 Thank you, Mr. Chair. Good morning, everybody. This is a plan that we are going to use. I'm going to use to present the paper, introduction, research questions, objective and hypothesis, methodology, empirical results, and some policy implications. Popular responses by citizens to lack of fairness has recently been at the root of regime change in the number of African countries. These voices typically called for more social inclusion and fair chances for everybody in society as ingrained in the concepts of equity, fairness, and social justice. Inequality typically stifles both macro and household economic growth, yet having both fair and unfair components. Measured inequality is typically a function of two major components, comprising inequality of circumstances to which an individual may not be held responsible, and inequality of effort to which an individual can largely be held responsible. Education is viewed essentially as an effort-related determinant of individual wages. It complements and substitutes for exogenous circumstances that enhance or constrain individual labour market opportunities. Education increases the skills and productivity of poor households, enhances their employability and earnings as well as their welfare. Resolving deficiencies in access and returns to education is therefore expected to be instrumental in augmenting the standards of living of the poor. Generally, educational expansion is expected to lead to an increase in the labour market participation opportunities open to economic agents and those and essential catalysts codified against inequality and poverty. Education is viewed as the single most important determinant of income, yet exploring literature relating education to income inequality reveals mixed results. While some find a positive relationship between schooling and inequality, others find a negative association between school enrolment and income inequality. However, from a state of unequal distribution of educational opportunities, we believe that investment in education and related infrastructures would increase labour market opportunities, relatively more so for those at the bottom of the income distribution than those at the top. In this context, the key question arises, is smoothing education more inequality reducing at lower than upper tails of the income distribution profile? This gives rise to the related objectives which are to evaluate the determinants of employment sector choices, to determine the nature of change in returns to formal education between 2005 and 2010 along the wage distribution profile and to evaluate the impact of education on measured inequality along the wage distribution profile. And some related hypotheses, other things being equal education is relatively more important in sanctioning wages and allocation of workers to various employment sectors. Returns to education were inclusive in the income labour market between 2005 and 2010 and smoothing education is more inequality reducing at lower than upper percentiles in the distribution of wages. There is some literature, the rule of education in causing or mitigating wage inequalities has been explained theoretically using the human capital theory, the dual labour market theory, the discrimination theory and the screening and signaling theory. Well, move on. This paper makes a number of empirical contributions by conducting analysis based on pooled individual records from the 2005 and 2010 Cameroon labour force service correcting for potential employment sector selection by us in the structural wage equation and running conditional quantile wage equations and designing factual and counterfactual experiments to elicit the impact of education on inequality along the wage distribution. The methodology to study the effects of education on wages, we exploit the 2005 and 2010 Cameroon labour force service by pulling them together. This enables the testing of how the effect of education on occupational choices and wages changed in the period 2005, 2010. By way of methodology, we follow a two-step econometric estimation procedure and conduct a factual and counterfactual experiment for inequality assessment. In terms of econometrics, the first step regression involves the estimation of a multinomial probit model of employment sector choices. The employment sectors were public, private, informal, and the small-scale agriculture was used as the reference category. After the multinomial probit, we generated three inverse-muse ratio, a la Hegma. In the second step, structural wage equations correcting for employment sector selectivity bias were estimated at the mean and across selected quintiles of the wage distribution. Using estimates of the selectivity corrected wage equations, factual and counterfactual experiments were designed. In particular, counterfactual distributions were simulated in which wage inequalities within selected quintiles were independent of variations in years of schooling. Inequalities computed by the Gini coefficient and the generalized entropic class of inequality measures using the factual and counterfactual distributions were compared to elicit the impact of education on inequality overall and along the wage distribution profile. These are some of the equations. The first equation number five there is our estimating equation. W is monthly wages. 2010 is a year dummy taking the value one for 2010 and zero for 2005. E is education which is captured by years of schooling. Then you have the interaction term there between the year dummy and the education which will capture the change between 2005 and 2010. The overall effect is given by our alpha two plus our alpha three. Then S, the sectors of employment, public, private, informal, while the small-scale agriculture is the reference category. Then C, the vector C is a vector of other households, other individual households and the market characteristics that we're controlled for while our vector lambda are the three inverse males ratios that we generated after the multinomial probit. Then equation number eight is the estimated counterpart of equation number five. Then in equation nine we simulated the factual distribution basically the exponential of the estimated wage equation that's the log of w hat plus the predicted v hat which is the residual. Then from the factual distribution we simulated the counterfactual distribution which is equation number ten which we simply at various selected quantiles we equalize education at those various levels. Then as far as impact of education and inequality is concerned we're captured by the expression number eleven where theta there is simply inequality from the factual distribution minus inequality from the counterfactual distribution divided by inequality from the factual distribution. When our theta is positive it indicates that education is inequality augmenting in the factual distribution which is tantamount to saying that it is inequality, education is inequality reducing in the counterfactual distribution. When theta is zero education is inequality neutral both in the factual and the counterfactual distribution and when theta is negative education is inequality reducing in the factual distribution which is tantamount to saying that education is inequality augmenting in the counterfactual distribution. Empirical results. Well we have just I'm just presenting results related to education which these are the marginal effects related to an additional year of schooling the baseline results are what we have in blue where we see that an additional year of schooling increases the probability of a formal sector employment more so for the public sector than the private sector while reducing the probability of informal sector employment. Then when we come to the effect between the two periods we find out that at least there are there is some marginal effect concerning public sector employment the probability increases between the two periods slightly then as far as the private sector is concerned there is almost no effect then as far as the informal sector is concerned it really the effect between the two periods is strong meaning that between those periods the probability of working in the informal sector was privileged. Then now we can just see the nature the distribution of lock wage and years of schooling by selected percentiles between in the period the entire period if we look at it carefully we see at least that there is some positive correlation between lock of wages and the years of schooling then the main issue here the lock wage estimates as far as education is concerned we have a baseline incremental and total returns to education as far as the baseline results are concerned we can observe that in the overall as well as the various percentiles or quantized education is increasing monotonically for the baseline results then as far as the returns to education are concerned between the two periods 2005 and 2010 the incremental results can be observed in the overall distribution then up to the 25th percentile which after the 25th percentile from the 50th percentile the incremental returns are diluted which is indicating that between these two periods returns to education were inclusive then as far as the total returns are concerned we observe that they were highest among workers situated at the 5th and the 10th percentile at the 5th percentile it was up to 10.5 percent and at the 10th percentile it was up to 12.6 percent then as far as inequality is concerned from the factual and counterfactual distribution of course in the factual distribution we expect inequality to be constant throughout but when we are looking at our counterfactual distribution which is an inequality equalizing distribution at the various percentiles we see that inequality in the counterfactual distribution is decreasing gradually as we move from lower percentiles to upper percentiles which of course is tantamount to saying that inequality in the factual distribution is increasing which the impact that we observed in our equation 11 can be seen here the absolute impact and the relative impact and we can see moving from lower percentiles to upper percentiles inequality is increasing there is a snowballing effect from lower percentiles to upper percentiles which is simply telling us that years of schooling actual years of schooling were inequality augmenting well they are using the generalized entropy measure for theta they equal zero we see it is basically tracing the same storyline as the results that we obtain from the Gini coefficient right then the policy implications simply leveling the playing field in terms of schooling opportunities would be an important public policy when trying to reduce inequality and poverty in this context a more balanced schooling profile would result in a more or less balanced distribution of labour market opportunities and earnings our findings endorse public policies that favour investments leading to educational expansion thanks for your kind attention