Many young people around the world — especially the disadvantaged — are leaving school without the skills they need to thrive in society and find decent jobs. These education failures are jeopardizing equitable economic growth and social cohesion, and preventing many countries from reaping the potential benefits of their growing youth populations. UNESCO’s 2012 Education for All Global Monitoring Report was launched today. ”Youth and Skills: Putting Education to Work” examines how skill development programs can be improved to boost young people’s opportunities for decent jobs and better lives. Supporting the Global Monitoring Report is a new World Inequality Database on Education. Luis Crouch, Head of GPE’s Global Practice Team took a look at the new database. As a data-oriented person I spent many pleasurable minutes with the new World Inequality Database on Education ( WIDE). However, I don’t think you have to be a data-cruncher to find pleasure and instruction in this web site. The site is so easy to manipulate, and the interpretation is so straightforward, that I think anyone will find it fun and useful. The data confirm findings that I and other colleagues have been noting for a while: the wealth-based disparities are really the most important. The feature that allows you to sort by level of disparity or inequality and also by level of average deprivation (or achievement) is easy to use and very instructive. If you sort by deprivation it shows that for most indicators, there is an initial growth in disparity when countries increase access. It appears as if, the privileged seem to take advantage of the change at first while the less privileged seem to catch up later. This means (if I’m right) that there is a sort of inverted U-shaped pattern to the inequality as a function of average achievement: the more achievement, the more inequality there is at first. It then decreases. Try, for instance, plotting the inequality in years of education, sorted by most deprived, and based on just the poorest and richest (the wealth variable), and just for the Arab states. It seems to really illustrate that U-shaped phenomenon. Rather than this being a reason for complacency, however, it seems to me that countries that eventually reduce disparity do it as a matter of policy. This could be seen as a warning to be vigilant about how one expands access to new areas. For instance, as interest in early childhood development (ECD) expands, is it possible to make sure that at least the publicly-subsidized forms of ECD are targeted to the poorer children and families, even at first? It may be difficult to do, from a political- economic point of view, but the data suggests it may be important to prevent this “initial-increase-in-inequality” phenomenon. It is also interesting to see that income or wealth seems to be the biggest source of education inequality. However, there may be something about the way the data is presented that exaggerates income or wealth as a factor. For instance, gender or urban/rural location are binary, whereas income or wealth have five categories. Does this then exaggerate, a little, the visual depiction of the impact of wealth? I think so, and would be happy to discuss it with others. Even so, the impact of wealth inequality is strikingly brought out by this data set. I tried the export function (exporting to Excel) and it works very well. This means that analysts can do all kinds of data analysis that is not possible in the web page itself. As far as I know this is a unique contribution to the data on inequality in education. Other sources of data that I use regularly (UNESCO Institute for Statistics and the World Banks EdStats) don’t show the inequality data as easily. So this is a really nice source to have, easy to use with the option to export the actual data for further analysis and comparisons. So, I think this is a really instructive and fun web site, not just for number crunchers but for anyone.