China implemented the two-child policy in 2016, however, potential impacts of this new policy on its population reality have not been adequately understood. Using population census data and 1% population sampling data during the period of 1982–2015, this study develops a fertility simulation model to explore the effects of the two-child policy on women’s total fertility rate, and employs Cohort Component Method in population projections to examine China’s demographic future with different fertility regimes. The fertility simulation results reveal that the two-child policy will make significantly positive effects on China’s total fertility rate through increasing second births, leading to a sharp but temporary increase in the first 5 years after the implementation of the new policy. In addition, population projections using simulated total fertility rates show that the Chinese population would reach its peak value around the middle 2020s and be faced with the reduction of labor force supply and rapid aging process, featured with remarkable increases in both size and share of the elderly population. The findings suggest that the two-child policy would undoubtedly affect China’s fertility rates and demographic future; however, the effects are mild and temporary.
In this study we examine the moderating effect of competitive strategy (including differentiation and cost‐leadership strategies) on the relationship between exploration and firm performance. We find that the moderating effect of differentiation strategy is positive while that of cost‐leadership strategy is negative. And, these moderating effects are stronger in a highly competitive context. This study offers an explanation for previous mixed findings on the linkage of exploration to firm performance and enriches the discipline's knowledge regarding the performance implications of exploration. Moreover, we respond directly to the appeal in research on competitive strategy to clarify the role played by competitive strategy in profiting from exploration. 相似文献
Residual marked empirical process-based tests are commonly used in regression models. However, they suffer from data sparseness in high-dimensional space when there are many covariates. This paper has three purposes. First, we suggest a partial dimension reduction adaptive-to-model testing procedure that can be omnibus against general global alternative models although it fully use the dimension reduction structure under the null hypothesis. This feature is because that the procedure can automatically adapt to the null and alternative models, and thus greatly overcomes the dimensionality problem. Second, to achieve the above goal, we propose a ridge-type eigenvalue ratio estimate to automatically determine the number of linear combinations of the covariates under the null and alternative hypotheses. Third, a Monte-Carlo approximation to the sampling null distribution is suggested. Unlike existing bootstrap approximation methods, this gives an approximation as close to the sampling null distribution as possible by fully utilising the dimension reduction model structure under the null model. Simulation studies and real data analysis are then conducted to illustrate the performance of the new test and compare it with existing tests. 相似文献