Combining microsimulation with CGE and macro modelling for distributional analysis in developing and transition countries

The purpose of the paper is to explore how microsimulation can be combined with CGE and macro modelling techniques to study distributional issues in transition and developing countries. This is an area of great current interest. (See, for example, Agénor et al., 2006; Cockburn, 2006; Cogneau & Robilliard, 2006; Bourguignon & Savard, 2008.) While microsimulation is essential in modelling the distributive effects of taxes and transfers, it is limited by the fact that it is often non-behavioural and by its inability to model prices, wages and macro variables. CGE and macro models, on the other hand, have in the past generally lacked the rich distributional detail found in microsimulation. The solution that is increasingly being advocated is to combine these different forms of modelling, either through ‘layering’ the models, or through their integration.

Different combinations of models are needed when dealing with different issues. For some purposes it is sufficient to stick with just one of the standard model types. For example, in some cases there may be little reason to expect complex distributional impacts and standard CGE modelling, with its representative household groups, may be enough. Conversely, if interest centres on distributional impacts and there is reason to believe that general equilibrium price effects will be unimportant, then conventional microsimulation may be sufficient. But there are cases where combining the approaches is necessary to get an accurate picture. A prototypical case is that of removing agricultural protection in developing countries. Tariff removal leads to lower food prices, benefitting poor consumers. on the other hand, lower agricultural prices reduce the wages of agricultural labourers, many of whom are also poor. The result is that some of the poor will benefit and others will lose. CGE is needed to capture the wage and price effects, and microsimulation is needed to net out gains and losses for individual households, allowing accurate distributional analysis to be performed.

Currently, in the developing world, the leading distributional issues surround the impact of factors like trade liberalisation, adjustment policies and financial crises on poverty and overall income inequality. In the transition economies similar issues are also important, but there are features that give some of these countries important aspects in common with higher income countries. Russia and the East European transition countries, although still poor relative to the West, are highly urbanized and industrialized. The portion of the economy in the informal sector is also relatively small compared with many developing countries. These aspects mean that food price and agricultural issues, for example, are less important than in many developing countries. Also, the tax, benefit and pension issues that face members of a formal-sector industrial workforce are more important. Conventional microsimulation has been developed to analyze policy issues and distributional impacts in highly urbanized and industrialized societies, and may therefore be more applicable and relevant in these transition countries than in many developed countries (DCs).

While there have been a few important contributions, neither microsimulation nor CGE modelling are so far very advanced in most transition countries.¹ Part of the reason may lie in data requirements and lack of modelling resources. And part may lie in the uncertainty about how to model these economies. But it may also be that the pace of change has been so rapid as to restrict the payoff to, and applicability of, such modelling. CGE models in particular assume that the base year is one of equilibrium, which is problematic. if transition begins to proceed in a more orderly way, better data become available, and more consensus develops about how to model these economies, improved prospects for microsimulation and CGE work will arise.

CGE and standard macro models provide static analyses. Ultimately, one is interested in the connection between growth and other phenomena including income distribution. There has been some interesting work in this area, but it is to be hoped that micro-macro links will extend more to dynamic modelling in the future. Tax and benefit changes can affect growth, for example through impacts on saving and investment, human capital formation, fertility, innovation, and incentives for the adoption of new technology. And trade reforms can stimulate growth through the expansion of export industries and FDi, as well as through capital accumulation/technology effects – at the same time generating possibly complex distributional impacts.

The rest of the paper is organized as follows. Section 2 briefly discusses the history and development of microsimulation. Next, in Section 3, we look at the basic aspects of SAM and CGE techniques. Section 4 then reviews recent attempts to merge or layer CGE and microsimulation models. Section 5 looks at work that has added explicit macroeconomic content to CGE, while growth is discussed in Section 6. Section 7 briefly discusses political economy aspects, while Section 8 concludes.

