Spatial Correlation in Household Choices in Rural Indonesia* John Gibson, Bonggeun Kim and Susan Olivia Received 21 January 2011; Accepted 23 June 2011 Many household choices in developing economies are correlated with choices of nearby households, because nearby locations have unobserved factors in common and households and their neighbors interact. However, models recognizing these spatial correlations are rarely used because few surveys give exact household locations. In the present paper, we use unusual data from rural Indonesia, where distances between households can be measured, to examine spatial effects in equations for non-farm enterprises’ share of household incomes and food’s share of total household budgets. Our results indicate that ignoring spatial correlations in household choices might cause bias and inferential errors and could distort recom- mended policy interventions aiming to raise living standards in rural Asia. Keywords: spatial correlation, non-farm enterprises, food Engel curves, Indonesia. JEL classification codes: C31, O17. doi: 10.1111/j.1467-8381.2011.02063.x I. Introduction Many household choices are correlated with choices made by nearby households, especially in developing countries where residential location strongly affects economic activity. These correlations can arise either because nearby locations share unobserved factors (e.g. access to markets or infrastructure quality) or because of interaction between one household and another (e.g. coordination problems when deciding to switch from farm to non-farm production). Empirical studies of rural and regional development use several proxies to capture these location effects, including distance to major population centers and markets, and economic and population density, but a common belief in this literature is that geographical dummy variables can capture all unobserved local factors (Jonasson and Helfand, 2010). Moreover, it is usually quite easy to estimate these geographic dummy variables due to the structure of household surveys; samples are usually clustered in groups of 10–20 rather than spread randomly across space. Because households in the *Gibson (corresponding author): Department of Economics, University of Waikato, Private Bag 3105, Hamilton, New Zealand. Email: jkgibson@waikato.ac.nz. Kim: Department of Economics, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-746, Korea. Email: bgkim07@ snu.ac.kr. Olivia: Department of Econometrics and Business Statistics, Monash University, Clayton, Victoria 3168, Australia. Email: Susan.Olivia@monash.edu. Asian Economic Journal 2011,Vol. 25 No. 3, 271–289 271 © 2011 The Authors Asian Economic Journal © 2011 East Asian Economic Association and Blackwell Publishing Pty Ltd