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urban population, the real effective exchange rate, import capacity, a "weather" variable, and trend in the following way: _21W_ FOOD IMPORTS = bo + bjURBAN POPULATION + b2REER + b3EMPORT CAPACITY + B4WEATHER + BsTREND + **_e_** Food imports are total volume figures, net of food aid. 22/ URBAN POPULATION is an index based on the total numbers of urban residents. The REER is the real effective exchange rate as defined by the IMF. It is an index which measures the evolution of a country's prices relative to those of its trading partners, adjustment for nominal exchange rate changes. The IMPORT CAPACITY is the sum of export earnings, net borrowing, foreign investment, and grant aid deflated by Africa's import price index.

The two variables, WEATHER and TREND, reflect the hypotheses for a supply side explanation of variation in food imports, which are unrelated to prices, policy variables, or urbanization. A secularly increasing TREND in food imports would support the hypothesis that there has been a deterioration in Africa's productive capacity. And poor WEATHER (rainfall as well as tornados or floods) that affect domestic production will necessitate higher imports. Clear cases of weather-related causes occurred during the period considered, including frequent severe drought between 1973 and 1984. Since 1984 Africa appears to have seen more normal rainfall patterns on average. The WEATHER variable is a proxy derived from annual data on cereal yields per hectare for each country. It is esimated as the percentage deviation from trend in cereal yields over the period.

Empirical results. Table 6 presents the results of the regression described above. All estimates of the coefficients in the equation agree in sign with a _priori_ expectations. All the explanatory variables are statistically significant at the 1 percent level, except for the REER which is significant at the _5_ percent level.

The estimated coefficient and significance for urban population indicates that urban growth is strongly associated with increased food imports. This variable held the strongest statistical relationship in terms of the addition to the r-squared when the variable was added to the equation (using ordinary least squares estimation). The addition to the r-squared was 0.237, as compared to 0.07 for import capacity, 0.05 for the weather variable, and 0.0017 for the trend.

It should be recognized with respect to the urban population variable that the estimated relationship may conceal an underlying, omitted variable which is correlated to both. In particular, government policies which alter the rural-urban terms of trade in favor of urban consumers - such as cheap food policies - will simultaneously encourage urban consumption of the subsidized foods (generally these programs include imported rice and wheat) and urban migration, which will likewise 21/ Data are from World Bank and FAO. Data for food imports, urban population, and import capacity are normalzed as indexes to abstract from country size (1970-87 average = 100).

L2/ Food aid is excluded from food imports because it is a component of grant aid and therefore it would create an endogeneity problem if included in the right-hand-side variable, "import capacity." Also, while emergency food aid shipments respond to domestic shortfalls in production, increasingly in the 1980s the relationship between "structural food aid" shipments and patterns of demand and supply in Africa were not obvious.