(c)  The number of firms: the number of corporations considered in the 

calculation of the quartiles is also given for all items. 

Uniform templates – The BACH database is based on harmonised templates for; (i) 
the income statement (Annex 1, Table A1.1), (ii) the balance sheet (Annex 1, Tables 
A1.2 and A1.3), (iii) economic and financial ratios (six financial structure ratios, 
seven financial and debt service ratios, ten profitability ratios, two activity and 
technical ratios and four working capital ratios) (See Annex 1, Tables A1.4, A1.5, 
A1.6, A1.7 and A1.8) and (iv) three notes (See Annex 1, Table A1.9). 

The BACH database is complemented by several documents that can be 
downloaded from the website. 

Calendar - It should be stressed that BACH data are released in accordance with a 
predefined calendar (Chart 1, Section 2.3.2). 

11   Weighted mean is the ratio between the sum of the individual values underlying the concept 

(numerator) and the sum of the individual values (denominator), taking into account in both cases the 
sample values in each combination (financial year/enterprise size class/business sector/sample). 
12   For each individual ratio, the individual figures for a certain sample are sorted in ascending order. This 

sample is then divided into four quarters: 

First quartile (Q1): 25% of the enterprises have a ratio below this quartile and 75% have a ratio above; 

Second quartile or median (Q2): the second quartile is the middle value that cuts the individual 
observations in half; and, 

Third quartile (Q3): 75% of the enterprises have a ratio below this quartile and 25% have a ratio above. 

Statistics Paper No 11, September 2015 

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Userguide - This describes the main contents and characteristics of the BACH 
database, in particular (i) database contents, (ii) national samples, (iii) methodology 
and warnings and (iv) release calendar and contacts. 

BACH Publications - The following additional products are also currently provided 
to users: 

(a)  Outlook booklets: joint studies analysing the relative performance of the 

European companies making use of the information available in BACH. Three 
booklets have already been issued:  

(i)  Outlook #1 (BACH, 2013): “SMEs in European countries”; 

(ii)  Outlook #2 (BACH, 2014): “Financial structure and profitability of 

European companies”; 

(iii)  Outlook #3 (BACH, 2015): “Deleveraging and its effects on the asset 

side”13. 

(b)  Yearly Get Insights: a yearly portfolio of selected economic and financial 

indicators providing a quick overview of the recent situation of European 
companies. Additionally, “Get Insights” explores developments in the European 
business environment and includes specific topics concerning companies. 

2.2 

The main potentialities and limitations of the database 

BACH’s potentialities may be summarised as follows: 

Potentiality #1: Increasing knowledge of non-financial corporations 
BACH is a free-of-charge database that gathers economic and financial information, 
allowing users to increase their knowledge of European non-financial corporations 
and to compare their performances across countries. 

Potentiality #2: Harmonised database 
BACH data are based on a common template for the most important accounting 
items available in order to increase, as much as possible, cross-country 
comparability. 

Potentiality #3: Data richness 
The BACH database gives access to a high level of detail, a wide scope and a high 
degree of disaggregation of all economic and financial information (See 2.1, 
describing the features and contents of the BACH Database). 

Potentiality #4: BACH Userguide 
BACH’s user-friendly Userguide can be downloaded from the BACH website. It was 
designed to provide users with access, as much as possible, to the data available. 

13   Outlook #3 is expected to be issued in October 2015. 

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The guide includes: (1) database contents; (2) national samples; (3) methodology 
and warnings; and (4) release calendar and contacts. 

Potentiality #5: Other BACH free services  
Joint analysis (the “Outlook series”) and yearly portfolios of selected indicators (Get 
Insights). These provide the vision of the BACH WG on the European NFC sector 
based on BACH data, as well as a portfolio of didactic tools. 

Finally, when using BACH it should be remembered that data come from different 
CBSOs in differing environments. Countries therefore rely on diverse subsets (which, 
as a consequence, have differing coverage rates) and varying detail of information. 
In this context, some general warnings should be taken into account: 

General warning #1: Heterogeneous samples 
National data samples are not completely harmonised, in particular due to 
differences in the exhaustiveness and representativeness of the relevant NFC 
population.  

Major caution should be exercised regarding national samples based on subsets of 
corporations. In these countries, the composition of a sample may change every 
year and the compilation of time series can raise problems from an analyst’s point of 
view. The year-on-year trend does not necessarily reflect economic phenomena, but 
may instead be a consequence of a change in the composition of the sample –
“sample composition bias”. On the other hand, some company segments may not be 
that well represented in the samples, as in the case of micro-enterprises. 

General warning #2: State of play of data update 
BACH data providers normally release information for the same year in two stages: 
(i) data are usually first provided during the fourth quarter of the following year, albeit 
with less coverage - provisional data and (ii) a second release normally takes place 
during the first half of the year following that, when more definite data are provided - 
final data. 

General warning #3: National deviations from the BACH concepts 
National data are not completely aligned with the concepts in the BACH templates. 
In particular, differences in national accounting practices cannot be completely 
eliminated. 

Box 1 
Variable samples and sliding samples 

As already mentioned, the BACH database provides, for each country and business 
sector/enterprise size class, two types of dataset using either a variable sample or a sliding sample. 
The main difference between the two consists of the following:  a sliding sample is based on data 
from a subgroup of enterprises from the annual database for two consecutive years, while the 
variable sample does not include this restriction.  

The data selection for a sliding sample is always based on the enterprise classification of “year n”. 
This means that new corporations (formed during “year n”) are not represented or, if applicable, the 

Statistics Paper No 11, September 2015 

13 

corporations that were first covered by the surveys launched in this “year n” are ignored in the 
statistics. Either condition could lead to an overestimation or an underestimation of the variables. It 
should be borne in mind that the statistics produced for the same corporations in two consecutive 
years (“year n” and “year n-1”) reflect, first and foremost, the structures of the samples. 

The following scheme explains the difference between the two types of samples available in the 
BACH database:  

Chart A 

2008

500 
companies

750
companies

600 
companies

2010

2009

2009

2009

Year

2008

2009

2009

2009

2010

Sample

Number of 
companies

Turnover
(M€)

-1

1

0

-1

1

500

500

750

600

600

80

78

95

83

86

I

G
N
D
L
S

I

E
L
B
A
R
A
V

I

I

G
N
D
L
S

I

A sliding sample includes the same set of firms over two consecutive years (“year n” and “year n-
1”). Thus, from the example above, for a given country/sector/enterprise size, three records are 
available just for the year 2009: 

(i) 

“Sample 0”: the variable sample for 2009 includes all the companies whose annual accounts are 

available in 2009 (750 units); 

(ii) 

“Sample 1”: the sliding sample for 2008-2009 contains the companies whose annual accounts 

are available in both 2008 and 2009 (500 units); 

(iii) 

 “Sample -1”: the sliding sample for 2009-2010 contains the companies whose annual accounts 

are available in both 2009 and 2010 (600 units). 

Source: BACH Userguide (2015) 

Statistics Paper No 11, September 2015 

14 

 
 
Box 2 
Variable samples versus sliding samples; a real example for French trade SMEs 

This box seeks to describe the importance of the two samples available in the BACH database: 
sliding and variable samples. For that purpose a selected analysis is used for the ratio “interest on 
financial debt over EBITDA” (Earnings before interest, tax, depreciation and amortisation) - French 
SMEs (trade sector) from 2000 to 2012. 

Both datasets, variable samples and sliding samples, reflect a decreasing trend for this ratio in the 
period from 2000 to 2012 (Chart A). 

Chart A 
Trade sector interest on financial debt over EBITDA | French SMEs (2000-2012) 

Variable sample
Sliding sample

24

22

20

18

16

14

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Source: Bank for the Accounts of Companies Harmonized (April 2015), http://www.bach.banque-france.fr/?lang=en. 
Notes: (1) Enterprise size 1 – SMEs, sample 0 – Variable samples and sample -1 and 1 – Sliding samples and 1/R22 – Interest on financial debt over 
EBITDA. 

However, analysis focused on the specific years 2001 and 2007 may produce opposite results, with 
variable samples showing a decrease, sliding samples showing an increase.  

The truth is that both conclusions are complementary; their justification relies on differing sample 
composition: 

(a)  Variable sample: incorporates the demography of the sample. This means that a change in the 

variable under analysis may be due to firms’ exits or entries into the market or into the sample. 
When the BACH sample coincides with the population, the demography reflected in the variable 
sample corresponds to the natural movement of firms in one economy. However, when the 
BACH sample is a subset of the population, the demography is much more that of the BACH 
sample than that of the population. Thus, in the case of the French SMEs shown in Chart A, the 
“artificial” change based on variable samples is merely because a company has entered/left the 
sample. In general, samples based on a subset of the NFC population are more affected by 
these “artificial” changes. 

(b)  Sliding sample: considers the same set of companies in a pair of two consecutive years. 

Hence, when the BACH sample is a subset of the NFC population, two-year trends are 
represented more accurately. However, if the BACH sample coincides with the NFC population, 

Statistics Paper No 11, September 2015 

15 

 
the demography dynamics of the NFC are not captured. For economic trend analysis for two 
consecutive years and cross-country comparability, sliding samples could be more appropriate.  

In this sense, the two samples available – variable and sliding – should be seen as important 
complements for different analysis. 

2.3 

Data providers, samples, sources and accounting 
practices 

The underlying data in BACH comes from different national data providers, almost all 
belonging to central banks. National samples and accounting practices have specific 
features that bear consideration. These issues are presented as follow: 

2.3.1. Data providers; 

2.3.2. Samples and sources; 

2.3.3. Accounting practices. 

2.3.1 

National data providers 

The main source of diversity in BACH comes from the institutional context of CBSOs, 
which is very closely related to the nature and purpose of their activities (Table 1). 
Although the overwhelming majority of CBSOs work for NCBs, their missions are 
dissimilar. In seven countries the main purpose of data collection is the production of 
statistics, although in one case (Slovakia) risk assessment is another aim. In 
Belgium, for example, the CBSO plays the role of commercial register in the 
collection of annual accounts. The major concern of the remaining CBSOs is the 
scrutiny of the business cycle, risk assessment of bank portfolios and/or supervisory 
obligations. 

Depending on the purpose and the specific situation of each CBSO, different 
methods of collecting information from companies are in place. As a consequence, 
different coverage/scope/detail is available in BACH national samples. 

Statistics Paper No 11, September 2015 

16 

 
Table 1 
Main features of national data providers 

Country 

Institution 

Department 

Main objective of data collection 

Austria 

National Central Bank 

Statistics 

Risk assessment, rating 

Belgium 

National Central Bank 

Microeconomic info. 

Publication of microdata 

Czech Rep. 

National Central Bank 

Monetary & Statistics 

Statistics 

France 

National Central Bank 

Germany 

National Central Bank 

Italy14 

Private company 

Corporate 

Statistics 

- 

Netherlands 

National Statistical Institute 

 Statistics 

Poland 

National Central Bank 

Portugal 

National Central Bank 

Slovakia 

National Central Bank 

Spain 

National Central Bank 

Statistics 

Statistics 

Statistics 

Statistics 

Risk assessment, rating 

Statistics 

Risk assessment, rating 

Statistics 

Statistics 

Statistics 

Statistics, risk assessment 

Statistics 

Note: National Statistical Institutes for future data providers (Denmark, Luxembourg, Romania and Turkey). 

CBSOs’ frameworks may account for more diversity in BACH in the near future, as it 
is expected that national statistical institutes (NSI) will compile data for BACH (in the 
case of Denmark, Luxembourg, Romania and Turkey). NSIs are typically related to 
the statistical function, although in a different institutional context.  

2.3.2 

National samples and sources 

National data samples in BACH differ in terms of: (i) representativeness of the NFC 
population, (ii) provisional/final data and (iii) breaks in time series. 

Generally speaking there are two types of national database. Administrative 
databases (Belgium and Portugal, since 2006) cover the entire population of NFCs, 
while the remainder is based on a subset of the population. Accordingly, Belgium 
and Portugal (since 2006) have very high NFC coverage rates in BACH. In 
accordance with the law, these two countries have full access to the financial 
statements of the entire population of non-financial corporations.  

Data providers from the other remaining countries collect NFC information from a 
subset of the population. In these cases large firms are often overrepresented. 
Furthermore, data availability in different countries depends on the state of 
transposition of the European Union Accounting Directive concerning the official 
publication of the annual accounts of limited liability companies. 

Table 2 shows that countries other than Belgium and Portugal present lower 
coverage rates in terms of number of companies in 2012, compared with that for 
turnover/number of employees covered. For instance, while covering 28% of 
companies, the coverage rate for French samples in 2012 rises to 81% in terms of 
turnover. 

14   The private company referred to in Table 1, Cerved Group Spa, develops statistical models for Italian 
banks. Together with Banca d’Italia, this company offers data for Italian companies to the BACH 
database. 

Statistics Paper No 11, September 2015 

17 

                                                                    
Table 2 
National samples 

Country 

Coverage rate – number of enterprises 

Coverage rate – turnover (T) or number of employees (E) 

Provisional figures (2013) 

Final figures (2012)  Provisional figures (2013) 

Final figures (2012) 

Austria 

14.1% 

Belgium* 

99.6% 

Czech Rep*  9.8% 

France  

26.9% 

Germany  

N/A 

Italy* 

6.4% 

Netherlands  69.0% 

Poland* 

3.0% 

Portugal 

94.1% 

Slovakia 

N/A 

Spain  

12.3% 

45.4% 

99.0% 

10.2% 

28.4% 

8.6% 

7.4% 

69.0% 

3.0% 

95.8% 

63.7% 

37.1% 

- 

99.7% (E) 

- 

81.5% (T) 

N/A 

75.4% (T) 

N/A 

56.0% (E) 

96.5% (T) 

N/A 

34.8% (E) 

- 

99.4% (E) 

- 

81.3% (T) 

69.9% (T) 

76.5% (T) 

N/A 

56.0% (E) 

97.7% (T) 

80% (T) 

62.1% (E) 

* For these countries 2013 data are final figures. 
Notes: (1) Coverage rates for Poland are approximated since this country does not have coverage rates available by business sector. 
Note that in Poland data is exhaustive in the case of companies with more than nine employees that deliver full balance sheet 
statements. (2) Coverage rate figures are not provided in a single format (turnover or number of employees) because only part of this 
information is available in the different countries. 

Note that almost all countries provide data in two different stages. In the first phase, 
data are released as leading indicators, but with a provisional character. At a later 
stage, the data is updated to be more definitive/final. More details on the release 
calendar for the BACH data are available in Chart 1.  

Chart 1 
BACH data release calendar 

(data of reference date December n) 

provisional
final

BE

CZ

DE

ES

FR

IT

NL

AT

PL

PT

SK

n

c
e
D

1
+
n
n
u
J

1
+
n

c
e
D

2
+
n
n
u
J

2
+
n

c
e
D

3
+
n
n
u
J

Source: BACH Userguide (2015). 

In general, provisional data have lower coverage when compared with final data (the 
difference can be substantial). For instance, in the case of Austria, according to the 
last update, 2012 final data have a coverage rate of 45% in terms of number of 
corporations, which compares with 14% for the 2013 provisional data. 

Statistics Paper No 11, September 2015 

18 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3 
Structure of the samples (structure of the populations in brackets) 

(%, 2012/2013) 

Country 

Manufacturing 

Electricity, gas 

Construction 

Trade 

Transport 

Austria (C) 2013  

Belgium (E) 2013* 

8 (16) 

24 (24) 

Czech Rep.  (C) 2013* 

25 (N/A) 

France (E) 2013 

Germany (T) 2012 

Italy (T) 2013* 

Netherlands (T) 2013 

Poland (C) 2013* 

Portugal (T) 2013 

Slovakia (T) 2012 

Spain (E) 2012 

24 (21) 

41 (38) 

39 (36) 

7 (7) 

27 (N/A) 

26 (26) 

33 (N/A) 

19 (19) 

1 (2) 

1 (1) 

3 (N/A) 

2 (1) 

18 (12) 

9 (8) 

0 (0) 

1 (N/A) 

6 (6) 

4 (N/A) 

1 (0) 

11 (16) 

10 (10) 

7 (N/A) 

9 (9) 

2 (3) 

3 (5) 

9 (8) 

10 (13) 

6 (6) 

6 (N/A) 

8 (9) 

23 (23) 

4 (4) 

21 (22) 

10 (10) 

16 (N/A) 

3 (N/A) 

22 (21) 

22 (26) 

30 (30) 

23 (20) 

30 (28) 

37 (37) 

9 (9) 

4 (4) 

5 (5) 

3 (3) 

6 (8) 

6 (6) 

30 (N/A) 

5 (N/A) 

23 (23) 

7 (6) 

* For these countries 2013 data are final figures. 
Notes: (1) C – weight of the sector in terms of the number of corporations, E – weight of the sector in terms of the number of 
employees, T – weight of the sector in terms of turnover, N/A – information not available. (2) Additional detail on other business 
sectors may be found in the BACH Userguide – National samples chapter (only in the Excel version). 

Despite differing underlying samples, the sectoral composition of national samples 
included in BACH is, to some extent, in line with the structure of the population of 
NFCs in these countries (Table 3). For example, in Spain almost half the number of 
employees of the non-financial sector in 2012 was concentrated in the manufacturing 
and trade sectors, 19% and 23% respectively. For the same country, and for the 
same year, the composition of the BACH sample by business sector reflects the 
population of Spanish companies, where the manufacturing and trade sample 
sectors were also the most important business sectors. 

Table 4 
Main features of underlying national databases 

Country 

Data source 

Breaks and biases in sample time span 

Austria 

Multiple sources 

No break in time series. Overrepresentation of holding companies, head offices and real estate 
firms 

Belgium 

Administrative data  Almost full coverage of all corporate sizes and business sectors. No break or bias in time series 

(since 2000) 

Czech Rep.  Statistical survey & 
Administrative data 

No break or bias in time series (since 2005) 

France 

Administrative data 
& Tax returns 

No break in time series. Very high coverage rate for firms with annual turnover of more than EUR 
750,000 

Germany 

Multiple sources 

No break in time series. Sample bias towards large companies 

Italy 

Multiple sources 

No break in time series. Bias towards large companies 

Netherlands  Administrative data  Statistics Netherlands will deliver only non-consolidated data for the period 2008-2011. Non-

consolidated in this sense means that individual tax payers only report for themselves (1:1) or 
that individual tax payers report for themselves and one other company (1:2). The basis for the 
BACH data is the database of the tax authorities (corporation tax) 

Poland 

Statistical survey 

Sample includes only companies with more than nine employees and that deliver full balance 
sheet statements. The sample covers about 70% of enterprises in this segment. Data according 
to NACE Rev.2 have been available since 2005 

Portugal 

Administrative data  A statistical survey used for collecting data before 2006. For that period, there was a bias 

towards large companies 

Slovakia 

Administrative data  No break in time series. Sample bias due to changes in class structures between years 

Spain 

Multiple sources 

Minor breaks due to changes to the Spanish GAAP (2008) 

Notes: (1) NACE is the Statistical Classification of the Economic Activities in the European Union enforced by law in all Member 
States. For more information on this topic, consult the Eurostat Manual available at 
http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-07-015/EN/KS-RA-07-015-EN.PDF. (2) GAAP is the acronym for 
Generally Accepted Accounting Principles, referring to the national financial accounting system. 

Statistics Paper No 11, September 2015 

19 

It should be emphasised that data sources and methods of collecting data may 
change over time. In addition, changes to accounting nomenclature and business 
sector classification may be made occasionally. These factors may cause some 
breaks and/or biases in time series. Table 4 shows the situation in BACH concerning 
data source and breaks and biases over time 15. 

Finally, it should be stressed there are specificities in national practices regarding 
sampling methods used for statistical outputs. Annex 3: National Sampling Methods 
provides more information on the specific practices adopted for each country. 

2.3.3 

National accounting practices 

The BACH database is the result of a substantial effort of data harmonisation both 
within and across countries with a view to: 

(a)  Minimising differences in accounting practices among countries and 

gaps/breaks in time series/limitations of national samples; 

(b)  Providing complete accounting statements; 

(c)  Meeting user needs while adjusting to the requirements of modern analysis. 

Table 5 
National accounting specificities 

Country 

Accounting 
standards 

IFRS compliance 

Austria 

National GAAP 

Legislation on GAAP has considerably restricted the implementation of IFRS in individual financial 
statements in recent years 

Belgium 

National GAAP 

No IFRS compliance. Possible but limited in the future 

Czech Rep.  National GAAP 

No IFRS compliance. Possible but limited in the future 

France 

National GAAP 

Progressive endorsement of IFRS through a selective approach. National rules evolved in 2005 
with the partial adoption of the following rules: IAS 2, IAS 8, IAS11, IAS 16, IAS 23, IAS 34, IAS 36, 
IAS 37 and IAS 38 

Germany 

National GAAP 

There are distinct differences between Germany GAAP and IFRS. The reform of the German 
Commercial Code in 2010 (BilMoG) only partially reduced the differences 

Italy 

National GAAP 
& IFRS 

IFRS for listed companies 

Netherlands  National GAAP 

At the moment not compulsory. However, listed companies need to apply IFRS 

& IFRS 

Poland 

National GAAP 

There are significant differences between Polish statements and IFRS items 

Portugal 

National GAAP 

The most recent GAAP (data since 2009) are very close to IFRS 

Slovakia 

National GAAP 
& IFRS 

The most recent national GAAP are very close to IRFS 

Spain 

National GAAP 

From 2000-2007 close to IFRS. Since 2008 in line with IFRS 

Note: IAS is the acronym for International Accounting Standard, created by the predecessor of the IASB. 

As can be seen in Table 5, national BACH data are compiled according to the 
Generally Accepted Accounting Principles (GAAP) in force in each country. Although 
national GAAPs have the Fourth Council Directive (Directive 78/660/EEC of 25 July 
1978) as common ground, differing national accounting practices represent a major 

15   For more information on this topic consult the ECCBSO Report on Products and Services of the 

European CBSOs (2013) available on https://www.eccbso.org/. 

Statistics Paper No 11, September 2015 

20 

                                                                    
concern for cross-country comparability in BACH. These non-convergent specificities 
are caused by differing cultural and economic backgrounds and non-aligned tax 
systems. 

Following the globalisation of business there has been a gradual and slow process of 
adjustment to the International Financial Reporting Standards16 (IFRS). However, 
convergence is not yet complete and, in some countries, might not happen in the 
foreseeable future. 

The definition of each individual item (the variables), inspired by the IFRS, has 
provided a benchmark for evaluating the degree of comparability among countries 
reporting to BACH. It has helped BACH data providers to seek common definitions 
and limit deviations. Final templates obtained at the end of this exercise reflect:  

(a)  a reduction in differences among country specificities, improving comparability;  

(b)  a minimisation of gaps/breaks in time series and limitations at national sample 

level.  

Despite these efforts, some national deviations remain in the BACH database and 
have been identified and documented. Deviations are presented by 
country/accounting item. For this purpose, a deviation arises when national data are 
not completely aligned with the “first best” definition (Annex 2: National Deviations 
provides additional details). At this level, four types of alert are made available for 
each country/variable:  

1.  Code “I”: national content has some important specificity/practice; 

2.  Code “D”: national content departs from the predefined BACH “first best” 

definition; 

3.  Code “N/A”: national content is not available;  

4.  Code “P_N/A”: national content is only partially available. 

When creating harmonised templates, attention was also paid to the level of detail 
required to ensure that major accounting items of information were not incomplete. 
BACH therefore provides a complete picture of the balance sheet and the income 
statement. 

Based on the items defined for the balance sheet and for the income statement, the 
last stage of the process of harmonisation was the creation of a set of standard 
economic and financial ratios. These indicators are intended to help users improve 
their analysis of the performance of European companies. In this regard, BACH 
ratios follow, as much as possible, current trends from the viewpoint of business 
analysts and researchers. 

16  

IFRS refers to international accounting rules developed by the International Accounting Standards 
Board (IASB). For additional information on this topic consult the IFRS Foundation website available at 
http://www.ifrs.org/. 

Statistics Paper No 11, September 2015 

21 

                                                                    
Finally, it should be noted that the BACH Userguide (only the Excel version) also 
provides transition tables for the national templates used in the data collection 
process and the BACH harmonised templates. 

Statistics Paper No 11, September 2015 

22 

3 

Recent developments and their future 
impact on BACH 

This section focuses on the revision of the European Union Accounting Directive, 
which will most probably impact BACH. Moreover, the European System of National 
and Regional Accounts (ESA2010) has been implemented in most EU countries, 
implying that some countries will revise their BACH data. The implementation of ESA 
2010 implies a change in the classification of the institutional sector, affecting holding 
companies.  

3.1 

Revision of the European Union accounting directives 

On 26 June 2013, the European Parliament and the Council adopted “Directive 
2013/34/EU on the annual financial statements, consolidated financial statements 
and related reports of certain types of undertakings”17. Box 3: Transposition of the 
new Accounting Directive sums up the national transposition of this Directive into 
national legislation of countries belonging to BACH. 

Accordingly, Member States must transpose Directive 2013/34/EU into their national 
laws and regulations by 20 July 2015. Member States may act to ensure that the 
new provisions are first applied to financial years beginning on 1 January 2016 or 
during the calendar year 2016. 

On the basis of Article 3 of Directive 2013/34/EU, Member States will define a new 
classification by size (Table 6), taking into account undertakings that on their balance 
sheet dates do not exceed the limits of at least two of the following three criteria (in 
the case of small undertakings the figures may be extended up to the maximum 
shown in brackets).  

Table 6 
New size classification according to Directive 2013/34/EU 

Size 
Classification 

Balance sheet total  
(in thousand euro) 

Net turnover 
(in thousand euro) 

Average number of employees during the financial 
year 

Micro-undertakings 

350 

700 

Small undertakings 

4 000 (6 000) 

8 000 (12 000) 

20 000 

40 000 

10 

50 

250 

Medium-sized 
undertakings 

Large undertakings 

Undertakings which on their balance sheet dates exceed at least two of the three criteria defined for 
medium-sized undertakings 

17   This new Accounting Directive amends Directive 2006/43/EC on statutory audits of annual accounts 
and consolidated accounts; repeals Directive 78/660/EEC, the so-called “Fourth Council Directive on 
the annual accounts of certain types of companies” and Directive 83/349/EEC, the so-called “Seven 
Council Directive on consolidated accounts”; and faithfully incorporates the provisions of Directive 
2012/6/EU on accounting requirements for micro-entities.  

Statistics Paper No 11, September 2015 

23 

 
                                                                    
For its current definition, classification by size is based on Recommendation 
2003/361/EC, which takes three variables into account (similarly to Directive 
2013/34/UE): turnover, number of employees and total assets. However, as for the 
previous definition, due to limited access to the number of employees and total 
assets variables, the enterprise size class in BACH only considers turnover 
thresholds. The following classification by size is applied in accordance with 
Recommendation 2003/361/EC:  

• 

SMEs, if turnover is lower than EUR 50 million (which compares to EUR 40 
million in the new Directive18). SMEs are also split into two categories, identified 
as follows: 

• 

Small companies, if turnover is lower than EUR 10 million (which 
compares to EUR 8 million (or EUR 12 million) in the new Directive); 

•  Medium-size companies, if turnover is between EUR 10 million and EUR 
50 million (which compares to figures between EUR 8 million (or EUR 12 
million) and EUR 40 million in the new Directive). 

• 

Large companies, if turnover is higher than EUR 50 million (which compares to 
companies with turnover higher than EUR 40 million in the new Directive). 

It is important to note that, under this classification, the Directive only intends to 
propose the financial information obligation that Member States are required to meet 
(or are exempted from) – the information is not for statistical purposes. 

