World Economics Data

Speed Read
  • The quality of GDP measurements are limited by the resources available to national statistics offices, this is correlated with an economy’s standard of living and other factors such as the degree of political interference.  
  • The use of GDP data for international comparisons is limited by a failure to rebase national accounts every five years resulting in underestimates of GDP by 20.9% in Africa, by 5.3% in the Americas and 1.1% in Asia.
  • International GDP comparisons are constrained since most countries use outdated national income standards which fail to measure informal and illicit economic activities estimated at between 8-30% of GDP in rich countries and 25-40% in emerging markets.
  • Other problems including the difficulty of measuring the output of government and the financial sectors means that for all but the richest countries, GDP data should not be trusted.

What is GDP

Gross Domestic Product (GDP) is an artificial construct which expresses, in a single number, the size of an economy over a particular accounting period, usually a quarter or a year.  The diverse economic activities undertaken in the period are expressed in a common unit, nominal value added, which has to be estimated in money for each activity and finally totalled. This process, known as national income accounting, allows estimates of the total gross nominal value added by the production of all goods and services within a national border, taking no account of the depreciation of the assets used to produce it nor of foreign production which may be consumed domestically.  

GDP is an important economic concept and is used to measure economic growth or contraction (yearly changes in GDP), labour productivity (GDP/ total labour hours) or living standards (GDP/capita). There are, however, a number of theoretical and methodological problems involved in its calculation which require resolution before useful comparisons can be made between economies and over time. Many GDP estimates published by national statistical offices and in international databases are compromises lying between best practice and available resources which limits the utility and accuracy of the data.

First, since GDP is measured in nominal values at market prices it is necessary to make adjustments when the measuring yardstick is volatile because of inflation/deflation in the absolute price level or the rates of exchange between different currencies. The calculation of real GDP, or GDP in constant prices, is an adjustment made to allow meaningful comparisons between income GDP over time, while estimates of GDP at Purchasing Power Parity (PPP) exchange rates permits comparisons between countries. Both adjustments require the calculation of indexes from prices of baskets of goods based on sampling techniques of varying complexity which are subject to methodological and statistical errors.

Second, the production boundary, or the limit between the human activities that are considered to be economic and included in GDP and those that are purely social, should be defined consistently. But compromises are inevitable and the limits of the production boundary differ between countries and over time.  Ideally, the production boundary contains all output with an opportunity cost valued at market prices, but this is difficult to measure exactly. For some activities such as government produced goods and services or the products of the financial services, education or health sector provision is either free, so a value needs to be imputed, or the value added by the activity is complex. Another question is whether or not, or the extent to which, informal, non-traded and illegal transactions are included within the production boundary.


Problems with GDP

GDP estimates are used to measure and compare economic performance and relative size between countries and over time in terms of growth, productivity, potential and living standards. The quality of GDP statistics published by national statistical offices and those contained in international databases such as those published by the World Bank are impacted by a number of general problems which limit their effective use. These are:

  1. The limited resources of national statistics offices.
  2. The failure to update base years regularly.
  3. The use of outdated standards of national income accounting.
  4. The degree to which the shadow economy (including informal activities) is measured.
  5. The measurement of the government and financial sectors.
  6. Government deliberate interference.


1. The Limited Resources of National Statistics Offices

Poorer countries generally report lower quality statistics because of the lack of resources available to national statistical offices. This problem is particularly serious across Africa where of 53 countries analysed, 36 had a GDP per capita measured in PPP terms of below US$5,000 while 22, had national income falling below US$2,000 per capita. This compares with typical developed world figures of approximately US$50,000 per capita.

In Africa, this problem of statistical capacity has been compounded by a loss or distortion of data due to wars, political instability and corruption, while the structure of economies and the nature of property rights means that there exist large unrecorded informal sectors. 

According to an extensive study of African economic statistics by Morten Jerven reported in his book Poor Numbers:


“…in most African states the database for aggregating measures of income and growth are weak. For large shares of the economy we have little or no information and the figures involve a great deal of guesswork.” [1]

As a result there are also serious discrepancies in the data on African economies published in international economic databases. Jerven (2013) notes that the UN reported annual national accounts for 45 sub-Saharan African countries between1991-2004, but it had only received data for less than half of  the 1,410 observations, but for 15 countries no data had been received at all. The World Bank also provides data for African countries in constant and current prices even when no figures, accurate or otherwise, have been provided by national statistical offices to undertake the necessary adjustments for inflation. The missing figures are supplied through “a method for filing the data gap”[2] a procedure which Jerven (2013) describes as “unclear.”  The result is that different international databases give different rankings about the size, growth rates and living standards of African economies. The same problem is found in many lower income countries in Asia, South and Central America and in the Middle-East.