The originator of microsimulation, Guy Orcutt, believed that one day rigorous and useful modelling of the overall economy could be done by aggregating the carefully modelled behavior of individual consumers and firms (see Orcutt, 1957 and Orcutt et al., 1976).² This is still the vision of many practitioners of microsimulation. However, the majority have taken it as a long-range objective and have mostly limited themselves to a more practical approach for the present. The focus is generally on distributional issues. The aim is to construct reliable models of individuals and households that will allow careful analysis of the impact of policy changes. While modelling the overall behaviour of the economy has not been the main agenda, from time-to-time attempts are made to graft macroeconomic content onto microsimulation models.³ And today, with many researchers attempting to combine microsimulation and CGE models in various ways, the achievement of Orcutt’s original vision is coming closer to reality in some cases.⁴

The foundation of microsimulation today is still found in models of household income distribution and consumption that take detailed account of taxes and transfers but leave household behaviour exogenous. The basis of a good accounting model of this type must be a rich database on a large representative sample of households. Constructing such a database is far from a trivial enterprise. No single household survey comes close to providing the required data. The best available household survey will be used as the ‘host’, but even the best surveys will not cover all the necessary variables, and the estimates they provide will be affected by reporting errors and differential response problems. This means that corrections need to be made to the data in the host survey, and that the host needs to be augmented by imputing values of omitted variables from other surveys or from administrative (for example social security or revenue authority) data. Totals for all kinds of income, consumption, transfers received, taxes paid, and other variables must reconcile with those available from independent sources, for example the National Accounts.

In addition to a highly developed database a microsimulation model for policy analysis needs to have a detailed and accurate tax and transfer simulator. Again this is far from trivial. Modern governments levy a cornucopia of taxes and provide a rich array of transfers. Moreover each is very complex. Just modelling the deductions, exclusions, exemptions and credits provided under the personal income tax is challenging in itself. Problems are multiplied when, as in many transition countries, there are substantial differences across different households in terms of eligibility for pensions and other transfers. Finally, tracing the impact of indirect taxes and tariffs is complicated by the fact that these are sometimes levied on intermediate goods, and therefore ‘cascade’ through the economy, producing a larger impact on consumer prices than if they were levied only at the final stage.⁵

In addition to the above, a microsimulation model must include sophisticated software to present and analyze results. Considering impacts of tax/transfer changes on inequality – both overall and within and across subgroups, poverty in all its nuances, progressivity, and benefit concentration is rich and complex. Lorenz curves and other concentration diagrams, Foster-Greer-Thorbecke (FGT) measures of poverty, and a battery of inequality indexes are brought to bear in the best current work.⁶

Examples of sophisticated microsimulation models of the accounting type are found in most OECD countries: for example, STINMOD in Australia, SPSD/M in Canada, TRIM3 in the United States, and TAXMOD and POLIMOD in the United Kingdom. Further, the European Union has spent several years funding the development of such a microsimulation model, EUROMOD, for 15 core member countries (c.f. Sutherland, 2001).

Current microsimulation is not confined by any means to static non-behavioural modelling. Models that endogenize labour supply and saving behaviour on the basis of econometric estimates of the relevant relationships are not uncommon. The methodology of such studies is discussed, for example, in Bourguignon et al. (2001) and Robilliard et al. (2008). (See also Section 4 below.) These are, of course, partial equilibrium models. Further, there has been substantial development of dynamic microsimulation models (see Section 6), which model demographic evolution over time, so that the impact of policy changes that will impose costs on, or deliver benefits to, particular age groups/household types can be traced more accurately into the forseeable future.⁷

Another area where microsimulation has played a role in the study of the overall economy is in the simulation of technological change and growth, which is discussed in Section 6 below.

CGE and microsimulation are being merged in current work in two different approaches. in the first approach the two types of models are ‘layered’. Second, the two kinds of models can be completely integrated. While the latter approach may appear to be the ideal, for some purposes the layered approach has advantages, as we shall see.

In this section we look at modelling exercises that provide pairwise links between microsimulation and macro models and between CGE and macro models, as well as efforts to link all three types of models.

Microsimulation has increasingly become dynamic in developing countries (see, for example, the surveys by Harding, 1996, and Kelly, 2004). sophisticated techniques have been developed in several countries to simulate the evolution of age cohorts or entire populations over time, examining, for example, the lifetime consequences of public pension arrangements or taxes and transfers. The consequences for the economic well-being of cohorts and income groups generated by varying economic growth records, and by differences in income growth for different elements of the population, have been carefully studied. This kind of work is unfortunately very costly and has substantial data and modelling requirements. We therefore find that these techniques are yet to be applied in developing or transition countries, although it is to be hoped that dynamic microsimulation models may begin to be seen in those countries before too long.

