The objective of this work is to provide a statistical tool that can drive local policies on the basis of urban specificities. For this purpose, very detailed and updated statistical information at fine geographic level is necessary. Typically, the former aspect has always been assured by the Census that until now had the limit of providing data on a decennial basis. Such a temporal discrepancy is no longer acceptable nowadays. The timeliness of the information is, on the other side, assured by sample surveys, which, unfortunately, have limitations on the territorial level dissemination: the estimates are, in fact, usually produced at regional level. From these considerations, it emerges the need to provide solutions that exploit the availability of new sources of information, such as administrative data. The integration of this information with survey data can overcome the lack of information at a more detailed territorial level, assuring simultaneously timely and accurate estimates. NSIs have started to produce social and economic indicators using administrative data at local level. However, due to a different taxonomy, these indicators do not coincide with those usually computed by means of sample surveys. Therefore, the information from administrative data is often not consistent with the information officially produced at the regional level with sample surveys. The aim of this work is, first of all, to compare the indicators computed by the two sources of information, for all the metropolitan cities in Italy, for some large municipalities and for functional aggregations of small municipalities. The following step is to use the administrative data as an auxiliary source for model based estimation or for projection-type estimators. The output of this step allows us to evaluate the results obtained on important indicators of social exclusion and well-being, typically produced with the EuSilc (European Union Statistics on Income and Living Conditions) survey. In particular, we focus small area estimates of poverty rate, low work intensity and quantile share ratio indicators, computed at provincial and metropolitan municipalities level.