Payout policy and cash-flow uncertainty

The importance of cash-flow uncertainty in payout policy has received little attention in empirical studies, while survey studies such as [Lintner, J., 1956. Distribution of incomes of operations among dividends, retained earnings, and taxes. American Economic Review 46, 97-113.] and [Brav, A., Graham, J., Harvey C., Michaely, R., 2005. Payout policy in the 21st century. Journal of Financial Economics 77, 483-527.] indicate its importance. With worldwide firm-level data, we present evidence that cash-flow uncertainty is an important cross-sectional determinant of corporate payout policy. Our results show that across countries, cash-flow uncertainty, as proxied by stock return volatility, has a negative impact on the amount of dividends as well as the probability of paying dividends. The impact of cash-flow uncertainty on dividends is generally stronger than the impact of other potential determinants of payout policy--such as the earned/contributed capital mix, agency conflicts, and investment opportunities. We also find that the effect of cash-flow uncertainty on dividends is distinct from the effect of a firm's financial life-cycle stage.Cash-flow uncertainty Dividend Repurchase

Towards the Reliable Prediction of Time to Flowering in Six Annual Crops. VI. Applications in Crop Improvement

Variation in time from sowing to flowering (f) was examined for 44 cultivars of soyabean, mungbean, black gram, ricebean, cowpea, chickpea, lentil and barley, when grown in up to 21 diverse environments obtained by making one or more sowings at each of six locations spanning tropical, sub-tropical and temperate climates in Australia. The utility of simple linear models, relating rate of development (l/f) towards flowering to mean photoperiod and temperature prevailing between sowing and flowering, was evaluated. The models were highly efficient, explaining most (86.7%) of the variation observed across species, cultivars and environments. They were particularly efficient in describing responses where cultivars were relatively well-adapted, in agronomic terms, and least efficient where cultivars were exposed to unfavourable temperature and, to a lesser extent, photoperiod. Opportunities for exploiting the models in applied crop improvement include their use in interpretation of G × E interaction, genotypic characterization and selection of parental genotypes, selection of test environments, designing screening procedures, and more efficiently matching genotypes to target environments. The main strengths of these linear, additive rate models in crop improvement are their wide applicability across species and genotypes, their relative simplicity, and the requirement for few genotype-specific response parameters. Their main weakness is their lack of precision in describing responses when plants are exposed to unfavourable photothermal extremes, albeit in circumstances that are sometimes unrealistic for cropping those particular genotypes