“You can see the computer age everywhere but in the productivity statistics” remarked Robert Solow in 1987. Today, a similar statement could be made about the internet and smartphones. The information revolution has touched almost every aspect of the economy in one way or another, and has had a powerful impact on daily lives. At the same time, real GDP per capita declined in the US from its 1995-2006 level of 2.2% to 1.3% in 2010-2016. Similar patterns are seen in other advanced economies. Several explanations have been offered for this apparent paradox. One is that a slow recovery from the Great Recession and financial sector crisis, combined with a global supply glut, has casted a long shadow on GDP growth. Another is that the effect of the Information Revolution on GDP has been much weaker than as claimed by enthusiasts, a position generally associated with the recent book by Robert Gordon (Gordon 2016). Yet another is that the procedures for estimating GDP have not kept pace with the changing technology, with a resulting omission or downward bias in the estimates.
There is also a growing conviction in the recent literature on GDP growth that at least part of the current round of innovation is more apt to affect the consumer directly and thus does not appear in measured GDP (Ahmad and Schreyer 2016, Brynjolfsson and Saunders 2009, Hulten 2015, Nakamura 2014, Varian 2009).1 These concerns are focused on the digital economy, but the idea can be traced back to a hypothesis advanced by Kelvin Lancaster in the 1960s in his New Approach to Consumer Theory (Lancaster 1966a, 1966b). Lancaster argued that consumer utility is derived from the characteristics of the goods acquired from producers and not from the goods themselves, and that there is a separate ‘consumption technology’ that transforms these goods, measured at production cost, into consumption ‘activities’ or ‘commodities’ that provide utility. This is relevant for the issues at hand, since once the idea of a separate technology for consumption is introduced, it is reasonable to expect that the technology might change over time in ways that make consumer choice more efficient.
What sort of innovation might cause the consumption technology to shift? Improvements in the development, storage, and dissemination of information – the ‘Information Revolution’ – are prime candidates. As Schmidt and Rosenberg (2014) put it in their book How Google works, “the internet has made information free, copious, and ubiquitous” to the consumer (and, one might add, timely). Applications like search engines, real-time traffic maps, product ratings and price comparisons, and a variety of online services have expanded the scope and efficiency of consumer choice. The advent of social media has connected people in networks that allow them to exchange information, some of it related to choices they face. Most of this information is available at little or no marginal cost to the user.
The efficiency of consumption can also increase through more-or-less costless improvements in product quality that allow better products that satisfy the same wants to be purchased for the same amount of money. The real cost of successive generations of personal computer equipment remained relatively constant even as computing power increased exponentially. Successive generations of mobile cellular telephones have followed a similar pattern to the point where today’s smartphone is many different products bundled into a small box – a mobile phone, a camera, a personal organiser, a music player, an eBook reader, and a device for accessing the internet. The savings relative to the individual purchase of these devices are large.
These increases in effective information and product quality allow the consumer to get more utility from a given amount of expenditure. They thus represent a shift in the utility function relative to the consumer budget constraint. This type of innovation might be called ‘output saving’, in contrast to the ‘resource-saving’ technical change of conventional growth theory in which innovation is associated with a costless shift in an aggregate production function and is resource-saving because fewer resources are needed to produce a given amount of output. Output-saving and resource-saving concepts are symmetric in that both represent costless shifts in their associated functions, utility and production. In a recent paper, we show that the two can be combined into a sources-of-welfare growth model in which the growth rate of consumer utility can be decomposed into separate output- and resource-saving terms, plus a resource-using term composed of the growth rates of labour and capital weighted by their shares in total GDP (Hulten and Nakamura 2017). The conventional Solow growth accounting model is the sum of the last two terms, with total factor productivity (TFP) here called resource-saving technical change (Solow 1957). Output-saving innovation is thus an add-on to the conventional model which allows innovation to go directly to the consumer, bypassing GDP. Its presence thus implies that conventional GDP may not be a sufficient statistic for estimating the impact of the ‘Internet Age’, and that GDP growth can slow while consumer welfare increases.
Attempting to sort this out empirically poses many hard problems. There is an important asymmetry between the output-saving and resource saving/using sides of the expanded growth account. GDP can be estimated using data on the prices and quantities observed in market transactions. The resulting estimates may not be perfect, but they are based on an objective price metric that links value to cost. Utility, on the other hand, is subjective and not directly observable, and there is no direct yardstick to measure whether (or by how much) utility has increased. However, indirect methods are available. The utility-augmented version of the sources-of-growth model can be reformulated in terms of the associated expenditure and indirect utility functions, and the related concepts of equivalent and compensating variations, as well as consumer surplus. These methods have been applied to the other issues involving consumer welfare, and have a prominent place in discussions of the benefits associated with the arrival of new goods in the marketplace.
