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Accurate Income Measurement for the Assessment of Public Policies

Final Report Summary - AIM-AP (Accurate Income Measurement for the Assessment of Public Policies)

The AIM-AP research project was established to improve the comparability, scope and applicability of tools, methods and data for the measurement of income and the analysis of the effects of policies on inequality, poverty and social inclusion. It included three linked projects.

The distributional effects of non-cash incomes and the implementation of a more comprehensive income definition. The aim was to investigate the distributional effects of the following non-cash income components: public education, imputed rents for owner occupied accommodation and public housing, public health care services and home production and employer-provided fringe benefits.

The implications of (and methods to account for) errors in targeting social benefits, tax evasion and measurement error in income data. This project relied on a series of national case studies to explore the implications of tax evasion and target inefficiency for measures of income distribution and the impact of tax-benefit policies. The likely presence of measurement error complicates matters considerably and was considered where possible.

Incorporation of the effects of indirect taxes, along with direct taxes and social benefits, in redistribution analysis. The aim was to develop a generic method of imputation of detailed household expenditures into income surveys for a selected set of EU countries. This permits comparative research on the incidence and distributional analysis of the combined set of policy instruments: direct taxes, benefits, and indirect taxes.

All three projects were designed to improve the degree of comparability of measurement and analysis across countries. Each project developed methodologies within a cross-national perspective and some cross-project results are combined. Where appropriate, the resulting data and method enhancements are being made generally accessible and re-useable by implementing them within EUROMOD, the EU tax-benefit model.

The aim of the project was to provide estimates of the distributional effects of three large non-cash income components (imputed rent, public education and public health care services) in seven European countries, to analyse their distributional effects and incorporate the corresponding estimates in EUROMOD. In the countries under examination - Belgium, Germany, Greece, Ireland, Italy, the Netherlands and the United Kingdom - the total monetary value of these non-cash incomes is around one third of the aggregate disposable income of the population. Using static incidence analysis, under the assumption that incomes in-kind do not create externalities, it is shown that non-cash incomes are far more equally distributed than cash incomes and, as a result, their inclusion in the concept of resources leads to considerable reductions in the measured levels of inequality and relative poverty. However, the relative ranking of countries in terms of inequality and/or poverty indicators is affected only marginally as we move from the distribution of disposable monetary income to the augmented income distribution that includes cash as well as non-cash incomes.

Nevertheless, it is doubtful whether results derived using the standard approach in the fields of public education and public health care can have a straightforward welfare interpretation. The reason is that using this approach we incorporate the value of the public services in the concept of household resources but ignore the problem of extra needs of public services recipients. Once these needs are taken into account with appropriate changes in the household equivalence scales used in the analysis, the results regarding these non-cash income components appear to be far more modest and, under particular circumstances may even appear to be inequality-increasing.

The results of a number of simulations using EUROMOD demonstrate clearly that it is both feasible and desirable to incorporate non-cash income components in standard tax-benefit microsimulation models and, further, they also show that the distributional effects of various policy simulations may change once the non-cash income components are accounted for. From this point of view, the objectives of the project were undoubtedly achieved. However, the results of the project also have a number of policy implications. First, international organisations or individual researchers interested in making meaningful cross-country or inter-temporal comparisons of inequality or poverty should take into account non-cash as well as cash income components. However, like monetary income components, care should be taken so that non-cash income components are measured in a consistent way across countries or within a particular country over time. As our results show that even seemingly similar data sets may not be as comparable as they appear at first sight.

Regarding imputed rent, care should be taken that the information available in income surveys can be exploited in order to estimate the net imputed rent of all households (in other words, not only information on gross imputed rent of homeowners). In the case of public education transfers, it is important that the income survey used for the distributional analysis provides as detailed as possible information on the actual use of public education services (that is, the survey should allow the identification of private education students as well as contain a detailed breakdown of the educational status of current students). In the case of public health care transfers, it may be desirable to obtain information that can be used in order to identify population members that are likely to underutilise systematically public health care services (for example, private health insurance policy holders).

It is important to account for fringe benefits and, particularly, home production of goods and services. Fringe benefits and home production and consumption of commodities are near cash income components and they can be accounted for relatively easily in income surveys. The latter is likely to be very important in the case of the EU when comparisons are made between 'old' EU Member States with fully commercialised agriculture with some 'new' Member States with large agricultural sectors and extensive consumption of own production. Accounting for home production of services is more problematic but, undoubtedly, these services improve the welfare of their recipients and should be included in distributional analyses. In order to obtain information on such services, income surveys should include questions on time use – certainly not an easy task. Moreover, methods of evaluation of the time spent on the production of home services are not uncontroversial.

Last but not least, more research effort is needed in two fronts. First, to identify and use appropriate equivalence scales when including public services with strong life-cycle patterns in distributional analyses. Second, to move beyond static analysis and examine the distributional effects of (private and public) non-cash incomes in a longitudinal framework.

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