High-resolution remote sensing data can predict household poverty in pastoral areas,Inner Mongolia,China

The accurate prediction of poverty is critical to efforts of poverty reduction,and high-resolution remote sensing(HRRS)data have shown great promise for facilitating such prediction.Accordingly,the present study used HRRS with 1 m resolution and 238 households data to evaluate the utility and optimal scale of HRRS data for predicting household poverty in a grassland region of Inner Mongolia,China.The prediction of household poverty was improved by using remote sensing indicators at multiple scales,instead of indicators at a single scale,and a model that combined indicators from four scales(building land,household,neighborhood,and regional)provided the most accurate prediction of household poverty,with testing and training accuracies of 48.57%and 70.83%,respectively.Furthermore,building area was the most efficient indicator of household poverty.When compared to conducting household surveys,the analysis of HRRS data is a cheaper and more time-efficient method for predicting household poverty and,in this case study,it reduced study time and cost by about 75%and 90%,respectively.This study provides the first evaluation of HRRS data for the prediction of household poverty in pastoral areas and thus provides technical support for the identification of poverty in pastoral areas around the world.

The Long-Run Performance of IPOs in Italy： A Comparison of Venture and Non-Venture-Backed Companies

The phenomena associated with the performance of newly listed companies has increased the interest of many researchers who have developed a vast literature on long-term underpricing and underperformance, which together with hot and cold issue markets, represent the three anomalies that have always accompanied with Initial Public Offerings （IPOs）. The objective of this work is to investigate the long-run performance of IPOs of venture and non-venture-backed companies. The analysis of a sample of 102 IPOs carried out in Italy in 1998-2005 revealed that both companies （venture-backed and non-venture-backed） showed negative values, thus, confirming the phenomenon of underperformance. During the 36 months following their listing, venture-backed companies seemed to register negative and statistically significant values both with the CARsVB methodology （-93.99%） and the Buy and Hold Abnormal Returns methodology （BHARsVB -88.37%）. Venture-backed companies, unlike non- venture-backed companies, seem to be able to restrain the losses, measured by both methods, in the first 12 months （CARsB - 12.38% -20.15% CARSNNVB; BHARsVB - 10.17%; BHARsNVB - 15.51%）. During the 36 months, however, the IPOs showed negative and statistically significant values regardless of whether they were venture or non-venture-backed. The test on the difference between the average abnormal returns of the two methodologies （CAARS and BHAARs） did not produce statistically significant results. The Wealth Relative was calculated and from the results it would appear that the portfolio of venture-backed IPOs does not register ＂brilliant＂ performances. The portfolio of 102 IPOs does not seem to beat the ＂market portfolio＂. In conclusion, therefore, the phenomenon of underperformance seems to be real in our country and is documented by strongly negative and statistically significant values obtained from the samples of IPOs analyzed.