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“SME Data Workshop”
Cairo Sheraton Hotel January 17, 2008
Workshop Report
Small and Medium Enterprise Policy (SMEPol)
Development Project
Canadian International Development Agency (CIDA)
International Development Research Centre (IDRC)
Table of Contents Executive Summary ....................................................................................................................3 SME Data Workshop Report ......................................................................................................6 A: Opening remarks and introductions........................................................................................6 B: Workshop Session 1: The Statistics Canada SME Statistics Program.......................................7
Lessons learned from the Canadian presentation.................................................................10 C: Workshop Session 2: European Statistics on SMEs, business demography and
entrepreneurship .................................................................................................................10
Lessons learned from the EuroStat presentation .................................................................13 D: Workshop Session 3: OECD work on SME statistics: policy and indicators.........................13
Lessons learned from the OECD presentation....................................................................15 E: Concluding panel session: .....................................................................................................15 Closing remarks ........................................................................................................................17 Annex 1: Agenda.......................................................................................................................18 Annex 2: List of participants .....................................................................................................20 Annex 3: International presenters..............................................................................................22 Annex 4: SME Statistics Program, Statistics Canada (PPT)........................................................23 Annex 5: European Statistics on SMEs, business demography and entrepreneurship (PPT)......27 Annex 6: OECD Work on SME Statistics: Policy and Indicators (PPT) ....................................37
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Executive Summary The major objective of the SME Data Workshop was to follow-up on the Roundtable on Micro, Small and Medium Enterprise (MSME) Data & Indicators, hosted by the SMEPol Project on September 26, 2007, by offering technical assistance from international experts on how to deal with the challenges of developing a MSME data system. It was attended by over 30 officials representing CAPMAS, the Ministry of Trade and Industry, the Ministry of Investment, the General Authority for Investment (GAFI), the Ministry of Finance, the National Authority for Social Insurance, the Information and Decision Support Centre (IDSC), the Social Fund for Development (SFD), the Egyptian Center for Economic Studies (ECES), the Delegation of the European Commission in Egypt, the SMEPol Unit, and members of the Press. The participant list is attached at Annex 2. The program consisted of three one-hour workshop sessions led by international experts from the Small Business & Special Surveys Directorate of Statistics Canada, the Structural Business Statistics Section of EuroStat, and the OECD Statistics Directorate. A discussion period followed each session. Issues discussed included the need for: internationally consistent employment size breakdowns for reporting SME data; a Business Register to be held by the National Statistical Office; linking of administrative data to the Business Register; identification of a small number of key statistical variables to measure the SME sector; use of a unique identifier number for each enterprise, consistently applied across ministries; and consultation with key stakeholders and users on the design of a SME Statistics Program. International exports also informed workshop participants about current data initiatives underway in the OECD and EuroStat related to business demography and entrepreneurship indicators and indicated how Egypt as a country could become directly involved. Overall lessons learned from the international experts include:
• Statistics Canada:
Policy demand is the driver behind the SME Statistics Program. The SME definition for statistical purposes should be based on (at least) employment
category breakdowns that are consistent with international standards. The base for the SME Statistics Program is the Business Register. Timely and quality data on SMEs is generated by linking administrative files (taxation,
payroll) with the Business Register data. It is important to have a unique identifier for each enterprise. Key variables such as sector, employment, revenue, and age of enterprise are sufficient to
start building a SME Statistics Program. The size of the informal economy can be estimated through sample surveys in selected
regions of the country and by using proxy indicators. Egypt should start by developing baseline measures for the formal economy (“track what
can be tracked”) and progress in a step-by-step fashion. Key stakeholders and data users should be consulted in the process of designing a SME
Statistics Program.
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• EuroStat
High policy demand is the driver behind the EuroStat project on Business Demography
– policymakers seek more information on business births and deaths as indicators for innovation and entrepreneurship.
The most critical thing to be done to improve SME data is building a Business Register. The Business Register should be held by the National Statistical Office.
The National Statistical Office should be the body responsible for harmonizing SME-related data across administrative units.
A simple business identifier number should be implemented and applied consistently across ministries.
The SME definition for statistical purposes should be consistent with international standards in terms of employment size category breakdowns.
Sampling surveys and censuses are an inefficient and costly alternative to collecting data on SMEs; a Business Register is much more efficient, less burdensome for SMEs and can be updated regularly, thus providing more timely policy input.
Egypt could participate in the OECD-EuroStat Entrepreneurship Indicators Programme upon official request.
• OECD
The National Statistical Office should be the coordinator and collector of SME data with
full access to administrative data held by other ministries. A common identifier should be used for each enterprise. Countries should use coherent and internationally consistent size classes for reporting on
SME data. The Business Register is the key to managing complex statistical systems and a pre-
condition for understanding the SME sector. Useful statistics can be obtained by linking the Business Register with the Trade Register. Egypt can learn useful lessons from other countries that have integrated their business
registers, such as Germany and the Netherlands. Egypt could participate in the next meeting of the International Working Group on
Business Registers taking place November 22-24, 2008, by extending a formal request to the OECD.
Egypt could also officially request Observer Status in the OECD and thus benefit from all SME statistical work being done by the OECD.
The workshop concluded that Egypt needs more consistent survey data on both the formal and informal enterprise sectors, including work to develop proxy indicators for the informal sector. It was agreed that current surveys are only a partial solution to Egypt’s SME data and information needs. It was noted that Egypt has a “jungle” of laws and regulations governing statistics and that these statistical laws need to be streamlined to be in line with the systematic statistical systems in Canada and the European countries. Participants agreed that Egypt needs an integrated statistical/ data system on MSMEs and that a procedure needs to be laid out for developing this. Participants recommended that Egypt start with sorting out data issues for the formal sector, where more data is available, and develop good reporting. This would at least help advance the Egyptian knowledge base about formal SMEs. Then the informal sector data issues could be
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tackled over time. It was proposed that a data initiative start with developing a very good database using data on employees and employers. This might produce a 70 percent complete database that could be developed from there. The Ministry of Finance/ SME Unit urged that ministries have to cooperate with each other in the collecting of data and the sharing of data across and between ministries. It was proposed that the Tax Authority agree to the necessary approach to integrate the tax data files and that the Social Fund for Development link its work on data collection with other ministries’ expertise. The SME Unit will recommend to the Minister of Finance that a Committee be formed to initiate a response to recommendations and suggestions arising from the workshop.
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SME Data Workshop Report
The major objective of the workshop was to follow-up on the Roundtable on Micro, Small and Medium Enterprise (MSME) Data & Indicators, hosted by the SMEPol Project on September 26, 2007, by offering technical assistance from international experts on how to deal with the challenges of developing a MSME data system. The workshop agenda is attached as Annex 1. It was attended by 30 officials representing CAPMAS, the Ministry of Trade and Industry, the Ministry of Investment, the General Authority for Investment (GAFI), the Ministry of Finance, the Tax Authority, the Social Insurance Fund, the Information and Decision Support Centre (IDSC), the Social Fund for Development (SFD), the Egyptian Center for Economic Studies (ECES), the Delegation of the European Commission in Egypt, the SMEPol Unit, and members of the Press. The participant list is attached at Annex 2. International experts leading the three workshop sessions were: Workshop Session 1: Mr. Terry Evers, Director of Small Business and Special Surveys
Division (SBSSD), Statistics Canada Workshop Session 2: Dr. Axel Behrens, Head of the Development and Analysis Section of
Structural Business Statistics (SBS), European Commission (and EuroStat) Workshop Session 3: Andreas Lindner, Head of the Trade and Globalisation Statistics
Section, OECD Statistics Directorate, OECD
A: Opening remarks and introductions 10:00 – 10:15 Mr. Mohamed Abdel Aziz, Manager, SME Unit, Ministry of Finance and Ms.
Lois Stevenson, SME Specialist, International Development Research Centre and SMEPol Project Coordinator
Mr. Mohamed Abdel Aziz, Manager of the Ministry of Finance SME Unit opened the session and reiterated the objectives for the workshop. Ms. Lois stressed how important good SME data is to proper policy formulation, reporting that according to regional analysis being completed by IDRC, much of the MENA Region is facing many of the same data and statistical challenges as Egypt. The workshop is intended to support the Egyptian government in dealing with some of these challenges. 10:15 – 10:45 Recap and confirmation of the major SME data and statistical challenges in
Egypt identified at September 26, 2007 Roundtable.
Ms. Lois Stevenson briefly recapped the major SME data and statistical challenges in Egypt based on the outcome of the September 26, 2007 Roundtable on MSME Data & Indicators (and reported in the Summary of Discussions & Conclusions Report available at: www.sme.gov.eg). She asked Workshop participants to briefly introduce themselves, followed by brief
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introductions by the three international experts. Bio-sketches for each of the speakers are attached as Annex 3. Mr. Terry Evers, Statistics Canada explained that his unit has a Cdn$7 million program working on wage rates and compensation data and SME surveys. There are two recurring surveys: one on the demand for financing, and a tri-annual survey measuring the costs of regulatory compliance for SMEs. His unit publishes a series of Small Business Profiles and a Market Research Handbook. StatsCan has been interested in a SME Statistics Program for a number of years, but in the past two to three years, has made significant progress. At the workshop, he will share the challenges StatsCan encountered in building the program and how they overcame these obstacles. He stated that users of StatsCan data need information and statistics and StatsCan has a responsibility to give them the data they need. Dr. Axel Behrens, EuroStat, explained that EuroStat provides official statistics to inform policies of the European Commission as well as regional policies. EuroStat does not do surveys or collect data from enterprises – it collects data from national statistical agencies and harmonizes and publishes the data. He will talk about data they have available on SMEs, their new program on business demography, and new projects, such as the Entrepreneurship Indicators Programme. Mr. Andreas Lindner, OECD, stated that work on SMEs has really only started since 2004 with the OECD meeting in Istanbul where a dedicated policy session with 55 countries led to a number of initiatives on SME data to address policy issues. He explained that the OECD is an international organization with 30 member countries, functioning as a think-tank with several Directorates, e.g. Trade, Agriculture, etc. In his work, everything starts with data. This data is discussed and leads to recommendations to governments. In his presentation, he will focus on the OECD’s work on SMEs, share findings from the Entrepreneurship Indicators Project and the Structural Business Statistics Project, and discuss issues of compatibility between the Trade Register and the Business Register. Lastly, he stressed the increasing importance of micro data in the age of globalization. The need for more micro data exists and statistical agencies need to organize better on how provide this data. He will share some case studies of how some countries are dealing with this.
B: Workshop Session 1: The Statistics Canada SME Statistics Program 10:45 - 11:45 Workshop leader: Mr. Terry Evers, Director of Small Business and Special
Surveys Division (SBSSD), Statistics Canada
Mr. Evers’ presentation started with a review of the context for SME statistics in Canada. He reported that policy areas are demanding detailed and high quality SME data to support policy development and that inter-ministerial support exists for the development of comparable SME data. Canada is also a partner in the OECD/ EuroStat standard demography statistics and entrepreneurship performance indicators projects. (His presentation is attached as Annex 4). Before the SME Statistics Program was started, there were several problems driving Canadian reform in this area:
• No standard definition for SMEs, so unable to create a comprehensive picture of SMEs in Canada or compare this to other countries;
• Lack of standard measures of SME performance to assess productivity, growth, competitiveness, etc;
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• Users did not know what data was available on SMEs or where or how to get it. He outlined the types of challenges Canada faced in developing its SME Statistics Program and how they overcame each one.
