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Purpose The purpose of this paper is to investigate the gender wage disparity in paid employment and self-employment. To achieve this objective, the Cameroon Household Consumption Survey of 2007 is used. The main question considered in this paper is why women paid employment and self-employment wages are relatively low. In a whole, what are the underlying factors that generate and explain wage gap between men and women householders in employment? Design/methodology/approach First, the paper uses the Oaxaca-Blinder Decomposition to explain wage gap. Thereafter, the Quantile Regression Decomposition using Machado and Mata approach is applied in order to see the gap at different levels of the wage distribution. Findings The main finding indicates that in both methods, the wage gap is due to an unexplained component in self-employment and explained component in paid employment, particularly with strong effects at the extreme of wage distribution. Research limitations/implications The topic of this paper helps to explain and analyse the functioning of the Cameroonian labour market. Practical implications The findings can be applied to narrow the gender wage gap by eliminating discrimination and approving the principle of equal opportunity, support policies that reduce obstacles preventing women from starting and developing their businesses to encourage more women to become entrepreneurs and achieve harmonisation between work and family life. Originality/value Using available data survey, this paper is the first to identify and decompose the causes of paid employment and self-employment gender wage gap in Cameroon.

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This paper develops estimators for simultaneous equations with spatial autoregressive or spatial moving average error components. We derive a limited information estimator and a full information estimator. We give the generalized method of moments to get each coefficient of the spatial dependence of each equation in spatial autoregressive case as well as spatial moving average case. The results of our Monte Carlo suggest that our estimators are consistent. When we estimate the coefficient of spatial dependence it seems better to use instrumental variables estimator that takes into account simultaneity. We also apply these set of estimators on real data.