Government policies that impose restrictions on the size of large establishments or firms, or promote small ones, are widespread across countries. In this paper, we develop a framework to systematically study policies of this class. We study a simple growth model with an endogenous size distribution of production units. We parameterize this model to account for the size distribution of establishments and for the large share of employment in large establishments. Then, we ask: quantitatively, how costly are policies that distort the size of production units? What is the impact of these policies on productivity measures, the equilibrium number of establishments and their size distribution? We find that these effects are potentially large: policies that reduce the average size of establishments by 20% lead to reductions in output and output per establishment up to 8.1% and 25.6% respectively, as well as large increases in the number of establishments (23.5%).
Grain refinement of magnesium alloy AZ31 was studied in multidirectional forging (MDF) under decreasing temperature conditions. MDF was carried out up to large cumulative strains of 5.6 with changing the loading direction during decrease in temperature from pass to pass. MDF can accelerate the uniform development of very fine-grained structures and an increase of the plastic workability at low temperatures. New grain structures with the minimal grain size of 0.23 mm can be developed by continuous dynamic recrystallization at a final processing temperature of 403 K. As a result, the multidirectional-forged alloy showed excellent higher strength as well as moderate ductility at room temperature, and also a superplastic elongation of over 300% at 423 K. The mechanisms of strain-induced and fine-grained structure development and of the excellent plastic deformation are discussed in detail.
Gene-environment (G-E) interactions have important implications for the etiology and progression of many complex diseases. Compared to continuous markers and categorical disease status, prognosis has been less investigated, with the additional challenges brought by the unique characteristics of survival outcomes. Most of the existing G-E interaction approaches for prognosis data share the limitation that they cannot accommodate long-tailed or contaminated outcomes. In this study, for prognosis data, we develop a robust G-E interaction identification approach using the censored quantile partial correlation (CQPCorr) technique. The proposed approach is built on the quantile regression technique (and hence has a solid statistical basis), uses weights to easily accommodate censoring, and adopts partial correlation to identify important interactions while properly controlling for the main genetic and environmental effects. In simulation, it outperforms multiple competitors with more accurate identification. In the analysis of TCGA data on lung cancer and melanoma, biologically sensible findings different from using the alternatives are made.
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