Participation Constraints in Pension Systems

We explore voluntary participation in pension arrangements. Individuals only participate when participation is more attractive than autarky. The benefit of participation is that risks can be shared with future generations. We apply our analysis to a pay-as-you-go system, a funded system without buffers and a funded system with buffers. Buffers play a particularly interesting role, because they raise the sensitivity of the contributions to the asset returns. In particular, compared to a system without buffer requirements, they require higher contributions when asset returns are low. Moreover, individual contributions may be increasing or decreasing in the size of the young cohort, depending on whether the fund has more or less reserves than required. We confine ourselves to recursive settings and study equilibria characterised by thresholds on the contribution that young generations are prepared to make assuming that the future young apply the same threshold. For standard parameter settings two such equilibria exist, of which only the one with the higher threshold is consistent with the initial young being prepared to start the system. Finally, we explore the social welfare maximising policy parameter settings for various levels of uncertainty and risk aversion

Voluntary Participation and Intergenerational Risk Sharing in a Funded Pension System

We explore the feasibility of a funded pension system with intergenerational risk sharing when participation in the system is voluntary. Typically, the willingness of the young to participate depends on their belief about the future young's willingness to do so. We characterise equilibria with voluntary participation and show that the likelihood of their existence increases with risk aversion and financial market uncertainty. We find that mandatory participation is often necessary to sustain a funded pension pillar and to let participants benefit from intergenerational risk sharing

Gene Discovery and Functional Genomics in the Pig

Advances in gene mapping and genomics in farm animals have been considerable over the past decade. Medium resolution linkage and physical maps have been reported, and specific chromosomal regions and genes associated with traits of biological and economic interest have been identified. We have reached an exciting stage in gene identification, mapping and quantitative trait locus discovery in pigs, as new molecular information is accumulating rapidly. Significant progress has been made by identifying candidate gene associations and low-resolution regions containing quantitative trail loci (QTL). However, we are still disadvantaged by the lack of tools available to efficiently use much of this new information. For example, current pig maps are neither of high enough resolution nor sufficiently informative at the comparative level for positional candidate gene cloning within QTL regions. As well, studying biological mechanisms underlying economically important traits such as reproduction is limited by the lack of molecular resources. This is especially important, as reproduction is very difficult to genetically improve by classical breeding methods due to the relatively low heritability and high expense in data collection. Thus, an improved understanding of porcine reproductive biology is of crucial economic importance, yet reproductive processes are poorly characterized at the molecular level. Recently, new methodologies have been brought to bear on a better understanding of pig molecular biology for accelerating genetic improvement in pigs. Several groups are developing molecular information in the pig, and the total Genbank sequence entries for porcine expressed genes have recently topped 100,000. Our Midwest EST Consortium has produced cDNA libraries containing the majority of genes expressed in major female reproductive tissues, and we have deposited nearly 15,000 gene sequences into public databases. These sequences represent over 8,900 different genes, based on sequence comparison among these data. Furthermore, we have developed computer software to automatically extract sequence similarity of these pig genes with their human counterparts, as well as the mapping information of these human homologues. Within our data set, we have identified nearly 1,500 pig genes with strong similarity to mapped human genes, and we are in the process of mapping 700 of these genes to improve the human-pig comparative map. This work and the complementary work of others can now be used to more rapidly understand and identify the genes controlling reproduction, so that genetic improvement of reproduction phenotypes can accelerate