Economic Reform in Tanzania and Vietnam: A Comparative Commentary

The economic reforms in Tanzania and Vietnam represent the two typical cases of transition economies in Asia and Africa, particularrly the transformation of the two developing economies from the planned to the market mechanism. In this paper, the two authors, Brian - a British economist and Dinh - a Vietnamese economist, have, basing on a comparative approach, enquired into various economic and social aspects of the economic reforms in the two countries, including the demographic transition, the change in population growth, the investment in human capital, the growth of GDP, the structural sransformation, the linkage between gricultural growth, rural development, food production and poverty alleviation, the reform in the industrial sector and the state enterprises, the change of ownership , the role of the State, the capital formation, the role of the domestic savings, foreign aid, investment and trade, the gains and losses from globalisation, with an aim to find the answer to the question why in the two cases, Tanzania seemed to follow the donors’ guidance better than Vietnam, but achieved smaller successes?Reform vesus Renovation; Fast Liberalisation vs Step-by-Step Transformation; Privatisation vs Equitisation; Multi-Sector Ownership vs Private Ownership Bias; Industrialisation vs Agriculture-Driven Growth; Active State vs Passive State.

Economic Reform in Tanzania and Vietnam: A Comparative Commentary

The economic reforms in Tanzania and Vietnam represent the two typical cases of transition economies in Asia and Africa, particularrly the transformation of the two developing economies from the planned to the market mechanism. In this paper, the two authors, Brian - a British economist and Dinh - a Vietnamese economist, have, basing on a comparative approach, enquired into various economic and social aspects of the economic reforms in the two countries, including the demographic transition, the change in population growth, the investment in human capital, the growth of GDP, the structural sransformation, the linkage between gricultural growth, rural development, food production and poverty alleviation, the reform in the industrial sector and the state enterprises, the change of ownership , the role of the State, the capital formation, the role of the domestic savings, foreign aid, investment and trade, the gains and losses from globalisation, with an aim to find the answer to the question why in the two cases, Tanzania seemed to follow the donors’ guidance better than Vietnam, but achieved smaller successes?http://deepblue.lib.umich.edu/bitstream/2027.42/40092/3/wp706.pd

The Impact of the Economic Stimulus on Domestic, Private Enterprises

In the year 2008 and the first half of 2009, the world witnessed the unfolding and heavy repercussions of the global financial crisis which affected Vietnam, among others, through the reduction of investments inflow, lower global commodity prices and trade. The government of Vietnam has acted quickly with its stimulus package, including a 4% interest rate subsidy for enterprises with the objective of preventing the economy from falling further. While there are some anecdotal evidences about the effectiveness of the stimulus package, there is no systematic evidence of the impact of the stimulus package. This paper makes use of the PCI 2008 enterprise survey data, a unique dataset which is only recently made by available to investigate the impact of the 4% interest rate subsidy component of the stimulus package. We find strong statistical evidence that the 4% interest rate subsidy has positive and important impacts on the enterprises, easing the severe effects of the global crisis.Freeware; Global financial crisis, fiscal stimulus, interest rate subsidy, Vietnam.

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks