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work is licensed under a Creative Commons IGO 3.0 AttributionNonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose. No derivative work is allowed.Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB's name for any purpose other than for attribution, and the use of IDB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license.Note that link provided above includes additional terms and conditions of the license. 2 AbstractIn Latin America and the Caribbean and in more advanced economies there is evidence that suggests an imbalance between the skills of the workforce and the skills demanded by the productive sector. If this skills mismatch is large and persistent it leads to significant economic and social costs: economies cannot use their human capital efficiently and many individuals cannot access good quality jobs. Correcting these imbalances is not easy; a number of frictions that are prevalent in the labor and education markets limit the capacity of households and education providers to provide skills in line with the requirements of the private sector. In light of this, governments around the world have developed tools that intend to anticipate skills demand in the labor market in order to inform the decision making of households, businesses, training providers and policy makers. A wide variety of tools have been developed, ranging from quantitative econometric models to more qualitative methods such as roundtables and focus groups. This report provides a description of the existing methodologies; an analysis of their objectives, strengths and weaknesses; and a description with illustrative examples. The report stresses the fact that an effective system to anticipate skills demand usually combines a variety of methods and surmounts challenges that are not only technical but also institutional.Systems to anticipate skills demand should not only focus on generating quality data but also on building processes to ensure that the information is effectively used in decision making.JEL codes: J08, J23, J24

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