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E. A. Haddad, A. C. Giuberti UDC ECONOMIC IMPACTS OF NATURAL RESOURCES ON A REGIONAL ECONOMY: THE CASE OF THE PRE-SALT OIL DISCOVERIES IN ESPÍRITO SANTO, BRAZIL 1 The Brazilian government has recently confirmed the discovery of a huge oil and natural gas field in the pre-salt layer of the country s southeastern coast. It has been said that the oil fields can boost Brazil s oil production and turn the country into one of the largest oil producers in the world. The fields are spatially concentrated in the coastal areas of a few Brazilian states that may directly benefit from oil production. This paper uses an interregional computable general equilibrium model to assess the impacts of pre-salt on the economy of the State of Espírito Santo, a region already characterized by an economic base that is heavily reliant on natural resources. We focus our analysis on the structural economic impacts on the local economy. 1 Eduardo A. Haddad, Ana Carolina Giuberti Text. 112 Природно-ресурсный потенциал региона Keywords: natural resources, impact analysis, inter-regional CGE model, Brazil, oil production, Dutch disease 1. Introduction The State of Espírito Santo is best described as a small Brazilian state. It is responsible for only 2.3% of Brazilian gross domestic product (GDP) and is a home to 1.9% of the country s population. 1 Its economy relies heavily on the natural-resources-based industry, the majority of whose products are exported. As Caçador and Grassi (2009) show, the commodity-based industry 2 was responsible for 76.6% of the State s manufacturing value added and for 92.5% of the State s total exports in Production in the commodity-based industry is concentrated in a few big companies (Vale, Samarco, ArcelorMittal Tubarão, Aracruz Celulose, and Petrobras) that operate at an international scale. Vale, Samarco, ArcelorMittal Tubarão, Aracruz Celulose, and ArcelorMittal Belgo together were responsible for 77.4% of the State s exports. The traditional industry 3 is the second most important industry in the local economy, although it represented only 8.6% of the State s manufacturing value added in 2005 (Caçador and Grassi, 2009, p. 463). Its importance lies in its geographic location: many industries, such as textile, clothing, and furniture are located upstate and are responsible for the creation of jobs and income in small municipalities. Moreover, the service sector related to foreign trade has an important role in the State s economy. In addition to being an exporter of commodities, the State can be considered the gateway for an important number of goods imported by trading firms. The establishment of these firms in the State of Espírito Santo was stimulated by the State Government through the Fundap 4 system, which gives financial incentives to firms that import from the State s ports. In 2006, these companies were responsible for 60.8% of the State s total imports and generated 30% of the ICMS 5 tax revenue, by far the most impor- tant State tax revenue (Caçador and Grassi, 2009, p ). Although it is a small economy inside the Brazilian federation, the State of Espírito Santo was recently projected to the national economic scenario after the discovery of a huge oil and natural gas field in the pre-salt layer of the country s southeastern coast. The first discoveries of oil and natural gas in the pre-salt layer were announced in 2006, but it was not until 2008 that the volume of barrels was confirmed. The new reserves amount to 14 billion barrels of oil and natural gas; this, together with the 14 billion barrels of the post-salt layer already known, duplicates the Brazilian reserves. 6 Of the 14 billion barrels, 3.5 billion barrels of oil of the light crude type are located on the coast of Espírito Santo. Besides that, the first oil well discovered is only 2.5 kilometers away from the FPSO JK (P-34) platform that, since 2006, has been extracting oil and natural gas from the post-salt layer of the State s coast. 7 This fact has led to the anticipation of oil extraction of the pre-salt layer. Currently, Petrobras, the government-controlled national oil company, extracts around 15,000 barrels a day from this well and 15,000 more from the well located at Rio de Janeiro s coast. 8 The company s goal to 2013 was to produce 100,000 barrels daily in the pre-salt layer of Espírito Santo s coast, with a predicted investment of R$ 10.3 billion (nearly US$ 5.7 billion) 9 between 2009 and This goal represents an expressive increase in the output of the local oil and natural gas extraction sector; compared with the actual production of 140,000 barrels a day in the post-salt layer, the expected growth of the production is 70%. For the pre-salt layer as a whole, the goal of Petrobras to 2013 was to extract 219,000 barrels of oil and 7 million m 3 of natural gas daily, with predicted investments of R$ 28.9 billion (nearly US$ 1 Estimates for Source: IBGE. Available at 2 The following sectors are considered part of the commodity industry: oil and gas extraction and related services, metal ore mining, pulp and paper manufacturing, basic metallurgy, manufacture of coke and petroleum refining, manufacture of nuclear fuel, ethanol production, and coal mining. 