Capital Taxation, Long-run Growth, and Bequests

It has been shown that higher capital taxes can have a growth-enhancing effect when combined with a revenue-compensating cut in wage taxes (Uhlig and Yanagawa 1996; European Economic Review 40, 1521–1540) or with an expansion in productivity-increasing public services (Rivas 2003;European Economic Review 47, 477–503).The present paper demonstrates that these results critically hinge on the existence of a bequest motive. It is shown that a wage-tax cut is no longer growth-enhancing when bequests are operative. By way of contrast, increasing productive public services may well boost growth. The theoretical findings are illustrated by numerical simulations based on US data.Capital income taxation, public spending, overlapping generations, growth, family altruism

Financing Social Security by Taxing Capital Income – A Bad Idea?

This paper examines the growth effects of an increase of capital income taxes with additional revenue being devoted to cut wage-related social security contributions to reduce unemployment. The analysis is carried out in an overlapping generations model with endogenous growth, unemployment and a social security system comprising pensions and unemployment benefits. It is shown that the reform not only promotes employment but may additionally stimulate economic growth. Calibrating the model to match data for the EU15 reveals that European countries can indeed gain in form of higher employment and growth if the initial capital income tax is not too high.Capital income taxation, social security, imperfect labor market, overlapping generations, growth

Taxing Human Capital Efficiently when Qualified Labour is Mobile

The paper studies the effect that skilled labour mobility has on efficient education policy. The model is one of two periods in which a representative taxpayer decides on labour, education, and saving. The government can only use linear tax and subsidy instruments. It is shown that the mobility of skilled labour well constrains government’s choice of policy instruments. The mobility does not however affect second best education policy in allocational terms. In particular, education should be effectively subsidized if, and only if, the elasticity of the earnings function is increasing in education. This rule applies regardless of whether labour is mobile or immobile.mobile labour, second-best efficient taxation, linear instruments, residence vs. source principle

The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird's-eye-view images contain sharper features (e.g. road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford RobotCar Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.Comment: equal contribution of first two authors, 8 full pages, 6 figures, accepted at IV 201

Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour

Being able to reason about how one's behaviour can affect the behaviour of others is a core skill required of intelligent driving agents. Despite this, the state of the art struggles to meet the need of agents to discover causal links between themselves and others. Observational approaches struggle because of the non-stationarity of causal links in dynamic environments, and the sparsity of causal interactions while requiring the approaches to work in an online fashion. Meanwhile interventional approaches are impractical as a vehicle cannot experiment with its actions on a public road. To counter the issue of non-stationarity we reformulate the problem in terms of extracted events, while the previously mentioned restriction upon interventions can be overcome with the use of counterfactual simulation. We present three variants of the proposed counterfactual causal discovery method and evaluate these against state of the art observational temporal causal discovery methods across 3396 causal scenes extracted from a real world driving dataset. We find that the proposed method significantly outperforms the state of the art on the proposed task quantitatively and can offer additional insights by comparing the outcome of an alternate series of decisions in a way that observational and interventional approaches cannot.Comment: 8 Pages, 4 Figures, To be published in the Proceedings of the 2023 IEEE Intelligent Vehicles Symposium, Final submission versio

CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments

Robots operating in real-world environments must reason about possible outcomes of stochastic actions and make decisions based on partial observations of the true world state. A major challenge for making accurate and robust action predictions is the problem of confounding, which if left untreated can lead to prediction errors. The partially observable Markov decision process (POMDP) is a widely-used framework to model these stochastic and partially-observable decision-making problems. However, due to a lack of explicit causal semantics, POMDP planning methods are prone to confounding bias and thus in the presence of unobserved confounders may produce underperforming policies. This paper presents a novel causally-informed extension of "anytime regularized determinized sparse partially observable tree" (AR-DESPOT), a modern anytime online POMDP planner, using causal modelling and inference to eliminate errors caused by unmeasured confounder variables. We further propose a method to learn offline the partial parameterisation of the causal model for planning, from ground truth model data. We evaluate our methods on a toy problem with an unobserved confounder and show that the learned causal model is highly accurate, while our planning method is more robust to confounding and produces overall higher performing policies than AR-DESPOT.Comment: 8 pages, 3 figures, submitted to 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Envisioning the qualitative effects of robot manipulation actions using simulation-based projections

