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Coming of voting age. Evidence from a natural experiment on the effects of electoral eligibility
(2024)
In recent years, several jurisdictions have lowered the voting age, with many more discussing it. Sceptics question whether young people are ready to vote, while supporters argue that allowing them to vote would increase their specific engagement with politics. To test the latter argument, we use a series of register-based surveys of over 10,000 German adolescents. Knowing the exact birthdates of our respondents, we estimate the causal effect of eligibility on their information-seeking behaviour in a regression discontinuity design. While eligible and non-eligible respondents do not differ in their fundamental political dispositions, those allowed to vote are more likely to discuss politics with their family and friends and to use a voting advice application. This effect appears to be stronger for voting age 16 than for 18. The right to vote changes behaviour. Therefore, we cannot conclude from the behaviour of ineligible citizens that they are unfit to vote.
Highlights
• Pathways for a circular economy towards the EU goals require policy support that, in turn, requires legitimacy.
• Legitimacy is often contested in the public discourse at all phases in the technological innovation system.
• Legitimacy remains poorly understood for ‘in-between’ technologies that struggle to move from the formative to the growth stage.
• The article explores legitimacy for chemical recycling primarily based on evidence from the UK, Germany, and Italy.
Abstract
The European Commission aims to increase the recycling of plastic packaging to 60% by 2025, requiring fundamental changes towards a more circular economy. Pathways for this transition require policy support that largely depends on their legitimacy in the public discourse. These normative aspects remain poorly understood for ‘in-between’ technologies, i.e., technologies that are no longer novel but struggle to move to the growth phase within the technological innovation system. Therefore, we ask: How do discourses shape technology legitimacy for in-between technologies? Drawing on the empirical example of chemical recycling, the analysis renders two principal findings. First, legitimising and delegitimising storylines present contesting views on in-between technologies regarding their technological aspects, environmental and social impacts, and economic and policy implications. Second, how discourses contribute to technology legitimacy depends on the actors and interests that drive the prevalent storylines in particular contexts.
Highlights
• Germany plans more long-distances water transfers to secure drinking water supply.
• Long-distance water transfers can unfold lock-ins that limit adaptive water governance.
• Our interdisciplinary case study shows how lock-ins emerge over different spaces and times.
• Commercialisation of water but also local protests contributed to various lock-ins.
• We therefore call for context-specific assessments of potentials and risks of LDWT.
Abstract
Germany plans to expand water transfers over long distances in the light of numerous and pressing challenges for drinking water supply. Research on inter- and intrabasin water transfers warns, however, that major investments in large-scale infrastructure systems accompanied by institutional logics and political interests often lead to a so-called lock-in. As a consequence, long-distance water transfers can limit the potential for adaptive water governance in the involved supply areas over decades with negative impacts for people and the environment. By using a case study in Germany as an example, we researched when, where and how such lock-ins around long-distance water transfers emerge. In the infrastructural development of the Elbaue-Ostharz transfer system we found various lock-ins that overlap in space and time. Some are located at the centre others at the margins of the infrastructure and commercialization of the water sector as well as hydraulic and hygienic concerns interlock with local protests in a way that the expansion of the long-distance water transfer infrastructure is presented continuously as imperative. Our findings contribute to a relational understanding of lock-ins of long-distance water transfers as contingent and diverse processes. Given the widespread occurrence of lock-ins, we argue for a context-specific assessment of potentials and risks of long-distance water transfers in times of multiple crises.
How does the design of debt repayment schedules affect household borrowing? To answer this question, we exploit a Swedish policy reform that eliminated interest-only mortgages for loan-to-value ratios above 50%. We document substantial bunching at the threshold, leading to 5% lower borrowing. Wealthy borrowers drive the results, challenging credit constraints as the primary explanation. We develop a model to evaluate the mechanisms driving household behavior and find that much of the effect comes from households experiencing ongoing flow disutility to amortization payments. Our results indicate that mortgage contracts with low initial payments substantially increase household borrowing and lifetime interest costs.
This paper contributes a multivariate forecasting comparison between structural models and Machine-Learning-based tools. Specifically, a fully connected feed forward non-linear autoregressive neural network (ANN) is contrasted to a well established dynamic stochastic general equilibrium (DSGE) model, a Bayesian vector autoregression (BVAR) using optimized priors as well as Greenbook and SPF forecasts. Model estimation and forecasting is based on an expanding window scheme using quarterly U.S. real-time data (1964Q2:2020Q3) for 8 macroeconomic time series (GDP, inflation, federal funds rate, spread, consumption, investment, wage, hours worked), allowing for up to 8 quarter ahead forecasts. The results show that the BVAR improves forecasts compared to the DSGE model, however there is evidence for an overall improvement of predictions when relying on ANN, or including them in a weighted average. Especially, ANN-based inflation forecasts improve other predictions by up to 50%. These results indicate that nonlinear data-driven ANNs are a useful method when it comes to macroeconomic forecasting.