The main objective of Directive 2013/34/EU is to reduce the administrative burden on 
small companies and to meet the needs of users and those who prepare annual 
accounts. The “Think Small First” approach of this Directive should “enable 
companies to prepare profit and loss accounts, balance sheets and notes that are 
more proportionate to their size and to the information needs of the users of their 
financial statements”.  

The number of companies benefiting from these simplifications should grow since 
more companies will be classified in the small category for accounting purposes than 
is currently the case, thanks to the increase of the turnover and balance sheet 
thresholds used to define small companies, and to the obligation to apply the size 
criteria on an individual, rather than consolidated, basis, when companies are part of 
a group. In the case of Belgium, for instance, nearly 60% of non-financial companies 
currently considered large will be considered small in the framework of the new 
Directive. 

The new Directive also aims, to a certain extent, to improve the quality and the 
comparability of the information disclosed. However, the new Accounting Directive 
offers national legislators many different options for widening the scope for 
differences among European countries, specifically for small companies. 

18   Note that, according to the Directive, a number of options may be considered regarding the 

classification previously outlined, for instance the creation or otherwise of a micro-undertaking 
category.  

Statistics Paper No 11, September 2015 

24 

                                                                    
When comparing the provisions of the new Accounting Directive with those of the 
Fourth and Seventh Directives, the major changes concern the requirements 
regarding the statutory accounts of small companies.  

Very limited changes are imposed, as such, by the Directive: 

(a) 

Items of exceptional size or incidence must be disclosed in the notes instead of 
in the profit and loss account; 

(b)  Member States may not require, in the notes for the small undertakings, more 

than that which is compulsory or permitted. 

The major changes will probably stem from the way different Member States will 
transpose the Directive into their national laws and regulations and will use the 
different options offered by the Directive, particularly:  

(a)  The creation or otherwise of a micro-undertakings category; 

(b)  The application or otherwise of the different possible simplifications and 

exemptions proposed for micro-undertakings (a drastically reduced balance 
sheet and profit and loss account, exemption from drawing up notes, filing only 
the balance sheet instead of publishing the financial statements, …); 

(c)  The application or otherwise of the different possible simplifications and 

exemptions for small companies: drawing up an abridged balance sheet and 
profit and loss account, exemption from preparing a management report, 
exemption from publishing the profit and loss account and management report, 
the requirement of additional optional information in the notes (e.g. information 
on the fixed assets, transactions with related parties). 

The creation of specific national regulations for micro-companies could possibly give 
rise to: 

(a)  A major loss of financial information for a huge number of companies, essential 

for micro and macroeconomic analysis and for statistical purposes; 

(b)  A distortion of competition among micro-undertakings of different countries 

operating in international markets. 

In conclusion, the scope for differences among European countries, including those 
participating in BACH, will continue to be a challenge, given the wide range of 
possibilities available. 

Box 3 
Transposition of the new Accounting Directive  

In terms of national transposition, as far as the BACH WG is aware, only Germany and France have 
partially or fully transposed this Directive. In Germany, nearly all the exemptions have been applied 
and the balance sheet and income statement have been simplified almost to the minimum required. 
In France, micro-companies are exempt from the obligation to complete the notes and may choose 

Statistics Paper No 11, September 2015 

25 

not to publish their annual accounts, their data only being accessible by public administrations. 
These new provisions are to be applied for the first time to annual accounts closing on 31 
December 2013 and filled from April 2014 onwards. 

Portugal has, to some extent, already transposed the Directive through the implementation of 
specific legislation for micro-entities in 2010. This national legislation uses thresholds other than 
those recommended by the Directive (EUR 500,000 for the turnover and balance sheet total and 
five FTEs for the number of employees) and introduces a more abridged balance sheet and income 
statement compared with that for other NFCs. Austria has not transposed the Directive, but 
accounting and disclosure rules have already been simplified for small and medium-sized 
enterprises in a number of areas. 

In Poland a project is under discussion, focusing mainly on the simplification of the balance sheet 
and income statement and a wide usage of the proposed exemptions. 

No transposition or project to do so has been reported for the other participating countries. Some of 
these, in particular Belgium, Denmark and Luxembourg, have decided that they will take a position 
on this issue when preparing the transposition of the new Accounting Directive. 

3.2 

Implementation of ESA 2010 – The case of holding 
companies 

By October 2014, ESA 2010 had been implemented in EU member countries. One of 
the significant changes introduced by ESA 2010 that will most probably impact the 
BACH database is that relating to the composition of the institutional sector of NFCs. 
Basically, some entities such as holding companies and special purpose entities 
(SPEs) will be reclassified into the financial sector (S.127. Captive financial 
institutions). As an example, ESA 2010 emphasises the distinction between holding 
companies, that do not undertake any management activities and should therefore 
be classified as financial corporations (S.12), and head offices. The latter exercise 
managerial control over their subsidiaries and should be allocated to the dominant 
NFC sector of their subsidiaries. 

Up to now, only corporations belonging to the NFC institutional sector have been 
covered in the BACH database. The new sectorisation of holding companies in ESA 
2010 may have implications and consequences for BACH because these 
corporations no longer belong to the NFC institutional sector. However, the main 
BACH aggregates affected can already be identified, namely: NACE codes K642 – 
Activities of holding companies, M701 – Activities of head offices and BACH 
aggregates Z0 – Total NACE including M701, but excluding K642.  

Although holding companies are identified separately in the BACH database, for any 
possible future adjustment the degree of that impact will not be uniform among 
countries. However, following the agreement between countries (see Annex 4: Task 
Force On Holding Companies And Head Offices: Recommendations), the final 
impact on BACH of the reclassification of holding companies into the financial sector 

Statistics Paper No 11, September 2015 

26 

 
will largely depend on the criteria adopted by each country. The BACH WG has been 
following the issue relating to the reclassification of HCs and HOs and, on the basis 
of questionnaires sent to BACH members, it can be concluded that, in terms of 
number of enterprises, according to the results for the BACH sample, the most 
affected countries could potentially be FR, PT and CZ. In terms of total assets, the 
most affected countries could be FR, PT, CZ, PL and ES. For the other countries, 
the impact of the reclassification is likely to be small.  

Statistics Paper No 11, September 2015 

27 

4 

Case study: the manufacturing sector  

This chapter presents a simple case study of a BACH sector-based analysis. For this 
purpose, the manufacturing sector was chosen. The analysis does not pretend to be 
exhaustive; it intends, instead, to serve two simultaneous objectives: 

(i)  To illustrate how rich the database is; 

(ii)  To identify the main challenges arising in country comparability, while 

presenting appropriate solutions to mitigate these. 

The European comparison is made for the year 2013, with the exception of Slovakia 
(final figures for 2012), and is based on data extracted, in April 2015, from the BACH 
database. According to the release calendar of the BACH Userguide, not all 
countries had reported final data for 2013. In this sense, data for some countries 
may be of a preliminary nature. 

Note #1: BACH preliminary data 
It is important to consult the release calendar in the BACH Userguide to ascertain 
the degree of completion of national data. Preliminary data normally have lower 
coverage rates. 

The comparison was conducted for the 11 European economies with available data 
in the BACH database: Austria, Belgium, Czech Republic, France, Germany, Italy, 
the Netherlands, Poland, Portugal, Slovakia and Spain. 

4.1 

The sector and its representativity in BACH 

The BACH database provides aggregated data at the level of 17 NACE sections and 
about 80 NACE divisions. There are, therefore, data for 24 divisions for the 
manufacturing sector.  

For 2012/2013, the manufacturing sector is relatively well covered by the different 
country samples in BACH, with coverage rates of above 50%, either in terms of 
turnover or number of employees (Table 7), except for Spain.19 These levels of 
representativeness allow the global trend of the manufacturing sector to be captured, 
although some national samples are biased towards large companies. For instance, 
the provisional data indicate that in Germany 9% of manufacturing sector companies 
represent 62% of the corresponding turnover.  

19   Note, however, that these data are provisional for Spain (2012 final figures, for instance, present a 

coverage rate above 60% in terms of number of employees).  

Statistics Paper No 11, September 2015 

28 

                                                                    
Table 7 
National samples of the manufacturing sector 

(2012/2013*) 

Country 

Coverage 
No. NFCs (C) 

Coverage 
Turnover (T)/No. employees (E) 

Weight of the sector 
in the sample 

Weight of the sector 
in the population 

Austria (2013) 

27% 

- 

Belgium (2013)* 

100% 

100% (E) 

Czech Rep. (2013)* 

18% 

- 

France (2013) 

Germany (2013) 

Italy (2013)* 

47% 

9% 

14% 

Netherlands (2013) 

69% 

Poland (2013)* 

- 

Portugal (2013) 

Slovakia (2012) 

Spain (2013) 

94% 

66% 

18% 

87% (E) 

62% (T) 

82% (T) 

- 

- 

97% (T) 

- 

33% (E) 

8% (C) 

24% (E) 

25% (C) 

24% (E) 

41% (T) 

39% (T) 

9% (C) 

- 

26% (T) 

33% (T) 

19% (E) 

16% (C) 

24% (E) 

N/A 

21% (E) 

38% (T) 

36% (T) 

9% (C) 

- 

26% (T) 

N/A 

19% (E) 

* For these countries 2013 relates to final figures. 
Source: Bank for the Accounts of Companies Harmonized (April 2015), http://www.bach.banque-france.fr/?lang=en.  
Notes: (1) Information for business sector section C, enterprise size 0 – All sizes and sample 0 – variable samples. (2) With the 
exception of Poland, in every data extraction all items are accompanied by coverage rate measurements. (3) The Excel version of the 
BACH Userguide (Chapter 2 – National Samples) gathers information for both the structures of the NFC population and NFC sample 
by country and business sector/enterprise size. 

Note #2: International comparisons 
There is a diversity of national specificities in BACH samples, the most important 
being the coverage of the NFC population. Although cross-country comparisons for 
global aggregates are appropriate, some caution is required for specific segments of 
corporations. Micro-enterprises, for example, are better captured in full coverage 
national samples than in partial coverage national samples.  

As previously mentioned, the BACH database has one important distinctive feature: 
the structure of national samples is, to some extent, in line with the structure of the 
population of NFCs in each country. For instance, in France, in 2013, the 
manufacturing sector in BACH represents 24% of the number of employees of the 
total BACH sample, while in the NFC population the weight of this sector is 21%.  

4.2 

The Importance and structure of the sector 

The manufacturing sector constitutes one of the most important sectors in the 
European economy. In the BACH sample (BACH average), this sector accounts for 
34% of NFC turnover (Chart 2).   

Statistics Paper No 11, September 2015 

29 

Chart 2 
Non-financial corporations’ turnover by business sector – BACH average 

(2012/2013 in percentage)* 

Manufacturing

Trade

Other services

Electricity and water

Construction

Mining and quarrying

Agriculture and fisheries

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Source: Bank for the Accounts of Companies Harmonized (April 2015), http://www.bach.banque-france.fr/?lang=en. 
* For Belgium, Czech Republic, Italy and Poland, data relate to final figures for 2013. For the remaining countries, data relate 
to provisional figures, except for Slovakia (final figures for 2012). 
Notes: (1) Enterprise size 0 – All sizes and sample 0 – Variable samples. (2) Sample structures obtained from the absolute values. (3) 
For the purpose of this article, NACE Revision 2 sections were aggregated as follows: Agriculture and fisheries (NACE A), Mining and 
quarrying (NACE B), Manufacturing (NACE C), Electricity and water (NACE D and E), Construction (NACE F), Trade (NACE G) and 
Other services (NACE H, I, J, L, Mc, N, P, Q, R, S). (4) The BACH average comprises the weighted mean for the European countries 
available in the BACH database. 

The analysis above makes implicit - BACH average 20 - the possibility to build 
additional aggregates from BACH data. The aggregate above corresponds to the 
ratio between the net turnover (absolute values) obtained for each business sector, 
identified in terms of sections of NACE (numerator) and the sum of the net turnover 
(absolute values) for all business sectors selected (denominator), taking into account 
in both cases the combination chosen (financial year/enterprise size class/sample).  
The results were made available for all BACH countries. For the purpose of this 
aggregate, the absolute value of turnover was used. Nevertheless, using a similar 
methodology, new variables can be also built.  

Note #3: New aggregates/new variables 
The provision of absolute values allows complementary standardised analysis in 
BACH, through building new indicators by combining made-available indicators in a 
different way.  

20   This BACH average could not be fully adjusted for the population of European companies as a whole, 
given the overweighting of some national samples in this aggregate, due to the exhaustiveness of data 
collection in some countries. 

Statistics Paper No 11, September 2015 

30 

 
                                                                    
When disaggregated by business sector (Chart 3), 
NACE 29 – Manufacture of motor vehicles, trailers and 
semi-trailers accounts for the highest share of 
European manufacturing sector gross value added 
(18%), followed by NACE 10 – Manufacture of food 
products (11%).  

More than half (56%) of the European manufacturing 
sector gross value added is concentrated in six (out of 
24) sectors of economic activity.  

The above analysis is similar to the previous analysis 
which used the net turnover for each business sector, 
identified in terms of sections of NACE. However, the 
data in Chart 3 take gross value added into 
consideration as well as each of the divisions 
specifically relating to the manufacturing sector (one 
section of NACE). 

Chart 3 
Manufacturing sector gross value added  

(Total countries, BACH average (2012/2013)*) 
By business sector 

C24
6%

C25
5% C26
5%

C19
7%

C21, 4%

C22, 4%

C27, 4%

C23, 3%

C30, 3%

C11, 2%

C17, 2%

Other
30%

C28
8%

C20
9%

C10
11%

C29
18%

C13, 1%

C15, 1%

C18, 1%

C32, 1%

C14, 1%

C16, 1%

C31, 1%

C33, 1%

* For Belgium, Czech Republic, Italy and Poland, data relate to final figures for 2013. For 
the remaining countries, data relate to provisional figures, except for Slovakia (final 
figures for 2012). 
Source: Bank for the Accounts of Companies Harmonized (April 2015), 
http://www.bach.banque-france.fr/?lang=en. 
Notes: (1) Enterprise size 0 – All sizes, 0 – Variable samples. (2) Sample structures 
obtained from the absolute values. (3) BACH average comprises the weighted mean for 
the European countries available in the BACH database. (4) NACE C10 – Manufacture 
of food products, NACE C20 – Manufacture of chemicals and chemical products, NACE 
C25 – Manufacture of fabricated metal products, except for machinery and equipment, 
NACE C26 – Manufacture of computer, electronic and optical products, NACE C28 – 
Manufacture of machinery and equipment n.e.c. and NACE C29 – Manufacture of motor 
vehicles, trailers and semi-trailers.    

