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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.
This thesis offers new insights on the effects of Start-Up Subsidies (SUS) for unemployed individuals as a special kind of active labor market program (ALMP) that aims to re-integrate individuals into the labor market via the route of self-employment. Moreover, this thesis contributes to the literature on methods for causal inference when the treatment variable is continuous rather than binary. For example, this is the case when individuals differ in their degree of exposure to a common treatment.
The analysis of the effects of SUS focuses on the main current German program called “Gründungszuschuss” (New Start-Up Subsidy, NSUS) after its reform in 2011. Average Effects on participants' labor market outcomes - as measured by employment and earnings - as well as subjective well-being are estimated mainly based on propensity score matching (PSM) techniques. PSM aims to achieve balance in terms of observed characteristics by matching participants with at least one comparable non-participant in terms of their probability to receive the treatment. This estimation strategy is valid as long as all relevant characteristics that explain selection patterns into treatment are observed and included in the estimation of the propensity score. To make our analysis as credible as possible, we control for a large vector of characteristics as observed through the combination of rich administrative data from the Federal Employment Agency as well as through survey data.
Chapters two to four of this thesis puts special emphasis on aspects regarding (the evaluation of) SUS programs that have received no or only limited attention thus far. The first aspect relates to the interplay of institutional details of the program and its effectiveness. So far, relatively little is known about the importance of SUS program features such as the duration of support. Second, there is no experimental benchmark evaluation of SUS available and thus, the reliability of non-experimental estimation techniques such as PSM is of crucial importance as estimates are biased when relevant confounders are omitted from the analysis. Third, there may be potentially detrimental effects of transitioning into (relatively risky) self-employment on subjective well-being among subsidized founders out of unemployment. These were to remain undetected if the analysis would focus exclusively on labor market outcomes of participants. The results indicate positive long-term effects of SUS participation on employment and earnings among participants. These effects are substantially larger than what estimated before the reform, indicating room for improvement in program design via changes in institutional details. Moreover, non-experimental estimates of treatment effects are remarkably robust to hidden confounding. Regarding subjective well-being, this thesis finds a positive long-run impact on job satisfaction and a detrimental effect on satisfaction with social security. The latter appears to be driven by adverse effects on social insurance contributions.
In chapter five, a novel automated covariate balancing technique for the estimation of causal effects in the context of continuous treatments is derived and assessed regarding its performance compared to other (automated) balancing techniques. Although binary research designs that only differentiate between participants and non-participants of some treatment remain the most-common case in empirical practice, many applications can be adapted to include continuous treatments as well. Often, this will allow for more meaningful estimates of causal effects in order to further improve the design of programs. In the context of SUS, one may further investigate the effects of the size of monetary support or its duration on participants' labor market outcomes. Both Monte-Carlo investigations and analysis of two well-known datasets suggests superior performance of the proposed Entropy Balancing for continuous treatments (EBCT) compared to other existing estimation strategies.
Reconstructing democracy
(2020)
Across the world, democracies are suffering from a disconnect between the people and political elites. In communities where jobs and industry are scarce, many feel the government is incapable of understanding their needs or addressing their problems. The resulting frustration has fueled the success of destabilizing demagogues. To reverse this pattern and restore responsible government, we need to reinvigorate democracy at the local level. But what does that mean? Drawing on examples of successful community building in cities large and small, from a shrinking village in rural Austria to a neglected section of San Diego, Reconstructing Democracy makes a powerful case for re-engaging citizens. It highlights innovative grassroots projects and shows how local activists can form alliances and discover their own power to solve problems.
Introduction
(2020)
Does political repression work and if so, under what conditions? Many contributions to the empirical study of non-democratic rule assume it does. As a consequence, strong convictions on political repression abound, but empirical investigations into the matter remain rare. This introduction sets the agenda for the chapters to come and outlines the answers given to the three motivating questions of this volume. First, what variants of political repression are there, and how do they interact? Second, what impact does the interaction of different forms of political repression have on the problem of authoritarian control? Finally, what difference does the complementary use of violence and restrictions make for the problem of authoritarian power-sharing?
It is well known that the inverted Collatz sequence can be represented as a graph or a tree. Similarly, it is acknowledged that in order to prove the Collatz conjecture, one must demonstrate that this tree covers all (odd) natural numbers. A structured reachability analysis is hitherto not available. This paper investigates the problem from a graph theory perspective. We define a tree that consists of nodes labeled with Collatz sequence numbers. This tree will be transformed into a sub-tree that only contains odd labeled nodes. The analysis of this tree will provide new insights into the structure of Collatz sequences. The findings are of special interest to possible cycles within a sequence. Next, we describe the conditions which must be fulfilled by a cycle. Finally, we demonstrate how these conditions could be used to prove that the only possible cycle within a Collatz sequence is the trivial cycle, starting with the number 1, as conjectured by Lothar Collatz.
