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This paper presents an empirical assessment of bilateral migration flows into the EU-15 countries. Using an extended gravity model, it identifies economic, welfare state, geospatial and linguistic variables as the principal determinants of migration flows into the EU-15 countries. As long as its effect is not offset by a high unemployment rate in the host country, the level of social protection expenditure influences migrants' choice of destination. However, albeit acting as a joint force with other economic, cultural and geospatial variables, the welfare state characteristics of the host country need to be reckoned with when studying European migration flows. Our empirical findings lend some support for a more unified or at least better coordinated social policy across the European Union.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Testing Mundell's Intuition of Endogenous OCA TheoryThierry Warin Phanindra V. Wunnava Hubert P. Janicki The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post World Net. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.

The existing literature on political budget cycles looks at the temptation for incumbent governments to run a greater deficit before an election by considering the characteristics of the incumbent. We propose here to look at the signals the incumbent receives from the voters. For this purpose, we consider the votes from the previous national elections and see whether they may influence the incumbent government to run a sound fiscal policy or an expansionary fiscal policy. However, since 1993 Europe has been equipped with two fiscal rules: a deficit and a debt ceiling. In this context, can we find evidence of a "political budget cycle" before 1993, and did the fiscal rules prevent the existence of a "political budget cycle" afterwards? To address these questions, we use a cross-sectional time series analysis of European countries from 1979 to 2005.

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Machine learning in finance has been on the rise in the past decade. The applications of machine learning have become a promising methodological advancement. The paper’s central goal is to use a metadata-based systematic literature review to map the current state of neural networks and machine learning in the finance field. After collecting a large dataset comprised of 5053 documents, we conducted a computational systematic review of the academic finance literature intersected with neural network methodologies, with a limited focus on the documents’ metadata. The output is a meta-analysis of the two-decade evolution and the current state of academic inquiries into financial concepts. Researchers will benefit from a mapping resulting from computational-based methods such as graph theory and natural language processing.

The growing use of artificial intelligence (A.I.) algorithms in businesses raises regulators' concerns about consumer protection. While pricing and recommendation algorithms have undeniable consumer-friendly effects, they can also be detrimental to them through, for instance, the implementation of dark patterns. These correspond to algorithms aiming to alter consumers' freedom of choice or manipulate their decisions. While the latter is hardly new, A.I. offers significant possibilities for enhancing them, altering consumers' freedom of choice and manipulating their decisions. Consumer protection comes up against several pitfalls. Sanctioning manipulation is even more difficult because the damage may be diffuse and not easy to detect. Symmetrically, both ex-ante regulation and requirements for algorithmic transparency may be insufficient, if not counterproductive. On the one hand, possible solutions can be found in counter-algorithms that consumers can use. On the other hand, in the development of a compliance logic and, more particularly, in tools that allow companies to self-assess the risks induced by their algorithms. Such an approach echoes the one developed in corporate social and environmental responsibility. This contribution shows how self-regulatory and compliance schemes used in these areas can inspire regulatory schemes for addressing the ethical risks of restricting and manipulating consumer choice.

We propose to measure economic convergence for three emerging countries: Brazil/China/India. A first result is that the higher the level of productivity in an industry, the lower its growth rate, showing a convergence to the productivity frontier represented by the U.S. A first contribution is to propose a new definition of convergence, based on labor productivity vis-à-vis the technological frontier. A second contribution is that we use industry-level data to measure convergence. In doing so, we aim to reduce the biases of using trade data collected at the national level as in previous models.