Education and Earnings for Poverty Reduction : Short-Term Evidence of Pro-Poor Growth from the Mexican Oportunidades Program

Education, as an indispensable component of human capital, has been acknowledged to play a critical role in economic growth, which is theoretically elaborated by human capital theory and empirically confirmed by evidence from different parts of the world. The educational impact on growth is especially valuable and meaningful when it is for the sake of poverty reduction and pro-poorness of growth. The paper re-explores the precious link between human capital development and poverty reduction by investigating the causal effect of education accumulation on earnings enhancement for anti-poverty and pro-poor growth. The analysis takes the evidence from a well-known conditional cash transfer (CCT) program — Oportunidades in Mexico. Aiming at alleviating poverty and promoting a better future by investing in human capital for children and youth in poverty, this CCT program has been recognized producing significant outcomes. The study investigates a short-term impact of education on earnings of the economically disadvantaged youth, taking the data of both the program’s treated and untreated youth from urban areas in Mexico from 2002 to 2004. Two econometric techniques, i.e. difference-in-differences and difference-in-differences propensity score matching approach are applied for estimation. The empirical analysis first identifies that youth who under the program’s schooling intervention possess an advantage in educational attainment over their non-intervention peers; with this identification of education discrepancy as a prerequisite, further results then present that earnings of the education advantaged youth increase at a higher rate about 20 percent than earnings of their education disadvantaged peers over the two years. This result indicates a confirmation that education accumulation for the economically disadvantaged young has a positive impact on their earnings enhancement and thus inferring a contribution to poverty reduction and pro-poorness of growth.

Renewable energy education : Report from a field trip to Melbourne, Australia, and short visits to India and New Zealand During Nov/Dec 1992

During November and December 1992 I visited several groups involved with renewable energy, most of them dealing with education. These groups and their work are described briefly in this report. The groups in Melbourne, Australia have come a long way with education in this field and we have a lot to learn from them. Government funding is needed for large scale work, but useful work can still be done at the community level with much smaller budgets.

Short Communication: A compelling argument for the gravity p-median model

The p-median model is used to locate P facilities to serve a geographically distributed population. Conventionally, it is assumed that the population always travels to the nearest facility. Drezner and Drezner (2006, 2007) provide three arguments on why this assumption might be incorrect, and they introduce the extended the gravity p-median model to relax the assumption. We favour the gravity p-median model, but we note that in an applied setting, Drezner and Drezner’s arguments are incomplete. In this communication, we point at the existence of a fourth compelling argument for the gravity p-median model.

The rapid analysis of heroin drug seizures using micellar electrokinetic chromatography with short-end injection

A simple and rapid method for the analysis of heroin seizures by micellar electrokinetic chromatography with short-end injection is described. Separations were performed using an uncoated fused silica capillary, 50 cm×50 mm I.D.×360 mm O.D. with an effective separation length of 8 cm. The system was run at 25°C with an applied negative voltage of –25 kilovolts. Injection of each sample was for 2 s at –50 mbar. UV detection was employed with the wavelength set at 210 nm. The background electrolyte consisted of 85:15 (water:acetonitrile, v/v) containing final concentrations of 25 mM SDS and 15 mM sodium borate, pH 9.5. Samples and standards were prepared in 0.1% v/v acetic acid and diluted in the run buffer containing 1 mg/ml of N,N-dimethyl-5-methoxytryptamine as an internal standard. Under these conditions a text mixture containing caffeine, paracetamol, morphine, codeine, heroin, and acetylcodeine was resolved within 1.5 min. The method was used to determine the concentration of heroin in heroin seizure samples, and the results were in good agreement with those obtained by a validated gas chromatographic method.

Enhancing the effectiveness of clustering with spectra analysis

For many clustering algorithms, such as K-Means, EM, and CLOPE, there is usually a requirement to set some parameters. Often, these parameters directly or indirectly control the number of clusters, that is, k, to return. In the presence of different data characteristics and analysis contexts, it is often difficult for the user to estimate the number of clusters in the data set. This is especially true in text collections such as Web documents, images, or biological data. In an effort to improve the effectiveness of clustering, we seek the answer to a fundamental question: How can we effectively estimate the number of clusters in a given data set? We propose an efficient method based on spectra analysis of eigenvalues (not eigenvectors) of the data set as the solution to the above. We first present the relationship between a data set and its underlying spectra with theoretical and experimental results. We then show how our method is capable of suggesting a range of k that is well suited to different analysis contexts. Finally, we conclude with further  empirical results to show how the answer to this fundamental question enhances the clustering process for large text collections.

A spectroscopy of texts for effective clustering

For many clustering algorithms, such as k-means, EM, and CLOPE, there is usually a requirement to set some parameters. Often, these parameters directly or indirectly control the number of clusters to return. In the presence of different data characteristics and analysis contexts, it is often difficult for the user to estimate the number of clusters in the data set. This is especially true in text collections such as Web documents, images or biological data. The fundamental question this paper addresses is: ldquoHow can we effectively estimate the natural number of clusters in a given text collection?rdquo. We propose to use spectral analysis, which analyzes the eigenvalues (not eigenvectors) of the collection, as the solution to the above. We first present the relationship between a text collection and its underlying spectra. We then show how the answer to this question enhances the clustering process. Finally, we conclude with empirical results and related work.

A SPATIAL COMPARISON OF SHORT AND LONG TERM MIGRATION TRENDS IN CHINA

http://digitalcommons.colby.edu/atlasofmaine2005/1019/thumbnail.jpg

Bleeding: A Collection of Poetry and Short Fiction

Poetry Short Stories

Short fiction of Thomas Hardy: an exploration and assessment

Hardy's novels and poetry have received extensive criticism, in due proportion to their merit. Hardy's short stories, however, have been virtually excluded from the annals of Hardy criticism, even though Hardy wrote over forty short stories, several of which are truly outstanding. In part, the reason for this neglect is because of the neglected state of the short story in Victorian England. Short fiction, published mainly only in periodicals and never collected in volume form, was obscured to a large extent by the highly popular serial novel. This thesis examines Thomas Hardy's short stories in the context of both the Victorian period and the Victorian short story genre, and explores the ways in which Thomas Hardy improved upon and deviated from some of the common types of short fiction being written in his day.

Positioning, linguistic theory and Orwell's short nonfiction

It is argued, making reference to an Orwell text sample, that linguistic theory illuminates "positioning" (as term and concept), that positioning's flexibility contributes to critical textual analysis and to attempts to understand complex human processes, but how far language may constrain as well as facilitate such understanding must remain open.

Image to text translation by multi-label classification

This paper presents an image to text translation platform consisting of image segmentation, region features extraction, region blobs clustering, and translation components. A multi-label learning method is suggested for realizing the translation component. Empirical studies show that the predictive performance of the translation component is better than its counterparts when employed a dual-random ensemble multi-label classification algorithm.