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Migration and development in Senegal : a system dynamics analysis of the feedback relationships
(2011)
This thesis investigates the reciprocal relationship between migration and development in Senegal. Therewith, it contributes to the debate as to whether migration in developing countries enhances or rather impedes the development process. Even though extensive and controversial discussions can be found in the scientific literature regarding the impact of migration on development, research has scarcely examined the feedback relationships between migration and development. Science however agrees with both the fact that migration affects development as well as that the level of development in a country determines migration behaviour. Thus, both variables are neither dependent nor independent, but endogenous variables influencing each other and producing behavioural pattern that cannot be investigated using a static and unidirectional approach. On account of this, the thesis studies the feedback mechanisms existing between migration and development and the behavioural pattern generated by the high interdependence in order to be able to draw conclusions concerning the impact of changes in migration behaviour on the development process. To explore these research questions, the study applies the computer simulation method ‘System Dynamics’ and amplifies the simulation model for national development planning called ‘Threshold 21’ (T21), representing development processes endogenously and integrating economic, social and environmental aspects, using a structure that portrays the reasons and consequences of migration. The model has been customised to Senegal, being an appropriate representative of the theoretical interesting universe of cases. The comparison of the model generated scenarios - in which the intensity of emigration, the loss and gain of education, the remittances or the level of dependence changes - facilitates the analysis. The present study produces two important results. The first outcome is the development of an integrative framework representing migration and development in an endogenous way and incorporating several aspects of different theories. This model can be used as a starting point for further discussions and improvements and it is a fairly relevant and useful result against the background that migration is not integrated into most of the development planning tools despite its significant impact. The second outcome is the gained insights concerning the feedback relations between migration and development and the impact of changes in migration on development. To give two examples: It could be found that migration impacts development positively, indicated by HDI, but that the dominant behaviour of migration and development is a counteracting behaviour. That means that an increase in emigration leads to an improvement in development, while this in turn causes a decline in emigration, counterbalancing the initial increase. Another insight concerns the discovery that migration causes a decline in education in the short term, but leads to an increase in the long term, after approximately 25 years - a typical worse-before-better behaviour. From these and further observations, important policy implications can be derived for the sending and receiving countries. Hence, by overcoming the unidirectional perspective, this study contributes to an improved understanding of the highly complex relationship between migration and development and their feedback relations.
The development of rural areas concerning food security, sustainability and social-economic stability is key issue to the globalized community. Regarding the current state of climatic change, especially semi-arid regions in uenced by monsoon or El Niño are prone to extreme weather events. Droughts, ooding, erosion, degradation of soils and water quality and deserti cation are some of the common impacts. State of the art in hydrologic environmental modeling is generally operating under a reductionist paradigm (Sivapalan 2005). Even an enormous quantity of process-oriented models exists, we fail in due reproduction of complexly interacting processes in their effective scale in the space-time-continuum, as they are described through deterministic small-scale process theories (e.g. Beven 2002). Yet large amounts of parameters - with partly doubtful physical expression - and input data are needed. In contradiction to that most soft information about patterns and organizing principles cannot be employed (Seibert and McDonnell 2002). For an analysis of possible strategies on the one hand towards integrated hydrologic modeling as decision support and on the other hand for sustainable land use development the 512 km2 large catchment of the Mod river in Jhabua, Madhya Pradesh, India has been chosen. It is characterized by a setting of common problems of peripheral rural semi-arid human-eco-systems with intensive agriculture, deforestation, droughts and general hardship for the people. Scarce data and missing gauges are adding to the requirements of data acquisition and process description. The study at hand presents a methodical framework to combine eld scale data analysis and remote sensing for the setup of a database focusing plausibility over strict data accuracy. The catena-based hydrologic model WASA (Güntner 2002) employes this database. It is expanded by a routine for crop development simulation after the de Wit approach (e.g. in Bouman et al. 1996). For its application as decision support system an agentbased land use algorithm is developed which decides on base of site speci cations and certain constraints (like maximum pro t or best local adaptation) about the cropping. The new model is employed to analyze (some) land use strategies. Not anticipated and a priori de ned scenarios will account for the realization of the model but the interactions within the system. This study points out possible approaches to enhance the situation in the catchment. It also approaches central questions of ways towards due integrated hydrological modeling on catchment scale for ungauged conditions and to overcome current paradigms.
Im Zuge der Covid-19 Pandemie werden zwei Werte täglich diskutiert: Die zuletzt gemeldete Zahl der neu Infizierten und die sogenannte Reproduktionsrate. Sie gibt wieder, wie viele weitere Menschen ein an Corona erkranktes Individuum im Durchschnitt ansteckt. Für die Schätzung dieses Wertes gibt es viele Möglichkeiten - auch das Robert Koch-Institut gibt in seinem täglichen Situationsbericht stets zwei R-Werte an: Einen 4-Tage-R-Wert und einen weniger schwankenden 7-Tage-R-Wert. Diese Arbeit soll eine weitere Möglichkeit vorstellen, einige Aspekte der Pandemie zu modellieren und die Reproduktionsrate zu schätzen.
In der ersten Hälfte der Arbeit werden die mathematischen Grundlagen vorgestellt, die man für die Modellierung benötigt. Hierbei wird davon ausgegangen, dass der Leser bereits ein Basisverständnis von stochastischen Prozessen hat. Im Abschnitt Grundlagen werden Verzweigungsprozesse mit einigen Beispielen eingeführt und die Ergebnisse aus diesem Themengebiet, die für diese Arbeit wichtig sind, präsentiert. Dabei gehen wir zuerst auf einfache Verzweigungsprozesse ein und erweitern diese dann auf Verzweigungsprozesse mit mehreren Typen. Um die Notation zu erleichtern, beschränken wir uns auf zwei Typen. Das Prinzip lässt sich aber auf eine beliebige Anzahl von Typen erweitern.
Vor allem soll die Wichtigkeit des Parameters λ herausgestellt werden. Dieser Wert kann als durchschnittliche Zahl von Nachfahren eines Individuums interpretiert werden und bestimmt die Dynamik des Prozesses über einen längeren Zeitraum. In der Anwendung auf die Pandemie hat der Parameter λ die gleiche Rolle wie die Reproduktionsrate R.
In der zweiten Hälfte dieser Arbeit stellen wir eine Anwendung der Theorie über Multitype Verzweigungsprozesse vor. Professor Yanev und seine Mitarbeiter modellieren in ihrer Veröffentlichung Branching stochastic processes as models of Covid-19 epidemic development die Ausbreitung des Corona Virus' über einen Verzweigungsprozess mit zwei Typen. Wir werden dieses Modell diskutieren und Schätzer daraus ableiten: Ziel ist es, die Reproduktionsrate zu ermitteln. Außerdem analysieren wir die Möglichkeiten, die Dunkelziffer (die Zahl nicht gemeldeter Krankheitsfälle) zu schätzen. Wir wenden die Schätzer auf die Zahlen von Deutschland an und werten diese schließlich aus.