1. SME definition: To deal with the issue of SME definitions and to overcome the challenge of reaching consensus on a definition (i.e. whether to use employment or revenue), StatsCan decided to adopt standard size groups based on employment, that were aligned with OECD and EuroStat categories: 0-4, 5-9, 10-19, 20-49, 50-99, 100-249, and >249. StatsCan now reports all SME data by these size groups, allowing users to define their own coverage. He suggested that there is no right or wrong answer to the question of whether to use employment or revenue for the definition, but StatsCan needed one that was empirically defendable and for which data was readily available.
2. Clear Program Objectives: To design the Program, they established clear Program
objectives based on producing an annual base of statistics to support policy development (SME business demography statistics and SME performance indicators) and leveraging existing annual administrative data. Core variables included those for which a discrete value was known for each and every business in Canada with acceptable quality. This would allow them to measure changes over time. They also had to reach agreement on the unit of observation and decided it had to be “the enterprise” (as opposed to “the establishment”) because for most SMEs, enterprise and establishment are the same thing.
He reported that of the 2.2 million Canadian enterprises, almost 60% have no employees; 25% have 1-4 employees; 7.6% have 5-9 employees; and only 8.8% have more than 10 employees.
3. Complete, timely and high-quality data for all businesses: The next major problem
was obtaining complete, timely, and high quality data for all businesses in Canada. They resolved this by using Canada Revenue Agency (CRA) administrative tax data to the maximum (e.g. payroll remittance data for employment data; income tax forms to obtain balance sheet and income statement data). This was facilitated by the fact that Canada uses a unique business number for every Canadian business, allowing tracking of firms across files.
The Program coverage is dictated by the Business Register, which includes all employer businesses, and all non-employer businesses with at least Cdn$30,000 in sales. Administrative data is loaded for each active business in the Business Register for each reference year.
4. Confidentiality: The last major problem to overcome was data confidentiality. Users
wanted the most detailed breakdowns (by sector, geographic region, business size and revenue) but in cases where there were a limited number of businesses in the sub-breakdown, data could not be released for reasons of confidentiality. The solution was to agree on a number of standard annual outputs that would be made freely available to all users (and without costs). Never is individual information for a company released to the public.
The SME Statistics Program is in its second full year. In 2006-07, StatsCan conducted e4xtensive consultations with stakeholders on concepts (e.g. size classes) and content (e.g. business demographics and performance indicators), and developed new employment measures to count
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full time and part time employment. In 2007-08, they created a SMEstats data warehouse with tax data for 1999-2006 (with key variables for sector, employment, revenue, and age of firm). In 2008-09, they plan to load additional administrative data into the data warehouse, including selected balance sheet and income statement variables, and to generate additional performance indicators (SME value-added, as a GDP proxy, and profit ratios). The Program will be able to support future research and analysis on special topics such as exports, innovation, R&D, etc by linking this file to sample survey data. Finally, Mr. Evers shared the indicators for entrepreneurship performance that are being utilized, including business birth rate, business death rate, new business population growth rate, 3 and 5 year firm survival rates, employment impacts, and measures for firm growth, value-added, innovation, productivity contribution, and export performance.
Participants asked a number of questions. Q: Does the Government of Canada offer incentives to SMEs to provide information. What is the mechanism for gathering data and disseminating the data? Is the data released to SMEs? A: All businesses in Canada must be registered, but the cost to them for doing this is low and very simple. The tax system is well organized. SMEs complain about having to report to the Canadian Revenue Authority (CRA), but StatsCan has an agreement with the CRA to share data. CRA gives StatsCan data on a monthly basis. StatsCan does share data with SMEs to help them make better business decisions, although never gives them micro data. Larger businesses have more capacity to come to StatsCan to buy data – they find this useful for their planning, but SMEs cannot afford to do this. StatsCan also has a useful alliance with the Canadian Federation of Independent Business, the largest SME membership organization in the country. Q: How does StatsCan guarantee the precision of the available data if SMEs under-report employment or revenue? If this happens, the quality of the data will be affected. A: Data is as good as reported by SMEs and “it’s probably as good as you’re going to get”. Employment data is better than revenue data because all SMEs have to register their employees for unemployment insurance, Canada Pension Plan, etc. If employers do not do this, their employees can report them. Q: The system in Egypt is entirely different because of the large size of the informal sector and informal enterprises lack confidence. They are afraid of taxation and social insurance – afraid of the government. How can Egypt develop better relationships with informal SMEs? A: Mr. Evers answered that there is no magic solution. StatsCan has responsibility as a data provider – it has to measure what it can measure. So the Egyptian government should develop baseline measures for the formal economy and start with that. “Track what you can track.” Be pragmatic. Start with what you have and progress in a step-by-step approach. Mr. Lindner added that the informal economy is a very important issue. OECD countries work with non-OECD countries and use National Accounts to measure the informal economy, for example in the Balkans. OECD can help Egypt through its “project on the shadow economy” to measure the size of its informal economy relative to GDP. He also mentioned there is smuggling across borders, which affects Trade statistics and the OECD has found ways to measure the size
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of this problem as well. He added that offering information back to SMEs, especially data on a regional level, can be used as an incentive for them to provide data. Q: What advice is there on the problem of measuring the informal economy? A: Mr. Evers explained that he went to the Bahamas to advise the government on developing a Business Register. The Bahamas also has a very large informal sector. The government there was using all kinds of data to measure the size of the sector, e.g. utility usage. Proxies for the size of the sector can also be estimated through sample surveys in selected regions of the country. Results from this sample will generate empirical evidence that can be used to make national projections. In some countries, Census Regions are used to define the sample area. Door-to-door surveys are needed to get a good profile of the informal economy – on sector, employment, etc. Mr. Lindner stated that he worked for two decades in transition countries, such as Russia. There, he used the equation approach – used consumption figures and then estimated whether production estimates were consistent with consumption figures. If there is a gap, it is likely the size of the informal economy. Ms. Stevenson added that a researcher by the name of Schneider has outlined a number of proxy methods to measure the informal economy and used these to benchmark over 135 developed and developing countries on the size of their “shadow economies”. Lessons learned from the Canadian presentation There were many useful lessons learned for Egypt in this case study example.
• Policy demand is the driver behind the SME Statistics Program. • The SME definition for statistical purposes should be based on (at least) employment
category breakdowns that are consistent with international standards. • The base for the SME Statistics Program is the Business Register. • Timely and quality data on SMEs is generated by linking administrative files (taxation,
payroll) with the Business Register data. • It is important to have a unique identifier for each enterprise. • Key variables such as sector, employment, revenue, and age of enterprise are sufficient to
start building a SME Statistics Program. • The size of the informal economy can be estimated through sample surveys in selected
regions of the country and by using proxy indicators. • Egypt should start by developing baseline measures for the formal economy (“track what
can be tracked”) and progress in a step-by-step fashion. • Key stakeholders and data users should be consulted in the process of designing a SME
Statistics Program.
C: Workshop Session 2: European Statistics on SMEs, business demography and entrepreneurship 12:00 – 13:00 Workshop leader 2: Dr. Axel Behrens, Head of the Development and
Analysis Section of Structural Business Statistics (SBS), Eurostat
Dr. Behrens stated that the European Union has a Law on Structural Business Statistics (SBS) and gave a general overview of SBS in the EU. (His presentation is attached as Annex 5.) The
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SBS consists of four sections: business registers; data production by activity (NACE) and quality assurance; statistics on manufactured goods; and development and analysis. Although the European Commission definition of SMEs is based on employment, annual turnover, and annual balance sheet total, EuroStat only uses employment in its data collection and reporting activity. Employment size categories are: 0; 1-4; 5-9; 10-19; 20-49; 50-249; and >249. SMEs (less than 250 employees) make up 99.3% of all EU enterprises. He explained that the Business Demography Project, a new initiative, responded to high political demand for data on enterprise births and deaths (considered indicators for innovation and entrepreneurship in an economy). While some data on this existed in some EU countries, it was largely not comparable, so there was a big need for harmonizing the data; data on such things as the number of enterprises and employees, the population of active enterprises, the real enterprise birth rate, the survival rate of new enterprises (up to five years of age), etc. The methodological challenge was how to capture real enterprise births and deaths. He indicated that data for this was taken from the Business Register and as one piece of advice, he stressed, that “if you do nothing else, invest in a good Business Register”. He shared some comparative data for birth rates, survival rates and death rates across different countries, revealing vast differences between countries. The newest development in EuroStat is the Entrepreneurship Program. They have created an additional series on “employer businesses”, to establish an “employer business demography”. They did this because policymakers are not that interested in self-employment, own-account enterprises and more interested in enterprise births where jobs are being created. EuroStat recently published a methodological manual on how to produce such a database. Dr. Behrens shared some comparative results from this employer business demography project. He also shared information of the “Factors of Business Success” (FoBS) project. This project targeted enterprises born in 2002 that had survived for three years – a sub-population of enterprises that are still managed by the founder, again using the Business Register as the sampling frame. The survey results rendered a “profile of successful entrepreneurs”. This survey has now been done in 15 countries. One of the interesting findings is that the extent to which entrepreneurs consider their enterprises as innovative increases with level of education of the founder, but profitability does not correlate with education level). Entrepreneurs consider their main start-up difficulties as contact with customers and administrative problems. Q: Does the survey ask questions about the outsourcing or sub-contracting behaviour of the successful firms? A: He doesn’t think so. Q: What are the failure factors? A: The survey has not looked at failures. It is up to policymakers to decide if they want data on failures, but such data is difficult to obtain because often it is not possible to locate the entrepreneur of a failed business. Q: Is a National Business Number sufficient to produce data on business birth and deaths? Egypt has started to issue identifier numbers of enterprises, for example, to micro and
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small enterprises that register through the SFD. Should a national number be issued to all enterprises? A: You should have a simple system for unique business numbers and apply it consistently. Q: How do you approach the harmonization of data that is collected by a number of stakeholders? Do you change the structure for each stakeholder or just the way they collect data? Who would be in charge of coordinating or leading such a harmonization effort? A: The most efficient mechanism for doing this is through the National Statistical Office, like in the Canadian example of Statistics Canada. In the EU, the Business Register is normally held by the National Statistical Office, with rare exception. Q: CAPMAS reported that they have a large amount of data on the number of enterprises in Egypt, in which sectors, etc. and even more statistics on industrial enterprises. They do an annual survey of industrial enterprises with more than 10 employees and take an inventory of all businesses that comply with law 159. Every five years, CAPMAS does a Census of employers with less than five employees (assets, revenues, wages, etc.) A: In response to this comment, Mr. Lindner stated that he understands the tension between policymakers and the National Statistical Office. Policymakers need urgent information and cannot wait for five year intervals to get it – so they end up hiring contractors to do more immediate surveys for them. He recommended that the Business Register belong to the National Statistical Office. This is a core issue and key statistical problem. There has to be an interface between the National Statistical Office and ministries. “It’s not always easy, but in good practice countries, the National Statistical Office has the Business Register.” This has to be the cross-country body to integrate Business Register activity. Core statistics should be produced by the National Statistical Office, and provided as a service to all Ministries. Dr. Behrens continued with his presentation on the Entrepreneurship Indicators Programme, the objective of which is to develop comparable measures of entrepreneurship across countries. When the project started in 2004-05, there was activity going on in different countries but no comparable indicators. The EU wanted to produce a regular scoreboard with common language, terminology and measures, making entrepreneurship data “mainstream”. The project was launched in 2006. A 15-country Entrepreneurship Indicators Working Group was established. EuroStat and the OECD partnered in their efforts in 2007. An Entrepreneurship Indicators Manual drafting group has been working on this; the potential coverage is 40+ countries. The Manual includes core definitions and a framework for understanding and developing the indicators. The basis will be the EuroStat/ OECD Manual on Business Demography (2007). Data collection is underway, using a model of the entrepreneurial process: Determinants of entrepreneurship (e.g. number of days to start a business, education access, access to financing, technology spillover, the regulatory framework, market conditions, culture); Performance (firms entering and exit, employment, self-employment/ start-up rates, firms employment rates, performance of growth firms, production, export share of 3-5 year old firms, value-added for young firms); and Impact (e.g. job creation, economic growth, poverty reduction, culture change, etc.). Not all specific indicators have been agreed to, but the working group has reached agreement on the broad categories.