Note #4: Granular data 
Information in BACH is provided with a reasonably significant level of detail in terms 
of business sectors and enterprise size classes. This level of detail allows several 
analyses to be conducted. 

4.3 

An assessment of the economic and financial situation of 
the sector 

When the performance of the manufacturing sector was compared across European 
countries, it was revealed that Netherlands and Belgium performed best, on average, 
in 2012/2013* at the level of EBITDA (Earnings before interest, tax, depreciation 
and amortisation) on net turnover (14% and 10% respectively, Chart 4). 
According to the same indicator, the French manufacturing sector generated less 
revenue per euro of turnover (7%). 

The above analysis is based on the weighted mean of EBITDA divided by net 
turnover. However, an analysis of the different quartiles of the underlying distribution 
provides a more complete picture. For example, Chart 4 shows that the high 

Statistics Paper No 11, September 2015 

31 

 
 
 
 
 
average performance of companies from the Netherlands and Belgium is very much 
influenced by extreme values. In fact, the inter-quartile range (the difference between 
the third and the first quartile) illustrates the heterogeneity of manufacturing 
companies from the Netherlands and Belgium (23% in both cases) compared to 
other countries. In these countries, while the weighted means are 14% and 10% 
respectively, the third quartiles come to 24% and 27% respectively. This means that 
25% of manufacturing sector companies from the Netherlands and Belgium has 
ratios of EBITDA to net turnover above 24% and 27% respectively21. 

Chart 4 
Manufacturing sector EBITDA over net turnover 

((2012/2013*) – BACH R33 in percentages) 

Weighted mean
1st Quartile
Median
3rd Quartile

30

25

20

15

10

5

0

-5

-10

NL

BE

AT

CZ

DE

PT

ES

PL

IT

FR

SK

* For Belgium, Czech Republic, Italy and Poland, data relate to final figures for 2013. For the remaining countries, data relate to 
provisional figures, except for Slovakia (final figures for 2012). 
Source: Bank for the Accounts of Companies Harmonized (April 2015), http://www.bach.banque-france.fr/?lang=en 
Notes: (1) Enterprise size 0 – All sizes, sample 0 – Variable samples and R33 – EBITDA on net turnover. (2) Quartiles not available for 
Czech Republic. 

Note #5: Quartile measurements  
These measurements are very useful for learning more about the underlying 
distribution. In particular, the quartile measurements allow the reader to assess the 
extent to which weighted means are influenced by extreme values. 

Alternatively, Chart 5 shows the return on equity indicator demonstrating that the 
Austrian manufacturing sector had the highest profitability (14%). On the other hand, 
southern Europe (Italy, Spain and Portugal) records the lowest profitability in the 
manufacturing sector. However, Belgium and the Netherlands, in contrast to results 
for EBITDA on net turnover, are also included in the group of countries with the 
lowest profitability in terms of return on equity. 

21   There is a diversity of national specificities in BACH samples, the most important being the coverage of 
the NFC population. Although cross-country comparisons for global aggregates are appropriate, some 
caution is required for specific segments of corporations. 

Statistics Paper No 11, September 2015 

32 

 
                                                                    
The analysis shows the importance of the indicators chosen for the purpose of each 
analysis. In particular, both EBITDA on net turnover and return on equity allow 
companies’ profitability to be analysed. In this category, the BACH database offers a 
total of ten different ratios. It should be noted that conclusions may be different 
according to the ratio used.  

Note #6: Richness of indicators 
BACH offers several indicators that try to cover a wide range of analysis. To this end, 
a battery of 29 ratios is provided, covering: financial structure, financial and debt 
service, profitability, activity and technical aspects and working capital features. 

Chart 5 
Manufacturing sector return on equity 

Weighted mean values (2012/2013*) – BACH R38 

Belgium

Czech Rep.

Germany

Spain

France

Italy

Netherlands

Austria

Portugal

Slovakia

0%

2%

4%

6%

8%

10%

12%

14%

16%

* For Belgium, Czech Republic, Italy and Poland, data are final figures for 2013. For the remaining countries, data correspond to 
provisional figures, except for Slovakia (final figures for 2012). For Austria this leads to an overestimation. 
Source: Bank for the Accounts of Companies Harmonized (April 2015), http://www.bach.banque-france.fr/?lang=en. 
Note: Enterprise size 0 – All sizes, sample 0 – Variable samples and R38 – Return on equity. 

The capital ratio (equity over total assets) highlights, inter alia, how debt is vital for 
the financing of the European manufacturing sector. Indeed, the BACH database 
reveals that debt represents more than half of the funding of the European 
companies of the manufacturing sector (Chart 6). In particular, the German 
manufacturing sector had the highest share of external funding in 2012/2013* (68% 
of total assets). The Netherlands, in turn, records the lowest value for this indicator 
(40% of total assets). 

For all European countries considered22, with the exception of Slovakia and 
Portugal, the average capital ratio of the manufacturing sector increased, or 
remained at the same level, between 2006 and 2012/2013*. 

22   Data for 2006 unavailable for the Netherlands. 

Statistics Paper No 11, September 2015 

33 

 
                                                                    
Chart 6 
Manufacturing sector capital ratio 

Weighted mean values (2006 and 2012/2013*) 

2006

2012/2013*

70%

60%

50%

40%

30%

20%

10%

0%

BE

CZ

DE

ES

FR

IT

NL

AT

PL

PT

SK

* For Belgium, Czech Republic, Italy and Poland, data relate to final figures for 2013. For the remaining countries, data relate to 
provisional figures, except for Slovakia (final figures for 2012). 
Source: Bank for the Accounts of Companies Harmonized (April 2015), http://www.bach.banque-france.fr/?lang=en. 
Note: (1) Enterprise size 0 – All sizes, sample 0 – Variable samples and E – Total equity in terms of total assets. (2) Data for 2006 
unavailable for the Netherlands. 

The analysis based on sliding samples includes companies that are available for a 
pair of two consecutive years (see Box 1: Variable samples and sliding samples 
and Box 2: Variable samples versus sliding samples; a real example for French 
trade SMEs). To this extent, sliding samples prevent effects simply due to 
companies’ inclusion in/exclusion from the samples 23. In a two-consecutive-year 
analysis, sliding samples increase cross-country comparability. The cost of using 
sliding samples comes from losing information (companies that are not available for 
two consecutive years). 

Note #7: Sliding/variable samples  
Sliding samples are more suitable for cross-country comparability and the analysis of 
two-consecutive-year trends. In turn, variable samples are preferable for countries 
where all NFCs are represented (e.g. Belgium and Portugal) or for capturing longer 
trends.  

Given the importance of debt in the funding of the European manufacturing sector it 
is worth analysing its underlying financial instruments. Chart 7 illustrates the 
structure of funding of the BACH European manufacturing sector for the years 
2012/2013*, by enterprise size. 

23   Some countries do not provide “pure” variable samples. In these cases, the second year of the sliding 

sample is provided as the variable sample. 

Statistics Paper No 11, September 2015 

34 

 
                                                                    
In 2013, independent of enterprise size, bank loans in the manufacturing sector were 
more significant in Poland. Germany registered the smallest weight for this item as 
regards large companies (4%). The lowest value for SMEs was recorded by the 
Netherlands (4%). 

Provisions are a source of manufacturing sector funding mainly in Germany and 
Austria for both enterprise size classes under consideration. Manufacturing sector 
funding via trade payables assumes a higher significance in Czech Republic, Italy 
and Slovakia. On the other hand, the Netherlands registers the highest figures for 
other non-financial creditors as a source of manufacturing sector funding. 

Chart 7 
Manufacturing sector structure of funding by enterprise size 

(2012/2013*) 

Credit Institutions (BACH item L2)
Trade payables (BACH item L4)
Provisions (BACH item Lp)
Other creditors (BACH item L3=L31+L32)

Other financial creditors (BACH item L31)
Other non-financial creditors (BACH item L32)
Others (BACH item L1,L5,L6)

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

l

a
t
o
T

s
E
M
S

e
g
r
a
L

l

a
t
o
T

s
E
M
S

e
g
r
a
L

l

a
t
o
T

s
E
M
S

e
g
r
a
L

l

a
t
o
T

s
E
M
S

e
g
r
a
L

l

a
t
o
T

s
E
M
S

e
g
r
a
L

l

a
t
o
T

s
E
M
S

e
g
r
a
L

l

a
t
o
T

AT

BE

CZ

DE

ES

FR

s
E
M
S

IT

e
g
r
a
L

l

a
t
o
T

s
E
M
S

e
g
r
a
L

l

a
t
o
T

s
E
M
S

e
g
r
a
L

l

a
t
o
T

s
E
M
S

e
g
r
a
L

l

a
t
o
T

s
E
M
S

e
g
r
a
L

NL

PL

PT

SK

* For Belgium, Czech Republic, Italy and Poland, data relate to final figures for 2013. For the remaining countries, data relate to 
provisional figures, except for Slovakia (final figures for 2012). 
Source: Bank for the Accounts of Companies Harmonized (April 2015), http://www.bach.banque-france.fr/?lang=en. 
Notes: (1) Enterprise size 0 – All sizes, Enterprise size 1 – SMEs, Enterprise size 2 – Large companies, sample 0 – Variable samples. 
(2) For a complete analysis of the differences in the source of financing of the countries, it is imperative to consult the BACH 
Userguide. In particular, it is important to note that neither the other financial creditor’s component nor the other non-financial creditor’s 
component is available for Austria; the BACH item L3 “other creditors” is provided instead. All values are expressed in terms of total 
liabilities (BACH item L). 

Note #8: Userguide  
Consultation of the BACH Userguide is imperative for an adequate analysis of the 
results. For instance, the document on deviations (see Annex 2: National Deviations) 
allows an understanding of national structural factors underlying dissimilar data 
results and trends among countries. 

Statistics Paper No 11, September 2015 

35 

 
Annex 1 
Overview of the information available in 
the BACH database 

Table A1.1 
Income Statement 

(as a % of net turnover) 

Code 

Income statement 

I1 

Net turnover 

General contents 

Includes sales of goods and services net of returns, deductions and rebates. Sales are net of VAT and 
excise taxes. 

I2 

I3 

I4 

I41 

Variation in stocks of finished goods and work in progress 

Includes changes in production inventories recognised in the income statement. 

Capitalised production  

Other income 

Includes costs capitalised by the entity and recognised as income in the period. 

Includes other income not identified in previous items (I1, I2 and I3). 

Of which: Operating subsidies and supplementary operating 
income  

Details of other income relating to operating subsidies and supplementary operating income. 

I42 

Of which: Financial income 

Details of other income relating to financial income. 

I43 

Of which: Extraordinary income 

Details of other income relating to extraordinary income. 

I5 

I6 

I7 

I8 

Cost of goods sold, materials and consumables  

Includes cost of materials and consumables used and the cost of goods sold in the period. 

External supplies and services 

Includes expenses relating to external supplies and services in the period. 

Staff costs 

Other expenses 

Includes staff expenses recognised in the period.  

Includes other expenses not identified in previous items (I5, I6 and I7). 

I81 

Of which: Operating taxes and other operating charges 

Details of other expenses relating to operating taxes and other operating charges. 

I82 

Of which: Provisions (net of reversals) 

Details of other expenses relating to provisions (net of reversals). 

I83 

I84 

I85 

I9 

Of which: Financial expenses other than interest on financial 
debt 

Details of other expenses relating to financial expenses, apart from interest on financial debt (included in 
I10). 

Of which: Extraordinary expenses and impairments (net of 
reversals), apart from those on inventories and receivables 

Details of other expenses such as extraordinary expenses and those relating to reductions/increases in fair 
value and impairment charges (net of reversals) other than those (net of reversals) included in I85. 

Of which: Impairments (net of reversals) on inventories and 
receivables 

Details of other expenses relating to impairment charges (net of reversals) on inventories and receivables. 

Depreciation and amortisation of intangible and tangible fixed 
assets 

Includes depreciation and amortisation of assets included in the items A11 and A12 and recognised in the 
period. 

I10 

Interest on financial debts 

Includes financing costs recognised in the period. 

I11 

Tax on profit  

Includes income taxes recognised in the period. 

It1 

It2 

It3 

Total income  

Total expenses  

(I1+I2+I3+I4) 

(I5+I6+I7+I8+I9+I10+I11) 

Net profit or loss for the period  

(It1-It2) 

Statistics Paper No 11, September 2015 

36 

 
Table A1.2 
Assets 

(as a % of total balance sheet) 

Code 

A1 

A11 

A12 

A13 

A131 

A2 

A21 

A3 

A4 

A41 

A42 

A5 

A51 

A52 

A6 

A7 

A 

Assets 

Fixed assets 

Intangible fixed assets 

Tangible fixed assets 

Financial fixed assets 

General contents 

 (A11+A12+A13) 

Includes brands, patents, copyrights, licenses, etc., even if such assets are 
held under finance lease contracts. This item also includes goodwill 
recognised separately. 

Includes land, buildings, machinery, administration and transport equipment, 
etc., even if such assets are held under finance lease contracts. This item also 
includes biological assets and investment properties. 

Includes shareholdings in the capital of other entities on a continuous basis, 
as well as loans made to such entities. 

Of which: Shares in affiliated undertakings and 
participating interests 

Details of financial fixed assets relating to investments in (shareholdings in the 
capital of) associates, subsidiaries and jointly controlled entities. 

