Scaling up infrastructure spending in the philippines: A cge top-down bottom-up microsimulation approach

Since Aschauer (1989) and Munnell (1990) stressed the important role of the public sector in funding infrastructure to stimulate economic development, a vast literature has dealt with this issue. Theoretical models and empirical studies have attempted to shed some light on this relationship. Some authors believed that a decline in productivity would be induced by slow expansion of public infrastructure investment (Bergman & Suan 1996; and Binder & Smith 1997). In policy circles, the role of infrastructure was somewhat neglected in the context of the stabilization and structural adjustment programs of the mid-eighties and nineties. For many international institutions and developing countries the focus was directed at liberalizing trade, improving macroeconomic balances and reacting to various external shocks. With improvements in these areas yet sluggish results in terms of poverty reduction in many countries, the end of the nineties saw major changes in development strategies by international financial institutions (IFI), development partners and governments of developing countries.

According to Estache (2007), infrastructure appears to be returning to the research agenda of development economists. This reflects a change in priorities of developing country governments, IFIs and multi- and bilateral donor agencies bringing infrastructure back to the top of the policy agenda. The Asian Development Bank organized a major conference entitled “Infrastructure Development: Private Solutions for the Poor” in October 2002 in Manila. This conference dealt with issues such as making infrastructure projects pro-poor, increasing private sector participation to scale up infrastructure and strengthening and increasing pro-poor public-private infrastructure partnerships. It built on a May 2000 conference organized by the Department for International Development (DFID-UK) and the World Bank. The World Bank’s world development report published in 2001 is another important illustration of this change, as is the implementation in many developing countries of Poverty Reduction Strategy Programs (PRSPs). More recently the Asian Development Bank (2009) published a report on investing in sustainable infrastructure to improve lives. Since the turn of the century, poverty has been at the centre of all development strategies. In many developing countries, growth is constrained by infrastructure bottlenecks and this is reflected in many investment climate surveys in which infrastructure ranks as the top priority (Estache, 2007).¹

Recently, governments of developing countries and various development partners have been investigating the determinants of poverty and the most effective roads out of poverty. One important determinant that has been identified is improvements in productivity. Education has received great attention as a tool to improve labour productivity. As a result, significant investments have been made, and major reforms have been implemented, to improve education in developing countries. At the same time, more and more analysts have raised the issue of deteriorated or obsolete infrastructure in developing countries as a stumbling block for growth. In many countries, infrastructure growth has not kept up with economic and demographic growth and in some instances infrastructure was not even maintained. This situation has led many analysts to return to the literature linking public expenditure to infrastructure. These stakeholders have argued that major investments to scale up infrastructure levels would transform their role from a constraint to an engine of growth that would contribute indirectly to poverty reduction in the long term.

At the same time, some researchers, such as Gupta et al. (2006), Foster and Killick (2006) and Mckinley (2005), have suggested that scaling up aid could have negative macroeconomic consequences, notably through Dutch disease (i.e., in which foreign inflows contribute to a real exchange rate appreciation that adversely affects a country’s international competitiveness). These conclusions have been challenged by others in empirical studies such as Berg et al. (2007) for five Sub-Saharan African countries, Li and Rowe (2007) for Tanzania, Mongardini and Rayner (2009) for a panel of 28 Sub-Saharan African countries. Many researchers have also investigated the impacts or challenges of scaling up aid to achieve the Millennium Development Goals (MDG), notably Bourguignon and Sundberg (2006) and Hailu (2007). Although the debate has not been settled, concern for risks associated with a large scaling up of aid (for infrastructure or other expenditures) continues to prevail in many policy circles.

Others argue that significant infrastructure spending may result in inflation and a resulting loss of competitiveness. There is also an important body of literature dealing with the crowding-out effects of public investment. Finally, some authors have raised concerns over the excessive operation and maintenance (O&M) cost burden of increased public infrastructure.

In a recent comprehensive report sponsored jointly by the African Development Bank, the World Bank and the Agence Française pour le Développement, edited by Foster and Briceño-Garmendia (2010), it was found that half of Africa’s growth was generated by infrastructure. In the report, the authors argue that improved infrastructure will accelerate urbanization, which has been the engine for growth in many countries, and will also improve regional integration. They focus on the potential contribution to the growth of various forms of infrastructure such as information and communication technologies, electricity, transportation (in a broad sense), water, irrigation and sanitation. They further decompose the respective contributions of investments in roads, railway, ports and airports. The report also investigates the impact on poverty, the role of institutions and the various options available. The main contribution of this report is to provide an estimate of infrastructure spending in Africa needed to achieve optimal growth rates. This price tag is estimated at 93 billion per year, of which one third is required for O&M.

