Climate change has become one of the most important development challenges worldwide. It affects various sectors, with agriculture the most vulnerable. In Ethiopia, climate change impacts are exacerbated due to the economy’s heavy dependence on agriculture. The Ethiopian government has started to implement its Climate Resilient Green Economy (CRGE) strategy, which is planned to foster development and sustainability while limiting GHG emissions by 2030. However, to the best of our knowledge, research on estimating the economic impacts of CO2emissions are limited. Moreover, studies estimating the productivity and welfare effects of Ethiopia’s target for reducing emissions in line with the CRGE are lacking. Therefore, this study aims to fill these significant research and knowledge gaps using a recursive dynamic ComputableGeneralEquilibrium (CGE) model to investigate CO2emissions’ impact on agricultural performance and householdwelfare. We simulate CO2emissions- induced variation in agricultural total factor productivity for the period 2010– 2030. The simulation results indicate that CO2emissions negatively affect agriculturalproductivity and householdwelfare. Compared to the baseline, real agricultural GDP is projected to be 4.5 percent lower in the 2020s under a no- CRGE scenario. Specifically, CO2emissions lead to a decrease in the production of traded and non-traded crops, but not livestock. Emissions also worsen the welfare of all segments of households, where the most vulnerable groups are the rural-poor households. Results also suggest that proper implementation of the CRGE strategy can significantly reduce the adverse effects of GHG emissions on agriculturalproductivity and householdwelfare.
(forthcoming) study the effects of tax reform on water use, economic growth, and income distribution in South Africa. Berrittella et al. (forthcoming) are an exception, using a multi- country CGE model including water resources (GTAP-W). They analyze the economic impact of restricted water supply for water-short regions. They contrast a market solution, where water owners can capitalize their water rent, to a non-market solution, where supply restrictions imply productivity losses. They show that water supply constrains could improve allocative efficiency, as agricultural markets are heavily distorted. The welfare gain may more than offset the welfare losses due to the resource constraint. In contrast to Berrittella et al.
While the 1970‟s oil shock awaked interest in biofuel, the recent years boom in more rapid development and consumption of biofuel as a substitute for conventional energy source has been primarily driven by mandates, subsidies, climate change concern, emissions targets and energy security. For instance, the European Union has mandated that biofuel account for 10% of the energy used in transportation by 2020 while India plans to meet 20% by 2017 and Brazil is planning to expand its biofuel exports. Ethiopia has entered in to a 10% blend of bio-ethanol. However, biofuel expansion is raising a number of controversies. On the one hand, increase in biofuel production is taken as one of the main reasons for the increase in world food prices (Mitchell, 2008; Heady and Fan, 2008; Baier et al 2009). The shift in resource (land, labor and water) use towards biofuel sector and away from cereal and livestock production sectors, poses a major concern. In addition, environmental benefits (carbon emissions offset) gained from such sources has also been an area of debate, as carbon emissions resulting from deforestation caused by cultivation of biofuel is probably higher (UNEP 2009; OECD, 2008; Dornbosch and Steenblik, 2007). On the other hand, in addition to being considered as important source of clean energy, optimists view biofuel as a potential for growth and development by providing employment opportunity to the rural poor, increasing price of agricultural products and enhancing agriculturalproductivity through technological spillovers (Hausmann, 2007; Arndt, 2010).
Water problems are typically studied at the level of the river catchment. About 70% of all water is used for agriculture, and agricultural products are traded internationally. A full understanding of water use is impossible without understanding the international market for food and related products, such as textiles. The water embedded in commodities is called virtual water. Based on a generalequilibrium model, we offer a method for investigating the role of water resources and water scarcity in the context of international trade. We run five alternative scenarios, analyzing the effects of water scarcity due to reduced availability of groundwater. This can be a consequence of physical constraints, and of policies curbing water demand. Four scenarios are based on a ‘‘market solution’’, where water owners can capitalize their water rent or taxes are recycled. In the fifth ‘‘non-market’’ scenario, this is not the case; supply restrictions imply productivity losses. Restrictions in water supply would shift trade patterns of agriculture and virtual water. These shifts are larger if the restriction is larger, and if the use of water in production is more rigid. Welfare losses are substantially larger in the non-market situation. Water-constrained agricultural producers lose, but unconstrained agricultural produces gain; industry gains as well. As a result, there are regional winners and losers from water supply constraints. Because of the current distortions of agricultural markets, water supply constraints could improve allocative efficiency; this welfare gain may more than offset the welfare losses due to the resource constraint.
