The Share Price Effects of Dividend Taxes and Tax Imputation Credits

We examine the hypothesis that dividend taxes are capitalized into share prices by focusing on investors' implicit valuations of retained earnings versus paid-in equity. Retained earnings are distributable as taxable dividends, whereas paid-in equity is distributable as a tax-free return of capital. Consistent with dividend tax capitalization, firm-level results for the United States indicate that accumulated retained earnings are valued less per unit than contributed capital. In addition, differences in dividend tax rates across U.S. tax regimes are associated with predictable differences in the magnitude of the implied tax discount for retained earnings, as are differences in dividend tax rates across Australia, Japan, France, Germany, and the United Kingdom.

Aeroacoustic and aerodynamic performances of an aerofoil subjected to sinusoidal leading edges

This paper presents the preliminary results on the aeroacoustic and aerodynamic performances of a NACA65-(12)10 aerofoil subjected to 12 sinusoidal leading edges. The serration patterns of these leading edges are formed by cutting into the main body of the aerofoil, instead of extending the leading edges. Any of the leading edges, when attached to the main body of the aerofoil, will always result in the same overall chord length. The experiment was mainly performed in an aeroacoustic wind tunnel facility, although a separate aerodynamic type wind tunnel was also used for the force measurements. These sinusoidal leading edges were investigated for their effectiveness in suppressing the laminar instability tonal noise (trailing edge self-noise) and turbulence–leading edge interaction noise. The largest reduction in aerofoil noise tends to associate with the sinusoidal leading edge of the largest amplitude, and smallest wavelength. However, noticeable noise increase at high frequency is also observed for this combination of serration. In terms of the aerodynamic performance, increasing the serration wavelength tends to improve the stall angles, but the lift coefficient at the pre-stall regime is generally lower than that produced by the baseline leading edge. For a sinusoidal leading edge with large serration amplitude, the effect of the reduction in “lift-generating” surface is manifested in the significant reduction of the lift coefficients and lift curve slope. The sinusoidal leading edge that produces the best performance in the post-stall regime belongs to the largest wavelength and smallest amplitude, where the lift coefficients are shown to be better than the baseline leading edge. In conclusion, large amplitude and small wavelength is beneficial for noise reduction, whilst to maintain the aerodynamic lift a small amplitude and large wavelength is preferred

Why does the Engel method work? Food demand, economies of size and household survey methods

Estimates of household size economies are needed for the analysis of poverty and inequality. This paper shows that Engel estimates of size economies are large when household expenditures are obtained by respondent recall but small when expenditures are obtained by daily recording in diaries. Expenditure estimates from recall surveys appear to have measurement errors correlated with household size. As well as demonstrating the fragility of Engel estimates of size economies, these results help resolve a puzzle raised by Deaton and Paxson (1998) about differences between rich and poor countries in the effect of household size on food demand

Authentication of saffron using 60 MHz 1H NMR spectroscopy

60 MHz proton NMR spectroscopy was used to analyse extracts from saffron spice and a range of potential adulterants and mixtures. Using a simple extraction procedure, good quality spectra were obtained which contain peaks from the characteristic metabolites picrocrocin and crocins, fatty acids and kaempferol. The spectra of samples from trusted suppliers were used to train one-class classification models by SIMCA, nearest neighbour and isolation forest methods. Applying these to spectra of saffron samples purchased from the online marketplace, it was found that 7 out of 33 samples were highly anomalous. From comparison with the spectra of known mixtures and confirmatory spectral analysis using 600 MHz NMR, it is probable that these contain considerable amounts of undisclosed foreign matter

The measurement of household consumption expenditures

Household-level data on consumer expenditures underpin a wide range of empirical research in modern economics, spanning micro-and macroeconomics. This research includes work on consumption and saving, on poverty and inequality, and on risk sharing and insurance. We review different ways in which such data can be collected or captured: traditional detailed budget surveys, less onerous survey procedures that might be included in more general surveys, and administrative or process data. We discuss the advantages and difficulties of each approach and suggest directions for future investigation. © 2014 by Annual Reviews. All rights reserved

Acute Consumption of Flavan-3-ol-Enriched Dark Chocolate Affects Human Endogenous Metabolism

