Category Archives: knowledge economy

30/9/17: Technological Revolution is Fizzling Out, as Ideas Get Harder to Find

Nicholas Bloom, Charles Jones, John Van Reenen, and Michael Webb’s latest paper has just landed in my mailbox and it is an interesting one. Titled “Are Ideas Getting Harder to Find?” (September 2017, NBER Working Paper No. w23782. the paper asks a hugely important question related to the supply side of the secular stagnation thesis that I have been writing about for some years now (see explainer here: and you can search my blog for key words “secular stagnation” to see a large number of papers and data points on the matter). Specifically, the new paper addresses the question of whether technological innovations are becoming more efficient - or put differently, if there is any evidence of productivity growth in innovation.

The reason this topic is important is two-fold. Firstly, as authors note: “In many growth models, economic growth arises from people creating ideas, and the long-run growth rate is the product of two terms: the effective number of researchers and their research productivity.” But, secondly, the issue is important because we have been talking in recent years about self-perpetuating virtuous cycles of innovation:

  • Clusters of innovation engendering more innovation;
  • Growth in ‘knowledge capital’ or ‘knowledge economies’ becoming self-sustaining; and
  • Expansion of AI and other ‘learning’ fields leading to exponential growth in knowledge (remember, even the Big Data was supposed to trigger this).

So what do the authors find?

“We present a wide range of evidence from various industries, products, and firms showing that research effort is rising substantially while research productivity is declining sharply.” In other words, there is no evidence of self-sustained improvements in research productivity or in the knowledge economies.

Worse, there is a diminishing marginal returns in technology, just as there is the same for every industry or sector of the economy: “A good example is Moore's Law. The number of researchers required today to achieve the famous doubling every two years of the density of computer chips is more than 18 times larger than the number required in the early 1970s. Across a broad range of case studies at various levels of (dis)aggregation, we find that ideas — and in particular the exponential growth they imply — are getting harder and harder to find. Exponential growth results from the large increases in research effort that offset its declining productivity.”

We are on the extensive margin when it comes to knowledge creation and innovation, which - to put it differently - makes ‘innovation-based economies’ equivalent to ‘coal mining’ ones: to achieve the next unit of growth these economies require an ever increasing input of resources.

Computers are not the only sector where the authors find this bleak reality. “We consider detailed microeconomic evidence on idea production functions, focusing on places where we can get the best measures of both the output of ideas and the inputs used to produce them. In addition to Moore’s Law, our case studies include agricultural productivity (corn, soybeans, cotton, and wheat) and medical innovations. Research productivity for seed yields declines at about 5% per year. We find a similar rate of decline when studying the mortality improvements associated with cancer and heart disease.” And more: “We find substantial heterogeneity across firms, but research productivity is declining in more than 85% of our sample. Averaging across firms, research productivity declines at a rate of around 10% per year.”

This is really bad news. In recent years, we have seen declines in labor productivity and capital productivity, and TFP (the residual measuring technological productivity). Now, knowledge productivity is falling too. There is literally no input into production function one can think of that can be measured and is not showing a decline in productivity.

The ugly facts presented in the paper reach across the entire U.S. economy: “Perhaps research productivity is declining sharply within every particular case that we look at and yet not declining for the economy as a whole. While existing varieties run into diminishing returns, perhaps new varieties are always being invented to stave this off. We consider this possibility by taking it to the extreme. Suppose each variety has a productivity that cannot be improved at all, and instead aggregate growth proceeds entirely by inventing new varieties. To examine this case, we consider research productivity for the economy as a whole. We once again find that it is declining sharply: aggregate growth rates are relatively stable over time, while the number of researchers has risen enormously. In fact, this is simply another way of looking at the original point of Jones (1995), and for this reason, we present this application first to illustrate our methodology. We find that research productivity for the aggregate U.S. economy has declined by a factor of 41 since the 1930s, an average decrease of more than 5% per year.”

This evidence further confirms the supply side of the secular stagnation thesis. Technological revolution has been slowing down over recent decades (not recent years) and we are clearly past the peak of the TFP growth of the 1940s, and the local peak of the 1990s (the ‘fourth wave’ of technological revolution).

