Young Economist 2018 winner: Sam Christie – best essay on topic

"GDP does not measure the output of the economy any more". Do you agree, and if so, what improvements would you suggest?

Sam Christie

Statisticians’ methodology of measuring the output of the economy has frequently changed since the first attempts at national accounting in the mid-17th century. However, since 1941, GDP has been the primary figure in valuing a nation’s output and, until the financial crisis, has been largely unchallenged (despite some environmentalist opposition in the 1970s). GDP is an outdated measure which contains increasingly prevalent flaws but there are a variety of ways in which its calculation could be improved.

One of GDP’s failures is its inability to measure innovation and changes in the quality of goods and services. The proliferation of choice available to economic agents since the 1990s, mainly in the form of information and communications, has augmented the power of the substitution effect. This has increased competition as consumers can more easily switch between brands and so, combined with technological progress reducing cost per unit, technology prices have fallen. Additionally, the quality of these goods has increased vastly in the time period. For example, the growth in computer power from 1940 to 2001 has averaged 55% a year whilst computer prices have fallen greatly (Nordhaus, 2001). GDP understates the decline in prices as these quality improvements are not taken into account and in 1996 a commission approximated that the US had overstated the rate of inflation (CPI) by over 0.6% a year; thus real GDP is underestimated (Boskin, 1996). However, hedonic pricing calculates a price index which studies the actual prices paid for goods and considers how these relate to increases in quality. Prices of goods and services are then regressed based on their characteristics. Their use gives a more accurate value of CPI and, therefore, real GDP, but is currently only used in a small number of high value products such as computers.

Furthermore, this boom in variety has resulted in large increases in consumer surplus, according to econometric estimates, and thus there is an increasing discrepancy between GDP and economic welfare (Hausman, 1996). This has been exacerbated by the growing number of intangible goods and services online such as Google and Wikipedia of which it is difficult to value the derived consumer welfare as they are free and so have no demand curve. As GDP can measure only market transactions, neglecting the value of free services leads to an underestimation of the figure. For example, estimates using the time spent on these online services value the increase in consumer surplus to be worth over $100 billion per year to the US economy but this is ignored by GDP (Brynjolfsson and Oh, 2012). This failure is salient as policymakers may undervalue the benefits associated with these zero-priced services and consequently underinvest in them.

Pre-GDP, less than half of the British population was employed in the services sector but the Digital Revolution has transformed the economy’s structure (Broadberry, 2005). This has increased imputations (the estimation of the value of an activity for which there is missing data) when constructing GDP, as the output of services is unclear and often difficult to quantify. In 2007, imputations accounted for 30.6% of France’s adjusted disposable household income due to the burgeoning of the services sector (OECD Annual National Accounts, cited in Stiglitz et al., 2010). This is significant as imputed values are invariably less reliable than observed values and so the probability of GDP accurately measuring output has fallen as services have grown in importance in the modern economy.

The ‘invariance principle’ states that the value of a service should not depend on the institutional arrangements in the country and thus government-provided services, where there is no associated market price, should still be included in GDP. Therefore, when no direct comparator exists to measure the value of a government service, its value is imputed using inputs. This method is flawed as GDP is an output-based measure and an increase in inputs does not always result in constant returns to scale. For example, the US health care service spends more per capita than the majority of European countries but has worse outcomes in terms of standard health indicators (Stiglitz et al., 2010). Furthermore, using inputs only, productivity changes are impossible to record for the public sector. Even Australia, who has already begun measuring both the inputs and outputs of government-provided services, encountered this difficulty as complications arose over whether the causation of these productivity changes were due to other factors (Diewert and Lawrence, 2006).

Using similar output-based measures, between 1995 and 2003 the UK would have demonstrated average growth of 2.75% a year instead of 3% (Atkinson, 2005). With the power of compound arithmetic, this difference in growth greatly influences the measured size of the economy over a sustained period of time.

