What are the socio-economic effects of the widespread introduction of robots, algorithms and digital technologies like artificial intelligence and machine learning? Following Frey and Osborne (London futures agiletown: the relentless march of technology and London's response. Deloitte, 2014, Technol Forecast Social Change 114(C), 254-280, 2017) we apply the computerisation probabilities to occupations in Austria. We conclude that about 40 percent of the Austrian workforce is active in occupations that are very likely to undergo substantial changes regarding task structure, skill requirement and working environment in the future, causing challenges and opportunities. We also provide evidence that compared to men, women in Austria seem more likely to be affected by technological changes, with sectoral orientation playing a role. Following EBRD (Skills, employment and automation. Chapter 2 in: EBRD (2018): Transition Report 2018-19, European Bank for Reconstruction and Development, London, 2018), we see a broader move towards job polarisation. We see this as distributive consequences of technological change and argue that the consequences of technology refashioning socio-economic development are influencing market processes, actors and inequalities. As in previous technological advances, coping with these changes will require efforts on the individual as well as on the political level.

This study explores productivity growth of 65 Austrian biogas plants from 2006 to 2014 using Data Envelopment Analysis. Productivity growth is measured by calculating the Malmquist productivity index, and the contributions of technical change, efficiency change, and scale change to productivity growth are isolated. The results reveal that the average annual productivity growth between 2006 and 2014 is 1.1 percent. The decomposition of the Malmquist productivity index shows that the annual scale change, technical change, and efficiency change for the average plant is 0.6 percent, 0.3 percent, and 0.3 percent, respectively. These results indicate that the exploitation of returns to scale is a major driver of productivity growth and technical change is rather low. A second-stage regression analysis reveals that rising feedstock prices incentivised efficiency improvements but initial capital subsidies did not have an impact on technical change and productivity growth.

The paper employs surveys among Austrian households to study ownership and purchase intentions of crypto-assets. About 1.6 percent of Austrians own crypto-assets and about 5 percent can be viewed as potential adopters. Owners, on average, have higher financial knowledge and are more risk-tolerant than non-owners. Distrust in banks or in conventional currencies is not found to be an important driver of ownership. Intentions to adopt are strongly affected by profit expectations and by beliefs that crypto-assets offer advantages for payments – most adopters or potential adopters hold both beliefs. Perceptions of high volatility or the risk of fraud and online theft dampen the demand for crypto-assets.

This study attempts to identify the short- and long-run components of the Kaldor-Verdoorn law (KV law). The law claims that demand dynamics drive productivity dynamics. The claim is tested with a panel of ten Central and Eastern European countries, where productivity and demand growth have been slowing since 2004-2006 and where fears of an end of convergent growth are spreading. Meanwhile, the gradual slowing of output and productivity growth applies not only to the region considered, but it is also a global phenomenon that is occurring despite remarkable technical progress and that is referred to as the productivity puzzle. However, this puzzle would be solved in light of the KV law. To test for its long-term properties, panel cointegration models with autoregressive distributed lags are applied. Our results confirm the law for the region; slower productivity growth is not due to "adverse technological progress" but to weakening external and domestic demand, which might block the implementation of product and process innovations.

The European Commission's Scoreboard of Macroeconomic Imbalances is a rare case of a publicly released early warning system. It was published first time in 2012 by the European Commission as a reaction to public debt crises in Europe. So far, the Macroeconomic Imbalance Procedure takes a one-size-fits-all approach with regard to the identification of thresholds. The experience of Central and Eastern European countries during the global financial crisis and in the resulting public debt crises has been largely different from that of other European countries. This paper looks at the appropriateness of scoreboard of the Macroeconomic Imbalances Procedure of the European Commission for this group of catching-up countries. It is shown that while some of the indicators of the scoreboard are helpful to predict crises in the region, thresholds are in most cases set too narrow since it largely disregarded the specifics of catching-up economies, in particular higher and more volatile growth rates of various macroeconomic variables.

In this paper we analyse the evolution of the current account as a percentage of GDP for a group of Central and Eastern European countries. Instead of analysing only the variable for unit roots, we go a step further and test for different speeds of mean reversion dependent on break dates endogenously determined. We apply the Bai and Perron method to find that most countries have managed to balance their current account, but some of them should keep an eye on the low speed of mean reversion and the deviation of the time trend from balance.