Europe Economic Data: The problems
In this paper the quality and the reliability of official national income accounting data is investigated for 41 countries across Europe.
Although for some countries the quality of national income statistics is constrained by the capacity and resources devoted to national statistics offices to follow international best practice, this is a less serious issue in Europe than in the Americas, Asia and in Africa. There is a global standard set by the United Nations for measuring national income, but poorer countries generally report lower quality statistics because of the reduced resources available to national statistical offices to implement best practice and together with the paucity of comprehensive household and business surveys available. This is a general problem across the world. Given the disparity in per capita income across Europe, some differences in the quality of economic statistics are inevitable. In 2013, GDP per capita across the continent in PPP terms (2011 relative prices) ranged from US$91,048 in Luxembourg to US$4,692 in Moldova with a median income of US$28,897. 
These problems can all be rectified by increasing the resources available for national income accounting exercises, but there are other significant data problems which distort the measurement of the relative size of national economies and the validity of inter-country comparisons of living standards and growth rates across Europe. The most significant specific data issues which impact of GDP estimation in Europe are:
Real growth in the activity of an economy is estimated by comparing GDP at current and at constant prices. Constant price estimates use the price relatives of a particular year, known as a base year or benchmark year, to weight the volume components of production. But since the structure of production and relative prices over time are dynamic, the pattern of relative prices and the industries surveyed in the base year become less relevant over time. The time elapsed between current estimates of GDP and the base year is of crucial importance to the accuracy of national income data. According to Jerven (2013) failures to regularly update a base year in which the structure and relative prices of an economy are monitored can lead to serious bias in the estimation of GDP. He refers to the case of Ghana when estimated GDP was uplifted by 60% overnight as a result of an update in the base year from 1993 to 2006. In 2014, the Nigerian National Bureau of Statistics released new estimates of GDP showing a rise of 59.5% after the economy’s base year was updated from 1990 to 2010. One of the reasons for these large uplifts is that better estimates of some economic activities such as telecommunications, tourism and financial services were made by statisticians while some previously unrecorded activities from the informal sector were included.
The best current international practice is to update a country’s base year every five years. However, most European countries (with the exception of Macedonia and Montenegro) now adopt the practice of chaining where price relatives are updated every year (See Table 1). Although chaining allows continual updates to be made to the structure of production and consumption, it requires considerable expenditure on resources by statistical offices.
Reassessing GDP in Europe
In this section, we report on an exercise to estimate the impact on estimated GDP if Macedonia and Montenegro updated their base years to 2013. In order to produce data that is not affected by exchange rate fluctuations we use World Bank GDP data which is estimated at (Purchasing Power Parity) PPP data in constant international US dollars order to make inter country comparisons of European countries.
The methodology used to estimate what would happen if the two countries updated their base years is crude and involves applying an estimated constant cumulative annual rate of growth to the years between the last reported base year and 2013 on top of the underlying rate of real growth. The longer the period between 2013 and the last base year and the higher the assumed cumulative growth rate, the higher the uplift that would be expected from rebasing. Analysing the existing economic data after carrying out this procedure shows that aggregate GDP in Montenegro and Macedonia may be underestimated by around 12%.
Two assumptions were necessary to carry this exercise out:
Inconsistent Base Year Data: The reported base years or the practice of chaining from the World Bank, and the IMF are shown by country in Table 1. Given the number of differences between these 3sources in reported base years or the practice of chaining, the source(s) generally used to calculate how many years the latest base year was out-of-date or if chaining was relevant was highlighted in bold. Furthermore, the number of years the two countries base year are calculated as the difference between the last reported base year and 2013.
Table 1: Reported Base Years by Source
Reported Base Year
Years out of date
Bosnia and Herzegovina
Sources: World Bank, IMF
Cumulative annual rate: The assumed annual rate that would act as a proxy by replicating over time the effect of the uplift created in one year by rebasing GDP was 1.6% per annum in Europe. This was based on an econometric model from a sample of 39 GDP national rebasing exercises carried out across the world excluding Africa which uses a higher rate of 2.52%.
