How we spend our time is changing rapidly. This column argues that an important driver is leisure-enhancing innovation, aimed at capturing our time, attention, and data. Leisure-enhancing technologies can help account for both the rise in leisure hours and the decline in productivity observed across the industrialised world. Their nature carries important implications for the long-run viability of the platforms’ business models, for measurement of economic activity, and for welfare.
Our time, attention, and data are central in today’s economy. We spend ever more time glued to our screens, while businesses innovate tirelessly to attract ‘eyeballs.’ Clearly, the leisure economy has redefined the way we spend our time. Perhaps less obviously, it has had a profound impact on the macroeconomy.
Economists have studied time allocation decisions at least since the seminal work of Becker (1965). More recently, research noted the decline in average hours worked and the steady rise in leisure hours (Aguiar and Hurst 2007, Boppart and Krusell 2016), and considered the impact of some technologies such as video games on labour market participation (Aguiar et al. 2017).
至少从贝克尔(Becker, 1965)的开创性著作开始，经济学家就开始研究时间分配决策。最近，研究注意到平均工作时间的下降和休闲时间的稳步增长(Aguiar and Hurst 2007, Boppart and Krusell 2016)，并考虑了电子游戏等一些技术对劳动力市场参与的影响(Aguiar et al. 2017)。
A new model to analyse the leisure economy
In a recent paper (Rachel 2019), I consider the underlying economic drivers and the long-run consequences of the leisure economy. The theory speaks to three salient real-world facts: the increase in leisure hours over the long-run and the dramatic rise in the use of leisure technologies more recently (Figure 1), the rising importance of the free economy (Figure 2), and the disappointingly low productivity growth across the developed world (the ‘productivity puzzle’).
Figure 1 Average time spent on media consumption per adult in the US
Notes: Figures for representative samples of total US population (whether or not they have the technology). Data on TV and internet usage and the usage of TV-connected devices are based on 248,095 individuals in 2016 and similar sample sizes in previous years. Data on radio are based on a sample of around 400,000 individuals. There are approximately 9,000 smartphone- and 1,300 tablet-panellists in the US across both iOS and Android smartphone devices. More than one device may be used at any time.
Figure 2 Free ad-supported consumer content in the US
Source: Nakamura et al. (2017). The figure shows the ratio of free consumer content, measured by the costs of production, to GDP. Thus, for example, it does not capture a welfare measure of the value of Facebook, but only measures the cost of providing it.
I focus on leisure-enhancing innovations – services that are supplied free of charge and are designed specifically to draw in viewers. Such innovations make economic sense because the attention they attract can profitably be used for advertising. Building this simple mechanism into a macroeconomic model of innovation-driven growth helps explain some of the salient puzzles observed in the data. The theory explains why so much innovation takes place in the leisure sector, and elucidates the puzzling disconnect between technology, which seems to be racing ahead, and productivity, which is stagnant. It accounts for the rapidly changing time-allocation patterns. It also carries implications for measurement of GDP, and, by highlighting the inefficiencies of market equilibrium, forms a useful framework for thinking about policy.
The model incorporates several aspects of leisure technologies that make them particularly fascinating. These services are often provided free of charge, which requires thinking carefully about the reasons why they are produced in the first place. They are non-rival, meaning that a person’s use does not prevent simultaneous use by others. In fact, they involve strong network effects; we like entertainment options that are popular.
Two novel mechanisms are considered: (i) supply of people’s out-of-work time and attention and (ii) demand for advertising.
On the first, consumers choose how much time to spend on ‘marketable‘ leisure – on the activities that allow for our time and attention to be used commercially, such as social media or TV – based on the abundance and attractiveness of the available leisure services (and, in the case with network effects, on how popular these options are in the population). Leisure innovations make these activities more desirable and drive higher ‘eyeball supply’ in equilibrium.
On the second, firms producing differentiated consumer goods and services can attempt to boost demand for their product through advertising. The central player in the theory – the platform – connects the two sides of this market, providing continually improving leisure services to customers, and turning their time and attention into valuable advertising service sold to the firms.
Lessons for the growth of the platforms
The theory suggests that the leisure sector inevitably emerges as a natural part of the growth process when both sides of the market are large enough to make the platform profitable. This may help explain the rapid emergence of the leisure economy over the past couple of decades. Today, firms involved in supplying these services are hugely profitable. The top six of the world’s largest companies by market capitalisation – Apple, Alphabet, Microsoft, Amazon, Facebook, and Alibaba – are platforms whose business models rely, at least in part, on capturing peoples’ time and attention. These platforms are often seen as the innovation leaders of today.
