Suchergebnisse

ABSTRACTThis paper proposes a model where the structure rather than the size of the financial sector explains its influence on income distribution. Because of information asymmetries, a financial sector dominated solely by profit‐maximizing financial intermediaries will increase income and wealth inequality as it gives preferential access to credit for high‐income agents, whereas a diversified inclusive financial sector with alternative models of finance, like cooperatives, will reduce the inequality gap. No full convergence in income distribution can be realized through finance only and there is still a need for redistribution policies. Accordingly, an objective function for cooperative financial institutions should define a desired pricing behaviour that can increase the income of members at a rate higher than the average growth rate of the economy.

AbstractThis paper analyses the influence of political institutions on the development of financial cooperatives. It proposes a political economy theory where autocratic regimes deliberately oppose the development of a well-functioning financial cooperative sector to maintain their political influence, and prevent the formation of strong pressure groups that can threaten the current politicalstatus quoand reduce the governing elites' economic benefits from underdeveloped and exclusive financial sector. Using panel data from 65 developing countries from 1995–2014, the results show that democracy, political rights and civil liberties promote financial cooperative development. These results are robust in controlling for endogeneity as well as other economic and institutional factors.

This paper analyses the influence of political institutions on the development of financial cooperatives. It proposes a political economy theory where autocratic regimes deliberately oppose the development of a well-functioning financial cooperative sector to maintain their political influence, and prevent the formation of strong pressure groups that can threaten the current political status quo and reduce the governing elites' economic benefits from underdeveloped and exclusive financial sector. Using panel data from 65 developing countries from 1995–2014, the results show that democracy, political rights and civil liberties promote financial cooperative development. These results are robust in controlling for endogeneity as well as other economic and institutional factors.

This paper adopts an alternative method for the analysis of the CAP's impact on farms' productivity based on a system of equations derived from a non-nested three-factors CES production function. With this method, we estimate the elasticity of substitution between labour, capital, and land in the EU agricultural sector, the magnitude and direction of technical change, and the impact of the CAP subsidies. The system of equations is estimated using the GMM estimator on a farm-level panel dataset covering 117,179 farms from all EU MS for the period from 2004 to 2015. Our results suggest that land, labour, and capital in EU farms are complementary production factors characterised by a slow decline or stagnation in the land-, labour-, and capital-augmented technical change. Higher levels of Pillar I and Pillar II CAP payments as percentage of total agricultural income have negative or no impact on farms' technical change, but higher nominal amounts of Pillar I decoupled subsidies, Pillar II investment and LFA subsidies have a positive impact. Moreover, the larger the share of subsidies in total agricultural income the stronger is the negative impact of the CAP on agricultural technical change.

This paper adopts an alternative method for the analysis of the CAP's impact on farms' productivity based on a system of equations derived from a non-nested three-factors CES production function. With this method, we estimate the elasticity of substitution between labour, capital, and land in the EU agricultural sector, the magnitude and direction of technical change, and the impact of the CAP subsidies. The system of equations is estimated using the GMM estimator on a farm-level panel dataset covering 117,179 farms from all EU MS for the period from 2004 to 2015. Our results suggest that land, labour, and capital in EU farms are complementary production factors characterised by a slow decline or stagnation in the land-, labour-, and capital-augmented technical change. Higher levels of Pillar I and Pillar II CAP payments as percentage of total agricultural income have negative or no impact on farms' technical change, but higher nominal amounts of Pillar I decoupled subsidies, Pillar II investment and LFA subsidies have a positive impact. Moreover, the larger the share of subsidies in total agricultural income the stronger is the negative impact of the CAP on agricultural technical change.