In this paper we provide a comparative analysis of funding mechanisms to finance infrastructure investment and associated O&M costs. We build on work by Adam and Bevan (2006), Levy (2007) and Estache et al. (2007), who explore how infrastructure investments funded through foreign aid can contribute to Dutch disease. In their 2006 paper, Adam and Bevan show that the impact of Dutch disease can be attenuated if non-tradable sectors also benefit from infrastructure investment externalities. They construct an aggregate model and apply it to Uganda to verify this. We extend this idea by dropping the dichotomous classification of sectors as tradable and non-tradable. We also introduce an additional element by imposing increases in public expenditure to maintain and repair the new public infrastructure as in Estache et al. (2007). We investigate fiscal policy and foreign aid as possible funding mechanisms. We further extend previous work by introducing distributional analysis with poverty indices and growth incidence curves. For this purpose, we adopt a top-down/bottom-up CGE microsimulation approach.

The paper is structured as follows: we present the CGE top-down/bottom approach in the context of CGE microsimulation approaches, then set forth our model and resolution strategy and present our simulations, followed by an examination of macro and distributional analysis. We end the paper with our concluding remarks and possible extensions.

Three main approaches have been used to study the impacts of macro reforms on income distribution and poverty. The first and most commonly used one is the representative household approach (CGE-RH), the second is the top-down, layered or microsimulation sequential approach (CGE-MSS), and the third is usually referred to as the CGE integrated multi-household (CGE-IMH) (see Davies (2009) for a detailed description of these approaches).

Without going into a complete review of these approaches, we wish to highlight a few of their drawbacks in order to situate the contribution of the Top-down/bottom-up approach. First, despite extensive applications for distributive analysis, the CGE-RH has been strongly criticized for its inability to capture intra group changes in distribution (see Savard (2005) & Robilliard et al. (2008) for an elaboration of this critique). The main drawback of the CGE-MSS approach is that it does not fully take into account the feedback effects to the CGE model of household behaviour in the microsimulation model. In fact, when micro-household behaviour aggregates perfectly, this approach implicitly integrates the feedback effect. However, when household behaviour does not aggregate perfectly, the aggregation error is lost in the process.² The interesting question is to know the size of this aggregation error. If the aggregation error is small, not taking it into account is unlikely to bias results, but this is not the case if the error is relatively large. This critique of the CGE-MSS approach has been highlighted in two literature reviews of macro-micro modelling for poverty analysis (Hertel and Reimer (2005) & Bourguignon and Spadaro (2006)). The third approach is the CGE-IMH, which is theoretically sound but presents a few challenges. First, it requires significant data treatment to balance out each household account and reconcile micro-household data with the national account data of the SAM. Also, large models with complex non linear functions can lead to convergence problems. These problems are raised in Rutherford et al. (2005), Chen and Ravallion (2004), Bourguignon and Savard (2008).

To circumvent these problems, we apply a more flexible variant of the CGE-IMH that draws from the CGE-MSS approach. The basic idea is to push the CGE-MSS approach further by explicitly taking into account the feedback effect of the microsimulation model. This makes it possible to take into account the aggregation error of the micro-household model in the CGE model. We refer to this approach as the “Top-down/bottom-up” (CGE-TD/BU) approach. The approach provides greater flexibility for modelling household behaviour compared to the CGE-IMH approach by introducing a bi-directional link between the CGE and microsimulation models to obtain a convergent solution. The main drawback of the approach is that convergence is not guaranteed and must be verified for each simulation. The approach allows the modeller to use the exact income and expenditure structure found in the household income and expenditure surveys, since perfect coherence between micro and macro data is not required. Another advantage is that there is no limit to the level of disaggregation in terms of production sectors and the number of households to be included in the model. Finally, and most importantly, the degree of freedom in choices of functional forms used to reflect the micro-economic heterogeneity of household behaviour is as great as in the CGE-MSS approach.

The basic idea of the approach is to use the CGE model to generate a price vector (including wage rates), and a household microsimulation (HHMS) model to capture household behaviour (consumption and labour supply) in response to these price changes. These micro-household responses are re-aggregated and fed back into the CGE model and so on until there is convergence. We will detail the procedure in the subsequent section.

