This paper explores how cross-sectional data can be exploited jointly with longitudinal data, in order to increase estimation efficiency while properly tackling the potential bias due to unobserved individual characteristics. We propose an innovative procedure and we show its implementation by analysing the determinants of consumption in Nicaragua, based on data from three Living Standard Measurement Study surveys from 1993, 1998 and 2001. The last two rounds constitute an unbalanced longitudinal data set, while the first is a cross-section of different households. Under the assumption that the relationship between observed and unobserved characteristics is homogeneous across time, information from longitudinal data is used to clean the bias in the unpaired sample. In a second step, corrected unpaired observations are used jointly with panel data. This reduces the standard errors of the estimation coefficients and might increase their significance as well, otherwise compromised by the limited variation provided by the short longitudinal data.