Economic growth, inequality and technology adoption in transitional economies

The stylized facts that motivate this thesis include the diversity in growth patterns that are observed across countries during the process of economic development, and the divergence over time in income distributions both within and across countries. This thesis constructs a dynamic general equilibrium model in which technology adoption is costly and agents are heterogeneous in their initial holdings of resources. Given the households‟ resource level, this study examines how adoption costs influence the evolution of household income over time and the timing of transition to more productive technologies. The analytical results of the model constructed here characterize three growth outcomes associated with the technology adoption process depending on productivity differences between the technologies. These are appropriately labeled as „poverty trap‟, „dual economy‟ and „balanced growth‟. The model is then capable of explaining the observed diversity in growth patterns across countries, as well as divergence of incomes over time. Numerical simulations of the model furthermore illustrate features of this transition. They suggest that that differences in adoption costs account for the timing of households‟ decision to switch technology which leads to a disparity in incomes across households in the technology adoption process. Since this determines the timing of complete adoption of the technology within a country, the implications for cross-country income differences are obvious. Moreover, the timing of technology adoption appears to be impacts on patterns of growth of households, which are different across various income groups. The findings also show that, in the presence of costs associated with the adoption of more productive technologies, inequalities of income and wealth may increase over time tending to delay the convergence in income levels. Initial levels of inequalities in the resources also have an impact on the date of complete adoption of more productive technologies. The issue of increasing income inequality in the process of technology adoption opens up another direction for research. Specifically increasing inequality implies that distributive conflicts may emerge during the transitional process with political- economy consequences. The model is therefore extended to include such issues. Without any political considerations, taxes would leads to a reduction in inequality and convergence of incomes across agents. However this process is delayed if politico-economic influences are taken into account. Moreover, the political outcome is sub optimal. This is essentially due to the fact that there is a resistance associated with the complete adoption of the advanced technology.

Candidate metastasis suppressor genes uncovered by array comparative genomic hybridization in a mouse allograft model of prostate cancer.

Background The purpose of this study was to identify candidate metastasis suppressor genes from a mouse allograft model of prostate cancer (NE-10). This allograft model originally developed metastases by twelve weeks after implantation in male athymic nude mice, but lost the ability to metastasize after a number of in vivo passages. We performed high resolution array comparative genomic hybridization on the metastasizing and non-metastasizing allografts to identify chromosome imbalances that differed between the two groups of tumors. Results This analysis uncovered a deletion on chromosome 2 that differed between the metastasizing and non-metastasizing tumors. Bioinformatics filters were employed to mine this region of the genome for candidate metastasis suppressor genes. Of the 146 known genes that reside within the region of interest on mouse chromosome 2, four candidate metastasis suppressor genes (Slc27a2, Mall, Snrpb, and Rassf2) were identified. Quantitative expression analysis confirmed decreased expression of these genes in the metastasizing compared to non-metastasizing tumors. Conclusion This study presents combined genomics and bioinformatics approaches for identifying potential metastasis suppressor genes. The genes identified here are candidates for further studies to determine their functional role in inhibiting metastases in the NE-10 allograft model and human prostate cancer.