What Do Credit Bureaus Do? Understanding Screening, Incentive, and Credit Expansion Effects

We develop a theoretical model that explains the primary empirical results emanating from a multi-year study of the impact of credit bureaus in Guatemala. Our theory derives “screening” and “incentive” effects of credit information systems that mitigate problems of adverse selection and moral hazard in credit markets. We also derive a “credit expansion” effect in which borrowers with clean credit records receive larger and more favorable equilibrium loan contracts. The credit expansion effect increases default rates, partially counteracting the first two effects. We create a simulation model that allows us to examine the relative magnitudes of these effects in relation to the order in which they occur

Competition and Microfinance

Competition between microfinance institutions (MFIs) in developing countries has increased dramatically in the last decade. We model the behavior of non-profit lenders, and show that their non-standard, client-maximizing objectives cause them to cross-subsidize within their pool of borrowers. Thus when competition eliminates rents on profitable borrowers, it is likely to yield a new equilibrium in which poor borrowers are worse off. As competition exacerbates asymmetric information problems over borrower indebtedness, the most impatient borrowers begin to obtain multiple loans, creating a negative externality that leads to less favorable equilibrium loan contracts for all borrowers

Population structure of pink salmon (Oncorhynchus gorbuscha) in British Columbia and Washington, determined with microsatellites

Population structure of pink salmon (Oncorhynchus gorbuscha) from British Columbia and Washington was examined with a survey of microsatellite variation to describe the distribution of genetic variation. Variation at 16 microsatellite loci was surveyed for approximately 46,500 pink salmon sampled from 146 locations in the odd-year broodline and from 116 locations in the even-year broodline. An index of genetic differentiation, FST, over all populations and loci in the odd-year broodline was 0.005, with individual locus values ranging from 0.002 to 0.025. Population differentiation was less in the even-year broodline, with a FST value of 0.002 over all loci, and with individual locus values ranging from 0.001 to 0.005. Greater genetic diversity was observed in the odd-year broodline. Differentiation in pink salmon allele frequencies between broodlines was approximately 5.5 times greater than regional differentiation within broodlines. A regional structuring of populations was the general pattern observed, and a greater regional structure in the odd-year broodline than in the even-year broodline. The geographic distribution of microsatellite variation in populations of pink salmon likely ref lects a distribution of broodlines from separate refuges after the last glaciation period

Adapting Classifiers To Changing Class Priors During Deployment

Conventional classifiers are trained and evaluated using balanced data sets in which all classes are equally present. Classifiers are now trained on large data sets such as ImageNet, and are now able to classify hundreds (if not thousands) of different classes. On one hand, it is desirable to train such general-purpose classifier on a very large number of classes so that it performs well regardless of the settings in which it is deployed. On the other hand, it is unlikely that all classes known to the classifier will occur in every deployment scenario, or that they will occur with the same prior probability. In reality, only a relatively small subset of the known classes may be present in a particular setting or environment. For example, a classifier will encounter mostly animals if its deployed in a zoo or for monitoring wildlife, aircraft and service vehicles at an airport, or various types of automobiles and commercial vehicles if it is used for monitoring traffic. Furthermore, the exact class priors are generally unknown and can vary over time. In this paper, we explore different methods for estimating the class priors based on the output of the classifier itself. We then show that incorporating the estimated class priors in the overall decision scheme enables the classifier to increase its run-time accuracy in the context of its deployment scenario