While dynamic microsimulation is still some way from arriving on the scene in development and transition studies, concern about growth has always been central in those areas and interest in the distributional effects of growth has been high for many years. There has therefore, unsurprisingly, now been significant attention to the development of dynamic versions of CGE microsimulation models and other micro-macro approaches.

A representative example of the kind of dynamic modelling that is now being performed is given by Annabi et al. (2005), who use an integrated dynamic CGE microsimulation model to study the potential poverty and inequality effects of unilateral trade liberalisation in senegal. The model is based on a 1996 sAM and a 1995 survey of 3,278 households. In contrast to static CGE work, which provides long-run analysis without allowing factor accumulation and can therefore be criticized as lacking realism, this work allows both capital accumulation effects and the transition from the initial situation to the post-reform world to be modelled. The hope is clearly that the results will have more predictive value than those of ststic CGE microsimulation.

Annabi et al. find that full tariff removal in senegal would lead to a small increase in poverty and income inequality in the short run. The formerly protected elements of agriculture and industry would also contract. In the long run, however, trade liberalisation would lead to substantial accumulation, particularly in services and industry, bringing significant overall welfare improvements and decreases in poverty. Interestingly, however, inequality in income distribution is predicted to increase, as urban residents and the non-poor benefit more, in relative terms, than the poor.

While the results of the Annabi et al. analysis are interesting, it is important to note that the dynamics being introduced on the CGE side are not matched by a movement towards a more dynamic approach on the microsimulation side. A beginning could be made in that direction in current work by following the first steps that were made in this area by microsimulation researchers in developed countries. In particular, it would not be too difficult to implement static ageing – the reweighting of households as the simulation proceeds to mimic predicted changes in demographic composition and labour force characteristics. One appealing aspect is that a dynamic CGE model itself generates predicted changes in the sectoral composition of the labour force, including rural-urban split, producing a key input into the static ageing of the microsimulation.

While there are thus interesting possibilities for integrated dynamic CGE microsimulation work in developing and transition countries, Bourguignon et al. (2002) cautions that substantial difficulties would be faced in extending the ‘layering’ approach to a dynamic setting. Achieving consistency between separate macro, CGE and microsimulation models running over time would multiply considerably the complexity involved in layering these models consistently in a static setting.

There are other strains of microsimulation work that could, in principle, be applied to the study of growth processes in developing and transition countries. An example is provided by the contributions of Wolfson (1996, 1999), Elliason (1996, 1997), and Ballot and Taymaz (1996). These authors were stimulated by Nelson and Winter (1982), who presented an evolutionary simulation model of firms producing with fixed coefficients production functions. These firms were subject to stochastic depreciation, but also could search for better techniques of production, either in a neighbourhood of their existing technique or imitatively by trying to copy the technique of a more successful firm. Nelson and Winter started their simulation in an initial state meant to mimic the U.S. in 1909. They argued that the evolution of technology and output they produced was as good as that provided by neoclassical growth models in most ways, and better in some.

Wolfson (1996) reported initial work on a model for Canada in the Nelson and Winter spirit, XECON, and Wolfson (1999) provided results from the completed model. Technology is much richer than in Nelson and Winter. There is an input-output structure and many commodities. The same commodity can be produced by different firms using different technologies. Learning and search for better techniques produce an evolution of the system over time.

Eliasson (1996) and Ballot and Taymaz (1996) report on simulations with the Swedish micro-to-macro model, MOSES (Model of the Swedish Economic System). MOSES has very realistic detail on firms. In the 1982 database there are 225 manufacturing firms, of which 154 are real firms, whose characteristics are specified on the basis of survey data. While Wolfson’s XECON is largely a theoretical tool, MOSES is meant to be a model of an actual economy. It incorporates bounded rationality and the accumulation of ‘competence capital’ through organizational learning in a heterogeneous environment. There is constant technological competition, ongoing learning, and both entry and exit of firms.

Eliasson (1996) stresses the importance of entry in the growth process. He finds that, over a period of 50 years, if free entry is allowed manufacturing output rises about 50% compared to a no-entry base run (about 45% for GNP). Eliasson is concerned about the dampening effect on growth of the strong restrictions on entry in even our advanced industrial economies. He indicates that only about 30% of the Swedish economy, for example, could be characterised as having free entry.

