Doing good when doing well: evidence on real earnings management

We provide evidence on earnings management by exploiting temporary exogenous shocks to utility firms’ sales arising from weather variation. We find that sample firms’ sales are highly sensitive to annual changes in average temperatures in the region where the firm operates, but this sensitivity disappears quickly as one moves down the income statement. This evidence, while indirect, is suggestive of earnings management activities. In search of direct evidence, we study charitable giving decisions by sample firms and uncover a significant positive sensitivity of charitable spending to weather-driven demand shocks, behavior that is highly consistent with the presence of real earnings management efforts. We find no convincing evidence supporting possible alternative explanations for this evidence, but we do find limited support for the presence of a larger giving–weather relation when earnings management incentives are likely to be elevated. If other real decisions with similar characteristics scale proportionally to charitable giving, our findings suggest that the overall magnitude of real earnings management activities could be quite substantial.

Effects of weather variation on waterfowl migration: lessons from a continental-scale generalizable avian movement and energetics model

We developed a nonbreeding period continental-scale energetics-based model of daily waterfowl movement to predict year-specific migration and overwinter occurrence. The model approximates energy-expensive movements and energy-gaining stopovers as functions of metabolism and weather, in terms of temperature and frozen precipitation (i.e., snow). The model is a Markov process operating at the population level and is parameterized through a review of literature. We examined model performance against 62 years of non-breeding period daily weather data. The average proportion of available habitat decreased as weather severity increased, with mortality decreasing as the proportion of available habitat increased. The most commonly used nodes during the course of the nonbreeding period were generally consistent across years, with the most inter-annual variation present in the overwintering area. Our model revealed that the distribution of birds on the landscape changed more dramatically when the variation in daily available habitat was greater. The main routes for avian migration in North America were predicted by our simulations: the Eastern, Central, and Western flyways. Our model predicted an average of 77.4% survivorship for the nonbreeding period across all years (range = 76.4 – 78.4%), with lowest survivorship during the fall, intermediate survivorship in the winter, and greatest survivorship in the spring. We provide the parameters necessary for exploration within and among other taxa to leverage the generalizability of this migration model to a broader expanse of bird species, and across a range of climate change and land use/land cover change scenarios.

Deep factor analysis for weather varied sense-through-foliage target detection

In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis (DFA) in signal denoising are studied. To extensively verify the universality of the DFA_based approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by DFA_based algorithm, which can improve the average classification accuracy in all seasons. Finally, by means of cross validation, the effectiveness of DFA_based algorithm on signal denoising and the influence on target detection accuracy are further studied. The method is stable and universal in any weather conditions, even in hazy and snowy days, which can be stable at about 93%.

Deep Factor Analysis for Weather Varied Sense-through-foliage Target Detection

In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis(DFA) in signal denoising are studied. To extensively verify the universality of the DFA_based approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by DFA_based algorithm, which can improve the average classification accuracy in all seasons. Finally, by means of cross validation, the effectiveness of DFA_based algorithm on signal denoising and the influence on target detection accuracy are further studied. The method is stable and universal in any weather conditions, even in foggy and snowy days, which can be stable at about 93%.

Millet Cultivation and Food Security in Tribal Region of Odisha, India: A Microlevel Analysis

As the world is facing challenges due to climate change and food insecurity, millet has proven its adaptivity to adverse agro-climates such as poor soil, minimal water, and significant weather variation. The present study attempts to assess the cultivation and consumption of millet in the tribal region. Hence, the tribally dominated Koraput district has been chosen as the study area. It is based on a primary survey of 150 millet cultivators. Although finger millet cultivation has been taken up by the farmers, it is mostly done for household consumption rather than sale at market. However, it is known that millet cultivation generates significant returns. Hence, farmers prefer to cultivate paddy instead of millet for commercial sales due to procurement and productivity issues, marketing problems. Furthermore, though millet along with rice is the staple food for a tribal household, rice consumption is highest in the food basket. This is prevalent as rice is being sold by the government at a very subsidised price. Hence, a proper strategy focussed on revamping millet cultivation and consumption would be beneficial in the fight against food insecurity and climate change, particularly in the tribal regions.

The Effects of Weather on Avian Growth and Implications for Adaptation to Climate Change

Climate change is forecasted to generate a range of evolutionary changes and plastic responses. One important aspect of avian responses to climate change is how weather conditions may change nestling growth and development. Early life growth is sensitive to environmental effects and can potentially have long-lasting effects on adult phenotypes and fitness. A detailed understanding of both how and when weather conditions affect the entire growth trajectory of a nestling may help predict population changes in phenotypes and demography under climate change. This review covers three main topics on the impacts of weather variation (air temperature, rainfall, wind speed, solar radiation) on nestling growth. Firstly, we highlight why understanding the effects of weather on nestling growth might be important in understanding adaptation to, and population persistence in, environments altered by climate change. Secondly, we review the documented effects of weather variation on nestling growth curves. We investigate both altricial and precocial species, but we find a limited number of studies on precocial species in the wild. Increasing temperatures and rainfall have mixed effects on nestling growth, while increasing windspeeds tend to have negative impacts on the growth rate of open cup nesting species. Thirdly, we discuss how weather variation might affect the evolution of nestling growth traits and suggest that more estimates of the inheritance of and selection acting on growth traits in natural settings are needed to make evolutionary predictions. We suggest that predictions will be improved by considering concurrently changing selection pressures like urbanization. The importance of adaptive plastic or evolutionary changes in growth may depend on where a species or population is located geographically and the species’ life-history. Detailed characterization of the effects of weather on growth patterns will help answer whether variation in avian growth frequently plays a role in adaption to climate change.

The urbanising force of global warming: the role of climate change in the spatial distribution of population

We study the relationship between changes in weather patterns and the spatial distribution of population and economic activity within countries. Our unique global dataset combines climatic and census data for the period 1950–2015 with satellite data on built-up areas, and light intensity at night for the 1990–2015 periods. We establish a global non-linear effect of climate on urbanisation. In particular, we find that deteriorating climatic conditions are associated with more urbanisation. This happens across the whole urban structure, with urbanisation increasing in both smaller and larger cities. But, we also find that weather variation can alter the national urban structure, including the pattern of urban concentration, as well as the size, density and spatial structure of large cities.