TY - RPRT A1 - Šedová, Barbora A1 - Čizmaziová, Lucia A1 - Cook, Athene T1 - A meta-analysis of climate migration literature T2 - CEPA Discussion Papers N2 - The large literature that aims to find evidence of climate migration delivers mixed findings. This meta-regression analysis i) summarizes direct links between adverse climatic events and migration, ii) maps patterns of climate migration, and iii) explains the variation in outcomes. Using a set of limited dependent variable models, we meta-analyze thus-far the most comprehensive sample of 3,625 estimates from 116 original studies and produce novel insights on climate migration. We find that extremely high temperatures and drying conditions increase migration. We do not find a significant effect of sudden-onset events. Climate migration is most likely to emerge due to contemporaneous events, to originate in rural areas and to take place in middle-income countries, internally, to cities. The likelihood to become trapped in affected areas is higher for women and in low-income countries, particularly in Africa. We uniquely quantify how pitfalls typical for the broader empirical climate impact literature affect climate migration findings. We also find evidence of different publication biases. T3 - CEPA Discussion Papers - 29 KW - migration KW - climate change KW - meta-analysis Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-499827 SN - 2628-653X IS - 29 ER - TY - JOUR A1 - Šedová, Barbora A1 - Kalkuhl, Matthias T1 - Who are the climate migrants and where do they go? BT - Evidence from rural India JF - World development N2 - In this paper, we move from the large strand of research that looks at evidence of climate migration to the questions: who are the climate migrants? and where do they go? These questions are crucial to design policies that mitigate welfare losses of migration choices due to climate change. We study the direct and heterogeneous associations between weather extremes and migration in rural India. We combine ERAS reanalysis data with the India Human Development Survey household panel and conduct regression analyses by applying linear probability and multinomial logit models. This enables us to establish a causal relationship between temperature and precipitation anomalies and overall migration as well as migration by destination. We show that adverse weather shocks decrease rural-rural and international migration and push people into cities in different, presumably more prosperous states. A series of positive weather shocks, however, facilitates international migration and migration to cities within the same state. Further, our results indicate that in contrast to other migrants, climate migrants are likely to be from the lower end of the skill distribution and from households strongly dependent on agricultural production. We estimate that approximately 8% of all rural-urban moves between 2005 and 2012 can be attributed to weather. This figure might increase as a consequence of climate change. Thus, a key policy recommendation is to take steps to facilitate integration of less educated migrants into the urban labor market. KW - climate change KW - migration KW - household analysis KW - India KW - econometrics Y1 - 2020 U6 - https://doi.org/10.1016/j.worlddev.2019.104848 SN - 0305-750X SN - 1873-5991 VL - 129 PB - Elsevier Science CY - Amsterdam ER - TY - THES A1 - Šedová, Barbora T1 - Heterogeneous effects of weather and climate change on human migration T1 - Heterogene Auswirkungen von Wetter und Klimawandel auf menschliche Migration N2 - While estimated numbers of past and future climate migrants are alarming, the growing empirical evidence suggests that the association between adverse climate-related events and migration is not universally positive. This dissertation seeks to advance our understanding of when and how climate migration emerges by analyzing heterogeneous climatic influences on migration in low- and middle-income countries. To this end, it draws on established economic theories of migration, datasets from physical and social sciences, causal inference techniques and approaches from systematic literature review. In three of its five chapters, I estimate causal effects of processes of climate change on inequality and migration in India and Sub-Saharan Africa. By employing interaction terms and by analyzing sub-samples of data, I explore how these relationships differ for various segments of the population. In the remaining two