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This chapter synthesizes two complementary streams in the economic thought of David M. Gordon, and explores their shared relevance to the rise of the “gig” economy in modern economies. Gordon made lasting contributions to the radical political-economic analysis of work and employment. At the microeconomic level of individual workplaces, he and his collaborators originally explained the factors affecting employers’ labor extraction strategies, through which they seek maximum work effort from waged employees while minimizing unit labor costs. At the macroeconomic or structural level, he linked that conflictual process to the broader institutional and structural features of the overall accumulation regime which is essential to any successful incarnation of capitalism. Employment practices and social structures have evolved considerably since Gordon’s passing, but his insights are still useful in understanding the rise of, and limits to, modern work arrangements. In particular, Gordon’s dual portrayal of the parameters of labor extraction, and their positioning within a broader structural and institutional context, provides a convincing explanation of both the recent rise of gig economy practices, and their potential limits.

Witchcraft in Honduras is an unprotected marginalized woman’s efforts to gain social, economic, and political power through an informal economy by utilizing the cultural belief in the witches’ supernatural power. The Honduran post-colonial Latin American culture allows for a persistent informal economy, in part, based on the commoditization of witchcraft and exorcism. The case study provides a specific example through ethnographic interviews of this under-researched informal economy driven by fear and economic desperation. Further research and analysis of these poorly understood and rarely recorded modern phenomena and the associated informal economy is needed.

The development of Research Management and Administration (RMA) in Europe is strongly connected with the development of the Science and Technology (S&T) policy of the European Union (EU). These policies were the result of a continuous debate between the member states and the European Commission and European Parliament.

Although there is no data on the early development of RMA, there are some publications on the history of the development of the S&T policy in Europe: the excellent publication ‘A History of European Union Research Policy’ by Luca Guzzetti (Guzzetti, 1995). Guzzetti’s book investigates the history of EU research policies from 1948 up to the preparation of the Fourth Framework Programme (FP) (1994–1998).

The RMA aspects are constructed mainly by oral history complemented with some written sources. The history shows a gradual development of the profession unevenly spread in time and European geography. This has mainly with the EU enlargement in the same period, when new member states were connected to the FP. The profession started with a few colleagues’ way back in the eighties of the last century as financial people were dealing with the first European financial reporting up to the present day where RMA is becoming a field of work attracting many new colleagues.

The author shows that extending the estimation window prior to structural breaks in cointegrated systems can be beneficial for forecasting performance and highlights under which conditions. In doing so, the author generalizes the Pesaran and Timmermann (2005)’s forecast error decomposition and shows that it depends on four terms: (1) a period ahead risk; (2) a bias due to a conditional mean shift; (3) a bias due to a variance mismatch; (4) a gap term valid only conditionally. The author also derives new expressions for the estimators of the adjustment matrix and a constant, which are auxiliary to the decomposition. Finally, the author introduces new simulation-based estimators for the finite sample forecast properties which are based on the derived decomposition. The author’s finding points out that, in some cases, parameter instability can be neglected by extending the window backward and forecasters can be insured against higher forecast risk under this model class as well, generalizing Pesaran and Timmermann (2005)’s result. The author’s result gives renewed importance to break tests, in order to distinguish cases when break-neglection is (not) appropriate.

Many recent chapters have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic variables. These chapters construct variables based on Google searches and use them as explanatory variables in regression models. We add to this literature by nowcasting using dynamic model selection (DMS) methods which allow for model switching between time-varying parameter regression models. This is potentially useful in an environment of coefficient instability and over-parameterization which can arise when forecasting with Google variables. We extend the DMS methodology by allowing for the model switching to be controlled by the Google variables through what we call “Google probabilities”: instead of using Google variables as regressors, we allow them to determine which nowcasting model should be used at each point in time. In an empirical exercise involving nine major monthly US macroeconomic variables, we find DMS methods to provide large improvements in nowcasting. Our use of Google model probabilities within DMS often performs better than conventional DMS methods.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.