Partial identification in nonseparable count data instrumental variable models

Summary This paper investigates undesirable limitations of widely used count data instrumental variable models. To overcome the limitations, I propose a partially identifying single-equation model that requires neither strong separability of unobserved heterogeneity nor a triangular system. Sharp bounds (identified sets) of structural features are characterised by conditional moment inequalities. Numerical examples show that the size of an identified set can be very small when the support of an outcome is rich or instruments are strong. An algorithm for estimation and inference is presented. I illustrate the usefulness of the proposed model in an empirical application to effects of supplemental insurance on healthcare utilisation.

Food Demand Elasticities by Income Group by Urban and Rural Populations for Pakistan

This paper presents food demand elasticity estimates for the Pakistan by urban and rural populations by income quartile for thirteen food groups. Income and price elasticities are estimated using a new food demand estimation technique based on demand for characteristics. This new technique requires far less data than the usual econometric approaches and so may be implemented relatively quickly and costeffectively. However, the resulting demand elasticity estimates depend directly on strong a priori assumptions made concerning food demand behaviour, but assumptions which do not depend on assumptions of weak or strong separability. Rather quite the opposite assumption is made - that the marginal rate of substitution'> between two foods depends directly on the levels of consumption of all other foods. The paper is organised as follows. The first section provides a brief overview of the methodology used for undertaking the demand estimations. The second section discusses the-data used for these estimations and food consumption patterns. The third section presents the demand elasticity estimates.