dummies for each region add up to zero allows a unique set of regression coefficients to be estimated. The coefficients of the regional dummy variables interacted with the time period dummies (referred to as the regional*time period dummies) can then be interpreted as the deviation from average expenditure in that region over the year (Suits, 1984) over the average of all other variables.<sup>24</sup> Since we suspected that cash crop income might also have an effect on seasonal spending patterns, we included a dummy for whether a household was a cotton farmer and a dummy for whether a household farmed cocoa and/or coffee. These crop dummies were interacted with the time period dummies. Since households with a high ratio of home consumption might also show different patterns of seasonality, we also added a dummy variable for households whose home consumption was greater or equal to 0.6 of purchased consumption, and interacted the home consumption dummy with the time period dummies.

We also suspected that households at different levels of expenditure might exhibit different seasonal patterns. To allow for this possibility, we stratified the 1985-1988 sample into two subsamples: households with expenditures of less than 100,000 CFAF per capita and those with expenditure greater than 100,000 CFA. Stratifying the sample at levels less than 100,000 CFAF per capita yielded too few observations for the months of March and April. And since our interest for the poverty profile is the seasonal correction for expenditures in the range of 75,000 CFAF, we wanted to pick a relatively low level of household expenditure as the cut-off point.

The regression analysis did reveal some seasonality (table 24). For nonexport-crop-growing households with a low ratio of consumption of home production to purchased food (the defaults), households interviewed in the June-November period in Other Cities had significantly lower expenditures, as did households interviewed in the December-March period in West Forest. In general, however, seasonality was not pronounced in the nonexport crop-growing households in the rural regions with low ratio of home-produced to purchased food expenditure. As might be expected, cocoa/coffeeand cotton-growing households interviewed in the December-March period when the crop is marketed (with low ratios of home produced to purchased food expenditure) had significantly higher levels of total expenditure. And among non-export crop-producing households with a high share of home produced to purchased food expenditure, expenditures were higher in households interviewed in the April-May period and lower in those inteniewed in the December-March period.

The major difference between the two income groups in the pattern of seasonality, aside from Abidjan, which is discussed below, is the case of cotton farmers. The expenditure of poor cotton farmers shows more pronounced seasonality than better off cotton farmers. Expenditure is highest among poor cotton farmers interviewed in December-March, and then falls off precipitously in the April-May period. For better off cotton farmers, expenditure is lower among households interviewed in the June-November period. These results suggest that better off cotton farmers may either have

**24** Suits' (1984) procedure imposes the restriction that the unweighted sum of the coefficients of each set of dumnmy variable **s** ums to zero. Rather than using the unweighted sum, however, we restricted the weighted sum to equal zero. In the case of the regional*time period dummy coefficients, the weights are the mean value of regional*time dummy variables, divided by the mean value of the regional dummy variable. The weights sum to one.

27