 So, what sort of precipitated our paper here was that the literature on education in Africa at the moment is bifurcated in that studies either focus on access to education, things like education for all and the Millennium Development Goals, or they focus on the quality of education and learning through things like SUCMEK, PISA, TIMS, those sort of assessments. The problem with that is if you just focus on access, you're not taking into account whether or not children are acquiring cognitive skills, which is what matters for labor market and social mobility, using work from Hanushek and Vosman's, showing that cognitive skills acquired rather than just years of education attained are the important statistic. And if you only look at school-based surveys, like SUCMEK or PISA, then it's also biased because you're not taking into account dropout profiles. You're just looking at the school sample at grade six, for example, with SUCMEK, and the problem is you've got lots of dropout that happens before grade six, which is very problematic. So what we try and do here is we use the SUCMEK dataset, which tested over 61,000 grade six students from across 14 African countries. And then we also use the DHS survey, so we use SUCMEK for quality and learning outcomes, and we use DHS for access and combine them into a single statistic. So in the paper we argue that something like the Millennium Development Goal of saying that we should expand access to include all children is insufficient because it doesn't take into account a learning criteria, whereas our new statistic, what we propose, is something called effective enrollment, which is not just whether or not children are enrolled, it's are they enrolled and learning. So the way that we do that is you can see in these graphs over here, we break down the grade six-aged population into not just whether or not children are in school, but if they are in school, have their acquired basic literacy and numeracy skills. And the reason why this is important, if you take a country like South Africa, where the traditional enrollment rate, if you look at something like the net enrollment rate in South Africa, it's 98 percent, 98 percent of grade six-aged children on school, but only 71 percent of grade six-aged children are actually literate. So the 98 percent enrollment rate overestimates the success of the South African schooling system. We then disaggregate it based on gender, wealth quintile and whether or not the school is in an urban or a rural area, and calculate the gaps in our new statistic of effective enrollment, which are these graphs over here. And one of the interesting findings from the paper is that the gender gap in every country is far smaller than the urban rural gap or the wealth gap. So the country with the largest gender gap, which is Malawi, the size of the gap, the pro-boy gap is only 10 percent, whereas the size of the gap for urban versus rural children is 20 percent. And in other countries, for example, Zambia, it's five times as high. So gender is not necessarily the largest dimension of inequality now, it's actually urban, rural and socioeconomic status. We extended the analysis in two other papers to look at changes over time in our new statistic and also the projections of how long it would take if everyone succeeded in achieving growth rates in effective enrollment of the fastest countries, in this case it was Namibia, that managed to expand literacy access, effective literacy access, not just enrollment, but effective enrollment of learning as well by 35 percent over the 2000 to 2007 period. The reason why we think our measure is superior to the others is that it takes into account both of these measures, not just only access or only learning, and we think and propose that this is the kind of statistic that should be used for something like the post-2015 Millennium Development Goals.