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THE IMPACT OF TECHNOLOGY PROGRESS AND CLIMATE CHANGE ON SUPPLY RESPONSE IN YEMEN PASQUALE LUCIO SCANDIZZO Centre for Economic and International Studies (CEIS), Faculty of Economics, University of Rome "Tor Vergata” DANIELE CUFARI Department of Economics Law and Institutions, Faculty of Economics, University of Rome “Tor Vergata” Yemen Source: WFP Yemen is one of the poorest countries in the World: • GDP per capita around 600 USD • Small land based: around 1.2 Mln Has of arablel and against 24 Mln of population • Oil sector is dominant: around 27% of GDP and 90% of merchandise exports • Scarcity of water and infrastructure Yemen Agroecological zones Source: IFPRI 1. Upper Highlands (above 1,900 m): temperate, rainy summer and a cool, moderately dry winter 2. Lower Highlands (below 1,900 m): Precipitation ranges from 0 mm to 400 mm and the temperature in the summer reaches 40°C. 3. Red Sea and Tihama Plain: tropical, hot and humid climate, while rainfall averages only 130 mm annually and occurs in irregular, torrential storms. 4. Arabian sea cost: average temperature of 25°C in January and 32°C in June, with an average annual rainfall of 127 mm 5. Internal Plateau: characterized by a desert environment 6. Desert Climate change poses a significant threat to Yemen’s development, with rising temperature projections and increasing in variance of rainfall Climate-related hazards in Yemen include extreme temperatures, floods, landslides, sea level rise, and droughts. Volatility increase and impact on agricultural productivity The volatility of the yield is negatively related with the productivity This negative effect, is enhanced by the increase of the variation of the rain, especially for the planting season Dependent Variable: NET VALUE OF PRODUCTION Method: Least Squares Sample: 1 90 Included observations: 90 Volatility increases Average Effects on farm productivity Percentages FARM_SIZE ((FARM_SIZE))^2 VARIABILITY OF SORGHUM YIELD VARIABILITY OF WHEAT YIELD HIGH RAINFALL VARIATION R-squared Adjusted R-squared Coefficient Std. Error t-Statistic Prob. 10661.47 -400.2723 -4.109969 -11.38326 -5380.643 1209.997 129.3618 1.349881 3.574484 2233.962 8.811153 -3.094208 -3.044690 -3.184589 -2.408565 0.0000 0.0027 0.0031 0.0020 0.0182 0.582808 0.563175 sorghum Wheat US dollars 20% -4% -270,4176 50% -10% -676,044 100% -20% -1352,088 20% -4% -283,93848 50% -11% -709,8462 100% -21% -1419,6924 Example of impact Impact of Climate Change (Authors’ estimates on unbalanced Panel data) Dependent variable: Logarithm of maize yield Independent variables: logarithm of average quantity and variance of rainfall in critical seasons Coefficient T-statistic -2 -5.40 Winter average 0.65 5.67 Spring+Fall average 0.63 4.42 Winter+Fall variance -0.25 -3.97 Variation of average spring rainfall -0.22 -1.81 R-squared 0.87 Constant Rainfall variance has a negative effect in the winter and the fall and the variation of rainfall in the spring, a likely manifestation of climate change, has also a negative effect Field Survey Descriptive statistics Farm size Persons living from farm activity Persons working in the farm N° of cropping seasons N° of cultivated crops Value added Ave. St. Dev. Value Added Per capita Value Added Log Value Added St. Dev. Value Added Cultivated land under cereals Cultivated land under pulses Cultivated land under vegetables Cultivated land under fruits Cultivated land under coffee Cultivated land under qat Cost of water Cost of fertilizers Cost of chemicals Cost of hired work Cost of land operations Other costs Total costs of production Unit Ha Nb Nb Nb Nb USD USD USD USD USD Ha Ha Ha Ha Ha N° observations 90 90 90 89 391 90 90 90 81 81 90 90 90 90 90 Ha 90 of Mean 1.18 11 4 2 6 6261 5023 529 8 1 0.49 0.10 0.08 0.23 0.01 Std. Dev. 1.69 8 3 1 3 12910 7604 965 2 1 0.57 0.26 0.47 0.60 0.02 0.28 0.67 Unit N° of observations Mean Std. Dev. USD/year USD/year USD/year USD/year USD/year USD/year USD/year 37 51 46 65 35 11 90 1649 82 126 489 292 1002 1692 1421 71 104 233 223 539 998 Adapting to climate change: Mathematical model Assuming that each option underlying