218
A further example of data volatility can be seen by examining the number of reported farming
households. In contrast to the commercial farms sector where the number of farming households is
reported to fluctuate around a mean of 3,000, significant changes in the number of reported
noncommercial farming households appears in the CSO data. From a high near 1.3 million in the early
1970s, the number of households drops to around 1 million for an extended period, until increasing
again to near 1.2 million in the mid-1980s (see table 7.11). Fluctuations of 300,000 households in the
farming population (plausibly 1 million or more individuals) is difficult to explain except perhaps as
a general problem with the data collection system.
Table 7.11: Number of noncommercial farm households
|
Year |
Copperbelt/ |
Central/ Southern/ Lusaka |
Northern/ |
Eastern |
Western |
All |
|
1971 |
144,400 |
273,000 |
499,600 |
230,400 |
148,600 |
1,296,000 |
|
1972 |
146,400 |
239,500 |
488,100 |
209,000 |
191,600 |
1,274,600 |
|
1973 |
97,600 |
203,300 |
348,100 |
273,200 |
109,000 |
1,031,200 |
|
1974 |
95,700 |
232,900 |
381,900 |
192,600 |
158,000 |
1,061,100 |
|
1975 |
111,800 |
247,400 |
318,600 |
128,500 |
157,500 |
963,800 |
|
1976 |
129,700 |
197,800 |
372,900 |
222,600 |
148,600 |
1,071,600 |
|
1982 |
124,200 |
180,580 |
320,890 |
208,350 |
151,900 |
985,920 |
|
1983 |
124,880 |
172,710 |
320,760 |
222,890 |
133,600 |
974,840 |
|
1984 |
135,970 |
183,360 |
318,120 |
247,350 |
152,120 |
1,036,920 |
|
1985 |
111,328 |
260,201 |
343,651 |
256,627 |
189,130 |
1,160,937 |
Source: Derived from Zambia CSO data.
VI. Revenues and expenditures
The CSO also provides data on farm expenditures and revenues. Tables 7.12-7.15 present
results for the two broad regions of commercial farms defined above, excluding data on farm livestock
revenue. They suggest a dramatic nominal increase in the kwatcha value of operations in the late
1980s, on both the revenue and the expenditure side, which corresponds to a period of hyperinflation.
When nominal values are deflated by a low-income CPI, farm sales and operating expenses for all
farm categories are shown to increase in real terms as well, although wide fluctuations are evident.
Results are not substantially different if the high-income CPI is used as a deflator. The rather
astounding 1989 increase in operating expenses (fertilizer and pesticide applications, machinery, seed,
etc.) in the Central, Southern, and Lusaka combined provinces on medium-sized farms cannot readily
be explained.