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Performance indicators will be ready in 2008 and the draft manual by the summer of 2008. A seminar on determinants will be held in June 2008. Q: Could Egypt participate in the Entrepreneurship Indicators Project in some way? A: Yes, Countries have to ask to become involved and there is a formal procedure. Egypt is involved in the MEDSTAT initiative but this initiative does not deal with business statistics. MEDSTAT is coming to an end and will not be continued. It will be replaced by bilateral country by country arrangements. Lessons learned from the EuroStat presentation
• High policy demand is the driver behind the EuroStat project on Business Demography – policymakers seek more information on business births and deaths as indicators for innovation and entrepreneurship.
• The most critical thing to be done to improve SME data is building a Business Register. The Business Register should be held by the National Statistical Office.
• The National Statistical Office should be the body responsible for harmonizing SME-related data across administrative units.
• A simple business identifier number should be implemented and applied consistently across ministries.
• The SME definition for statistical purposes should be consistent with international standards in terms of employment size category breakdowns.
• Sampling surveys and censuses are an inefficient and costly alternative to collecting data on SMEs; a Business Register is much more efficient, less burdensome for SMEs and can be updated regularly, thus providing more timely policy input.
• Egypt could participate in the OECD-EuroStat Entrepreneurship Indicators Programme upon official request.
D: Workshop Session 3: OECD work on SME statistics: policy and indicators 13:15 – 14:15 Workshop leader 3: Mr. Andreas Lindner, Head of the Trade and
Globalisation Statistics Section, OECD Statistics Directorate, OECD
Mr. Lindner started by describing the OECD mission and structure and the situation of SMEs in OECD countries. (His presentation is attached as Annex 6.) The genesis of the OECD work on SMEs is policy driven and involves stocktaking of policy issues and solutions and stocktaking of SME statistics. An OECD survey of the business frames used for SME statistics in member countries revealed several inconsistencies and limitations (e.g. using different sources, different thresholds, different updating intervals, different statistical units, different definitions for the variables for measuring the SME sector, etc.). He stressed the importance of having the National Statistical Office as the coordinator and collector of SME data, enabling that Office with full access to administrative data, and using a common identifier for enterprises across administrative units. He also advocated use of coherent size classes to capture and report data on SMEs in order to facilitate international comparisons, agreeing with the previous workshop presenters that the appropriate cut-offs are: 0, 1-4, 5-9, 10-19, 20-49, 50-249, and >249. He emphasized that use of an identifier number for each enterprise allowed for better tracing of firms over time (entry, exit, growth, etc.).
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Mr. Lindner also mentioned the importance of being able to link SME statistics with Trade statistics by linking the Business Register data with the Trade Register data. By doing this, governments can identify trade patterns by sector, niche markets, where SMEs are trading by country, etc., data that can be used for policy advice. However, not all OECD countries have formal Trade Registers, although in over half of the countries, the Trade Register is linked to the Business Register and data from fiscal authorities. The Trade Register can be updated using Customs declarations. He referred to the OECD-EuroStat Entrepreneurship Indicators Program (EIP) as well, sharing detail on the EIP definitions of entrepreneurs, entrepreneurial activity and entrepreneurship, and important principles guiding the program. Next, Mr. Lindner talked about the increasing importance of having a Business Register. He described this as the key to managing the complex statistical systems in a resource-effective and efficient way, and a pre-condition for understanding the SME segment of the economy. He agreed with Mr. Evers that a coherent Business Register can answer most, if not all, of the needed information about SMEs without routinely burdening SMEs with requests for survey data. The next OECD meeting of the International Working Group on Business Registers will be held 22-24 November, 2008. Mr. Lindner suggested that the Egyptian government make a formal request to the OECD to participate in that meeting. He indicated that Business Registers can include: enterprise registers (name, activity, and ID number of the enterprise); VAT registers, income declarations; accounts data; foreign trade register; farm register; patent register; etc. He shared case studies from the Netherlands and Germany as countries that have excelled in the Business Register area. Q: What would be required, organizationally, to implement a Business Register project, like Germany did, what kind of costs would be involved, and how long would the process take? A: First, Germany did a national stocktaking. The National Statistical Office is under the Ministry of the Interior. Its vetting power and prerogative is recognized, but all Ministries have their own databases for policy. The opportunity was to integrate them into a common database. The approach used in Germany would have to be adapted for Egypt, but the same principles could be applied. Dr. Behrens added that in some EU countries where statistical systems are more mature (i.e. developed a number of years ago), they have more difficulty adapting to emerging developments in the statistical field. New member countries have been able to build up their statistical systems in a much easier way. It is very important that ministries are willing to cooperate with each other in the project, but it does not have to be expensive. The investment can be further reduced by learning from what others have done in terms of structural design. Mr. Evers agreed with the approach of adapting an organizational structure and design, but added there will be costs to reconcile business registers into one integrated Business Register. If there is a single identifier number for enterprises, for example, care has to be taken to ensure it relates to the same thing across Registers. The process can be a very labour intensive one.
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Q: What are some models and examples of problems of collecting data on informal enterprises and the solution to these? A: Mr. Lindner responded that there are ways of addressing the informal issue. Give informal enterprises incentives; do not ask them for the same information more than once; streamline questions asked by the Chamber of Commerce, SFD, etc; give information back to them. Q: What about OECD stats on SMEs, like contribution of SMEs to employment, GDP, etc? Is there a regular report produced that Egypt can use as a guide for what indicators to use? A: This information can be found on the OECD website (ww.oecd.org). Lots of research papers can be found there. Q: Does the OECD cooperate with developing countries and can it provide financial or technical assistance to Egypt, and if so, through what process? A: Several OECD Directorates offer assistance. Paris 21 has a capacity-building institution within it to focus on developing countries. Developing countries can seek Observer status with the OECD; it is more and more the practice of allowing developing countries to sit as observers. The Egyptian government would have to send an official letter to the OECD Director-General requesting Observer Status. Lessons learned from the OECD presentation
• The National Statistical Office should be the coordinator and collector of SME data with full access to administrative data held by other ministries.
• A common identifier should be used for each enterprise. • Countries should use coherent and internationally consistent size classes for reporting on
SME data. • The Business Register is the key to managing complex statistical systems and a pre-
condition for understanding the SME sector. • Useful statistics can be obtained by linking the Business Register with the Trade Register. • Egypt can learn useful lessons from other countries that have integrated their business
registers, such as Germany and the Netherlands. • Egypt could participate in the next meeting of the International Working Group on
Business Registers taking place November 22-24, 2008, by extending a formal request to the OECD.
• Egypt could also officially request Observer Status in the OECD and thus benefit from all SME statistical work being done by the OECD.
E: Concluding panel session: 14:15 – 15:00 Key lessons learned during the day and how it can help Egyptians move
forward to improve the statistical system and produce better data on the SME sector.
In this session, several participants stated that they had benefited greatly from the workshop and remarked on the high calibre of the international speakers. One such participant indicated that he
15
learned that Egypt may have to be content with the SME definition issue (others agreed) and instead of debating that further, devote more time to more important issues. He recommended that Egypt start with sorting out data issues for the formal sector, where more data is available, and develop good reporting. This would help advance the Egyptian knowledge base about formal SMEs at least. Then the informal sector data issues could be tackled over time. There are 80,000 regulations in Egypt – these have to be eliminated to help SMEs. Another participant proposed that we start with developing a very good database using data on employees, employers. This might give us a 70 percent complete database and we can develop from there. On the issue of a SME definition, Mr. Lindner advocated for a definition that is in line with international organizations. Q: In light of the merger of tax departments, how will the Tax Authority deal with combining data sets? A: In 2006 there was a public decree to merge the Sales and Income Tax Authorities. In the future, SMEs will have to deal with only one tax agency with one report on sales and profits. Mr. Evers remarked that, if there really is an initiative to integrate the sales and income tax files, Egypt has a golden opportunity not only to bring these files together with a common identifier, but to provide the single business identifier and core business descriptions to the National Statistical Office to start developing the formal Business Register. The Tax Authority official stated that the tax files are sorted into three groups: small tax payers, medium tax payers, and large tax payers. They will have a Register for the 3 groups. CAPMAS reported that they have the right to collect information from all SMEs in Egypt and that this information is collected at the level of the governorate. Mr. Evers commented on the use of Census surveys, stating that in the long run, these are not sustainable. Information becomes stale and there is a huge operational cost to surveying firms. The notion of the Business Register is that once you have the core data (employment, sales, etc.), then you can refresh the Business Register quarterly or annually. Dr. Behrens supported this viewpoint, citing the experience of Portugal. A representative from the Ministry of Trade and Industry added that Egypt needs more consistent survey data and proxy indicators for the informal sector. Current surveys are only a partial solution to information needs. Another participant noted that Egypt has a “jungle” of laws and regulations governing statistics and that these statistical laws need to be streamlined to be in line with the systematic statistical systems in Canada and the European countries. The SFD endorsed the need for more consistent data collection and indicated its willingness to link their work with other people’s expertise.
16
Closing remarks
Mr. Abdel Aziz presented closing remarks. He concluded that Egypt needs an integrated statistical/ data system on MSMEs and that we need to lay out a procedure for developing this. He proposed that the Tax Authority agree to the necessary approach to integrate the tax data files. He urged that ministries have to cooperate with each other in the collecting of data and the sharing of data across and between ministries. He informed that participants will receive a copy of the workshop outcome report and that the SME Unit will recommend to the Minister of Finance that a Committee be formed to initiate a response to recommendations and suggestions arising from the workshop.
