Global Weather Sensitivity: A Comparative Study - albertotroccoli.org

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movement of Morocco and Tunisia, countries very similar in terms of economy and geography, it seems reasonable to suggest that correction values correspond to real structural differences between countries. Finally, there is little to no correlation between the relative sensitivity scores found in Table 1 and the total weather sensitivity scores found in Table 3. This is not surprising, as total scores are based predominantly on the size of the country’s GDP and not on weather volatility. The United States, the largest economy in the world, has the largest total weather sensitivity, valued at roughly $2.5 trillion, but a small weather sensitivity score. In contrast, Bolivia, which has the lowest total weather sensitivity, valued at roughly $2 billion, ranks 20th in relative weather sensitivity. It should be noted that countries with small total weather sensitivity values can still be heavily affected by the weather in relative terms. This is particularly relevant in developing countries, where large percentages of the population live close to subsistence levels. Risk management strategies that protect against weather-related fluctuations in economic output can potentially provide a crucial social safety net in places where none other exists. 4.2 Growth from <strong>Weather</strong> Hedging Table 4 shows the total capital-at-risk (CAR) for all sixty-eight countries and their expected economic growth from hedging all of their CAR. These results correspond to the calculations in section 3.5. Note that in some cases the CAR is greater than the total weather sensitivity shown in Table 3. This is due to the fact that the CAR is a function of sectoral sensitivity and the sensitivity scores shown in Table 1, while the total weather sensitivity is a function only of sector sensitivities. An estimate of the percent growth for each country is also included. In absolute terms the United States, China, and Russia, are expected to grow 16
the most from weather hedging, and New Zealand, Switzerland, and Finland, the least. Japan is the only country for which it would not be beneficial to hedge its weather risk and its growth is therefore zero. In percentage terms, Kuwait, Azerbaijan, and Saudi Arabia are expected to grow the most, and Italy, Germany, and Switzerland, the least. The total expected growth of all sixty-eight countries is $258 billion, or roughly 1% of their cumulative output. 5 Conclusions The results of this paper suggest three conclusions: first, weather sensitivity is diverse and heterogeneous, affecting different countries in different ways; second, the benefits to global economic growth from managing weather risk could be substantial; and lastly, future research on this topic will be critical in removing some of the biases inherent in this analysis. Gathering accurate weather and economic data from countries around the world is central to this task. Each of these will be addressed in turn. The results of this paper show a wide range of weather sensitivities for different countries, from 0.58 in Pakistan to 15.73 in Brazil. This range suggests that weather sensitivity is not uniform, homogeneous, or general, but rather is determined by a wide range of factors and local conditions. These scores, aggregated at the country level, only partially reflect this diversity. Within countries weather conditions vary greatly. This heterogeneity makes the task of managing weather risk at the country level particularly challenging. Assessing the dependence of the economy on industries such as agriculture, mining, tourism, and manufacturing, as well as other industries that are particularly weather sensitive, is a good first step for managing weather risk. A good understanding of how much weather conditions vary in that country is also important. Businesses likely adapt to the climate in which they work; but variations in the weather 17
Page 1 and 2: Global Weather Sensitivity: A Compa
Page 3 and 4: output in 11 sectors using a transc
Page 5 and 6: and climate response functions, mos
Page 7 and 8: least 10 years of data history were
Page 9 and 10: around one, with higher values (mea
Page 11 and 12: seasonal and regional trends. To en
Page 13 and 14: 4 Results and Discussion 4.1 Weathe
Page 15: the United Arab Emirates, Kuwait, a
Page 19 and 20: is available, researchers should ex
Page 21 and 22: [9] Mendelsohn, R, Dinar, A, Willia
Page 23 and 24: Country SS w/ Corrections Rank SS w
Page 25 and 26: Table 2: Range of Weather Sensitivi
Page 27 and 28: Rank Country Weather Sensitivity (U
Page 29 and 30: Rank Country Weather Sensitivity (U
Page 31 and 32: Country Capital At Risk Expected Gr
Page 33 and 34: Country Capital At Risk Expected Gr
Page 35 and 36: Country 1 2 3 4 5 6 7 8 9 Chile 6%
Page 37 and 38: Country 1 2 3 4 5 6 7 8 9 Switzerla
Page 39 and 40: Country CV CDD CV HDD CV P recip Ge
Page 41 and 42: Table 9: Corrections Data Country Y
Page 43: Country Yield/Hectare Correction To

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