3 By traditional industry we consider food and beverage production, textile industry, clothing and accessories manufacturing, leather manufacturing, luggage and footwear manufacturing, wood products manufacturing, and furniture manufacturing. 4 Fund for the Development of Port Activities. 5 Value added tax on goods and services. 6 FOLHA ONLINE. Petrobras anuncia descoberta de reservas de petróleo em pré-sal do ES. 21 de novembro de Available at: ult91u shtml . Accessed 12/02/ AGÊNCIA ESTADO. Lula e Petrobras inauguram exploração do pré-sal nesta terça. 2 de setembro de Available at:,0. htm Accessed 12/02/ G1. Pré-sal vai produzir 1,8 milhão de barris por dia em 2020, diz Gabrielli. G1. Brasília, 08 de novembro de Available at: 0,,MUL ,00.html Accessed 12/02/ PETROBRAS (2009). E. A. Haddad, A. C. Giuberti billion) between 2009 and For 2020, the goal is million barrels of oil and 40 million m 3 of natural gas daily. If one considers that the Brazilian oil output in 2008 was million barrels a day, one can see the magnitude of the presalt layer exploitation 1. The facts described above give rise to the question: Will this discovery of natural resources be a curse for the local economy? Although early economists stressed that natural resources would have a positive role in economic development, strong empirical evidence has shown a negative correlation between resource abundance and economic growth: resource-abundant countries tend to grow more slowly than resource-poor countries. Moreover, as the availability of natural resources per se should not mitigate economic growth, the literature on the natural resource curse has pointed out a number of explanations for these empirical results: the linkage theory, described in Gelb (1988); the Dutch disease effect, whose core model was presented by Corden and Neary (1982) and was also present in the model of Sachs and Warner (1999); the fluctuation of the terms of trade, with the implication for the volatility of the revenue associated with the natural resources (Auty, 2001); the crowding-out effect (Buffie 1993; Sachs and Warner, 2001); and rent-seeking effects (Torvik, 2002). The methodology used to describe and analyze the natural resource curse varies across studies, and a great number of them use cross-country regressions or panel data. 2 By far most studies concentrate on the country s economy as a whole, and only a few on regional economies. As far as we know only Papyrakis and Gerlagh (2007), Li and Polanski (2009), and Shao and Qi (2009) take on a regional approach, the former for the US states, and the latter two for Chinese provinces, although their focus was not exclusive on the Dutch disease mechanism or on the computable general equilibrium (CGE) methodology, on which we focus here. Papyrakis and Gerlagh (2007) used cross-state regressions to verify the effect of resource abundance on the per capita economic growth rate of 49 US states for which they had data. Their findings show that resource abundance can have a negative impact on growth through indirect channels such as investment levels, schooling rates, and openness. Li and Polanski (2009) studied the natural resource curse through the linkage theory. For them, the main eco- 1 PETROBRAS (2009). 2 The literature on the natural resource curse is quite extensive; we mention here a few examples of recent works in this line of investigation, which also review previous studies: Sachs and Warner, 1999, 2001; Papyrakis and Gerlagh 2004, nomic explanation for the curse resides on the low intraregional linkages, both backward and forward linkages, of the natural resource sector with the supply chain. Using regional input-output data for China for 1997 and 2002, the authors verified the low linkage of this sector and the smaller growth of Chinese regions that based their growth on natural resources compared with other regions. Shao and Qi (2009) used cross-province panel data regression for provinces in Western China to analyze the effects of energy exploitation on growth. As a result, they found that energy exploitation has both direct and indirect negative effects on growth. The indirect effect hinders economic growth through science and technology innovation, human capital investment, and corruption. The Dutch disease effect implies a structural change in the economy due to the boom in the natural resource sector. As the natural resource sector is a tradable sector, the inflow of revenue causes the real exchange rate to appreciate, which in turn dampens exports and the growth of the other tradable sectors, usually the more dynamic manufacturing sector or