Many governments are going ‘beyond GDP’ to measure standards of living and to base policy on such wider considerations. One of the more advanced approaches is the Living Standards Framework used by the New Zealand Treasury as a complementary input into the policy process. This paper uses the Framework as a case study to highlight shortcomings and unresolved theoretical and empirical issues in the underlying theoretical model (i.e., the capital approach to development based on mainstream neoclassical economics). In particular, innovation is noticeable mostly by its absence, despite being the main driver of living standards in the long-run. It is argued that innovation should be at the centre of the Framework. Moreover, one must go beyond standard welfare analysis and use a model of the innovation–subjective wellbeing nexus in order to assess the many, potentially very complex, wellbeing implications of innovation. Adoption of such a perspective, although currently resisted by many policy makers, seems to fit well with the ‘normative turn’ in innovation economics. It does not make one a neo-Luddite. Instead, adoption might help overcome resistance to innovation. This should be especially important at a time when the spread of digital technologies is forecast to cause major societal disruptions.
Schumpeter (1947, p.155, fn.12) states that ‘... the question of appraisal of social gains from entrepreneurship, absolute and relative to the entrepreneurial shares in them, and of the social costs involved in a system that relies on business interests to carry out its innovations, is so complex and perhaps even hopeless that I beg to excuse myself from entering into it’.
Baumol (2010) extends neoclassical welfare economics to capture the impacts of innovation (‘innovative entrepreneurship‘) and emphasises the enormous beneficial spillovers and other externalities from innovation that accrue to people not directly associated with innovation. This implies that, contrary to the standard view, zero spillovers are incompatible with optimality.
Perez (2013) further argues that environmental constraints, while undoubtedly the greatest challenge, might also be the greatest opportunity for massive green innovation and changes towards sustainable lifestyles that usher in a new golden age.
SWB is now explicitly included (as a row across the bottom of the figure), as is the distribution within the population and over time (added across the right-hand side of the figure). Otherwise Figure 1 is unchanged.
Also see the slightly different version in Gleisner et al. (2012, Figure 12, p.230).
Strictly speaking, this is only correct if some restrictive assumptions are satisfied; for example, constant returns to scale in production, constant population growth and per capita consumption being independent of population size (World Bank, 2006).
This is a key assumption made in most of mainstream macroeconomics and growth theory. It has hardly ever been tested empirically. Only recently have researchers begun to use long-term historical data to test, and seemingly confirm, the assumption for Great Britain and the United States (Greasley et al., 2014a, 2014b).
That is, HC per worker calculated as a function of years of schooling is adjusted using adult survival rates that proxy for health status (World Bank, 2011, p.97).
Liu finds that although HC stocks have increased over time, HCpc has declined in some countries (Israel, Korea, Norway and the US) because of population aging outweighing increases in education levels. It has remained broadly stable in Australia, Canada, France and New Zealand.
Because of data problems, Hamilton and Liu (2014) exclude Korea as an outlier when calculating the average share of the IC residual across countries.
They comment that ‘There are no obvious explanations for this low share ... New Zealand stands out ... as having the highest share of human capital after Korea, in spite of a projected real income growth of only 0.77 per cent . The other notable feature for New Zealand is the large negative figure for net foreign assets, more than 5% of total wealth’ (Hamilton and Liu, 2014, p.84).
Some of Arrow et al.‘s (2012) results might be attributable to their use of schooling-based HC instead of a more comprehensive HC measure. On this and other criticisms of Arrow et al.‘s approach, see Hamilton (2012).
It is also being implemented by Statistics New Zealand as one of its main approaches to measuring sustainable development. See Statistics New Zealand's website on sustainable development, available from http://www.stats.govt.nz/sustainabledevelopment [accessed May 2013].
On path dependency, see Arthur (1994). For a recent survey of complexity economics, see Antonelli (2011) and the contributions to this volume.
Note that the World Bank has no entry for innovation in its indices. World Bank (2011) has one reference to technical progress – in the context of decomposing IC (p.99) – which is proxied by a time dummy.
Parts of this section are based on Engelbrecht (2014a). See this paper for further references.
The differences between the models used by Swann and Engelbrecht are discussed more extensively in Engelbrecht (2013), which argues for a ‘unified well-being theory of innovation’ and notes its relevance for the normative assessment of social innovations. There seems to be a much clearer recognition of the important links between, on the one hand, SWB, and, on the other hand, social innovation and social and welfare policies, than of the links between SWB and technological innovation (see Newton, 2007; O'Donnell, 2013, pp.102–3). Deeming (2013) argues that measurement and analysis of SWB is central to the development of social policy.
The wellbeing effects of processes are captured in economics by the concept of ‘procedural utility’ (Frey et al., 2004), whereas outcome utility is associated with mainstream economic theory. Empirical evidence of the importance of procedural utility has been accumulating (see Frey and Stutzer, 2005; Block and Koellinger, 2009; Schneck, 2014).
See Helliwell and Huang (2011) and Dewe and Cooper (2012). Research on workplace SWB and productivity is also multifaceted and has a long history. For a survey, see Zelenski et al. (2008); they find that despite there being many inconsistent findings, overall there seems to be a positive relationship between the two.
See Helliwell (2012) on the importance of the social context for SWB, and the implications for the management of public and private institutions.
Income inequality seems to have a complex relationship with SWB. However, much of the conflicting empirical evidence might be attributable to estimation issues (Verme, 2011).
This is well established in the psychological literature (Isen et al., 1987).
Many other possible direct and indirect links among elements of the general model of the innovation-SWB nexus are discussed in Engelbrecht (2014a).
Potts (2011) is another evolutionary economist who advocates a hands-off approach when it comes to happiness policies. He focuses on happiness-signalling effects, which imply that economic agents learn from each other about what creates happiness, much like the price-signalling effects emphasised by Hayek.