17
Annex 1: Agenda
SME Data Workshop January 17, 2008
9:30 – 15:00
Cairo Sheraton, Nefertiti Room
AGENDA
Registration and welcome refreshments
Opening remarks: Mr. Mohamed Abdel Aziz, Manager, SME Unit, Ministry of Finance and Ms. Lois Stevenson, SME Specialist, International Development Research Centre and SMEPol Project Coordinator
Introductions of workshop participants
Recap and confirmation of the major SME data and statistical challenges in Egypt identified at September 26, 2007 Roundtable.
Introductions of three international experts and themes for the day.
Workshop leader 1: Mr. Terry Evers, Director of Small Business and Special Surveys Division (SBSSD), Statistics Canada
This presentation will focus on how Stats Can built its comprehensive SME Statistics Program, including obstacles, solutions, current status and future of Statistics Canada's SME Statistics Program. There will be several lessons learned for Egyptian officials on how such a comprehensive system could be approached in Egypt.
Refreshment break Workshop leader 2: Dr. Axel Behrens, Head of the Development and Analysis Section of Structural Business Statistics (SBS), Eurostat
This presentation will focus briefly on the role of Eurostat and then the latest developments regarding: • the new Regulation (law for EU Member States) for structural business statistics
including SME statistics,
9:30 – 10:00
10:00 – 10:15
10:15 – 10:45
10:45 – 11:45
10:45 – 12:00
12:00 – 13:00
18
• business demography as part of the new Regulation for structural business statistics and the link to SME statistics,
• joint Eurostat/OECD entrepreneurship indicators project; and the link to business demography and SME statistics
Refreshment Break Workshop leader 3: Mr. Andreas Lindner, Head of the Trade and Globalisation Statistics Section, OECD Statistics Directorate, OECD
This presentation will focus briefly on the role of OECD Statistics and then on issues related to: • Growing importance of SMEs for job creation, growing policy interest, OECD
initiatives to tackle this • SME characteristics in OECD countries
o Problems, challenges, indicators • The interface SMEs, Entrepreneurship Indicators and Business Demography • Difficulties of international SME comparisons due to different definitions,
thresholds, etc. • How to tackle building up a comprehensive SME data base for policy making.
o The importance of good and effective register design – lessons learned • The need to integrate different administrative sources into one
comprehensive and multi-output data warehouse • The cost effectiveness of good business register design • Some country experiences
o Linking business statistics to today’s globalized worlds – an imperative • Some statistical challenges stemming from globalization • Its application to SMEs: linking SBS data to trade statistics • Some indicators
Concluding panel session: Key lessons learned during the day and how it can help Egyptians move forward to improve the statistical system and produce better data on the SME sector.
Lunch and networking
13:00 – 13:15
13:15 – 14:15
14:15 – 15:00
15:00 – 16:00
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Annex 2: List of participants
Organization Name & Title Telephone Fax E-mail 1. CAPMAS Ahmed Said Morsy Ahmed ,
Manager of Research Department
2402 41 70; 0101314025
226 36 464
2. CAPMAS Dorreya Abbass Mohamed 24024031 – ext. 236
3. CAPMAS Hanaa Abd El Hady Abd Allah
24024170 22636404 [email protected]
4. Egyptian Center for Economic Studies (ECES)
Dr. Nihal El-Megharbel , Principal Economist
24619037 – 24619038
24619045 [email protected]
5. European Commission (EuroStat)
Dr. Axel Behrens, Head, Development and Analysis Section of Structural Business Statistics (SBS)
6. European Union Rodrigo Romero van Cutsem, Trade Expert, Trade, Science & Enterprise Section
3749 4680/218 3749 5363 [email protected]
7. General Authority for Investment (GAFI)
Dr. Amr Hamdi Eldin Hassan, General Manager for SMEs, Investing in the Governorates Sector
8. General Authority for Investment (GAFI)
Mohamed Mohamed Abou Serea, Economic Researcher, One-stop Shop
2405 5452; 2405 5616 [email protected]
9. Industrial Modernization Centre
Amr Mohamed Taha Information/Monitoring & Evaluation Manager
25722228 25772870 [email protected]
10. Information and Decision Support Centre (IDSC)
Dr. Sohair El Sherif, Manager of Project – Warehouse Info. For SMEs
27929292 27929222 [email protected]
11. Information and Decision Support Centre (IDSC)
Dr. Ashraf Shaheen, Executive Manager, Information Dissemination
7929 292 7929 222 [email protected]
12. International Development Research Centre (IDRC)
Lois Stevenson, Senior SME Specialist & SMEPol Project Coordinator
25789129 27730139 [email protected]
13. Ministry of Finance Dr. Mohamed Soruor, Advisor to the Assistant of the Minister of Finance
2792 0774;
14. Ministry of Finance/ SMEPoL
Mohamed Abdel Aziz, SMEPol Unit Manager
25789129 27730139 [email protected]
15. Ministry of Finance/ SMEPoL
Samer Sayed, IT Specialist
25789129 27730139 [email protected]
16. Ministry of Finance/ SMEPoL
Nerveen Osman, SME Specialist
25789129 27730139 [email protected]
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17. Ministry of Investment Nesma Mostafa Abass, Economic Researcher, Technical Office of the Minister
240 55651/2/3 24055635/36 [email protected]
18. Ministry of Trade & Industry
Hussien Abdel Mottaleb, SMEs Manager
19. Ministry of Trade & Industry
Azza Mohamed Kamal, Head of the Agriculture, SPS &TBT Department, Central Department of World Trade Organization, Trade Agreements Sector
2342 2342 2342 1768 [email protected]; [email protected]
20. Ministry of Trade & Industry
Dr. Beiomy El Shimy, General Manager – SME department
23420982 [email protected]
21. Ministry of Trade & Industry
Sayada Abd Allah, SME Department
22. Ministry of Trade & Industry
Doaa Abdel Salam Mahram, International Trade Researcher
23. Ministry of Trade & Industry
Sherein Fayed [email protected]
24. National Authority for Social Insurance
Hisham Kandil, Undersecretary of the Ministry of Insurance
2792 1140
25. National Authority for Social Insurance
Mohamed Said Abdel Lataf Ali, Director, Inspection Department
2787 0391;
26. National Authority for Social Insurance
Samy Mohamed Abdel Hady Mohamed, General Manager of Internal
2593 7891 2591 2453
27. OECD Mr. Andreas Lindner, Head, International Trade and Globalization Statistics
28. Social Fund for Development
Emil Michelle George, Social Fund Representative
3332 2039 [email protected]
29. Social Fund for Development
Maha Osama Mehanna, Assistant to the Manager
3332 2262 [email protected]
30. Social Fund for Development
Samir Amin Abdel Hamid, Manager of Services Unit for SMEs
3761 0290 3749 2830
31. Statistics Canada Mr. Terry Evers Director, Small Business and Special Surveys Division
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Annex 3: International presenters
Mr. Terry Evers is the Director of Small Business and Special Surveys Division (SBSSD), Statistics Canada. Mr. Evers has a degree in Geography and Sociology as well as a diploma in Informatics. Having spent 34 years in Statistics Canada, he has extensive experience in all aspects of survey design, systems development, data collection and dissemination. Over the years he has worked on a wide variety of business and social statistical programs.
As Director of SBSSD, Mr. Evers is responsible for the Small Business Program in Statistics Canada which includes a large recurring national survey on SME demand for financing and the SME Statistics Project. Also, under his responsibility are many ad hoc and recurring business surveys conducted on a cost recovery basis for other Federal and Provincial Government Departments. These include surveys on compensation, working conditions, energy use, cost of regulatory compliance etc.
Prior to this, Mr. Evers was the Assistant Director in the Business Register Division of Statistics Canada. As such, he was responsible for all activities related to the collection, processing, profiling and updating of the Business Register. Mr. Evers currently sits on the OECD Entrepreneurship Indicators Steering Group, Industry Canada’s Small Business Research Advisory Committee, Aboriginal International Business Development Committee and the Paper Burden Reduction Initiative Working Group.
Dr. Axel Behrens is permanent official of the European Commission and currently heads the Development and Analysis Section of Structural Business Statistics (SBS), after having worked in price statistics, regional statistics and short term statistics. He received his doctorate in 1991 at the University of Kiel in international economics. Before entering the European Commission in 1995, he was assistant professor at the University of Konstanz.
Under his responsibility falls the new SBS Regulation setting the standards for all EU Member States in terms of structural business statistics, including the new parts on business services and business demography. Furthermore, he is in charge of the new Foreign Affiliates statistics (FATS). The current work of his section includes also some ad hoc studies, like the Factors of Business Success for newly born enterprises, an ad hoc project on international sourcing and a project on demand for services. Besides that, he is vice chairman of the OECD steering group concerning entrepreneurship indicators. The collection of those indicators, mainly based on business demography has started on EU and OECD level. A first publication is foreseen for spring 2008.
Mr. Andreas Lindner, a German international economist and statistician, is Head of the Trade and Globalisation Statistics Section of the Statistics Directorate at OECD. He holds a master degree in economics from Freiburg University, Germany and an English degree from Cambridge University. In his long career of 31 years at OECD, he specialised in various statistical key domains such as business statistics, trade statistics, globalisation statistics, R&D statistics and agricultural statistics. An elected member of the International Statistical Institute (ISI), he is also member of the IAOS and of several international statistical groups. At OECD he chairs the Steering Group on linking business statistics to trade statistics and is organiser and OECD chair of the Working Group on International Merchandise Trade Statistics and Trade in Services Statistics, which has held major annual meetings since 1999. More recently, he is particularly involved in statistical system/register design issues as member of the Steering Group of the Wiesbaden City Group on Business Survey Frames (a UN City group, formerly known as International Roundtable on Business Registers). He organises the forthcoming meeting of this international expert group, hosted by OECD in November 2008.
He was responsible for the statistical workshop at the OECD Ministerial meeting on SMEs (Istanbul, 2004), which led to the development of SME statistics at OECD, the entrepreneurial indicators project and business demography statistics. He is playing a key role in further developing SME statistics in the context of linking SMEs to trade statistics, adaptation of business statistics to globalisation and statistical system design.
