The CGE model

The CGE model used in this study is a version of a global general equilibrium model developed by van der Mensbrugghe (2005) and Zhai (2008). The model has its intellectual roots in the group of multi-country, applied general equilibrium models used over the past two decades to analyze the impact of trade policy reforms (Shoven & Whalley, 1992; Hertel, 1997). A novel feature of the model is its incorporation of recent heterogeneous-firms trade theory into an empirical global CGE framework. The model features intra-industry firm heterogeneity in productivity and fixed cost of exporting, which enables us to investigate the intra-industry reallocation of resources and the exporting decision by firms, and thereby capture both the intensive and the extensive margin of trade.⁴ The model is calibrated to the Global Trade Analysis Project (GTAP) (version 7) global database and implemented in General Algebraic Modeling System (GAMS) programming language. It includes seventeen countries/regions and seventeen sectors. This section overviews the key features of the model, which is a revised version of the one developed by Zhai (2008).

2.1 Production and trade

The agriculture and mining sectors are assumed to have perfect competition. In each of these two sectors, there is a representative firm operated under a constant returns to scale technology. The manufacturing and service sectors are characterized by monopolistic competition, and their structure of production and trade follows Melitz (2003). Each sector with monopolistic competition consists of a continuum of firms, which are differentiated by the varieties they produce and the productivity levels. Firms face fixed production costs, resulting in increasing returns to scale. There is also a fixed cost and a variable cost associated with the exporting activities. On the demand side, the agents are assumed to have Dixit-Stiglitz preference over the continuum of varieties. As each firm is a monopolist for the variety it produces, it sets the price of its product at a constant markup over its marginal cost. A firm enters domestic or export markets if and only if the net profit generated from its domestic sales or exports in a given country is sufficiently large to cover the fixed cost. This zero cutoff profit condition defines the productivity thresholds for firm entry to domestic and export markets, and in turn determines the equilibrium distribution of non-exporting firms and exporting firms, as well as their average productivities. Usually, the combination of a fixed export cost and a variable (iceberg) export cost ensures that the exporting productivity threshold is higher than that for production for domestic market, i.e. only a small fraction of firms with high productivity engage in export markets. These firms supply both domestic and export markets.

Production technology in each sector is modeled using nested constant elasticity of substitution (CES) functions. At the top level, the output is produced as a combination of an aggregate intermediate input and an aggregate primary factor. At the second level, the aggregate intermediate input is split into each commodity input according to the Leontief technology. The aggregate primary factor is produced by a capital-land bundle and aggregate labor. Finally, at the bottom level, the capital-land bundle is decomposed into capital and land (for the agriculture sector) or natural resources (for the mining sector), and aggregate labor is decomposed into unskilled and skilled labor. At each level of production, there is a unit cost function that is dual to the CES aggregator function and demand functions for corresponding inputs. The top-level unit cost function defines the marginal cost of sectoral output.

2.2 Income distribution, demand, and factor markets

Income generated from production accrues to a single representative household in each region. A household maximizes utility using the Extended Linear Expenditure System (ELES), which is derived from maximizing the Stone-Geary utility function. The consumption/savings decision is completely static. Savings enter the utility function as a “good” and its price is set as equal to the average price of consumer goods. The reason for treating savings in this way is that savings represent a stream of future consumption from the intertemporal perspective and, hence, contribute to consumer welfare in the long run. Investment demand and government consumption are specified as a Leontief function. In each sector a composite good defined by the Dixit-Stiglitz aggregator over domestic and imported varieties is used for final and intermediate demand.

All commodity and factor markets are assumed to clear through price adjustment. There are five primary factors of production. Although agricultural land is treated as a fixed, immobile factor, both capital and two types of labor (skilled and unskilled) are fully mobile across sectors within a country or region. In the natural resource sectors of forestry, fishing, and mining, a sector-specific factor is introduced into the production function to reflect the resource constraints. These sector-specific factors are modeled using upward sloping supply curves. For other primary factors, stocks are fixed.

2.3 Macro closure

There are three macro closures in the model: the net government balance, the investment and savings balance, and the trade balance. We assume that government consumption and savings are exogenous in real terms. In our subsequent exercises, we assume no fiscal policy changes, and the government budget is automatically balanced through changes in income tax on households.

Following the GTAP model (Hertel, 1997), the savings and investment balance (or current account balance) is endogenized through the assumption of a fictitious global bank. The global bank collects savings from all regions and allocates investment to each region so as to equalize the changes in expected rates of return on capital across regions. The expected rate of return on capital in region s, Rs, is defined as follow:

where rs denotes current economy-wide rate of return on capital in region s, p¹_sis the aggregate price of investment goods in region s, K_s and K^e_s are the levels of aggregate capital stock at the beginning of the period and at the end of the period, respectively, in region s. The parameter s is an elasticity parameter determining the extent to which the expected rate of return is discounted by the increase in future stocks. With the levels of regional investment determined by the global bank, the net capital flow to each region is endogenous (subject to the constraint of the global balance) to match the changes in regional savings and investment balance.⁵

2.4 Calibration

The model is calibrated to the GTAP global database (version 7.0). Some elasticity and technological parameters related to the model's firm heterogeneity specification are not available in the GTAP database. These parameters are set mainly based on a review of the relevant literature. Table 1 [ PDF 122KB | 1 page ] reports these parameters. The markup ratios are set to 20%–25% for manufacturing sectors and 35% for services sectors. The choices of markup ratios, together with the optimal pricing rule for monopolistic firms, imply that the elasticity of substitution between varieties is 5.0–6.0 for manufacturing sectors and 3.85 for service sectors, which is consistent with the recent empirical findings of Broda and Weinstein (2006). The shape parameters of the Pareto distribution of firm productivity are calibrated to match the profit ratio in total markup, which is estimated to be 64.5% based on French firm data by Arkolakis (2006).

The GTAP database 7.0 uses 2004 as the base year. As shown in Table 2 [ PDF 122KB | 1 page ], there has been a significant widening in the global current account imbalances since then. To make our analysis more relevant for current policy context, we updated the model's database to reflect the larger global current account imbalance before we ran various counterfactual scenarios. This was accomplished by shocking the real exchange rate in each country/region to obtain a new benchmark equilibrium, with the current account of each country/region consistent with the level of the 2006–2008 average. After updating the base year data, five counterfactual scenarios were examined, and their results were compared with this newly obtained benchmark equilibrium.

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