Inventories 

Includes raw materials and consumables, goods, work in progress and 
finished products, as well as consumable biological assets. 

Of which: Payments on account 

Details of inventories relating to payments on account. 

Trade receivables 

Other receivables 

Current 

Non-current 

Deferred assets 

Current 

Non-current 

Includes credit granted to customers for sales or services net of advances 
received (except for payments received on account for orders, included in L5). 

Includes other accounts receivable (except trade receivables), as well as non-
current assets held for sale (net of any associated liabilities). 

Includes deferred tax assets and expenses to be recognised in future periods. 

Other financial assets, current 

Includes financial assets held for trading and derivatives. 

Cash and bank 

Includes amounts available in cash, demand deposits and other deposits in 
financial institutions. 

TOTAL BALANCE SHEET 

 (A1+A2+A3+A4+A5+A6+A7)=E+L 

Notes: (1) Assets are presented at a value net of accumulated depreciation, accumulated impairment losses and other adjustments. 

Statistics Paper No 11, September 2015 

37 

  
  
  
  
Table A1.3 
Equity and Liabilities  

(as a % of total balance sheet) 

Code 

E1 

E2 

E 

Lp 

Lp1 

L1 

L11 

L12 

L2 

L21 

L22 

L3 

L31 

L311 

L312 

L32 

L321 

L322 

L4 

L5 

L6 

L61 

L62 

L 

Equity and liabilities 

General contents 

Capital, reserves, earnings and other equity instruments 

Includes paid-up capital, reserves, treasury stock and other equity 
instruments. Capital subscribed but not paid up is deducted from this 
item. This item also includes the cumulative net income for previous 
periods, net income for the period as well as dividends paid in advance. 

Revaluations, adjustments on financial investments and 
other comprehensive income 

Includes revaluation surplus for tangible and intangible assets, 
adjustments on financial assets recognised in equity and other changes 
in equity. 

TOTAL EQUITY  

Provisions 

(E1+E2) 

Includes all provisions. 

Of which: Provisions for pensions and similar obligations  Details of provisions relating to liabilities for post-employment benefits. 

Bonds and similar obligations 

Includes bonds and securities issued by the entity. 

Current 

Non-current 

Amounts owed to credit institutions 

Includes the entity’s debt to finance companies and credit institutions 
(including leasing). 

Current 

Non-current 

Other creditors   

Other financial creditors 

Current 

Non-current 

(L31+L32) 

Includes the entity’s remaining funding (excluding funding identified in 
L1 and L2). 

Other non-financial creditors 

Includes other accounts payable (except trade payables). 

Current 

Non-current 

Trade payables 

Includes debts to suppliers of goods and services, net of advances 
(except for payments on account, included in A21). 

Payments received on account for orders, current 

Includes payments received by the entity on account of orders. 

Deferred liabilities 

Current 

Non-current 

Includes deferred tax liabilities and income to be recognised in future 
periods. 

TOTAL LIABILITIES  

(Lp+L1+L2+L3+L4+L5+L6) 

Statistics Paper No 11, September 2015 

38 

  
  
  
  
  
  
  
  
  
  
 
Table A1.4 
Financial Structure Ratios  

(as a %) 

R1 

R11 

R12 

R13 

R14 

R15 

R16 

Ratio 

Definition 

Contents 

Assets to Equity ratio 

Total balance sheet/Total equity 

Numerator: 

Liabilities to Equity ratio 

Total liabilities/Total equity 

Numerator: 

Denominator: 

Current assets/Total assets 

Current assets/Total balance 
sheet 

Other financial assets and cash 
and bank/ Total assets 

Other financial assets and cash 
and bank/Total balance sheet 

Non-current debt/ Total assets 

Non-current debt/Total balance 
sheet 

Denominator: 

Numerator: 

Denominator: 

Numerator: 

Denominator: 

Numerator: 

Denominator: 

A 

E 

L 

E 

(A2+A3+A41+A51+A6+A7) 

A 

(A6+A7) 

A 

(L12+L22+L312+L322) 

A 

Current debt/Total assets 

Current debt/Total balance sheet  Numerator: 

(L11+L21+L311+L321+L4+L5) 

Denominator: 

A 

Notes: (1) This set of information is completed following the structures provided in the balance sheet (Tables A1.2 and A1.3) and income statement (Table A1.1). (2) Some ratios are 
not computed for holding companies. (3) Edition of the ratios: denominator>0. 

Table A1.5 
Financial and Debt Service Ratios  

(as a %) 

R2 

R21 

R22 

R23 

R24 

R25 

R26 

R27 

Ratio 

Definition 

Financial income net of charges 
other than interest/EBITDA 

Financial income net of charges 
other than interest/EBITDA 

Contents 

Numerator: 

Denominator: 

EBITDA/Interest on financial debt  EBITDA/Interest on financial 

Numerator: 

debts 

Interest burden 

EBT/EBIT 

Interest and similar charges/Net 
turnover 

Interest and similar charges/Net 
turnover 

Interest and similar 
charges/Gross operating profit 

Interest and similar charges/ 
Gross operating profit 

Net financial income/Gross 
operating profit 

Financial income net of charges/ 
Gross operating profit 

Gross operating profit/Total net 
debt 

Gross operating profit/Total net 
debt 

Denominator: 

Numerator: 

Denominator: 

Numerator: 

Denominator: 

Numerator: 

Denominator: 

Numerator: 

Denominator: 

Numerator: 

Denominator: 

(I42-I83) 

(I1+I2+I3+I41+I42-I5-I6-I7-I81-
I83) 

(I1+I2+I3+I41+I42-I5-I6-I7-I81-
I83) 

I10 

(I1+I2+I3+I4-I5-I6-I7-I8-I9-I10) 

(I1+I2+I3+I4-I5-I6-I7-I8-I9) 

(I83+I10) 

I1 

(I83+I10) 

(I1+I2+I3+I41-I5-I6-I7-I81) 

(I42-I83-I10) 

(I1+I2+I3+I41-I5-I6-I7-I81) 

(I1+I2+I3+I41-I5-I6-I7-I81) 

(L1+L2+L3+L4+L5+L6-A6-A7) 

Notes: (1) EBITDA is calculated without provisions, impairments and extraordinary items; EBIT and EBT are calculated to include these items. (2) This set of information is completed 
following the structures provided in the balance sheet (Tables A1.2 and A1.3) and income statement (Table A1.1). (3) Some ratios are not computed for holding companies. (4) 
Edition of the ratios: denominator>0. 

Statistics Paper No 11, September 2015 

39 

 
 
Table A1.6 
Profitability Ratios  

(as a %) 

R3 

R31 

R32 

R33 

R34 

R35 

R36 

R37 

R38 

R39 

R310 

Ratio 

Definition 

Contents 

Gross value added/Net turnover 

Gross value added/Net turnover 

Numerator: 

(I1+I2+I3+I41-I5-I6-I81) 

Gross operating profit/Net 
turnover (ROS) 

Gross operating profit/Net 
turnover 

Denominator: 

Numerator: 

Denominator: 

I1 

(I1+I2+I3+I41-I5-I6-I7-I81) 

I1 

EBITDA/Net turnover 

EBITDA/Net turnover 

Numerator: 

(I1+I2+I3+I41+I42-I5-I6-I7-I81-I83) 

Net operating profit/Net turnover 

Net operating profit/Net turnover 

Numerator: 

(I1+I2+I41-I5-I6-I7-I81-I82-I85-I9) 

Denominator: 

I1 

EBIT/Net turnover 

EBIT/Net turnover 

EBT/Net turnover 

EBT/ Net turnover 

Denominator: 

Numerator: 

Denominator: 

Numerator: 

Denominator: 

I1 

(I1+I2+I3+I4-I5-I6-I7-I8-I9) 

I1 

(I1+I2+I3+I4-I5-I6-I7-I8-I9-I10) 

I1 

Net financial income/Net turnover  Net financial income/Net turnover  Numerator: 

(I42-I83-I10) 

Return on equity 

Net profit or loss for the 
period/Total equity 

Net operating profit/Total assets 

Net operating profit/Total balance 
sheet 

Profit or loss for the year before 
taxes/Equity 

Profit or loss for the year before 
taxes/Total equity 

Denominator: 

Numerator: 

Denominator: 

Numerator: 

Denominator: 

Numerator: 

Denominator: 

I1 

It3 

E 

(I1+I2+I3+I41-I5-I6-I7-I81-I82-I85-
I9) 

A 

It3+I11 

E 

Notes: (1) EBITDA is calculated without provisions, impairments and extraordinary items; EBIT and EBT are calculated to include these items. (2) This set of information is completed 
with the structures provided in the balance sheet (Tables A1.2 and A1.3) and income statement (Table A1.1). (3) Some ratios are not computed for holding companies. (4) Edition of 
the ratios: denominator>0. 

Table A1.7 
Activity and Technical Ratios 

(as a %) 

R4 

R41 

R42 

Ratio 

Definition 

Contents 

Asset turnover ratio 

Net turnover/Total balance sheet 

Numerator: 

Employee expenses/Gross value 
added 

Staff costs/Gross value added 

Numerator: 

Denominator: 

I1 

A 

I7 

Denominator: 

(I1+I2+I3+I41-I5-I6-I81) 

Notes: (1) This set of information is completed following the structures provided in the balance sheet (Tables A1.2 and A1.3) and income statement (Table A1.1). (2) Some ratios are 
not computed for holding companies. (3) Edition of the ratios: denominator>0. 

Statistics Paper No 11, September 2015 

40 

 
 
Table A1.8 
Working Capital Ratios 

(as a %) 

R5 

R51 

R52 

R53 

R54 

Ratio 

Definition 

Contents 

Inventories/Net turnover 

Inventories/Net turnover 

Numerator: 

Trade receivables/Net turnover 

Trade receivables/Net turnover 

Numerator: 

Denominator: 

Trade payables/Net turnover 

Trade payables/Net turnover 

Numerator: 

Denominator: 

Operating working capital/Net 
turnover 

Operating working capital/Net 
turnover 

Denominator: 

Numerator: 

Denominator: 

A2 

I1 

A3 

I1 

L4 

I1 

(A2+A3-L5-L6) 

I1 

Notes: (1) This set of information is completed following the structures provided in the balance sheet (Tables A1.2 and A1.3) and income statement (Table A1.1). (2) Some ratios are 
not computed for holding companies. (3) Edition of the ratios: denominator>0. 

Table A1.9 
Notes 

(as a % of turnover) 

N 

N1 

N2 

N3 

Note 

Contents 

Acquisitions less sales and disposals of intangible assets 

Acquisitions less sales and disposals of tangible assets 

Acquisitions less sales and disposals of financial fixed assets 

Includes acquisitions less sales and disposals in the period of 
assets included in item A11 of the balance sheet. 

Includes acquisitions less sales and disposals in the period of 
assets included in item A12 of the balance sheet. 

Includes acquisitions less sales and disposals in the period of 
assets included in item A13 of the balance sheet. 

Statistics Paper No 11, September 2015 

41 

 
 
Annex 2 
National deviations 

Table A2.1 
Income statement - Comparisons* 

I1 

I2 

I3 

I4 

I41 

I42 

I43 

I5 

I6 

I7 

I8 

I81 

I82 

I83 

I84 

I85 

I9 

I10 

I11 

It1 

It2 

It3 

AT 

- 

- 

- 

D 

D 

- 

- 

- 

- 

- 

D 

D 

N/A 

- 

- 

N/A 

- 

- 

- 

- 

- 

- 

BE 

I 

P_N/A 

P_N/A 

- 

I 

- 

- 

I 

P_N/A 

- 

- 

- 

- 

P_N/A 

- 

- 

- 

D 

- 

- 

- 

- 

CZ 

FR 

DE 

IT 

NL 

PL 

PT 

SK 

ES 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

D 

- 

- 

- 

N/A 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

D 

- 

- 

D 

- 

- 

- 

- 

- 

- 

- 

- 

I 

- 

- 

- 

- 

D 

N/A 

- 

- 

- 

N/A 

- 

- 

- 

- 

I 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

N/A 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

I 

D 

N/A 

N/A 

N/A 

N/A 

- 

- 

I 

- 

- 

- 

D 

- 

- 

- 

I 

I 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

I 

N/A 

D 

N/A 

D 

D 

N/A 

N/A 

D 

N/A 

D 

D 

I 

- 

- 

I 

- 

- 

- 

N/A 

- 

- 

- 

- 

- 

- 

- 

D 

- 

- 

I 

- 

- 

- 

- 

- 

- 

I 

- 

- 

D 

D 

- 

- 

- 

- 

- 

- 

D 

D 

- 

- 

I 

- 

- 

- 

- 

*Detailed information for each country is available in the BACH Userguide (only in the Excel file); Legend: (1) “-“ means the concept is in accordance with the general content; (2) “I” 
means additional information for the user (available in the Excel file of the BACH Userguide); (3) “D” means possible deviation from the defined concept; (4) N/A means unavailable; 
(5) “P_N/A” means partially unavailable (e.g. unavailable for one size class). 
Note: (1) Austria (AT), Belgium (BE), Czech Republic (CZ), France (FR), Germany (DE), Italy (IT), Netherlands (NL), Poland (PL), Portugal (PT), Slovakia (SK) and Spain (ES). (2) 
Although data are not yet available for some countries, future methodological differences from the BACH concept are already available. 