For improving sustainability and resilience of EU farming system, the current state needs to be assessed, before being able to move on to future scenarios. Assessing sustainability and resilience of farming systems is a multi-faceted research challenge in terms of the scientific domains and scales of integration (farm, household, farming system level) that need to be covered. Hence, in SURE-Farm, multiple approaches are used to evaluate current sustainability and resilience and its underlying structures and drivers. To maintain consistency across the different approaches, all approaches are connected to a resilience framework which was developed for the unique purposes of SURE-Farm. The resilience framework follows five steps: 1) the farming system (resilience of what?), 2) challenges (resilience to what?), 3) functions (resilience for what purpose?), 4) resilience capacities, 5) resilience attributes (what enhances resilience?). The framework was operationalized in 11 case studies across the EU. Applied approaches differ in disciplinary orientation and the farming system process they focus on. Three approaches focus on risk management: 1) a farm survey with a main focus on risk management and risk management strategies, 2) interviews on farmers' learning capacity and networks of influence, and 3) Focus Groups on risk management. Two approaches address farm demographics: 4) interviews on farm demographics, and 5) AgriPoliS Focus Group workshops on structural change of farming systems from a (farm) demographics perspective. One approach applied so far addresses governance: 6) the Resilience Assessment Tool that evaluates how policies and legislation support resilience of farming systems. Two methods address agricultural production and delivery of public and private goods: 7) the Framework of Participatory Impact Assessment for sustainable and resilient farming systems (FoPIA-SURE-Farm), aiming to integrate multiple perspectives at farming system level, and 8) the Ecosystem Services assessment that evaluates the delivery of public and private goods. In a few case studies, additional methods were applied. Specifically, in the Italian case study, additional statistical approaches were used to increase the support for risk management options (Appendix A and Appendix B). Results of the different methods were compared and synthesized per step of the resilience framework. Synthesized results were used to determine the position of the farming system in the adaptive cycle, i.e. in the exploitation, conservation, release, or reorganization phase. Dependent on the current phase of the farming system, strategies for improving sustainability and resilience were developed. Results were synthesized around the three aspects characterizing the SURE-Farm framework, i.e. (i) it studies resilience at the farming system level, (ii) considers three resilience capacities, and (iii) assesses resilience in the context of the (changing) functions of the system. (i) Many actors are part of the farming system. However, resilience-enhancing strategies are mostly defined at the farm level. In each farming system multiple actors are considered to be part of the system, such as consultants, neighbors, local selling networks and nature organizations. The number of different farming system actors beyond the focal farmers varies between 4 (in French beef and Italian hazelnut systems) and 14 (large-scale arable systems in the UK). These large numbers of actors illustrate the relevance of looking at farming system level rather than at farm level. It also suggests that discussions about resilience and future strategies need to embrace all of these actors. (ii) At system level there is a low perceived capacity to transform. Yet, most systems appear to be at the start of a period in which (incremental) transformation is required. At system level, the capacity to transform is perceived to be relatively low, except in the Romanian mixed farming system. The latter may reflect a combination of ample room to grow and a relatively stable environment (especially when compared to the past 30 to 50 years). The relatively low capacity to transform in the majority of systems is not in line with the suggestion that most systems are at the start of (incremental) transformation, or, at least, reached a situation in which they can no longer grow. Further growth is only deemed possible in the Belgium dairy, Italian hazelnut, Polish fruit and Romanian mixed farming systems. (iii) System functions score well with regard to the delivery of high-quality and safe food but face problems with quality of rural life and protecting biodiversity. Resilience capacities can only be understood in the context of the functions to be delivered by a farming system. We find that across all systems required functions are a mix of private and public goods. With regard to the capacity to deliver private goods, all systems perform well with respect to high-quality and safe food. Viability of farm income is regarded moderate or low in the livestock systems in Belgium (dairy), France (beef) and Sweden (broilers), and the fruit farming system in Poland. Across all functions, attention is especially needed for the delivery of public goods. More specifically the quality of rural life and infrastructure are frequently classified as being important, but currently performing bad. Despite the concerns about the delivery of public goods, many future strategies still focus on improving the delivery of private goods. Suggestions in the area of public goods include among others the implementation of conservation farming in the UK arable system, improved water management in the Italian hazelnut system, and introduction of technologies which reduce the use of herbicides in Polish fruit systems. It is questionable whether these are sufficient to address the need to improve the maintenance of natural resources, biodiversity and attractiveness of rural areas. With regard to the changing of functions over time, we did not find evidence for this in our farming systems.