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**Livestock Insurance in Mongolia**

# **Analysis of the Risk Profile Across Mongolia**

Data were obtained for 36 sums with at least one sum in nearly<sup>everyaimag</sup> in Mongolia. These data were mostly complete for cattle, sheep,<sup>and goatsfrom</sup> 1969-2000 (year 1975 and 1979 are missing). Camel and horse mortality<sup>rateswere</sup> obtained for 1991 to 2000. Special procedures were used to fill in the<sup>dataseries for</sup> camels and horses. Regressions of the following form were<sup>fitusing the available</sup> data: Camel Mortality, = C + b,x Cattle Mortality, + b2 x Sheep Mortalityt + b3 x Goat<sup>Mortality,</sup> Horse Mortality, = C + b,x Cattle Mortality, + b2 x Sheep Mortality,+ b3 x Goat<sup>Mortalityt</sup>

# where t = years 1991-2000.

These regressions had reasonably high R squared values and<sup>forthosefew</sup> that did not, a simple procedure was used to scale the cattle mortality<sup>inthesame</sup> fashion as existed in the 10 years that were available (the ratio of<sup>camelmortality</sup> over cattle mortality). Missing years were replaced with estimated data.<sup>Whilethis</sup> may introduce some problems, the likely bias is in the direction<sup>ofaddingslightly</sup> too much covariate risk. The intent is to get a complete cost accounting<sup>of offering</sup> this insurance for all species in every sum and aimag in Mongolia.

Once the data adjustments were complete, there was a complete<sup>matrixfor all</sup> five species for each of the 30 years and in each aimag. The aimag estimates basically assume that all sums within the aimag will have similar relative<sup>riskasthe</sup> ones that were available. There may be some spreading of risk<sup>withinanaimagthat</sup> are not reflected using this approach. However, again the intent<sup>ittogivesome</sup> indication of how a mortality index might perform across Mongolia.<sup>Thebiasthat is</sup> introduced with the methods employed here is in the right direction<sup>forthis typeof</sup> work. The covariant risk will be slightly overstated due to this bias.

Given the matrix of mortality rates, any number of index<sup>policiescanbe</sup> designed. The 1999 value of number of head by aimag are used to represent<sup>valueat</sup> risk throughout the time period. This is a common practice to use today's<sup>valueat</sup> risk with a time series of events in order to model the losses. Since<sup>therisksare</sup> spatial in nature it is important that they be calculated using the current<sup>valuesat</sup> risk. This effectively weights the risk spatially by the location of<sup>thelivestockin</sup> Mongolia. The underlying assumption is that the 30 years represent<sup>thelikelyfuture</sup> distribution of events. Also, using the actual events for the 30 years<sup>maintainsthe</sup> spatial correlation among aimags. This is very important as it allows<sup>foramore</sup> complete picture of the covariate risk across Mongolia. Again,<sup>theassumption,as</sup> with most statistical data work, is that the past is a good predictor<sup>ofthefuture.</sup> There are no apparent trends in the mortality rate data. And while<sup>2000and 2001are</sup> unusual events, there are similar events in longer series of data for<sup>Mongolia.Itis</sup> too early to tell if there are any structural changes that make Mongolia<sup>more riskyfor</sup> livestock herders.