Statistics Paper No 11, September 2015 

42 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A2.2 
Assets - Comparisons* 

BE 

CZ 

FR 

A1 

A11 

A12 

A13 

A131 

A2 

A21 

A3 

A4 

A41 

A42 

A5 

A51 

A52 

A6 

A7 

A 

AT 

- 

D 

- 

- 

- 

- 

- 

- 

- 

D 

N/A 

- 

N/A 

N/A 

- 

- 

- 

- 

D 

- 

- 

- 

D 

- 

- 

P_N/A 

N/A 

- 

P_N/A 

- 

- 

- 

- 

- 

N/A 

N/A 

- 

- 

- 

- 

- 

- 

D 

N/A 

N/A 

- 

N/A 

N/A 

- 

- 

- 

- 

D 

D 

- 

- 

- 

- 

D 

- 

D 

DE 

- 

D 

- 

- 

- 

- 

- 

D 

D 

D 

N/A 

N/A 

- 

D 

D 

- 

- 

- 

- 

D 

- 

- 

- 

- 

IT 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

NL 

PL 

D 

N/A 

N/A 

N/A 

N/A 

- 

D 

- 

- 

P_N/A 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

PT 

- 

- 

- 

- 

- 

- 

- 

- 

- 

D 

D 

- 

D 

- 

- 

- 

- 

SK 

ES 

D 

D 

D 

D 

- 

D 

D 

D 

D 

- 

- 

- 

- 

- 

N/A 

- 

- 

- 

- 

- 

- 

*Detailed information for each country is available in the BACH Userguide (only in the Excel file); Legend: (1) “-“ means the concept is in accordance with the general content; (2) “I” 
means additional information for the user (available in the Excel file of the BACH Userguide); (3) “D” means possible deviation from the defined concept; (4) N/A means unavailable; 
(5) “P_N/A” means partially unavailable (e.g. unavailable for one size class). 
Note: (1) Austria (AT), Belgium (BE), Czech Republic (CZ), France (FR), Germany (DE), Italy (IT), Netherlands (NL), Poland (PL), Portugal (PT), Slovakia (SK) and Spain (ES).(2) 
Although data are not yet available for some countries, future methodological differences from the BACH concept are already available. 

Statistics Paper No 11, September 2015 

43 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A2.3 
Equity and Liabilities - Comparisons* 

E1 

E2 

E 

Lp 

Lp1 

L1 

L11 

L12 

L2 

L21 

L22 

L3 

L31 

L311 

L312 

L32 

L321 

L322 

L4 

L5 

L6 

L61 

L62 

L 

AT 

- 

N/A 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

N/A 

N/A 

N/A 

N/A 

N/A 

N/A 

- 

- 

- 

N/A 

N/A 

- 

BE 

- 

- 

- 

- 

CZ 

D 

N/A 

- 

- 

FR 

- 

D 

- 

- 

P_N/A 

N/A 

N/A 

P_N/A 

N/A 

N/A 

- 

- 

- 

- 

D 

- 

D 

- 

- 

- 

- 

- 

- 

N/A 

N/A 

- 

- 

- 

- 

- 

- 

- 

- 

N/A 

N/A 

N/A 

N/A 

N/A 

N/A 

D 

- 

- 

N/A 

N/A 

- 

- 

- 

- 

- 

D 

D 

- 

- 

- 

D 

- 

- 

N/A 

- 

- 

D 

D 

N/A 

- 

DE 

D 

N/A 

- 

D 

- 

- 

- 

- 

- 

- 

- 

- 

D 

D 

- 

N/A 

N/A 

N/A 

D 

- 

D 

N/A 

N/A 

- 

IT 

NL 

PL 

PT 

SK 

ES 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

I 

I 

I 

I 

- 

- 

- 

I 

N/A 

I 

- 

- 

- 

- 

- 

- 

N/A 

N/A 

- 

- 

- 

- 

- 

- 

- 

D 

D 

- 

- 

D 

- 

- 

- 

D 

- 

- 

D 

- 

- 

- 

D 

- 

- 

- 

- 

- 

- 

- 

D 

- 

- 

D 

- 

- 

- 

D 

- 

- 

- 

- 

- 

- 

- 

- 

D 

D 

- 

N/A 

N/A 

N/A 

N/A 

N/A 

D 

D 

D 

N/A 

D 

D 

*Detailed information for each country is available on the BACH Userguide (only in the Excel file); Legend: (1) “-“ means the concept is in accordance with the general content; (2) “I” 
means additional information for the user (available in the Excel file of the BACH Userguide); (3) “D” means possible deviation from the defined concept; (4) N/A means unavailable; 
(5) “P_N/A” means partially unavailable (e.g. unavailable for one size class). 
Note: (1) Austria (AT), Belgium (BE), Czech Republic (CZ), France (FR), Germany (DE), Italy (IT), Netherlands (NL), Poland (PL), Portugal (PT), Slovakia (SK) and Spain (ES).(2) 
Although data are not yet available for some countries, future methodological differences from the BACH concept are already available. 

Table A2.4 
Notes - Comparisons* 

N1 

N2 

N3 

AT 

N/A 

N/A 

N/A 

BE 

- 

- 

- 

CZ 

N/A 

N/A 

N/A 

FR 

D 

D 

D 

DE 

N/A 

N/A 

N/A 

IT 

N/A 

- 

- 

NL 

N/A 

N/A 

N/A 

PL 

N/A 

N/A 

N/A 

PT 

- 

- 

P_N/A 

SK 

ES 

D 

D 

D 

D 

D 

D 

*Detailed information for each country is available on the BACH Userguide (only in the Excel file); Legend: (1) “-“ means the concept is in accordance with the general content; (2) “I” 
means additional information for the user (available in the Excel file of the BACH Userguide); (3) “D” means possible deviation from the defined concept; (4) N/A means unavailable; 
(5) “P_N/A” means partially unavailable (e.g. unavailable for one size class). 
Note: (1) Austria (AT), Belgium (BE), Czech Republic (CZ), France (FR), Germany (DE), Italy (IT), Netherlands (NL), Poland (PL), Portugal (PT), Slovakia (SK) and Spain (ES).(2) 
Although data are not yet available for some countries, future methodological differences from the BACH concept are already available. 

Statistics Paper No 11, September 2015 

44 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 3 
National sampling methods and 
metadata on data collection 

Table A3.1 
National sampling methods and metadata on data collection 

Austria 

Sampling methods 

The data collected are the product of a cooperation between the Oesterreichische Nationalbank and the Austrian Institute for SME Research. The data pool 
contains the individual company financial statements of legally independent firms domiciled in Austria and operating outside the banking and insurance 
sectors. 

Survey 

Not applicable. 

Survey unit 

All available annual accounts (no survey) of the smallest legal entity (firm) providing a balance sheet and a profit and loss account following the legislation for 
large  limited liability companies in Austrian local GAAP. 

Obligatory or 
voluntary survey 

Not applicable. 

Gross sample size 

Yearly balance sheet data from about 60,000 NFCs (final data, considerably fewer provisional data). 

Size of national 
population 

Statistics Austria reports in the Structural Business Statistics 2013   314,325 ENTERPRISES (this number includes sole proprietorships and partnerships 
too). Please note that the Austrian BACH data DO NOT include sole proprietorships and unlimited partnerships. 

Cut-off points 

Not applicable. 

NACE level 

A-S, without O. 

Timing/period of 
collection 

Annual accounts are available from 2000 onwards. 

Timeliness 

Provisional data November N+1 (11 months), Final data June N+2 (18 months). 

Grossing up factors 

Not applicable. 

Representativeness 

The final annual data used for BACH statistics cover about 45% of the total number of NFCs. 

Other remarks 

None. 

Belgium 

Sampling methods 

Data cover all limited liability non-financial corporations (NFCs).  

Survey 

The Belgian CBSO collects and publishes the annual accounts of Belgian NFC, using standard formats.  

Survey unit 

Obligatory or 
voluntary survey 

Two types of standard format are used: the extended format (1,100 codified items) is used by large companies, while the abbreviated format (500 codified 
items) can be used by small companies. Company size is determined according to the three criteria recommended by the Accounting Directive: turnover, 
balance sheet total and number of employees.  

Filing annual accounts is mandatory for all limited liability NFCs. 

Gross sample size 

-  

Size of national 
population 

According to the most recent data, around 397,000 of NFCs, of which 25.000 are considered to be large, file their annual accounts with the CBSO. 

Cut-off points 

- 

NACE level 

All sectors of economic activity for the group of NFC are covered. 

Timing/period of 
collection 

Annual accounts are available from 2000 onwards. 

Timeliness 

Ten months for provisional figures, 14 months for final information.  

Grossing up factors 

Not applicable. 

Representativeness 

Around 99% for final data and between 80 and 99% for provisional data. 

Other remarks 

Czech Republic  

Sampling methods 

The sampling method (used since 2004) was determined to ensure the representativeness of the activities of non-financial corporations. The samples for the 
annual survey are defined to cover all NACE sectors and all size classes.  

Survey 

For all years, data have been collected from the annual statistical statement produced by the Czech Statistical Office. The structure of this statement is 
available at http://apl.czso.cz/pll/vykazy/pdf1. 

Survey unit 

The statement comprises about 70 variables for balance sheet items and about 100 variables for the income statement.  

Obligatory or 
voluntary survey 

The CZSO annual statement is mandatory for predetermined entities. 

Statistics Paper No 11, September 2015 

45 

 
 
 
 
Gross sample size 

The CZSO annual statement covers a minimum of around 23,200 corporations each year (as regards the BACH structure of NACE, the same number relates 
to 2013). 

Size of national 
population 

According to recent data, approximately 237,000 non-financial corporations have been included in the NFC population in the last few years (concerning the 
BACH structure of NACE). 

Cut-off points 

None. 

NACE level 

The CZSO annual statement covers all NACE groups (all sectors of economic activity). 

Timing/period of 
collection 

The data which enable all BACH requirements on NACE levels etc. to be met have been fully available from the CZSO annual statement since 2007 (only the 
main sectoral classification in NACE, Revision 2 was available from 2004 to 2006). 

Timeliness 

Preliminary data in T+11, final data in T+15. 

Grossing up factors 

Not applicable. 

Representativeness 

Annual data used for BACH statistics cover about 9.8% of the total number of non-financial corporations, about 67.6 % of employees and 75.9% of turnover 
(in 2013). 

Other remarks 

None. 

France 

Sampling methods 

Not applicable (administrative data). 

Survey 

The French data come from the FIBEN database, which is administered by the Banque de France. FIBEN is an administrative dataset based on tax 
statements.  

Survey unit 

FIBEN includes information from firms’ balance sheets and income statements (ca. 620 variables) 

Obligatory or 
voluntary survey 

Not applicable (administrative data). 

Gross sample size 

Yearly balance sheet data from ca. 300,000 NFCs operating in France. 

Size of national 
population 

NFCs: 3,237,174 companies (2011, source: INSEE).  
Entire population (including financial sector): 3,303,592 (2011, source: INSEE). 

Cut-off points 

- 

NACE level 

FIBEN collects firms’ industry affiliation using the six-digit industry code. All non-financial sectors are included.  

Timing/period of 
collection 

Data are available from 1999 onwards. 

Timeliness 

Provisional data available at T+12 months, final data available at T+24 months. 

Grossing up factors 

Not applicable. 

Representativeness 

FIBEN is comprehensive for firms that have annual sales greater than EUR 750,000.  

Other remarks 

- 

Germany 

Sampling methods 

German BACH data are based on the balance sheets and income statements of non-financial enterprises, which have been collated in the Bundesbank’s 
Financial Statements Data Pool. This data pool is a joint undertaking between the Bundesbank and a number of banks and other financial institutions with the 
aim of voluntarily integrating their financial statement databases for statistical purposes. The data pool contains the individual company financial statements 
of legally independent firms domiciled in Germany and operating outside the banking and insurance sectors. 

Survey 

Not applicable. 

Survey unit 

All available annual accounts (no survey) of the smallest legal entity (firm) providing a balance sheet and a profit and loss account following the legislation for 
large limited liability companies in German local GAAP. 

Obligatory or 
voluntary survey 

Not applicable. 

Gross sample size 

The Bundesbank’s Financial Statements Data Pool contains about 140,000 financial statements per year. 

Size of national 
population 

The German NFC population contains about 3.7 million firms (including sole proprietors). 

Cut-off points 

Not applicable. 

NACE level 

A-S, without O. 

Timing/period of 
collection 

Continuous collection. 

Timeliness 

The vast majority of annual accounts are available 24 months after the end of the financial year. 

Grossing up factors 

Not applicable. 

Representativeness 

The Bundesbank’s Financial Statements Data Pool covers about 3% of firms and 60-70% of turnover. 

Other remarks 

Due to comparability issues only the accounts of limited liability companies are provided to the BACH data base. The German Bach sliding samples cover 
between 50,000 and 60,000 enterprises per year. 

Italy 

Sampling methods 

A non-statistical sample. There are no criteria for stratification. 

Survey 

Survey unit 

Data are collected by the banking system, following an agreement. According to the agreement, every bank must collect a number of balance sheets 
depending on its size.  

Although the CERVED database is based on a survey, there are two types of format for NFCs: an extended format and an abbreviated format. The first 
contains details regarding the nature of creditors. 

Statistics Paper No 11, September 2015 

46 

 
 
 
Obligatory or 
voluntary survey 

Gross sample size 

Size of national 
population 

Not applicable. 

The CERVED database covers about 55,000 corporations each year. The statistics compiled from this annual survey, based on a sliding sample, include 
about 50,000 corporations over two consecutive years. The database comprises all resident enterprises with a commercial, industrial or agricultural nature as 
their principal activity, as well as non-resident entities that are permanently established in Italy. 

Around 1,000,000 corporations per year are included in the NFC population. 

Cut-off points 

Not applicable.  

NACE level 

The CERVED database covers all sectors of economic activity, with the exception of divisions 84, 98 and 99. 

Timing/period of 
collection 

Balance sheet data are available from 2000. 

Timeliness 

11 months for provisional figures and 15 months for final information. 

Grossing up factors 

Not applicable. 

Representativeness 

The CERVED database covers, on average, around 75% of the turnover of the total NFCs. It is biased towards medium and large companies. 

Other remarks 

Netherlands 

Sampling methods 

Not applicable (administrative corporation tax data). 

Survey 

The BACH data is based on the database of the tax authorities (corporation tax). Statistics Netherlands has delivered only non-consolidated data for the 
period 2008-2013. Non-consolidated means that individual tax payers report only for themselves or that individual tax payers report for themselves and one 
subsidiary.  

