CONCLUSIONS AND OUTLOOK FOR THE FUTURE
24.6.1 What Has Been Achieved So Far?
Tax-benefit modeling is now in widespread use to provide evidence in the policy-making process. Tax-benefit models are used within governments to provide costings of policy reforms and impact assessments of distributional and incentive effects.
They are used to assess progress towards meeting targets within relevant policy domains (and may be used to set feasible targets in the first place). They are used to explore the implications of alternative reform options. Other participants in the policy-making process (opposition political parties, special interest groups, NGOs, international organizations, and civil society generally) may also put forward their own perspectives and alternative proposals on the basis of microsimulation analysis. All of them may draw on the growing body of microsimulation-informed economic analysis from academic research. Within academia, microsimulation is also an accepted and recognized part of the toolbox in applied public economics, other branches of applied economics, and other disciplines, such as quantitative social policy, sociology, and political science. Evidence for this is provided by the increasing frequency of publication of articles making use of microsimulation in mainstream journals, as is clear from the references included in this chapter, and reliance on microsimulation analysis in the economic debate, as illustrated by Mirrlees et al. (2010).Microsimulation modeling provides an opportunity for fruitful links between the policy-making and academic communities. There are many instances in which methodological developments within academic policy-focused research have provided new and more sophisticated tools that can be adopted for use by policy-making institutions. One example is the modeling of labor supply responses, which is increasingly included in microsimulation models used by government agencies.
There are also instances in which innovation has taken place within government agencies in response to particular policy needs, as well as instances of the analytical needs of policymakers providing the impetus for academic developments. One example from the European Union is the adoption of social targets for Europe 2020 and the need to develop methods of forecasting micro-level indicators. Forging such links can bring additional benefit in the form of more open channels of communication with the official producers and providers of micro-data about the data requirements of microsimulation models and the potential benefits for policy-making.In our view there are four major strands of technical/methodological achievement and ongoing progress in the use of tax-benefit microsimulation for the analysis of policy and income inequality. A formal framework for disentangling the effect of policies on income distribution is an important step toward better understanding how various studies have approached measuring these effects and their consistency. A coherent framework can no doubt greatly increase the clarity and transparency of microsimulation studies and facilitate links with other relevant methodological literature. The devil is in the details and microsimulation modeling offers these in abundance.[564]
Behavioral microsimulation is no longer limited to the academic sphere, and it has an increasing impact on policy-motivated analysis. Further developments of behavioral models in terms of policy scope (e.g., extending economic modeling to cover areas such as housing, mobility, and saving) and their robustness based on the comparison with ex-post evaluation studies may strengthen their role in the policy and economic debate. Moreover, the cross-fertilization between the analytical and the computational approach to the optimal taxation problem based on behavioral microsimulation models could reinforce the link between public finance theory and applied research.
The analysis of tax-benefit policies with a clear impact on the labor market participation and the evaluation of the impact of macroeconomic shocks would clearly benefit from the availability of counterfactuals that consider feedback effects between the micro and macro level. A fully integrated micro-macro model, although daunting in terms of the time and resources required to create it, is potentially an incredibly powerful tool for moving beyond the partial equilibrium framework in which microsimulation models operate, for disentangling the effects of macro changes on individual resources, and for extending the policy scope of the analysis through the linkage to environmental models. However, the practical, conceptual, and methodological challenges are formidable. Even so, falling short of full model integration, improving methods of linking microsimulation analysis to macroeconomic data in various ways has been, and remains, an important part of the developing toolbox.
Cross-country comparisons of policy effects, and especially policy swap analysis, inform our understanding of the variation in the effects of policies in different economic and sociodemographic contexts, and, at the same time, these comparisons offer the opportunity for cross-country “policy learning.” The development of EUROMOD, and other multicountry models, has facilitated this type of analysis, while maintaining comparability of concepts and measurement and consistency in the operation of policy rules. There is potential to extend the approach to global regions other than the EU, such as southern Africa, Latin America, or the Balkan region (where, arguably, policy learning is most relevant). There is also potential to extend beyond the EU to include all OECD countries to aid comparisons, for example, between the EU and the US.
