to process relevant (new) information, is an important and highly robust determinant of the decision to adopt new technologies (see, e.g., Feder et al. 1985 and Strauss et al. 1991), as well as of the decision to adopt new technologies early (see, e.g., Wier and Knight 2000). To the extent that women are less educated than men, which is generally the case in the poorer parts of the developing world, the more likely women are to delay adoption or to forgo adoption entirely.

The importance of own-schooling for adoption is probably greater in the case of female-headed households, where the potential for positive education spillovers from better-educated adult (male) members are limited or non-existent. Doss and Morris (2001), for example, find in their study of maize farmers in Ghana that female farmers in male-headed households tend to have less formal schooling than male farmers, and that female farmers in female-headed households have even less. They also find that education is an important positive determinant of the adoption decision. Similarly, Croppenstedt et al. 2003 find that literacy and schooling are important determinants of fertilizer use in Ethiopia, with the effect on intensity of use particularly strong when the farmer has four or more years of schooling. They find that very few female-headed households are literate, and virtually none have four or more years of formal schooling.

_Social networks and social learning._ Learning about a new technology and its use from other farmers in the community (via imitation or information exchange within social networks) has been shown to be an important determinant of the adoption decision (see, e.g., Munshi 2004, Foster and Rosenzweig 1995, Conley and Udry 2001, Conley and Udry 2004, and Bandiera and Rasul 2005). The ability and the extent of learning from others is also shown to be positively correlated with the level of formal schooling, suggesting an indirect pathway through which formal schooling again impacts the adoption decision.

Studies also find that farmers learn from farmers of similar type, particularly in terms of demographic characteristics. For example, Conley and Udry (2004), in their study of pineapple farming technologies in Ghana, find that farmers are more likely to have information links with other farmers of the same gender, clan, and age, and that these links were important for technology diffusion. Similarly, Wier and Knight (2000) find that 88 percent of adopters indicated that their decision was influenced by somebody of the same gender. Disaggregating this statistic by gender, they find that 53 percent of female-headed households were influenced by other female-headed households, though the share of female-headed households in the sample was only 22 percent. To the extent that female farmers have less-extensive or poorer-quality information networks, knowledge transfer through these networks are more likely to be impaired.

_Agricultural extension_ . Agricultural extension services are also seen as an important instrument for the provision of information on new technologies and their use (Evenson 2001 and Anderson and Feder 2003). For example, in reviewing 22 studies of the determinants of the adoption of improved varieties and fertilizer in East Africa, Doss et al. (2003) find that extension services, in terms of frequency, appear to be the single-most important positive determinant of the adoption decision. For various reasons, however, extension services often fail to reach female farmers, in particular female-headed farming households, even though female farmers often indicate a strong demand for such services (Saito et al. 1994). Summarizing evidence from 6 studies in SubSaharan Africa in the 1980s, Quisumbing (1994) reports that male-headed households were 26