Regional Heterogeneity, Geography and Agglomeration Effects in Efficiency Analysis: The Case of Dairy Farming in Europe
Castro Medina, Daniel Mauricio
Kumulative Dissertation
Angenommen am:
2015-02-12
Erschienen:
2015-03-10
Betreuer:
Brümmer, Bernhard Prof. Dr.
Gutachter:
Cramon-taubadel, Stephan Von Prof. Dr.
Gutachter:
Kneib, Thomas Prof. Dr.
Zum Verlinken/Zitieren: http://hdl.handle.net/11858/00-1735-0000-0022-5EDF-C
Dateien
Name:
TESIS_final_publication.pdf
Größe:
3,1 MB
Format:
PDF
Beschreibung:
Dissertation
Lizenzbestimmungen:
Zusammenfassung
Englisch
Natural conditions and location play an essential role in the determination of the agricultural production and its technical (in)efficiency . The present work analyzes by means of different model specifications and statistical techniques how location and environmental variables determine the production possibilities and how far farmers allocate from their potential production in the dairy sector in Europe. The aim of this work is to make use of innovative statistical techniques that allows having a better understanding of the impact of the natural conditions and the location in which the production takes place. Since these conditions are not homogeneous over space and in some cases over time, this source of heterogeneity necessarily impacts the estimators of efficiency when national or transnational analyses are performed. That is, assuming that the technology of all producers in different locations is the same or ignoring the different conditions and spatial interactions in which the production takes place might be a very strong assumption that leads to incorrect conclusions and even more, mistaken policy decisions. This work gives some clues of how regional heterogeneity can be included in the estimation of efficiency. Furthermore, this work also explores the implications of a phenomenon in economics that can be attributed to location and natural conditions. Local agglomeration, understood as the benefit from the concentration of the dairy activity in a limited area. Results presented in this work proved that accounting for such heterogeneity in some cases improved efficiency estimation. Models accounting for regional heterogeneity were in all cases preferred over those that ignore it. The results also show that efficiency scores were improved, which suggests a subestimation of efficiency when heterogeneity is not accounted for. The present work has also used natural conditions (THI, altitude and LFA) and variables that might be seen as indicators of adaptation to natural conditions (cow per ha., feeding per cow, buildings per cow) to unveil different technologies in the dairy sector Europe. The cluster analysis employed suggested substantial differences in the technologies employed in different region in Europe. Measuring farms’ efficiency with adequate frontier gives a more realistic measure of efficiency since farmers are now compared with those that face similar natural conditions and employ similar technologies. Finally, it was shown how agglomeration effects have a positive impact on productivity and efficiency. The innovation presented in this work assumed that effects of agglomeration are not only limited to the region in which the farm is located.
Keywords: Stochastic frontier; efficiency analysis; linear mixed models; regional heterogeneity; Bayesian analysis; latent class model; production heterogeneity; Local agglomeration; size of firms