There is room for improvement and for development in two key areas. The first relates to the data and methods that are available for input into and adoption by microsimulation models.
Our understanding of how available micro-data may be improved and reconciled with other information, as well as the potential of new forms and sources of data that may improve the quality and scope of simulation or facilitate linkage with other models (macro, environmental, etc.), are areas for attention. In terms of methodological improvements, more attention is clearly needed to assess statistical significance and reliability of results obtained with microsimulation models drawing on various statistical methods.The second area for improvement relates to the organization of microsimulation activities. There is much duplication of effort (with many models doing the same or similar things in some countries), combined with problems oflack of transparency (i.e., lack of documentation, results that are not reproducible by others). Furthermore, most existing models are not made available or accessible to the people who might make use of them. The final two subsections explore the outlook for microsimulation and policy analysis along these two dimensions.
24.6.2 Data and Methodological Developments
Microsimulation models require access to appropriate and good quality micro-datasets that are themselves well-documented and validated against independent information. The trend toward making more use of register (administrative) data to supply information on income receipt (and in some cases many other variables) is welcome in the sense that it reduces measurement problems and underreporting and potentially frees up resources (e.g., survey interview time) for the collection of more or better quality data in other dimensions. At the same time, such linkage may introduce new problems. It may delay the delivery time of the micro-data if there are limits on the speed of obtaining and processing administrative information. Use of administrative information may also raise new concerns about data confidentiality, which may result in additional restrictions on the ways in which the datasets can be accessed and by whom.
There seem to be trade-offs between using high-precision data and widespread access.Technological developments may offer possible ways around these trade-offs, if models and their micro-level data (both input and output) are housed on a suitably secure server and accessed remotely. This is a mode of working that was pioneered for income distribution analysis by LIS32 and, in spite of the additional complexities associated with microsimulation modeling, has also been successfully deployed in a few other cases. These include the WIDER African models, as well as two adaptations of national components of EUROMOD: Mefisto for Flanders (Decancq et al., 2012) and Soresi for Austria.33 In each of these cases, the broad aim of the models is to provide access to modeling capacity by civil society, with the simulation and output options offered to users structured and restricted accordingly. More critically in this context, in each case the providers of the input micro-data have given permission for such access over the web. It remains to be seen whether it will be possible to make use of high-precision administrative data in this way. Even so, there would be other technical and pedagogical challenges to be overcome in offering to the public the full flexibility of a model like EUROMOD using remote access.
More generally there is potential to extend the policy scope and applicability of microsimulation models through the statistical linkage of data from different sources. Given the increasing complexity of tax-benefit systems that operate through direct and indirect taxes, wealth and property taxes, and cash and noncash benefits, microsimulation models can help in understanding the overall effect on individual material wellbeing only if more comprehensive surveys become available, cross-links between various administrative datasets are utilized further, or systematic and rigorous matching procedures are implemented and documented. A prime example is the analysis of the effects of indirect taxes, because any conclusion about the incidence and regressivity of taxes can be easily biased by the data inconsistency observed, in particular, at the tails of the income distribution (see Decoster et al., 2010; Brewer and O’Dea, 2012).
Finally, making progress on many of the technical challenges associated with microsimulation modeling, most notably the modeling of take-up and compliance behavior, is also inhibited by lack of suitable data. For example, nonreceipt of a benefit entitlement may be explained in many ways, ranging from (among other causes) measurement error in the survey responses, lack of information about eligibility on the part of a nonclaimant, or a decision not to claim due to the costs of claiming. It is likely that the relative importance of each factor varies with national context and specific benefit. Accurate modeling of the probability of taking up (i.e., receiving, given positive entitlement) a particular benefit, in principle, needs to take any one or many possible causes into account, which would typically be demanding in terms of the data requirements. Modeling of tax noncompliance at the individual level is even more demanding given the concealed nature of such activities and a potentially wider range of possible factors and interactions at play. Progress in these areas can therefore be expected to be patchy and uneven, depending on the specific problems and the data possibilities.
