Suchergebnisse

The at-risk-of-poverty rate (AROP) is one of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Timeliness of this indicator is crucial for monitoring of the social situation and of the effectiveness of tax and benefit policies. However, partly due to the complexity of EU-SILC data collection, estimates of the number of people at risk of poverty are published with a significant delay. This paper extends and updates previous work on estimating ('nowcasting') indicators of poverty risk using the tax-benefit microsimulation model EUROMOD. The model's routines are enhanced with additional adjustments to the EU-SILC based input data in order to capture changes in the employment characteristics of the population since the data were collected. The nowcasting method is applied to twenty-seven EU Member States. Median income and AROP rates are estimated up to 2016. The performance of the method is assessed by comparing the predictions with actual EU-SILC indicators for the years for which the latter are available. If nowcasts are unreliable we explain the main reasons behind the differences between the nowcasted and SILC-based indicators. For countries with stable and reliable results we discuss the main drivers behind the nowcasted trends.

The distributional impact of policy changes is usually considered in terms of equivalised household income, assuming that each individual within the household is being affected in the same way, as a result of complete income pooling. The aim of this paper is to extend this approach by introducing a gender perspective in the analysis of policy effects. We use EUROMOD, the tax-benefit microsimulation model for the EU, to estimate the effects of changes in tax-benefit policies over the period 2008-2014 separately for men and women. The paper consists of two parts. First, we apply the standard approach based on the equal income sharing assumption but focus on lone parent families - a specific household type which makes gendered policy effects easier to observe. This analysis is performed for 18 EU countries: Belgium, Bulgaria, the Czech Republic, Denmark, Germany, Estonia, Ireland, Spain, France, Italy, Latvia, Luxembourg, Hungary, the Netherlands, Poland, Romania, Finland and Sweden. Second, we estimate the policy effects for men and women in couples. To obtain gender specific effects, we redefine income at the individual level by allocating income components to each adult within the household according to a set of assumptions. We present three alternative scenarios of intra-household income sharing. All scenarios assume that all individual incomes (e.g. earnings, individual benefits) are retained by their recipients, while common incomes (e.g. family benefits, housing allowances) are distributed following three different sets of sharing rules, which are defined in relation to the primary and the secondary earner status. We compare the outcomes of men and women in these three scenarios and in the baseline which assumes equal income sharing. This analysis is performed for six countries which differ in terms of the degree of defamilialisation their welfare regimes provide: Belgium, the Czech Republic, Spain, France, Romania and Finland.

Policy over the past years has seen a gradual movement away from universal social benefits towards the provision of more targeted benefit schemes. Using the European tax-benefit microsimulation model EUROMOD, this paper aims to compare the effectiveness of income-tested benefits at different points in the economic cycle. This objective is considered in terms of coverage of households with incomes falling below various thresholds and importance in terms of the fraction of total resources that these benefits provide. The prevalence and relative weight of income-tested benefits throughout the income distribution is also examined. We compare the situation in 2009 with that in 2014 (or 2013) for fifteen EU Member States experiencing differing economic conditions over the period in question, including those which have been affected comparatively little by the crisis as well as those which have witnessed severe reductions in economic activity and employment levels and those in strong recovery by 2014. As EU-SILC micro-data containing household income for 2013 or 2014 are not available yet, standard EUROMOD routines are enhanced with additional adjustments to the EU-SILC based input data in order to take into account changes in the labour market. We attempt to indicate the sensitivity of the estimated policy effectiveness indicators to these particular changes. We conclude by discussing the methodological pitfalls and main findings of this research.

Very low work intensity and at-risk-of-poverty are two of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Their timeliness is critical for tracing the effectiveness of policy interventions towards reaching this target. However, due in part to the complicated nature of microdata collection and processing, official Eurostat estimates of these indicators become available with a significant delay. This paper presents a method of estimating ('nowcasting') very low work intensity and poverty risk using the tax-benefit microsimulation model EUROMOD based on EU-SILC data and combined with up-to-date macro-level statistics from the Labour Force Survey. The method is applied to 12 EU Member States for the period 2009-2013. Its performance is assessed by comparing the EUROMOD estimates with the official Eurostat statistics for the years for which the latter are available. The most important measurement issues of the work intensity indicator that are relevant in the context of nowcasting are also discussed.

The at-risk-of-poverty rate is one of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Timeliness of this indicator is crucial for monitoring of the social situation and of the effectiveness of tax and benefit policies. However, partly due to the complexity of EU-SILC data collection, estimates of the number of people at risk of poverty are published with a significant delay. This paper extends and updates previous work on estimating ('nowcasting') indicators of poverty risk using the tax-benefit microsimulation model EUROMOD. The model's routines are enhanced with additional adjustments to the EU-SILC based input data in order to capture changes in the employment characteristics of the population since the data were collected. The nowcasting method is applied to seventeen EU Member States. AROP rates are estimated up to 2014 for ten countries and 2013 for the remaining seven countries. The performance of the method is assessed by comparing the predictions with actual EU-SILC indicators for the years for which the latter are available.

