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Tytuł pozycji:

Bridging between economy-wide activity and household-level consumption data: Matrices for European countries.

Tytuł:
Bridging between economy-wide activity and household-level consumption data: Matrices for European countries.
Autorzy:
Cai M; Joint Research Centre, European Commission, 41032 Seville, Spain.
Vandyck T; Joint Research Centre, European Commission, 41032 Seville, Spain.
Źródło:
Data in brief [Data Brief] 2020 Mar 12; Vol. 30, pp. 105395. Date of Electronic Publication: 2020 Mar 12 (Print Publication: 2020).
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Original Publication: [Amsterdam] : Elsevier B.V., [2014]-
Contributed Indexing:
Keywords: Budget survey; Equity; Input–Output; Macro models; Micro data; National accounts; Reclassification
Entry Date(s):
Date Created: 20200328 Latest Revision: 20200928
Update Code:
20240105
PubMed Central ID:
PMC7083776
DOI:
10.1016/j.dib.2020.105395
PMID:
32215306
Czasopismo naukowe
This dataset represents bridging matrices between two different data classification systems: consumption by purpose (COICOP) and products by activity (CPA). While the former classification is used in household budget and expenditure surveys, the latter represents the industry sector dimension that is typically adopted in national accounts and input-output tables. We collect input data from Eurostat on total household consumption for 35 COICOP and 63 CPA categories for the year 2015. Based on these data, we construct bridging or concordance tables for 30 European countries using recently developed matrix balancing techniques. The resulting tables enable data conversion between consumption- and production-based statistics, facilitating research that integrates macroeconomics, multi-sectoral international trade and heterogeneous agents in household-level expenditure micro-data. Although they are a necessary input in several types of research, they are often constructed on an ad hoc and region-specific basis and not shared publicly. As such, making this dataset available will be useful for computable general equilibrium and input-output models and for carbon footprint and life cycle analyses that incorporate rich consumption micro-data, for instance to shed light on distributional aspects of climate and energy policies. Furthermore, by eliminating a barrier raised by differences in statistical classifications, this dataset may foster collaboration between different research teams and may facilitate soft-linking between complementary analytical tools used for policy support.
(© 2020 The Authors. Published by Elsevier Inc.)

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