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Annex 4: SME Statistics Program, Statistics Canada (PPT)
23
SME Statistics ProgramSME Statistics Program
Small Business and Special Surveys Small Business and Special Surveys DivisionDivision
Statistics CanadaStatistics Canada
Conference Conference ““Past, Present and Future: Past, Present and Future: SMEPOLicySMEPOLicy in Egyptin Egypt””January 2008January 2008
SME StatisticsIntroduction
Strong interest in SMEs because of contribution to employment and innovation (Government priorities)
Policy areas demanding detailed / high quality SME data to support policy development geared specifically at SME growth as opposed to business start-ups
Industry Canada, Canadian Federation of Independent Business, Provincial Departments for Economic Development
International Ministerial support for the development of comparable SME dataOECD / Eurostat support for the development of standard demography statistics and entrepreneurship performance indicators
1
SME StatisticsBusiness Problem
No standard definitions for SMEsIn STC / nationally / internationally
Unable to create a comprehensive picture of SMEs in CanadaLimits comparisons to other countries
Lack standard measures of SME performance to assess productivity, growth, competitiveness etc.Accessibility
Users don’t know what data STC has on SMEs; how / where to get it; and how to interpret it because of different concepts in use
2
SME Statistics ProgramObstacle
Different users / Different requirements Achieving consensus on a definition of small and medium Achieving consensus on how to measure size – Emp vs Rev
How resolvedAdopt standard size groups based on employment aligned with OECD and Eurostat
0, 1-4, 5-9, 10-19, 20-49, 50-99, 100-249, > 249Report all data by size group allowing users to define their owncoverage
Carry revenue as an auxiliary variable so outputs by revenue can be produced
3
SME Statistics ProgramObstacle
Managing expectationsHow resolved
Clear definition of Program ObjectivesProduce on an annual basis statistics to support policy development and international country comparisons
SME business demography statistics SME performance indicators
No official industrial estimates or employment estimatesLeverage existing administrative data Funding for data gaps must come from stakeholders
4
SME Statistics ProgramObstacle
Agreeing on program content How resolved
The base program would only include core statisticsCore defined as:
Variables available from existing administrative sources on an annual bases (Longtitudinal)Variables for which a discrete value is known for each and everybusiness in Canada with acceptable quality
In so doing the data is both comprehensive and consistent over time Allow us to measure change over time
5
24
SME Statistics ProgramObstacle
Agreeing on the unit of observationHow resolved
For international comparisons the unit of observation needs to be enterprise as it has the same legal definition internationally
For SMEs enterprise and establishment are one and the sameIt is only with large enterprises that establishment / enterprise becomes an issue
6
SME Statistics ProgramEnterprise Counts 0 employees 1,290,000 58.3%1-4 employees 560,000 25.3%5-9 employees 169,000 7.6%10-19 employees 92,000 4.2%20-49 employees 56,000 2.5%50-99 employees 18,000 0.8%100-249 employees 8,000 0.4%> 249 employees 20,000 0.9%
(116,000 establishments)Total Enterprises 2,213,000
Note: At present > 249 not included
7
SME Statistics ProgramObstacle
Getting complete, timely, high quality data for all businesses in Canada
How resolvedExploit Canada Revenue Agency administrative data to the maximum
Use Payroll Remittance Data to obtain employment data Use T1 and T2 tax data to obtain balance sheet and income statement data Full set of data for a given tax year is available by September of the following year (ex. Tax year 2006 – complete data available by September 2007)
Facilitated by unique Business Number8
SME Statistics ProgramObstacle
Ensuring that the SME Statistics Program remains fully aligned with Statistics Canada’s Business Register
Business counts from the SME Statistics Program need to be consistent with annual, quarterly, monthly survey estimates
How resolvedThe program coverage is dictated by the Business Register
All employer business in CanadaAll non-employer business with at least $30K in GST sales
Administrative data is loaded for each active business in the Business Register for each reference year
9
SME Statistics ProgramObstacle - Confidentiality
Users want most detailed data possible (Industry – 6 digit NAICS, Geography – sub provincial breakdown, business size or revenue size)For any given dimension this is not a problemMulti dimensional cross tabulations and selected sub-populations (high growth / gazelles) problematic
Small numbers of businesses in the larger size groups
How resolvedStandard annual outputs free - additional customized outputs generated as required on a cost recovery basis
Users need to decide what the most important dimension(s) is to maximize the useful information presented in the output tables
10
StatusPhase 1 (FY 06 - 07)
Extensive consultation with stakeholders on concepts (size classes based on Emp and Rev) and content (Bus demographics and performance measures)Development of new employment measures
• Based on Payroll Remittance Data to Canada Revenue Agency• Avg 12 month head count for EMP• Annual EI contributions to determine full time / part time Emp
Phase 2 (FY 07-08)Created SMEstats data warehouse with tax data for 1999 –2006 (Key variables included Ind, emp, rev, age)
11
25
Bus. Demography Statistics
Tax data 1999 - 2006Demographic DimensionCharacteristic Ind Prov Emp size Rev size
# Firms Y Y Y YEmp count Y Y YAge of Business Y Y Y
12
Entrepreneurship Performance Indicators
Export performance, small firms
Average firm size after 3 and 5 yearsProportion 3 and 5 year survival
Innovation performance, young or small firms
Employment 3 and 5 year old firmsSurvival rate, 3 and 5 years
Productivity contribution, young firms
Ownership rate business populationNet business population growth
Value-added by young firmsOwnership rate start-upsBusiness churn
Gazelle rate by turnoverGazelle rate by employmentEmployer firm death rate
High growth firm rate by turnover
High growth firm rate by employment
Employer firm birth rate
WealthEmploymentFirms
Phase 3 (FY 08-09)Load additional admin data into DW
Selected balance sheet and Income statement variablesProfit, salaries & wages, depreciation, net interest paid etc
Generate additional performance indicators SME Value added (GDP proxy)Profit ratios
14
Future PerspectiveLeveraging the SME Statistics Program
Given that the Program has core micro data for each business in Canada we will be able to create custom tabs for users on a costrecovery basis
Provincial Governments has already expressed interest in sub-provincial data
The Program will be able to support future research and analysis on special topics such as exports, innovation, research and development by linking this file to sample survey data
Contains all core business characteristics for the universe of SMEs
15
26
Annex 5: European Statistics on SMEs, business demography and entrepreneurship (PPT)
27
European Statistics on SMEs, business demographyand entrepreneurship
Annex 5
The European Commission…is a supranational organisation and:
the executive branch of the European Union, i.e. implementing decisions the body for proposing legislation (right of initiative)upholding the Union's treaties (guardian of the treaty)running day-to-day of the European Union
The European CommissionCosists of Directorates General (DGs)
EU Policies– Agriculture– Regional– others
External RelationsGeneral services– e.g. Publication Office– Statistical Office (Eurostat)
Internal Services
Eurostatis providing official statistics, especially for EU policies (agriculture, regional, Lisbonprocess)negotiating and setting standards withmethodological manualsproposing legislation for statistics to bedelivered from Member States and standards to be complied with (including legislativefollow-up)does not conduct surveys!
Eurostat – Structural Business Statistics
Consists of four sections:business registersdata production by activity (NACE) andquality insurancestatistics on manufactured productsdevelopment and analysis
Eurostat – Structural Business Statistics
The collection by activity (NACE 4-digit) is based on a legal act (SBS recast):Annex I: GeneralAnnex II: IndustryAnnex III: Distributive TradeAnnex IV: Construction
28
Eurostat – Structural Business Statistics
cont.Annex V: InsuranceAnnex VI: Credit InstitutionsAnnex VII: Pension FundsAnnex VIII: Business Services (new)Annex IX: Business Demography (new)
SMEs-
Size Class Breakdown
Definition SMEsCommission definition of SMEs (2003/361)– SMEs:
• Employment less than 250 persons• Annual turnover less than €50 Million• Annual balance sheet total less than €43 Million
– Small enterprise• Employment less than 50 persons• Annual turnover and/or annual balance sheet total
less than €10 Million– Microenterprise:
• Employment less than 10 persons• Annual turnover and/or Annual balance sheet total
less than €2 Million
In the data collection, we do not follow the definition
-Just employment
Size Class Breakdown for number of persons employed
12-910-1920-4950-249250+
For the data from Annex I – IV, i.e. for the non-financial business economy at NACE 3-digit.
No employment breakdown for theAnnexes V – VII
Annex VIII has an employmentthreshold of 20+ only
Annex IX on employees (0,1-4,5-9,10+)
Some facts on SMEs in the European Union
SMEs represent 99.8% of all EU-27 enterprises in the non-financial business economy, employing two thirds of the workforce (66.7%) and generating 56.9% of total value added; the average large enterprise in the EU-27's non-financial business economy employed just over 1 000 persons, compared with 4.4 persons being employed by the average SME;
29
Some facts on SMEs in the European Unionlarge enterprises accounted for 90.0% of those employed in the air transport sector, 88.2% of those working in post and telecommunications, and around 85% in two of the energy related activities In contrast, SMEs employed 92.3% of those persons employed in recycling, 88.3% of the motor trades' workforce, and 88.2% of those employed in the construction sector; SMEs employed 81.2% of the non-financial business economy workforce in Italy, a share that is close to 50% in Slovakia and the United Kingdom;
Business Demography
Background
High political demand (EU Commission and ECB)– Data on enterprise births and deaths in Europe– Business Demography data as an indicator of
innovation and successful entrepreneurship– Greenbook on Entrepreneurship (2003),
followed by “Entrepreneurship Action Plan”.– Lisbon process of the EU: Structural Indicators– Similar statistics already in several Member
States, need for harmonisation
Variables and breakdowns
Number of enterprises and persons employed– Population of active enterprises– Real enterprise births– Survival of newly born enterprises (up to 5 years)– Real enterprise deaths
NACE: 2 or 3 level codes (subsections / divsions, groups) + special aggregates (ICT, services)Size classes according to employees (0, 1-4, 5-9, 10+)Legal forms (sole proprietorships, partnerships and limited liability companies)
Methodology
“Real” births and deaths– New combination of production factors
– Identification of births/deaths by excluding entry/exit due to take-overs, mergers, split-offs, break-ups, …
Taking data from business register– 1st advantage: no response burden on enterprises.
– 2nd advantage: availability of populations of newly born enterprises as sampling frames for specific surveys, e. g. “Factors of Business Success”
State of affairs
22 EU Member States plus Norway and SwitzerlandES, (DE), FI, (FR), IT, LU, NL, PT, SE, UK, CY, CZ, EE, HU, LT, LV, MT, (PL), SI, SK, BG, RO, CH, NOAvailable data– Eurostat website– Publications: Statistics in Focus, detailed
tables
30
(1)Enterprise birth rate, by NACE activity
Enterprise birth rate, business economy
Enterprise survival rate, business economy
Enterprise death rate, business economy
Further development
In the framework of the OECD/Eurostatprogramme on entrepreneurship– creation of additional series of enterprises
with employees (birth only with at least 1 employee) – employer business demography
– comparability of data with US, Japan…– Methodological manual published, but
further need to harmonise methodologies of EU and OECD, e.g. for high-growth enterprises
Employer Business Demography
Can’t we just drop the size class “0 employees”?– Problem: non-employer becoming employer, i.e. they
enter population of active enterprises with employeesbut are not counted as “employer birth”
– Same problem with deaths
– Solution: “entry by growth” as case of “employer birth”“exit by decline” as case of “employer death”
31
Employer Business DemographyComparison of birth rates / death rates1. Employer births/deaths based on new metholodology
• Covers non-employers becoming employers (births) and vice-versa (deaths)
2. Births/deaths in 2007 harmonised data collection• Covers all enterprises
3. Employer births/deaths, “size class 0 employees dropped”• Old methodology• Only enterprise births / deaths with at least one employee in
the year of birth / death• No “entry by growth” / “exit by decline”
HU birth rate 2005
0,0%
5,0%
10,0%
15,0%
20,0%
25,0%
30,0%
C2K C D E F G H I J K
Employers / all / all except “0 employees”
NL birth rate 2005Employers / all / all except “0 employees”
0,0%
2,0%
4,0%
6,0%
8,0%
10,0%
12,0%
14,0%
C2K C D E F G H I J K
Birth rates 2005, all 5 countries, C to KEmployers / all / all except “0 employees”
0,0%
2,0%
4,0%
6,0%
8,0%
10,0%
12,0%
14,0%
16,0%
18,0%
20,0%
HU IT NL RO SK
Using the BD population as a sampling frame
The survey on « factors of business success »
Background
Successful implementation of Business Demography: births, deaths, and related employment variablesQuestions of interest– What are the factors determining the success /
survival of newly born enterprises?– What are obstacles that new enterprises face?– What support to them is most useful?– What is the profile of the successful entrepreneur?
