Behavioral motivations for self-insurance under different disaster risk insurance schemes

This paper presents a lab-in-the-field experiment with 2111 Dutch homeowners in floodplain areas to examine the impacts of financial incentives and behavioral motivations for self-insurance under different flood insurance schemes. We experimentally varied the insurance type (mandatory public versus voluntary private) and the availability of a premium discount incentive for investing in flood damage mitigation measures. This set-up allowed us to examine the existence of moral hazard, advantageous selection and the behavioral motivations of individual agents who face these different insurance types, without the selection bias that makes a causal inference from survey studies problematic. The main results show that a premium discount can increase investments in self-insurance under both private and public insurance. Moreover, we find no support for moral hazard in our natural disaster insurance market, but we do find a substantial share of cautious people who invest both in private insurance as well as in self-insurance, indicating advantageous selection. The results have implications for the design of insurance schemes to cope with increasing natural disaster risks

Multiscale Analysis of Biological Data by Scale-Dependent Lyapunov Exponent

Physiological signals often are highly non-stationary (i.e., mean and variance change with time) and multiscaled (i.e., dependent on the spatial or temporal interval lengths). They may exhibit different behaviors, such as non-linearity, sensitive dependence on small disturbances, long memory, and extreme variations. Such data have been accumulating in all areas of health sciences and rapid analysis can serve quality testing, physician assessment, and patient diagnosis. To support patient care, it is very desirable to characterize the different signal behaviors on a wide range of scales simultaneously. The Scale-Dependent Lyapunov Exponent (SDLE) is capable of such a fundamental task. In particular, SDLE can readily characterize all known types of signal data, including deterministic chaos, noisy chaos, random 1/fα processes, stochastic limit cycles, among others. SDLE also has some unique capabilities that are not shared by other methods, such as detecting fractal structures from non-stationary data and detecting intermittent chaos. In this article, we describe SDLE in such a way that it can be readily understood and implemented by non-mathematically oriented researchers, develop a SDLE-based consistent, unifying theory for the multiscale analysis, and demonstrate the power of SDLE on analysis of heart-rate variability (HRV) data to detect congestive heart failure and analysis of electroencephalography (EEG) data to detect seizures

Green Tax Reform, Endogenous Innovation and the Growth Dividend

We study theoretically and numerically the effects of an environmental tax reform using endogenous growth theory. In the theoretical part, mobile labor between manufacturing and R&D activities, and elasticity of substitution between labor and energy in manufacturing lower than unity allow for a growth dividend, even if we consider preexisting tax distortions. The scope for innovation is reduced when we consider direct financial investment in the lab, or elastic labor supply. We then apply the core theoretical model to a real growing economy and find that a boost in economic growth following such a carbon policy is a possible outcome. Lump-sum redistribution performs best in terms of effciency measured by aggregate welfare, while in terms of equity among social segments its progressive character fails when we consider very high emissions reduction targets

Wheat genetic resources have avoided disease pandemics, improved food security, and reduced environmental footprints: A review of historical impacts and future opportunities

The use of plant genetic resources (PGR)—wild relatives, landraces, and isolated breeding gene pools—has had substantial impacts on wheat breeding for resistance to biotic and abiotic stresses, while increasing nutritional value, end-use quality, and grain yield. In the Global South, post-Green Revolution genetic yield gains are generally achieved with minimal additional inputs. As a result, production has increased, and millions of hectares of natural ecosystems have been spared. Without PGR-derived disease resistance, fungicide use would have easily doubled, massively increasing selection pressure for fungicide resistance. It is estimated that in wheat, a billion liters of fungicide application have been avoided just since 2000. This review presents examples of successful use of PGR including the relentless battle against wheat rust epidemics/pandemics, defending against diseases that jump species barriers like blast, biofortification giving nutrient-dense varieties and the use of novel genetic variation for improving polygenic traits like climate resilience. Crop breeding genepools urgently need to be diversified to increase yields across a range of environments (>200 Mha globally), under less predictable weather and biotic stress pressure, while increasing input use efficiency. Given that the ~0.8 m PGR in wheat collections worldwide are relatively untapped and massive impacts of the tiny fraction studied, larger scale screenings and introgression promise solutions to emerging challenges, facilitated by advanced phenomic and genomic tools. The first translocations in wheat to modify rhizosphere microbiome interaction (reducing biological nitrification, reducing greenhouse gases, and increasing nitrogen use efficiency) is a landmark proof of concept. Phenomics and next-generation sequencing have already elucidated exotic haplotypes associated with biotic and complex abiotic traits now mainstreamed in breeding. Big data from decades of global yield trials can elucidate the benefits of PGR across environments. This kind of impact cannot be achieved without widescale sharing of germplasm and other breeding technologies through networks and public–private partnerships in a pre-competitive space

A Note on the Different Interpretation of the Correlation Parameters in the Bivariate Probit and the Recursive Bivariate Probit

This note makes the point that, if a Bivariate Probit (BP) model is estimated on data arising from a Recursive Bivariate Probit (RBP) process, the resulting BP correlation parameter is a weighted average of the RBP correlation parameter and the parameter associated to the endogenous binary variable. Two corollaries follow this proposition: i) a zero correlation parameter in a BP model, usually interpreted as evidence of independence between the binary variables under study, may actually mask the presence of a RBP process; and ii) the interpretation of the correlation parameter in the RBP is not the same as in the BP -- i.e. the RBP correlation parameter does not necessarily reflect the correlation between the binary variables under study