FUENTES DE INFORMACION 1 Bibliografía

rate in a country as noted in Section 4.4. In particular, in countries where the household consumption surveys were carried out only a couple of years ago, the inflation rate might not accurately indicate the true picture of the consumer basket in that country. To explore the accuracy of inflation figures in other African countries, this section will look at the movement of prices for each component of the weights in the five years to January 2007. Analysis in this section excludes South Africa, where appropriate data could not be obtained, and Botswana, which changed its weights in 2006, making the process of little use. For the other countries, the inflation components’ monthly index data used for this analysis started on the following months and years: Ghana and Kenya, from February 2002; Mauritius, August 2002; and Nigeria, February 2003. The data from Ghana has a gap between January and June 2004. The start dates were determined by the availability of data from the central banks or Central Statistical Office.

Table 4.5 shows the average difference of the movement in components of the inflation rate in Kenya compared with food inflation, which contributes 51% to the rate. The table uses the following equation:

Y F

A  4.4

where Arepresents the average difference in Table 4.5, while Fis food weights and

Yrepresents the other components in the table under the ‘Components’ column.

Components Weights Average difference6 St. Deviation T- Stats Probability

Housing 11.7 0.8633 3.0482 2.1937 0.0143

Clothing 9.0 1.0124 3.1440 2.4943 0.0064

House Goods 5.8 0.9090 3.0210 2.3306 0.0099

Education 6.0 0.9196 2.9729 2.3960 0.0082

Other 17.0 0.5317 3.1200 1.3199 0.0934

Table 4.5: The difference between food inflation and other inflation components in Kenya

The results above show that food inflation has, on average, been increasing at a faster rate than all the other components of the consumer basket in Kenya. The probability figures for this average superior increase in the food inflation rate are below the 10% level of significance, with most except the ‘Other’ category passing the 5% level of significance. Surprisingly, these results are not echoed in Table 4.6, which shows that food inflation has been increasing at above non–food inflation over the five years from February 2002.

Average difference St. Deviation T- Stats Probability Food & Non-food 0.7887 3.0193 2.0233 0.0217

Clothing & Non-clothing (0.6835) 1.8464 (2.8675) 0.0021

Housing & Non-housing (0.5355) 1.8162 (2.2839) 0.0113

Other & Non-other (0.1702) 2.0499 (0.6432) 0.2611

Table 4.67: The difference between inflation components and others in Kenya

Month–to–month food inflation rose at over 0.78% more than non-food inflation in Kenya. This movement is statistically significant at 5% level of significance since the probability is 2.17%. The food inflation components in Ghana (see Table 4.7) do not increase at a

6 _{Average difference in this case refers to the mean of the monthly differences between food inflation and}

each item listed on the table between February 2002 and January 2007.

statistically significant rate compared with other components, and the only significant difference is the lower increase experienced by clothing items against non-clothing items.

Components Average difference St. Deviation T- Stats Probability

Food-Non Food 0.1199 6.2104 0.1419 0.4443

Clothing-Non Clothing (0.8678) 3.6803 (1.7327) 0.0418

Table 4.7: The difference between inflation components and others in Ghana

While non-clothing items are increasing at a faster rate than clothing items, this is not much of a problem since clothing contributes only 10% to the inflation basket. These results could suggest that differential increases in the inflation components are not the main pressure for regular inflation weight reconfiguration. This evidence suggests that population tastes and technological changes are vital to Ghana changing its weights more often than they currently do.

The increase in prices in Nigeria and Ghana, especially the difference between food and other inflation components, are statistically similar over the period. However, transport and housing increase at a faster rate than non-transport and non-housing inflation respectively in Nigeria, which is surprising, given that the country has its own oil. Table 4.8 below presents this evidence.

Average difference St. Deviation T- Stats Probability Food & Non Food (0.4429) 4.1870 (0.7329) 0.2327

Housing-Non Housing 0.6535 3.1549 1.4352 0.0749

Transport-Non Transport 1.3045 4.3116 2.0961 0.0183

Table 4.8: The difference between inflation components and others in Nigeria

The expectation in Nigeria, where there is fuel extraction, is that housing, which includes fuel and transport, would increase at a slower rate than other items since it is an oil producer, while other countries in Africa import the substance. In Mauritius, food inflation also increases faster than non-food inflation at 10% level of significance, as shown in Table 4.9 below.

Average difference St. Deviation T- Stats Probability

Food-Non Food 0.1373 0.7472 1.3500 0.0885

Housing-Non Housing 0.1221 0.6372 1.4087 0.0793

Table 4.9: The difference between inflation components and others in Mauritius

As noted earlier, the fact that food contributes over 30% to the weights makes this statistically significant increase a concern, especially as weights are changed after five years. These findings would suggest a time series with multiple regimes. The statistically significant higher increase in housing inflation compared to non-housing inflation is not so worrying given that this component contributes only 9% to the inflation rate.

The implication of a component of the weights increasing at a faster rate than other components has two effects on inflation. First, it affects inflation directly since an increase in component will result in a weight proportionate increase in inflation, and this is not a problem since inflation will include this increase. Second, a sustained statistically significant increase results in consumers spending more money on the component than provided for or reflected in the weights, and this can be corrected by conducting regular household consumption surveys. However, the challenge in Africa, as indicated earlier, is that governments cannot afford regular household surveys, and they conduct these more than five years apart, which results in the distortions in the inflation rate calculation between these surveys. The distortion in the inflation rate is also fuelled if the component that increases at a statistically significant rate than others has the highest basket weight in the calculation of the inflation rate. Mauritius, for instance, changes weights every five years, and the concern is the food inflation rate, which is statistically higher than for non–food items between the five year surveys, while it contributes 32% to the computation of inflation. Since food is a basic need, inevitably

prices increase faster than other items, in order to maintain the unchanged quantity requirements for this component in line with the hierarchy of needs. Apart from Mauritius, the analysis above is also applicable to Kenya, where the situation is even worse since monthly food inflation increases at over 0.78% more than non–food inflation, and the weights are based on a household consumption survey carried out in 1997. The above evidence points to the need for at least annual consumer surveys to ensure that the inflation rate is more accurate, especially where food is a high–weighted component of the inflation weights in the country.