Benefit Take-Up and Labor Supply Incentives of Interdependent Means-Tested Benefit Programs for Low-Income Households

Bruckmeier, Kerstin; Wiemers, Jürgen

doi:10.1057/s41294-017-0041-5

Cited by 19 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Germany, the working poor are eligible to claim social benefits even if they are in regular employment, but eligibility and the total value of benefits depend on the household context (Bruckmeier & Wiemers, 2018). First, it depends on the type of household, i.e., the number of individuals that have to make a living from the labor income.…”

Section: Social Benefits As An Additional Source Of Incomementioning

confidence: 99%

Wage Inequality in Germany after the Minimum Wage Introduction

Schänk

Bossler

2020

SSRN Journal

View full text Add to dashboard Cite

Section: Social Benefits As An Additional Source Of Incomementioning

confidence: 99%

Wage Inequality in Germany after the Minimum Wage Introduction

Schänk

Bossler

2020

SSRN Journal

View full text Add to dashboard Cite

“…Fuchs et al (2020) considered employment status, household type, poverty gap, education level, housing type, gender, and so on as influencing variables through quantitative analysis of non-take-ups and regarded the size of local government as a regional influencing factor (Paz-Fuchs & Wynn, 2019). Hümbelin (2019) analyzed the factors affecting the effectiveness of non-take-up discovery using administrative data from Canton City, employing variables that reflect both personal and regional characteristics such as urban size, language, political ideology, and poverty level (Bruckmeier & Wiemers, 2018; Harnisch, 2019; Hümbelin, 2019).…”

Section: Introductionmentioning

confidence: 99%

Regional Factors Influencing Non-Take-Up for Social Support in Korea Using a Spatial Regression Model

2021

View full text Add to dashboard Cite

The purpose of this study is to analyze the influential factors of non-take-up citizens, who do not receive social benefits, to increase their discovery rate. A spatial regression model was used to analyze the variables affecting the discovery rates. As a result of the study, there was a difference in the percentage of welfare blind spots by region. In addition, when the proportion of the elderly population, the number of unemployment benefit recipients, etc. increased, that of welfare blind spots also increased; the lower the population density was, the higher the rate of increase in welfare blind spots became. Accordingly, in order to resolve the welfare blind spots at the local level, it is necessary to reinforce policy support for the elderly population and reduce the unemployment rate. Particularly, the policy will have to be prepared to resolve the welfare blind spots in rural areas with low population density.

show abstract

“…However, this is moderate by European comparison (Eurofund, 2015, Table 1). Also, other social policy programs in Germany feature even higher non-take-up rates (Bruckmeier and Wiemers, 2018, Table 3). These authors point to a complicated benefit structure and argue the expected utility of the entitlements as well as information costs and stigmatization explain take-up behavior.…”

Section: Institutional Backgroundmentioning

confidence: 99%