INTRODUCTION AND OVERVIEW
24.1.1 WhatisMicrosimulation?
Microsimulation methods are increasingly used to evaluate the effects of policies on income distribution. Microsimulation refers to a wide variety of modeling techniques that operate at the level of individual units (such as persons, firms, or vehicles), with rules applied to simulate changes in state or behavior.
These rules may be deterministic or stochastic, with the result being an estimate of the outcomes of applying these rules, possibly over many steps involving many interactions. These estimates are also at the micro level, allowing analysis of the distribution of the outcomes and changes to them, as well as the calculation of any relevant aggregate.[526]In the social sciences, Guy Orcutt and his colleagues pioneered microsimulation models in the 1950s as a new approach to analyzing the impact of social and economic policies, which accounted for the characteristics and behavior of the microlevel units under investigation (Orcutt, 1957; Orcutt et al., 1961). Microsimulation is commonly applied to many areas relevant to public policy, such as transportation, location planning for public services and commercial developments, and demand for health care and longterm care.[527] The microsimulation approaches considered here are those that primarily address questions related to the impact of tax-benefit policies on income distribution. Models simulating the effects of social and fiscal policies on household income were first developed in the 1980s when the essential inputs—micro-data from household surveys and accessible computing power—began to be made available.
These early tax-benefit microsimulation models were arithmetic, recalculating the components of household disposable income (usually cash benefits, direct taxes, and social contributions) for each household in a representative microdataset under different sets of policy rules.
They could answer “what if’ questions about the effects of specific policy reforms on each household’s income and hence on the overall income distribution and the aggregate public budget. Some early studies include Atkinson et al. (1983) and Betson et al. (1982). These models could also readily be used to calculate indicators of work incentives on the intensive margin (Atkinson and Sutherland, 1989; Bourguignon et al., 1993). Since then, this “static” modeling approach has not only proliferated, but it has also been refined in a number of directions, influenced by developments in data availability, methodology, speed, capacity of accessible computing power, and the demands made by policymaking and policy analysis.Microsimulation models are often categorized as “static,” “dynamic,” or “behavioral” (see Harding, 1996a). The first type applies purely deterministic policy rules to microdata in combination with data adjustments such as reweighting. The characteristics of the micro units stay constant. Dynamic models, on the other hand, “age” the micro units through time, changing their characteristics in response to natural processes and the probabilities of relevant events and transitions (Li and O’Donoghoue, 2013). Behavioral models use microeconometric models of individual preferences to estimate the effects of policy changes on behavior, often in terms of labor supply. In practice the distinction between modeling approaches is no longer necessarily useful because modern microsimulation analysis often combines elements of each type, according to the question being addressed. For example, labor supply models require the calculation of budget sets (household income under alternative labor supply scenarios) for individuals, and these are usually generated by static tax-benefit models. Behavioral reactions, as well as static calculations, are relevant in dynamic microsimulations. In seeking to simulate the effects of policy changes in a variety of economic environments, so-called static models may borrow elements from dynamic model methodology, and in seeking to simplify the dynamic modeling process, the reverse can also be true (Caldwell, 1990).
In practice, dynamic models mainly address questions about the effects of policies that take time to evolve, such as pensions (e.g., Borella and Coda Moscarola, 2010; Dekkers et al., 2010; Flood, 2007) and long-term care reform (e.g., Hancock, 2000; Hancock et al., 2013), often focusing on the cost, winners, and losers, as well as questions about intrapersonal redistribution over the lifecycle (Harding, 1993).Without tax-benefit microsimulation modeling, and before it was widespread, analysis of the effects of taxes and benefits on household income, as well as calculation of work incentive indicators, was limited to “model family” calculations for stylized households, sometimes referred to as “tax-benefit models.” These calculations are carried out, for example, by the Organization for Economic Cooperation and Development (OECD) for the purpose of making cross-country comparisons (OECD, 2007), but, depite being useful for understanding the net effects of policies in particular standardized cases, such models cannot give full information about impacts on income distribution.
This chapter provides an overview of microsimulation approaches for exploring the effects of policy on income distribution, and it highlights some particular state-of-the-art or innovative studies that have been carried out in this area. The main emphasis is on static modeling methods, though we also consider extensions accounting for behavioral reactions (Section 24.3.3) and highlight the main modeling features of dynamic modeling (Section 24.5.2), referring to the existing reviews. We have not attempted to create a comprehensive review of the models themselves. Their proliferation would make such a task not only daunting, but quickly out-of-date. There are already a number of reviews and collections describing the models and the analyses using them, a selection of which we summarize below.
24.1.2 Microsimulation in the Economic Literature
There are several distinct motivations for using a microsimulation model to simulate the impact of a given policy on income distribution.
