Microeconomic Behavior of Agents in a Credit-Output Market in an Agricultural Setting

Rural agents engage in interlocking market transactions to minimize costs due to underdevelopment of rural markets. This study aims to model the economic behavior of agents in a credit-output market. Results indicate the prevalence of high interest rates in developed areas. Where income is low, transaction costs are high and the market is segmented, informal lenders are useful on efficiency grounds. Hence, the first-best solution appears to be augmenting farmer’s income.economic/development modelling, income, rural sector, credit market, transaction cost

Microinsurance: Does Traditional Regulation Apply?

Microinsurance--insurance services for low-income households--has emerged as a phenomenon in recent years. This Policy Notes draws attention to the need for an effective regulatory and supervision framework for said phenomenon to assure access of low-income households to insurance and to maintain the soundness of the insurance industry.microinsurance, mutual benefit associations

Credit Subsidy in Philippine Agriculture

This study attempts to measure subsidies to agricultural credit in recent years and provides policy implications. It finds that credit policy has evolved, from provision of subsidized credit to one that is more market-oriented, focusing on providing access to credit to farmers while exposing them to market interest rates. Nevertheless, there remains a significant public outlay for credit largely through unintended default subsidy. It recommends that publicly supported credit be provided solely through competent government financial institutions under independent regulatory oversight, rather than through agencies (such as the Agricultural Credit Enhancement Fund) or through government-owned and controlled corporations (such as QUEDANCOR). Government may also need to invest in other support services that would attenuate agricultural risk and encourage greater private sector participation in agricultural lending

Recursive Neural Networks for Semantic Sentence Representation

Semantic representation has a rich history rife with both complex linguistic theory and computational models. Though this history stretches back almost 50 years (Salton, 1971), recently the field has undergone an unexpected shift in paradigm thanks to the work of Mikolov et al., 2013(a & b) which has proven that vector-space semantic models can capture large amounts of semantic information. As of yet, these semantic representations are computed at the word level, and finding a semantic representation of a phrase is a much more difficult challenge. Mikolov et al., 2013(a&b) proved that their word vectors can be composed arithmetically to achieve reasonable representations of phrases, but this ignores syntactic information due to the commutativity of the arithmetic composition functions (addition, multiplication, etc.), causing the representation for the phrase “man bites dog” and “dog bites man” to be identical. This work hopes to introduce a way of computing word level semantic representations alongside a parse tree based approach to composing those word vectors to achieve a joint word-phrase semantic vector space. All associated code for this thesis was written in Python and can be found at https://github.com/liamge/Pytorch_ReNN

Developing Principles for the Regulation of Microinsurance (Philippine Case Study)

Low-income households find it hard to cope with the risks brought about by an illness or injury, death of a family member, man-made calamities, and natural disasters. Demand for microinsurance products is growing and both formal and informal microinsurance schemes have started to emerge to address this need. This paper seeks to provide a better understanding of the microinsurance market in the Philippines and to draw certain principles for microinsurance regulation from a review of the Philippine experience with microinsurance. The Philippine experience on the provision of microinsurance services and the interaction between the insurance providers and the regulator may help inform the development of certain principles for microinsurance regulation

Developing Principles for the Regulation of Microinsurance: Philippine Case Study

Illness or injury, death of a family member, man-made calamities and natural disasters have a devastating effect on those poor households' cash flow, liquidity, and earning capacities and thus, on household welfare. Demand for microinsurance products is growing in view of continuing risks to household welfare and the seeming inability of the government to address this issue. This study seeks to provide a better understanding of the microinsurance market in the Philippines and to draw certain principles for microinsurance regulation from a review of the Philippine experience with microinsurance. The study describes how policies, legal, regulatory, and supervisory framework governing insurance have shaped the development of the market and vice versa. The Philippine experience on the provision of microinsurance services and the interaction between the insurance providers and the regulator may help inform the development of certain principles for microinsurance regulation

Seasonal and interannual variability of North American isoprene emissions as determined by formaldehyde column measurements from space

Formaldehyde (HCHO) columns measured from space by solar UV backscatter allow mapping of reactive hydrocarbon emissions. The principal contributor to these emissions during the growing season is the biogenic hydrocarbon isoprene, which is of great importance for driving regional and global tropospheric chemistry. We present seven years (1995-2001) of HCHO column data for North America from the Global Ozone Monitoring Experiment (GOME), and show that the general seasonal and interannual variability of these data is consistent with knowledge of isoprene emission. There are some significant regional discrepancies with the seasonal patterns predicted from current isoprene emission models, and we suggest that these may reflect flaws in the models. The interannual variability of HCHO columns observed by GOME appears to follow the interannual variability of surface temperature, as expected from current isoprene emission models

Galaxy And Mass Assembly: Galaxy Zoo spiral arms and star formation rates

Understanding the effect spiral structure has on star formation properties of galaxies is important to complete our picture of spiral structure evolution. Previous studies have investigated connections between spiral arm properties and star formation, but the effect that the number of spiral arms has on this process is unclear. Here, we use the Galaxy And Mass Assembly (GAMA) survey paired with the citizen science visual classifications from the Galaxy Zoo project to explore galaxies’ spiral arm number and how it connects to the star formation process. We use the votes from the GAMA-Kilo Degree Survey Galaxy Zoo classification to investigate the link between spiral arm number and stellar mass, star formation rate, and specific star formation rate (sSFR). We find that galaxies with fewer spiral arms have lower stellar masses and higher sSFRs, while those with more spiral arms tend towards higher stellar masses and lower sSFRs, and conclude that galaxies are less efficient at forming stars if they have more spiral arms. We note how previous studies’ findings may indicate a cause for this connection in spiral arm strength or opacity

On predicting the outcomes of chemotherapy treatments in Breast cancer

Chemotherapy is the main treatment commonly used for treating cancer patients. However, chemotherapy usually causes side effects some of which can be severe. The effects depend on a variety of factors including the type of drugs used, dosage, length of treatment and patient characteristics. In this paper, we use a data extraction from an oncology department in Scotland with information on treatment cycles, recorded toxicity level, and various observations concerning breast cancer patients for three years. The objective of our paper is to compare several different techniques applied to the same data set to predict the toxicity outcome of the treatment. We use a Markov model, Hidden Markov model, Random Forest and Recurrent Neural Network in our comparison. Through analysis and evaluation of the performance of these techniques, we can determine which method is more suitable in different situations to assist the medical oncologist in real-time clinical practice. We discuss the context of our work more generally and further work.Postprin