the agricultural sector in poor developing countries. Some authors (e.g., David, 1995) argue this cannot be called a disease, and consider it an adjustment to the new longrun equilibrium of the economy. It can be argued, however that this structural change is unwanted, because it reduces the capacity of the economy to maintain sustainable growth after the boom. Therefore, the Dutch disease mechanism implies general equilibrium effects in the economy, which makes CGE models appropriate tools to study these effects. Thus, this paper makes a new contribution to the literature, combining the regional analysis with the CGE approach in evaluating the Dutch disease effect. It uses an interregional CGE model, called B-MARIA-ES (BMES), to assess the impacts of pre-salt on the economy of the State of Espírito Santo. We focus our analysis on the structural economic impacts, both in the short run and the medium run. The remainder of this article is organized as follows. The next section describes the interregional CGE model used in this study. Section 3 presents the overall features of Espírito Santo s economy and of the oil and natural gas extraction sector. Section 4 discusses the results of the simulations, and the final section presents the conclusion of this study. 2. The BMES Model To evaluate the effects of exploiting the pre-salt in Brazil under different economic en- 114 Природно-ресурсный потенциал региона vironments (closures), we departed from the B-MARIA-27 model, described in detail elsewhere (Haddad and Hewings, 2005). Its structure represents a further development of the Brazilian Multisectoral And Regional/Interregional Analysis Model (B-MARIA), the first fully operational interregional CGE model for Brazil. 1 Its theoretical structure departs from the MONASH- MRF Model (Peter et al., 1996), which represents one interregional framework in the ORANI suite of CGE models of the Australian economy. The interstate version of B-MARIA used in this research, the BMES model, contains over 480,000 equations, 2 and it is designed for policy analysis in a comparative-static framework. The behavior of agents is modeled at the regional level, accommodating variations in the structure of regional economies. The model recognizes the economies of two Brazilian regions: the State of Espírito Santo and the rest of the country. Results are based on a bottom-up approach national results are obtained from the aggregation of regional results. The model identifies 55 sectors in each region producing 110 commodities through a transformation process based on a constant elasticity of transformation (CET) specification. 3 The model also recognizes one representative household in each region, regional governments and one federal government, and a single foreign consumer who trades with each region. Special groups of equations define government finances, accumulation relations, and regional labor markets. The mathematical structure of the suite of B-MARIA models is based on the MONASH-MRF Model for the Australian economy. It qualifies as a Johansen-type model in that the solutions are obtained by solving the system of linearized equations of the model. A typical result shows the percentage change in the set of endogenous variables after an exogenous change is carried out, compared with their values in the absence of such change, in a given environment. The schematic presentation of Johansen solutions for such models is standard in the literature. More details can be found in Dixon et al. (1992). 1 The complete specification of the model is available in Haddad (1999). 2 There are 289 block equations and 346 block variables in the condensed model; the entire model contains 327 block equations and 397 block variables. 3 The sectors/products are mapped into the three categories of Corden and Neary s core model: Booming sector B (sector 3/ product 19), Lagging sector L (sectors 1-39, but 3/products 1-89, but 19), and Non-tradable sector (sectors 40-55/products ) CGE Core Module The basic structure of the CGE core module is very standard and comprises three main blocks of equations determining demand and supply relations, and market clearing conditions. In addition, various regional and national aggregates, such as aggregate employment, aggregate price level, and balance of trade, are defined here. Nested production functions and household demand functions are employed. For production, firms are assumed to use fixed proportion combinations of intermediate inputs and primary factors at the first level. At the second level, substitution is possible between domestically produced and imported intermediate inputs, on one hand, and between capital, labor, and land, on the other. At the third level, bundles of domestically produced inputs are formed as combinations of inputs from different regional sources. The modeling procedure adopted in BMES uses a constant elasticity of substitution (CES) specification at