Cities between Competitiveness and Cohesion: Discourses, Realities and Implementation (GeoJournal Library)
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The central aim of many analyses in population studies and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the ´´causes of effects´´ (e.g. ´´What accounts for the decline of fertility rates?´´) by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the ´´effects of causes´´ (e.g. ´´What is the effect of women’s labour force participation on fertility?´´) in natural sciences and in psychology is to conduct randomised experiments. In population studies, experimental designs are unfeasible. Nevertheless, quasi or natural experiments are sometimes performed to estimate treatment effects. However, most research is based on non-experimental designs (also called observational or survey designs). Inferring the effects of causes – or treatment effects – from other than experimental data is tricky. However, treatment effects can be inferred from non-experimental data with a counterfactual approach. In such a perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin (1974). Other important contributions include the work of James Heckman and collaborators and of Charles Manski and collaborators.

Von Händler/Antiquariat, Rhein-Team Lörrach Ivano Narducci e.K. [57451429], Lörrach, BW, Germany.
- The central aim of many analyses in population studies and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the causes of effects (e.g. What accounts for the decline of fertility rates ) by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the effects of causes (e.g. What is the effect of women s labour force participation on fertility ) in natural sciences and in psychology is to conduct randomised experiments. In population studies, experimental designs are unfeasible. Nevertheless, quasi or natural experiments are sometimes performed to estimate treatment effects. However, most research is based on non-experimental designs (also called observational or survey designs). Inferring the effects of causes or treatment effects from other than experimental data is tricky. However, treatment effects can be inferred from non-experimental data with a counterfactual approach. In such a perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin (1974). Other important contributions include the work of James Heckman and collaborators and of Charles Manski and collaborators. 299 pp. Englisch.

Von Händler/Antiquariat, Rheinberg-Buch [53870650], Bergisch Gladbach, NRW, Germany.
Neuware - The central aim of many analyses in population studies and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the causes of effects (e.g. What accounts for the decline of fertility rates ) by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the effects of causes (e.g. What is the effect of women s labour force participation on fertility ) in natural sciences and in psychology is to conduct randomised experiments. In population studies, experimental designs are unfeasible. Nevertheless, quasi or natural experiments are sometimes performed to estimate treatment effects. However, most research is based on non-experimental designs (also called observational or survey designs). Inferring the effects of causes or treatment effects from other than experimental data is tricky. However, treatment effects can be inferred from non-experimental data with a counterfactual approach. In such a perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin (1974). Other important contributions include the work of James Heckman and collaborators and of Charles Manski and collaborators. 299 pp. Englisch.

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The central aim of many analyses in population studies and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the "causes of effects" (e.g. "What accounts for the decline of fertility rates?") by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the "e, This book offers a cross-European analysis of urban and regional strategies of reconciling welfare and competitiveness. It develops an understanding of the strategy challenge to reconcile competitiveness with cohesion, and provides an analysis of cities and regions as actors in multi-level governance settings. The book offers a European comparative view on housing, labour markets, enterprises, ethnical issues, gender dimensions, urban development projects, transport, and sustainability.

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The book is the result of a COST Action financed by the European Science Foundation. It analyses the key concepts, ideas and processes driving competitiveness and cohesion agendas across Europe and demonstrates the implications and the effects of contemporary urban regeneration policies and politics in Europe. The case study material ensures that an in depth understanding of policy processes is conveyed to readers, and attention is drawn to the problems and paradoxes of policy, but also to the possibilities for policy innovation and learning across national and disciplinary borders. The material A.O. studies the cities of Amsterdam, Antwerp, Athens, Bratislava, Istanbul, Maribor, London, Rotterdam, Vienna, and Zurich. Specific policy fields which are looked at relate to housing, labour markets, enterprises, integration of ethnical groups, urban regeneration, or transport. The book shows that spatial and urban policy continues to be a key site of policy intervention and experimentation. Different national welfare systems, political cultures, and socio-economic conditions combine and recombine to address policy problems and opportunities. Collectively, the authors argue that the examined policy initiatives reflect and reproduce these broader changes and shifting ways of thinking about the appropriate relationships between citizens, businesses, and the state.

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Discourses, Realities and Implementation, Discourses, Realities and Implementation.