Figure 3 below presents the household income impacts. S1 generates positive in- come effects for all the households while S2 generates negative effects. The magni- tudes of income increases range between 2.09 and 2.55 %. It is very interesting that the calculated income changes in S1 indicate that income inequality would not increase because of the tariff-cut. It is also worth to mention that indeed the factor remunerations are the main source of household income but how the factor prices are determined in the model is mostly dependent on how the factor market closures are defined, so again the results are highly dependent on the closure rules. Household consumption expenditures and the welfare implications are presented in Table 9. The results indicate that in S1, the consumption growth and the EVs are positive for all household groups while EVs are negative in S2. The positive EV in S1 is the manifestation of positive consumption growth and the negative EV values are associated with negative consumption. The consumption growth and the associated EV values are highest for the ‘ marginal agricultural farm- households ’ and ‘ small-scale agricultural farm households ’ in S1. The consumption and welfare effects in S1 are channeled through product and factor markets. Because of the tariff-cut, the Bangladeshi households will enjoy cheaper commodities on one hand, and the reallocation of the resources from non-competitive sector to competitive sector that will increase factor demand on the other, and therefore will increase factor prices. So, decreases of prices and increase of household income contribute
Coxhead & Warr (1991) used a CGE model for Philippines to investigate the distributional effects of technical progress in Philippine agriculture. They show, in a small open economy, technical improvements in farming are likely to benefit poor, especially if the technical change is labour-using, land-saving. It produces a redistribution of income from landlords to labourers. A technical change which substitutes capital for labour with no increase in output in irrigated agricultural sector triggers a reduction in real wage in the same sector. Households owning only labour lose while real incomes of households that do not depend on labour show a slight increase. Coxhead and Warr (1995)used the same model to trace the effects of differential rates of technical progress in the irrigated and non-irrigated agricultural sectors on income distribution of factor owning household groups, poverty and economic welfare within a small open economy with open agricultural trade and agricultural trade under restrictions. The results clearly showed that reduced poverty from technical progress is substantially greater when agricultural trade is unrestricted at a constant world price.
Diversification of the source of household income is a common practice in many countries but factors influencing this decision differ. Households in developing economies are not an exception to this phenomenon (Lemi, 2006). Agricultural households expand the sources of their income due to pull and push factors. A common pull factor is that a non-agricultural activity generates extra income. On the other hand, a common push factor is to minimize risks and cope with shocks. Both types of diversification influence the well-being of rural households. Pull factors increase income and improve welfare of the households, whereas the push factors are expected to reduce poverty levels of the households (Nega et al., 2009). Traditionally, it is assumed that the entire rural economy depends on agriculture with the non-agricultural sector contributing negligibly. However, this has changed recently and it is widely recognized that non-agricultural activities make considerable contributions to economic growth, reduce poverty and limit rural-urban migration (Lanjouw & Lanjouw, 2001). Empirical evidence indicates that non-agricultural activities on average constitute 40- 45% of the total income for rural African households. Furthermore, non-agricultural activities are found to improve household income and wealth and hence contribute significantly to the survival strategies of households (Barrett et al., 2001).
