Application of the Fuzzy Approach for Agricultural Production Planning in a Watershed, a Case Study of the Atrak Watershed, Iran

Watersheds are large-scale regions where the agricultural production planning is associated with multiple objectives, usually, including economic, social and environmental targets. Uncertainty plays an important role in all agricultural planning because some factors are not fully controllable while some input data or parameters such as demand, resources, costs and objective functions are imprecise. This paper applies fuzzy multi-objective mathematical programming model to the Atrak watershed agricultural development plan. The model focuses on attaining three objectives simultaneously, namely, profit maximization, employment maximization and erosion minimization and these are subjected to 88 constraints. Results of the model indicate that, when compared with the current cropping structure, the implementation of the optimal cropping pattern could increase profit and employment and decrease soil erosion significantly. (Authors' abstract

Oil Exports and the Iranian Economy

This paper develops a long run growth model for a major oil exporting economy and derives conditions under which oil revenues are likely to have a lasting impact. This approach contrasts with the standard literature on the "Dutch disease" and the "resource curse", which primarily focus on short run implications of a temporary resource discovery. Under certain regularity conditions and assuming a Cobb Douglas production function, it is shown that (log) oil exports enter the long run output equation with a coefficient equal to the share of capital. The long run theory is tested using a new quarterly data set on the Iranian economy over the period 1979Q1-2006Q4. Building an error correction specification in real output, real money balances, inflation, real exchange rate, oil exports, and foreign real output, the paper finds clear evidence for two long run relations: an output equation as predicted by the theory and a standard real money demand equation with inflation acting as a proxy for the (missing) market interest rate. Real output in the long run is shaped by oil exports through their impact on capital accumulation, and the foreign output as the main channel of technological transfer. The results also show a significant negative long run association between inflation and real GDP, which is suggestive of economic inefficiencies. Once the effects of oil exports are taken into account, the estimates support output growth convergence between Iran and the rest of the world. We also find that the Iranian economy adjusts quite quickly to the shocks in foreign output and oil exports, which could be partly due to the relatively underdeveloped nature of Iran’s financial markets

Oil Exports and the Iranian Economy

This paper develops a long run growth model for a major oil exporting economy and derives conditions under which oil revenues are likely to have a lasting impact. This approach contrasts with the standard literature on the "Dutch disease" and the "resource curse", which primarily focus on short run implications of a temporary resource discovery. Under certain regularity conditions and assuming a Cobb Douglas production function, it is shown that (log) oil exports enter the long run output equation with a coefficient equal to the share of capital. The long run theory is tested using a new quarterly data set on the Iranian economy over the period 1979Q1-2006Q4. Building an error correction specification in real output, real money balances, inflation, real exchange rate, oil exports, and foreign real output, the paper finds clear evidence for two long run relations: an output equation as predicted by the theory and a standard real money demand equation with inflation acting as a proxy for the (missing) market interest rate. Real output in the long run is shaped by oil exports through their impact on capital accumulation, and the foreign output as the main channel of technological transfer. The results also show a significant negative long run association between inflation and real GDP, which is suggestive of economic inefficiencies. Once the effects of oil exports are taken into account, the estimates support output growth convergence between Iran and the rest of the world. We also find that the Iranian economy adjusts quite quickly to the shocks in foreign output and oil exports, which could be partly due to the relatively underdeveloped nature of Iran's financial markets.growth models, long run relations, Iranian economy, oil price, foreign output shocks, error correcting relations

Does Inflation Slow Long-Run Growth in India?

