Distributional National Accounts (DINA) with household survey data: Methodology and results for European countries

Arbeitspapier

The paper builds Distributional National Accounts (DINA) using household survey data. We present a transparent and reproducible methodology to construct DINA whenever administrative tax data are not available for research and apply it to various European countries. By doing so, we build synthetic microdata files which cover the entire distribution, include all income components individually aligned to national accounts, and preserve the detailed socioeconomic information available in the surveys. The methodology uses harmonized and publicly available data sources (SILC, HFCS) and provides highly comparable results. We discuss the methodological steps and their impact on the income distribution. In particular, we highlight the effects of imputations and the adjustment of the variables to national accounts totals. Furthermore, we compare different income concepts of both the DINA and EG-DNA approach of the OECD in a consistent way. Our results confirm that constructing DINA is crucial to get a better picture of the income distribution. Our methodology is well suited to build synthetic microdata files which can be used for policy evaluation like social impact analysis and microsimulation.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: wiiw Working Paper ; No. 180

Classification and Topics

• Classification: Wirtschaft ; Large Data Sets: Modeling and Analysis ; Personal Income, Wealth, and Their Distributions ; Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
• Subject: Distributional national accounts ; survey data ; income inequality

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Ederer, Stefan ; Humer, Stefan ; Jestl, Stefan ; List, Emanuel
• Event: Veröffentlichung
• (who): The Vienna Institute for International Economic Studies (wiiw)
• (where): Vienna
• (when): 2020

Further information

• Handle: http://hdl.handle.net/10419/223085
• Last update: 10.03.2025, 11:42 AM CET