Does board gender diversity affect firm performance?Empirical evidence from Standard & Poor’s 500 Information Technology Sector

The essence of this study is to investigate the influence of the board gender diversity on firms’accounting and market-based performance using a sample of Standard&Poor’s 500 companies belonging to the information technology sector over 12 years.Using the pooled ordinary least squares(OLS)method,the outcomes provide evidence for a positive influence of women on corporate boards on both measures of company performance,except for the percentage of female executives in the case of return on assets(ROA).After estimating the fixed effects and random-effects through panel data,the econometric outcomes show no statistically significant association among board gender diversity and ROA but a positive influence of the number and percentage of women on board on price-to-earnings ratio.

Statistical models for meta-analysis: A brief tutorial

Aggregate data meta-analysis is currently the most commonly used method for combining the results from different studies on the same outcome of interest. In this paper, we provide a brief introduction to meta-analysis, including a description of aggregate and individual participant data meta-analysis. We then focus the rest of the tutorial on aggregate data metaanalysis. We start by first describing the difference between fixed and random-effects meta-analysis, with particular attention devoted to the latter. This is followed by an example using the random-effects, method of moments approach and includes an intercept-only model as well as a model with one predictor. We then describe alternative random-effects approaches such as maximum likelihood, restricted maximum likelihood and profile likelihood as well as a non-parametric approach. A brief description of selected statistical programs available to conduct random-effects aggregate data meta-analysis, limited to those that allow both an interceptonly as well as at least one predictor in the model, is given. These descriptions include those found in an existing general statistics software package as well as one developed specifically for an aggregate data metaanalysis. Following this, some of the disadvantages of random-effects meta-analysis are described. We then describe recently proposed alternative models for conducting aggregate data meta-analysis, including the varying coefficient model. We conclude the paper with some recommendations and directions for future research. These recommendations include the continued use of the more commonly used random-effects models until newer models are more thoroughly tested as well as the timely integration of new and well-tested models into traditional as well as meta-analytic-specific software packages.

Using Biophysical Variables and Stand Density to Estimate Growth and Yield of <i>Pinus patula</i>in Antioquia, Colombia

Timberland investment opportunities in Colombia are expected to increase as a result of the peace agreement recently signed between the Colombian government and the Revolutionary Armed Forces of Colombia. This new socio-political environment may facilitate the expansion of commercial forest plantations on a wider range of site conditions that are currently considered in existing biometric tools. Data from 1119 temporary plots of unthinned, unmanaged, and genetically unimproved Pinus patula plantations in the Antioquia region were combined with a large set of biophysical attributes to identify spatial variation in yield. A wide array of biophysical covariates was explored to characterize the most favorable environmental conditions for the species, and to identify potential explanatory variables to be included in forest yield models. The mathematical form of the model is the von Bertalanffy-Chapman-Richards type, with parameters: asymptote, intrinsic growth rate and allometric constant. The parameters were expressed as linear functions of soil pH, terrain slope, the mean annual temperature to mean annual precipitation ratio, and stand density. The statistical contribution of selected covariates was evaluated using the likelihood ratio test. The model was validated using an independent set of 133 observations. The spatial representation of the model depicts the timber production potential and allows for the identification of the most suitable geographical areas to establish Pinus patula plantations in Antioquia, Colombia. The estimated yield model provides a reliable baseline for timber production, and insight into timberland investments in Colombia.

An Empirical Estimation of Effects of Priority Forestry Programs on Farmers’ Incomes in China

China has launched six Priority Forestry Programs (PFPs) since 1998, i.e. the Natural Forest Protection Program, the Cropland Conversion to Forest and Grassland Program, Sandification Control Program for the Vicinity of Beijing & Tianjin , Wildlife Conservation and Nature Reserve Development Program, Forest Industrial Base Development Program and Shelterbelt Development Programs for regions such as Three North and the Yangtze River Catchments. The Government of China has made different policies for these PFPs, such as subsidies, low-interest loans and revenue offsets. Using a fixed-effect model and panel data from 2 353 households in 9 counties of Sichuan, Hebei, Shaanxi, and Jiangxi provinces, this paper studies effects of PFPs on farmers’ incomes. The empirical results indicate that the effects of PFPs on farmers’ incomes are mixed. Overall, the impact of Conversion of Cropland to Forestland and Grassland Program is significantly positive, whereas that of the Natural Forest Protection Program and the Sandification Control Program around Beijing & Tianjin is negative. To a lesser extent, the Shelterbelt Development Programs have a positive impact. In the meantime, different effects of PFPs on farmers’ incomes are also mixed for different provinces and different households are also mixed.