Microsimulation can be used to quantify the role of existing policies on income inequality or poverty in a given context. More importantly, it is a tool to aid the design of new policies with particular objectives and to evaluate actual or proposed reforms in dimensions that were not taken into account in the original design. Moreover, it can also be used to show how alternative approaches could result in better outcomes in some respect. From a practical policy perspective, one of the main uses of microsimulation modeling for the design of policy is to assess the approximate budgetary cost of a new policy given its objectives, such as the desire to reduce the poverty gap or to increase work incentives for particular groups. Such analysis rarely sees the light of day except in its final form as a costed reform proposal.Evidence from microsimulation modeling is also used to inform academic economic debates about the impact of policy reforms and the optimal design of policy (Blundell, 2012). In general terms, a microsimulation approach allows the researcher to conduct a controlled experiment by changing the parameters of interest while holding everything else constant and avoiding endogeneity problems in identifying the direct effects of the policy under analysis (Bourguignon and Spadaro, 2006). The use of tax-benefit microsimulation models to calculate counterfactual states and scenarios underpins much microeconomic analysis of the causal impact of fiscal policy reforms. A prime example is the use of the Institute of Fiscal Studies tax-benefit microsimulation model, TAXBEN for the UK, to provide empirical evidence for the arguments about tax design put forward in the authoritative Mirrlees Review (Mirrlees et al., 2010). Moreover, the counterfactuals shed light on the potential ingredients of optimal tax analysis, which cannot be derived in a quasiexperimental setting. This is demonstrated by the developments of the computational optimal income taxation theory, applied by Aaberge and Colombino (2013) to Norway and by Blundell and Shepard (2012) to the UK.
Microsimulation modeling is increasingly recognized as part of the policy evaluation literature, in which it is one of the key ingredients of a careful, evidence-based evaluation of the design of tax-benefit reforms. Although, in general, this literature has been more focused on ex-post analysis, Keane (2010) and Blundell (2012), among others, have underlined the need to consider both ex-ante and ex-post approaches to study the effects of policy changes. In this context, tax-benefit microsimulation models can offer insights in two ways. First, they are unique tools for conducting ex-ante analysis through the simulation of counterfactual scenarios reflecting alternative policy regimes. Such counterfac- tuals are needed both for the “morning-after” evaluation of tax-benefit reforms and for more complex structural models that reveal individual behavioral changes based on simulated budget constraints and an estimated model of individual and family choices (see Section 24.3.3). Second, by developing a counterfactual scenario, tax-benefit microsimulation models enable the researcher to disentangle ex-post what would have happened without a given policy. Although ex-post analysis is typically conducted by means of qua- siexperimental approaches, based on difference-in-difference, matching, and selection estimators, the cross-fertilization between ex-ante and ex-post approaches has contributed to the increasing credibility of analysis based on detailed microsimulation models, making them a core part of the causal policy evaluation literature. A prime example is the quasiexperimental analysis used to validate structural models of labor supply that use microsimulation models to derive the budget sets faced by individuals (see, among others, Blundell, 2006).
Furthermore, microsimulation features in the strain of literature that involves micromacro linkage, aiming to measure the effects of macroeconomic changes (including macroeconomic policy) on income distribution.
More specifically, the linkage ofmicro- simulation models to macroeconomic models allows one to consider the interactions of macroeconomic policies or shocks with the tax-benefit systems (see Section 24.3.4). Ignoring the tax-benefit policy effects on income distribution can be justifiable in some circumstances, for example, when analyzing their impact in developing countries, because they may be very limited in size, and reform to social expenditures or macroeconomic shocks could be much more relevant for redistribution, but it is more problematic in the context of mature welfare states (Bourguignon and Bussolo, 2013).The literature on microsimulation has expanded enormously in the last 20 years, along with the spread and development of this methodology. An attempt to cover all relevant publications would be a daunting task, and, therefore, we aim to provide some of the most important methodological references with relevant illustrations in the rest of this chapter. For further and broader material, we refer the reader to a number of reviews and workshop and conference volumes that provide good surveys, both of model applications and of models themselves, reflecting how state-of-the-art modeling has evolved since the beginning of the 1990s: Harding (1996b), Gupta and Kapur (2000), Mitton et al. (2000), Gupta and Harding (2007), Harding and Gupta (2007), Lelkes and Sutherland (2009), Zaidi et al. (2009), Dekkers et al. (2014), and O’Donoghue (2014).[528] Forsurveys of the models themselves, see Merz (1991), Sutherland (1995), Klevmarken (1997), Gupta and Kapur (2000), O’Donoghue (2001), Zaidi and Rake (2001), Gupta and Harding (2007), Urzda (2012), and Li and O’Donoghoue (2013). In addition, several books focus on specific models, providing excellent examples of opening the “black box” often associated with complex economic models. For example, Harding (1993) describes the details of her dynamic cohort microsimulation model used to evaluate lifetime income distribution and redistribution for Australia; Redmond et al. (1998) provide an extensive discussion of the inner workings of POLIMOD, a static tax-benefit model for the UK; and Bargain (2007) offers a collection of applications using EUROMOD, the EU tax-benefit model. Furthermore, the microsimulation community established the International Microsimulation Association (IMA) in 2005, and since 2007, it has been possible to follow the latest developments in the field through the International Journal of Microsimulation, a refereed online journal published under the auspices of the IMA.[529]
24.1.3 Summary of Chapter
The remainder of this chapter is structured as follows. Before getting into the ways in which microsimulation can be used to understand the effects of policy changes, Section 24.2 describes how it can be used to improve the information generally available for the analysis of income distribution and redistribution. Simulated estimates of tax liability and benefit entitlement can be used alongside the values recorded in survey and administrative microdatasets to understand and improve on the deficiencies in the latter (e.g., to impute gross income from net if the former is not available or measured satisfactorily in the source data). Furthermore, indicators that cannot be collected in surveys or through administrative processes but are of value in understanding the relationships between policy and income distribution, such as indicators of work incentives, can be calculated using microsimulation models.
