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Sébastien Pérez-Duarte

9 January 2009
OCCASIONAL PAPER SERIES - No. 100
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Abstract
The first part of this paper provides a brief survey of the recent literature that employs survey data on household finance and consumption. Given the breadth of the topic, it focuses on issues that are particularly relevant for policy, namely: i) wealth effects on consumption, ii) housing prices and household indebtedness, iii) retirement income, consumption and pension reforms, iv) access to credit and credit constraints, v) financial innovation, consumption smoothing and portfolio selection and vi) wealth inequality. The second part uses concrete examples to summarise how results from such surveys feed into policy-making within the central banks that already conduct such surveys.
JEL Code
C42 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Survey Methods
D12 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Empirical Analysis
D14 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance
Network
Eurosystem Monetary Transmission Network
12 July 2011
WORKING PAPER SERIES - No. 1359
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Abstract
In this article, we use a stylized model of the labor market to investigate the effects of three alternative and well-known bargaining solutions. We apply the Nash, the Egalitarian and the Kalai-Smorodinsky bargaining solutions in the small firm's matching model of unemployment. To the best of our knowledge, this is the first attempt to implement and systematically compare these solutions in search-matching economies. Our results are twofold. First from the theoretical and methodological viewpoint, we extend a somewhat flexible search-matching economy to alternative bargaining solutions. In particular, we prove that the Egalitarian and the Kalai-Smorodinsky solutions are easily implementable within search-matching economies. Second, our results show that even though the traditional results of bargaining theory apply in this context, they are generally qualitatively different from the standard results, and the differences are quantitatively weaker than expected. This is of particular relevance in comparison with the results established in the earlier literature.
JEL Code
C71 : Mathematical and Quantitative Methods→Game Theory and Bargaining Theory→Cooperative Games
C78 : Mathematical and Quantitative Methods→Game Theory and Bargaining Theory→Bargaining Theory, Matching Theory
J20 : Labor and Demographic Economics→Demand and Supply of Labor→General
J60 : Labor and Demographic Economics→Mobility, Unemployment, Vacancies, and Immigrant Workers→General
30 September 2013
STATISTICS PAPER SERIES - No. 3
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Abstract
Traditional literature on sampling techniques focuses mainly on statistical samples and covers non-random (non-statistical) samples only marginally. Nevertheless, there has been a recent revival of interest in non-statistical samples, given their widespread use in certain fields like government surveys and marketing research, or for audit purposes. This paper attempts to set up common rules for non-statistical samples in which only data on the largest institutions within each stratum are collected. This is done by focusing on the statistics compiled by the European System of Central Banks (ESCB) on the interest rates of monetary financial institutions (MFIs) in countries of the European Union. The paper concludes by proposing a way of establishing common rules for non-statistical samples based on a synthetic measurement of a mean of absolute errors.
JEL Code
C42 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Survey Methods
E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects
Annexes
2 December 2013
ANNEX
10 September 2015
STATISTICS PAPER SERIES - No. 11
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Abstract
The Bank for the Accounts of Companies Harmonized (BACH) is a free-of-charge database containing the aggregated accounting data of non-financial incorporated enterprises for, so far, 11 European countries. While the individual accounts feeding the database were originally prepared in line with national accounting standards consistent with European Accounting Directives, they have been harmonised with a view to preserving, to the greatest extent possible, the cross-country comparability of the resulting data. This article presents the methodology underpinning BACH, including the content of the database. It describes the characteristics of national samples and outlines the harmonisation process. BACH is a unique tool for analysing and comparing the financial structure and performance of firms across European countries. A simple case study is also presented in support.