Survey unit 

Balance Sheets and Income (P&L) Statements of companies. 

Obligatory or 
voluntary survey 

Filing of corporation tax is obligatory.  

Gross sample size 

For BACH: Yearly balance sheet data for ca. 300,000 non-financial corporation tax payers operating in the Netherlands. 

Size of national 
population 

Cut-off points 

Corporation tax: ca. 630,000  corporation tax payers of which ca. 470,000 are non-financial corporation tax payers. 

Statistics Netherlands only delivers the non-consolidated data for the corporation tax data. Non-consolidated means that individual tax payers report only for 
themselves or that individual tax payers report for themselves and one subsidiary. 

Nace level 

All sectors of economic activity within the group of NFCs are covered. 

Timing/period of 
collection 

Data are available from 2008 onwards. 

Timeliness 

Provisional data available at T+24 months, final data available at T+36 months. 

Grossing up factors 

Not applicable. 

Representativeness 

Annual data used for BACH statistics cover ca. 60% of the number of non-financial corporation tax payers operating in the Netherlands. 

Other remarks 

None. 

Poland 

Sampling methods 

No sampling method is applied because in Poland the survey comprises all companies with more than nine employees and that deliver full balance sheet 
statements.  

Survey 

Data are collected by the Central Statistical Office (CSO). The annual survey covers entities keeping accounting ledgers and employing ten or more persons.  
As regards legal form the survey covers entities organised in the form of commercial companies (personal and with share capital), partnerships, state-owned 
enterprises, co-operatives, branches of foreign entrepreneurs, state organisational entities, and natural persons conducting business activities.  
Data according to the classification NACE Revision 2 are available from 2005. (Data according to NACE Revision 1 are available from 1995). 

Survey unit 

Comprises more than 350 variables (from the balance sheet and the profit and loss account). 

Obligatory or 
voluntary survey 

Gross sample size 

Size of national 
population 

An annual survey is obligatory for all the above-mentioned entities.  

The CSO annual survey covers about 55,000 non-financial enterprises each year. It covers about 3% of the national population in terms of number of 
entities.  

According to data from 2013, the non-financial enterprises sector comprises about 1,771,000 entities.  

Cut-off points 

Imposing NACE Revision 2 in 2005.  

NACE level 

The data cover all sectors of activity excluding: credit institutions, brokerage and insurers, investment and pension societies, higher educational schools, 
individual households in the agricultural sector, independent public healthcare centres and cultural institutions that are legal units. 

Timing/period of 
collection 

Annual survey from 2005 to 2013. (Data according to NACE Revision 1 are available from 1995). 

Timeliness 

11/12 months after the accounting year. 

Grossing up factors 

- 

Representativeness 

The annual survey covers about 3% of entities in terms of number of enterprises and about 56% in terms of number of employees.  

Other remarks 

Portugal 

Sampling methods 

The sampling method (used from 2000 to 2005) was determined to ensure the representativeness of the activities of NFCs) and their size class, enabling a 
time-series analysis of a relevant number of companies to be produced and providing accurate coverage of activities. Stemming from these objectives, the 

Statistics Paper No 11, September 2015 

47 

 
 
 
 
 
samples for the annual survey were defined to cover two components, one random and the other non-random. From 2006 data cover all NFCs. 

Survey 

From 2000 to 2005 data were collected through the Central Balance Sheet Database (CBSD) annual survey. Since 2006 data have been collected through 
the Informação Empresarial Simplificada (Simplified Corporate Information, Portuguese acronym: IES), which comprises the information for the annual 
accounts of corporations. 

Survey unit 

Comprised more than 600 variables from 2000 to 2005 and has consisted of more than 1,800 variables since 2006. 

Obligatory or 
voluntary survey 

The CBSD annual survey (from 2000 to 2005) was voluntary.  
IES (since 2006) is mandatory for all NFCs. 

Gross sample size 

The CBSD annual survey covered about 17,500 corporations each year. The statistics compiled from this annual survey, based on a sliding sample, included 
about 15,000 corporations over two consecutive years. IES comprises all resident enterprises with a commercial, industrial or agricultural nature as principal 
activity as well as non-resident entities with a permanent establishment in Portugal. 

Size of national 
population 

According to more recent data, more than 350,000 corporations per year are included in the NFC population. 

Cut-off points 

From 2000 to 2005 data were based on a sample (see other remarks). Since 2006 data have covered all NFCs.  

NACE level 

The CBSD annual survey (used from 2000 to 2005) covered all sectors of economic activity within the group of NFCs in the annual reporting sample. Since 
2006 data have covered all NFCs. 

Timing/period of 
collection 

An annual survey (through the CSBD annual survey, from 2000 to 2005) and annual accounts of corporations (through IES, since 2006). 

Timeliness 

10/12 months (through the CSBD annual survey, from 2000 to 2005) and six months (through IES, since 2006).  

Grossing up factors 

Not applicable. 

Representativeness 

The CBSD annual survey (used from 2000 to 2005) covered around 5% of the total number of enterprises and 40% of the total number of employees. IES 
(since 2006) covers more than 90% in the majority of the cases. 

Other remarks 

Slovakia 

It should be noted that data from 2000 to 2005 present a range of characteristics suggesting that any analysis of the results should be undertaken with 
caution. This is mostly because the indicators should not be interpreted as corresponding to the overall results for Portuguese NFCs. For instance, the overall 
results of the corporations reflect, in terms of activities, better coverage of large corporations as well as that of corporations in the manufacturing, electricity, 
gas and water, and transport and communication sectors. On the other hand, there is less coverage in the trade and repairs sector. This conclusion is 
reached by comparing the proportion of turnover in the corporations in the sample with that of the total population of NFCs. 

Sampling methods 

A non-statistical sample. There are no criteria for stratification. 

Survey 

Financial statement data consist of companies’ annual balance sheets and income statements. Financial statements are compiled by DataCentrum and 
provided by the Ministry of Finance. 

Survey unit 

The balance sheets (145 variables) and income statements of companies (60 variables). 

Obligatory or 
voluntary survey 

Providing financial statements is mandatory for determined entities (e.g. public limited companies, join stock companies, public companies…). 

Gross sample size 

The BACH database covers about 127,000 corporations (2012 figures).  

Size of national 
population 

According to our reference database around 164,000 corporations per year are included in the NFC population. 

Cut-off points 

None. 

NACE level 

All NACE groups.  

Timing/period of 
collection 

Data available since 2005. 

Timeliness 

Preliminary data in T+12, final data in T+16. 

Grossing up factors 

None. 

Representativeness 

Annual data used for BACH statistics cover about 77% of the number of non-financial corporations.  

Other remarks 

None. 

Spain  

Sampling methods 

A non-statistical sample. There are no criteria for stratification 

Survey 

Survey unit 

Obligatory or 
voluntary survey 

Gross sample size 

Size of national 
population 

Cut-off points 

NACE level 

The Spanish contribution of the Central Balance Sheet Data Office Annual Survey to the BACH sample is prepared by integrating the CBA (voluntary 
reporting) and CBB (accounts filed with the mercantile registers) databases. 

In CBA there are two types of questionnaire: an extended questionnaire for companies with more than 100 employees (655 variables) and an abbreviated 
questionnaire for those with fewer than 100 employees (320 variables). Although CBB is not a survey, there are three types of questionnaire: extended 
format (556 variables), abbreviated format and SME format (304 variables in each one of them). 

The CBA annual survey is voluntary. CBB is mandatory for all NFCs. 

CBA covers about 10,000 corporations each year, based on a sliding sample over two consecutive years. CBB comprises around 800,000 corporations 
which have deposited their annual accounts in the mercantile registries; once this information has been filtered the CBB sample comprises around 600.000 
corporations per year. 

According to the Central Business Register, around 1,100,000 corporations per year are included in the NFC population. 

The figures from 2000 are compiled under two different Accounting Laws: PGC1990 from 2000 to 2007 and PGC2007 in force since 2008. In any case, the 
information is well connected in the time series without any significant breaks in the database (although the basic data may have these breaks, the data 
stored in the database have been treated in a way that avoids any distortion in the analysis). 

CBA and CBB cover all NACE Revision 2 divisions, with the exceptions of 64, 65, 66, 84, 94, 97, 98 and 99, all of which are activities not undertaken by 
NFCs. 
The strict application of the recommendation of the Task Force on Head Offices, Holding Companies and SPEs has meant that certain holding companies, 

Statistics Paper No 11, September 2015 

48 

 
 
without autonomy of decision, carrying out these activities (under 642 NACE), have been included in the NFC sector. 

Timing/period of 
collection 

CBA and CBB are available jointly from 2000. 

Timeliness 

Ten months for provisional figures of CBA and CBB, and 22 months for final information.  

Grossing up factors 

Not applicable. Banco de España´s CBSO grosses up figures for an internal statistics used in the compilation of the Financial Accounts of the Economy, but 
cannot be used in the diffusion of the BACH database. 

Representativeness 

The merger of CBA and CBB covers, on average, around 50% of the Gross Value Added of the total non-financial corporations. CBA is biased towards 
medium and large companies, whereas CBB is focused on the small and micro-companies of the population. 

Other remarks 

Statistics Paper No 11, September 2015 

49 

 
 
Annex 4 
Task force for holding companies and 
head offices: recommendations 

To develop a solution and guidelines that can clearly identify the kind of entities to be 

classified as financial institutions, a task force for holding companies, head offices 

and similar entities was set up by the ECB, Eurostat and the Organisation for 

Economic Cooperation and Development (OECD) in 2013, with the following goals: 

(a)  To achieve comparable treatment in the national statistics of entities 

denominated as holding companies, head offices and special purpose entities 
(SPEs) and similar, in international manuals: SNA 2008, ESA 2010 and Balance 
of Payments and International investment Position Manual: Sixth Edition (BPM 
6); 

(b)  To develop a typology and a uniform classification of these entities, taking into 

account the recommendations of various international groups: the Eurostat WG 
on multinationals (TF MUNA), the United Nation expert group on the impact of 
globalisation on national accounts (GGNA), the OECD WG on financial statistics 
and national accounts, the OECD WG on international investment statistics, and 
Eurostat and ECB in the field of external statistics. 

The main conclusions of the task force can be summarised as follows:  

(a)  The standard criteria for an entity to become an institutional unit must also be 

met (autonomy of decision) in the case of holding companies, head offices and 
other SPEs. Entities of this type owned by non-residents will always be 
regarded as institutional units; 

(b)  Entities owned by an individual resident unit, with “no employees or 

compensation for employees” can be used as an indicator of their lack of 
independence. However, the criterion is not sufficient to conclude that there is 
no institutional independence; 

(c)  Having multiple parent companies/shareholders is a sufficient condition for a 

unit to be an institutional unit. Head offices should always be considered as 
separate institutional units; 

(d) 

In relation to the identification of holding companies and head offices: 

• 

The holding of at least 50% of total assets materialised in shares and other 
shareholdings from subsidiary entities may be considered to be an 
indicator that identifies this type of entity; 

Statistics Paper No 11, September 2015 

50 

• 

• 

The delimitation in this type of entity regarding employment should take 
national circumstances into account. In general, a number of employees 
greater than three may be considered to be an indicator that it is a head 
office; 

The test for the identification of institutional units (IU) such as holding 
companies, head offices, SPEs or similar is shown below (decision tree, 
Chart A4.1). 

Chart A4.1 
Test for the identification of institutional units such as holding companies, head 
offices, SPEs or similar 

Entity owned by non-residents

No

Is the entity owned by several 
companies or shareholders?

No

Yes

Yes

Yes

The entity is an institutional unit 
(IU)

The entity must be classified in a 
sector

The entity is an IU

The entity must be classified in a 
sector

Can the entity act independently?

The entity is an IU

The entity must be classified in a 
sector

No

The entity is an artificial entity 
rather than an IU

The entity must be consolidated 
with the parent company

Source: Task force for holding companies and head offices. 
Note: The absence of employees or compensation of employees may be an indicator of lack of independence that must be 
investigated, but it is not enough to determine that the entity is not an institutional unit. 

Statistics Paper No 11, September 2015 

51 

 
Annex 5 
The ECCBSO  

The main task of the ECCBSO (https://www.eccbso.org/) is to improve the analysis 
of non-financial corporate enterprise data through the exchange of information and 
by joint studies, based on the work carried out by the national Central Balance Sheet 
Data Offices which collect, store and disseminate descriptive and accounting data for 
companies and, starting from this information, develop economic and financial 
research. 

The ECCBSO was set up in 1987 as an initiative of Banque de France, National 
Bank of Belgium and the Italian Centrale dei Bilanci. 

Currently, the ECCBSO consists, apart from the European Central Bank (ECB), of 
31 members representing 28 different countries: Austria (Oesterreichische 
Nationalbank), Belgium (National Bank of Belgium), Bulgaria (Българска народна 
банка), Czech Republic (Česká Národní Banka), Denmark (Danmarks Nationalbank 
and Danmark Statistik), Finland (Tilastokeskus Statiskcentralen and Suomen 
Pankki), Germany (Deutsche Bundesbank), France (Banque de France), Greece 
(Τράπεζα της Ελλάδος), Hungary (Magyar Nemzeti Bank), Ireland (Central Bank of 
Ireland), Italy (Banca d’Italia and Cerved Group Spa), Luxembourg (STATEC – 
Luxembourg and Banque Centrale du Luxembourg), Macedonia (Народна банка на 
Република Македонија), Malta (Central Bank of Malta), Montenegro (Centralna 
banka Crne Gore), the Netherlands (Centraal Bureau voor de Statistiek and ), 
Poland (Narodowy Bank Polski), Portugal (Banco de Portugal), Romania (Banca 
Naţională a României), Serbia (Народна банка Србије), Slovakia (Národná Banka 
Slovenska), Slovenia (Banka Slovenije), Spain (Banco de España), Sweden 
(Statistiska Centralbyråns), Turkey (Türkiye Cumhuriyet Merkez Bankası) and the 
United Kingdom (Bank of England). The European Commission is also a part of the 
ECCBSO as a permanent observer and of the IFRS Foundation and the Bank for 
International Settlements (BIS) as an observer. 

