Corruption: Who pays for the bill?

This paper intends to study who pays for corruption in Brazil from 2005 to 2011. Politicians may decide to charge the spillovers of corruption at a municipal level through taxes or to charge it to the entire country through voluntary transfers. The used measure of corruption is based on audit reports conducted on randomly selected municipalities from 2005 to 2011.In order to address this question an IV strategy was computed using as instrument for the number of observed cases of corruption the dummy variable of being audited or not. We evaluated the impact of corruption on taxes and on voluntary transfers and concluded that with an increase in the number of observed cases of corruption the first decreases and the latest registers an increase. Therefore, considering all Brazilian municipalities, mayors prefer to spread the bill all over the country than charging it locally.

An agent-based simulator to estimate domestic energy use

Throughout recent years, there has been an increase in the population size, as well as a fast economic growth, which has led to an increase of the energy demand that comes mainly from fossil fuels. In order to reduce the ecological footprint, governments have implemented sustainable measures and it is expected that by 2035 the energy produced from renewable energy sources, such as wind and solar would be responsible for one-third of the energy produced globally. However, since the energy produced from renewable sources is governed by the availability of the respective primary energy source there is often a mismatch between production and demand, which could be solved by adding flexibility on the demand side through demand response (DR). DR programs influence the end-user electricity usage by changing its cost along the time. Under this scenario the user needs to estimate the energy demand and on-site production in advance to plan its energy demand according to the energy price. This work focuses on the development of an agent-based electrical simulator, capable of: (a) estimating the energy demand and on-site generation with a 1-min time resolution for a 24-h period, (b) calculating the energy price for a given scenario, (c) making suggestions on how to maximize the usage of renewable energy produced on-site and to lower the electricity costs by rescheduling the use of certain appliances. The results show that this simulator allows reducing the energy bill by 11% and almost doubling the use of renewable energy produced on-site.