The International Consortium on Agricultural Biotechnology Research (ICABR)

 

Welfare Effects of Agricultural Biotechnology in the European Union
Topic: Impact of Agricultural Biotechnology

Matty DEMONT
Flanders Interuniversitary
Institute for Biotechnology (VIB)
Dept of Agricultural & Environmental Economics,
Catholic University Leuven,
Belgium

Eric TOLLENS
Dept of Agricultural & Environmental Economics,
Catholic University Leuven,
Belgium

Since 1995, genetically modified organisms (GMOs) have been introduced commercially into US agriculture. These innovations are developed and commercialised by a handful of vertically coordinated "life science" firms who have fundamentally altered the structure of the seed industry. Enforcement of intellectual property rights (IPRs) for biological innovations has been the major incentive for a concentration tendency in the upstream sector. On the one hand, this monopolisation may increase long-run social welfare through an increased rate of investment in R&D. On the other hand, due to their monopoly power, these firms are capable of charging a "monopoly rent", extracting a part of the total social welfare. A popular argument used by the opponents of agricultural biotechnology (AgBiotech) is the idea of an input industry extracting all benefits generated by these innovations. Are life science firms able to appropriate all benefits or is there a limit to their monopoly power? In the US, the first ex post welfare studies reveal that farmers are receiving the largest part of the benefits followed by the gene developers who receive the next largest share. However, up to now no parallel ex ante study has been published for the European Union (EU). Hence, this research project aims at calculating the total benefits of selected AgBiotech innovations in the EU and their distribution among member countries, producers, consumers, input suppliers and government.

We develop a partial equilibrium welfare framework (Alston, Norton and Pardey, 1995) which explicitly recognizes that research protected by intellectual property rights generates monopoly profits, and makes it possible to partition these rents among consumers, farmers, and the innovating input firms (Moschini and Lapan, 1997). Until now, few studies have been published calculating the welfare effects of AgBiotech innovations using the model of Moschini and Lapan (1997). They are applied on typical US export crops like Bt cotton (Falck-Zepeda, Traxler and Nelson, 2000) and RRę soybeans (Moschini, Lapan and Sobolevsky, 2000). The major difference with the EU is the fact that these American studies regard an ex post setting, while the recent moratoriums on GMOs in the EU and the absence of empirical farm level impact data oblige us to use ex ante assumptions about yield increases, cost reductions and technology fees. However, this limitation makes it particularly interesting, because studying the potential welfare effects associated with AgBiotech in the EU reveals the benefits foregone or costs of a complete ban of GMOs in the EU. Secondly, the actual situation of consumer and environmental concerns regarding GMOs in the EU advances the challenge of completing the conventional models by including policy instruments like moratoriums, labelling regimes and identity preservation (separation of markets), consumer refusal (negative demand shifts) and environmental externalities (decrease in pesticide use, environmental risk). So far, no complete rational cost-benefit analysis has been carried out for the EU. Thirdly, the specific institutional features and market interventions of the EU reshape the model and its expected outcome profoundly.

The simulation model will be applied for the case of herbicide tolerant sugar beets. Using @RISK 4.0, subjective prior distributions of non-deterministic parameters (elasticities, yield increases, cost reductions, technology fees, etc.) are included in the model to generate posterior distributions of the outcomes of the model via stochastic simulation and scenario methods. These non-deterministic inputs of the model will be ranked according to their influence on the main outcomes of the model: (1) the size and (2) the distribution of the benefits of AgBiotech between (a) producers, consumers, input suppliers, governments and distributors and (b) member countries and regions of the European Union.

Features of the macro-economic simulation model


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