Microsimulation attempts to describe economic and social events by modelling the behaviour of individual agents. These models have proved useful in evaluating the impact of policy changes at the micro level. Spatial microsimulation models contain geographic information and allow for a regional or local approach to policy analysis. This paper builds on previous work on urban systems by employing similar modelling techniques for the analysis of rural areas [Birkin, M., Clarke, M., 1988. SYNTHESIS--a synthetic spatial information system for urban and regional analysis: methods and examples. Environment and Planning A 20, 1645-1671; Ballas, D. et al., 1999. Exploring microsimulation methodologies for the estimation of household attributes. The Fourth International Conference on GeoComputation, 25-28 July 1999, Fredericksburg, Virginia, USA (paper presentation); Ballas, D, Clarke, G. P., 2000. GIS and microsimulation for local labour market policy analysis. Computers, Environment and Urban Systems 24, 305-330; Ballas, D., Clarke, G. P., 2001a. Modelling the local impacts of national social policies: a spatial microsimulation approach. Environment and Planning C: Government and Policy 19, 587-606; Ballas, D., Clarke, G. P., 2001b. Towards local implications of major job transformations in the city: a spatial microsimulation approach. Geographical Analysis 33, 291-311]. It describes the development of the simulation model for the Irish local economy (SMILE) model. SMILE is a static and dynamic spatial microsimulation designed to analyse the impact of policy change and economic development on rural areas in Ireland. This paper focuses on the static model. First, we describe the European and Irish policy environment and indicate the importance of building spatial models to analyse change in rural Ireland. Second, we review existing literature on regional and local modelling techniques and the use of spatial models as tools for policy analysis. Third, we describe the SMILE static model. The model generates synthetic spatially referenced population for the Irish Republic at the small area level--the population micro data is produced for district electoral divisions (DEDs). Finally, we show how data from other sources can be linked to the static model output. We link data from the Irish National Farm Survey on farm system, income and off-farm employment of operator to the SMILE static model. We use this example to show its potential as a tool for policy analysis.