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In this article, we examine the effects of political change on name changes of units within central government ministries. We expect that changes regarding the policy position of a government will cause changes in the names of ministerial units. To this end we formulate hypotheses combining the politics of structural choice and theories of portfolio allocation to examine the effects of political changes at the cabinet level on the names of intra-ministerial units. We constructed a dataset containing more than 17,000 observations on name changes of ministerial units between 1980 and 2013 from the central governments of Germany, the Netherlands, and France. We regress a series of generalized estimating equations (GEE) with population averaging models for binary outcomes. Finding variations across the three political-bureaucratic systems, we overall report positive effects of governmental change and ideological positions on name changes within ministries.
The legitimacy and effectiveness of international organizations are often linked directly to issues of representation—not only on their high-level governing boards and in top leadership but also within their staff. This article explores two key questions of bureaucratic representation in the critical cases of the International Monetary Fund and World Bank. First, we seek to unpack three essential dimensions of staff representation—nationality, education, and gender—to explain how representation may matter for international organizations. Second, we aim to describe the multiple dimensions of representation in the International Monetary Fund and the World Bank over the past twenty years by deploying a novel dataset on staff demographics, focusing on ranks with decision-making authority within the institutions. Our descriptive analysis reveals that the International Monetary Fund and the World Bank have made considerable efforts to diversify their bureaucracies. Nonetheless, representation remains uneven; for example, nationals from middle- and low-income countries, women, and staff without economics degrees from prominent US- or UK-based universities are less present in key leadership positions. These results may be well explained by the particular needs of the institutions’ technical mandates and limits in the supply of qualified staff and, as such, need not be seen as suboptimal. Nonetheless, perceived imbalances in representation may continue to pose external legitimation and operational challenges to the International Monetary Fund and the World Bank in a complex political environment where such multidimensional representation is important to sustaining the buy-in of donor and borrower countries alike. To this end, we recommend that the International Monetary Fund and the World Bank enhance their diversity and inclusion efforts by increasing transparency via reporting disaggregated data on workforce composition and introducing annual requirements to publish progress reports with management feedback to strengthen internal and external accountability.
Public sector organizations at all levels of government increasingly rely on Big Data Algorithmic Systems (BDAS) to support decision-making along the entire policy cycle. But while our knowledge on the use of big data continues to grow for government agencies implementing and delivering public services, empirical research on applications for anticipatory policy design is still in its infancy. Based on the concept of policy analytical capacity (PAC), this case study examines the application of BDAS for early crisis detection within the German Federal Government—that is, the German Federal Foreign Office (FFO) and the Federal Ministry of Defence (FMoD). It uses the nested model of PAC to reflect on systemic, organizational, and individual capacity-building from a neoinstitutional perspective and allow for the consideration of embedded institutional contexts. Results from semi-structured interviews indicate that governments seeking to exploit BDAS in policymaking depend on their institutional environment (e.g., through research and data governance infrastructure). However, specific capacity-building strategies may differ according to the departments' institutional framework, with the FMoD relying heavily on subordinate agencies and the FFO creating network-like structures with external researchers. Government capacity-building at the individual and organizational level is similarly affected by long-established institutional structures, roles, and practices within the organization and beyond, making it important to analyze these three levels simultaneously instead of separately.
Introduction
(2022)
Chinese CSP for the world?
(2022)
For three consecutive five-year plans since 2006, China has worked on building up an internationally competitive CSP industry and value chain. One big milestone in commercializing proprietary Chinese CSP technology was the 2016 demonstration program of 20 commercial-scale projects. China sought to increase and demonstrate capacities for domestic CSP technology development and deployment. At the end of the 13th five-year period, we take stock of the demonstrated progress of the Chinese CSP industry towards delivering internationally competitive CSP projects. We find that in January 2021, eight commercial-scale projects, in total 500 MW, have been completed and three others were under construction in China. In addition, Chinese EPC’s have participated in three international CSP projects, although proprietary Chinese CSP designs have not been applied outside China. The largest progress has been made in molten-salt tower technology, with several projects by different companies completed and operating successfully: here, the aims were met, and Chinese companies are now at the global forefront of this segment. Further efforts for large-scale demonstration are needed, however, for other CSP technologies, including parabolic trough - with additional demonstration hindered by a lack of further deployment policies. In the near future, Chinese companies seek to employ the demonstrated capabilities in the tower segment abroad and are developing projects using Chinese technology, financing, and components in several overseas markets. If successful, this will likely lead to increasing competition and further cost reductions for the global CSP sector.
Energy models are used to inform and support decisions within the transition to climate neutrality. In recent years, such models have been criticised for being overly techno-centred and ignoring environmental and social factors of the energy transition. Here, we explore and illustrate the impact of ignoring such factors by comparing model results to model user needs and real-world observations. We firstly identify concrete user needs for better representation of environmental and social factors in energy modelling via interviews, a survey and a workshop. Secondly, we explore and illustrate the effects of omitting non-techno-economic factors in modelling by contrasting policy-targeted scenarios with reality in four EU case study examples. We show that by neglecting environmental and social factors, models risk generating overly optimistic and potentially misleading results, for example by suggesting transition speeds far exceeding any speeds observed, or pathways facing hard-to-overcome resource constraints. As such, modelled energy transition pathways that ignore such factors may be neither desirable nor feasible from an environmental and social perspective, and scenarios may be irrelevant in practice. Finally, we discuss a sample of recent energy modelling innovations and call for continued and increased efforts for improved approaches that better represent environmental and social factors in energy modelling and increase the relevance of energy models for informing policymaking.
The past three decades of policy process studies have seen the emergence of a clear intellectual lineage with regard to complexity. Implicitly or explicitly, scholars have employed complexity theory to examine the intricate dynamics of collective action in political contexts. However, the methodological counterparts to complexity theory, such as computational methods, are rarely used and, even if they are, they are often detached from established policy process theory. Building on a critical review of the application of complexity theory to policy process studies, we present and implement a baseline model of policy processes using the logic of coevolving networks. Our model suggests that an actor's influence depends on their environment and on exogenous events facilitating dialogue and consensus-building. Our results validate previous opinion dynamics models and generate novel patterns. Our discussion provides ground for further research and outlines the path for the field to achieve a computational turn.
The sequence of isomorphism—
(2022)
Isomorphism has been widely used to describe why trends penetrate entire organizational fields. However, research so far has neglected the temporal aspects of such diffusion processes and the organizational reasons underlying the introduction of new management tools. We argue that during reform waves, the reasons for adopting the new tools differ over time. Using comparative data from two surveys on quality management in the field of higher education and the health sector, we show that early adopters are more likely to be motivated by instrumental reasons, while late adopters will more likely be motivated by institutional reasons.