Ballot and Taymaz (1996) add a training and human capital block to MOSES. In contrast to the usual case, firms in their model invest in the general human capital of their workers, because when they find a useful innovation they earn short-run rents, including rents on the general human capital of their workers. And an important change is made in how firms search for innovations. Rather than using adaptive rules, genetic algorithms are used. This makes the modelling truly evolutionary, and leads to discussion of the simulation as a complex system.

It is interesting to ask what value there could be in applying this kind of growth simulation to developing and transition economies. The environment facing firms in these countries is generally more severe and less predictable than in, for example, Canada or Sweden, but this may make the kind of modelling performed by these authors more rather than less relevant. For example the restrictions faced by business in a country like Russia represent severe entry barriers – just what Eliasson is worried about, and a phenomenon that his style of model is designed to address.

Another area of work on growth processes that cannot be ignored here is the contributions made by ‘new classical’ macroeconomics. Models with rigorous micro underpinnings and in some cases rich detail on heterogeneous consumers and firms have been generated. In the 1970s and 1980s, macroeconomists at Minnesota and other ‘freshwater’ North American economics departments began to develop dynamic general equilibrium models of the economy. These were initially quite controversial since they modelled unemployment as an equilibrium phenomenon, assumed rational expectations, and neglected the monetary side of the economy. However, as the models became more sophisticated they began to be more widely accepted. Today, a growing group of young researchers are working with dynamic GE models with heterogenous consumers and workers whose characteristics they specify by reference to microdata. (See for example the pioneering contributions of Huggett, 1996; Krusell & Smith, 1998; Quadrini, 2000; Ventura, 1999.) In addition to savings, these models have endogenized labour supply and human capital investments. The researchers not only examine simulated distributions of earnings, consumption, and income, but have also shown interest in the distributions of wealth generated. Experiments have been done to replace progressive income taxes with consumption taxes, or with proportional income taxes. Studies of the impact of altering pension regimes have also been performed.

It has been recognized for some time that CGE models have important potential applications in political economy analysis.²⁴ (See for example Dervis et al., 1982: 401–402.) Much of the output concerns distributional effects of alternative policies, and the tussle over distribution is of course the bread and butter of politics. To the extent that voters or, say, regional or other subnational governments, act in their self interest, their behaviour may be predicted from distributional analysis. Examples of research applying this kind of insight can be found in Groenewold et al. (2000) who model the reaction of regional governments to redistributive policies imposed by a federal government, and in Yeldan (1998) who examines the role of different interest groups in the 1994 Turkish economic crisis in the light of CGE analysis.

Some authors have been very imaginative in their application of CGE to political economy questions. A good example is provided by De Janvry et al. (1991b). (See also De Janvry and Subbarao, 1986; and De Janvry et al., 1991a.) De Janvry et al. argue that in Ecuador traditionally the coastal agricultural interests had controlled the political process, but that in the 1980’s oil boom an elite of the more highly educated members of the population took over. However, since either elite formed a minority of the population, it needed the support of another group to hold power. The rural poor provided the most promising political ally for both groups, in view of their relatively large numbers.

De Janvry et al. analyzed the effects of alternative stabilization policies on the incomes of the alternative voter groups. However, they went beyond mere analysis of the possible political effects of given policy changes. They determined the policies that particular political coalitions could adopt if they wished to achieve or maintain power.

This immediately suggests the possibility of using CGE political economy analysis not just by social scientists but by political parties and leaders themselves. One could, in fact, observe that the proof of whether the technique has predictive power will lie in whether it receives such use in the future.

In the standard CGE model the choice of factor and household disaggregations has an impact on the political insights that can be obtained. If one differentiates labour only according to the categories skilled/unskilled, male/female, and urban/rural, for example, then it is impossible to study the effects of policies that discriminate between workers in different cohorts, regions, or industries, or between, say, skilled blue collar workers and the university educated. similarly, if representative households are defined, say, according to income group, then many politically-relevant dimensions are missed. The solution advocated, for example, by De Janvry et al., is to choose groupings carefully, in the light of one’s political analysis. However, this approach imposes limits at the start that may be difficult to correct in the light of evolving insights about the political process.