Autonomous robots that are to perform complex everyday tasks such as making pancakes have to understand how the effects of an action depend on the way the action is executed. Within Artificial Intelligence, classical planning reasons about whether actions are executable, but makes the assumption that the actions will succeed (with some probability). In this work, we have designed, implemented, and analyzed a framework that allows us to envision the physical effects of robot manipulation actions. We consider envisioning to be a qualitative reasoning method that reasons about actions and their effects based on simulation-based projections. Thereby it allows a robot to infer what could happen when it performs a task in a certain way. This is achieved by translating a qualitative physics problem into a parameterized simulation problem; performing a detailed physics-based simulation of a robot plan; logging the state evolution into appropriate data structures; and then translating these sub-symbolic data structures into interval-based first-order symbolic, qualitative representations, called timelines. The result of the envisioning is a set of detailed narratives represented by timelines which are then used to infer answers to qualitative reasoning problems. By envisioning the outcome of actions before committing to them, a robot is able to reason about physical phenomena and can therefore prevent itself from ending up in unwanted situations. Using this approach, robots can perform manipulation tasks more efficiently, robustly, and flexibly, and they can even successfully accomplish previously unknown variations of tasks

Who Is Bowling Alone? Quantile Treatment Effects of Unemployment on Social Participation

The author gratefully acknowledges funding from the Spanish Ministry of Science, Innovation and Universities' Juan de la Cierva Research Grant Programs (IJCI-2017-33950), the European Research Council (ERC-2014-StG637768, EQUALIZE project); and the CERCA Programme, Generalitat de Catalunya.This article examines heterogeneity in the effect of unemployment on social participation. Whereas existing studies on this relationship essentially estimate mean effects, we use quantile regression methods to provide a broader and more complete picture. To account for the potential endogeneity of job loss, we estimate quantile treatment effects (on the treated) based on entropy balancing and focus on unemployment due to plant closures. Using German panel data, we show that the effect of unemployment varies across the distribution of public social activities. It is large and negative for individuals in the middle and lower part of the distribution of public activities, whereas those participating a lot are not affected. By contrast, the effect of unemployment on private social participation is virtually zero for individuals at the lower part of the outcome distribution and weakly positive in the middle. Our findings suggest that active labor market policies should account for targetgroup specific elements, tailored to those individuals which are most adversely affected by unemployment

Evaluating temporal observation-based causal discovery techniques applied to road driver behaviour

Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. The creation of models to describe these relationships is typically accomplished through the application of causal discovery techniques. However, as it stands observational causal discovery techniques struggle to adequately cope with conditions such as causal sparsity and non-stationarity typically seen during online usage in autonomous agent domains. Meanwhile, interventional techniques are not always feasible due to domain restrictions. In order to better explore the issues facing observational techniques and promote further discussion of these topics we carry out a benchmark across 10 contemporary observational temporal causal discovery methods in the domain of autonomous driving. By evaluating these methods upon causal scenes drawn from real world datasets in addition to those generated synthetically we highlight where improvements need to be made in order to facilitate the application of causal discovery techniques to the aforementioned use-cases. Finally, we discuss potential directions for future work that could help better tackle the difficulties currently experienced by state of the art techniques

SMOS Sea Ice Thickness Data Product Quality Control by Comparison with the Regional Sea Ice Extent

Brightness temperature data from wave Imaging Radiometer using Aperture Synthesis (MIRAS) on board the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission have been used to derive the thickness of thin sea ice for the Arctic freeze-up period. To control the long-term geophysical quality for level 3 SMOS sea ice thickness products we derive a regional extent parameter that can be compared to independent standard ice extent products such as the NSIDC sea ice index. This metric allows to identify first-order quality problems such as data gaps and to observe the evolution of the Arctic sea ice growth in key regions. The regionalized SMOS sea ice thickness extent corresponds in general well with the corresponding NSIDC Sea Ice Index. The occurrence of severe RFI problems has so far mainly been limited to the initial period of the SMOS measurements during the season 2010/2011. Otherwise the comparison does not reveal any significant quality problems of the SMOS sea ice thickness data