The at-risk-of-poverty rate is one of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Timeliness of this indicator is critical for monitoring the effectiveness of policies. However, due to complicated nature of the European Union Statistics on Income and Living Conditions (EU-SILC) poverty risk estimates are published with a 2 to 3 year delay. This paper presents a method that can be used to estimate (nowcast) the current at-risk-of-poverty rate for the European Union (EU) countries based on EU-SILC microdata from a previous period. The EU tax-benefit microsimulation model EUROMOD is used for this purpose in combination with up to date macro-level statistics. We validate the method by using EU-SILC data for 2007 incomes to estimate at-risk-of-poverty rates for 2008-2012, and where possible compare our predictions with actual EU-SILC and other external statistics. The method is tested on eight EU countries which are among those experiencing the most volatile economic conditions within the period: Estonia, Greece, Spain, Italy, Latvia, Lithuania, Portugal and Romania.

The at-risk-of-poverty rate (AROP) is one of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Timeliness of this indicator is crucial for monitoring of the social situation and of the effectiveness of tax and benefit policies. However, partly due to the complexity of EU-SILC data collection, estimates of the number of people at risk of poverty are published with a significant delay. This paper extends and updates previous work on estimating ('nowcasting') indicators of poverty risk using the tax-benefit microsimulation model EUROMOD. The model's routines are enhanced with additional adjustments to the EU-SILC based input data in order to capture changes in the employment characteristics of the population since the data were collected. The nowcasting method is applied to twenty-five EU Member States. AROP rates are estimated up to 2015 for twenty countries and 2014 for the remaining five countries. The performance of the method is assessed by comparing the predictions with actual EU-SILC indicators for the years for which the latter are available.

The at-risk-of-poverty rate is one of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Timeliness of this indicator is critical for monitoring the effectiveness of policies. However, due in part to the complicated nature of the European Union Statistics on Income and Living Conditions (EU-SILC), estimates of the number of people at risk of poverty are published with a 2 to 3 year delay. This paper presents a method of estimating ('nowcasting') the current distribution of income between households, including the at-risk-of-poverty rate, using a tax-benefit microsimulation model (EUROMOD) based on the EU-SILC, combined with up-to-date macro-level statistics. The method is applied to 13 EU Member States experiencing differing economic conditions over the period, including those which have been affected comparatively little by the crisis as well as those which have suffered a major reduction in economic activity and employment.

This paper introduces the Hypothetical Household Tool (HHoT), a new extension of EUROMOD, the tax-benefit microsimulation model for the European Union. With HHoT, users can easily create their own hypothetical data, which enables them to better understand how policies work for households with specific characteristics. The tool creates unique possibilities for an enhanced analysis of taxes and social benefits in Europe by integrating results from microsimulations and hypothetical household simulations in a single modelling framework. Furthermore, the flexibility of HHoT facilitates an advanced use of hypothetical household simulations to create new comparative policy indicators in the context of multi-country and longitudinal analyses. In this paper, we highlight the main features of HHoT, its strengths and limitations, and illustrate how it can be used for comparative policy purposes.

This paper introduces the Hypothetical Household Tool (HHoT), a new extension of EUROMOD, the tax-benefit microsimulation model for the European Union. With HHoT, users can easily create their own hypothetical data, which enables them to better understand how policies work for households with specific characteristics. The tool creates unique possibilities for an enhanced analysis of taxes and social benefits in Europe by integrating results from microsimulations and hypothetical household simulations in a single modelling framework. Furthermore, the flexibility of HHoT facilitates an advanced use of hypothetical household simulations to create new comparative policy indicators in the context of multi-country and longitudinal analyses. In this paper, we highlight the main features of HHoT, its strengths and limitations, and illustrate how it can be used for comparative policy purposes.

This paper presents baseline results from the latest version of EUROMOD (version F6.36+), the tax-benefit microsimulation model for the EU. First, we briefly report the process of updating EUROMOD. We then present indicators for income inequality and risk of poverty using EUROMOD and discuss the main reasons for differences between these and EU-SILC based indicators. We further compare EUROMOD indicators across countries and over time between 2009 and 2012. Finally, we provide estimates of marginal effective tax rates (METR) for all 27 EU countries in order to explore the effect of tax and benefit systems on work incentives at the intensive margin. Throughout we highlight both the potential of EUROMOD as a tool for policy analysis and the caveats that should be borne in mind when using it and interpreting results. This paper updates the work reported in EUROMOD Working Paper EM3/2013.

This paper provides a description of the latest public release of EUROMOD (version F6.0++), a tax-benefit microsimulation model for the EU. First, we briefly report the process of constructing and updating EUROMOD. We then present indicators for income inequality and risk of poverty using EUROMOD and discuss the main reasons for differences between these and EU-SILC based indicators. We further compare EUROMOD indicators across countries and over time between 2007 and 2010. Finally, we provide estimates of marginal effective tax rates (METR) for all 27 EU countries in order to explore the effect of tax and benefit systems on work incentives at the intensive margin. Throughout we highlight both the potential of EUROMOD as a tool for policy analysis and the caveats that should be borne in mind when using it and interpreting results.