It is well known that the inverted Collatz sequence can be represented as a graph or a tree. Similarly, it is acknowledged that in order to prove the Collatz conjecture, one must demonstrate that this tree covers all odd natural numbers. A structured reachability analysis is hitherto not available. This paper investigates the problem from a graph theory perspective. We define a tree that consists of nodes labeled with Collatz sequence numbers. This tree will be transformed into a sub-tree that only contains odd labeled nodes. The analysis of this tree will provide new insights into the structure of Collatz sequences. The findings are of special interest to possible cycles within a sequence. Next, we describe the conditions which must be fulfilled by a cycle. Finally, we demonstrate how these conditions could be used to prove that the only possible cycle within a Collatz sequence is the trivial cycle, starting with the number one, as conjectured by Lothar Collatz.
It is well known that the inverted Collatz sequence can be represented as a graph or a tree. Similarly, it is acknowledged that in order to prove the Collatz conjecture, one must demonstrate that this tree covers all odd natural numbers. A structured reachability analysis is hitherto unavailable. This paper investigates the problem from a graph theory perspective. We define a tree that consists of nodes labeled with Collatz sequence numbers. This tree will be transformed into a sub-tree that only contains odd labeled nodes. Furthermore, we derive and prove several formulas that can be used to traverse the graph. The analysis covers the Collatz problem both in it’s original form 3x + 1 as well as in the generalized variant kx + 1. Finally, we transform the Collatz graph into a binary tree, following the approach of Kleinnijenhuis, which could form the basis for a comprehensive proof of the conjecture.
Social Media, Quo Vadis?
(2020)
Over the past two decades, social media have become a crucial and omnipresent cultural and economic phenomenon, which has seen platforms come and go and advance technologically. In this study, we explore the further development of social media regarding interactive technologies, platform development, relationships to news media, the activities of institutional and organizational users, and effects of social media on the individual and the society over the next five to ten years by conducting an international, two-stage Delphi study. Our results show that enhanced interaction on platforms, including virtual and augmented reality, somatosensory sense, and touch- and movement-based navigation are expected. AIs will interact with other social media users. Inactive user profiles will outnumber active ones. Platform providers will diversify into the WWW, e-commerce, edu-tech, fintechs, the automobile industry, and HR. They will change to a freemium business model and put more effort into combating cybercrime. Social media will become the predominant news distributor, but fake news will still be problematic. Firms will spend greater amounts of their budgets on social media advertising, and schools, politicians, and the medical sector will increase their social media engagement. Social media use will increasingly lead to individuals’ psychic issues. Society will benefit from economic growth and new jobs, increased political interest, democratic progress, and education due to social media. However, censorship and the energy consumption of platform operators might rise.
Social Media, Quo Vadis?
(2020)
Over the past two decades, social media have become a crucial and omnipresent cultural and economic phenomenon, which has seen platforms come and go and advance technologically. In this study, we explore the further development of social media regarding interactive technologies, platform development, relationships to news media, the activities of institutional and organizational users, and effects of social media on the individual and the society over the next five to ten years by conducting an international, two-stage Delphi study. Our results show that enhanced interaction on platforms, including virtual and augmented reality, somatosensory sense, and touch- and movement-based navigation are expected. AIs will interact with other social media users. Inactive user profiles will outnumber active ones. Platform providers will diversify into the WWW, e-commerce, edu-tech, fintechs, the automobile industry, and HR. They will change to a freemium business model and put more effort into combating cybercrime. Social media will become the predominant news distributor, but fake news will still be problematic. Firms will spend greater amounts of their budgets on social media advertising, and schools, politicians, and the medical sector will increase their social media engagement. Social media use will increasingly lead to individuals’ psychic issues. Society will benefit from economic growth and new jobs, increased political interest, democratic progress, and education due to social media. However, censorship and the energy consumption of platform operators might rise.
Climate-related costs and benefits may not be evenly distributed across the population. We study distributional implications of seasonal weather and climate on within-country inequality in rural India. Utilizing a first difference approach, we find that the poor are more sensitive to weather variations than the non-poor. The poor respond more strongly to (seasonal) temperature changes: negatively in the (warm) spring season, more positively in the (cold) rabi season. Less precipitation is harmful to the poor in the monsoon kharif season and beneficial in the winter and spring seasons. We show that adverse weather aggravates inequality by reducing consumption of the poor farming households. Future global warming predicted under RCP8.5 is likely to exacerbate these effects, reducing consumption of poor farming households by one third until the year 2100. We also find inequality in consumption across seasons with higher consumption during the harvest and lower consumption during the sowing seasons.