The basic model used for the analysis and the algorithm used for its resolution are presented in detail in Bourguignon and Savard (2008). We provide a summary of the model hypotheses and modifications required to capture the productivity of scaling up infrastructure investment and the O&M costs of the new infrastructure, which is adapted from Estache et al. (2007). We start with the CGE model, which we present in general before focusing on the special features of the infrastructure application. We follow with a description of the microsimulation model and complete the presentation with an examination of the resolution process.

The CGE model

The CGE model is disaggregated into 20 sectors and comprises 873 equations. Production is determined through a 3-tier system: the total production of the branch (XS) is made up of a fixed share between value-added (VA) and intermediate consumptions (CI). VA is a combination of composite labour (LD) and capital (KD), which are related with a Cobb-Douglas function. Producers minimize their cost of producing VA subject to the Cobb-Douglas function. We introduce an infrastructure externality parameter into this function, which we describe in more detail below. Optimal labour demand equations are derived from this process. Labour is decomposed into formal and informal labour, and the choice of combinations between these two factors is determined by a constant elasticity of substitution (CES) function.³ We assume that capital is not mobile between sectors, as it is quite difficult in the short to medium term to convert capital in order for it to be used in another production sector. Intermediate consumptions are determined by a fixed share (Leontief) assumption.

The labour market is quite original with respect to most macro/micro models. Our dual labour market is not perfectly segmented. The nominal wage rate in the formal market is exogenous and it is also above the equilibrium market wage;⁴ hence, we have an excess supply of labour on this market. Workers choose to offer their labour on the formal and informal markets or stay unemployed based on their reservation wage and on the prevailing wages on the two labour markets. The informal nominal wage rate is flexible and clears this market. The labour market mechanisms implemented are found in the CGE-RH modelling context in Fortin et al. (1997), Savard and Adjovi (1998), Devarajan et al. (1999), Agénor et al. (2003) and Stifel and Thorbecke (2003), among others. Figure 1 provides a graphic representation of the labour market (similar to the model presented in Thomas and Vallée (1996)).

Figure 1

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On the left hand side we have the informal sector wage and on the right hand side, the formal sector wage. The total labour available in the economy is measured by the distance 0ⁱ-0^f. Moving from 0ⁱ to 0^f we have the supply of labour on the informal market (Lsi) and from point 0^f towards 0ⁱ we have the labour supply on the formal market (Lsf). On the left hand side we have the aggregate labour demand of the informal sector depicted by (Ldi) and on the right hand side, the aggregate labour demand in the formal sector (Ldf). The exogenous formal wage is w¹, and w^1* represents the equilibrium wage on the formal market if the nominal wage was not fixed. Employment in the formal sector is measured by the distance 0^f – e. When w¹ > w^1* we have rationing on this market. The workers rationed out are not forced into unemployment as they can decide to supply their labour on the informal sector market if their reservation wage is inferior to the nominal informal wage. The rationed unemployed are measured by the distance e – d and the waiting unemployment, by the distance b – d. Further details are provided in the presentation of the microsimulation model further on.

We only have one representative household in the CGE model and its income is composed of wage payments (from the two labour categories), capital payments, dividends and transfers from other agents. As opposed to what we would find in the CGE-IMH approach, the labour endowment is endogenous (as stated above), although this is only factored in the micro model. Workers can move in and out of unemployment as well as between the formal and informal markets but these movements will be computed in the microsimulation model and transferred into the CGE model in the resolution procedure.

The key assumptions to capture the impact of infrastructure spending concern their production externalities and the government budget constraint to fund O&M costs. A complete description of the model can be found in Estache et al. (2007). This first Equation (1) is the government budget constraint (Equation 1) where government income (Yg) is spent on public services or expenditures (G) and on government savings (Sg), which will be used entirely for public investment.