28/9/17: Irish Migration: Some Good News in 2017

While headline figures for net migration to Ieland paint an overall positive picture in the annual data (provided on April-April basis) for 2017, there are some creases on the canvas, both good and bad.

Top line numbers are good: net inward migration posted a print of 19,800 in 2017, up on 16,200 in 2016 and 5,900 in 2015. This marks the third year of positive inflows. However, on a cumulative basis, the last three years are still falling short of offsetting massive outflows recorded in 2010-2014. Cumulatively, between 2010 and 2017, the overall net migration stands at -65,900. Taking last two years’ average net inward immigration, it will take Ireland almost 4 years to cover the shortfall. Worse, on pre-crisis trend (omitting peak inward migration years of 2005-2007), we should be seeing inward net migration of around 27,100, well above the current rate. And on a cumulative basis, were the pre-crisis trends to remain unbroken, we would have added 487,600 residents between 2000 and 2017, instead of the actual addition of 394,500 over the same period. 

So things are improving and getting toward healthy, but we are not quite there, yet.

And there are other points of concern. Primary one is the fact that net inward migration remains negative for Irish nationals: in 2017, net outflow of Irish nationals fell to 3,400 from 8,700 in 2016. However, the figures continue to record net outflows for 8th year in a row. Over the period of 2010-2017, Ireland lost net 139,800 nationals.

On a positive side, there is net inflow of all other nationalities into Ireland, with non-EU nationals inflows jumping (net basis) to 15,7000 in 2017, the highest levels on record (albeit records only start from 2006). It is impossible to tell from CSO figures which nationalities are driving these numbers - a crucial point when it comes to assessing the nature of inflows.

Final point worth making is a positive one: in 2017, Ireland recorded another year or growth in - already strong - net inflows of skills and human capital as reflected both in age demographics and educational attainment. By educational attainment, third level graduates and higher category of net inflows posted another historical record in 2017 at 23,600, topping 2016 record of 20,800. Since 2009, including the years of the acute crisis of 2010-2012, Ireland added net 61,000 new immigrants and returned migrants with third level and higher education. This is consistent with continued recovery in human capital-intensive sectors of the economy and is a huge net positive for Ireland.

Hence, overall, the figures for migration are on the balance positive, although some pockets of weaknesses continue to remain and pose a challenge to the arguments about the breadth and depth of the recovery to-date.

22/10/15: Gig Economy and Human Capital: Evidence from Entrepreneurship and Self-Employment

In a couple of weeks, I will be speaking about the role of human capital in the emergence of the new economy at the CXC Corporate event “Globalization & The Future of Work Summit” in Dublin.

Without preempting what I am going to say, here are some key points of interest.

Human capital-centric growth is overlapping, but distinct from the so-called “Gig Economy”, primarily because of the different definition of what constitutes two respective workforces.

Take, for example, the U.S. data. Based on research by the American Action Forum by Rinehart and Gitis (2015) we can define three types of the broadly-speaking “Gig Economy” workers: “For our most narrow measurement of gig workers (labeled Gig 1) we simply include independent contractors, consultants, and freelancers. Our middle measurement (Gig 2) includes all Gig 1 workers plus temp agency workers and on-call workers. Our broadest measurement (Gig 3) includes all Gig 2 workers plus contract company workers.”

The respective numbers engaged in three categories in 2014 range between 20.5 million and 29.7 million with growth rates over the recent years outpacing economy-wide jobs expansion rates across all categories of the Gig Economy workers.

Still, the key problem with identifying underlying trends in the development of the Gig Economy is the lack of data on specifics of occupational choices of the self-employed individuals and the relationship between these choices and human capital held by the Gig Economy participants relative to the traditional employees.

To see the indicators of links between the Gig Economy and human capital, we have to look at the more established literature concerning transition to entrepreneurship.