Interestingly, there are also implications for the future of such a business model. Specifically, for the economy with leisure technologies to adopt a balanced growth path – that is, to grow steadily over the long run – additional restrictions are needed. First, consumers’ enjoyment of leisure should not diminish as leisure consumption rises. This condition may be embedded directly in preferences or it may emerge as a result of the aforementioned network effects.
More importantly, balanced growth requires the technology used by platforms to produce marketing services that exhibit increasing returns to scale. This could be justified by platforms becoming increasingly better informed about consumers’ tastes and preferences, and being able to target advertising more effectively, perhaps through the use of all-powerful machine learning models. But it is not completely clear whether this can continue ad infinitum. For instance, Varian (2017) shows that the prediction accuracy of machine learning algorithms increases with a square root of the number of observations, suggesting that decreasing returns may eventually kick in.
Leisure, innovation and growt
As the range of leisure options increases, people spend more time on leisure, meaning that ever more resources are directed towards innovation in the leisure sector. This has adverse implications for the tangible economy. In particular, in the paper, I show analytically that the growth rate of knowledge, or total factor productivity (TFP), declines once the leisure sector emerges. The long-run growth rate is ultimately driven by the growth of peoples’ creative time at work (specifically, the growth rate of R&D hours). Because leisure technologies successfully compete for peoples’ time, including creative time, productivity growth slows.
This channel provides a novel explanation for the productivity puzzle: technology (inclusive of leisure technology) races ahead, but productivity stagnates.
While more work is needed to quantify those effects more precisely, a simple, illustrative calibration suggests that they may be substantial. The endogenous emergence of the leisure sector explains nearly a one percentage point decline in TFP growth and over a one percentage point decline in the equilibrium real interest rate (Figure 3).
Figure 3 Endogenous transition of the economy following the emergence of the leisure-sector
Measurement and welfare
The model also carries implications for the measurement of GDP. Because they are priced at zero, the value of leisure services is difficult to measure. Indeed, as currently measured, GDP does not capture any of the value these services generate. My model can be used for counterfactual measurement analysis. For example, one way of measuring the value of leisure services is to value leisure time at an ongoing market wage rate. Figure 4 shows the comparison between the level of GDP as measured currently and the path under that alternative assumption.
The bottom line is that the growth rate of the economy following the transition is permanently lower, consistent with recent experience in the US and other advanced countries. However, GDP growth as measured by statistical agencies today does not account for the true value of leisure services and so exaggerates the extent of the slowdown.
Figure 4 The level of GDP with and without the value of leisure services (measured at the ongoing market wage rate)
Finally, the theory provides a framework for thinking about policy. The market equilibrium is inefficient, but moreover, it is unclear whether in principle there is too much or too little free leisure services. On the one hand, platforms may undersupply, providing only as much as is needed for advertising and not fully taking into account consumers’ utility benefit – an effect similar to one identified by Spence and Owen (1977). Against that, the adverse impact of free leisure services on growth and productivity highlights the reason why there may be too much of it. More detailed quantification may be able to provide an idea of the relative size of these externalities, ultimately informing the optimal policy response to this immensely important phenomenon.
Aguiar, M, and E Hurst (2007a), “Measuring trends in leisure: The allocation of time over five decades”, The Quarterly Journal of Economics 122(3): 969–1006.
Becker, G S (1965), “A theory of the allocation of time”, The Economic Journal 75(299): 493–517.
Boppart, T, and P Krusell (2016), “Labor supply in the past, present, and future: A balance-growth perspective”, NBER Working Paper 22215.
Nakamura, L, J Samuels and R Soloveichik (2017), “Measuring the ‘free’ digital economy within the GDP and productivity accounts”, FRB of Philadelphia Working Paper 17-37.
中村，L, J Samuels和R Soloveichik(2017)，“在GDP和生产率核算中衡量‘自由’的数字经济”，费城联邦储备银行工作论文17-37。
Rachel, L (2019), “Leisure-enhancing technological change“, mimeo.
Spence, M, and B Owen (1977), “Television programming, monopolistic competition and welfare”, Quarterly Journal of Economics 91(1): 103–126.
Varian, H (2017), “Artificial intelligence, economics, and industrial organization”, in The Economics of Artificial Intelligence: An Agenda, National Bureau of Economic Research.