Perspectives of children with disabilities and their guardians on factors affecting inclusion in education in rural Nepal: “I feel sad that I can’t go to school”

Globally, children with disabilities are significantly less likely to attend school compared to their peers without disabilities and, even if they do attend, have poorer educational outcomes. In order to understand why these inequalities persist, this study explores the barriers and enablers to accessing education. We focus on the perspectives of guardians and children with disabilities – voices that have thus far been underrepresented – complemented by perspectives from local and national level stakeholders. Data was collected in three rural districts in Nepal, using semi-structured interviews; data was analysed thematically. Overall, the research found that challenges to inclusion are complex, involving a mixture of individual, family, school, community and policy level factors. Notable barriers were attitudes towards education for children with disabilities, the low capacity of schools to provide an inclusive education, as well as the interplay of additional ‘push factors’ such as poor health and poverty.     

Monetary policy shocks and interest rates: Further evidence on the liquidity effect

Monetary Policy Shocks and Interest Rates: Further Evidence on the Liquidity Effect. — This essay tests whether innovations in monetary policy are inversely linked with changes in interest rates. Using Mishkin’s efficient markets framework and the measures of policy innovations constructed by Boschen and Mills and Bemanke and Mihov, we find strong evidence that expansionary monetary policy shocks lower interest rates. We argue that the failure of most studies to find a significant liquidity effect is due to the endogeneity of the monetary aggregates which are used to measure policy shocks.     

Optimal Rationing Policies and Production Quantities for Products with Several Demand Classes

This paper considers a situation in which several distinct classes of customers exist for a single product or service in a fixed, finite planning period. If such a product is also perishable and if backlogging is not permitted, it may sometimes be worthwhile to reject “low-revenue customers” upon arrival, depending on the number of units on hand and time left until the end of the period. The objectives of this study are to derive optimal rationing policies at any state of the planning period and to formulate a procedure for calculating the optimal production quantity at the beginning of the period for various cost (production) functions, given that an optimal rationing policy is to be followed. The approach used is backward-recursion dynamic programming. Résumé Ce mémoire examine une situation dans laquelle plusieurs classes distinctes de clients existent pour un seul produit ou service au cours d'une période de planification déterminée. Si cette denrée est également périssable et que le stockage n'est pas permis, il est parfois avantageux de rejeter les “clients à bas revenus,” tout dépendant du nombre d'unités en main et du temps qui reste avant la fin de la période. Les objectifs de l'éude visent à créer des politiques de rationnement optimal au cours de la période de planification et de formuler une méthode de calcul des quantités optimales de production en début de période relativement à plusieurs fonctions relatives au coǔt (production), compte tenu de l'application d'une politique de rationnement optimal. Il s'agit ici de programmation dynamique à récursivité rétrograde.     

The next scientific revolution

For decades, computer scientists have tried to teach computers to think like human experts. Until recently, most of those efforts have failed to come close to generating the creative insights and solutions that seem to come naturally to the best researchers, doctors, and engineers. But now, Tony Hey, a VP of Microsoft Research, says we're witnessing the dawn of a new generation of powerful computer tools that can "mash up" vast quantities of data from many sources, analyze them, and help produce revolutionary scientific discoveries. Hey and his colleagues call this new method of scientific exploration "machine learning." At Microsoft, a team has already used it to innovate a method of predicting with impressive accuracy whether a patient with congestive heart failure who is released from the hospital will be readmitted within 30 days. It was developed by directing a computer program to pore through hundreds of thousands of data points on 300,000 patients and "learn" the profiles of patients most likely to be rehospitalized. The economic impact of this prediction tool could be huge: If a hospital understands the likelihood that a patient will "bounce back," it can design programs to keep him stable and save thousands of dollars in health care costs. Similar efforts to uncover important correlations that could lead to scientific breakthroughs are under way in oceanography, conservation, and AIDS research. And in business, deep data exploration has the potential to unearth critical insights about customers, supply chains, advertising effectiveness, and more.     

Green commuter planning: A role for business

Evidence is presented confirming the environmental importance of work‐related travel. A recent guide to setting up a green commuter plan is summarized, as is a report evaluating the effectiveness of staff transport reduction programmes in the USA and Holland. The elements of an effective and comprehensive ‘green change’ strategy are discussed. These include site location, external lobbying, identifying cultural issues and marketing, as well as internal changes to direct transport‐related systems. Further research, multidisciplinary and applied, is recommended. Copyright © 1999 John Wiley & Sons, Ltd and ERP Environment.     

Money stock targeting and money supply: A closer examination of the data

In a recent paper Mercenier and Sekkat (1988) use a linear-quadratic model to examine the willingness of a monetary authority in a small open economy to target its exchange rate. Based on their empirical results, the authors conclude that the Bank of Canada has displayed a willingness to use the money supply to target the Canada—US exchange rate. We re-examine their empirical results using a different estimation approach and with different assumptions about the forcing process of the exogeneous variables. We also extend the sample period to include more recent observations. While we find some weak evidence to support their conclusion, the results, in general, suggest that a linear-quadratic model may not be a particularly useful representation of the assumed exchange rate targeting by a monetary authority.     

Closing information asymmetries: A scale agnostic approach for exploring equity implications of broadband provision

A persistent challenge for telecommunications policy is the determination of broadband provision footprints in both space and time. In the United States, Form 477 data from the Federal Communications Commission (FCC) provide a valuable snapshot of broadband provision at the block level, but there are often inconsistencies with the underlying data. These inconsistencies include overly-optimistic self-reports from providers, requirements to report broadband provision within inflexible census administrative units, and a modest temporal reporting schedule (biannual) with a significant, one year temporal lag. These uncertainties are often compounded by telecommunications providers refusing to disclose any information on service footprints, geographic expansion plans or the characteristics of populations served by broadband. This type of obfuscation drastically limits the ability of policy analysts to evaluate outcomes (both positive and negative) associated with service provision and the digital divide, more generally. With the recent entry of Google Fiber to several metropolitan markets in the United States, many of the broadband reporting and evaluation challenges have re-emerged. The purpose of this paper is to leverage basic data mining techniques, a scale agnostic geographic framework and exploratory spatial data analysis (ESDA), to uncover the geographic intricacies of Google Fiber (Fiber) service for both Provo, Utah and Austin, Texas and compare them to Form 477 reports. In addition, we use basic statistical approaches to explore issues of broadband access and equity, accounting for the differences in socio-economic and demographic status of the populace served/not served for both cities, as well as basic pricing within and between communities.