Flavan-3-ols and methylxanthines have potential beneficial effects on human health including reducing cardiovascular risk. We performed a randomized controlled crossover intervention trial to assess the acute effects of consumption of flavan-3-ol-enriched dark chocolate, compared with standard dark chocolate and white chocolate, on the human metabolome. We assessed the metabolome in urine and blood plasma samples collected before and at 2 and 6 h after consumption of chocolates in 42 healthy volunteers using a nontargeted metabolomics approach. Plasma samples were assessed and showed differentiation between time points with no further separation among the three chocolate treatments. Multivariate statistics applied to urine samples could readily separate the postprandial time points and distinguish between the treatments. Most of the markers responsible for the multivariate discrimination between the chocolates were of dietary origin. Interestingly, small but significant level changes were also observed for a subset of endogenous metabolites. H-1 NMR revealed that flavan-3-ol-enriched dark chocolate and standard dark chocolate reduced urinary levels of creatinine, lactate, some amino acids, and related degradation products and increased the levels of pyruvate and 4-hydroxyphenylacetate, a phenolic compound of bacterial origin. This study demonstrates that an acute chocolate intervention can significantly affect human metabolism

A comparison of variate pre-selection methods for use in partial least squares regression: a case study on NIR spectroscopy applied to monitoring beer fermentation

This work investigates four methods of selecting variates from near-infrared (NIR) spectra for use in partial least squares (PLS) regression models to predict biomass and chemical changes during beer fermentation. The fermentation parameters studied were ethanol concentration, specific gravity (SG), optical density (OD) and dry cell weight (DCW). The four selection methods investigated were: Simple, where a fingerprint region is chosen manually; CovProc, a covariance procedure where variates are introduced based on the magnitude of the 1st PLS vector coefficients; CovProc-SavGo, a modification to CovProc where the window size of a Savitzky-Golay filter applied to the spectra is also optimised; and Genetic Algorithm (GA), where variates are selected based on the frequency of appearance in 8-variate multiple linear regression models found from repeated execution of the GA routine. The analysis found that all four methods produced good predictive models. The GA approach produced the lowest standard error in prediction (SEP) based on leave-one-out cross validation (LOO-CV), although this advantage was not reflected in the standard error in validation values, SEV, where all four models performed comparably. From this work, we would recommend using the Simple approach if a suitable fingerprint region can be identified, and using CovProc otherwise

A specific case in the classification of woods by FTIR and chemometric: discrimination of Fagales from Malpighiales

Fourier transform infrared (FTIR) spectroscopic data was used to classify wood samples from nine species within the Fagales and Malpighiales using a range of multivariate statistical methods. Taxonomic classification of the family Fagaceae and Betulaceae from Angiosperm Phylogenetic System Classification (APG II System) was successfully performed using supervised pattern recognition techniques. A methodology for wood sample discrimination was developed using both sapwood and heartwood samples. Ten and eight biomarkers emerged from the dataset to discriminate order and family, respectively. In the species studied FTIR in combination with multivariate analysis highlighted significant chemical differences in hemicelluloses, cellulose and guaiacyl (lignin) and shows promise as a suitable approach for wood sample classification

Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study

Genomics-based technologies produce large amounts of data. To interpret the results and identify the most important variates related to phenotypes of interest, various multivariate regression and variate selection methods are used. Although inspected for statistical performance, the relevance of multivariate models in interpreting biological data sets often remains elusive. We compare various multivariate regression and variate selection methods applied to a nutrigenomics data set in terms of performance, utility and biological interpretability. The studied data set comprised hepatic transcriptome (10,072 predictor variates) and plasma protein concentrations [2 dependent variates: Leptin (LEP) and Tissue inhibitor of metalloproteinase 1 (TIMP-1)] collected during a high-fat diet study in ApoE3Leiden mice. The multivariate regression methods used were: partial least squares “PLS”; a genetic algorithm-based multiple linear regression, “GA-MLR”; two least-angle shrinkage methods, “LASSO” and “ELASTIC NET”; and a variant of PLS that uses covariance-based variate selection, “CovProc.” Two methods of ranking the genes for Gene Set Enrichment Analysis (GSEA) were also investigated: either by their correlation with the protein data or by the stability of the PLS regression coefficients. The regression methods performed similarly, with CovProc and GA performing the best and worst, respectively (R-squared values based on “double cross-validation” predictions of 0.762 and 0.451 for LEP; and 0.701 and 0.482 for TIMP-1). CovProc, LASSO and ELASTIC NET all produced parsimonious regression models and consistently identified small subsets of variates, with high commonality between the methods. Comparison of the gene ranking approaches found a high degree of agreement, with PLS-based ranking finding fewer significant gene sets. We recommend the use of CovProc for variate selection, in tandem with univariate methods, and the use of correlation-based ranking for GSEA-like pathway analysis methods