The Impact Of Tax Benefit Systems On Low Income Households In The Benelux Countries. A Simulation Approach Using Synthetic Datasets

Computing the tax-benefit position of similar typical households across countries is a method widely used in comparative fiscal- and social policy research. These calculations provide convenient summary pictures of certain aspects of tax-benefit systems. They can, however, be seriously misleading because they reduce very complex systems to single point estimates. Using an integrated European tax-benefit model (EUROMOD), we substitute the typical household by a synthetic dataset, which can be used across countries. By varying certain important household characteristics (notably income), this dataset captures a much larger range of household situations. The calculations performed on this range of households not only show the tax-benefit position of many individual households but also demonstrate which household characteristics determine taxes and benefits in each country. Hypothetical calculations such as those presented here do not exploit the ability of EUROMOD to determine the impact of social and fiscal policies on actual populations. Nevertheless, they can be a valuable contribution to understanding tax-benefit systems since they allow us to separate the effects of tax-benefit rules from those of the population structure. We compute and compare disposable incomes for a large range of pre-tax-and-benefit income (so called budget constraints) of households in the Benelux countries. Disposable incomes are then decomposed to separately show the effects of each simulated tax and transfer payment. Based on these results, we illustrate the performance of the three tax-benefit systems in terms of ensuring a minimum level of household income.European Union, Microsimulation, Poverty, Benelux, Average Production Worker

Multi-source statistics:Basic situations and methods

Many National Statistical Institutes (NSIs), especially in Europe, are moving from single‐source statistics to multi‐source statistics. By combining data sources, NSIs can produce more detailed and more timely statistics and respond more quickly to events in society. By combining survey data with already available administrative data and Big Data, NSIs can save data collection and processing costs and reduce the burden on respondents. However, multi‐source statistics come with new problems that need to be overcome before the resulting output quality is sufficiently high and before those statistics can be produced efficiently. What complicates the production of multi‐source statistics is that they come in many different varieties as data sets can be combined in many different ways. Given the rapidly increasing importance of producing multi‐source statistics in Official Statistics, there has been considerable research activity in this area over the last few years, and some frameworks have been developed for multi‐source statistics. Useful as these frameworks are, they generally do not give guidelines to which method could be applied in a certain situation arising in practice. In this paper, we aim to fill that gap, structure the world of multi‐source statistics and its problems and provide some guidance to suitable methods for these problems

Speed, algorithmic trading, and market quality around U.S. macroeconomic news announcements

This paper documents that speed is crucially important for high-frequency trading strategies based on U.S. macroeconomic news releases. Using order-level data on the highly liquid S&P 500 ETF traded on NASDAQ from January 6, 2009 to December 12, 2011, we find that a delay of 300 ms or more significantly reduces returns of news-based trading strategies. This reduction is greater for high impact news and on days with high volatility. In addition, we assess the effect of algorithmic trading on market quality around macroeconomic news. In the minute following a macroeconomic news arrival, algorithmic activity increases trading volume and depth at the best quotes, but also increases volatility and leads to a drop in overall depth. Quoted half-spreads decrease (increase) when we measure algorithmic trading over the full (top of the) order book