=> FoBS project
32
Methodology
Target population– Enterprises that were born in 2002 and survived for 3
years, until the time of the survey in 2005.– Sub-population of enterprises that are still managed
by the founder => profile of the successful entrepreneur
Business Registers used as sampling frames for these populationsSurveys in 15 countries (questionnaires, phone, interviews)
Elements of the questionnaire (1)
Start-up conditions and profile of the entrepreneur– Motivation for start-up– Financing– Difficulties at start-up– Entrepreneur’s characteristics: education,
experience, gender, age, citizenshipPresent situation– Employment, turnover– Co-operation, networking– Difficulties developing the enterprise
Elements of the questionnaire (2)
Future plans– Future of the enterprise (continuing, selling,
closing down)– Development of employment, turnover,
investments
Availablity of data
Participating countries– CZ, DK, EE, IT, LV, LT, LU, AT, PT, SI, SK, SE,
BG, ROAvailable data– Eurostat website– Publications: Statistics in Focus
Main findings of the FoBS survey (1)
Experience of having worked in the activity and in running an enterprise help, but are not essential, in becoming a successful entrepreneur; entrepreneurs consider contacts with customersand administrative problems as their main start-up difficulties; dealing with outstanding invoices to customers is one of the start-up difficulties more often perceived as problematic for men than for women;men are more optimistic about the profitability of their enterprise;
Main findings of the FoBS survey
the degree to which entrepreneurs consider their enterprises to be innovative increases with their educational level;The highest education does not bring about thehighest level of profitability;
the most often-cited motivations for starting-up an enterprise are the desire to be one’s own bossand the prospect of making more money.
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Motivation for starting a business
Entrepreneurship Indicators Entrepreneurship Indicators ProgrammeProgramme
Developing Comparable Measures Developing Comparable Measures of Entrepreneurship of Entrepreneurship
Entrepreneurship Indicators Programme
2004‐2005
OECD statistical activity initiated by:
Kauffman Foundation (USA)
Danish‐led Consortium (ICE)better international entrepreneurship data
The European Commission under the lead of Eurostat was also ‐ exploring entrepreneurship
‐ survey on Factors of Business Success (FoBS)
OECD Proposal
Observation: There is a lot of activities in manycountries, but there are no comparable indicators
Produce a regular Scoreboard or Compendium
Make entrepreneurship data “mainstream”
Common language, terminology, measures
Launched “Programme” in 2006
Infrastructure
Entrepreneurship Indicators Steering GroupPolicy and Statistical Experts
15 Countries plus International Organisations
Formal partnership with Eurostat in 2007EIP is OECD‐Eurostat Entrepreneurship Indicators Programme
Manual drafting group
Potential coverage of some 40 countries
Out‐reach to non‐members in 2008 and beyond
OECD‐Eurostat Countries
• Australia
• Austria
• Belgium
• Bulgaria
• Canada
• Cyprus
• Czech Republic
• Denmark
• Estonia
• Finland
• France
• Germany
• Greece
• Hungary
• Iceland
• Ireland
• Italy
• Latvia
• Liechtenstein
• Lithuania
• Japan
• Korea
• Luxembourg
• Malta
• Mexico
• Netherlands
• New Zealand
• Norway
• Poland
• Portugal
• Romania
• Slovakia
• Slovenia
• Spain
• Sweden
• Switzerland
• Turkey
• United Kingdom
• United States
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Entrepreneurship Measurement Manual
EssentialCore Definitions (see OECD presentation)
Framework for understanding and developing
Indicators and data specifications
Manual planned for 2008
Manual drafting group installed
The basis will be the Eurostat‐OECD Manual on Business Demography (2007)
Data collection now underway by OECD & EU
EntrepreneurialPerformance
DeterminantsImpact
A Simple Model of the entrepreneurial process
EntrepreneurialPerformance
DeterminantsImpact
A Simple Model of the entrepreneurial process
Job creation
Economic growth
Poverty reduction
Culture
others
EntrepreneurialPerformance
DeterminantsImpact
A Simple Model of the entrepreneurial process
EntrepreneurialPerformance
DeterminantsImpact
A Simple Model of the entrepreneurial process
Market conditions
Accessto finance
R&D and technology
Entrepreneurial
capabilities
Culture
Regulatoryframework
EntrepreneurialPerformance
DeterminantsImpact
A Simple Model of the entrepreneurial process
35
EntrepreneurialPerformance
Firms
Employment
Economic performance
Employer firm birth rate*
Employer firm death rate*
Business churn
Net business population growth
Survival rate 3 years
Survival rate 5 years
Proportion 3 years survival
Proportion 5 years survivalEmployment rate 3 year old firms
% adults starting firms
Ownership rate business population
Ownership rate start-ups
Value-added young firms
Productivity contribution
Productivity growth contribution
Export share of 3 year old firms
Average firm size after 5 years
Rate of high-growth firmsconcerning employment*
Rate of employment Gazelles*
Employment rate 5 year old firms
Average firm size after 3 years
Rate of high-growth firmsconcerning turnover/profits*
Rate of turnover/profit Gazelles
Export share of 5 year old firms
EntrepreneurialPerformanceDeterminants Impact
Job creation
Economic growth
Poverty reduction
Culture
Market conditions
Accessto finance
R&D and technology
Entrepreneurial
capabilitiesCultureRegulatory
framework
Competition
Anti-trust laws
Public procurement
Degree of public
involvement
Access to the domestic market
Access to foreign markets
Access to debt financing
Access to VC
Access to other Types of
Equity
Business angels
University/ industry interface
Technological cooperation
between firms
Technology diffusion
Broadbandaccess
Patent system,standards
R&D investmentTraining andexperience ofentrepreneurs
Business andEntrepreneurshipEducation (skills)
Entrepreneurshipinfrastructure
Immigrationand E-ship
Risk attitudein societies
Attitudestowards
entrepreneurs
Desire forbusiness
ownership
Court-legalframework
Labour market
regulation
Productregulation
Environmentregulation
Administrative burdens for entry
Bankruptcyregulation
Administrative burdens for
growth
Social and healthsecurity
Income taxes
Business taxes
Capital taxes
Safety andhealth
regulationEntrepreneurship
Education (mindset)
Stock markets
Firms
Employment
Economic performance
Employer firm birth rate*Employer firm death rate*
Business churnNet business population growth
Survival rate 3 yearsSurvival rate 5 years
Proportion 3 years survivalProportion 5 years survival
Employment rate 3 year old firms
% adults starting firms
Ownership rate business populationOwnership rate start-ups
Value-added young firmsProductivity contribution
Productivity growth contributionExport share of 3 year old firms
Average firm size after 5 years
Rate of high-growth firmsconcerning employment*
Rate of employment Gazelles
Employment rate 5 year old firmsAverage firm size after 3 years
Rate of high-growth firmsconcerning turnover/profits*
Rate of turnover/profit Gazelles
Export share of 5 year old firms
Timetable
Publication with Performance Indicators –Spring 2008Draft Methodological Manual – summer 2008Consultation phase for the manual – 2008Publication of final Methodological Manual –end 2008Seminar on Determinants – June 2008Further work on Determinants – 2008/2009
Any questions?
36
Annex 6: OECD Work on SME Statistics: Policy and Indicators (PPT)
37
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
OECD Work on SME Statistics: Policy and Indicators
Conference
“Past, Present and Future: SME Policy in Egypt”
Cairo, January 15-17, 2008Andreas Lindner OECD/STD/TAGS 2
Annex 6
Preamble:“An opinion without data is just another person’s opinion”
Quote from A. Schleicher, OECD, who conducted the well-known PISA study
Structure of this presentation
1. What is and how works the OECD ?
2. Work on SMEs
3. Linking SBS and Trade
4. The OECD-Eurostat Enterpreneurship Indicators Program
5. Business registers – the key
6. PS: Impact of globalisation on statistics
4
1. What is the OECD?An Organisation of 30 member countries committed to democracy and the market economy
A provider of comparative data, analysis and forecasts
so that governments can:
- compare policy experiences
- seek answers to common problems
- identify good practice
- co-ordinate policies
Global Partners
OECD member countriesCountries invited to open talks on potential membership
Countries to which OECD is offering enhanced engagement
OECD members, future members, andEEPs
30 member countries
AUSTRALIAAUSTRIA BELGIUM CANADACZECH REPUBLIC DENMARK FINLAND FRANCE GERMANY GREECE HUNGARY ICELAND IRELAND ITALY JAPAN
KOREALUXEMBOURGMEXICONETHERLANDSNEW ZEALANDNORWAYPOLANDPORTUGALSLOVAK REPUBLICSPAINSWEDENSWITZERLANDTURKEYUNITED KINGDOMUNITED STATES
• Countries invited to membership talks
CHILEESTONIAISRAELRUSSIASLOVENIA
• Enhanced engagement program (EEP)
BRAZILCHINAINDIAINDONESIASOUTH AFRICA
38
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
The OECD missionArticle 1 of the OECD Convention:
Support economic growth
Boost employment
Raise living standards
Maintain financial stability
Assist other countries’ economic development
Contribute to growth in world trade
Who drives the OECD’s work?
