This paper examines the issue of horizontal inequalities in Vietnam over the past 20 years. Using data from three recent Vietnam population censuses (1989, 1999, and 2009) and three Vietnam Household Living Standard Surveys (1998, 2008, 2012), we estimated numerous measures on inequalities between five groups against four welfare indicators. Our results show that horizontal inequality matters in Vietnam, in particular for ethnicity, region, and rural/urban groups. While there has been an improvement in horizontal inequality in education, this paper shows little change in other welfare indicators, in particular poverty. We also found that horizontal inequality does matter for poverty reduction in Vietnam and it needs more attention when designing poverty policies in the future.
This paper considers variable and factor selection in factor analysis. We treat the factor loadings for each observable variable as a group, and introduce a weighted sparse group lasso penalty to the complete log-likelihood. The proposal simultaneously selects observable variables and latent factors of a factor analysis model in a data-driven fashion; it produces a more flexible and sparse factor loading structure than existing methods. For parameter estimation, we derive an expectation-maximization algorithm that optimizes the penalized log-likelihood. The tuning parameters of the procedure are selected by a likelihood cross-validation criterion that yields satisfactory results in various simulation settings. Simulation results reveal that the proposed method can better identify the possibly sparse structure of the true factor loading matrix with higher estimation accuracy than existing methods. A real data example is also presented to demonstrate its performance in practice. 相似文献