Download full text
(1.175Mb)
Citation Suggestion
Please use the following Persistent Identifier (PID) to cite this document:
https://nbn-resolving.org/urn:nbn:de:0168-ssoar-74537-2
Exports for your reference manager
The Use of Hypothetical Household Data for Policy Learning: Comparative Tax-Benefit Indicators Using EUROMOD HHoT
[journal article]
Abstract Tax-benefit microsimulation models are typically used to quantify the effect of specific policy changes on the income distribution based on representative microdata. Such analysis evaluates policies by considering how different tax-benefit elements interact given personal, household and labour marke... view more
Tax-benefit microsimulation models are typically used to quantify the effect of specific policy changes on the income distribution based on representative microdata. Such analysis evaluates policies by considering how different tax-benefit elements interact given personal, household and labour market characteristics. Using hypothetical household data instead helps address broader questions of policy design and systemic (cross-national) differences. This article introduces the Hypothetical Household Tool (HHoT) in combination with the microsimulation model EUROMOD to analyse European tax-benefit policies from a comparative perspective. It presents a series of applications from social welfare analysis illustrating how hypothetical data can benefit comparative academic and policy research.... view less
Keywords
social policy; redistribution; tax policy; cash benefit; social insurance; EU; simulation; indicator; data capture
Classification
Basic Research, General Concepts and History of Social Policy
Free Keywords
hypothetical households; comparative indicators; microsimulation
Document language
English
Publication Year
2020
Page/Pages
p. 170-189
Journal
Journal of Comparative Policy Analysis: Research and Practice, 22 (2020) 2
Issue topic
Comparing the Development of Social Impact Bonds across Different Countries and Policy Sectors
DOI
https://doi.org/10.1080/13876988.2019.1609784
ISSN
1572-5448
Status
Published Version; peer reviewed