Chapter 2 Measurement of inequality

Cowell, Frank

doi:10.1016/s1574-0056(00)80005-6

“…This axiom, sometimes also called 'symmetry', requires that the underlying social welfare function uses only the information about the income variable and not about, for example, some other characteristics which might be discernible in a sample or an enumeration of the population (Cowell 2000). This assumption is usually invoked for welfare orderings, whether we look at inequality (Atkinson 1970) or at poverty (Greer, Foster and Thorbecke 1984).…”

Section: The Anonymity Axiommentioning

confidence: 99%

Removing the anonymity axiom in assessing pro-poor growth

Grimm

¹

2006

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe recent focus on 'pro-poor growth' led also to an intense debate on how exactly to define and to measure pro-poor growth. However, all suggested measures, irrespective whether they use the absolute or the relative definition of pro-poor growth have in common that they are based on the anonymity axiom, i.e. they do not distinguish between changes in horizontal and vertical inequality. That means usual assessments of pro-poor growth look at distributional changes over time and ignore how specific groups or households moved. Such a perspective may provide a very incomplete picture given that the common objective of most studies investigating the pro-poorness of growth is to test whether specific policy reforms where beneficial to the initially poor or not. Using panel data from Indonesia and Peru, this paper analyzes and illustrates empirically the implications of removing the anonymity axiom from measurements of pro-poor growth. It is shown that postulating anonymity, when assessing pro-poor growth can lead to misleading conclusions on how a specific policy affected the incomes of the initially poor. For both countries, the analysis shows substantial convergence to the mean, which is, at least for the case of Indonesia, robust to measurement error in the expenditure data.JEL Classification: D31, D63, O15.

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“…A first conclusion is that between-country inequality variation is more significant 1 A comprehensive survey of the topic can be found in Cowell (2000).…”

Section: Overviewmentioning

confidence: 99%

“…It is known that for values above one, the Atkinson index is very sensitive to abnormally low incomes (Cowell, 1995). With this in mind, we estimate the four values to have a broader picture of how θ affects the levels and trends of inequality.…”

Section: Inequality Measurementmentioning

confidence: 99%

The Construction and Interpretation of Combined Cross-Section and Time-Series Inequality Datasets

François

¹

,

Rojas‐Romagosa

²

2005

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes.You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. licence. www.econstor.eu Please send questions and/or remarks of nonscientific nature to driessen@tinbergen.nl. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicatedMost TI discussion papers can be downloaded at http://www.tinbergen.nl. The Construction and Interpretation of Combined Cross-Section and Time-Series Inequality Datasets Joseph F. Francois Tinbergen Institute (Erasmus University Rotterdam) and CEPR Hugo Rojas-Romagosa Tinbergen Institute (Erasmus University Rotterdam) August 2005Abstract: The inequality dataset compiled in the 1990s by the World Bank and extended by the UN has been both widely used and strongly criticized. The criticisms raise questions about conclusions drawn from secondary inequality datasets in general. We develop techniques to deal with national and international comparability problems intrinsic to such datasets. The result is a new dataset of consistent inequality series, allowing us to explore problems of measurement error. In addition, the new data allow us to perform parametric non-linear estimation of Lorenz curves from grouped data. This in turn allows us to estimate the entire income distribution, computing alternative inequality indexes and poverty estimates. Finally, we have used our broadly comparable dataset to examine international patterns of inequality and poverty.Keywords: Income distribution datasets, inequality trends, Lorenz curve estimation, poverty estimation JEL codes: D31, C80, O15We acknowledge support from the EU research and training network (RTN) "Trade, Industrialization, and Development," as well as research support from DFID and the World Bank. All errors are of course our own.Address correspondence to: J. Francois, Tinbergen Institute, Erasmus University Rotterdam, Burg Oudlaan 50-H8-18, 3000DR Rotterdam, NETHERLANDS. Email: francois@few.eur.nl. Data are available at www.intereconomics.com/francois/data.html. OverviewThere is a sizeable literature regarding the interaction between income inequality and other economic variables, such as growth, poverty, trade and ...

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“…In the case of scale-invariant distributional measures (see Cowell, 2000) and linearly homogenous tax-benefit systems, the decomposition of other effects (equations [4][5][8][9] simplifies because the effect of a change in nominal levels becomes (approximately) zero at the population level. 14 Furthermore, equation 4 is now equivalent to 5 and equation 8 is equivalent to equation 9, reducing the overall number of combinations from four to two.…”

Section: Decomposing Static Policy Effectsmentioning

confidence: 99%

Microsimulation and Policy Analysis

Figari

¹

,

Paulus

²

,

Sutherland

³

2015

Handbook of Income Distribution

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ISER Working Paper Series www.iser.essex.ac.uk Microsimulation and Policy AnalysisNon-technical summary This paper is prepared as a chapter forthcoming in the Handbook of Income Distribution, Volume 2 (edited by A. B. Atkinson and F. Bourguignon, Elsevier-North Holland). Microsimulation methods are increasingly used to study the effects of policies on the income distribution contributing both to policy debates and academic literature. In general terms, microsimulation refers to modelling changes in the state or behaviour of micro-level units. We provide an overview of approaches that address questions related to the impact of taxbenefit policies on household income distribution. We first discuss how microsimulation modelling contributes to the analysis of the income distribution more broadly, pointing out that it can enrich survey and administrative data by checking its consistency, adding further details and enhancing analytical flexibility. In addition, it provides the basis for calculating indicators that cannot otherwise be estimated directly from micro-data such as measures of work incentives, net support for children and automatic stabilisers embedded in tax-benefit systems. We then focus more specifically on the role of microsimulation in policy evaluation. Taxbenefit models can provide information on household disposable income and its components under various policy scenarios and as such constitute a unique tool for ex ante policy analysis, though equally can be used for ex post analysis. We review the formal framework underlying such microsimulation-based decomposition analysis of income distribution and suggest a broader perspective to accommodate a wider range of applications. Our main emphasis is on static modelling methods, though we also consider extensions accounting for behavioural reactions and links with macroeconomic models. As microsimulation models are often concerned with current and future policies, we discuss various approaches to predict the income distribution given that data providing information on the current situation becomes available with a time lag, distinguishing between nowcasting and forecasting. We emphasise the role of comparative analysis as cross-country differences in tax-benefit systems and population structures provide a basis to assess the robustness of results and generalise conclusions. We provide empirical illustrations drawing mainly on analysis using the EU-wide tax-benefit model EUROMOD. Covering 27 countries and made generally accessible this is now one of the most widely used models. There are many remaining challenges to providing estimates of the effects of policy and policy changes that can be used with confidence within policy analysis and we consider three major issues: reconciling the simulated income distribution and that measured using the original micro-data; accounting for benefit non take-up and tax non-compliance, as well as validating and assessing the statistical reliability of microsimulation estimates. While our main focus is on the distributio...

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Chapter 2 Measurement of inequality

Cited by 481 publications

References 259 publications

Removing the anonymity axiom in assessing pro-poor growth

Removing the anonymity axiom in assessing pro-poor growth

The Construction and Interpretation of Combined Cross-Section and Time-Series Inequality Datasets

Microsimulation and Policy Analysis

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