El objetivo de este trabajo es contribuir a cubrir el déficit de información respecto a la estimación del PIB municipal presentando un método de cálculo basado en la estimación de la productividad sectorial diferencial de la ciudad respecto a su Comunidad Autónoma. Se utilizan los diferenciales salariales sectoriales entre las ciudades y las Comunidades Autónomas estimados a partir de la Muestra Continua de Vidas Laborales para el período 2010-2016. Los resultados parecen indicar que se están recogiendo aspectos relevantes de la economía de las ciudades como, entre otros, potenciales economías de aglomeración.

This paper compares five small area estimators. We use Monte Carlo simulation in the context of both artificial and real populations. In addition to the direct and indirect estimators, we consider the optimal composite estimator with population weights, and two composite estimators with estimated weights: one that assumes homogeneity of within area variance and squared bias and one that uses area-specific estimates of variance and squared bias. In the study with real population, we found that among the feasible estimators, the best choice is the one that uses area-specific estimates of variance and squared bias.

In this article we propose using small area estimators to improve

Most methods for small-area estimation are based on composite estimators derived from design-or model-based methods. A composite estimator is a linear combination of a direct and an indirect estimator with weights that usually depend on unknown parameters which need to be estimated. Although model-based small-area estimators are usually based on random-effects models, the assumption of fixed effects is at face value more appropriate. Model-based estimators are justified by the assumption of random (interchangeable) area effects; in practice, however, areas are not interchangeable. In the present paper we empirically assess the quality of several small-area estimators in the setting in which the area effects are treated as fixed. We consider two settings: one that draws samples from a theoretical population, and another that draws samples from an empirical population of a labor force register maintained by the National Institute of Social Security (NISS) of Catalonia. We distinguish two types of composite estimators: a) those that use weights that involve area specific estimates of bias and variance; and, b) those that use weights that involve a common variance and a common squared bias estimate for all the areas. We assess their precision and discuss alternatives to optimizing composite estimation in applications.