the lower levels to combine goods from different sources. Given the property of standard CES functions, non-constant returns are ruled out. One can modify assumptions on the parameter values to introduce external scale economies of the Marshallian type. Changes in the production functions of the manufacturing sectors in each region were implemented to incorporate non-constant returns to scale, a fundamental assumption for the analysis of integrated interregional systems. We kept the hierarchy of the nested CES structure of production, which is very convenient for the purpose of calibration (Bröcker, 1998), but we modified the hypotheses on parameter values, leading to a more general form. Non-constant returns to scale were introduced in the group of equations associated with primary factor demands within the nested structure of production. The sectoral demand for the primary factor composite (in region r), y, relates to the total output, z, in the following way: y = az r, with the technical coefficient a and the parameter ρ specific to sector j in region r. This modeling procedure allows for the introduction of Marshallian agglomeration (external) economies, by exploring local properties of the CES function (Figure 1). The treatment of the household demand structure is based on a nested CES/linear expenditure system (LES) preference function. Demand equations are derived from a utility maximization problem, whose solution follows hierarchical steps. The structure of household demand follows a nesting pattern that enables different elasticities of substitution to be used. At the bottom level, substitution occurs across different domes- E. A. Haddad, A. C. Giuberti 115 Fig. 1. Nested Structure of Production in the BMES model tic sources of supply. Utility derived from the consumption of domestic composite goods is maximized. At the subsequent upper level, substitution occurs between domestic composite and imported goods. Equations for other final demand for commodities include the specification of export demand and government demand. Exports face downward sloping demand curves, indicating a negative relationship with their prices in the world market. One feature presented in BMES refers to the government demand for public goods. The nature of the input-output data enables the isolation of the consumption of public goods by both the federal government and the regional governments. However, productive activities carried out by the public sector cannot be isolated from those by the private sector. Thus, government entrepreneurial behavior is dictated by the same cost minimization assumptions adopted by the private sector. A unique feature embedded in the B-MARIA family of models is the explicit modeling of the transportation services and the costs of moving products based on origin-destination pairs. The model is calibrated taking into account the specific transportation structure cost of each commodity flow, providing spatial price differentiation, which indirectly addresses the issue related to regional transportation infrastructure efficiency. Such structure is physically constrained by the available transportation network, modeled in a geo-coded transportation module. 1 Other 1 See Haddad and Hewing (2005) for more details. definitions in the CGE core module include tax rates, basic and purchase prices of commodities, tax revenues, margins, components of real and nominal gross regional product (GRP)/ GDP, regional and national price indices, money wage settings, factor prices, and employment aggregates Structural Database The CGE core database requires detailed sectoral and regional information about the Brazilian economy. National data (such as input-output tables, foreign trade, taxes, margins and tariffs) are available from the Brazilian Statistics Bureau (IBGE). At the regional level, a full set of statelevel accounts were developed at FIPE, University of Sao Paulo. These two sets of information were put together in a balanced interstate social accounting matrix, for the year Previous work in this task has been successfully implemented in interregional CGE models for Brazil (e.g., Haddad, 1999; Haddad and Hewings, 2005) Behavioral Parameters The benchmark figures for the regional elasticities of substitution were 3.0 for tradable goods and 2.0 for non-tradables. Parameter values for international trade elasticities were set at half the values of the corresponding regional trade elasticities. Substitution elasticity between primary factors was set to 0.5. The parameters of scale economies were set to one in all sectors and regions, except for the core manufacturing sectors in the rest of the country, which were set to 0.8. Elasticities of transformation in the CET specification were fixed at 0.05 for all sectors. The marginal budget shares in regional household consumption were calibrated from the social accounting mat
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