(1) $S_g=Y_g-G$

We assume that public spending is exogenous and that public savings (the budget surplus) is implicitly exogenous as it is equal to public investment, which is exogenous. Hence, to fund new public infrastructure, the government will need an endogenous source of revenue such as a tax instrument or a foreign transfer (aid). We introduce an additional assumption, namely that an increase in public infrastructure investment will generate higher O&M costs for the government. We draw this assumption from the estimations of Fay and Yepes (2003).⁵

The externality Equation (2) is the other important assumption, given its role in increasing the total productivity of factors in the value added Equation (3). For this, we draw on the vast literature linking public infrastructure to private sector factor productivity, including Dumont and Mesplé-Somps (2000) in a CGE context, although our externality function does not include private investment. This function was also used in Estache et al. (2008). The function defining the externality is the following:

(2) ${\theta }_i={\left(\frac{Itp}{Itpo}\right)}^{{\xi }_i}$

where θ_i is the externality or sectoral productivity effect, which is a function of the ratio of new public investment (Itp) over past public investment (Itpo) with a sector-specific elasticity (ξ_i).⁶ The externality is introduced in the value added (Va_i) equation:

(3) $V{\alpha }_i={\theta }_i{A}_{i}L{d}_i^{ai}K{d}_i^{1-a_i}$

where A_i is the scale parameter, Ld_i, the labour demand, Kd_i, the capital demand, and α, the Cobb-Douglas parameter. Hence, an increase in θ_i represents a Hicks neutral productivity improvement, like the one modelled in Yeaple and Golub (2007).⁷ With this formulation, the infrastructure investment can act as a source of comparative advantage because the function is sector specific.

Commodity markets are balanced through adjustments in market prices. The current account balance is fixed; accordingly, the nominal exchange rate varies to allow the real exchange rate to clear the current account balance. The GDP deflator is used as the numeraire in the model. We also assume in a standard manner that the Philippines is a small open economy. Armington’s (1969) assumption is adopted for the demand of imported goods (imperfect substitution with constant elasticity of substitution function (CES)) and constant elasticity of transformation (CET) functions are used to model export supply.⁸

The household microsimulation model (HHMS)

The construction of the household microsimulation model (HHMS) relies on data for all of the 39,520 households from the 1997 Family Income and Expenditure Survey (FIES) and on three rounds of the Labour Force Survey (LFS) run between 1997 and 1998.⁹ The HHMS comprises a representation of household income structures and expenditure behaviour as well as their labour supply decisions. Household consumption is modelled with a linear expenditure demand system (LES). We use the calibration method proposed by Dervis et al. (1982). Savings and income tax rates are calibrated according to the observed data in the survey. All transfers received and paid are exogenous. We consider that capital endowments are fixed at the levels observed in the 1997 FIES. In the FIES, we have information on the sector of activity of the household head and on the amount of non-wage income. This allows for a mapping of the sector of origin for household capital income. Based on information from the FIES and LFS, we classified wage earners into formal and informal workers according to the category of work specified in the survey.¹⁰

Our labour market mechanism is drawn from the Roy (1951) model, which was revisited by Heckman and Sedlacek (1985) and further enriched by Magnac (1991). We selected the non competitive version of the models presented by Magnac (1991), as it includes a formal and informal market with unemployment. The formal market in non competitive; it has a rigid nominal wage and workers face a cost of entry to access this market. The fixed formal wage is above the market equilibrium wage, which creates excess supply on this market. The rigid wage can reflect various interventions on the labour market such as labour union contracts, efficiency wages and regulated wages.¹¹ The labour supply model is estimated with the two-step Heckman procedure. More details on the labour supply model and the results of the estimation results can be found in Bourguignon and Savard (2008). The estimated model allows us to compute the worker-specific cost of entry, the reservation wage and the potential wage. Each of these elements is used in constructing our labour supply in the HHMS.

The key feature of the labour supply model is the introduction of an endogenous labour supply, but it also serves to compute changes in income, consumption and welfare changes at the household level. We introduce mobility between the three statuses of workers and potential workers, namely informal work, formal work and unemployment. The transformation at the micro level allows us to allocate changes in labour supply to the informal sector and unemployment in the HHMS model. We now describe how we go from the labour supply model to our HHMS model to construct our two labour supplies.

For the formal market, we construct two queues. The first concerns the formal sector workers who can be laid off in the context of a decrease in formal labour demand generated by the CGE model. The second concerns unemployed and informal market workers who supply their labour to the formal market when formal labour demand increases in the CGE model. We assume that firms have perfect knowledge of the workers’ productivity (implicitly their potential wage).

If formal labour demand increases, workers are drawn from the queue of informal and unemployed workers. To be included in the queue, two conditions from our labour supply model must be satisfied by workers: the formal wage minus the cost of entry into the formal sector must be greater than both the reservation wage of the worker and the informal wage.¹² Workers satisfying these two conditions are then ranked based on their potential wage and formal sector firms recruit the most qualified workers from this queue.