     2. Failure to update base years

The longer the distance in years between the current year and the base year chosen to measure changes in output, the less accurate estimates of GDP are. Since inflation or deflation in the price level means that the yardstick used to measure GDP – money – varies over time. In order to distinguish real growth in an economic activity from nominal growth at market prices a GDP deflator, an index of all prices in an economy, is estimated and used to compare GDP at current and at constant prices. A constant price index benchmarks relative prices at a base year to value the components of national output, but since the structure of production and relative prices over time are dynamic, base year surveys become less relevant over time. In one of its accounting standards SNA 1993 (see Section 3) the United Nations recommends that national accounts are rebased every five years and that chained volume indexes are calculated. But many poorer countries with limited resources rebase less frequently than every five years because of the time and cost involved in the process. (see Section 1). Richer countries including members of the OECD have adopted the practice of chaining where price relatives are updated every year. Although chaining allows continual updates to be made to the structure of production and consumption, it requires considerable expenditure on resources by statistical offices.


The longest gaps between base years are found again in Africa. Jerven (2013) refers to the case of Ghana where 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. There are several reasons for the changes that accompany rebasing in developing countries. Better estimates of the weight of some economic activities in total output that have grown rapidly such as telecommunications, tourism and financial services and those that have declined were made by statisticians. In some cases rebasing is often accompanied by the employment of the latest national accounting standards (See below) while previously unrecorded activities from the informal sector are included in GDP.



3. The System of National Accounts

Many countries are using outdated national accounting standards. National income measurement is governed by a global standard: the United Nations System of National Accounts (SNA)[3] which is the internationally agreed standard set of recommendations on how to compile and measure economic activity.[4] It was established by the United Nations Statistical Commission (UNSC) to facilitate international comparability of economic statistics.[5] The first UNSC guide to the SNA was published in 1953. There have since been three revisions to recommend SNA standards: SNA 1968, SNA 1993, and SNA 2008, all approved by UNSC.[6] If two countries adopt the same SNA standards, this means that their economic statistics are broadly comparable in terms of the definitions used and the accounting methodologies applied. But the longer it takes a country to update its SNA the less reliable the data becomes for economic comparisons to a country with a more recent SNA version.[7] Furthermore, SNA recommendations are advisory not mandatory and even if two countries are using SNA 1993 as their standard of national accounting, for example, some recommended standards may be used by Country A, but not by Country B.

The SNA, like its European counterpart, which is called the “European System of National and Regional Accounting” (ESA), contains a huge body of mutually consistent concepts, definitions and classifications for measuring economic activity and several economic phenomena. In practice, it is also used as a base of reference for the production of sectoral and territorial economic statistics.[8] These standards are not mandatory and the adoption by a country of a standard such as those mentioned above does not imply that all of the recommendations are implemented.

The UNSC formally adopted SNA 2008 in 2009 and some of the recommendations of the new standard have been implemented by some countries. Significant differences between SNA 1993 and SNA 2008 include the treatment of government accounts, capital expenditure, intellectual property and the measurement of the informal sector and illegal activities, areas which are particularly important in developing and emerging-market countries.[9]


4. The Informal Economy

All economies have many unrecorded economic transactions which bias downwards official estimates of GDP. The central issue of national income measurement is deciding which economic activities should and can be included in the official accounts or where the production frontier lies.[10] For example, housework services by women, non-paid self-sufficient peasant farming activities, drug running, prostitution, illicit gambling are all productive activities, but they are difficult to account for and have generally been excluded from GDP estimates. The SNA 1993 national income accounting standard recognised the importance of the informal economy particularly in developing economies defining it as belonging to the household sector, but there was no explicit methodological recommendation as to how to measure its size. The SNA 2008 standard goes a lot further in defining the informal sector and in placing the onus of measurement on national statistical offices adopting the standard to include estimates within the framework of national income accounting. 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.


Given that there are estimates that the informal economy constitutes a high proportion of national income, the efforts made so far to include the informal economy in GDP estimates are inadequate so official national income statistics are often misleading.[11] For example, the implementation of ESA 2010 in Europe to incorporate the informal economy raised the GDP of the EU-28 countries by an additional 1.4% in 2010.  However, informal economic activities have been estimated to vary between less than 8 per cent of national income and over 30 per cent of national income in OECD countries. In less developed countries, the informal sector constitutes typically between 25 and 40 per cent of national income and represents up to 70 per cent of non-agricultural employment. In such countries, informal activity often arises because of the inadequacies of legal systems when it comes to formalising business registration rather than as a result of deliberate evasion activity.[12]


Moreover, there is still no consensus on how to define the informal economy.[13] According to Schneider and Williams (2013) a broad definition is: those economic activities and the income derived from them that circumvent or otherwise avoid government regulation, taxation, or observation.[14] Nevertheless, measurement of these activities are notoriously difficult as they are deliberately hidden from official transactions. Survey measures, for instance, tend to understate the size of the informal economy because even in the most careful circumstances, people do not like to admit shadow work. Also, the several approaches used in practice tend to give different results.