This paper examines the long-run relationship between consumer price index industrial workers (CPI-IW) inflation and GDP growth in India. We collect data on a sample of 14 Indian states over the period 1989-?2013, and use the cross-sectionally augmented distributed lag (CS-DL) approach of Chudik et al. (2013) as well as the standard panel ARDL method for estimation? to account for cross-state heterogeneity and dependence, dynamics and feedback effects. Our findings suggest that, on average, there is a negative long-run relationship between inflation and economic growth in India. We also find statistically-significant inflation-growth threshold effects in the case of states with persistently-elevated inflation rates of above 5.5 percent. This suggests the need for the Reserve Bank of India to balance the short-term growth-inflation trade-off, in light of the long-term negative effects on growth of persistently-high inflation

Oil Exports and the Iranian Economy

This paper develops a long run growth model for a major oil exporting economy and derives conditions under which oil revenues are likely to have a lasting impact. This approach contrasts with the standard literature on the "Dutch disease" and the "resource curse", which primarily focus on short run implications of a temporary resource discovery. Under certain regularity conditions and assuming a Cobb Douglas production function, it is shown that (log) oil exports enter the long run output equation with a coefficient equal to the share of capital. The long run theory is tested using a new quarterly data set on the Iranian economy over the period 1979Q1-2006Q4. Building an error correction specification in real output, real money balances, inflation, real exchange rate, oil exports, and foreign real output, the paper finds clear evidence for two long run relations: an output equation as predicted by the theory and a standard real money demand equation with inflation acting as a proxy for the (missing) market interest rate. Real output in the long run is shaped by oil exports through their impact on capital accumulation, and the foreign output as the main channel of technological transfer. The results also show a significant negative long run association between inflation and real GDP, which is suggestive of economic inefficiencies. Once the effects of oil exports are taken into account, the estimates support output growth convergence between Iran and the rest of the world. We also find that the Iranian economy adjusts quite quickly to the shocks in foreign output and oil exports, which could be partly due to the relatively underdeveloped nature of Iran’s financial markets.growth models, long run relations, Iranian economy, oil price and foreign output shocks, and error correcting relations

Global VAR (GVAR) Database, 1979Q2-2016Q4

This is the latest version of the Global VAR (GVAR) dataset. The GVAR is a global modelling framework for analyzing the international macroeconomic transmission of shocks, taking into account drivers of economic activity, interlinkages and spillovers between different countries, and the effects of unobserved or observed common factors. This dataset includes quarterly macroeconomic variables for 33 economies (log real GDP, y, the rate of inflation, dp, short-term interest rate, r, long-term interest rate, lr, the log deflated exchange rate, ep, and log real equity prices, eq), as well as quarterly data on commodity prices (oil prices, poil, agricultural raw material, pmat, and metals prices, pmetal), over the 1979Q2 to 2016Q4 period. These 33 countries cover more than 90% of world GDP. You can download the data, as well as a description of the compilation, revision and updating of the GVAR Database, from here: http://www.econ.cam.ac.uk/people-files/faculty/km418/research.html#gvar It would be appreciated if use of the updated dataset could be acknowledged as: “Mohaddes, K. and M. Raissi (2018). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2016Q4. University of Cambridge: Faculty of Economics (mimeo)”

The Adaptive Investment Effect: Evidence from Chinese Provinces

This paper investigates the so-called “adaptive investment effect", a redirection of investment in productive capital towards adaptive capital with a view to mitigating the negative effects of climate change. We estimate the costs associated with the adaptive investment effect using data on Chinese provinces and find that the impact of investment on economic growth is reduced by between 27% and 37% in provinces investing more in adaptive capital. This implies that the social cost of carbon is higher than existing studies suggest, making it more urgent for policymakers to take action against climate change

Global VAR (GVAR) Database, 1979Q2-2019Q4

This is the latest version of the Global VAR (GVAR) dataset. The GVAR is a global modelling framework for analyzing the international macroeconomic transmission of shocks, taking into account drivers of economic activity, interlinkages and spillovers between different countries, and the effects of unobserved or observed common factors. This dataset includes quarterly macroeconomic variables for 33 economies (log real GDP, y, the rate of inflation, dp, short-term interest rate, r, long-term interest rate, lr, the log deflated exchange rate, ep, and log real equity prices, eq), as well as quarterly data on commodity prices (oil prices, poil, agricultural raw material, pmat, and metals prices, pmetal), over the 1979Q2 to 2019Q4 period. These 33 countries cover more than 90% of world GDP. $$\$$ It would be appreciated if use of the updated dataset could be acknowledged as: “Mohaddes, K. and M. Raissi (2020). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2019Q4. University of Cambridge: Judge Business School (mimeo)”