The president is elected every three years from the member institutions represented 
on the committee. The presidency is currently held by Banco de Portugal, in the 
person of the Director of the Statistics Department, João Cadete de Matos, until 
November 2016. 

The ECCBSO consists of four working groups (WGs):  

The BACH WG: this WG is responsible for the management of the BACH database, 
which contains aggregated and harmonised accounting-related data for non-financial 
incorporated enterprises in European countries. In particular, this WG is dedicated to 
the maintenance, updating and promotion of BACH, as well as to the development of 
related products. The WG must participate actively in discussions regarding the 
importance and the economic and financial situation of European companies; 

Statistics Paper No 11, September 2015 

52 

WG on IFRS impact and CBSO databases: this WG focuses primarily on the 
impact of the IFRS24, issued by the IASB, on the accounting procedures of non-
financial corporations, and their effects on the European CBSO databases and 
questionnaires, trying to assess both potential opportunities and risks. To that end, 
the group has developed IFRS-compliant standard formats (using for that purpose 
the IFRS taxonomy defined by the IASB-XBRL team) and has also created the 
ERICA (European Records of IFRS Consolidated Accounts) database. This 
database includes around 1,000 non-financial groups from the participating countries 
(Austria, Belgium, France, Italy, Germany, Greece, Portugal and Spain); 

Risk Assessment WG: This WG was set up in 1987 to draw up the so-called “white 
paper” on corporate analysis in Europe and to draft reports on the analysis of stocks 
and flows conducted in the various CBSOs. The group then expanded its project 
and, since 1999, its members have been working in cooperation with the relevant 
divisions of the ECB, homogenising the risk analysis carried out by NCBs. These 
analyses mainly concern corporations whose liabilities have been used as collateral 
in monetary policy operations. The group has also carried out research on default 
prediction models and, jointly with the European Credit Assessment Framework 
(ECAF) task force, has developed studies on technical details of the future ECAF;  

Financial Statement Analysis Working Group: this WG focuses on the analysis of 
the asset position, capital structure and profitability of NFCs in an international 
setting. It has produced four studies in total. The latest research paper, “Profitability, 
Equity Capitalization and Net Worth at Risk – How resilient are non-financial 
corporations in a crisis environment?” was published in January 2013 (under its old 
name “Study Group of the ECCBSO”) and deals with companies’ reaction to the 
financial crisis in terms of key financial ratios. Net Worth at Risk (NWaR), a concept 
developed by the group, relates to the minimum capital needed by a company in 
order to absorb potential losses during a period of two years and independent of a 
crisis. The group is currently carrying out research on trade credits.  

24  

 IFRS refers to international accounting rules developed by the International Accounting Standards 
Board (IASB). For additional     information on this topic, consult the IFRS Foundation website available 
at: http://www.ifrs.org/. 

Statistics Paper No 11, September 2015 

53 

                                                                    
Annex 6 
BACH’s history and the BACH WG 

The BACH project was launched in 1985 by the General Directorate Economic and 
Financial Affairs of the European Commission (DG ECFIN). BACH was created in 
order to analyse the financial structures and performance of European companies. 
The idea of the project was to take advantage of the information supplied by the 
CBSOs, which use companies’ individual accounts and, therefore, have more 
detailed information than that available in national accounts. The BACH database 
was developed by a ECCBSO working group.  

In the late nineties another database was created in the ECCBSO context – the 
European Sectoral References Database (ESD) – to provide NFC financial ratios. 
The aim here was to provide sectoral benchmarks in order to compare companies’ 
performances. It included dispersion statistics (quartiles) and was primarily 
developed as an additional tool for credit and financial institutions’ risk analysis. 

In 2010, national providers decided to combine BACH and ESD in a single 
application, given their high degree of complementarity. As a second step, following 
a joint effort to harmonise data, the two databases were merged in a more user-
friendly and comparable framework. In essence, the current BACH database reflects 
the merger of the previous BACH and ESD databases.  

Based on the knowledge that national accounting and reporting standards do not 
fully converge, a synchronised stock-taking exercise to settle countries’ specificities 
was undertaken in BACH, in order to increase data comparability. The result was an 
increase in cross-country comparability and a more user-friendly and integrated 
Userguide, which contains complementary information useful for a better 
understanding of the BACH database. 

As a result of this joint effort, a new BACH database was released in 2013.  

The Bank for the Accounts of Companies Harmonized Working Group (BACH WG) 
is responsible for the management of the BACH database, which contains the 
aggregated and harmonised accounting related data of non-financial incorporated 
enterprises for European countries. In particular, this WG is devoted to the 
maintenance, updating and promotion of BACH, as well as to the development of 
related products. The WG participates actively in discussions regarding the 
importance and the economic and financial situation of European companies. 

Statistics Paper No 11, September 2015 

54 

 
 
Participants in the BACH WG currently come from 17 different institutions:  

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

Banca Naţională a României 

Banco de Portugal 

Banca d’Italia 

Narodowy Bank Polski 

National Bank of Belgium 

Centraal Bureau voor de Statistiek 

Banque de France 

Cerved Group Spa 

Banco de España 

Deutsche Bundesbank 

Česká Národní Banka 

STATEC 

•  Oesterreichische Nationalbank 

• 

• 

• 

• 

European Central Bank 

Danmarks Nationalbank 

Národná Banka Slovenska 

Central Bank of the Republic of Turkey 

Statistics Paper No 11, September 2015 

55 

References 

Bank for the Accounts of Companies Harmonized, (2013), Outlook #1 – SMEs in 
European countries. 

Bank for the Accounts of Companies Harmonized, (2014), BACH Userguide. 

Bank for the Accounts of Companies Harmonized, (2014), Outlook #2 - Financial 
structure and profitability of European companies. 

Bank for the Accounts of Companies Harmonized, (2015), Outlook #3 Deleveraging 
and its effects on the asset side. 

Banco de Portugal, (2008), “Simplified reporting: inclusion of the Simplified 
Corporate Information in the Statistics on Non-Financial Corporations from the 
Central Balance Sheet Database”, Supplement 1/2008 to the Statistical Bulletin. 

Cochran, William G. (1977), Sampling techniques,  3rd edn., Wiley, New York.  

European Banking Authority, (2012), Assessment of SME proposals for CRD 
IV/CRR. 

European Committee of the Central Balance Sheet Data Offices, (2013), Products 
and Services of the European CBSOs. 

Eurostat, (2008), “NACE Rev. 2, Statistical Classification of Economic Activities in 
the European Community”, Methodologies and Working Papers. 

Eurostat (2015), “Contributions of each institutional sector to macroeconomic 
developments”, Retrieved March 06, 2015. 

Levy, Paul S. and Lemeshow, S, (1999), Sampling of populations: methods and 
applications, 3rd edn., Hoboken. 

Särndal, Swensson and Wretman, (2010). “Sampling: Design and Analysis”, Model 
Assisted Survey Sampling, Springer Series in Statistics, 1992, Brooks Cole. 

Relevant websites 

Bank for the Accounts of Companies Harmonized database: 
http://www.bach.banque-france.fr/  

European Committee of Central Balance Sheet Data Offices website: 
https://www.eccbso.org/  

Statistics Paper No 11, September 2015 

56 

Relevant legislation 

Commission Recommendation 2003/361/EC of 6 May 2003 concerning the definition 
of micro, small and medium-sized enterprises  

Directive 2012/6/EU of the European Parliament and of the Council of 14 March 
2012 amending Council Directive 78/660/EEC on the annual accounts of certain 
types of companies as regards micro-entities 

Directive 2013/34/EU of the European Parliament and of the Council of 26 June 
2013 on the annual financial statements, consolidated financial statements and 
related reports of certain types of undertakings, amending Directive 2006/43/EC of 
the European Parliament and of the Council and repealing Council Directives 
78/660/EC and 83/349/EEC 

Fourth Council Directive 78/660/EEC of 25 July 1978 based on Article 54 (3) (g) of 
the Treaty on the annual accounts of certain types of companies 

Regulation (EC) No 1893/2006 of the European Parliament and of the Council of 20 
December 2006 establishing the statistical classification of economic activities 
(NACE Revision 2) and amending Council Regulation (EEC) No 3037/90 as well as 
certain EC Regulations on specific statistical domains 

Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 
May 2013 on the European system of national and regional accounts in the 
European Union 

Seventh Council Directive 83/349/EEC of 13 June 1983 based on Article 54 (3) (g) of 
the Treaty on consolidated accounts 

Statistics Paper No 11, September 2015 

57 

 
 
Abbreviations 
BACH 

Bank for the Accounts of Companies Harmonized 
Balance of Payments and International investment 
Position Manual: Sixth Edition 
Central Balance Sheet Data Offices 

Central Balance Sheet Database 
General Directorate Economic and Financial Affairs 
of the European Commission 
European Banking Authority 
Earnings Before Interest, Tax, Depreciation and 
Amortisation 
European Credit Assessment Framework 

European Central Bank 
European Committee of Central Balance Sheet Data 
Offices 
European Records of IFRS Consolidated Accounts 

European System of National and Regional Accounts 

European Sectoral References Database 

BPM6 

CBSO 

CBSD 

DGECFIN 

EBA 

EBITDA 

ECAF 

ECB 

ECCBSO 

ERICA 

ESA 

ESD 

GAAP 

Generally Accepted Accounting Principles 

IAS 

IASB 

IES 

IFRS 

IU 

NACE 

NCB 

NFC 

NSI 

NWaR 

OECD 

SME 

SPE 

WG 

International Accounting Standard 

International Accounting Standards Board 
Informação Empresarial Simplificada (Simplified 
Corporate Information) 
International Financial Reporting Standards 

Institutional Unit 
Statistical Classification of Economic Activities in the 
European Community 
National Central Bank 

Non-financial corporations 

National Statistical Institute 

Net Worth at Risk 
Organisation for Economic Cooperation and 
Development  
Small and Medium-sized Enterprises 

Special Purpose Entities 

Working Group 

Statistics Paper No 11, September 2015 

58 

 
 
 
 
Bank for the Accounts of Companies Harmonized Working Group (BACH WG) 1356-bach-ut@banque-france.fr 
This report was drafted by the Working Group on the Bank for the Accounts of Companies Harmonized. The project was launched 
during the presidency of Ms Ana Margarida de Almeida (Chairperson) and Vitor Silveira (Secretariat) counting on the valuable 
contribution of Ms Margarida Brites (ex-Chairperson). The project was concluded during the presidency of Ms Paula Menezes 
(Chairperson) and Fábio Albuquerque (Secretariat). 

Members of the BACH WG 

Alina Tarta 
Banca Naţională a României; e-mail: alina.tarta@bnro.ro 

Ana Margarida de Almeida 
Banco de Portugal; e-mail: ammalmeida@bportugal.pt  

Antonio De Socio 
Banca d’Italia; e-mail: antonio.desocio@bancaditalia.it  

Begoña Gutiérrez del Olmo 
Banco de España; e-mail: bego.golmo@bde.es  

Bozena Wtulich 
Narodowy Bank Polski; e-mail: bozena.wtulich@nbp.pl  

Cécile Buydens 
National Bank of Belgium; e-mail: cecile.buydens@nbb.be  

Chantal Lemmens-Dirix 
Centraal Bureau voor de Statistiek; e-mail: c.dirix@cbs.nl  

Christophe Cahn 
Banque de France; e-mail: christophe.cahn@banque-france.fr  

Ewa Sokolowska 
Narodowy Bank Polski; e-mail: ewa.sokolowska@nbp.pl  

Fábio Albuquerque 
Banco de Portugal; e-mail: fhalbuquerque@bportugal.pt  

Fabrizio Balda 
Cerved Group Spa; e-mail: fabrizio.balda@cervedgroup.com  

Franck Lemaire 
Banque de France; e-mail: franck.lemaire@banque-france.fr  

Henri Demarteau 
Centraal Bureau voor de Statistiek; e-mail: hdeu@cbs.nl  

José Ramón Cano 
Banco de España; e-mail: joser.cano@bde.es  

© European Central Bank, 2015 

Postal address 
Telephone 
Website 

60640 Frankfurt am Main, Germany 
+49 69 1344 0 
www.ecb.europa.eu 

Karelle Thiebot-Goget 
Banque de France; e-mail: karelle.thiebot-goget@banque-france.fr  

Lars Holstein 
Deutsche Bundesbank; e-mail: lars.holstein@bundesbank.de  

Luis Ángel Maza 
Banco de España; e-mail: lmaza@bde.es  

Margarida Brites 
Banco de Portugal; e-mail: mpbrites@bportugal.pt  

Matthias Bürker 
Banque de France; e-mail: matthias.burker@banque-france.fr  

Miroslav Kalous 
Česká Národní Banka; e-mail: miroslav.kalous@cnb.cz  

Patricia Gonzalez 
STATEC; e-mail: patricia.gonzalez@statec.etat.lu  

Paula Menezes 
Banco de Portugal; e-mail: pamenezes@bportugal.pt  

Reinhard Konczer 
Oesterreichische Nationalbank; e-mail: reinhard.konczer@oenb.at  

Sébastien Pérez-Duarte 
European Central Bank; e-mail: sebastien.perez_duarte@ecb.int  

Stinne Skriver Jørgensen 
Danmarks Nationalbank; e-mail: ssjo@nationalbanken.dk  

Tomáš Eder 
Národná Banka Slovenska; e-mail: tomas.eder@nbs.sk  

Vítor Lopes Silveira 
Banco de Portugal; e-mail: vfsilveira@bportugal.pt

All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced electronically, 
in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

Information on all of the papers published in the ECB Statistics Paper Series can be found on the ECB’s website, 
http://www.ecb.europa.eu/pub/research/statistics-papers/html/index.en.html. 

ISSN 
ISBN 
DOI 
EU catalogue No 

2314-9248 (online) 
978-92-899-1907-4 
10.2866/539767 
QB-BF-15-003-EN-N