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 past thirty years have seen a dramatic decline in the rate of income convergence across states and in population flows to high-income places. These changes coincide with a disproportionate increase in housing prices in high-income places, a divergence in the skill-specific returns to moving to high-income places, and a redirection of low-skill migration away from high-income places. We develop a model in which rising housing prices in high-income areas deter low-skill migration and slow income convergence. Using a new panel measure of housing supply regulations, we demonstrate the importance of this channel in the data.

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.

Recent empirical evidence suggests that skill-biased technological change accelerated during the Great Recession. We use a neoclassical growth framework to analyze how business cycle fluctuations interact with a long-run transition towards a skill-intensive technology. In the model, the adoption of new technologies by firms and the acquisition of new skills by workers are concentrated in downturns due to low opportunity costs. As a result, shocks lead to deeper recessions, but they also speed up adoption of the new technology. Our calibrated model matches both the long-run downward trend in routine employment and key features of the Great Recession.

A systematic review is provided on artificial agent methodologies applicable to control engineering of autonomous vehicles and robots. The paper focuses on some fundamentals that make a machine autonomous: decision making that involves modelling the environment and forming data abstractions for symbolic processing and logic-based reasoning. Most relevant capabilities such as navigation, autonomous path planning, path following control, and communications, that directly affect decision making, are treated as basic skills of agents. Although many autonomous vehicles have been engineered in the past without using the agent-oriented approach, most decision making onboard of vehicles is similar to or can be classified as some kind of agent architecture, even if in a naïve form. First the ANSI standard of intelligent systems is recalled then a summary of the fundamental types of possible agent architectures for autonomous vehicles are presented, starting from reactive, through layered, to advanced architectures in terms of beliefs, goals, and intentions. The review identifies some missing links between computer science results on discrete agents and engineering results of continuous world sensing, actuation, and path planning. In this context design tools for ‘abstractions programming’ are identified as needed to fill in the gap between logic-based reasoning and sensing. Finally, research is reviewed on autonomous vehicles in water, on the ground, in the air, and in space with comments on their methods of decision making. One of the main conclusions of this review is that standardization of decision making through agent architectures is desirable for the future of intelligent vehicle developments and their legal certification.

The paper reviews the macroeconomic data describing the British economy from 1760 to 1913 and shows that it passed through a two-stage evolution of inequality. In the first half of the 19th century, the real wage stagnated while output per worker expanded. The profit rate doubled and the share of profits in national income expanded at the expense of labour and land. After the middle of the 19th century, real wages began to grow in line with productivity, and the profit rate and factor shares stabilized. An integrated model of growth and distribution is developed to explain these trends. The model includes an aggregate production function that explains the distribution of income, while a savings function in which savings depended on property income governs accumulation. Simulations with the model show that technical progress was the prime mover behind the industrial revolution. Capital accumulation was a necessary complement. The surge in inequality was intrinsic to the growth process: technical change increased the demand for capital and raised the profit rate and capital’s share. The rise in profits, in turn, sustained the industrial revolution by financing the necessary capital accumulation. After the middle of the 19th century, accumulation had caught up with the requirements of technology and wages rose in line with productivity.

This paper examines the evolving structure of the American economy, specifically the trends in employment, value added, and value added per employee from 1990 to 2008. Employing historical time series data from the Bureau of Labor Statistics and the Bureau of Economic Analysis, US industries are separated into internationally tradable and nontradable components, allowing for employment and value-added trends at both the industry and the aggregate level to be examined. Value added grew across the economy, but almost all of the incremental employment increase of 27.3 million jobs was on the nontradable side, where government and health care are the largest employers and provided the largest increments (an additional 10.4 million jobs) over the past two decades. There are obvious questions about whether those trends can continue; without fast job creation in the non- tradable sector during this period, the United States would already have faced a major employment challenge. The non- tradable sector also experienced much slower growth in value added per employee; because value added per employee is highly correlated with income, it goes a long way to explain the stagnation of wages across large segments of the workforce. The evolution of the US economy supports the notion of there being a long-term structural challenge with respect to the quantity and quality of employment opportunities in the United States.