One interesting set of studies here comes from the Italian Survey of Household Income and Wealth (SHIW), a large biannual household survey conducted by the Banca d’Italia. A 2007 paper by Federici, Ferrante and Vistocco looked at the links between institutional structures, technological innovation and human capital in determining the propensity to transition from employment to entrepreneurship. Looking at the general literature on the subject, the authors state that “…institutions are more important than technology (i.e., technological specialization and/or industry composition) in fostering or restricting entrepreneurship and that the interactions between institutions and occupational choices may be complex and non linear”. The authors caution against directly linking self-employment rates with entrepreneurship rates, as “countries displaying the same self-employment rates, might be endowed with very different amounts and qualities of entrepreneurial skills devoted to innovation and business ventures (or, on the other hand, they might not)”.

To better pinpoint the link between entrepreneurship, self-employment and the institutional and technological drivers for risk taking, Federici, Ferrante and Vistocco augment the survey data with a set of variables describing the social and institutional environment in which self-employed and traditional workers are operating. Crucially, “in addition to standard indexes of economic and social infrastructure at the local level, [the authors] include a measure of creativity developed by Florida (2004).”

The conclusions are strong: “in Italy, both institutional and technological factors have shaped entrepreneurial opportunities requiring, tacit knowledge embedded in social networks and in the cultural background of families… Hence, well-educated people lacking privileged access to tacit knowledge and, in particular, an appropriate family background, could find themselves up against a considerable barrier to entrepreneurship and occupational mobility.” In simple terms, the Gig Economy-related value added can and should be considered within the context of family and cultural institutions as much as technological enablement environment.

As per traditional metrics of human capital, the study conclusions appear to be contradicting the core literature on entrepreneurship. “The evidence of the highly significant negative role of education in entrepreneurial selection is very strong in comparison with the majority of international studies showing that education has either a positive impact (Blanchflower, 1998) or a statistically non-significant effect on occupational choices”. In other words, formal education seems to be more conducive to employment choices in traditional environments (e.g. full time jobs),w it exception, perhaps, of professional skills-based activities.

The negative links between education and propensity to engage in entrepreneurial activity is, however, in line with other Italian study based on the same data, authored by Sabatini (2006).

However, U.S. data-based studies frequently find existence of a U-shaped relationship between income and propensity to transition to self-employment, with highest propensities concentrated around low income earners and high income earners, while lower propensities occurring for middle income earners. One recent example of this evidence is Moutray (2007). In so far as formal education is an instrument for income, especially for sub-populations excluding very high income earners, this suggests that the negative relationship between self-employment and education found in the case of Italy can be culturally conditioned and does not translate to other economies.

Another interesting aspect of transition to the ‘Gig Economy’ relating to the links between human capital and creativity or cultural institutions was uncovered by a 2011 paper by Mitra and Abubakar who looked at data from the Local Authority Districts of Thames Gateway South Essex (TGSE) in East of England. The study attempted “to explore and identify key determinants of business formation in Knowledge Intensive sectors (which include the creative industries) of regions outside the major metropolitan conurbations, and their possible differences with other Non-Intensive Sectors.”

The authors found that human capital is “positively correlated with new business entry in Knowledge intensive sectors”, but at the same time, it is “negatively correlated with new startups in non-knowledge intensive sectors”. Per authors: “This finding suggests that while entrepreneurship in knowledge based and creative industries requires highly skilled labour, in non knowledge based industries, low skilled labour is the primary determinant of new firm creation. Our findings also appear to suggest the need for higher skills/educated base in order to boost the growth of new businesses” in high knowledge-intensity sectors.

Werner and Moog (2009) use data from the German Socio-Economic Panel (SOEP) to map out significant linkages between entrepreneurial learning (and entrepreneurial human capital) and the probability of transition from traditional employment to self-employment. One interesting aspect of their findings is that learning-by-doing occurring (in their sample) during tenure of working for an SME has positive impact on ability to transition to entrepreneurship, confirming similar findings from other European countries. This also confirms findings that show that working for SMEs results in more frequent exits into self-employment and that such exits more frequently result in transition to full entrepreneurship than for self-employment entered from employment in larger firms.