24.6.3 The Case for a Collaborative Approach
Few models are accessible beyond their producers. This leads to a proliferation of many similar models and the (largely wasteful) duplication of effort that this involves. It also limits access to models because building from scratch is time-consuming and requires specialist skills; there are significant barriers to entry. Furthermore, the need to provide in the public domain documentation or validation of models that are essentially private to their producers is rarely acted upon. This lack of transparency inhibits proper evaluation of microsimulation-based studies, and lack of access inhibits the reproducibility of microsimulation analyses. Together, these factors may reduce the chances of microsimulation-based studies being published in the top scientific journals. As Wolfson (2009, p. 29) says:
.... microsimulation modelling still has not achieved the kind of scientific status it deserves. One reason is that many potential users are concerned about the 'black box' nature of microsimulation models. An important step, therefore, is for microsimulation modelling to become a 'glass box' activity, including for example public availability of the model and open source code.
Models are also expensive to maintain and keep up to date. If there were fewer, better models that were made generally accessible, this would improve efficiency and quality. A collaborative approach would also bring the various types of use and user closer together and, with the appropriate level of (technical) model flexibility, could also facilitate innovations such as model linkages. EUROMOD and TAXSIM provide two rather different examples of models that already take this approach. EUROMOD makes available both tax-benefit codes and input data to anyone with permission to access the original micro-data sources, while TAXSIM provides online access to the tax calculator that may be linked to input data of the user’s own choosing.[565]
Of course, there are also good reasons why microsimulation models are developed as individually or institutionally private investments. In some cases the necessary micro-data cannot be made available more widely (e.g., in the case of government models, especially those using administrative data). In the academic sphere, there are few incentives to share technical developments as public goods in the matter suggested, especially if they embody a large time investment and if they do not themselves attract academic reward.
If the benefits of an open and collaborative approach are to be realized the main challenges are to find ways of organizing and funding arrangements that account for the longterm investment aspect, due to the need to maintain models, as well as engage in initial construction. This would include developing an incentive structure that recognized the academic value of the work done on the “public good” research infrastructure, while
34
eliciting contributions in some form from the users of the models who might otherwise “free ride.” In the end, cooperation within the microsimulation community and particularly between academic researchers and policy makers will contribute to the integration of microsimulation for policy analysis into the mainstream of economic policy-making (Atkinson, 2009).
ACKNOWLEDGMENTS
We wish to thank Tony Atkinson, Franrjois Bourguignon, and Brian Nolan for their comments and suggestions on the early drafts of this chapter. We are also grateful to Paola De Agostini, John Creedy, Mathias Dolls, Carlo Fiorio, Horacio Levy, Marcello Morciano, Andreas Peichl, Iva Tasseva, and Alberto Tumino for comments and useful discussions, as well as information or permission to make use of their analysis. Paulus and Sutherland acknowledge the support for this work from the core funding of the Research Centre on Micro-Social Change and from the UK Economic and Social Research Council (grant RES-518-28-001).
APPENDIX A. INCREASING UK CHILD BENEFIT IN 2001 AND 2013: THE NET EFFECTS
In both 2001 and 2013, the UK Child Benefit was delivered as a universal benefit for all children under the age of 19 in full-time nonadvanced education. In both years there were two rates, one for the oldest child (£15.50 and £20.30 per week, respectively) and one for any other children (£10.35 and £13.40 per week, respectively). As an illustration, we double these values and use EUROMOD to calculate the net budgetary cost after the operation of the rest of the tax and benefit systems, and we also show how the gain per child would vary across the household income distribution.
In 2001, Child Benefit was disregarded by the income tax system but was taken into account for the assessment of Income Support (and income-related Job Seeker’s Allowance), Housing Benefit, and Council Tax Benefit, some of the main UK means-tested benefits for working-age people and their families. (The Working Families Tax Credit disregarded Child Benefit.) As the table shows, although the gross cost of the increase in Child Benefit is estimated at £8.85 billion per year, once the reduced entitlements to these benefits are taken into account, the net cost falls to £7.01 billion or 79% of the gross.
