# 5. Calibration

With the basic structure of the model specified, model behavior will depend importantly on the values of certain key parameters. As with the standard MFMod model the majority of parameters are estimated econometrically either on Jamaican data or in the cases where not enough data exists are imposed using results from the literature or cross-country regressions (Burns & Jooste, 2019). A similar approach is followed for this model, but with the added complication that many of the parameters are unobservable. Below is a brief description of the rationale for the default parameter values chosen. Results from sensitivity analyses for alternative choices are presented in Figure 5 and Figure 12.

## 5.1. Protection and adaptation capital

The actual effectiveness of adaptation capital is not well understood. Measuring it historically, would require knowing how much of a country’s capital is dedicated to adaptation and by how much this reduced damages that would have occurred in the absence of adaptation. One estimate (Multihazard Mitigation Council, 2018) suggests that the benefit-cost rate of hurricane adaptation is 10:1 at existing levels of adaptation investment. Importantly, this would not translate into a 1 = 10 unless adaptation investments equaled the expected value of hurricane damages (which is unlikely to be the case). γγ The literature discussed in Appendix B is somewhat vague in describing its calibration methods. Nevertheless, this work follows De Bruin et al (2009) in setting the curvature parameter of the protection function, 2 , to 0.3 (close to the inverse of their parameter of 3.6). The effectiveness parameter is set to 1 = 1 , implying that if an amount equal to the expected value of hurricane damages γγ were invested each year in adaptation, the productive capital stock would be protected from hurricanes. γγ

## 5.2. Insurance

Premia for natural disaster insurance can vary widely, with empirical estimates rising as high 700% percent (Froot, 2001). Disaster insurance markups tend to be higher than those in other insurance policies, because the rare and large payouts required for natural disasters are much more difficult to handle for insurance companies. Some authors have used mark-ups in the range of 50-100% (Lemoyne de Forges, Bibas, & Hallegatte, 2011), while the markup for insurance assumed in World Bank (2017) was 50%.

This paper uses the insurance mark-up formula discussed earlier, calculating it on the discrete cumulative distribution function for climate damages in Jamaica (World Bank, 2018). This data implies a mean damage of = 165 million USD or 1.15% of GDP and a standard deviation of 672 million . Using this information in the markup equation and using an industry average weight for DD<sup>�</sup> <u>( )</u> SSDD(DD) = , gives an insurance markup = 0.61 , which is the value used in this paper. SSDD DD ββ= 0.15 5.3. Contingency fund μμ<sup>II</sup> = ββ DD<sup>�</sup> 5.3. Contingency fund The mark up on the contingency fund is assumed to be zero ( = 0 ) and its return relatively low = 0.02 . This implies that a contingency fund would require only negligible administrative cost and that it μμ<sup>CC</sup> DD<sup>CC</sup> would earn a 2% nominal interest rate on the fund volume (assuming that the fund would be invested in liquid investments, e.g. money market funds, that earn a lower rate of return). Note that the relative performance of the different policies depends critically on the assumptions on cost. With a markup of 0, the low 2% return can also be interpreted as the net return. If administrative costs were higher, say 1 or 2 percent, then the current assumptions would be equivalent to a 3 or 4 percent return on investments.

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