The learning-by-doing effect of pre-transition experience for starting entrepreneurs and self-employed is also confirmed by the UK study by Panos, Pouliakas and Zangelidis (2011) who looked at the self-employment transition dynamics for individuals with dual job-holding and the links between this transition and human capital and occupational choice between primary and secondary jobs. The study used a wide (1991-2005) sample of UK employees from the British Household Panel Survey (BHPS). The authors investigated, sequentially, “first, the determinants of multiple job-holding, second, the factors affecting the occupational choice of a secondary job, third, the relationship between multiple-job holding and job mobility and, lastly, the spillover effects of multiple job-holding on occupational mobility between primary jobs.” The findings indicate that “dual job-holding may facilitate job transition, as it may act as a stepping-stone towards new primary jobs, particularly self-employment.” An interesting aspect of the study is that whilst the major effects are present in the lower skilled distribution of occupations, there is also a significant and positive effect of dual-jobs holding on transition to self-employment for professional (highly skilled) grade of workers.

Finally, there is a very interesting demographic dimension to transition to self-employment, explored to some extent in the U.S. data by Zhang (2008). The paper focused on the topic of elderly entrepreneurship. The author conjectures that in modern (ageing) demographic setting, “the “knowledge economy” could elevate the value of elderly human capital as the “knowledge economy” is less physically demanding and more human-capital- and knowledge-based.” Zhang (2008) largely finds that professional, skills-based self-employment and entrepreneurship amongst the older generations of workers can act as an important force in reducing adverse impact of ageing on modern economies.

The common thread connecting the above studies and indeed the rest of the vast literature on entrepreneurship, self-employment and transition from traditional employment to more projects-based or client-focused forms of engagement in the labour markets is increasingly shifting toward the first type of the ‘Gig Economy’ engagement. This typology of the ‘Gig Economy’ is becoming more human capital and skills-intensive and is better aligned with the ‘knowledge economy’ and the ‘creative economy’ than ever before. In simple terms, therefore, the ‘Gig Economy’ not only reaches deeper than the traditional view of the shared services (Uber et al) growth trends suggest.

While both increasing in importance and broadening the set of opportunities for economic development, the modern ‘Gig Economy’ is presenting significant challenges to social, cultural and policy norms that require swift addressing. These challenges are broadly linked to the need to Create, Attract, Retain and Enable key human capital necessary to sustain long term development and growth of the ‘Gig Economy’.

With that, tune in to my talk at the CXC Corporate event “Globalization & The Future of Work Summit” (link: in few weeks time for the details as to what should be done to put global ‘Gig Economy’ onto the sustainable development and growth track.


Will Rinehart, Ben Gitis, “Independent Contractors and the Emerging Gig Economy” July 29, 2015,

Federici, Daniela and Ferrante, Francesco and Vistocco, Domenico, "On the Sources of Entrepreneurial Talent in Italy: Tacit vs. Codified Knowledge" (July 24, 2007)

Sabatini, Fabio, "Educational Qualification, Work Status and Entrepreneurship in Italy: An Exploratory Analysis" (June 2006). FEEM Working Paper No. 87.2006

Velamuri, S. Ramakrishna and Venkataraman, S., "An Empirical Study of the Transition from Paid Work to Self-Employment". Journal of Entrepreneurial Finance and Business Ventures, Vol. 10, No. 1, pp. 1-16, August 2005

Moutray, Chad M., "Educational Attainment and Other Characteristics of the Self-Employed: An Examination Using Data from the Panel Study of Income Dynamics" (December 11, 2007). Hudson Institute Research Paper No. 07-06.

Mitra, Jay and Abubakar, Yazid, "Entrepreneurial Growth and Labour Market Dynamics: Spatial Factors in the Consideration of Relevant Skills and Firm Growth in the Creative, Knowledge-Based Industries" (August 23, 2011). University of Essex CER Working Paper No. 1.

Werner, Arndt and Moog, Petra M., "Why Do Employees Leave Their Jobs for Self-Employment? – The Impact of Entrepreneurial Working Conditions in Small Firms" (November 1, 2009).

Panos, Georgios A. and Pouliakas, Konstantinos and Zangelidis, Alexandros, "Multiple Job Holding as a Strategy for Skills Diversification and Labour Market Mobility" (August 23, 2011). University of Essex CER Working Paper No. 4.

Zhang, Ting, "Elderly Entrepreneurship in an Aging U.S. Economy: It's Never Too Late" (September 8, 2008). Series on Economic Development and Growth, Vol. 2.