Economic Analysis of Heterogeneity in Land Management

Land-use dynamics are recognized by the National Research Council as one of eight grand challenges in the environmental sciences (National Research Council 2001). Meeting this challenge requires the development of models that are based on behavioral theories linked to specific geographical areas (National Research Council 2001; Irwin 2010) and that acknowledge both the social and biophysical heterogeneity of landscapes (Jacobson 2002, Chen et al. 2011). By advancing models that analyze heterogeneity in landowner objectives and behaviors, this thesis makes an important contribution to the emerging literature aimed at improving scientific understanding of land-use dynamics. To analyze heterogeneity in landowners we conducted empirical economic analyses driven by two research questions: (1) How, if at all, do landowner objectives vary among landowners and across landscapes? (2) How, if at all, do land management behaviors vary among landowners and across landscapes? The first research question seeks to understand how attitudes and preferences vary among landowners, and how they are distributed over space. The second explores how attitudes and space affect management behavior implemented by landowners. We conducted two empirical economic analyses to answer these research questions. The first analysis employs a landowner segmentation approach that groups landowners based on their attitudes regarding land ownership. Statistical tests are then used to determine differences between groups and to explore the spatial distribution of the groups. The second analysis utilizes discrete regression models of six management behaviors to gauge the significance of these landowner groupings and the location of land parcels for explaining variation in landowner behaviors. Our analysis revealed interesting findings about heterogeneity in landowner objectives and behaviors. First, our segmentation analysis divulged heterogeneity in objectives across owners. We identified three unique groupings: passive residents, retreat owners, and active residents. We found evidence of distinct spatial patterns across these groupings and also revealed a relationship between the location of active resident owners and population density. Second, our discrete regression models brought to light insights about heterogeneity in landowner behaviors. For timber management, trail construction and posting land, the landowner groupings helped significantly explain variation in these behaviors, offering support for heterogeneity across owners. The discrete regression models revealed few relationships between our measures of the urban-rural gradient and landowner behavior. This research advances understanding of heterogeneity in landowner objectives and management behaviors. This research presents valuable insights to Maine policymakers and stakeholders seeking to reach out to segments of landowners and to foster greater cooperation among landowners