Assessing the Performance of GM Maize Amongst Smallholders in KwaZulu-Natal, South Africa

The distributional impact of biased technological change depends both on the factor-saving (or using) biases and the factor endowments in the economy. If a labor-saving technology is introduced in a land-scarce/ labor-abundant economy, labor incomes will fall and poverty will increase. GM white maize, developed in the United States, is now being used by both large-scale commercial farmers and smallholders in South Africa (SA). In Asia, importing labor-saving machinery increased unemployment, and interviews with the few early adopters in SA suggest that herbicide-tolerant (RR) maize adoption can result in a reduction in labor use per unit of output by about 50% (Gouse, Piesse, & Thirtle, 2006). But, the ultimate impact depends on the change in output as well as the bias. In addition, labor for land preparation, planting, and weeding is the constraint in much of Sub-Sahara Africa (SSA). If land is infertile but plentiful, planting area and output could double and labor demand for all other tasks increase substantially. Thus, a labor-saving technology need not displace labor. It depends on the factor endowments and urbanization, and, in addition, high levels of HIV/AIDS now exacerbate labor scarcity in many communities, including rural KwaZulu-Natal where our study areas are located. This article investigates the efficiency of the different technologies using a series of techniques, starting with yields—which are the simplest partial-productivity measures—and allowing for seed costs. Then, farm accounting—particularly gross margins—is used to compare profitability, before fitting a stochastic frontier production function, to estimate relative efficiency levels with respect to all inputs. The more original part investigates the impacts of the GM varieties on labor use. The approach taken focuses on labor use by task and by laborer, which is unusual if not entirely original (see, for instance, Fernandez-Cornejo, Hendricks, & Mishra, 2005). The next section provides some background on GM maize in SA. Then, we describe the current samples, with summary statistics and partial productivity measures. Next, we give a brief review of stochastic frontiers, followed by the results. The final section and the conclusion provide a warning that this type of analysis of relatively small samples is very dangerous unless the researchers actually know the farmers and the enumerators well enough to scrutinize the results carefully.