Browsing by Subject "Ricardische Analyse"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Publication
    Microeconometric analysis of the impacts of climate change on German agriculture : applications and extensions of the Ricardian approach
    (2015) Chatzopoulos, Thomas; Lippert, Christian
    The so-called Ricardian approach is an econometrics-based climate change impact assessment frequently used by agricultural and environmental economists. The intuition behind this approach is that, in the long run, the optimal behavior of farms is climate-dependent. In essence, the approach explores the role of climate in determining farm profitability and potential adaptation, by regressing economic or behavioral measures of agricultural outcomes against climatic and various other land and site attributes. The overall output of the approach enables (i) the identification of profitability differentials due to climate differentials, (ii) marginal implicit pricing of climate, and (iii) a probabilistic exploration of long-run adaptation strategies. This cumulative dissertation took up the challenge of improving specific conceptual and methodological aspects of the Ricardian approach in order to render it a more realistic impact assessment tool. In particular, we aimed at a more efficient treatment of the variables that proxy climate, and at the imposition of structure on equations that can reflect adaptation. Three empirical studies were pursued for over 270,000 German farms at three spatial scales: districts (N = 439), community associations (n = 3,515), and communities (n = 9,684). For this reason, secondary data of various formats (e.g., farm census records, measurements by weather stations, digital images) on a host of characteristics (e.g., farm-specific, climatic, topographical, geographical) were extensively processed (e.g., integrated, geocoded, spatially interpolated, zonally rearranged) and spatially matched. We took a multi-model and multi-stage approach from an instrumental-variables (IV) perspective, which we coupled with advances from the subfield of spatial econometrics. From an empirical viewpoint, our results showed that historical climate change has generally been beneficial to the sector as a whole. The impact of historical mean annual temperature (precipitation) on average land rental prices is positive (concave). Indicatively, permanent-crop and vegetable farms value temperature more than the rest farm types, whereas forage farms, and to a certain extent mixed farms, stand out for their resilience to precipitation. Climate change in the near decades is likely to be beneficial, but the magnitude of benefits depends on the farm type one looks at.
  • Thumbnail Image
    Publication
    Spatial econometric methods in agricultural economics : selected case studies in German agriculture
    (2013) Schmidtner, Eva; Dabbert, Stephan
    The location of agricultural activities is determined by location factors that are spatially heterogeneous, such as climate and soil; for the spatial distribution of some agricultural specialties, spatial dependence, i.e., beneficial and self-enhancing effects resulting from a concentration of these agricultural activities, might also play a role. Thus, the dimension ?space? might be of importance in analysing agricultural research settings. This cumulative dissertation consists of three articles addressing current research questions on the spatial distribution of agricultural activities and agricultural profitability in Germany. To account for the geographic location of attributes, spatial econometric analysis tools are used. The first article addresses the determinants of the uneven spatial distribution of organic farming in Germany. In addition to traditional location factors, positive agglomeration effects might also influence the spatially heterogeneous concentration of organic agriculture. Conventional farmers might be more likely to convert to organic farming given an easy communication with organic farmers located nearby and a geographically close and strong institutional network. First, a theoretical model explaining the decision of a farmer to convert from conventional to organic agriculture is established. Next, secondary data at the German county level are analysed by using spatial lag models. Data on organic farming refer to the year 2007. The results suggest that agglomeration effects matter in organic agriculture. For the previous analysis, aggregated data at a relatively low spatial resolution are used, which might lead to results that are artificially generated through the process of data aggregation. The second article addresses the question whether results can be confirmed at different spatial levels, assuming that agglomeration effects are important in organic farming. The results of spatial lag models are compared at two measurement scales, the German counties and community associations. Secondary data are also used in this analysis; for the organic sector, 2007 data are considered. The analysis indicates that essential factors determining the decision to convert from conventional to organic farming are sustained at different spatial resolutions. The results at the lower spatial resolution are shown to be not artificially generated through the aggregation process in this case, which strengthens the relevance of the previous study. The third publication assesses the effects of different indicators of soil characteristics on the estimation results of a Ricardian analysis. The study draws on data from the official farm census conducted in 1999 and on weather data from the German National Meteorological Service at the county level for the time period 1961-1990. Additionally, different soil data bases are considered to control for soil quality. The results of spatial error models suggest that rental prices are determined by climate and non-climate factors. Accounting for different methods of measuring soil quality does not influence the results of the analysis. To estimate the effects of changing climatic conditions on future land rents, data from the regional climate model REMO for the time period 2011-2040 are used. The models show that projected climate levels will have an overall positive but spatially heterogeneous effect on the income from agriculture in Germany. The empirical analyses presented illustrate that spatial econometrics can offer appropriate tools for analysing agriculture. In all three cases theoretical considerations and diagnostic tests for spatial dependence suggest using spatial analysis techniques. The use of alternative specifications of the spatial neighbourhood matrix further supports the stability of results. The general approach and methods used could be translated to other issues in agricultural economics such as potential agglomeration effects in hog production or the future impact of climatic factors on the spatial distribution of viticulture. Thus, spatial econometrics might offer an interesting approach to various spatial research questions in agricultural economics, in addition to the applications that were selected for this thesis.