JEL Code
C81 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access
M41 : Business Administration and Business Economics, Marketing, Accounting→Accounting and Auditing→Accounting
18 December 2015
STATISTICS PAPER SERIES - No. 12
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Abstract
Non-response is a common issue affecting the vast majority of surveys, and low non-response is usually associated with higher quality. However, efforts to convince unwilling respondents to participate in a survey might not necessarily result in a better picture of the target population. It can lead to higher, rather than lower, non-response bias, for example if incentives are effective only for particular groups, e.g. in a business survey, if the incentives tend to attract mainly larger companies or enterprises encountering financial difficulties. We investigate the impact of non-response in the European Commission and European Central Bank Survey on the Access to Finance of Enterprises (SAFE), which collects evidence on the financing conditions faced by European small and medium-sized enterprises compared with those of large firms. This survey, which has been conducted by telephone biannually since 2009 by the European Central Bank and the European Commission, provides a valuable means of searching for this kind of bias, given the high heterogeneity of response propensities across countries. The study relies on so-called
JEL Code
C81 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access
C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods
D22 : Microeconomics→Production and Organizations→Firm Behavior: Empirical Analysis
Annexes
18 December 2015
ANNEX
22 May 2017
STATISTICS PAPER SERIES - No. 21
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Abstract
Analysis of consolidated accounting data of European listed groups shows significant differences in some key ratios between countries However, the figures do not reveal whether these differences result from a distinct composition of the countries’ populations in terms of branches of activity (structural effect) or from intrinsic disparities in the behaviour of groups from various countries. This paper will address this issue using ratio decomposition techniques. A comparative overview of decomposition methodologies available in the literature will be provided, as well as an in-depth description of the methodology used. This will be applied to decompose the difference in the financial debt ratio, the equity ratio and the EBIT margin across countries for one specific year and to consider any dissimilarities in financial debt ratios over a limited period of time. The study will be based on the data available in the ERICA dataset from the European Committee of Central Balance Sheet Data Offices (ECCBSO), which includes accounting data of listed groups from Austria, Belgium, France, Germany, Greece, Italy, Portugal and Spain. The aggregate ratios of each country will be compared against a benchmark composed of the aggregate ratios for the eight countries together.
JEL Code
C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation
L22 : Industrial Organization→Firm Objectives, Organization, and Behavior→Firm Organization and Market Structure
L25 : Industrial Organization→Firm Objectives, Organization, and Behavior→Firm Performance: Size, Diversification, and Scope
M4 : Business Administration and Business Economics, Marketing, Accounting→Accounting and Auditing
16 October 2018
WORKING PAPER SERIES - No. 2187
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Abstract
The financial accounts of the household sector within the system of national accounts report the aggregate asset holdings and liabilities of all households within a country. In principle, when household wealth surveys are explicitly designed to be representative of all households, aggregating these micro data should correspond to the macro aggregates. In practice, however, differences are large. We first discuss conceptual and generic differences between those two sources of data. Thereafter we investigate missing top tail observation from wealth surveys as a source of discrepancy. By fitting a Pareto distribution to the upper tail, we provide an estimate of how much of the gap between the micro and macro data is caused by the underestimation of the top tail of the wealth distribution. Conceptual and generic differences as well as missing top tail observations explain part of the gap between financial accounts and survey aggregates.
JEL Code
C46 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Specific Distributions, Specific Statistics
D31 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions
E01 : Macroeconomics and Monetary Economics→General→Measurement and Data on National Income and Product Accounts and Wealth, Environmental Accounts
Network
Household Finance and Consumption Network (HFCN)
13 December 2019
STATISTICS PAPER SERIES - No. 31
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Abstract
Much of the literature on inequality, both that on the theoretical features of inequality measurement and that on the discussion of the results of empirical analysis, has preferred to focus on income inequality. This paper looks into the analysis of wealth inequality, which can be performed by carefully adapting the techniques used in the case of income distributions. The paper focuses on the measurement of inequality itself and includes an application to European data on wealth. We summarise the main inequality measures used in the economic literature, expanding the focus to lesser known but relevant ones, grounding their use in socio-economic theory and highlighting the connections between them. In particular, we investigate how each measure captures the same movement in the wealth distribution and why different measures can lead to differences in the observed change in inequality over time or across countries. In the main theoretical contribution of the paper we obtain a novel decomposition of changes in inequality measures as a set of equalising and disequalising factors, which sheds some light on the different results across indicators. We complement the analysis by focusing on the decomposition of wealth inequality measures, gaining an understanding of the contributions of inequality by wealth component and socio-demographic characteristics. The distribution of wealth of European households obtained by the Household Finance and Consumption Survey (HFCS) in 2010 and 2014 is used for empirical analysis and application of our methods.
JEL Code
D31 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions
D63 : Microeconomics→Welfare Economics→Equity, Justice, Inequality, and Other Normative Criteria and Measurement
Network
Household Finance and Consumption Network (HFCN)