5. Measurement of Government and Financial Sectors

Government Sector:  Most of the outputs of the public sector are not sold at market prices which causes a measurement problem. Traditionally, only inputs to the government sector such as salaries were reflected in the National Accounts. This treatment meant that the output of the government sector in terms of defence, welfare, education, and health etc. was measured by final expenditure which by definition imposed zero productivity growth for the sector. In reality while some government expenditure may add to the total value added in the economy such as health care and education others such as some forms of welfare expenditure and expenditure on unnecessary regulation may actually reduce value added and GDP. The input equals output method cannot distinguish among these effects and simply assumes that a bureaucrats salary has the same dollar impact on GDP as an equivalent salary of a doctor, for example.

There have been a number of measurement improvements undertaken over time within the OECD-Eurostat countries to move towards output based measures of government output, particularly as a result of ESA 1995, but these are not universal and progress has been made unevenly on a country by country basis. Most other regions use compensation based measures of government output making intercountry comparisons almost meaningless. This problem is recognised, if not solved by the World Bank’s rolling Intercountry Comparison Programme (ICP):

“The input approach was used in 2005 based on government salaries for a number of occupations.  Because of quality differences, productivity adjustments were made in Asia, Africa, and Western Asia.  Regional linking factors were computed from compensation data from 75 countries representing all regions including Eurostat-OECD. The linking factors were computed without adjustments for productivity and independently of the regional PPPs.”[15]

This is potentially a very serious source of bias in the estimation of GDP given the relative size of the public sector across the world. For example, data from the World Bank shows that total government expenditure varies significantly across the world, but even when it is low it remains a significant proportion of national income. Government final consumption expenditure as a proportion of GDP in 2013 was 15.6% in Argentina and 26.8% in Denmark.

Financial Sector: The global financial crisis raised some profound questions about how the output of the financial sector is counted in GDP.[16] There is little clarity about the services that banks provide to customers, much less whether statisticians are correctly measuring those services.[17] As currently measured, however, it seems likely that the value of financial intermediation services is significantly overstated in the national accounts.[18]  Fee revenues are used by statisticians to measure output but relatively few financial services involve direct fees or commissions.[19] For example, a large proportion of the profits that banks earn as a compensation for risk-bearing – the spread between loan and deposit rates on their loan book – are accounted for as output by the banking/financial sector.[20] But bearing risk is not, in itself, a productive activity, although the management of risk is.  However, the current accounting is unclear about the distinction between risk-bearing and risk management. The outcome is a potentially significant overestimation of GDP of the financial sector. According to Andrew Haldane and co-authors, the statistical mirage affects all countries’ GDP. One study of the United States concludes:

“Making conservative assumptions, we show that the current official method overestimates the service output of the commercial banking industry by at least 21% (amounting to $116.8 billion in 2007:Q4 for example) and GDP by 0.3% ($52.9 billion in 2007:Q4 for example) between 1997 and 2007.”[21]

Another study suggests that for the Eurozone, adjusting for banks’ risk-taking would reduce the measured output of the financial sector by 25-40 percent. If the same factor were applied in the United Kingdom, the measured contribution of the financial sector would have been 6-7.5 percent of GDP in 2008, rather than 9 percent.[22]


6. Government Deliberate Interference
Governments can either manipulate GDP data directly or through the calculation of price indexes such as the GDP deflator. Governments in countries undergoing severe inflation have a long history of hiding the true extent of their inflationary problems which are often reflected not in official data, but in their inability to maintain a stable domestic currency.[23]  So called “troubled currencies” are associated with elevated rates of inflation, and in some extreme cases, hyperinflation. In many cases, governments fabricate inflation statistics to hide their economic problems. In the extreme, countries simply stop reporting inflation data.  As Professor Steve Hanke puts it “official economic data from countries with troubled currencies often amount to nothing more than ‘lying statistics’ and should be treated as such.”24] A current snapshot is shown in the table below, illustrating the gap between black-market exchange-rate data for three “troubled” currencies and the implied inflation rates for each country.

One example of political intervention in the production of inflation statistics has been witnessed in Argentina. In 2007, the Argentine administration decided to interfere with the calculation of the official Consumer Price Inflation Index (CPI) estimated by the National Statistics Institute (INDEC) and a few months later, the wholesale price index (WPI) was also modified, as well as the official Household and Employment, Manufacturing Survey. This has had a positive upwards bias on GDP and there have been accumulating gaps between the official estimation of inflation and alternative ones.
Argentine GDP are estimated using volume index indicators and a base year of 1993, not by deflating the value of production or value added at current prices by chaining. The extent to which the political intervention has biased GDP upwards has been recorded by Coremberg (2013) who applied the standard SNA 1993 methodology to the main basic series that constitute GDP in Argentina to produce a reproduced ARKLEMS[25] series from 1993 to 2012 to compare against official GDP. The main result of this procedure was a series that replicates almost exactly the official Argentine GDP growth from 1993 to 2007. After that year, however, an important gap appears. This gap increases over time as is shown in the Figure below.

