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residuals of that esimated trend to be a proxy for the effect of weather on agriculture in each year. _Il/_ The results are consistent with expectadons overall (figure 6) and for individual countries and years where drought (Ghana and Senegal, 1983) or abundant harvests (Burkina 1985, 1986) are known to have occurreJ.

Major shocks such as war, civil strife, and natural disasters such as floods or cyclones have also had devastating effects on specific African countries. The potendal impact of these phenomenon are taken into account in the model by including the percentage of the populatdon affected by 'disasters" as recorded in "Major Disasters Worldwide" (USAID).

The real effective exchange rate (REER) is included in the model (an increase in the REER indicates appreciation). As a measure of the eompetitiveness of agriculture it incorporates some of the incentives accounted for by the real producer price. However, REERs also provide a more general measure of distortions in product and faitor markets, as well as serving as a proxy for the indirect effects of the exchange and trade controls that usually accompany overvaluation.

The results of the pooled cross-section time-series model _JAI_ are presented in table 1 (and Annex table 14). They include a number of variations of lagged and moving averages in the independent variables, and two subgroups of countries; a) those exporting primarily tree crops,<sup>and</sup> b) those exporting primarily annual crops. (Crop specific and country models are presented below.) The weather variable is strongly significant in all of the equadons. The coefficient ranges from 0. _15_ to 0.46 and is highest for the annual crop producers. The coefficients can be interpreted as a 10 percent drop from trend for cereal yields associated with a 1.5 to 4.6 percent decline in exports. The "disasters" variable has the expected sign and is significant in most cases.

The estimated price elasticities of export supply for all countries range from 0.1 to 0.3, and the elasticities with respect to REE range from -0.1 to -.25. For countries exporting primarily tree crops (where longer lagged responses are expected) the REER is the most consistent explanatory variable with elasticities ranging from -0.14 to -0.25; the price elasticities for tree crop exporters are only significant when the REER is excluded (with an elasticity of 0. 115) or in one case significant with the wrong sign (an elasticity of -0.06).

Higher price elasticities can be expected from countries exporting annual crops such as tobacco and cotton, as farmers are more able to respond quickly to changes in relative price incentives by ,3/ Cereals are the most susceptible crop to moisture stress, and for most countries variation in average yields of cereals will result primarily from variations in weather. While exported crops may respond differently to specific weather patterns, the deviations from trend can be expected to be of the same sign. Year to year variations in cereal yields could arise due to fluctuations in ferdlizer avallability or other policy related factors, but with few excepdons ferdlizer is not widely used on cereals and other factors are not likely to dominate the effects of rainfall.

_J1/_ The pooling of cross-section tme-series data can be accomplished several ways depending on the characteristics of the data. Since these data are time-wise autoregressive and crosssecdonally correlated, a hree-stage procedure is required to produce consistent, unbiased estimates foliowing Kmenta (pp. 512-514), and using the SAS Park estimation procedure from SAS SUGI Supplemental software.