The 2008 financial crisis highlighted GDP’s flaws in measuring the value of the financial sector. Since 1993, the UN System of National Accounts utilised the concept of ‘financial intermediation services indirectly measured’ (FISIM), which finds the difference between banks’ borrowing and lending rates and a risk-free reference rate, multiplied by the stock of outstanding balances. This is difficult to measure, especially when converted into real terms (Haldane et al., 2010), and is further complicated as financial services represent intermediate consumption by households and other businesses, but it is not obvious how to allocate the amount between the two categories (Coyle, 2015).

Moreover, the use of a risk-free reference rate means that increased risk is recorded as increased growth because, during high-risk lending, banks charge a higher rate of interest to protect their losses. However, excessive risks are not always a productive activity. During the fourth quarter of 2008, FISIM showed the fastest growth of the financial sector on record as its share of the UK economy grew to 9% and then again to 10.4% the following year (Coyle, 2015). At a time when the banking system had collapsed, these figures are evidently incorrect. Using multiple reference rates which fully represent the risk of loans and deposits in each asset class (Fixler and Zieschang, 2010) would have the effect of increasing the reference rate specific to the type of activity and thus reducing measured output. If the UK had adjusted for banks’ risk taking, the output of the financial sector would have been 6-7.5% of GDP instead of the 9% originally recorded (Colangelo and Inklaar, 2012).

Finally, the standard of living for future generations partly depends on the quality and quantity of physical capital that we pass onto them. However, GDP does not account for the depreciation of assets and, therefore, is a less relevant measure of economic welfare than Net Domestic Product (NDP) as the figure cannot show the sustainability of current rates of consumption (Spant, 2003). GDP includes the expenditure to replace worn-out capital but this does nothing to improve living standards or increase productivity as there has been no net increase in the level of capital stock. The increase in software investment has exacerbated this problem, causing an increasing discrepancy between GDP and NDP, as software has a shorter lifespan than traditional machinery (by approximately eight years) and hence GDP has become an even poorer reflection of economic welfare (Stiglitz et al., 2010). However, NDP is also an imperfect measure; it ignores the depletion of natural assets, raising concerns over the environmental sustainability of the statistic, whilst GDP is more effective at measuring overall production (Spant, 2003).

These deficiencies have proliferated with the development of the economy and the following changes should be enacted to better measure output and increase the comprehensiveness of GDP. Firstly, despite the expense, one must take into account quality changes, and the extension of hedonic price indices to more products is necessary. A further improvement would be adjusting FISIM’s risk-free reference rate to multiple rates to better measure the output of the financial sector. Additionally, the UK should change its measurement of services so that both inputs and outputs are recorded whilst minimising the use of imputations, making it possible to measure productivity changes and corresponding increases in quality. This would require definitive decisions regarding the output of certain services. For zero-priced goods and services, it is critical to begin measuring the value of consuming intangibles. Recording the time spent on these websites and using the opportunity cost of working additional hours gives the most accurate representation of consumer welfare and should be included in GDP (Brynjolfsson and Oh, 2012). The UK should seek to improve its estimates for depreciation, making them as reliable as possible, and move towards measuring NDP. However, due to NDP’s imperfections, GDP should also be regularly published alongside this measure and used together to formulate policies. NDP and changes to services’ measurement is relatively unproven and may be difficult at current levels of technology.

Nonetheless, these are certainly viable for the future and we must strive for a more complete measurement of GDP.

It is essential to create a comprehensive measure of GDP which better reflects the economy we live in today. GDP certainly remains the best existing estimate of national output when considering the various subjective alternatives and it would be misguided to claim that the statistic no longer measures the output of the economy. However, GDP does not measure output to the degree of precision that is currently practically feasible. Implementing the above changes will result in a more accurate figure that goes beyond just a measure of production and becomes a true reflection of social welfare.

Word Count: 1499 (excluding references and in-text citations).



