Oil Palm Production and Educational Outcomes: Gender-Differentiated Evidence from Cameroon

AbstractOil palm production continues to expand in many developing countries in the tropics. Its expansion has been associated with economic gains, but oil palm production could also have immense social implications, especially affecting human capital development with significant labour implications. We use a farm household dataset from a native but emerging oil palm production zone, Cameroon to examine the relationship between oil palm production and child educational outcomes such as enrollment rate, attendance rate and the number of school days missed. Using different analytical techniques, we show that oil palm production is positively associated with the enrollment rates of both boys and girls. We do not find any statistical relationship between oil palm production and attendance rates by gender. However, we find evidence of a strong negative association between oil palm production and the number of school days missed by boys. That is, oil palm production is associated with more school days attended by boys. Exploring the mechanism that could be explaining these results, we show that households may be investing the income gains from oil palm production in the human capital development of their children. Our results are robust over different regression estimators and alternative specifications. We also show that the results are unlikely to be driven by omitted variable bias. These findings have implications on whether oil palm production could stir integrated growth and human capital development, especially in rural areas.Keywords: Oil palmchild laboureducationschool enrollmentschool attendanceincomesustainable development goalsCameroonJEL CODES: I25I31J24Q12Q18 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 These differences may be due to structural conditions and the policy frameworks in these countries.2 This is usually “claimed” ownership as it is not backed by any formal land titles.3 Angrist and Pischke highlight that instrumental variable estimators could be used to control for any biases emerging from measurement error.4 This variable is constructed by summing all the households in a village that produce oil palm, excluding the representative household and dividing by the total number of households in the village.5 Reasons for these differences could be due to (1) the IV approach must have corrected an upward bias which may have been caused by omitted variables, such that OLS estimates are downward biased towards zero. (2) there may be attenuation bias in the OLS model which is corrected in the IV model.Additional informationFundingThis research was funded by the Foundation Fiat Panis, Ulm, Germany. Additional financial support was received from the chair group of Economic and Agricultural Policy at the University of Bonn, Germany.