chapters, I present two systematic literature reviews. First, I undertake a comprehensive meta-regression analysis of the econometric climate migration literature to summarize general climate migration patterns and explain the conflicting findings. Second, motivated by the broad range of approaches in the field, I examine the literature from a methodological perspective to provide best practice guidelines for studying climate migration empirically. Overall, the evidence from this dissertation shows that climatic influences on human migration are highly heterogeneous. Whether adverse climate-related impacts materialize in migration depends on the socio-economic characteristics of the individual households, such as wealth, level of education, agricultural dependence or access to adaptation technologies and insurance. For instance, I show that while adverse climatic shocks are generally associated with an increase in migration in rural India, they reduce migration in the agricultural context of Sub-Saharan Africa, where the average wealth levels are much lower so that households largely cannot afford the upfront costs of moving. I find that unlike local climatic shocks which primarily enhance internal migration to cities and hence accelerate urbanization, shocks transmitted via agricultural producer prices increase migration to neighboring countries, likely due to the simultaneous decrease in real income in nearby urban areas. These findings advance our current understanding by showing when and how economic agents respond to climatic events, thus providing explicit contexts and mechanisms of climate change effects on migration in the future. The resulting collection of findings can guide policy interventions to avoid or mitigate any present and future welfare losses from climate change-related migration choices. N2 - Während die geschätzten Zahlen zukünftiger Klimamigranten alarmierend sind, deuten die wachsenden empirischen Belege darauf hin, dass der Klimawandel nicht automatisch zu mehr Migration führt. Denn auch wenn klimabezogene Einflüsse die Entscheidung zur Migration zunehmend beeinflussen, wird diese durch eine Vielzahl von Faktoren, wie beispielsweise den sozioökonomischen und politischen Bedingungen, beeinflusst. Der Zusammenhang zwischen Klimawandel und Migration ist also stark kontextabhängig. Diese Dissertation besteht aus fünf Artikeln und zeigt, wann und wie Klimamigration entsteht, indem sie die heterogenen klimatischen Einflüsse in Entwicklungsländern untersucht. Gestützt auf ökonomische Migrationstheorien analysiere ich Datensätze aus den Natur- und Sozialwissenschaften mithilfe von Methodiken der ökonometrischen Kausalanalyse, der Geoinformationssysteme und der systematischen Literatursynthese. In drei von fünf Kapiteln schätze ich die kausalen Auswirkungen des Klimawandels auf Ungleichheit und Migration in Indien und Subsahara Afrika. Durch die Verwendung von Interaktionstermen und die Analyse von Teilstichproben untersuche ich in Regressionsmodellen, wie sich diese Beziehungen für verschiedene Bevölkerungsgruppen unterscheiden. In den verbleibenden zwei Kapiteln fasse ich die ökonometrische Literatur zur Klimamigration systematisch zusammen. Zunächst führe ich eine umfassende Meta-Regressionsanalyse durch, um die allgemeine Klimamigrationsmuster zusammenzufassen und die widersprüchliche Evidenz zu erklären. In einem zweiten Schritt untersuche ich die ökonometrische Klimamigrationsliteratur aus einer methodologischen Perspektive, um Best-Practice-Leitlinien für künftige empirische Analysen von Klimamigration bereitzustellen. Insgesamt bestätigen die Ergebnisse dieser Dissertation, dass die klimatischen Einflüsse auf menschliche Migration heterogen sind und von den sozioökonomischen Merkmalen der einzelnen Haushalte wie dem Wohlstand und Bildungsniveau, der Abhängigkeit von der Landwirtschaft oder dem Zugang zu Anpassungstechnologien und Versicherungen, mitbestimmt werden. Ich finde beispielsweise, dass ungünstige klimatische Schocks zu einem Migrationsanstieg im ländlichen Indien führen, sie aber die Migration im landwirtschaftlichen Subsahara Afrika, wo das durchschnittliche Einkommensniveau viel niedriger ist, verhindern. Ich habe zudem herausgefunden, dass im Gegensatz zu lokalen klimatischen Schocks, die in erster Linie die Binnenmigration in die Städte verstärken und damit die Urbanisierung beschleunigen, globale Schocks über landwirtschaftliche Erzeugerpreise die Abwanderung in benachbarte