Abstract Background: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. Objective: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. Methods: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. Results: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. Conclusions: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.
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Social enterprises and government share the ultimate goal of solving societal problems, which provides a lot of potential for collaboration between the two parties. While the local government level is the most relevant for social enterprises, little research has been done on the relationship between social entrepreneurs and local government officials. However, in the Netherlands, social enterprises experience these relations as far from optimal, evidenced by the fact that they named ‘regulations and government policy’ as the most important obstacle for increasing their impact in a 2015 sector survey. Therefore, a pilot project was started with social entrepreneurs in an Amsterdam neighbourhood, forming a learning network aiming to improve relations with local government. In the network, an innovative tool was developed in the form of a set of five illustrated stereotypes of social entrepreneurs with certain views towards local government. These stereotypes serve both as a reflection tool for social entrepreneurs and as a communication tool to open dialogue between social entrepreneurs and local government. We conclude that in an applied research project, it is crucial to place focus on the final phases in which results are reformulated into practical tools to match target groups, and resulting tools are distributed through targeted events and publications.
ABSTRACT. It is now generally accepted that the quality of the regulatory arrangements should be appraised not only by looking at the institutional design, but also by evaluating the factual enforcement and implementation of regulations. It is therefore advised that national governments take a more active stance in supervising the regulatory enforcement by different regulatory agencies. However, in some cases, government’s activism might be an impeding factor in regulatory enforcement. That this is not so crazy idea shows the analysis of the regulatory enforcement by Lithuanian Competition Authority in the area of competition policy during the years of integration to the European Union. For example, not only political and financial independence of the Competition Authority was difficult to establish, but also functions and competences of the regulatory agency were changed a number of times, which hampered the effectiveness of the agency’s performance while enforcing the competition law. In addition to often changes of functions, also the scope of competences was changing. As a result, the variety of tasks attributed to the Lithuanian Competition Authority caused the growing overload of work, which further hindered its regulatory practice. The question is who can be blamed for that? Was it just the inexperience of the government who was seeking for the best institutional design and could not stop with redesigning the regulatory agency or was it the intentional behaviour guided by some concrete interests as a result of a regulatory capture? The analysis of the regulatory enforcement during the period of 15 years does not allow for disregarding of the second possibility.
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