Following recent international oil price increases, there has been considerable interest in how this external factor can affect the South African economy. This paper reports results from a computablegeneralequilibrium (CGE) analysis of an increase (up to 30 per cent) in international oil prices. Background information is provided, which puts the magnitude of the price variations in historical context. We describe the procedure used to adjust the social accounting matrix (SAM), which is used to calibrate the model, to account explicitly for crude oil. Then, the effects of the crude oil price increase are traced through the economy, from markets, industries through to factors, households and the government. Predictably, the shock hurts the economy: a 20 per cent increase results in a drop in GDP of 1 per cent. It is found that the major impact is to be found in the petroleum industry itself, whereas the effects on liquid fuel dependent industries such as transport is not as large as may be supposed. In agriculture, it is found that the depreciating currency has a positive effect, offsetting most of the negative effects of higher petroleum prices, particularly in export-oriented areas. In a long-term scenario, capital and skilled labour becomes mobile, and the results suggest that such reallocation may not be to the overall advantage of the economy.
When comparing the spatial distribution of the losses across the three models for the concave recovery curve of the Emilia-Romagna flood (Fig. 4), we find some interesting results. First, the two “IO-based” models (i.e. the ARIO and MRIA models) show large differences in the spatial distribu- tion of losses. Whereas the ARIO model shows negative re- sults in all regions, the MRIA model only shows negative re- sults in the affected region. What is notable is that the losses in the affected region are higher in the MRIA model (as also shown in Table 5). As such, by allowing for substitution be- tween producers in the model, the affected region is affected more heavily, while the non-affected regions benefit. This can be explained by the inefficiency losses, which are mod- elled in the MRIA model, but not in the ARIO model. Sec- ond, we find some interesting similarities between the IEES – Rigid and IEES – Flexible with, respectively, the ARIO and MRIA models. The rigid version of the IEES model, with immobile production factors, shows relative little substitu- tion effects, resulting in negative (albeit small) effects in al- most all non-affected regions. The flexible version, however, shows, similar to the MRIA model, benefits in all other re- gions due to large substitution effects. For the IEES models, it is important to note that the productivity shocks decrease the demand for labour and capital because of lower produc- tive capacity and income. This means that remunerations of capital and labour go down and in the IEES – Flexible model capital, and labour can move towards non-affected regions. This determines the exacerbation of the profit and losses dy- namics and is the main cause for the difference in regional economic losses between the IEES – Flexible and – Rigid model.
In this respect, the Social Accounting Matrix (SAM), serving as a database of our CGEM, is the one developed by the National Accounting Department in 2007. To meet the objective of the analysis (micro-simulation) , the population was split into the MCS in five quantiles (from the poorest 20% to the richest 20%). This classification is based on data from the Household Living Standards Survey conducted by the HCP in 2007, which distinguishes in household income between the remuneration of factors of production, in particular labor and capital, and transfers they receive from other economic agents, including the State. Labor and capital incomes are mainly attributed to households and businesses. In this perspective, the labor factor is disaggregated into three categories according to the level of education and the degree held by the individuals in the household. Three levels of qualification of the workforce are distinguished according to whether it is low, medium or highly qualified. Government revenue comes from direct and indirect taxes.