Official GDP shows a positive gap of 12.2% in 2012 with respect to reproduced GDP. During the period of political intervention in the production of official statistics, reproduced Argentina GDP grew by 15.9% between 2007 and 2012 (3% annually), a fall in the rate of growth of GDP with respect to the earlier period 2002-2007 of 47% (8.1% annual rate). However, after intervention official figures show a higher, almost doubled rate of GDP growth between the years 2007-2012: 30% (5.3% annual rate).

Another problem is government corruption which can also infect all parts of an economy and its accurate measurement in systematic ways.[26] Often a direct result of the government’s concentration of economic or political power, corruption manifests itself in many forms such as bribery, extortion, nepotism, cronyism, patronage, embezzlement, and graft. For example, excessive and redundant government regulations provide opportunities for bribery or graft. In addition, government regulations or restrictions in one area may create informal markets in another. As a result, corruption and informal economy are positively correlated.[27] That is, countries with more corruption and bribery have larger informal economies. However, the relationship between corruption and informal economy appears to rely on the national income levels as well as the effectiveness of the legal system.[28]



The quality of economic data across much of the world is poor. The resources available to national statistics offices is limited, many countries use outdated national income standards and base years need updating in many countries and the shadow economy is largely uncounted. The result of all these deficiencies means that ranking countries by GDP and GDP per capita outside of the OECD is almost meaningless. Accurate and transparent statistics are essential indicators of economic potential. The issue of data quality needs to be urgently addressed to provide investors with more accurate guidelines. 

According to Jerven (2013) to improve matters:

“…a change in the structure of funding for statistical offices is needed. We need not only more funding, but funding that is geared towards reliable, frequently disseminated surveys.”

World Economics has made an attempt to assess the extent to which GDP may be underestimated by just using out of date base years. The methodology used 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 African GDP may be underestimated by 20.9%, GDP in the Americas by 5.3% and by 1.1% in Asia.

The differences between GDP measured in accordance with SNA 1993 and SNA 2008 are also 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%. Unfortunately, the use of SNA 2008 is limited to developed countries which limits international comparisons since most developing countries are using SNA 1993 while a few still use SNA 1968.

Given all the problems discussed comprehensive business surveys can provide more accurate estimates of the direction and speed of economic activity than official GDP estimates which are several months out of date and subject to revision. At present World Economics produces regular surveys of business conditions in many countries across the world including emerging markets and the developed world. Indicators of the speed and direction of the underlying economies are available from the Sales Managers’ Indexes. The Indexes which are published monthly provide data relating to the current month on the World Economics website. In the USA, a country with higher quality GDP statistics, these measures of private sector business conditions correlate closely with GDP. In the case of China, where official data is widely mistrusted, the SMI data suggests that actual GDP growth may be half of that published by the authorities.

[1]  Jerven (2013).p3.

[2] Jerven(2013) p22

[3] See Jerven (2013:9-10).

[6] See Jerven (2013:9-10).

[10] See Jerven (2013:12-13).

[11] See Schneider and Williams (2013:14).

[12] See Schneider and Williams (2013:21-22).

[14] These include illegal activities such as drug dealing and manufacturing, prostitution, gambling, smuggling, fraud, human trafficking and weapon trafficking. Also, activities within family household such as tax evasion through unreported income from self-employment, wages, and assets from unreported work related to legal services and goods.

[16] Coyle (2014:98).

[18] In the United Kingdom’s national accounts, the financial sector appear to have grown twice as fast as the economy as a whole since 1850. Most of its growth have been concentrated in two periods, the episodes of globalization between the 1970s and 2007. Real GDP doubled between 1980 and 2008, but the measured real value-added trebled. Similar trends are evident in the United States (where the share of the financial sector in total GDP rose from 2 percent in the 1950s to 8 percent in 2008) and in Europe. This was in Andrew Haldane’s phrase, more mirage than miracle. See Coyle (2014:98) for more details.

[19] For a more detailed explanation about the intermediation services that the financial system provides see Haldane (2011).

[20] As the OECD GDP statistic manual puts it: “Measurement using the general formula [for constructing GDP] would result in their value added being very small, if not negative

[24] See

[25] Specifically Argentina KLEMS where KLEMS (K-capital, L-labor, E-energy, M-materials, and S-purchased services) refers to broad categories of intermediate inputs that are consumed by industries in their production of goods and services. 

[27] See Hibbs and Piculescu (2005)