Do Politicians Shirk when Reelection Is Certain? Evidence from the German Parliament

Does stiffer electoral competition reduce political rent-seeking behavior? For a microanalysis of this question, I construct a new data set spanning the years 2005 to 2012 covering biographical and political information of German members of parliament (MPs) and including attendance rates in voting sessions for the first time. For the parliament elected in 2009, I show that indeed MPs who expect to face a close race in their district show significantly and relevantly lower absence rates in parliament beforehand. MPs of governing parties seem to react less to electoral competition. These results are confirmed by an analysis of the parliament elected in 2005, by several robustness checks, and also by employing an instrumental variable strategy exploiting convenient peculiarities of the German electoral system. This study is also the first to analyze how MPs elected via party lists react to different levels of electoral competition

Divided We Reform? Evidence from US Welfare Policies

Divided government is often thought of as causing legislative deadlock. I investigate the link between divided government and economic reforms using a novel data set on welfare reforms in US states between 1978 and 2010. Panel data regressions show that under divided government a US state is around 25% more likely to adopt a welfare reform than under unified government. An analysis of close elections providing quasi-random variation in the form of government and other robustness checks confirm this counter-intuitive finding. The empirical evidence is consistent with an explanation based on policy competition between governor, senate, and house

Scalable Probabilistic Similarity Ranking in Uncertain Databases (Technical Report)

This paper introduces a scalable approach for probabilistic top-k similarity ranking on uncertain vector data. Each uncertain object is represented by a set of vector instances that are assumed to be mutually-exclusive. The objective is to rank the uncertain data according to their distance to a reference object. We propose a framework that incrementally computes for each object instance and ranking position, the probability of the object falling at that ranking position. The resulting rank probability distribution can serve as input for several state-of-the-art probabilistic ranking models. Existing approaches compute this probability distribution by applying a dynamic programming approach of quadratic complexity. In this paper we theoretically as well as experimentally show that our framework reduces this to a linear-time complexity while having the same memory requirements, facilitated by incremental accessing of the uncertain vector instances in increasing order of their distance to the reference object. Furthermore, we show how the output of our method can be used to apply probabilistic top-k ranking for the objects, according to different state-of-the-art definitions. We conduct an experimental evaluation on synthetic and real data, which demonstrates the efficiency of our approach

Essays in empirical political economics

This dissertation consists of four distinct empirical studies within the field of political economics. They all deal with either political competition or political reforms. The first study investigates the relationship between divided government and the adoption of economic reforms. The second study shows the relevance of gubernatorial reelection concerns during the US Welfare Reform. The third study analyzes how political competition affects the behavior of German Members of Parliament. The fourth study deals with status quo bias in German voters’ policy reform preferences

Trial and Error? Reelection Concerns and Policy Experimentation during the U.S. Welfare Reform

We study the political economy of policy innovations during the U.S. welfare reform in 1996. Specifically, we investigate how reputation concerns among governors influence the decision to experiment with welfare policies. In line with a political agency model, our empirical results suggest that governors with high reputation among the electorate are less likely to experiment with welfare policies than governors with low reputation. Yet, governors with high reputation who are less concerned about reelection actually experiment more than governors striving for reelection. Overall, our findings imply that reelection concerns may inhibit innovation in the public sector

Offline Writer Identification Using Convolutional Neural Network Activation Features

Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR 2013 benchmark database and the CVL dataset. While we perform comparably to the state of the art on CVL, our proposed method yields about 0.21 absolute improvement in terms of mAP on the challenging bilingual ICDAR dataset.Comment: fixed tab 1

Quinaprilat during cardioplegic arrest in the rabbit to prevent ischemia-reperfusion injury

AbstractObjectives: This study evaluated intracardiac angiotensin-converting enzyme inhibition as an adjuvant to cardioplegia and examined its effects on hemodynamic, metabolic, and ultrastructural postischemic outcomes. Methods: The experiments were performed with an isolated, erythrocyte-perfused, rabbit working-heart model. The hearts excised from 29 adult New Zealand White rabbits (2950 ± 200 g) were randomly assigned to four groups. Two groups received quinaprilat (1 μg/mL), initiated either with cardioplegia (n = 7) or during reperfusion (n = 7). The third group received l-arginine (2 mmol/L) initiated with cardioplegia (n = 7). Eight hearts served as a control group. Forty minutes of preischemic perfusion were followed by 60 minutes of hypothermic arrest and 40 minutes of reperfusion. Results: All treatments substantially improved postischemic recovery of external heart work (62% ± 6%, 69% ± 3%, and 64% ± 5% in quinaprilat during cardioplegia, quinaprilat during reperfusion, and l-arginine groups, respectively, vs 35% ± 5% in control group, P <.001) with similarly increased external stroke work and cardiac output. When administered during ischemia, quinaprilat significantly improved recovery of coronary flow (70% ± 8%, P =.028 vs quinaprilat during reperfusion [49% ± 5%] and P =.023 vs control [48% ± 6%]). l-Arginine (55% ± 7%) showed no significant effect. Postischemic myocardial oxygen consumption remained low in treatment groups (4.6 ± 1.2 mL · min−1 · 100 g−1, 6.0 ± 2.2 mL · min−1 · 100 g−1, and 4.7 ± 1.6 mL · min−1 · 100 g−1 in quinaprilat during cardioplegia, quinaprilat during reperfusion, and l-arginine groups, respectively, vs 4.2 ± 0.8 mL · min−1 · 100 g−1 in control group), even though cardiac work was markedly increased. High-energy phosphates, which were consistently elevated in all treatment groups, showed a significant increase in adenosine triphosphate with quinaprilat during ischemia (2.24 ± 0.14 μmol/g vs 1.81 ± 0.12 μmol/g in control group, P =.040). Ultrastructural grading of mitochondrial damage revealed best preservation with quinaprilat during ischemia (100% [no damage], P =.001 vs control). Conclusion: These experimental findings have clinical relevance regarding prevention of postoperative myocardial stunning and low coronary reflow in patients undergoing heart surgery.J Thorac Cardiovasc Surg 2002;124:352-6