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Published in: Social Indicators Research 2/2019

06-09-2018

The Measurement of Targeting Design in Complex Welfare States: A Proposal and Empirical Applications

Authors: Sarah Marchal, Wim Van Lancker

Published in: Social Indicators Research | Issue 2/2019

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Abstract

The extent to which welfare states target resources to the poor and the effect this may have on redistribution and public support remains an important question in contemporary social policy and welfare state research. Usually in this line of research, targeting is measured as the extent of transfers accruing to the lowest income groups. Such an outcome measure depends on both policy design and contextual factors, such as the composition of the population. For some research questions however, researchers may want to separate the effect of the design of benefit schemes, i.e. targeting design, from the context in which targeting takes place. For instance to assess the effect of policy design on redistributive outcomes, or to track whether policymakers resorted to more or less targeting in their benefit schemes over time. Therefore, in this article we develop an institutional targeting indicator that captures the policy intention to target towards the poor. Our indicator summarizes policy design into one parameter, and captures the complexity of benefit design in contemporary welfare states in a meaningful way. Drawing on the OECD Benefits and Wages data that capture the rules and legislation of tax benefit systems, we demonstrate different empirical applications for this indicator.

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Appendix
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Footnotes
1
Apart from more categorical targeting, certain implementation characteristics can also be intended to target the most needy (van de Walle 1998). Self-selection into work programmes or difficult application procedures can be used in order to make sure only the most needy apply for a benefit. In this paper, we disregard this type of targeting, since its exact effects are hard to predict.
 
2
In other cases researchers may be more interested in the targeting of benefits in proportion to incomes. This is usually done by calculating the progressivity index, which subtracts the Gini coefficient of gross earnings from the concentration coefficient (see also the discussion of the OECD indicator scoreboard above). A value of zero than indicates a tax or a benefit which is proportional to incomes, values above zero a progressive tax (or benefit) and below zero a regressive tax (or benefit). Subtracting the Gini coefficient of gross earnings, or contextualizing with this measure, may also aid in comparing concentration coefficients when the underlying income distribution differs, and in order to assess the relative pro-poorness of benefit transfers (see Marx et al. 2016).
 
3
All Gini coefficients and concentration coefficients reported in this paper are calculated with Van Kerm’s (2009) sgini command for stata.
 
4
In contrast, the benefit amounts in contributory schemes are usually tied to previous earnings, which makes it more complex to interpret targeting design. In Sect. 5.4, we discuss the interpretation of targeting design for contributory benefit schemes.
 
5
We follow the OECD classification of these different benefits. This is particularly relevant for the assessment of refundable tax credits. These are included as benefits if the OECD classifies these as in-work benefits or child benefits. If the OECD includes them as income taxes, they are not included in our indicator. In Fig. 6 in the “Appendix” we explore the impact of this classification on our targeting design measure. We return to this issue in Sect. 6.
 
6
Despite its functional equivalence to social assistance, the OECD classifies Temporary Assistance to Needy Families (TANF) as a child benefit since access is heavily conditioned on the presence of children in the household.
 
7
The OECD classification includes the child tax credit under family benefits.
 
8
Following Van Mechelen and Bradshaw (2013) and Van Lancker and Van Mechelen (2015), we operationalize all child related support as the difference in net income between a hypothetical family with children and a hypothetical family that is for all intents and purposes the same, bar the presence of children in the household. This measure hence includes the impact of child tax credits and tax allowances, as well as higher housing allowances or social assistance benefits to cover the presence of children in the household.
 
9
Please note that it is equally possible to repeat this exercise when we use a functionally equivalent operationalization of child support, cf. supra.
 
10
It is more intuitive to think about targeting in tax systems through their digression from a proportional system, rather than by assessing the amount of taxes that is liable by different households. An assessment of the redistributive function of taxes may therefore be better served by alternative indicators, that aim to capture the progressivity of tax rates, or the redistributive impact of the tax benefit system regardless of the targeting design of benefits (see OECD 2017 for an example).
 
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Metadata
Title
The Measurement of Targeting Design in Complex Welfare States: A Proposal and Empirical Applications
Authors
Sarah Marchal
Wim Van Lancker
Publication date
06-09-2018
Publisher
Springer Netherlands
Published in
Social Indicators Research / Issue 2/2019
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-018-1995-z

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