CouncilOversight and strategic direction
Representatives of member countries and of the European Commission; decisions taken by consensus
Committees
Discussion and implementation
Representatives of member countries and of invited non-Members work with the OECD Secretariat on specific issues
Secretariat
Analysis and proposalsSecretary-GeneralDeputy Secretaries-GeneralDirectorates
Economics and trade
Analyse and publish comparative data
Produce forecasts
Develop policies for growth and stability
Foster open markets
Encourage expansion of financial services
Promote cross-border investment
Share best practices
Social cohesion
Ensure equal access to education for all
Promote effective and accessible health systems
Fight social exclusion and unemployment
Bridge the “digital divide“ between rich and poor
GovernancePromote effective public administration
Encourage companies to run their affairs better
Ensure transparent and fair tax systems
Foster fair competition
Fight corruption and money-laundering
Promote high ethical standards
Encourage citizen-participation in policy-making
OECD’s way of working
Analysis
Data Collection
Discussion
Peer reviews,multilateral surveillance
Implementation
Decisions
39
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
The OECD Secretariat
Two official languages: English and French
Staff members are international civil servants
No quota system for national representation
2300 staff at Paris headquarter
2. The genesis of OECD work on SMEs
• A policy-driven process
• Stocktaking of policy issues and solutions
• Stocktaking of SME statistics
• First OECD Conference for Ministers responsible for SMEs on "Enhancing the Competitiveness of SMEs in the Global Economy: Strategies and Policies", held in BOLOGNA on 13-15 June 2000
• Second OECD Ministerial Conference on “Promoting Entrepreneurship and Innovative SMEs in a Global Economy” in ISTANBUL on 3-5 June 2004,
15
The Istanbul Ministerial meeting recommended for statistics:
1. Promote international convergence of statistical concepts and processes
2. Foster greater international comparability of statistics
3. Promote development of an integrated business statistical register
4. Promote data linking to make better use of existing data and reduce respondent burdenon SMEs
5. Carry out policy-relevant empirical analysesto underpin evidence-based policy making
Andreas Lindner OECD/STD/TAGS 16
The Istanbul Ministerial Declaration contains the following Ministerial
Acknowledgement relating to Statistics:…”invite the OECD to
consider…developing a robust and comparable statistical base on which SME policy can be developed. The Action Plan emerging from the Istanbul Conference Special Workshop on SME Statistics provides a good basis for this work”
Andreas Lindner OECD/STD/TAGS 17
OECD implemented the Istanbul recommendations:
• Creation of the CFE (Centre for Entrepreneurship) to steer the policy-process and liaise with OECD Statistics Directorate
• Creation of a Entrepreneurship Indicators Unit and programme (EIP)
• Staff increase for SBS statistics to further SME data and to build up a Business Demography database and indicators
• Intensified work on Business Register design and statistical system issues
• Building up of an indicators programme, linking SBS and Trade Statistics
Andreas Lindner OECD/STD/TAGS 18
SMEs in OECD countries
• Comprehensive questionnaire sent out to OECD NSO’s as part of the Istanbul Meeting
• Analysis of results, change in data collection, linkage to policy and OECD agendas
• Confirmation that SMEs are not known well enough
• But what could be done since we are talking about > 90% of the business population?
Andreas Lindner OECD/STD/TAGS 19
40
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
20
A “forest” of statistical units• The EU focus on “enterprise” can be considered
as a compromise taken in 1993 to accommodate very different national practices
• The system of international standardisation is incoherent with respect to statistical units
• EU terms need to be translated into world-wide methodology
• The legal dimension needs to be distinguished from the economic dimension
• The economic dimension is likely to become more important
21
OECD survey: business frames used for SME statistics
• Half of countries reported use of combination of different sources, sometimes according to sectors and/or size classes
• Agriculture often the exception to the rule (agr. = from statistical source)
• Very different thresholds across countries
22
Identified limitations of Business Frames for SBS:
• Different updating intervals limit comprehensive coverage
• A specific SME frame is the exception• Confidentiality issues limit availability of data for other
users/producers• General concern about quality and coverage of
demographic data, in particular for deaths• Difficulties were reported as to the proper allocation of
activities to industries• The quality of the Business Frame was generally
considered as appropriate, although improvements are foreseen in many countries with respect to SMEs, change of activity, legal status etc.
23
The traceability issue• Ability to trace change through a unique
identifier code for the local unit/establishment would allow to better capture unit activity and de-registration
• Similarly, the introduction of links between legal units in enterprise groups and groups of companies would enhance identification of changes in size, ownership and location for units involved in events such as mergers and splits
24
The statistical units “mess”
• General impression: the same name does not necessarily mean the same thing in another country. True? Evidence suggests that this is the case.
• Is the enterprise vs. establishment split meaningful? Why not calling it “economic statistical unit”?
• Despite Council Regulations, the situation in the EU seems less harmonized than one might assume
25
Council Regulation 696/93: 8 different statistical units (legal, geographical,
activity criteria)• the Enterprise;• the Institutional Unit; • the Enterprise Group;• the Kind-of-activity Unit (KAU);• the Unit of Homogeneous Production (UHP); • the Local Unit;• the Local Kind-of-Activity Unit (local KAU);• the Local Unit of Homogeneous Production
(local UHP).
41
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
26
UN classification ISIC
• the enterprise;
• enterprise group;
• kind-of-activity unit (KAU);
• local unit;
• establishment;
• homogeneous unit of production.
27
The Canadian statistical structure:
• Enterprise: the highest level of the hierarchy with complete financial statements and information about international links. An enterprise may have one or more companies
• Company: A somewhat homogeneous production structure with balance sheets data. May have one or more establishments.
• Establishment: The most homogeneous production level with economic key data (output, inputs, wages…). May have one or more location.
• Location: A unique physical production unit.
28
A wealth of statistical units• The EU focus on “enterprise” can be considered
as a workable compromise taken in 1993 to accommodate very different national practices
• The system of international standardisation is incoherent with respect to statistical units
• EU terms need to be translated into world-wide methodology
• The legal dimension needs to be distinguished from the economic dimension
• The economic dimension is likely to become more important
29
Collection and compilation strategies matter
• In only two countries, Germany and the US, the NSO is not in charge of official statistics on SMEs.
• Almost all countries have different treatment for core and specific statistics
• Some countries have developed tools to monitor the response burden. The strategy developed is therefore to collect core variables through the integration of census/surveys based data and administrative data
30
Collection and compilation strategies (2)
• In the vast majority of countries the NSO is fully in charge of data collection
• In the remaining countries, the NSO plays an important coordinating role (Germany is the exception where the NSO has “outsourced” SME data collection)
• In the majority of countries, SME core statistics are differentiated from specific SME variables. The typical pattern is a reduced sample for core data as opposed to – often voluntary – thematic surveys
31
Data linkage with administrative sources: access matters
• Very different access patterns for NSOs to individual records could be identified, ranging from full to no access.
• Where access to administrative and other sources is partial, differences in the definition of variables have been commonly seen as a major impediment to the use of administrative sources, different observation units, classification and the absence of a unique identification number have also been mentioned.
• Sometimes technical problems hinder access to administrative sources. Countries generally have identified the main sources they would like to access
42
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
32
Access and linkage with administrative sources
• The picture regarding access of NSOs to administrative SME data is mixed. Country practice differs ranging from full access via partial access to no access:
• Although about 2/3 of responding countries state that NSOs have full access, half of them reported problems in uses or little practical experiences
• Five countries reported only partial access• Japan and Switzerland reported that no access
was granted to NSOs
33
Access and linkage with administrative sources (2)
• In the case of access, but no usage, the main reason were different basic units and absence of links between registers and administrative data
• Similarly, the main impediments to a better use of available data in the two distinct sources were:
• Different definitions of variables• No common identifier• Different classifications and thresholds
OECD SME examples:• In “micro-based” OECD economies the share of SME
employment is very significant (Italy, Spain, Portugal, Australia, Korea, and others (see next slide)
• These countries’ SMEs also generate significant value added in manufacturing (+/- 20%), see slide 2
• The sheer number of micro and small-medium enterprises supports the argument for “bottom-up”support measures instead of “top-down”
• Coherent Size–classes cut-offs facilitate international comparisons: o, 1-4, 5-9, 10-19, 20-49, 50-249, 250+ seem appropriate (see slide 3)
Enterprises with less than 20 employees, manufacturing, 2005 or later
35
SMEs are an important part of employment and generate a significant
% of Value Added
36
Enterprise distribution by size-class in %
37
43
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
3. Linking SBS and Trade
• Another joint OECD-Eurostat project
• OECD works with Non-EU OECD countries to yield more global coverage
• Annual review of progress at the International WPTGS meetings in September
• Steering group set up, comprising OECD (chair), Unites States, Canada, Norway, Israel, Eurostat
38 39
Why linking systems together?The example of business statistics and
trade• Global economies require data on who is trading
and what are the economic characteristics of trade operators
• This question requires establishing a direct relation between foreign trade and industrial statistics.
• But these two statistical domains are based upon different concepts (products versus economic activities) and use different classifications (SITC, HS, ISIC, CPC).
40
Why linking systems together?The example of business statistics and
trade• Central issue of such an analysis is to try to
classify trade operators according to economic enterprise characteristics
• But this depends on the possibility of using or developing common identifiers between the trade register and the business register
• Countries largely differ in their ability to perform such a linking.
41
The present situation (OECD questionnaire result)
The situation in Non-EU countries of OECD can be summarized as follows:
• Six out of eight countries reported having a trade register, only 1 country reported having a formal trade register
• How is the trade register updated? Customs declarations provide the basis. In half of the responses there is a link to the Business Register and data from fiscal authorities is used as well
42
The present situation (cont’d)• Which is/are the unit(s) of reference in the
trade register(s)? Enterprises and establishments constitute together with legal units the reference units used.
• Is there at least one common unit of reference between the business register and customs forms/register? All countries, except one, reported “yes”.
• Is (or can) the basic statistical unit of the business register linked to customs forms/ register of traders? Almost all countries reported that they can link the basic statistical unit to customs forms/trade registers
An example: Who are the Norwegian exporters?
43
44
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
Norway: SME trade is significant
4445
Norway: concentration of exports by ISIC Sectors
Norway: Concentration of exports (% ), by ISIC Sectors, 2003
57
6571
7985
97 99
16
26
39
59
72
9296
38
53
68
80
88
97 99
4855
60
6976
9296
0
10
20
30
40
50
60
70
80
90
100
Top 5 Top 10 Top 20 Top 500 Top 100 Top 500 Top 1000
% o
f tot
al e
xpor
ts
C-EGOthersTotal
46
Norway: Number of enterprises by number of partner country
Number of enterprises according to number of partner countries (exports), in % of total enterprises, 2003
39
169 11
6 6 310
54
179 9
4 3 2 2
68
146 5 3 2 1 2
54
168 8 4 3 2 4
01020304050607080
1 part
ner co
untry
2 part
ner co
untrie
s
3 part
ner co
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s
4-5 part
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ountrie
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6-7 part
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8-10 p
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11-13
partn
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14+ p
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% o
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C-EGOthersTotal
The SBS-Trade linking allows much more:
• Determine export specialization patterns, in particular for SMEs
• Detecting niche markets• Identifying with whom and how many
other countries SMEs are trading• Give eventually policy advice and foster
enabling framework conditions for SMEs based upon this evidence
• …and much more!
47
4. The OECD –Eurostat E(ntrepreneurship) I(ndicators)
P(rogram)• Made possible through policy attention
obtained at the Istanbul Ministerial
• Recognition of “rubbish in, rubbish out”
• Kauffman Foundation approached OECD
• And Danish consortium FORA joined in
• Eurostat and OECD joined forces in 2007
Entrepreneurship Measurement Manual
Boring but essentialCore Definitions
Framework for understanding and developing
Indicators and data specifications
OECD-Eurostat Manual planned for 2008
A good start: the Eurostat-OECD Manual on Business Demography Statistics (2007)
Data collection now underway by OECD & EU
45
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
EIP Definitions • Entrepreneurs: those persons (business owners) who
seek to generate value, through the creation or expansion of economic activity, by identifying and exploiting new products, processes or markets.
• Entrepreneurial activity: the enterprising human action in pursuit of the generation of value, through the creation or expansion of economic activity, by identifying and exploiting new products, processes or markets.