Throughout the chapter we provide some empirical illustrations drawing mainly on analysis using the EU-wide tax-benefit model EUROMOD (Sutherland and Figari, 2013). Covering 27 countries and made generally accessible, this has become one of the most widely used models. We have chosen to highlight EUROMOD at least partly because it is generally available to use, and readers can reproduce, update, and extend the chapter’s examples of analysis with relative ease. More information about EUROMOD is provided in Box 24.1.
EUROMOD aims to simulate as many of the tax and benefit components of household disposable income as possible, and generally, the following instruments are simulated: income taxes, social insurance contributions, family benefits, housing benefits, social assistance, and other income-related benefits. Instruments that are not simulated are taken directly from the data. These include most contributory benefits and pensions (due to the lack of information on previous employment and contribution history) and disability benefits (because of the need to know the nature and severity of the disability, which is also not present in the data).
EUROMOD input data for most countries are derived from the European Union Statistics on Income and Living Conditions (EU-SILC). In common with most sources of microdata used as input into microsimulation models, the EU-SILC was not designed for this purpose (Figari et al., 2007). A significant amount of preparation of the data, including imputing necessary information that is missing, needs to be done. For example, if gross income values are not directly recorded during the data collection operations and are imputed in an unsatisfactory way, a net-to-gross procedure is applied to the net income variables in order to derive the gross values used in the policy simulation.
EUROMOD includes some simple adjustments for the non-take-up of some benefits and evasion of taxes in some countries. In common with other adjustments and assumptions (e.g., the updating of nonsimulated incomes to a more recent point in time than the data income reference point) these can be changed or “switched off’ by the user, depending on the analysis being done.
Baseline systems in EUROMOD have been validated and tested at the micro level (i.e., case-by-case validation) and the macro level. For each system simulated in EUROMOD, Country Reports are available on the EUROMOD web pages with background information on the tax-benefit system(s), a detailed description of all taxbenefit components simulated, a general overview of the input data, and an extended summary of the validation process.
For more information about EUROMOD and its applications, see the official website (https://www.iser.essex.ac.uk/euromod) and Sutherland and Figari (2013). supply responses. This is followed in Section 24.3.4 by a review of the ways changes in income distribution can be linked to macroeconomic processes. Section 24.3.5 covers the use of microsimulation, in conjunction with macrolevel statistics or forecasts, to provide estimates of income distribution for periods beyond those covered by the latest microdata. These projections might be for the current situation (nowcasting) or sometime in the future (forecasting). Finally, Section 24.3.6 focuses on the ways in which microsimulation can be used to inform cross-country comparisons of the effects of policies.
Ofcourse, there are many remaining challenges to providing estimates ofthe effects of policy and policy changes that can be used with confidence within policy analysis, and Section 24.4 considers three major ones. First, Section 24.4.1 considers the issues around reconciling the simulated income distribution and that measured using the original microdata (from surveys particularly, but also administrative sources). A major difference between the two distributions can undermine confidence in microsimulation results but has a number of interrelated causes, some of which can point to problems in survey data (e.g., income underreporting), and can be mitigated using information from simulations, and others that cannot (e.g., small and unrepresentative samples of high-income earners). Simulations can overestimate income if the non-take-up of benefits is not accounted for and also distorted if there is tax evasion. These issues, and how they may be accounted for in microsimulation models, are discussed in Section 24.4.2. Finally, it is important that the reliability of microsimulation estimates is possible to ascertain. This applies both in terms of how well point estimates match up to information from other sources (validation) and the need for statistical reliability indicators that can be applied to microsimulation estimates. Section 24.4.3 considers these issues.
Although the main focus of this chapter is the contribution to policy analysis of (direct) tax and (cash) benefit microsimulation of household incomes at the national level at a given point in time, Section 24.5 considers a somewhat broader scope, in some dimensions. Section 24.5.1 discusses a broadening of the outcome income measure to include the effect of noncash benefits and, particularly, indirect taxes. Section 24.5.2 reviews the main features of dynamic microsimulation models used in analyzing the long-term redistributive effects of policies and the incidence of tax-benefit systems over the lifetime rather than cross-sectionally at a point in time. Section 24.5.3 discusses the use of microsimulation to explore the effects of policies at a lower level than that of the nation (e.g., Spanish regions or US states) and at a higher level (e.g., the European Union or world regions such as southern Africa).
The final section concludes by first summarizing our view of the achievements of microsimulation for policy analysis to date and then by exploring the outlook for the future along two dimensions: the need for data improvements and methodological developments and the need to consider ways to organize development, maintenance, and access to microsimulation models for policy analysis purposes.
24.2.