The Impacts of Higher Energy Prices on Indonesia’s and West Java’s Economies using INDOTERM, a Multiregional Model of Indonesia

Indonesia’s national and regional/local policy makers are becoming increasingly concerned with disparities between regions. Aggregate incomes and expenditures in one region may change proportionally more than national changes. This paper contains a technical summary of the structure and special features of INDOTERM model, a member of the TERM family (TERM = The Enormous Regional Model). It treats West Java and the rest of Indonesia as separate economies. We discuss the data required to prepare a version of INDOTERM that represents all the provinces of Indonesia. Finally, we present a long-run simulation of the impacts of the recent hike in global energy prices on the Indonesian economy combined with possible depletion of Indonesia’s crude oil supplies. The special features for future development of INDOTERM are multiple household incomes and expenditures and a “top-down” extension representing sub-provincial municipalities. Nationally, Indonesia’s real income losses due to resource depletion are more than compensated by the sharp increase in the terms of trade arising from the increase in global demand for energy. West Java and the rest of Indonesia fare similarly, as a large proportion of the composite region consists of the remaining provinces of Java which have a similar economic structure to West Java. The relatively sparsely populated outer islands that are relatively rich in mineral resources are not represented separately. Using a “top-down” extension of West Java’s 25 municipalities and districts in INDOTERM, the simulation shows that Kabupaten Indramayu fares best. This local region also loses from the decline in crude oil productivity, and indeed the output loss more than outweighs the increase in natural gas production for this effect. But it gains substantially from the energy price hikes: the increase in nominal income has a substantial positive effect on the municipality, with local industries, including trade and motor repairs experiencing output increases in excess of 40%. Overall, the municipality experiences a gain in factor income of 7.3%, whereas most other regions of West Java lose income in the scenario.Computable General Equilibrium, Regional CGE, Indonesia

CGE modelling of the resources boom in Indonesia and Australia using TERM

The sharp increase in Australia's terms of trade since 2003-04 has dramatic regional and sectoral implications. Mining-intensive regions have gained from the jump in export prices. Import-competing sectors have faced greater competition both from falling import prices and due to rising demand for domestic factors from the mining sectors. The drought of 2006 will widen the gap between winning and losing regions. In Indonesia, even if we assume that the oil extraction sector is facing resource depletion, a long-run terms-of-trade improvement may result in aggregate consumption increasing should real GDP fall relative to the base case. The TERM framework is highly suitable for modelling Brazil and China, each with around 30 regions.Resource /Energy Economics and Policy,

Course: Computable General Equilibrium Modelling

I – Introdução à teoria do equilíbrio geral • Fundamentos teóricos • Aplicações clássicas II – Modelo ORANI-G: teoria e simulações ilustrativas • Estrutura teórica • Base de dados • Fechamentos • Simulações ilustrativas5 p.Economi

Would Trade Liberalization Help the Poor of Brazil?

This paper addresses the potential effects of world agricultural trade liberalization on poverty and regional income distribution in Brazil, using an inter-regional applied general equilibrium (AGE) and a micro-simulation model of Brazil tailored for income distribution and poverty analysis by using a detailed representation of households. The model distinguishes 10 different labor types and has 270 different household expenditure patterns. Income can originate from 41 different production activities located in 27 different regions in the country. The AGE model communicates to a micro-simulation model that has around 112,000 Brazilian households and 264,000 adults. Poverty and income distribution indices are computed over the entire sample of households and persons, before and after the policy shocks. The simulated trade liberalization scenario causes agriculture to expand considerably and so, given the importance that agriculture still has for the poorest in Brazil, it has positive impacts on poverty in Brazil. The only states which show an increase in the number of poor households are Sao Paulo and Rio de Janeiro, where the bulk of the manufacturing activities in Brazil are concentrated. There is an even more positive impact on inequality. The higher fall in the poverty gap is shown to occur mainly on the poorest household groups, suggesting that the poorest among Brazil’s poor would benefit more from global trade liberalization.Distorted incentives, agricultural and trade policy reforms, national agricultural development, Agricultural and Food Policy, International Relations/Trade, F13, F14, Q17, Q18,

Regional Inequality, Poverty and Economic Integration in Brazil.

Gains and losses from trade liberalization are often unevenly distributed inside a country. For example, if budget shares vary according to household income, changes in commodity prices will redistribute an overall welfare change between household types. Household incomes will also be differentially affected. Sectoral differences in factor-intensity mean that changes in industrial structure cause redistribution of income between primary factors. Particular primary factors (such as capital, or less skilled labour) may contribute disproportionately to the incomes of certain household types. The fortunes of such households indirectly depend on the prospects of particular sectors. We emphasize these distributive issues, especially those arising from the income side. At the same time we distinguish households by regions (within the country). The regional distinction sharpens the contrast between groups of households. Particular regions have their own patterns of economic activity and so are differently affected by changes in the industrial protection structure. Since regional household incomes depend closely on value-added from local industries, economic change will tend to redistribute income between regional households. If the regional concentration of poverty is more than we could predict by regional primary factor endowments and industry structure, the addition of a regional dimension will add power to our analysis of income distribution beyond the mere addition of interesting regional detail. The paper deals with these issues more fully. We extend previous regional modeling of Brazil to include the intra-household dimension, addressing poverty and income distribution issues that may be caused by trade integration. An applied general equilibrium (AGE) inter-regional model of Brazil underlies our analysis, with a detailed specification of households. The model is static and solved with GEMPACK. The Representative Household (RH) hypothesis is abandoned; instead a micro-simulation (MS) model is used to track changes in household income and expenditure patterns. This micro-simulation model is built upon two Brazilian household studies: (1) the Household Budget Survey (POF, IBGE, 1999) covers detailed expenditure patterns for 16,013 households and 11 regions in Brazil in 1996; (2) the National Household Sample Survey (PNAD, IBGE, 1997) is a yearly survey that includes detailed information about household employment and income sources, with 331,263 observations. We integrate the two data sources to produce a detailed mapping of expenditure and income sources for 250,000 Brazilian households, distinguishing 50 activities, 80 commodities, and 27 regions. We link the AGE and MS models together, solving them iteratively to get consistency between results. After a shock the AGE model communicates changes in wages and employment by industry and labour type to the MS model that individually simulates the changes in employment, income and expenditure patterns for each household. The new expenditure pattern is then communicated to the AGE model, and the process is repeated until the two models converge. The final results from the MS model enable us to estimate changes in poverty and income distribution measures, both nationally and for regions within Brazil. We use the model to analyze poverty and income distribution impacts of the Free Trade Area of Americas formation upon the Brazilian economy. In the particular simulation we examine, freer trade leads to increased employment, especially for lower-paid workers. Poor households, which contain more enemployed adults, benefit most. This leads to a reduction in poverty in all 27 Brazilian states.