In contrast, in the 2013 system, the Child Benefit is disregarded in the assessment of all means-tested payments, but higher-income parents who pay income tax at the 40% (or higher) marginal rate have the value of their Child Benefit included in their tax calculation. Thus, as shown in the table, the cost of the increase in Child Benefit is offset to a small extent by an increase in income tax liabilities. In addition, in 2013, there was a cap on the overall sum of benefits that could be received by families in some circumstances. This would result in some families not receiving all or any of their Child Benefit increase. In 2013, the gross cost of the increase in Child Benefit is estimated at £11.55 billion per year, and once the reduced entitlements to these benefits are taken into account, the net cost falls to £11.14 billion or 96% of the gross.
Source: EUROMOD version F6.20, using Family Resources Survey data for 2008—2009, adjusted to2001 and 2013 prices and incomes.
There are different distributional consequences of these differences between gross and net effects, as shown in Figure 24.A1 below. This shows the average net weekly increase in income per child by decile group of equivalized household income under the 2001 and 2013 policy systems. Under the 2001 system, those in the lower income groups receive less, because some of the additional income is withdrawn as reduced entitlement to the means-tested benefit. (This applies to a lesser extent in the bottom decile group in which families simulated to not take-up their entitlements to means-tested benefits are mainly located.) In 2013, however, it is children in higher income households who benefit to a lesser extent, due to the clawback through income tax (the effect of the benefit cap is small and concentrated in the lower-middle of the distribution).
Figure 24.A1 Doubling Child Benefit in the UK: Average net gain per child in £ per week. Notes: Deciles are based on equivalized household disposable income in the respective years and are constructed using the modified OECD equivalence scale to adjust incomes for differences in household size and composition. The lowest income group is labeled “1” and the highest “10.” Source: EUROMOD version F6.20, using Family Resources Survey data for 2008-2009, adjusted to 2001 and 2013 prices and incomes.
The point of this illustration is to demonstrate how the interactions matter and need to be understood when designing policy scenarios. Similarly, the policy analyst needs to account for the interactions in order to understand the effects of policy changes. If policymakers wanted to double the payment made to all children in 2001, they would have needed to increase child amounts within the other benefits as well as in Child Benefit. On the other hand, if the goal had been to reduce the number of families subject to means tests (without anyone losing), then the illustrative reform would have done just that (for example, reducing the number of all households receiving Council Tax Benefit). If the goal in 2013 had been to reduce the reach of means-testing, the means-tested payment rates for children would have needed to be reduced at the same time as Child Benefit increase.
Appendix b. comparison of simulated estimates of income tax with administrative statistics, UK 2010-2011
Here we illustrate the type of validation of simulated income tax that can be carried out using published tables from administrative data of tax revenues. The exercise also suggests ways in which the input micro-data might be adjusted, or not. In this exercise, the input data are the UK Family Resources Survey (FRS) 2009-2010 updated to 2010-2011 incomes and prices.
Simulated income tax liabilities are compared with statistics on income tax paid by band of taxable income, published by the HM Revenue and Customs (HMRC, Table 3.3). The first point to note is that the tax paid in any year may not match the liability for tax on income earned in that year, because of adjustments carried over from previous years.
The first row in the top panel of the table below shows the ratio of microsimulation model (EUROMOD) estimates to those of HMRC in three dimensions: the number of taxpayers (defined as individuals with positive taxable income before deduction of any personal allowances), their total taxable income (before deduction of allowances), and the total tax liability/revenue. The number of taxpayers is underestimated by 7% and taxable income by more: 13%. Also shown are the ratios for the lowest taxable income group (under £10,000 per year) and highest income group (over £150,000).[566]
Ratio of EUROMOD estimates to HMRC statistics
| Taxpayers | Taxable income | Tax revenue | |
| EUROMOD | |||
| All | 0.93 | 0.87 | 0.85 |
| Taxable income bgcolor=white>0.91 | |||
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