Results: In 2013, the two countries analysed had a combined GDP of US$34.6 billion. The uplift applied to Montenegro was (7 x 1.6%) 11.2% and that applied to Macedonia was (8 x 1.6%) 12.8% raising the estimated GDP for both countries if rebasing occurs to US$38.9 billion.
The harmonized System of National Accounts (SNA) was created by the international community to facilitate the comparability of economic statistics and standards. Since 1953 there have been three revisions to recommended SNA standards, in 1968, 1993, and in 2008, all approved by the United Nations Statistical Commission. SNA 2008 has already been implemented in Australia in 2009, Canada in 2012, in the USA in 2013 and New Zealand in 2014, causing problems in making international comparisons between this group of countries and those using SNA 1993, or earlier versions.
In Europe, three countries use SNA 1993, which only aids global comparisons between them and other countries that still use this outdated standard. However, among the 28 European Union member countries, a separate system of national accounting is used, the European Standard of Accounts (ESA). This is broadly based on the SNA system and so far there have been two European standards ESA95, (based on SNA 1993) and ESA 2010 (based on SNA 2008). Two countries Russia and Ukraine have implemented SNA 2008. See Table 2.
Table 2: Accounting Standards by Country
GDP US$PPP 2013
European National Accounting Standard
GDP US$PPP 2013
UN National Accounting Standard
Source: World Bank, IMF, Eurostat
In October 2014, the first set of revised GDP estimates based on ESA 2010 were published for the 28 European Union countries which lead to large estimated shifts in national income levels in most member states. The main methodological differences between SNA 1993 and SNA 2008, which have been replicated between ESA 95 and ESA 2010, include the treatment of government accounts, capital expenditure and intellectual property. ESA 2010 has also incorporated a number of improvements in statistical analysis including better means of monitoring and measuring the informal sector and some illegal activities. The implementation of this change means that whereas the national accounts of the 28 European Union countries are broadly comparable between themselves and with the USA, Canada, Australia and New Zealand, comparisons are less reliable for the other European countries listed above. The longer it takes for these other European countries to adopt the new SNA 2008 standard the less reliable will be economic comparisons between themselves and other countries in Europe and the rest of the developed world.
The differences between GDP measured in accordance with SNA 1993 and SNA 2008 are significant. The adoption by the US of most of the accounting guidelines of SNA 2008 led to a comprehensive revision of its national income and product accounts. In the US revised estimates of GDP and other major macroeconomic aggregates made by the Bureau of Economic Analysis (BEA) produced a 3.6% upward revision of GDP in 2012. In Europe, the implementation of the methodological changes in ESA 2010 produced a rise in GDP in 2010 of 2.3%.
The main methodological changes brought about by the adoption of the new European standard are shown in Table 3 although it is noteworthy that most of the size of the GDP revision, just over eighty percent of it, was due to the decision to capitalise research and development expenditures.
Table 3: Effects of major ESA 2010 changes on EU GDP 2014
Source of Revision to GDP
Percent of EU 28 GDP 2010
Capitalisation of research and development
Capitalisation of military weapon systems
Inexpensive tools for common operators
Government public/private sector delineation
Employers pension schemes
Other Methodological Changes
Source: Eurostat (2015)
The capitalisation of expenditures on weapons systems revised EU‑28 GDP by 0.17% respectively, while the revised treatment of small tools, such as saws, spades, knives, axes, hammers and other hand tools, where ESA 2010 eliminated the monetary threshold for the purchase of these items to be recognised as capital expenditure, accounted for a revision of 0.07%. The change to the sector classification of government and the recording of employers’ pension schemes revised GDP up by 0.06% each. At the national level the largest impacts from methodological changes from the implementation of ESA 2010 GDP levels were registered for Sweden (4.4%) and Finland (4.0%) while the smallest impact was registered in Bulgaria (0.4%). The results of the application of the methodological changes arising from the implementation of ESA 2010 for each country is shown in Table 4.