• Entrepreneurship is the phenomenon associated with entrepreneurial activity.
Some Important Principles
Entrepreneur vs. EntrepreneurshipNot just small or young firmsNot all firms are entrepreneurialThey are doing something “new”Some entrepreneurs fail“Value” can be defined in different waysIndicators focus on business entrepreneurship
Definitions and Indicators EIP offers broad definition of entrepreneurshipNo single measure perfectly reflects definition Therefore…..We must distinguish aspects and types of entrepreneurshipEmployers vs. non‐employersMeasures of High Growth and GazellesInnovative firmsExporting firms
For example…………….
Sub‐Dividing Entrepreneurial Firms
Low Growth Replicators
Degree of Innovation
High Growth Replicators
Com
pany
Gro
wth
Low Growth Innovators
High-Growth Replicators
Low Growth Replicators
High-Growth Innovators
The entrepreneurial process
Determinants -> Performance -> Impact
See Eurostat presentation on the EIP subject
54
More information on EIP at OECD: Tim Davis, OECD
[email protected] information on Business Demography at
OECD:Nadim Ahmad, OECD
[email protected] information on SME statistics at OECD:
Benoît [email protected]
55
46
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
5. Business Registers• … will become more and more key for managing
the increasing complex statistical systems in a resource-effective and efficient way…
• Are a pre-condition for finding answers to the SME segment of an economy, which…– Can not be surveyed in its entirety– Is key for job growth– Has no “statistical education” and cannot answer or
does not want answering to statistical questions– Needs to know where they stand in comparison to
competitors and internationally
Business Registers are key for SME data
• …because the SME population is too large for direct surveying (except special sample surveys)
• …SME Entrepreneurs struggle for economic survival and hate any statistical response burden
• …rightly can suspect that much of the requested information exists already “somewhere”
• A coherent BR System could answer most, if not all, of the needed information and should be considered a priority for an efficient and effective national statistics system
57
OECD will host the next meeting of the international Wiesbaden
Group on Business Registers22-24 November 2008
A system of statistical registers
59
Business Registers (the red box) are the backbone of a statistical system
Business registers include:
• Enterprise registers (name, activity, ID..)
• VAT registers
• Income declarations
• Accounts data
• Foreign Trade register
• Farm register
• Patent register, etc. …60
Integrated register design: an example of good practice - The Netherlands(excerpt of the Statistics Netherlands
presentation to the Wiesbaden Group)• Business survey frames specialists design the
national statistical inquiry system
• The Business Register in relation to
changed Data Collection Strategies is currently the “hot topic”
• The Wiesbaden Group on Business Survey Frames analyses and shares progress
61
47
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
Wiesbaden, October 23, 2007 62
Dutch data collection strategy (1)
1. Use of administrative data (if available)2. Self administered reporting
– Electronic questionnaires• Internet• E-mail• Other media (e.g. CD-Rom)
– Paper questionnaires
3. (CA)TI: (Computer assisted) telephone Interviewing
4. (CA)PI: (Computer assisted) personal interviewing
Wiesbaden, October 23, 2007 63
Dutch data collection strategy (2)Differentiation according strata by size and complexity of
enterprises
1. Large and complex enterprises (1%): tailor-made and co-ordinated data collection
2. Small enterprises (90%): use of administrative data. Data collection by survey should be avoided
3. Medium sized enterprises (9%): mixed mode; optimal combination of the use of administrative data and data collection by survey
4. “Holiday period” for surveyed entity!
Wiesbaden, October 23, 2007 64
Dutch data collection modes
• Joined data collection in co-operation with other data collecting organisations: once questioned, multiple used.-> negotiations on definitions (units and variables)(e.g. chambers of commerce, business associations)
• New data collection techniques and procedures -> e.g. XBRL (extensible business reporting language)
Wiesbaden, October 23, 2007 65
Dutch register developments
• End nineties: introduction of the single administrative business register, initially operated as a unit reference system for some administrative systems (tax, chambers of commerce, statistics, social security)
• In 2008 the introduction of the new trade register (NHR) as the single exhaustive administrative business register. The authentic variables (e.g. identification number) must be used (by law) in all official registers.
• Statistics Netherlands has full and free access to registers containing data useful for statistical purposes (regulated in the Dutch law on statistics)
Could we learn from this experience ?
• Yes. Integration and resource-efficiency, coupled with a lowest possible burden on respondents are the new Dutch model
• …but such an approach may be more difficult the bigger the country
• …and also raise legal/confidentiality issues as to the full access of the NSO in other countries
66
Another example of good practice: Germany
(excerpt of Germanys FSO presentation to the Wiesbaden
Group)• Conception phase precedes data collection
• Analysis precedes dissemination
• Metadata is an integral part of the whole process
• The Business Register is a coherent system of business registers
• …capable to link multi-sources and
• …capable of allowing multi-uses depending on the questions to be answered
48
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
68
The New German Business Register in the statistical production process
disseminationof results
conceptionphase
datacollection
dataprocessing analysis
meta data
business register
69
survey 1?
?
?
survey 3
survey 5
Present situation of business statistics
survey 2 survey 4
survey 6
The new German maxim for business statistics
administrative data 1
administrative data 2
administrative data 3
surv
ey 1
surv
ey 2
survey 3 survey 5 survey 6survey 4
The statistical business register as an instrument for data combination
data available in the statistical office
administrative datain the businessregister
survey data of the
statistical office
statistical business register
core ofbusiness register
register unit
1
3
2
administrative data already
used by the statistical
office
register informationabout surveyparticipation
5
4
Data combination of a register unit
- z.B. exports
- e.g. leasing
- e.g. investment
- survey ID
- e.g. turnover
- e.g. legal form
- e.g. tax – ID
-e.g. labour administrationID
- e.g. labour
administration employees
- e.g. VAT
- e.g. turnover
surv
eyda
taof
the
stat
istica
l offi
ce
43
regi
ster
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5
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e
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ster
2
adm
inist
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e
data
in th
ebu
sines
s regi
ster
- e.g. turnover fromsurvey
- business register ID
administrative data in the
business register
statistical business register
core ofbusiness register
administrative data already
used by the statistical office
register information about
survey participation
data available in the statistical office
( … )
administrativedata
administrativedata
accountingdata of
enterprises
data of chambers of commerce
survey data of the
statisticel office
data of marketresearchinstitutes
data of scientificinstitutions
enterprise 1enterprise 2
local unit
administrativedata
49
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
Contact information:
• Business Registers and Statistical System:
• Business and Trade Statistics:
• SBS and Trade Linkages:
• Globalisation Indicators:
Andreas Lindner [email protected]
• Trade Indicators & Business and Trade Statistics:
Florian Eberth [email protected]
74
Thank you very much for your attention!
Questions?
75
How globalisation affects statistics
A postscriptum
77
Postscriptum The impact of globalisation on statistical
units/definitions• One of the most visible manifestations of a more
globalised world is trade. The remarkable growth of trade flows is undisputable, in particular for emerging major players (see next slide)
• But, as can be seen from the remarks later under “Linking systems together…”, even trade and business statistics are rather separate entities needing reconciliation/standardisation…
78
Relative growth of exports of goodsGrowth over the period 1996-2005,
OECD total = 1
79
The impact of globalisation on statistical units/definitions (cont’d)
• The real problem is that statistical systems have been “overtaken” by the speed of globalisation obliging national reporting systems and structures to align to this paradigm shift
• In other words, “national” concepts need to incorporate the international dimension to adequately reflect the reality of today’s production process
50
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
80
Statistical units/definitions were more or less in tune
Administrative & legal system
Statistical System Economic System
81
…but they went out of tune
Administrative & legal system
Statistical System Economic System
82
As a consequence, both Administrative & Legal and Statistical Systems…
• Need to adapt to (=better integrate) the new reality
• …where the production process is international,
• …with more complex structures/processes exist (outsourcing, flatter vertical integration…)
• …and where control and ownership become more dominant features than legal forms
83
A new statistical unit is needed: the Enterprise Group (EG)
• EU definition: – An enterprise group is an association of enterprises
bound together by legal and/or financial links. A group of enterprises can have more than one decision-making centre, especially for policy on production, sales and profit. It may centralize certain aspects of financial management and taxation.
– It constitutes an economic entity which is empowered to make choices, particularly concerning the units which it comprises.
– EUROSTAT is actively working on the EG Register
84
EGs and EG-sub-groups: a scenario based upon real data
• The economic picture of a country crucially depends on the degree of inclusion or exclusion of statistical units– France has launched in 2005 a major high-
level project of re-designing its enterprise statistics system
– This project was carried out by the CNIS, the National Council for Statistical Information
– In April 2007, the final report was presented
85
EGs and EG-sub-groups: a scenario based upon real data
• Some conclusions:• INSEE, the French NSO, will use this report for defining
procedures to incorporate enterprise groups in the collection system of economic statistics
• An affiliate is no longer considered as an equivalent ot an enterprise unit
• An affiliate can not be considered as enterprise (lack of autonomy)
• A MNE has to be considered globally (an MNE and its branches think and define themselves globally)
• Many economic actors consider the EG (or sub-group of it) as the relevant unit of analysis
• Users want to analyse market realities, not hierarchical structures within EGs
51
Andreas LindnerOECD/STD/TAGS
Cairo SME Conference and Workshop January 2008
86
EGs and EG-sub-groups: a scenario based upon real data
• The CNIS recommends the addition of a Global Statistical Unit (GSU) for EGs
• It is at this level where the strategic direction and decisions on affiliates are taken (mergers, acquisitions, investment, relocations etc,)
• This GSU is independent of national frontiers• This logic of definition leads to a statistical
representation of the national economy as the sum of all French GSUs world-wide
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EGs and EG-sub-groups: a scenario based upon real data
• This approach is innovative in macro economic statistics since it goes systematically beyond the national territory (to our knowledge, only the BEA in the US has a similar approach)
• Most “traditional” economic statistics take the national approach in terms of national territory
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Simulation done by the CNIS in France
France World
(A)Foreign-controlled
Companies2 M
(B)LegallyIndependantCompanies
6,3 M
(C)FrenchGroups2,1 M
(D) International French Groups: Df = 4,2 M Dw = 3,6 M
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Simulation done by the CNIS in France
• Truncated “Global Statistical Unit (GSU)”:
A+B+C+Df = 14,6 million salaried employees• French truncated GSU
B+C+Df = 12,6 million salaried employees
90
Simulation done by the CNIS in France
• “World-wide” French GSU:
B + C + Df + Dw = 16,2 million salaried employed
• A world-wide “French” measurement, including the activities abroad from French EGs would yield 16,2 million employees, compared to 14,6 million in the “territorial”view; a plus of 1,6 millions
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International production processes necessitate a fresh look at international
trading and contracting
• More an better data needed on re-exports• More and better data needed on further
processing and merchanting• FATS => inward FATS alongside SBS needed,
idem for a reduced set of outward FATS data (more difficult)
• MNEs => consistent definition of EGs needed and integration into registers– Profiling of domestic operations needed– Difficulty: consolidation/deconsolidation of financial
results of EGs
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