In the presence of a decrease in formal sector labour demand, the least productive formal sector workers, based on their potential wage computed from the labour supply model, will be laid off. The destination of laid off workers depends on the reservation wage of the workers compared to the prevailing informal sector wage. If the reservation wage is above the informal wage the workers become unemployed, and if it is lower they supply their labour to the informal market. The reservation wage check with the informal sector wage is not only applied to laid off workers in the formal sector but also to active informal sector workers and the unemployed; the change in informal wage can modify the status of either. This procedure provides us with the new labour endowments in the HHMS to compute the new income level, expenditure and change in welfare. The final step in the HHMS consists in computing the variation in aggregate labour supply in the informal sector and the variations in aggregate consumption that will be used to link back to the CGE model.

Resolving the model is done by iterating between the CGE and HHMS models. Hence, we build on the CGE-MSS approach by integrating the feedback effect of the microsimulation model back into the CGE model. This top to bottom and bottom to top resolution process is performed until convergence and is equivalent to a cobweb type of resolution of market equilibrium. Performing this bi-directional link is equivalent to including all 39,520 households from the household survey in the model.¹³

The Top Down loop presented in Figure 2 is implemented in the following way. First, the simulation is performed in the CGE model and we feed the variations in prices (goods and factors) and formal labour demand from this model into the HHMS model. As explained in the previous sub-section this labour demand change, and the variation in the informal wage, allows us to determine the labour endowment of workers.

Figure 2

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Once this is done, we can determine the new income and consumption level of each household. We then aggregate consumption and informal labour supply and compute the variations in these two variables. These variations are finally introduced into the CGE module where aggregate consumption and informal labour supply is exogenous. We repeat this loop until converging results are obtained for these two variables.¹⁴ We know that there are conditions that satisfy the stability of the cobweb resolution approach and these conditions also apply to the resolution of the TDBU approach. Our application of the CGE-TDBU approach to the Philippines is programmed with GAMS software. Our CGE model has 873 equations and endogenous variables, whereas the microsimulation model has 829,920 equations with 829,920 endogenous variables.

In order to analyze the macro and distributional impacts of scaling up infrastructure investment under different funding mechanisms, we contrast two types of infrastructure investments; a non productive one and a productive one.¹⁵ These simulations are well justified in the context of the economic crisis and the larger debate on scaling up infrastructure in developing countries described in the introduction. We simulate an increase of 30 percent in public investment on infrastructure with respect to the level of investment in the reference period. We compare three funding mechanisms consisting of increases in the value added tax rate, the income tax rate and foreign aid. It is important to highlight that the effective tax rates observed in the SAM are relatively small in the reference period: between 2 and 3 percent for the two weighted average tax rates. Hence, the increase in these taxes required to fund the program is large in percentage terms, but not in nominal terms. The increases necessary for the three funding schemes are presented in Table A.3 in the appendix. Productive externalities contribute to increased economic activity, which increases government revenues. Hence, the funding requirements are not equal to the direct investment and O&M costs.

A comparative analysis of productive and non-productive investments under three funding mechanisms allows us to highlight the most efficient funding mechanism. We explore the effect on different macroeconomic and sectoral variables in addition to the distributional impacts. To simplify the presentation, we focus on the macroeconomic variables and the key sectoral impacts contributing to welfare and distributional impacts, namely market prices and returns to factors. We compare funding options throughout the section. We concentrate on productive investments with a brief comparative analysis to non productive investments at the end of the section. Distribution analysis follows in the subsequent section. When looking at results (Tables 1 to 3), it is important to keep in mind that our current account balance is fixed.¹⁶

Before moving on to the specific simulations, we can begin with a few general comments. First, the impacts on GDP and most macro variables are relatively modest. This results from the fact that, in nominal terms, the 30% increase in public investment is not large compared to size of the economy (0.48%). However, this is not an important issue as we are mostly interested in the comparative analysis between the different scenarios. The second point is that all simulations produce a positive impact on GDP, although the non productive simulations produce a very small impact. This positive effect comes in part from the productivity gains of the infrastructure but also from the economic activity generated by these investments.¹⁷ The positive effect helps create employment in the construction sector and in the public services to operate and maintain this new infrastructure. The sectors supplying goods and services more intensively to these two sectors, and their workers, benefit the most. The expansion of the construction sector and public services creates employment directly and indirectly with an increase in the informal wage. This effect is possible given the presence of unemployment in the model. Hence, these new workers contribute to increasing GDP.