Länder antreiben. Diese Ergebnisse erweitern unser derzeitiges Verständnis, indem sie verdeutlichen, wann und wie Akteure auf unterschiedliche Klimaereignisse mit der Entscheidung zur Migration reagieren. Die daraus resultierenden Erkenntnisse können helfen, Entscheidungsträger auf drei wichtige Arten zu informieren. Erstens, wenn man weiß, wer die Klimamigranten sind und welche Destinationsziele sie wählen, wird Klimamigration vorhersehbarer und damit kontrollierbarer. Dies kann verhindern, dass sie zu einer humanitären Krise wird. Zweitens hilft die Identifizierung von Bevölkerungsgruppen, die nicht in der Lage sind, sich durch Migration an die veränderten klimatischen Bedingungen anzupassen, dabei, unfreiwillige Immobilität zu vermeiden, was wiederum auch eine potenzielle humanitären Krise verhindert. Drittens können all diese Informationen helfen, Kosten und Nutzen der Klima(im)mobilität genauer zu bewerten und so die Social Cost of Carbon genauer einzuschätzen. KW - migration KW - weather KW - climate change KW - agriculture KW - food prices KW - inequality KW - econometrics KW - Landwirtschaft KW - Klimawandel KW - Ökonometrie KW - Lebensmittelpreise KW - Ungleichheit KW - Migration KW - Wetter Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-536733 ER - TY - JOUR A1 - Şener, Ulaş T1 - Rodrik, Dani (2015): Economics Rules: The Rights and Wrongs of the Dismal Science / rezensiert von Ulaş Şener JF - European journal of economics and economic policies : intervention ; EJEEP Y1 - 2017 U6 - https://doi.org/10.4337/ejeep.2017.03.08 SN - 2052-7764 SN - 2195-3376 VL - 14 SP - 375 EP - 377 PB - Elgar CY - Cheltenham ER - TY - JOUR A1 - Şahin, Muhittin A1 - Egloffstein, Marc A1 - Bothe, Max A1 - Rohloff, Tobias A1 - Schenk, Nathanael A1 - Schwerer, Florian A1 - Ifenthaler, Dirk T1 - Behavioral Patterns in Enterprise MOOCs at openSAP JF - EMOOCs 2021 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-517350 SN - 978-3-86956-512-5 VL - 2021 SP - 281 EP - 288 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - GEN A1 - Ślęzak, Jakub A1 - Metzler, Ralf A1 - Magdziarz, Marcin T1 - Codifference can detect ergodicity breaking and non-Gaussianity T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - We show that the codifference is a useful tool in studying the ergodicity breaking and non-Gaussianity properties of stochastic time series. While the codifference is a measure of dependence that was previously studied mainly in the context of stable processes, we here extend its range of applicability to random-parameter and diffusing-diffusivity models which are important in contemporary physics, biology and financial engineering. We prove that the codifference detects forms of dependence and ergodicity breaking which are not visible from analysing the covariance and correlation functions. We also discuss a related measure of dispersion, which is a nonlinear analogue of the mean squared displacement. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 748 KW - diffusion KW - anomalous diffusion KW - stochastic time series Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-436178 IS - 748 ER - TY - GEN A1 - Ślęzak, Jakub A1 - Metzler, Ralf A1 - Magdziarz, Marcin T1 - Superstatistical generalised Langevin equation BT - non-Gaussian viscoelastic anomalous diffusion N2 - Recent advances in single particle tracking and supercomputing techniques demonstrate the emergence of normal or anomalous, viscoelastic diffusion in conjunction with non-Gaussian distributions in soft, biological, and active matter systems. We here formulate a stochastic model based on a generalised Langevin equation in which non-Gaussian shapes of the probability density function and normal or anomalous diffusion have a common origin, namely a random parametrisation of the stochastic force. We perform a detailed analysis demonstrating how various types of parameter distributions for the memory kernel result in exponential, power law, or power-log law tails of the memory functions. The studied system is also shown to exhibit a further unusual property: the velocity has a Gaussian one point probability density but non-Gaussian joint distributions. This behaviour is reflected in the relaxation from a Gaussian to a non-Gaussian distribution observed for the position variable. We show that our theoretical results are in excellent agreement with stochastic simulations. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 413 KW - anomalous diffusion KW - generalised langevin equation KW - superstatistics KW - non-Gaussian diffusion Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-409315 ER - TY - JOUR A1 - Ślęzak, Jakub A1 - Metzler, Ralf A1 - Magdziarz, Marcin T1 - Superstatistical generalised Langevin equation BT - non-Gaussian viscoelastic anomalous diffusion JF - New Journal of Physics N2 - Recent advances in single particle tracking and supercomputing techniques demonstrate the emergence of normal or anomalous, viscoelastic diffusion in conjunction with non-Gaussian distributions in soft, biological, and active matter systems. We here formulate a stochastic model based on a generalised Langevin equation in which non-Gaussian shapes of the probability density function and normal or anomalous diffusion have a common origin, namely a random parametrisation of the stochastic force. We perform a detailed analysis demonstrating how various types of parameter distributions for the memory kernel result in exponential, power law, or power-log law tails of the memory functions. The studied system is also shown to exhibit a further unusual property: the velocity has a Gaussian one point probability density but non-Gaussian joint distributions. This behaviour is reflected in the relaxation from a Gaussian to a non-Gaussian distribution observed for the position variable. We show that our theoretical results are in excellent agreement with stochastic simulations. KW - anomalous diffusion KW - generalised langevin equation KW - superstatistics KW - non-Gaussian diffusion Y1 - 2018 U6 - https://doi.org/10.1088/1367-2630/aaa3d4 SN - 1367-2630 VL - 20 IS - 023026 SP - 1 EP - 25 PB - Deutsche Physikalische Gesellschaft / Institute of Physics CY - Bad Honnef und London ER - TY - JOUR A1 - Ślęzak, Jakub A1 - Metzler, Ralf A1 - Magdziarz, Marcin T1 - Codifference can detect ergodicity breaking and non-Gaussianity JF - New Journal of Physics N2 - We show that the codifference is a useful tool in studying the ergodicity breaking and non-Gaussianity properties of stochastic time series. While the codifference is a measure of dependence that was previously studied mainly in the context of stable processes, we here extend its range of applicability to random-parameter and diffusing-diffusivity models which are important in contemporary physics, biology and financial engineering. We prove that the codifference detects forms of dependence and ergodicity breaking which are not visible from analysing the covariance and correlation functions. We also discuss a related measure of dispersion, which is a nonlinear analogue of the mean squared displacement. KW - diffusion KW - stochastic time series KW - anomalous diffusion Y1 - 2019 U6 - https://doi.org/10.1088/1367-2630/ab13f3 SN - 1367-2630 VL - 21 PB - Deutsche Physikalische Gesellschaft CY - Bad Honnef ER - TY - GEN A1 - Ślęzak, Jakub A1 - Burnecki, Krzysztof A1 - Metzler, Ralf T1 - Random coefficient autoregressive processes describe Brownian yet non-Gaussian diffusion in heterogeneous systems T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - Many studies on biological and soft matter systems report the joint presence of a linear mean-squared displacement and a non-Gaussian probability density exhibiting, for instance, exponential or stretched-Gaussian tails. This phenomenon is ascribed to the heterogeneity of the medium and is captured by random parameter models such as ‘superstatistics’ or ‘diffusing diffusivity’. Independently, scientists working in the area of time series analysis and statistics have studied a class of discrete-time processes with similar properties, namely, random coefficient autoregressive models. In this work we try to reconcile these two approaches and thus provide a bridge between physical stochastic processes and autoregressive models.Westart from the basic Langevin equation of motion with time-varying damping or diffusion coefficients and establish the link to random coefficient autoregressive processes. By exploring that link we gain access to efficient statistical methods which can help to identify data exhibiting Brownian yet non-Gaussian diffusion. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 765 KW - diffusion KW - Langevin equation KW - Brownian yet non-Gaussian diffusion KW - diffusing diffusivity KW - superstatistics KW - autoregressive models KW - time series analysis KW - codifference Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-437923 SN - 1866-8372 IS - 765 ER -