It is not only the exports sector that expands in response to the policy shock. Table 3 shows that other non-tradable sectors of the economy of Ghana have equally expanded. Some of the other sectors that have expanded include administration, health, water, education, trade, transport and communication, real estate, mining, trading, other services, etc. Majority of the sectors have expanded to provide supporting services to the export sector (backward linkages). Examples of these services include road transport, business services including telecommunication, public sector services, water and electricity, health and education. The expansion of the service sector which includes retail trade is significant in that it provides employment for many people. Construction contracts because as a non-tradable it had benefited enormously from the tariff protection. These results suggest that additional trade liberalisation brings welfare gains to Ghana. The findings confirm those of Wang and Zhai (1998) for China, Siddique et al (2008) for Pakistan, but contradict that of Pradhan and Sahoo (2008) for India. Sectoral impact
In the past two decades an increasing number of researchers have sought to determine the impact of supply and demand shocks in one sector on the economy as a whole. Domestic or international shocks such as the outbreak of SARS or the terrorist attacks of September 11, 2001 adversely affect industries such as air transport, tourism and the economy as a whole. This indicates a need to understand the nature of the impact of shocks and policy changes in order to gain greater insight into the workings of such changes and determine ways of minimizing their adverse effects. However, much of the research with reference to developing country up to now has been descriptive in nature or has relied on input-output (I-O) analysis. The major objective of this study is to develop and applied Computablegeneralequilibrium (CGE) models to investigate the effects of a range of alternative policies or exogenous tourist expenditure shocks. Despite the existence of varied tourist attractions, comprising warm weather, tropical beaches, abundant wildlife in natural
In economic literature, trade stimulates economic growth and reduces poverty, because trade acts as a channel through which surplus of domestic production can exchange the products of foreign countries. Trade enhances the allocation of domestic resources derived from the apparent comparative advantages of member countries and fosters economic growth of the economy. In economic theory, trade liberalization is the outcome of productivity gains through increased in competition, efficiency, innovation and attainment of latest technology. The impact of trade liberalization on growth is keenly debated issue in the field of modern economics. Most of the economic literature considers that trade liberalization leads to improvement in social welfare through spreading out the allocation of domestic resources. The policy of trade can works through price changes because price changes leads to substitution effect of production and consumption of goods and services. The level and composition of exports and imports (trade balance) can influence through changes in prices. Trade liberalization leads to more effective and efficient reallocation of resources through changes in relative prices. In general, liberalization of trade enhances the economic scope through expansion of market share and transfer of knowledge (technical knowhow). The sources of economic growth are efficiency gains from specialization and economies of scale (reduction of cost of production) which is the ultimate outcome of trade. Winters (2000) was developed the theoretical outline of trade reforms linkage with poverty reduction. Winters (2002) has explained how trade liberalization affects poverty reduction throughout various channels such as economic growth, price changes, market and government. Baldwin (2003) mentioned that lower trade restrictions countries achieve faster economic growth than those countries with higher restrictive trade policies.
Three types of policy simulations are performed in line with the model closure described above. First, the weighted average of EAC tariffs is set at zero i.e. imports from EAC enter Uganda free of duty - including category B goods exports from Kenya. The reason for including category B goods is to avoid the modelling difficulties associated with isolating these goods in the model. Since the 10% tariff on category B goods was a temporary measure, applying uniform condition to EAC imports is appropriate. In the second simulation, the average weight of non-EAC COMESA tariffs is set at zero (i.e. imports from COMESA countries enter Uganda free of tariffs) to demonstrate the likely impact that Uganda’s membership to COMESA free trade area would have on the poor in Uganda. In the third simulation, tariffs are set at zero across the board (i.e. EAC, COMESA and ROW imports, including sensitive products). Although this simulation is not identical to what happens in the real world, the purpose is to demonstrate the potential effect of complete tariff reduction.
Assumptions about household demands. (a) The representative household is assumed to maximise a nested Klein-Rubin/CES utility function subject to its aggregate budget constraints. (b) Substitution is allowed between commodities and between sources of commodities using a nested Linear Expenditure System (LES)-CES demand system.
The principal resources to produce goods and services generally consist of four inputs: land, labour, capital and entrepreneurship (St John and Stewart, 1997; Callander, 1992; Marshall, 1959. In Thai agriculture in 1960 – 1980, land and labour used to be the major contributions to agricultural growth. However, after the closing of the land frontier since the mid-1980s, the contribution from capital has been more important. The study by Poapongsakorn et al. (2006) concluded that during 1981 – 2003, the majority of agricultural growth was from capital, which contributed around 60% of the growth in agricultural sector (see Table 2.1). It is predicted that there will be young professional farmers making more intensive use of land (Jitsanguan, 2001; Poapongsakorn et al., 2006). In addition, Coxhead and Plangpraphan (1998)’s study points out that, in the Thai agricultural sector, machinery is positively related with land demand but negatively with labour demand. They summarize that agricultural labour can be clearly substituted by agricultural machinery. As a result of this issue, Poapongsakorn et al. (2006) and Siamwalla (1996) forecast that Thai farmers will have a commercial attitude and use innovative production methods. Therefore, this section provides an overview of labour and capital in Thai agricultural sector.