The SNA 2008 standard recommends a number of methods and improved statistical sampling to estimate the informal economy which have been replicated in Europe by the implementation of ESA 2010 which also produced a number statistical improvements which led to a further upward estimate of EU-28 by an additional 1.4% in 2010. These included making estimates of illegal activities and in total raised total GDP in the 28 countries by 1.4%. The total EU28 uplift as a result of both the methodological and statistical improvements arising from the application of ESA 2010 was 3.7%. The data from Eurostat also provides a country-by-country breakdown of the impact of the implementation of ESA 2010 broken down by methodical changes and statistical improvements. At the national level the largest impacts from statistical improvements on 2010 GDP levels were registered for the Netherlands (5.9%) while a negative impact was registered in Luxembourg (-1.4%).
The assumed revision estimate that would act as a proxy if the non-EU countries implemented ESA 2010 was 3.7%. Applying the rebasing, the adjustments for the implementation of ESA 2010 and the implied adjustment for ESA 1995 to ESA 2010 and SNA 1993 to SNA 2008 produces a rise in estimated European GDP in 2013 of US$827 billion or 3.6% measured in PPP dollars.
The full results of all adjustments are shown in Table 4.
Table 4: European Revision of GDP by % Size of Uplift
GDP PPP 2013 Actual (US$ million)
ESA 2010 % Uplift
Total Uplift %
GDP PPP 2013 Revised (US$ million)
Bosnia & Herzegovina
However, the estimates of the shadow economy and illegal activities undertaken as a result of the application of ESA 2010 across the European Union are dwarfed when a comparison is made with the data presented by Freidrich Schneider, a leading expert on the size and importance of grey economic activity across the world. Based on his estimates for 2007 which covers 38 of the European countries investigated in this analysis, Table 5 shows that according to Schneider (2013) the relative size of the shadow economies across Europe ranged from 8.1% of GDP in Switzerland to 46.8% in the Ukraine. The size of shadow economy across the 28 European Union economies ranged from 9.4% in Luxembourg to 32.7% in Bulgaria.  In contrast, the statistical improvements enacted by ESA 2010 ranged from -1.2% in Latvia to 5.9% in the Netherlands. Generally, although the estimates were smaller in scale there was little correspondence with Schneider’s estimates. The implementation of ESA 2010 which implemented a number of statistical improvements including making estimates of illegal activities raised total GDP in the 28 countries by only 1.4%.
Table 5: The Shadow Economy % of National Income
% of National Income
41% - 50%
31% - 40%
30 % and Below
Source: Schneider and Williams (2013).
*Shadow Economy data for Moldova only available for year 2006.
Europe is split between countries using the same, albeit outdated, national accounting methodology SNA1993 or ESA 1995, and those who use the newer ESA 2010, which is based on SNA 2008. This makes it relatively sound to compare the European Union countries with OECD countries such as the US, Canada, Australia and New Zealand, but it will make comparisons between non-EU countries and the OECD problematic as SNA 2008 is progressively adopted. The information already available on estimates of the size of the shadow economy economies (See Table 5) suggests that significant additional upward revisions may be needed despite the adoption of ESA 2010 and SNA 2008 since the new standard only goes so far in devoting resources to measuring informal and illicit activities. In Europe, however, Tables 1 and 4 show that unlike many other parts of the world the extensive use of chaining means that out of date base years is less of a problem. This means that there would be only one effective change in the rankings of countries in Europe by GDP size as a result of rebasing with Macedonia moving up by a few places.
Eurostat (2015), “Annual national accounts - how ESA 2010 has changed the main GDP aggregates”
Jerven, M. (2013) Poor Numbers: How we are misled by African Development Statistics and what to do about it, Cornell.
Schneider, F and Williams, C. C., (2013) The Shadow Economy, Institute of Economic Affairs, Hobart Paper 172.
 A number of countries were excluded from this study as no GDP PPP data was available for the year 2013: Andorra, Channel Islands, Faeroe Islands, Greenland, Isle of Man, Liechtenstein, Monaco, and San Marino.
 The United Nations System of National Accounts (SNA): http://unstats.un.org/unsd/nationalaccount/
 GDP per capita figures at PPP are from the World Bank, World Development Indicators http://data.worldbank.org/products/wdi
 This means that there would be no effective change in the rankings of countries in Europe by GDP size as a result of rebasing.
 No Shadow Economy data was available for the following countries: Kosovo, Montenegro, and Serbia.