Central bank intervention in the form of quantitative easing (QE) during times of low interest rates is a controversial topic. The author introduces a novel approach to study the effectiveness of such unconventional measures. Using U.S. data on six key financial and macroeconomic variables between 1990 and 2015, the economy is estimated by artificial neural networks. Historical counterfactual analyses show that real effects are less pronounced than yield effects.
Disentangling the effects of the individual asset purchase programs, impulse response functions provide evidence for QE being less effective the more the crisis is overcome. The peak effects of all QE interventions during the Financial Crisis only amounts to 1.3 pp for GDP growth and 0.6 pp for inflation respectively. Hence, the time as well as the volume of the interventions should be deliberated.
When estimating misspecified linear factor models for the cross-section of expected returns using GMM, the explanatory power of these models can be spuriously high when the estimated factor means are allowed to deviate substantially from the sample averages. In fact, by shifting the weights on the moment conditions, any level of cross-sectional fit can be attained. The mathematically correct global minimum of the GMM objective function can be obtained at a parameter vector that is far from the true parameters of the data-generating process. This property is not restricted to small samples, but rather holds in population. It is a feature of the GMM estimation design and applies to both strong and weak factors, as well as to all types of test assets.
Highlights
• We present a novel alternative to the die-in-the-cup experiment.
• Participants’ payoffs depend on their reported mothers’ birthdays.
• We find that subjects lied to obtain real monetary payoffs.
• The extent of lying is small and insensitive to several design variations.
Abstract
We ask a representative sample of German household decision-makers to enter their mother's birthday, with potential payments depending on the month and the day they state. Thus, we create an incentive to lie. Compared to the die-under-the-cup experiment, our alternative has a lower probability that the income-maximizing outcome is true. Furthermore, it is better suited for online surveys and samples in which gambling is socially stigmatized. We conduct different variations of this game to crystalize design recommendations for researchers interested in our tool. Participants lied to receive higher payoffs, but only with real monetary incentives and only to a relatively small extent. Our results are largely insensitive to several design elements that we vary, such as the probability of being paid and the magnitude of the payoffs.
Neanderthal diet has been on the spotlight of paleoanthropological research for many years. The majority of studies that tried to reconstruct the diet of Neanderthals were based on the analysis of zooarchaeological remains, stable isotopes, dental calculus and dental microwear patterns. In the past few years, there have been a few studies that linked dental macrowear patterns of Neanderthals and modern humans to diet and cultural habits. However, they mostly focused on maxillary molars. Although mandibular molars have been widely used in microwear dietary research, little is known about their usage at the macroscopic scale to detect information about human subsistence strategies. In this study, we compare the macrowear patterns of Neanderthal (NEA), fossil Homo sapiens (FHS), modern hunter-gatherers (MHG), pastoralists, early farmers and Australian Aborigines from Yuendumu mandibular molars in order to assess their utility in collecting any possible information about dietary and cultural habits among diverse human groups. We use the occlusal fingerprint analysis method, a quantitative digital approach that has been successfully employed to reconstruct the diet of living non-human primates and past human populations. Our results show macrowear pattern differences between meat-eater MHG and EF groups. Moreover, while we did not find eco-geographical differences in the macrowear patterns of the fossil sample, we found statistically significant differences between NEA and FHS inhabiting steppe/coniferous forest. This latter result could be associated with the use of distinct technological complexes in these two species, which ultimately could have allowed modern humans to exploit natural resources in a different way compared to NEA.
Non-matrix-matched calibration of laser ablation ICPMS (trace/major) element data is a common quantification strategy. However, LA sampling is associated with downhole elemental fractionation, potentially causing inaccuracies if the magnitude of fractionation between the sample and reference material (RM) differs. Here, we estimate fractionation factors (FFs) for different elements (El) in a range of RMs relative to NIST SRM610/612 (FFEl/Ca-NIST) and evaluate element-specific corrections for downhole fractionation using these measured FFEl/Ca-NIST. Significantly different mean El/Ca values were observed before and after correction, particularly for the alkali elements (all RMs), and B, Fe, and Zn (some RMs), notably improving accuracy, especially for the alkali elements. In cases where this methodology does not result in an accuracy improvement, this may help identify underlying issues in reported/reference values for RMs, given that this phenomenon should be accounted for. Overall, we recommend considering routine assessment of FFs and applying a FF correction to enhance data quality.
Highlights
• Since there is only a low level of evidence, it is difficult to agree on state-of-the-art standards or to provide recommendations and guidelines.
• The value of combining several monitoring devices for dual or triple guidance must be challenged.
• The principle of fascial plane blocks is suitable to avoid traumatic needle-to-nerve contact. However, local toxicity must be regarded as a possible mechanism for nerve injuries.
• Block procedures might be conducted during sedation or general anesthesia when considering the individual patients' clinical situations and the expertise of the anesthesiologist.