Pastoral Farmer Goals and Intensification Strategies

Focus groups were held with four pastoral sectors (sheep, dairy, deer, and beef) to investigate intensification strategies available to each sector. Focus groups first identified drivers of intensification in their sector, then identified the strategies they perceived as available, and evaluated the identified strategies in terms of favourability. For a researcher selected intensification strategy in each pastoral sector, benefits, barriers and solutions, and the relationship between farmer goals and the selected strategy was examined. The three main drivers of intensification in the sheep industry were profit, higher land values and return on capital. The researcher chosen strategy, high fecundity sheep, was viewed by the focus group as having benefits of increased financial security, increased profit, better return on capital and better land utilisation. However the strategy was seen as conflicting with other desirable goals such as lifestyle, social life, work variety, self reliance, environmental concerns and animal welfare. The three main drivers of intensification in the dairy sector were declining market prices, need for increased profit and need for increased productivity. The researcher chosen strategy, robotic milking, was viewed as having benefits of: reduced labour requirements, enhanced lifestyle, greater job satisfaction, reduce operational costs and increased profit. Implementation cost was viewed as a barrier as was the need for new specialised technical skills. The three main drivers of intensification in the deer industry were return on investment, competition from other land uses and returns per hectare compared with other pastoral sectors. The researcher chosen strategy, 100kg weaner by 1st June, had benefits of increased management options, increased profit, achievement of animals’ genetic potential, better predictability and a higher kill-out yield. The strategy presents challenges to animal welfare – an important consideration for the group. Three industry enterprises (dairy, calf rearers, and beef finishers) are involved in beef production. All three agreed that profit was the main driver for intensification. The researcher chosen strategy was dairy/beef progeny. Benefits of this strategy for the industry were: increased profit, access to prime markets, higher yielding quicker growing animals, and better behaved animals. The primary barrier to the success of this strategy was the need for co-operation across the three industry enterprises and the processors, and the need to ensure increased profits are distributed to all parts of the chain. Dairy farmers (the source of 65% of animals farmed for beef) were particular concerned about animal welfare issues and the consequent financial risks presented to their operations by this strategy.Agribusiness, Agricultural Finance, Consumer/Household Economics, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Food Consumption/Nutrition/Food Safety, Land Economics/Use, Livestock Production/Industries, Risk and Uncertainty,

Algorithms for the Detection of Resolved and Unresolved Targets in the Infrared Bands

This dissertation proposes algorithms for the detection of both resolved and unresolved targets in the infrared bands. Recent breakthroughs in deep learning have spurred major advancements in computer vision, but most of the attention and progress has been focused on RGB imagery from the visual band. The infrared bands such as Long Wave Infrared (LWIR), Medium Wave Infrared (MWIR), Short Wave Infrared (SWIR) and Near Infrared (NIR) each respond differently to physical phenomena, providing information that can be used to better understand the environment. The first task addressed is that of detecting vehicles in heavy clutter in MWIR imagery. A specialized network using a combination of analytically derived filters and a convolutional neural network trained using a novel objective function based on a target to clutter ratio is proposed which shows significant advantages in probability of detection and false alarm rate. The next task is that of domain adaptation where the network is deployed in a scenario different from that for which it was trained. The previously described network is adapted on the fly to improve results for new clutter data. Next, the task of hostile fire detection is considered where the unresolved image of an anti-tank guided missile launch is detected. An analysis of the relative utility of the IR bands is conducted, and data driven and parametric learning algorithms are presented which achieve a high probability of detection with a very low false alarm rate on a multi-spectral data set created by combining real IR video with radiometrically correct, synthesized missile launches at varying ranges. Finally, two methods for classifiers in the field to estimate the actual class probabilities of their environment to improve results are presented

Knowledge-Driven Contrast Gain Control is Characterized by Two Distinct Electrocortical Markers

Sensitivity to variations in luminance (contrast) is fundamental to perception because contrasts define the edges and textures of visual objects. Recent research has shown that contrast sensitivity, in addition to being controlled by purely stimulus-driven mechanisms, is also affected by expectations and prior knowledge about the contrast of upcoming stimuli. The ability to adjust contrast sensitivity based on expectations and prior knowledge could help to maximize the information extracted when scanning familiar visual scenes. In the present study we used the event-related potentials (ERP) technique to resolve the stages that mediate knowledge-driven aspects of contrast gain control. Using groupwise independent components analysis and multivariate partial least squares, we isolated two robust spatiotemporal patterns of electrical brain activity associated with preparation for upcoming targets whose contrast was predicted by a cue. The patterns were sensitive to the informative value of the cue. When the cues were informative, these patterns were also able to differentiate among cues that predicted low-contrast targets and cues that predicted high-contrast targets. Both patterns were localized to parts of occipitotemporal cortex, and their morphology, latency, and topography resembled P2/N2 and P3 potentials. These two patterns provide electrophysiological markers of knowledge-driven preparation for impending changes in contrast and shed new light on the manner in which top-down factors modulate sensory processing