The results for each simulation are divided into four effects: input, output, income and macroeconomic effects. The results of the first two simulations produced opposite outcomes in terms of the four effects. Simulation 2 accelerated the capital intensification of all agricultural sectors, whereas Simulation 1 led to more capital intensity in some agricultural sectors. The effects of the input reallocation had a simultaneous impact on output in every sector. Simulation 1 led to a fall of almost all outputs in the agricultural sectors, whereas there was an increase in agricultural output in Simulation 2. In terms of domestic income effects, as a result of the decline of the average price of factors in Simulation 1, there was a decrease in factor incomes belonging to households and enterprises. Consequently, government revenue decreased by 0.7%. In contrast, Simulation 2 resulted in an increase in all incomes above. Finally, regarding macroeconomic variables, Simulation 1 had a negative impact on private consumption, government consumption, investment, imports and exports, resulting in Gross Domestic Product (GDP) decreasing by 0.8%. On the other hand, Simulation 2 had a positive impact on those same variables, affecting a 0.4% rise of GDP. The effects of Simulation 3 were very small in everything compared with the first two simulations. The effect of Simulation 4 was mostly dominated by Simulations 1 and 2; the negative results of Simulation 1 were compensated by the positive effects of Simulation 2.
To export involves sunk costs incurred for market research, advertisement, establishing distribution networks etc. Firm level data confirms that entry into exporting is a self-selection process in which only the more productive firms become exporters (Clerides et al., 1998, Melitz, 2003). But even when domestic firms are productive enough to enter export markets, they may be unfamiliar with overseas markets and foreign consumers may be unfamiliar with Chinese products. The presence of large multinationals with well established international trade networks and extensive knowledge of international markets, can reduce the information barriers facing both domestic firms and foreign consumers (Aitken et al., 1997, Greenaway and Kneller, 2008, Lawless, 2009). Even if domestic firms do not currently find exporting profitable, the success of multinational firms in international markets can stimulate domestic firms to improve their productivity and product quality to meet international standards so as to emulate them (Alvarez and López, 2005). FDI from the East Asian economies has transferred labour-intensive, export-oriented assembly to the coastal provinces in China (Deng et al., 2007), and the export of FIEs accounts for more than 50% of China’s total export volume in the last ten years.
The disparity in income distribution, welfare of the public, and poverty have been attracting the interest of the various groups of people such as policy makers, social scientists, politicians, and the society at large. Income distribution, welfare, and poverty are major problems in many developing countries, including Indonesia. These problems might become so severe and if there is no action is taken, most likely there will be followed by social unrest and political instability. Poverty and disparity in income contribute to lagging in development and chaos. The tragedies of Malari in 1975 and May 1998 were two examples of social unrest during Suharto era. Until now, the people of Indonesia still looking for the answer of “if the socio-economic situation in Indonesia was comparable to those of Swiss, did the students’ movement and demonstration take place until the Suharto’s administration collapsed?” (Tambunan, 2006).
Thissen (1998) suggests one more classification according to the determination of the parameters of the model: by calibration or by econometric technique. The vast majority of CGE models use calibration in order to determine parameters. The econometric method was initiated by Jorgenson (1984); in his study he built a generalequilibrium model with stochastically specified submodels. Among the advantages of cal ibration is the relative simplicity of finding the parameters’ values. Few data are needed and one set of observations can be used. Calibration uses data for only one period of time, which can be both, an advantage and a disadvantage. In case the economy experiences significant changes in its structure, the calibration method is superior to the econometric one. Econometrics use data for several years, which may not be similar to the year of consideration, while calibration is done with the same data as used in the model. At the same time, if no considerable structural changes have taken place, econometrics can give better estimates. Besides that, econometric models incorporate stochastic disturbances in order to capture the effect of omitted variables and errors, while calibration assumes that this stochastic disturbances term is zero and does not include this information. Finally, econometric models give indicators of accuracy of determined variables, while calibration does not give information on reliability of parameters.