Abstract. Aim: The goal of the present study was to determine the prevalence and distribution of high-risk human papilloma virus (HPV) genotypes in women from two districts in and aroundProphylactic vaccination against HPV has been conducted in many countries for the prevention of cervical cancer. There are a number of variants such as monovalent (HPV16), bivalent (HPV16 and 18), quadrivalent (HPV6, 11, 16 and 18) and nonavalent (HPV6,11,16,18, 31, 33, 45, 52 and 58) vaccines (5,6). In Vietnam, the measures for cervical cancer prevention include, primary screening for HPV and vaccination against HPV (7). It has been shown that there are differences in the genotype distribution in women with multiple HPV infections not only among women of different ethnicity but also women from different regions (8). Moreover, the tendency of HPV genotypes to cluster in multiply infected women has been frequently observed (8). Knowledge about the type-specific distribution and the prevalence of HPV cervical infection in women from diverse areas and ethnicity is a prerequisite for the planning of an efficient and adequate screening and vaccination strategy with the best possible outcome. Some studies have been conducted within Vietnam, especially in Ho Chi Minh City and Hanoi (7, 9-11), but also in populations from Thai Nguyen, Hue and Can Tho (9). These studies showed a prevalence of high-risk HPV infection, ranging between 2-11%.

This study employs a chance-constrained data envelopment analysis (CDEA) approach with two models (model A and model B) to decompose provincial productivity growth in Vietnamese agriculture from 1995 to 2007 into technological progress and efficiency change. The differences between the chance-constrained programming model A and model B are assumptions imposed on the covariance matrix. The decomposition allows us to identify the contributions of technical change and the improvement in technical efficiency to productivity growth in Vietnamese production. Sixty-one provinces in Vietnam are classified into Mekongtechnology and other technology categories. We conduct a Mann-Whitney test to verify whether the two samples, the Mekong technology province sample and the other technology sample, are drawn from the same productivity change populations. The result of the Mann-Whitney test indicates that the differences between the Mekong technology category and the other technology category from two models are more significant. Two important questions are whether some provinces in the samples could maintain their relative efficiency rank positions in comparison with the others over the study period and how to further examine the agreements between the two models. The Kruskal-Wallis test statistic shows that technical efficiency from both models for some provinces are higher than those of them in the study period. The Malmquist results show that production frontier has contracted by around 1.3 percent and 0.31 percent from chance-constrained model A and model B, respectively, a year on average over the sample period. To examine the agreements or disagreements in the total factor productivity indexes we compute the correlation between Malmquist indexes, which is positive and not very high. Thus there is a little discrepancy between the two Malmquist indexes, estimated from the chance-constrained models A and B.

This paper employs the spatial econometric approach to undertake a research of labor productivity convergence of the industrial sector among sixty provinces in Vietnam in the period 1998-2011. It is shown that the assumption of the independence among spatial units (provinces in this case) is unrealistic, being in contrast to the evidence of the data reflecting the spatial interaction and the existence of spatial lag and errors. Therefore, neglecting the spatial nature of data can lead to a misspecification of the model. We decompose the sample data into the sub-periods 1998-2002 and 2003-2011 for the analysis. Different tests point out that the spatial lag model is appropriate for the whole period of the sample data (1998-2011) and the sub-period (2003-2011), therefore, we employ the maximum likelihood procedure to estimate the spatial lag model. The estimation results allow us to recognize that the convergence model without a spatial lag variable and using ordinary least square to estimate has the problem of omitting variables, which will have impact on the estimated measure of convergence speed. And this problem dominates the positive effect of factors such as mobilizing factors, trade relation, and knowledge spillover in the regional scope.