<mods:mods version="3.3" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mods:titleInfo><mods:title>What Does Gender Yield Gap Tell Us about Smallholder Farming in Developing Countries?</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">E B</mods:namePart><mods:namePart type="family">Nchanji</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">O A</mods:namePart><mods:namePart type="family">Collins</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">E</mods:namePart><mods:namePart type="family">Katungi</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">A</mods:namePart><mods:namePart type="family">Nduguru</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">C</mods:namePart><mods:namePart type="family">Kabungo</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">E M</mods:namePart><mods:namePart type="family">Njuguna</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">C O</mods:namePart><mods:namePart type="family">Ojiewo</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>This study examines the extent of the productivity gap between male and female bean&#13;
producers, its discriminatory nature and implications for the policymakers in agriculture in Tanzania.&#13;
Generally, women are distinctively “invisible” in agriculture, due to social norms and even from the&#13;
national agricultural policy perspective. Their discrimination arises from uncounted and unaccounted&#13;
for farm work, and their productivity is reduced by triple roles, limited access to education, having&#13;
triple effects on access to technology, training and land rights. In research, issues of concern to them&#13;
such as nutritious food crops, varietal selection on important attributes, household food security,&#13;
convenient home storage and small-scale processing are widely ignored through unfavourable&#13;
policy design. Given the above discriminatory issues surrounding women in agriculture, they are&#13;
hypothesised to be less productive and often lag behind male counterparts in crop production.&#13;
To test the above hypothesis, a three-stage stratified sampling method was used to collect crosssectional&#13;
data in 2016 across four regions of Tanzania. Then, an Oaxaca-Blinder decomposition&#13;
method (at means) was used to apportion the sources of the difference between men and women&#13;
into explained and unexplained variations. Further improvements through the newly developed&#13;
Re-Centered Influence Functions (RIFs) remarkably improved outcomes as the differences were&#13;
analysed through unconditional partial effects on quantiles. Using a counterfactual approach and&#13;
correcting for selection bias, the model provided consistent estimates for easy comparison of the two&#13;
groups. Besides this, it emerged that interventions such as providing improved bean seed varieties&#13;
and training farmers on good agricultural practices reduced the gender yield gap and provided&#13;
a potential avenue for addressing the discrimination observed in productivity among males and&#13;
females. Controlling for selection bias also improved the model, but the real discrimination was&#13;
observed at the 50th percentile, where the majority of the respondents lay within. However, if a&#13;
female’s age, family size, additional years of schooling and discretion to spend income from beans&#13;
were taken away, they would be worse off. Our study finds that females comprised 25 percent&#13;
of the sample, had 6 percent lower productivity, provided 64.70 percent on-farm labour and had&#13;
0.32 hectares less land compared to males, ceteris paribus. Access to improved varieties contributed to a&#13;
35.4 percent improved productivity compared to growing indigenous/local varieties. The implication&#13;
is that the gender yield gap can be reduced significantly if efforts are focused on preventing or&#13;
correcting factors causing discrimination against women.</mods:abstract><mods:classification authority="lcc">Smallholder Farmers</mods:classification><mods:classification authority="lcc">Agriculture-Farming, Production, Technology, Economics</mods:classification><mods:classification authority="lcc">Gender Research</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2020-12</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>MDPI</mods:publisher></mods:originInfo><mods:genre>Article</mods:genre></mods:mods>