**Page 11**

**Livestock Insurance in Mongolia**

governors give monthly reports on livestock, including information on movement from one sum to another. Records are kept of where the animals have been moved. Thus, by all counts, it should be quite difficult to create grossly false numbers to trigger an indemnity payment. Nonetheless, it is reasonable to implement an outside auditing process should this index insurance be adopted. Local and aimag politics may still provide enough incentives to government officials that they may be tempted to 'create losses'. The systems for developing the census and the reporting of losses are standard across Mongolia. These systems should not be changed. The historic records are the basis for rating any index on mortality rates.

The only recommended modification is that some thought be given to using auditors from nearby aimags. It would be better to have these professionals involved for a variety of reasons: 1) local officials are more likely to trust and cooperate with these professionals than someone form an insurance company or the national government; 2) these professionals know the systems; and 3) they also have a stake in any potential fraud that may be emerging within a particular region. If anyone is 'creating losses' it is likely that the whole system will have to pay for such fraud at some point. Importantly, the loss experience within region should be used for rate adjustment. Thus, any cheating will first result in higher rates in the region where the cheating occurs. This, of course, will be a slow process and should not be relied on to provide the only due diligence incentives.

# _How might mortality insurance work?_

The sum of Saintsagaan in the aimag of Dundgobi is used to illustrate how such mortality insurance may work. There were 2,800 cattle, 63,200 sheep, 43,300 goat, and 11,100 horses in this sum in 2000. This sum had extremely serious losses in 2000 with the mortality rate on cattle at 72%; sheep at 41%; goats at 44%; and horses at 33%. The simple fact that each of these species had serious losses in the same year illustrates the covariant risks that are present within a sum. More importantly the fact that many other sums across Mongolia experienced serious losses in the same year further makes the point about covariant risk. The correlation of losses was strong among species with an aimag and across aimags.

Figure 1 and 2 show the statistics for the 30 years of data for mortality rates for sheep in this sum. Sorting the 30 years of data from the minimum to the maximum creates the cumulative distributions. Since there are thirty years, each year represents a 1 in 30 frequency. Thus, the minimum value is a 1/30 event, the percentile on the second value is the previous value + 1/30, and so on until the maximum event is achieved and set as the 100'h percentile. This array can now be used to establish a number of possible triggers that represent frequency of the event. Using frequency to define a disaster is an important criterion. Public policy makers should be able to relate to frequency. In simple terms; How often does an event have to occur before it is a disaster? If one thinks of living in the desert, the rainfall is always very low. Noone should decide that every year is a disaster because it never rains in the desert. A 1 in 5 year event may be a reasonable trigger. Such events are generally serious in Mongolia and getting paid 1 of 5 years should be frequent