It seems clear that this is an area where integrated CGE microsimulation would have great advantages. It is not necessary to adopt any prior grouping of households or individuals. There is no limit to the alternative ways in which people can be grouped, and the sensitivity of results to a wide variety of changes in the details of tax and benefit proposals can be investigated. A possible downside is that the tool could be ‘hijacked’ for political purposes. While the economist may wish to focus attention on state-of-the-art inequality and poverty measures, for example, there may be pressures from policy-makers and governments to focus on redistribution between key groups of voters instead. Use of CGE microsimulation to perform political economy analysis may therefore turn out to be something of a two-edged sword.

While there do not appear to be any political economy studies that have yet used CGE microsimulation, the latter field is young and it seems very likely that such studies will be forthcoming. Already, one can look at the losers and gainers from policy changes and proposed reforms in CGE microsimulation work and make informal guesses about the likely extent and composition of voter support for such initiatives. In the Cockurn (2006) study of Nepal, for example, one sees that a projected gain from trade liberalisation for a majority that includes most urban dwellers and higher income rural residents, suggesting that such liberalisation is politically feasible. Or in the work of Rutherford et al. (2007) for Russia one can see possible majority alliances in favour of domestic reforms but one will also search in vain for such a grouping that would support the Doha Development Agenda. It would seem that a reasonable guess can be made about which of these policies would ‘fly’ with voters. It would be naive, of course, to think that the existence of majorities modelled to gain from particular policies means that the latter will necessarily be adopted, even in fully democratic countries. The identification of the size and composition of gaining and losing groups is only an input into political economy analysis. Outcomes depend on the nature and details of political institutions and strategic interactions between the relevant groups of political actors.

This paper has overviewed recent work that has attempted to bring together microsimulation, CGE, and macro models to perform distributional analysis in developing and transition countries. We have seen that different combinations of such models, including those where only a single model-type is used, are appropriate for different problems. For short-run impact analysis, microsimulation on its own may be appropriate. However, we naturally want to know about the interrelationship between the changes in disposable income, consumption and labour supply found in a sophisticated microsimulation on the one hand, and general equilibrium price changes or changes in macro variables on the other hand. In the case of national subregions, or countries embedded in free-trade areas, it can be argued that microsimulation may adequately be combined with pure macro models. That is, CGE modelling may not be necessary. For distinct national economies, however, the first step beyond microsimulation should likely be integration with CGE modelling. Ultimately, structures in which macro, CGE and microsimulation models are either layered or integrated can also be pursued.

There has now been considerable research on combining microsimulation with CGE and macro models in developing countries. This research shows both that distributional results from microsimulation tend to change significantly when CGE is added, and that the converse is true as well. Further, avoiding microsimulation by assuming that income distribution follows an assumed functional form within household categories in CGE exercises produces significantly different results from full CGE microsimulation. Taking such shortcuts has been shown to minimize estimated poverty and inequality impacts in some important cases. Also, the impacts of growth shocks, trade liberalisation and other shocks on distribution are shown to be much more complex in exercises that add full microsimulation to CGE and macro modelling. such insights as the Cockburn (2006) finding that trade liberalisation will tend to reduce poverty for the urban poor in Nepal, while increasing it for the rural poor but especially the moderately poor in that group, are not to be found in more aggregated models.

We have also seen that promising work has now been done in dynamic integrated CGE microsimulation work in developing countries. Refinement of CGE microsimulation dynamics, at least exploiting the possibilities for static ageing, is one fruitful direction in which his work can be further developed. Macroeconomic or financial aspects can conceivably also be explored in this context, but again the most productive approach is likely to be an integrated one.

Much microsimulation work has been done in developed countries. CGE work is most advanced, on the other hand for LDCs. Currently, several groups of development researchers are putting these two approaches together, and in some cases are adding macroeconomic and financial modelling as well. In contrast, with a few conspicuous exceptions, little such work is being done for the transition economies. It is to be hoped that increased modelling efforts will be seen for the transition countries, yielding strong microsimulation, CGE and macro models, separately and in combination.

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Author details

1. James B. Davies

   Department of Economics, University of Western Ontario, Canada

   For correspondence

   jdavies@uwo.ca