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Orešković and Porsdam Mann draw a distinction between ‘fast’ and ‘slow’ science. Whereas the latter involves rigorous and laborious adherence to the scientific method, the former represents the reality that much scientific work faces time pressures which at times force shortcuts. The distinction can be seen to operate in contemporary research into the coronavirus pandemic: whereas the development of vaccines and treatments usually requires years of meticulous laboratory work and several more years of clinical testing, the many millions suffering from the disease need a treatment now. However, by taking too many safeguards off the treatment discovery and testing pipelines, or by refusing to act in accordance with scientific advice, governments risk sacrificing the public’s trust not only in the government’s scientific bona fides but in the scientific process itself. This is a heavy price to pay, argue Orešković and Porsdam Mann, and point to evidence indicating that the success of Germany and Japan in combating COVID-19 can be traced to public trust in science and government, as well as scientifically-informed and respectful national leadership.
Managing the Work-Nonwork Interface: Personal Social Media Use as a Tool to Craft Boundaries?
(2021)
What is Design Theory?
(2021)
What does the right to social security mean if the majority of the world’s population still lives in overwhelming insecurity? What is the significance and role of international social security standards, developed by the International Labour Organization (ILO2) over decades? What are the economic, labour market and political factors determining differences between countries with respect to population coverage by social security schemes and systems? How can past and recent experiences of countries in the Global North and in the Global South be used to expand social security coverage, and what role can be played by the new standard in this area – the ILO Social Protection Floors Recommendation 202, adopted in 2012?
The future of work
(2021)
Driven by the exponential increase in the computational power of machines, data digitalization and scientific advancement in robotics and automation, the current wave of technological change is seemingly unprecedented in speed and scale. It transforms manufacturing and businesses making them more flexible, decentralized and efficient (Lasi et al. 2014). Even though technological change is nothing new, some argue that it is different this time. The new technologies have not only the potential to substitute labor (Nomaler and Verspagen 2018), they also change the way people work. The trend towards new forms of employment is no longer a marginal phenomenon.
The changing world poses many challenges to public policies, including social policies – among them social protection policies, which are the main focus of this handbook. Here, in this part of the handbook, we take on a number of these challenges: demographic changes and their interaction with social protection policies; roles of social protection in coping with the consequences of the COVID-19 pandemic (both topics discussed in Chapter 39 and 43 by Woodall); the challenges of globalisation (discussed in Chapter 40 by Betz) and the limitations it imposes on state sovereignty and its ability to decide on the size of publicly funded programmes, in particular social protection; challenges to labour markets and social effective protection coverage posed by automation and digitalisation of businesses (discussed in Chapter 41 by Gassmann) and, last but not least, potential roles of social protection in facilitating population’s adjustments to climate change (discussed in Chapter 42 by Malerba).
Policy analysis is the cornerstone of evidence-based policy making.1 It identifies the problems, informs programme design, supports the monitoring of policy implementation and is needed to evaluate programme impacts (Scott 2005). Rigorous and credible policy evidence is necessary to ensure the transparency and accountability of policy decisions, to secure political and public support and, hence, the allocation of financial resources. Sound policy analysis helps design effective and efficient programmes, thereby maximizing programme impact.
In recent years, the basic income grant (BIG) discourse has gained attention worldwide as a potential policy option in social protection as testified by recent public debates, ongoing pilot projects, campaigning efforts,1 policy measures during Covid-19 and the surge in academic research. A BIG refers to regular cash transfers paid to all members of society irrespective of their socio-economic status, their capacity or willingness to participate in the labour market or having to meet pre-determined conditions (Offe 2008; Van Parijs 1995, 2003; Wright 2004, 2006). Despite the recent hype around BIG, Iran is the only country worldwide with a scaled-up BIG (Tabatabai 2011, 2012). Other programmes have never gone beyond pilot programmes. This raises the question why this is the case.
With the roll out of social protection programmes to national scale, questions about implementation and delivery move more and more into the centre of debate (e.g. UNDP 2020; UNDP and UNCDF 2014; Kramon 2019). This concerns in particular the local level, where key processes of implementation are taking place, but where at the same time institutional, operational and financial capacities are often the weakest. While social protection programmes are usually based on a clearly defined set of operational rules and regulations – usually set out in a programme manual – in practice these processes often tend to look quite different. Although many social protection programmes have explicitly excluded traditional authorities from playing an active role in programme delivery, there is ample evidence from across countries that in many local contexts, these ‘informal institutions’ continue to play an important role in the delivery of social protection programmes.