Atkinson, A.B., (2005). Measurement of UK government output and productivity for the national accounts. Journal of the Statistical and Social Inquiry Society of Ireland, 34, p.153. [Online]. Available from: http://www.tara.tcd.ie/bitstream/handle/2262/8840/JssisiVolXXXIV152_160.pdf?sequence=4&isAll owed=y. [Last accessed 25 June 2018].

Boskin, M.J., (1996). Toward a more accurate measure of the cost of living: Final report to the Senate Finance Committee from the Advisory Commission to Study the Consumer Price Index. Advisory Commission to Study the Consumer Price Index, p.61. [Online]. Available from: https://babel.hathitrust.org/cgi/pt?id=mdp.39015041731095;view=1up;seq=137. [Last accessed 25 June 2018].

Broadberry, S., (2005). Britain’s 20th Century Productivity Performance in International Perspective. Warwick: Warwick University working paper, p.33. [Online]. Available from: https://warwick.ac.uk/fac/soc/economics/staff/sbroadberry/wp/labmkt5.pdf. [Last accessed 25 June 2018].

Brynjolfsson, E. and Oh, J., (2012). The attention economy: measuring the value of free digital services on the Internet. [Online]. Available from: https://pdfs.semanticscholar.org/9ff9/bec84357dacc286b570937a955f358a9a8b5.pdf. [Last accessed 25 June 2018].

Colangelo, A. and Inklaar, R., (2012). Bank output measurement in the euro area: a modified approach. Review of Income and Wealth, 58(1), pp.142-165. [Online]. Available from: https://www.econstor.eu/bitstream/10419/153638/1/ecbwp1204.pdf. [Last accessed 25 June 2018].

Coyle, D., (2015). GDP: A brief but affectionate history. Princeton: Princeton University Press.

Diewert, W. E. and D. Lawrence, (2006). Measuring the Contributions of Productivity and Terms of Trade to Australia’s Economic Welfare, Report by Meyrick and Associates to the Australian Government. Canberra, Australia: Productivity Commission, pp.17-33. [Online]. Available from: http://www.oecd.org/sdd/productivity-stats/37503743.pdf. [Last accessed 25 June 2018].

Fixler, D. and Zieschang, K., (2010). Deconstructing FISIM: should financial risk affect GDP? In Communication à la 31e conférence générale de l’International Association for Research in Income and Wealth, Sankt-Gallen (Suisse): pp. 22-28. [Online]. Available from: https://www.researchgate.net/profile/Dennis_Fixler/publication/265022326_Deconstructing_FISIM

_should_financial_risk_affect_GDP/links/5527cc870cf2e089a3a1dd13/Deconstructing-FISIM-should- financial-risk-affect-GDP.pdf. [Last accessed 25 June 2018].

Haldane, A., Brennan, S. and Madouros, V., (2010). What is the contribution of the financial sector: Miracle or mirage? The Future of Finance, 87. [Online]. Available from: http://www.cetking.com/wp- content/uploads/2012/12/futureoffinance5.pdf#page=91. [Last accessed 25 June 2018].

Hausman, J.A., (1996). Valuation of new goods under perfect and imperfect competition. Chicago: University of Chicago Press, pp. 207-248. [Online]. Available from: http://www.nber.org/chapters/c6068.pdf. [Last accessed 25 June 2018].

Nordhaus, W., (2001). The progress of computing. Yale Cowles Foundation Discussion Paper No. 1324. [Online]. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=285168. [Last accessed 25 June 2018].

Spant, R., (2003). Why net domestic product should replace gross domestic product as a measure of economic growth. International Productivity Monitor, (full), pp.39-43. [Online]. Available from: http://www.csls.ca/ipm/7/spant-e.pdf. [Last accessed 25 June 2018].

Stiglitz, J.E., Sen, A. and Fitoussi, J.P., (2010). Report by the commission on the measurement of economic performance and social progress. Paris: Commission on the Measurement of Economic Performance and Social Progress. [Online]. Available from: http://ec.europa.eu/eurostat/documents/118025/118123/Fitoussi+Commission+report. [Last accessed 25 June 2018].