The linkage effects of raw material processing in economic development: A survey of modeling and other approaches

Processing of primary raw materials has become an important and widely recommended strategy for economic development. Yet, even as the councils of many LDCs call for primary commodity producers to gain greater participation in downstream activities, there remain doubts on fundamental questions:What comparative advantage and what disadvantages do processing activities have in the LDCs?What linkages do processing activities offer?What is the potential contribution of processing to economic goal attainment in the LDCs?In this article we survey the material on processing, particularly with respect to modeling direct and indirect linkages between processing and the LDC economies. The article examines the following topics: (1) the literature on processing in economic development; (2) modeling processing linkages; and (3) outstanding questions regarding processing linkages.We focus on quantifiable linkages between the processing industry and the rest of the economy. There is also the possibility of other linkages, such as the development of entrepreneurship and management talent and technology, that are more difficult to quantify. It is important to distinguish between micro linkages, which refer to the direct impact on supplier and user sectors, and macro linkages, which include indirect general economic effects transmitted through government revenue and expenditures, the money supply, and foreign exchange availabilities.     

The duration of unemployment in Russia

This paper uses longitudinal survey data to assess factors affecting the duration of unemployment in Russia. We examine four types of marginalised labour force participants, according to ILO guidelines and survey responses, and we estimate duration models for each type. It turns out that the sets of characteristics with the strongest effects on the duration are remarkably similar across the different unemployment definitions and model specifications. Therefore, despite the formidable practical measurement problems, problematic groups of individuals can actually be identified. Received: 27 January 1999/Accepted: 27 January 2000     

Do flexible work schedules reduce turnover in U.S. federal agencies?

Agency theory suggests that when agencies adopt flexible work schedules, employees will be more likely to remain with the organization, because these programs demonstrate that the organization cares about their well-being in that flexible work schedules give them more flexibility regarding when, where, and how they perform their work. To test this proposition, cross-sectional panel data at the agency level were obtained from two federal government sources: Federal Employee Viewpoint Survey and FedScope. Furthermore, transfers and quits were the two forms of turnover examined. After lagging the independent variables behind turnover over several years so as to provide a robust test of causality, the results show only limited support for agency theory. Specifically, teleworking was found to lower quits. However, teleworking was not found to reduce transfers or turnover, in general. Furthermore, alternative work schedules were not found to have an impact on quits, transfers, or turnover generally.     

Confronting or Complementing? A Case Study on NGO–State Relations from Kerala, India

NGOs have, of late, found some of their traditional domains, such as provision of micro-credit and participatory development, coinciding with or being taken away by the state. How do they position themselves and retain relevance vis-a-vis the state in the changed scenario? Tracing the trajectory of interventions of a local NGO in Kerala, India, this article shows that NGOs exhibit ‘multiple identities’—selective collaboration, gap-filling and posing alternatives—in the process of engagement with the state. The ‘strategizing’ of such identities may hold the key to their relevance vis-a-vis the state.     

Statistical Microcomputing—Like it is

A statistical software package is a collaborative effort between a program's authors and users. When statistical analysis took place exclusively on mainframe computers, the entire statistical community was served by some three to six major packages, which helped to ensure that program errors would be quickly uncovered and corrected. The current trend toward performing statistical analysis on microcomputers has resulted in an explosion of software of varying quality, with more than 200 packages for the IBM PC alone. Since all of these programs are competing for the same base of knowledgeable users, the number of sophisticated users per package is dramatically less than for mainframe packages; the net result is that problems in any particular package are more likely to go unnoticed and uncorrected. For example, the most widely used shareware package contains major errors that should cause it to be rejected out of hand, and three best-selling packages analyze unbalanced two-factor experiments using an approximate technique originally developed for hand calculation. Several strategies are offered to help author and user reveal any problems that might be present in their software.     

Quantitative Risk Assessment of FMD Virus Transmission via Water

Foot-and-mouth disease (FMD) is a viral disease of domesticated and wild cloven-hoofed animals. FMD virus is known to spread by direct contact between infected and susceptible animals, by animal products such as meat and milk, by the airborne route, and mechanical transfer on people, wild animals, birds, and by vehicles. During the outbreak of 2001 in the Netherlands, milk from dairy cattle was illegally discharged into the sewerage as a consequence of transport prohibition. This may lead to contaminated discharges of biologically treated and raw sewage in surface water that is given to cattle to drink. The objective of the present study was to assess the probability of infecting dairy cows that were drinking FMD virus contaminated surface water due to illegal discharges of contaminated milk. So, the following data were collected from literature: FMD virus inactivation in aqueous environments, FMD virus concentrations in milk, dilution in sewage water, virus removal by sewage treatment, dilution in surface water, water consumption of cows, size of a herd in a meadow, and dose-response data for ingested FMD virus by cattle. In the case of 1.6 x 10(2) FMD virus per milliliter in milk and discharge of treated sewage in surface water, the probability of infecting a herd of cows was estimated to be 3.3 x 10(-7) to 8.5 x 10(-5), dependent on dilution in the receiving surface water. In the case of discharge of raw sewage, all probabilities of infection were 100 times higher. In the case of little dilution in small rivers, the high level of 8.5 x 10(-3) is reached. For 10(4) times higher FMD virus concentrations in milk, the probabilities of infecting a herd of cows are high in the case of discharge of treated sewage (3.3 x 10(-3) to 5.7 x 10(-1)) and very high in the case of discharge of raw sewage (0.28-1.0). It can be concluded that illegal and uncontrolled discharges of contaminated milk into the sewerage system may lead to high risks to other cattle farms at 6-50 km distance of the location of discharge within one day. This clearly underlines current measures that prohibit such discharges, and also asks for strict control. This risk assessment clearly demonstrated the potential significance of FMD virus transmission via water, and the results will be useful on an international scale, and could also serve as a basis for other FMD risk-assessment models.     

Using unlabelled data to update classification rules with applications in food authenticity studies

Summary.  An authentic food is one that is what it purports to be. Food processors and consumers need to be assured that, when they pay for a specific product or ingredient, they are receiving exactly what they pay for. Classification methods are an important tool in food authenticity studies where they are used to assign food samples of unknown type to known types. A classification method is developed where the classification rule is estimated by using both the labelled and the unlabelled data, in contrast with many classical methods which use only the labelled data for estimation. This methodology models the data as arising from a Gaussian mixture model with parsimonious covariance structure, as is done in model-based clustering. A missing data formulation of the mixture model is used and the models are fitted by using the EM and classification EM algorithms. The methods are applied to the analysis of spectra of food-stuffs recorded over the visible and near infra-red wavelength range in food authenticity studies. A comparison of the performance of model-based discriminant analysis and the method of classification proposed is given. The classification method proposed is shown to yield very good misclassification rates. The correct classification rate was observed to be as much as 15% higher than the correct classification rate for model-based discriminant analysis.