In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are indicators. This approach allows us to incorporate and test more information on spatial dependence and offers more flexibility than the representation in terms of Wy or Wx. Furthermore, we adapt the ML estimator included in the software package Mx to estimate SEMs with spatial dependence. We present illustrations based on Anselin's Columbus, Ohio, crime dataset.

This paper analyses household preferences for in-house pipedwater in urban and rural Indonesia via a hedonic price model, specified as a constrained autoregression-structural equation model (ASEM). ASEM reduces bias due to time-varying omitted variables and measurement errors. In addition, it provides a convenient way of testing and correcting for endogeneity. On the basis of the Indonesia Family Life Survey data set, we find that on average urban and rural households have the same willingness to pay for in-house piped water, that is, 34.24 per cent of their monthly house rent. For the 25 per cent urban and rural households with lowest expenditure, this percentage is equivalent to 9.41 per cent and 7.57 per cent of their monthly expenditure, respectively. The findings support a need for further investment in in-house piped water in both areas, particularly for the households with the lowest expenditure levels