The Doha Round, poverty, and regional inequality in Brazil

This paper addresses the potential effects of the Doha round of trade negotiations on poverty and income distribution in Brazil, using an applied general equilibrium (AGE) and micro-simulation model of Brazil tailored for income distribution and poverty analysis. Of particular importance is the fact that the representative household hypothesis is replaced by a detailed representation of households. The model distinguishes 10 different labor types and has 270 different household expenditure patterns. Income can originate from 41 different production activities (which produce 52 commodities), located in 27 different regions in the country. The AGE model communicates to a micro-simulation model that has 112,055 Brazilian households and 263,938 adults. Poverty and income distribution indices are computed over the entire sample of households and persons, before and after the policy shocks. Model results show that even important trade policy shocks, such as those applied in this study, do not generate dramatic changes in the structure of poverty and income distribution in the Brazilian economy. The simulated effects on poverty and income distribution are positive, but rather small. The benefits are concentrated in the poorest households.Environmental Economics&Policies,Economic Theory&Research,Poverty Assessment,Inequality,Services&Transfers to Poor

Ethanol expansion and indirect land use change in Brazil

In this paper we analyze the Indirect Land Use Change (ILUC) effects of ethanol production expansion in Brazil through the use of an inter-regional, bottom-up, dynamic general equilibrium model calibrated with the 2005 Brazilian I-O table. A new methodology to deal with ILUC effects is developed, using a transition matrix of land uses calibrated with Agricultural Censuses data. Agriculture and land use are modeled separately in each of 15 Brazilian regions with different agricultural mix. This regional detail captures a good deal of the differences in soil, climate and history that cause particular land to be used for particular purposes. Brazilian land area data distinguish three broad types of agricultural land use, Crop, Pasture, and Plantation Forestry. Between one year and the next the model allows land to move between those categories, or for Unused land to convert to one of these three, driven initially by the transition matrix, changing land supply for agriculture between years. The transition matrix shows Markov probabilities that a particular hectare of land used in one year for some use would be in an other use next period. These probabilities are modified endogenously in the model according to the average unit rentals of each land type in each region. A simulation with ethanol expansion scenario is performed for year 2020, in which land supply is allowed to increase only in states located on the agricultural frontier. Results show that the ILUC effects of ethanol expansion are of the order of 0.14 hectare of new land coming from previously unused land for each new hectare of sugar cane. This value is higher than values found in the Brazilian literature. ILUC effects for pastures are around 0.47. Finally, regional differences in sugarcane productivity are found to be important elements in ILUC effects of sugar cane expansion.

The regional economic consequences of Less Favoured Area support in Poland: A regional general equilibrium analysis.

The Common Agricultural Policy (CAP) is one of the most complex and also the most costly of all EU policies. It comprises over 40 financing streams, including Pillar I and Pillar II measures which are highly regulated. In the case of Poland, these are directed to all 16 NUTS2 regions. We are modelling here the regional and thus national consequences of the CAP's most costly Pillar II measure in Poland, so called Less Favoured Areas support (LFA). It is complex, when we recognise the multipurpose of this measure and significant amount of funds directed at a large number of regions. To handle the regional complexity of this problem, we require a multi-regional model. Such a model must be detailed in its disaggregation of industries, commodities and households if it is to be capable of reflecting the complexity of this measure. As such, we use a large-scale multi-regional CGE model. The model is tailored to reflect the complexity of the rural development policy (Pillar II), of which LFA is the largest part in Poland. Of the 82 region-specific sectors in the model, over 20 is related to agricultural production. The model distinguishes rural and urban households in each region and is based on the most recent IO tables of 2005. We propose a framework for mapping the individual financing stream of the LFA to the specific structural variables relating to specific type of land (LFA and nonLFA) in each region. As to our best knowledge such an approach was never conducted before with respect not only of LFA but also Pillar II measures in general

WebProt\'eg\'e: A Cloud-Based Ontology Editor

We present WebProt\'eg\'e, a tool to develop ontologies represented in the Web Ontology Language (OWL). WebProt\'eg\'e is a cloud-based application that allows users to collaboratively edit OWL ontologies, and it is available for use at https://webprotege.stanford.edu. WebProt\'ege\'e currently hosts more than 68,000 OWL ontology projects and has over 50,000 user accounts. In this paper, we detail the main new features of the latest version of WebProt\'eg\'e