Structural development accounting

We construct and estimate a unified model combining three of the main sources ofcross-country income disparities: differences in factor endowments, barriers to technologyadoption and the inappropriateness of frontier technologies to local conditions. The keycomponents are different types of workers, distortions to capital accumulation, directedtechnical change, costly adoption and spillovers from the world technology frontier. Despiteits parsimonious parametrization, our empirical model provides a good fit of GDP data forup to 86 countries in 1970 and 122 countries in 2000. Removing barriers to technologyadoption would increase the output per worker of the average non-OECD country relativeto the US from 0.19 to 0.61, while increasing skill premia in all countries. Removing barriersto trade in goods amplifies income disparities, induces skill-biased technology adoptionand increases skill premia in the majority of countries. These results are reverted if tradeliberalization is coupled with international IPR protection.

El impacto de los servicios sanitarios sobre la salud

This paper reviews what has increased medical-care spending bought in terms of health benefitswith longitudinal data from the U.S and, more limited, from Spain. Health services contributionto health has been positive in average, especially during the last 50 years for the U.S andthe last 30 years for Spain. This contribution differs among countries and is much greater forsome diseases (cardiovascular) than for others (cancer). Benefits from health care interventionscan be valued on basis on the social willin gness to pay, observed or declared on the process ofestablishing health policy priorities. 30.000 euros per Quality Adjusted Life Year could providean efficiency threshold for financing publicly health services in Spain: Consensus andlegitimacy of the political process of establishing health priorities becomes, however, moreimportant than any approximate number. Attention is paid finally to bridging the gap betweenefficacy (the possibilities given by innovation and resources devoted to health care) andeffectiveness (the distance to the frontier) of the everyday working of a health system with itsinappropriate care and limited application of the existing knowledge.

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.