analysis of poverty effects of various candidate biofuel
crops in South Africa
Nicholas N. Ngepah
Competition Commission South Africa, Private Bag X23,
Lynnwood Ridge 0040, and University of Johannesburg,
Johannesburg, South Africa. E-mail:
012841 4729 or 073378 9075.
Fax: 012841 4322.
Accepted 7 February, 2011
The aim of this study was to investigate the poverty
reduction impact of various potential biofuel crops in South
Africa. A simple pro-poor development framework (in which
income is substituted for by its function) is specified.
After analysis for outliers with considerable leverage, a
robust regression option was used to carry out estimations
for physical output, values and inputs of each crop. For
reasons of data availability, the crops considered were
maize, wheat, sorghum and sugarcane for bioethanol, and
groundnuts, soybeans and sunflower for biodiesel. The
results suggest that various crops have different impacts on
the different poverty measures. If a biofuel strategy’s
intent is to promote (income) poverty reduction, then for
South Africa sugarcane should be prioritised for bioethanol
and groundnut for biodiesel. Other crops like maize and
sunflower would require stronger support to small farmers.
The finding also suggests that poverty reduction comes
mainly by employment of the poor in commercial farming.
There is suggestion that investment in farming by the poor
is often inadequate and would generally result to poverty
exacerbation. The implication is that the capital base of
the poor must be broadened for them to effectively
participate in farming. This has to be done without stifling
commercial farming which is simultaneously contributing to
poverty reduction through employment. These recommendations
hold for sugarcane, groundnut and maize. However, a weakness
worth mentioning is that the data is likely to underestimate
or completely ignore most of the subsistence producers whose
production is mainly for own consumption. Therefore, poverty
impact could equally experience a downward bias in the
models estimated here.
Biofuel crops, comparative analysis, poverty effect, South