The impact on the aggregate household is positive for all simulations and we observe a crowding out effect on private investment in all scenarios. A fall in the unemployment rate and an increase in the nominal informal wage rate are also found in all simulations. The increase in government income is the same in all simulations as this is implicitly directly tied to the simulated increase in public investment and the O&M cost associated with the investment. Other variables, such as the nominal exchange rate, and firm income and savings, exhibit qualitative and quantitative differences.

For the distributional analysis, we apply poverty indices and compute growth incidence curves. The poverty index chosen (FGT) is the additively decomposable P_α proposed by Foster, Greer and Thorbecke (1984).¹⁸ To complete the distributional analysis, we computed the growth incidence curve (GIC) proposed by Ravallion and Chen (2003).

Growth incidence curves

We computed the GIC for the three productive scenarios (Figure 3 to 5). A caveat must be highlighted for comparison of the two tools used. In the first case, households remain in the same group whereas for the GIC curves, a household in the top of the distribution can drop to the bottom and this is not fully captured given that the approach compares households at a specific rank and not the households before and after the simulation. The two tools are thus complementary.

Figure 3

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Figure 4

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Figure 5

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It is interesting to note that in the first scenario (Value Added Tax), we observe in Figure 3 the largest gains at the two extremes of the distribution. If we remove the bottom and top two deciles, we note a slightly positive GIC. This is consistent with the decomposition of poverty analysis in which more educated households seem to benefit the most.

Moving on to the income tax funding option, we have a clearer positively sloped GIC compared to the previous one, in which we needed to exclude the extreme deciles. In this case, the bottom 5 percent are clearly losers compared to the rest of the distribution, with the exception of the top percentile, which also seems to be a loser. This apparently counterintuitive result reflects the fact that general equilibrium price effects, which favour the richer percentiles, dominate the direct income effect of increasing the income tax.

The foreign aid scenario presents a much more horiztonal or proportional impact compared to the two previous scenarios. The bottom part of the distribution is similar to the VAT option and the top of the distribution is similar for the three scenarios.

In this context, no option seems to exhibit pro-poor features, with the third one looking to be the closest to that option and the income tax being the most favourable to the households in the top half of the distribution. When comparing with the FGT indices, we had the VAT as being the most interesting option, followed by foreign aid and finally the income tax option. In the case of the GIC, if the objective is to choose the least regressive option, one would prefer the foreign aid option followed by the VAT option, with the income tax option being the least preferred.

In this paper we present the main steps to implementing a variant of the CGE integrated multi-household approach that offers more flexibility compared to the standard version of this approach, namely introducing endogenous labour supply and unemployment into the model. We also illustrate how to exploit this modelling exercise to analyze the distributional impact of scaling up infrastructure investment on macroeconomic, sectoral variables in the Philippines. The approach allows us to capture numerous issues surrounding infrastructure expansion among which are productivity externalities, job creation, relative price changes, crowing out, Dutch disease, funding issues, fiscal constraints and distributional analysis. We build on the models presented in papers such as Adam and Bevan (2006) and Estache et al. (2008) by introducing rigorous distributional analysis. Our macro results are different than those of these authors given the presence of unemployment, which contributes to attenuating or reversing the Dutch disease effect that is also softened by the production externality assumption. We also present a framework that allows the analyst to explore the poverty impact of such programs and identify the most favourable funding option based on two distributional analyses (FGT and GIC). As in Estache et al. (2008), we do not observe strong differences at the macro level when comparing funding options to scale up infrastructure. However, our poverty analysis allowed us to clearly rank the performance of each funding option to establish that the VAT is most favourable, followed by foreign aid and finally income taxes.

Our static modelling framework does not allow us to fully capture the negative impact of crowding out of private investment. Another issue is that the returns on investment in infrastructure might come in the medium to long term. To improve the analysis on this front, a recursive dynamic framework would be a more appropriate tool. In our future research we will extend this model in this direction. The major challenge for this work program will be to introduce a capital reallocation mechanism at the micro level. To our knowledge, only one set of authors (Annabi et al., 2005) applied a dynamic CGE microsimulation model, but they failed to rigorously capture the distributional factor growth issue as stated in Davies (2009). The uniform redistributed capital growth underestimated the distributional impact of the growth elements in the model and this issue needs to be resolved along the lines suggested by Davies (2009) in further research.

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Author details

1. Luc Savard

   GREDI, Faculté d’administration, Université de Sherbrooke, Canada

   For correspondence

   luc.savard@usherbrooke.ca