A latent class application to the multidimensional measurement of poverty

The paper presents the multidimensional measurement as a transparent and easy-to-interpret method to measure poverty, where poverty is measured with a set of direct and indirect poverty indicators side-by-side. Multidimensional measurement is formalised and compared to the traditional, one-dimensional measurement. This formalisation is based on the idea about a set of indicators that are measuring different manifestations of the same latent variable. The Latent Class Model (LCM) is proposed as a method to select a valid and reliable set of poverty indicators for multidimensional measurement. The LCM is used to test if these different poverty indicators really measure the same latent referent - an assumption on which the multidimensional measurement is based. Before this method presented here, constructing and selecting indicators for the multidimensional measurement of poverty has relied practically on theory and substance only. Naturally, the method presented here can be used generally for studying and developing multidimensional measurements.