• The quality of ultrasound equipment and education provided by the corresponding anesthesia department is highly relevant
We study the many implications of the Eurosystem collateral framework for corporate bonds. Using data on the evolving collateral eligibility list, we identify the first inclusion dates of bonds and issuers and use these events to find that the increased supply and demand for pledgeable collateral following eligibility (a) increases activity in the corporate securities lending market, (b) lowers eligible bond yields, and (c) affects bond liquidity. Thus, corporate bond lending relaxes the constraint of limited collateral supply and thereby improves market functioning.
Does political conflict with another country influence domestic consumers' daily consumption choices? We exploit the volatile US-China relations in 2018 and 2019 to analyze whether US consumers reduce their visits to Chinese restaurants when bilateral relations deteriorate. We measure the degree of political conflict through negativity in media reports and rely on smartphone location data to measure daily visits to over 190,000 US restaurants. A deterioration in US-China relations induces a significant decline in visits not only to Chinese but also to other foreign ethnic restaurants, while visits to typical American restaurants increase. We identify consumers' age, race, and cultural openness to moderate the strength of this ethnocentric effect.
This paper empirically analyses whether post-global financial crisis regulatory reforms have created appropriate incentives to voluntarily centrally clear over-the-counter (OTC) derivative contracts. We use confidential European trade repository data on single-name sovereign credit default swap (CDS) transactions and show that both seller and buyer manage counterparty exposures and capital costs, strategically choosing to clear when the counterparty is riskier. The clearing incentives seem particularly responsive to seller credit risk, which is in line with the notion that counterparty credit risk (CCR) is asymmetric in CDS contracts. The riskiness of the underlying reference entity also impacts the decision to clear as it affects both CCR capital charges for OTC contracts and central counterparty clearing house (CCP) margins for cleared contracts. Lastly, we find evidence that when a transaction helps netting positions with the CCP and hence lower margins, the likelihood of clearing is higher.
Highlights
• Out of the six edible pumpkin seeds found in Cameroonian C. sativus showed most potent anti-proliferative effects on prostate cells.
• Its oil conserved almost all the effects of raw seeds and prevented benign prostatic hyperplasia (BPH).
• It exhibited potent anti-inflammatory activities in rat with BPH.
Abstract
Pumpkin seeds are claimed to treat prostate tumour/cancer. The in vitro (ability to inhibit cell growth through MTT assay) and in vivo (ability to prevent testosterone-induced BPH in rats at the doses of 125, 250, 500 and 1000 mg/kg BW) of six edible pumpkin seeds found in Cameroonian were assessed. The endpoints were cell growth arrest, prostate mass and volume, prostatic epithelium height, prostatic proteins, prostate specific antigen (PSA) and inflammatory cytokines. In vitro, C. sativus seeds exhibited the most potent antiproliferative effects on DU145 and PC3 prostate cancer cells and its oil conserved almost all the effects of raw seeds. Further, it prevented the increased of prostate relative mass and volume, prostate epithelium height, PSA and testosterone dose-dependently compared to normal rats. This effect is thought to be mediated through antiandrogenic, estrogenic and anti-inflammatory activities, evidenced by a decreased in IL-1β, IL-6 and TNFα level. Overall, this results justify its traditional use.
The 2011 Arab Spring marked the opening of the Central Mediterranean Route for irregular border crossings between Libya and Italy, which produced heterogeneous reductions of bilateral smuggling distances between country pairs in the Mediterranean region. We exploit this source of spatial and temporal variation in bilateral distance along land and sea routes to estimate the elasticity of irregular migration intentions for African and Near East countries. We estimate an elasticity of migration intentions to smuggling distances exceeding −3, mainly driven by countries with weak rule of law and high internet penetration. Our findings are consistent across irregular migration measures both at the aggregate and individual levels. We show that irregular migration elasticity is higher for youth, relatively skilled individuals and those with an informative advantage (having a social network abroad or a mobile phone).
Highlights
• We present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series.
• The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios.
• The model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33.
Abstract
High-rate Global Navigation Satellite System (HR-GNSS) data can be highly useful for earthquake analysis as it provides continuous high-frequency measurements of ground motion. This data can be used to analyze diverse parameters related to the seismic source and to assess the potential of an earthquake to prompt strong motions at certain distances and even generate tsunamis. In this work, we present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. We explored the potential of the model for global application and compared its performance using both synthetic and real data from different seismogenic regions. The performance of our model at this stage was satisfactory in estimating earthquake magnitude from synthetic data with 0.07 ≤ RMS ≤ 0.11. Comparable results were observed in tests using synthetic data from a different region than the training data, with RMS ≤ 0.15. Furthermore, the model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33, provided that the data from a particular group of stations had similar epicentral distance constraints to those used during the model training. The robustness of the DL model can be improved to work independently from the window size of the time series and the number of stations, enabling faster estimation by the model using only near-field data. Overall, this study provides insights for the development of future DL approaches for earthquake magnitude estimation with HR-GNSS data, emphasizing the importance of proper handling and careful data selection for further model improvements.