Methods of analysis and empirical evidence of farm structural change

The dissertation aims to develop and apply new empirical methods to analyze and model farm structural change. Changes of the farm structure are not only important for the sector itself but may have broader economic, social and environmental consequences for a region. Understanding this process is important for assessing the impact of (agricultural-) policies. A common approach to analyze farm structural change are Markov chains. The dissertation provides a Bayesian estimation framework that allows to more consistently and transparently combine individual and aggregated data in the estimation of non-stationary Markov models compared to existing methods. It is shown that the data combination improves precision and numerical stability of the estimation. Building on this, a Bayesian prediction framework for EU farm structural change is developed exploiting the available information more fully. Secondly, farm interdependences and their importance for farm structural change are analyzed. It is argued that the assumption of independence between farm behavior as implied by the Markov approach may become problematic in specific applications. Empirical evidence is provided that these interactions are indeed important to consider for a consistent aggregation of farm level results when assessing policy effects at regional level. Specifically, it is shown for the case of Norway that it is important to consider neighboring farm characteristics when analyzing the influence of direct payments on farm survival. To the knowledge of the author, the study is the first to show empirically that spatial interdependence at farm level is important for farm structural change. With respect to policy assessment, the results indicate that direct payments a farm receives itself have a positive influence on farm survival while neighboring direct payments have a negative one. For an overall assessment of the policy effects it is thus necessary to consider the interdependencies between farms. Ignoring these interdependencies might lead to an overestimation of the effects of direct payments.