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A keresés eredménye

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Beruházás a gyerekekbe – a hátrányos helyzet továbbörökítésének megszakítása: A nemzeti szakpolitikák vizsgálata - Magyarország (2013)

Átlagos bérhátrány európai térképe (2009)

Középpontban az adatok - A romák - FRA EU-MIDIS (2009)

Investing in children - Breaking the cycle of disadvantage - A study of national policies: Cyprus (2016)
Investing in children - Breaking the cycle of disadvantage - A study of national policies: Cyprus

EU Network of Independent Experts on Social Inclusion. Study of national policies - Czech Republic (2016)
EU Network of Independent Experts on Social Inclusion. Study of national policies - Czech Republic

EU Network of Independent Experts on Social Inclusion: Study of national policies - Denmark (2016)
EU Network of Independent Experts on Social Inclusion: Study of national policies - Denmark

EU Network of Independent Experts on Social Inclusion: Study of national policies - Estonia (2016)
EU Network of Independent Experts on Social Inclusion: Study of national policies - Estonia

Investing in children: Breaking the cycle of disadvantage - A study of national policies: Ireland (2016)

The interaction between minimum wages, income support, and poverty - Research note 10/2015 by Manos Matsaganis, Márton Medgyesi and Alexandros Karakitsios (2015)
Minimum wages have emerged as a key policy issue in several countries in Europe (for example, in Germany and Italy) and beyond (for example, in the US). Furthermore, at EU level, discussions on a common European benchmark have gained momentum since European Commission President J.-C. Juncker came out in favour of an EU minimum wage as an essential component of the European Social Model. This Research Note attempts to throw light on the interaction between minimum wages, income support, and poverty. It focuses on two closely connected aspects of this issue. On the one hand, the latest EU-SILC data is used to examine the relationship between low wages and poverty, looking at the individual characteristics and household circumstances of those workers earning less than 50% of average hourly wages. On the other hand, the European tax-benefit model EUROMOD is deployed to simulate the effects on poverty of raising national minimum wages to that threshold (i.e. 50% of average hourly wages), taking into account interactions with social assistance and other tax-benefit policies, and assuming no negative impact on employment or behavioural effects. The main finding is that raising minimum wages to that level would have at best modest effects in terms of poverty reduction, though better coordination of minimum wages with other tax-benefit policies, and in particular with in-work benefits, could improve overall anti-poverty performance.

The characteristics of workers on low wages - Research note 9/2015 by Erhan Ozdemir and Terry Ward (2015)
This Research Note has two main aims. The first is to examine the characteristics of employees with low pay, which is defined in the main part of the study as those with hourly earnings of less than 50% of the average. The second is to compare the EU-SILC with the Labour Force Survey (LFS) as a source of data on the characteristics of the low-paid in order to assess their consistency. This is an important prelude to using the EU-SILC as the basis for examining the extent to which low pay is a major determinant of low household income and, accordingly, the strength of the link between low pay and in-work poverty. Since the LFS covers a much larger sample of households than the EU-SILC, it should be a more reliable source of data on the characteristics of those in employment. A comparison between the two in terms of low-wage earners, therefore, provides a check on the reliability of the EU-SILC data. In order to identify the low-paid in terms of their earnings relative to the average, the data on net monthly earnings reported by the LFS, which is in the form of deciles, is converted into monetary amounts using the EU-SILC, on the implicit assumption that the distribution of earnings according to the two surveys is similar. Earnings are then converted into hourly amounts on the basis of the LFS data on usual hours worked. The characteristics of the low-paid examined consist of gender, age, educational attainment levels, hours of work, the type of employment contract, occupation, sector of activity and country of birth (to reflect migrant background).