The Impact of Reducing the Pension Generosity on Inequality and Schooling

We investigate the impact of a reduction in the pension replacement rate on the schooling choice and on inequality in an overlapping generations model in which individuals differ by their life expectancy and in their cost of attending schooling. Within our framework we illustrate that many pension systems are ex ante regressive due to the difference in life expectancy across skill groups. We then derive the level of progressivity that needs to be implemented to restore an equal treatment of the pension system across skill groups

Education, lifetime labor supply, and longevity improvements

This paper presents an analysis of the differential role of mortality for the optimal schooling and retirement age when the accumulation of human capital follows the so-called “Ben-Porath mechanism”. We set up a life-cycle model of consumption and labor supply at the extensive margin that allows for endogenous human capital formation. This paper makes two important contributions. First, we provide the conditions under which a decrease in mortality leads to a longer education period and an earlier retirement age. Second, those conditions are decomposed into a Ben-Porath mechanism and a lifetime-human wealth effect vs. the years-to-consume effect. Finally, using US and Swedish data for cohorts born between 1890 and 2000, we show that our model can match the empirical evidence

Optimal investment and location decisions of a firm in a flood risk area using impulse control theory

Flooding events can affect businesses close to rivers, lakes or coasts. This paper provides an economic partial equilibrium model, which helps to understand the optimal location choice for a firm in flood risk areas and its investment strategies. How often, when and how much are firms willing to invest in flood risk protection measures? We apply Impulse Control Theory and develop a continuation algorithm to solve the model numerically. We find that, the higher the flood risk and the more the firm values the future, i.e. the more sustainable the firm plans, the more the firm will invest in flood defense. Investments in productive capital follow a similar path. Hence, planning in a sustainable way leads to economic growth. Sociohydrological feedbacks are crucial for the location choice of the firm, whereas different economic settings have an impact on investment strategies. If flood defense is already present, e.g. built up by the government, firms move closer to the water and invest less in flood defense, which allows firms to generate higher expected profits. Firms with a large initial productive capital surprisingly try not to keep their market advantage, but rather reduce flood risk by reducing exposed productive capital

Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation

Biomedical text tagging systems are plagued by the dearth of labeled training data. There have been recent attempts at using pre-trained encoders to deal with this issue. Pre-trained encoder provides representation of the input text which is then fed to task-specific layers for classification. The entire network is fine-tuned on the labeled data from the target task. Unfortunately, a low-resource biomedical task often has too few labeled instances for satisfactory fine-tuning. Also, if the label space is large, it contains few or no labeled instances for majority of the labels. Most biomedical tagging systems treat labels as indexes, ignoring the fact that these labels are often concepts expressed in natural language e.g. `Appearance of lesion on brain imaging'. To address these issues, we propose constructing extra labeled instances using label-text (i.e. label's name) as input for the corresponding label-index (i.e. label's index). In fact, we propose a number of strategies for manufacturing multiple artificial labeled instances from a single label. The network is then fine-tuned on a combination of real and these newly constructed artificial labeled instances. We evaluate the proposed approach on an important low-resource biomedical task called \textit{PICO annotation}, which requires tagging raw text describing clinical trials with labels corresponding to different aspects of the trial i.e. PICO (Population, Intervention/Control, Outcome) characteristics of the trial. Our empirical results show that the proposed method achieves a new state-of-the-art performance for PICO annotation with very significant improvements over competitive baselines.Comment: International Workshop on Health Intelligence (W3PHIAI-20); AAAI-2

Measuring private transfers between generations and gender: an application of national transfer accounts for Austria 2015

Few data sources provide information on private transfers between generations and gender. We use a novel approach based on the National Transfer Accounts methodology to estimate the value of intra-family transfers between generations by age, gender and parental status in Austria 2015. The paper considers monetary transfers together with transfers of consumption goods and transfers of services produced by non-market work. Our results show that parents use one third of their disposable income and up to four hours of daily non-market work for their children. The total size of the intra-family transfers corresponds to 38 per cent of primary income

Aiding first incident responders using a decision support system based on live drone feeds

In case of a dangerous incident, such as a fire, a collision or an earthquake, a lot of contextual data is available for the first incident responders when handling this incident. Based on this data, a commander on scene or dispatchers need to make split-second decisions to get a good overview on the situation and to avoid further injuries or risks. Therefore, we propose a decision support system that can aid incident responders on scene in prioritizing the rescue efforts that need to be addressed. The system collects relevant data from a custom designed drone by detecting objects such as firefighters, fires, victims, fuel tanks, etc. The drone autonomously observes the incident area, and based on the detected information it proposes a prioritized based action list on e.g. urgency or danger to incident responders

The environment, life expectancy, and growth in overlapping generations models: A survey

It is widely accepted that environmental and demographic changes will significantly influence the future of our society. In recent years, an increasing number of studies has analyzed the interlinkages among economic growth, environmental factors, and a specific demographic variable, namely life expectancy, applying an overlapping generations framework. The aim of this survey is threefold. First, we review the role of life expectancy and pollution for sustainable growth. Second, we discuss the role of intervening factors like health investment and technological progress as well as institutional settings including government expenditures, tax structures, and inequality. Finally, we summarize policy implications obtained in different models and compare them to each other

Optimal time allocation in active retirement. Working Paper 02/2019

We set up a lifecycle model of a retired scholar who chooses opti-mally the time devoted to different activities including physical activity,continued work and social engagement. While time spent in physicalactivity increases life expectancy, continued scientific publications in-creases the knowledge stock. We show the optimal trade off betweenthese activities in retirement and its sensitivity with respect to alterna-tive settings of the preference parameters

Should I stay or should I go: Modelling disaster risk behaviour using a dynamic household level approach

In the last decades, many parts of the world faced an increase in the number of extreme weather events and worsening climate conditions endangering the livelihood of households in developing countries that rely on their local environment. While various empirical studies have identified key factors of exposure and vulnerability to disaster risk, we still lack a conceptual understanding of how these forces interact and how they impact household decision making. To gain insight into these mechanisms we set up a dynamic household model where households face environmental hazards. To respond to the risk, households can either relocate to a safer area or undertake preventive measures. Both actions require material and immaterial resources, which constrain the household's decision. Households are assumed to be heterogeneous with respect to key empirically identified factors for individual disaster risk: education, income, risk awareness, time preference and their access to preventive measures. This paper provides analytical insights into the short-run decision making of households derived from the theoretical framework as well as an extensive numerical investigation. To parameterize and calibrate the model we use data from Thailand and Vietnam. The roles of household characteristics on the short-term decision-making and long-run outcomes of households' well-being and disaster risk is discussed. We conclude the paper with an extensive evaluation of different policy interventions including housing and prevention cost subsidies as well as income transfers with respect to their heterogeneous effects on different sub-populations

Multi-score Learning for Affect Recognition: the Case of Body Postures

An important challenge in building automatic affective state recognition systems is establishing the ground truth. When the groundtruth is not available, observers are often used to label training and testing sets. Unfortunately, inter-rater reliability between observers tends to vary from fair to moderate when dealing with naturalistic expressions. Nevertheless, the most common approach used is to label each expression with the most frequent label assigned by the observers to that expression. In this paper, we propose a general pattern recognition framework that takes into account the variability between observers for automatic affect recognition. This leads to what we term a multi-score learning problem in which a single expression is associated with multiple values representing the scores of each available emotion label. We also propose several performance measurements and pattern recognition methods for this framework, and report the experimental results obtained when testing and comparing these methods on two affective posture datasets