The costs of error in setting reference rates for reduced deforestation
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To measure the deforestation reduced by a policy, we need to compare deforestation
rates under a policy with deforestation rates in the absence of policy. Unfortunately
the deforestation rate in the absence of a policy, or reference rate, is ex ante
difficult to forecast and ex post impossible to observe. This means that reference
rates will be set with error and we will not know how large the error will be. The
challenging nature of setting reference rates is reflected in the number of proposals
for reference rate design. In this paper I show how these proposals ignore forecast
error. As a consequence, these proposals have basic structural weaknesses that increase
the costs of reduced deforestation policy. I propose that a criteria for reference
rates is to minimise the cost of forecast error. These ideas are illustrated with a cross
country dataset on agricultural expansion. I show that the best forecasting model
differs by country and that a country’s best forecasting model can be very simple.