The coefficient of years of schooling reflects the average returns to education, representing the change in wages due to a change in years of schooling  1 ln _w_  _S_  .<sup>8</sup> Psacharapolous and Patrinos (2004) provide an overview of returns to education estimates worldwide and over time using the Mincerian approach, and conclude that they are in the neighbourhood of 10%, with large variations around the mean. Their country specific estimates for men vary from zero returns for Italy [in 1989] to 28% for Jamaica [1989], and for women from negative returns of ‐ 0.8% for Suriname [1993] to 41% for Puerto Rico [1959].

Current and past work shows a general awareness that these comparisons suffer from a number of limitations. A first constraint is the difference in data sample coverage. While estimates for a country’s average returns to education should be based on representative samples, this is not always the case. The estimation sample may contain only formal wage workers, excluding casual and informal wage workers. The data may be obtained from firm surveys, focusing on representativeness for a subset of firms rather than workers.<sup>9</sup> Other causes of concern relate to the method of analysis used. Coefficients obtained from the ‘dummy variable’ (second) approach need to be adapted before they can be compared to those obtained from the continuous approach presented above. There is also substantial variation in what is included as right hand side variables, as some models include occupational variables, leading to weaker returns, while others do not.<sup>10</sup> A key concern that has received much attention relates to the estimation method, as coefficients obtained from OLS are biased since they do not take endogeneity into account. IV or control function estimation addresses this concern provided they make use of valid instrumental variables (Blundell, Daerden and Sianesi 2005). Standard estimates also typically neglect heterogeneity in returns across different levels of education. Yet, investment in education may depend on whether returns are convex or concave, which is a subject of debate, including in Tanzania. While many of these concerns had long been neglected in analysis for developing countries, and especially for African countries (see Schultz 2004), they are now increasingly taken into account.<sup>11</sup>

> 8 The alternative method replaces years of schooling by dummy variables for different levels of education, and obtains the returns to education by dividing the estimated coefficient by the corresponding years of schooling for each level of education relative to the level of education below.

> 9 Firm survey often target a specific sector, for instance the manufacturing sector, or the private sector.

> 10 Glewwe  (1996) also suggests that substantial variability in school quality could bias returns to education and therefore should be included as right hand side variables. .

> 11 Note that recent work indicates that the Mincerian model and its underlying assumptions do not necessarily hold in all contexts, even when addressing these concerns.  Heckman, Lochner and Todd (2003) find empirical support for the model’s predictions for the US for 1940s and 1950s, but not thereafter. Relaxing key assumptions seem to lead to different inference, and the authors conclude that Mincerian returns, in their context, do not necessarily provide good guidance to policy analysis.  Caution is therefore needed, both in the interpretation of the results and

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