These methods have also been applied in the emerging empirical literature on the digital economy, along with other approaches like the use of advertising revenues as a proxy for the value of internet applications. This literature is already too large to survey here, but a summary of the evidence for selected applications is that their value lies in the range of $100 billion to $1 trillion.2 Viewed against the overall size of GDP, currently around $18 trillion, the effects seem relatively small. On the other hand, the digital economy has grown rapidly over the last two decades. The percentage of US households with a computer increased from 23% in 1993 to almost 80% in 2012, and the percentage with internet went from 18% in 1997 to nearly 75% in 2012 (US Census 2012). Surveys by the PEW Research Center found that the percentage of adults who use at least one social media site increased from 7% in 2005 to 65% in 2015 (Perrin 2015), and that the market penetration of smartphones more than doubled from 2011 to 2016, from 35% to 77% (Anderson 2015, Pew 2017). Whether the rapid uptake is enough to offset the declining growth rate of real GDP since 2007 is an open question, given their relatively small GDP share (Byrne et al 2016, Syverson 2016; Groshen et al 2017 suggest that this may not be the case). But, it is a question whose answer may well become more important as the Information Revolution proceeds apace with the continued development of applications that make use of cloud computing, Big Data, and the growing power of artificial intelligence.
In any case, ignoring the output-saving dimension of innovation is not the answer. The measurement difficulties involved in determining the magnitude of the non-GDP benefits of innovation are substantial, but the development of the GDP accounts was itself a daunting challenge. An experimental innovation satellite account that supplements the existing GDP accounts would be a helpful step in the direction of bringing the benefits of output-saving innovation into the picture. A start at such an innovation-focused account has already been made in the area of resource-costly innovation, with the capitalisation of intangible expenditures like R&D and artistic originals into the US national GDP accounts. Other types of intangible capital are also important for innovation and need to be added to this list, but they too involve significant measurement difficulties and would require the development of new data sources (Corrado and Hulten 2014). An innovation satellite account would pull together current research in these difficult areas and point to other areas where current data limitations are a problem. The experimental nature of the satellite account would also allow for tentative and incomplete estimates of innovations whose hard-to-measure benefits are currently outside the scope of conventional practice but are nonetheless of central importance for understanding the Digital Age.
Authors’ note: The views expressed in this column are solely those of the authors and should not be attributed to any organization with which they are affiliated, including the Federal Reserve Bank of Philadelphia or the Federal Reserve System.
Ahmad, N and P Schreyer (2016), “Measuring GDP in a digitalized economy”, OECD, working paper, April.
Anderson, M (2015), “Technology Device Ownership: 2015”.
Brynjolfsson, E and A Saunders (2009), “What the GDP gets wrong (Why managers should care)”, MIT Sloan Management Review, 51(1): 95-98.
Byrne, D M, J G Fernald and M B Reinsdorf (2016), “Does the United States have a productivity slowdown or a measurement problem”, Brookings Institute, Papers on economic activity, March.
Corrado, C A and C R Hulten (2014), “Innovation accounting”, in D W Jorgensen, J S Landefeld and P Schreyer (eds), Measuring economic progress and economic sustainability, Studies in Income and Wealth, vol 72, The University of Chicago Press for the National Bureau of Economic Research, Chicago: 595-628.
Groshen, E L, B C Moyer, A M Aizcorbe, R Bradley and D Friedman (2017), "How government statistics adjust for potential biases from quality change and new goods in an age of digital technologies: A view from the trenches”, Journal of Economic Perspectives, 31(2): 187–210.
Gordon, R J (2016), The rise and fall of American growth: The US standard of living since the Civil War, The Princeton Economic History of the Western World, Princeton, New Jersey: Princeton University Press.
Hulten, C R and L Nakamura (2017), “Accounting for growth in the age of the internet: The importance of output-saving technical change”, National Bureau of Economic Research, Working Paper 23315.
Hulten, C R (2015), “Measuring the economy of the 21st century”, NBER Reporter, 4: 1-7.
Lancaster, K J (1966a), “Change and innovation in the technology of consumption”, American Economic Review, 56: 14-23.
Lancaster, K J (1966b), “A new approach to consumer theory”, The Journal of Political Economy, 74(2): 132-157.
Nakamura, L (2014), “Hidden value: How consumer learning boosts output”, Business Review, Federal Reserve Bank of Philadelphia, Q3: 9-14.
Perrin, A (2015), “Social networking usage: 2005-2015”, Pew Research Center, October.
Pew (2017), “Mobile Fact Sheet”, Pew Research Center.
Syverson, C (2016), “Challenges to mismeasurement explanations for the US productivity slowdown”, NBER, Working paper 21974.
Schmidt, E and J Rosenberg (2014), How Google Works, (with Alan Eagle), Grand Central Publishing, September.
Varian, H (2009) “Economic Value of Google”, 2009.
Solow, R M (1987) “We’d better watch out”, New York Times Book Review, 12 July.
Solow, R M (1957) “Technical change and the aggregate production function”, Review of Economics and Statistics, 39: 312–20.
US Census (2014) “Computer and internet access in the United States: 2012”, 3 February.
 Concerns about the ability of measured GDP and the associated price measures to capture the benefit of new and innovative goods go back decades, with the contributions of Hausman, Nordhaus, and Triplett (see Hulten 2015 for a more detailed survey).
 The many relevant references are surveyed in Hulten and Nakamura (2017).