value evolves as a Brownian Motion with zero drift and constant variance (1) 𝑑 𝑉𝑖 𝑦𝑗 = 𝜎𝑗 𝑉𝑖 𝑦𝑑𝑧𝑗 where j denote the j-th option and i denote the i-th farmer. The economic value of the ith farm can be represented by the equation: 𝑊𝑖 = (2) 𝑉𝑖 𝜌 + 𝐽 𝑗=1 𝐴𝑖𝑗 (𝑉𝑖 𝑦𝑗 )𝛽 where 𝐴𝑖𝑗 (𝑉𝑖 𝑦𝑗 )𝛽 is the value of the jth option to adapt of the ith farmer and β = 1 2 + (3) 1 2𝜌 + 2 2 𝜎 (Dixit and Pindyck, 1994). For adoption To be acceptable for option j: 𝑉𝑖 𝑦𝑗∗ = 𝛽1 𝛽1 −1 ∗ 𝐼𝑗 where 𝐼𝑗 represents the cost of adoption. At farmer level, the option value over an infinite time horizon for farmers who have not adopted (yet) is given by: (4) 𝑉𝑖 𝑦𝑗 𝛻𝑗∗ 𝑦𝑗∗ 𝛽1 ∗ 𝛻𝑗∗ 𝑦𝑗∗ 𝜌𝛽1 where 𝛻𝑗∗ 𝑦𝑗∗ = 𝑏𝑖 i. e. the coefficient estimated in the regression on an estimate of the increment of value added due to the adoption Econometric Results: Value Added equations Constant p-value Standard Deviation of value added Gender (0=female, 1=male) Dummy high value crops (farmers growing qat, coffee, fruits and vegetables) Dummy terrace irrigation Dummy land partly owned and partly rented Dummy changes of agricultural practices in response to climate change Age group 15-29 Education from 4 to 8 years VALUE ADDED IN USD OLS TLS -12587.00 -14278.00 0.00 0.00 1.45 1.54 0.00 0.00 1829.00 0.07 1353.29 0.00 2056.57 0.05 4264.50 0.00 1315.21 0.01 3075.57 0.00 1908.04 0.01 Dummy alternative form of irrigation apart from terrace R-squared Adjusted R-squared 3134.16 0.097 4727.64 0.049 13123.55 0.00 0.96 0.95 0.86 0.85 Adapting to climate change: option values (US $ per year) Item Opportunity option: high value crops (qat, coffee, fruits and vegetables) Growth option: terrace rehabilitation Coping option: changes of practices in response to climate change Opportunity option: education Underlying (increase in Value Added per farm) Estimates of strike prices Volatility Value of Option 1353 835 0.33 815 2057 1269 0.50 1373 1315 811 0.39 787 1908 1897 0.30 985 Option values for introducing Drought Tolerant maize (US dollars/ha) Variable Obs Mean Std. Dev. Min Max 7849.05 Valueadded/ha 32 2257.081 1980.829 73.7 Val. added GM/ha 32 2708.498 2376.994 88.44 9418.86 Difference of VA 32 451.4163 396.1647 14.74 1569.81 Beta 32 1.37 0 1.37 1.37 hurdle 32 3.69 0 3.69 3.69 Underlying 32 15377.96 13495.78 502.14 53477.16 Estim. investments 32 4166.212 3656.293 136.04 14488.09 option value 35% 32 11320.98 9935.358 369.6668 option value 55% option value 75% 32 32 11958.85 12507.17 10495.16 10976.37 390.4953 408.3998 39368.94 41587.15 43493.96 Adapting to climate change: Option Values (US dollars per year) Underlying (increase in Value Added per farm) Estimates of strike prices Volatility Option value Volatility + Option value + Volatility ++ Option value ++ 1353 835 33% 815 53% 916 73% 988 Opportunity option: adoption of drought tolerant maize 1358 416 35% 1131 55% 1196 75% 1250 Growth option: terrace rehabilitation 2057 1269 50% 1373 70% 1485 90% 1594 1315 811 39% 787 59% 898 79% 993 1908 1897 30% 985 50% 1188 70% 1335 Opportunity option: high value crops (qat, coffee, fruits and vegetables) Coping option: changes of practices in response to climate change Opportunity option: education Option values contribution 7000 6000 5000 Opportunity option: education Coping option: changes of practices in response to climate change 4000 Growth option: terrace rehabilitation 3000 Opportunity option: adoption of drought tolerant maize Opportunity option: high value crops (qat, coffee, fruits and vegetables) 2000 1000 0 Option value Option value + Option value ++ CONCLUSIONS • Climate Change threats provide the incentives to adapt trough a class of projects, which construct capabilities and open real options as a major source of opportunities. • The options to adapt to climate change in Yemen, exist not only as a reactive and coping responses of existing farming system, but also as accumulation of capabilities to flexibly create a whole set of new farming systems • The adoption of the GM technology appears to be an especially valuable option for the country to adapt to some of the harshes conditions that may be determined by climate change Thank you for your attention