White Paper

The rise of the “gig economy” has attracted wide attention from both scholars and the popular media. Much of this attention has been devoted to jobs mediated through various online platforms. While non-traditional work arrangements have been a perennial subject of debate and study, the perception that new technology is producing an accelerated pace of change in the organization of work has fueled a resurgence of interest in how such changes may be affecting both workers and firms. This paper provides a typology of work arrangements and reviews how different arrangements, and especially gig activity, are captured in existing data. A challenge for understanding recent trends is that household survey and administrative data paint a different picture, with the former showing little evidence of the growth in self-employment that would be implied by a surge in gig activity and the latter providing evidence of considerable recent growth. An examination of matched individual-level survey and administrative records shows that a large and growing fraction of those with self-employment activity in administrative data have no such activity recorded in household survey data. The share of those with self-employment activity in household survey data but not administrative data is smaller and has not grown. Promising avenues for improving the measurement of self-employment activity include the addition of more probing questions to household survey questionnaires and the development of integrated data sets that combine survey, administrative and, potentially, private data.

In the light of social and economic challenges posed by rapid population ageing there is an increased need to understand ageism – how it is expressed and experienced, its consequences and the circumstances that contribute to more or less negative attitudes to age. Ageism is the most prevalent form of discrimination in the UK (Abrams et al., 2011a), estimated to cost the economy £31 billion per year (Citizens Advice, 2007). It restricts employment opportunities, and reduces workplace productivity and innovation (Swift et al., 2013). Ageism also results in inequality and social exclusion, reducing social cohesion and well-being (Abrams and Swift, 2012; Stuckelberger et al., 2012; Swift et al., 2012). Not only is ageism a barrier to the inclusion and full participation of older people in society, but it also affects everyone by obscuring our understanding of the ageing process. Moreover, by reinforcing negative stereotypes, ageism can even shape patterns of behaviour that are potentially detrimental to people’s self-interest (Lamont et al., 2015). Here we review national and some international research from the last 25 years to reveal what our core attitudes to ageing are and how they result in discrimination and other damaging consequences. We outline the prevalence of perceived age-based discrimination and its consequences for individuals and society, and then explore the individual and societal factors that contribute to more positive or negative attitudes to age and their application to reducing experiences of ageism. We conclude by considering areas that are likely to be key for policy, research and practice.

Prospective studies of professions and skills is for economic actors an approach that is both increasingly necessary and increasingly complex. Necessary because business organization is facing increasingly intense so the need to adapt to market fluctuations and changing consumer needs, leveraging technological and organizational resources themselves changing. Complex, because the mutations are singularly accelerated in some parts of the economy, which requires a reflection both more frequent and more divided on the professional skills of the workforce. [googletranslate_en]

We analyze the effects of declining population growth on the adoption of automation technology. A standard theoretical framework of the accumulation of traditional physical capital and of automation capital predicts that countries with a lower population growth rate are the ones that innovate and/or adopt new automation technologies faster. We test the theoretical prediction by means of panel data for 60 countries over the time span from 1993 to 2013. Regression estimates provide empirical support for the theoretical prediction and suggest that a 1% increase in population growth is associated with approximately a 2% reduction in the growth rate of robot density. Our results are robust to the inclusion of standard control variables, the use of different estimation methods, the consideration of a dynamic framework with the lagged dependent variable as regressor and changing the measurement of the stock of robots.

Far-reaching changes in technology, climate, and global economic integration are transforming the world of work in ways that we do not yet fully understand. Will the swift technological advances of the Fourth Industrial Revolution raise the standards of living for everyone? Or will robots massively displace workers leading to a jobless future where only a few benefit from the fruits of innovation? Will mitigation efforts be able to cushion the adverse effects of climate change, including food shortages and mass migration, which would place extra pressure on urban labor markets? Will countries continue to integrate commercially and financially, fostering growth and employment? Or will trade wars become a norm in a world increasingly fragmented and inward-looking? In sub-Saharan Africa, these uncertainties meet a dramatic increase in population and a rapid expansion in the labor force, which is becoming increasingly urban.

The U.S. labor share of income has been on a secular downward trajectory since the beginning of the new millennium. Using data that are disaggregated across both state and industry, we show the decline in the labor share is broad-based, but the extent of the fall varies greatly. Exploiting a new data set on the task characteristics of occupations, the U.S. input-output tables, and the Current Population Survey, we find that in addition to changes in labor institutions, technological change and different forms of trade integration lowered the labor share. In particular, the fall was largest, on average, in industries that saw: a high initial intensity of “routinizable” occupations; steep declines in unionization; a high level of competition from imports; and a high intensity of foreign input usage. Quantitatively, we find that the bulk of the effect comes from changes in technology that are linked to the automation of routine tasks, followed by trade globalization.

The effect of technological innovations on labor market outcomes has been widely studied. According to a recent World Bank report, 1.8 billion jobs in developing countries are at risk of being automated. However, little is known about how technological innovations will affect Central Asia. I hypothesize that, in Central Asian economies, a firm’s level of computerization is negatively correlated with its workforce size. Given that a high proportion of Central Asian workers fill manual, low-skilled positions, a substantial number of these positions should, in theory, be susceptible to technology-induced labor substitution. To test my hypothesis, I use the World Bank’s Business Environment and Enterprise Performance Survey (BEEPS) to assess the relationship between technological advancement, as measured by changes in a firm’s personal computer (PC) adoption, and changes in its workforce size for the countries of Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan. My analysis finds no relationship between a firm’s computer use and its workforce size. This finding withstands a battery of robustness checks. The World Bank asserts that the rapid growth and diffusion of digital technologies, along with the growing importance of the digital economy, necessitate a discussion among policymakers and policy researchers about the consequences of these new technologies. This paper contributes to a better understanding of the regional effect of technology on labor, which could help guide assessments of policy options in order to maximize the benefits, and minimize the adverse impacts, of new technologies.