Spatial Cournot competition in a circular city with transport cost differentials

For an even number of firms with identical transport cost, spatial Cournot competition in a circular city generates a continuum of equilibria. We establish that any transport cost differential between the firms, however small, may eliminate all the equilibria, except the partial agglomeration equilibrium pattern characterized in Matsushima (2001).Agglomeration

Delivered Pricing, Positive Externalities and Firm Dispersion

This note examines firm locations in a delivered pricing model with positive production externalities. We find that, quite counter intuitively, firms will disperse rather than move closer, when production externalities are positive and reciprocal. Furthermore, we see a divergence between the private and social optimal locations, which is in contrast to the coincidence of these locations in the standard delivered pricing model.Location dispersion

Is Tax sharing Optimal? An Analysis in a Principal-Agent Framework

We study the effects of a statutory wage tax sharing rule in a principal - agent framework with moral hazard (à la Holmstrom, 1979) using the approach of Bose, Pal, Sappington (2007) to model the stochastic relationship between the agent's unobserved effort and his observed performance. The analysis indicates that tax sharing with positive legislated contributions from both the employer and employee does not maximize any of the outcomes -- employee effort, wages, profits or welfare. Moreover, a rule which specifies a corner solution, with 100% of the tax statutorily levied on the employer will maximize effort, expected profit and expected welfare while 100% of the tax statutorily levied on the employee will maximize expected wages.moral hazard, taxes, principal-agent model

Optimal Training, Employee Preferences and Moral

We study an agency model with moral hazard, when the employer offers complementary training/development programs that will increase the productivity of the employee’s effort. Since it is costly for an employer to offer training and development opportunities and given that employees are not identical, how will an employer choose the quantity and allocation of such programs? Does the quantity and type of training offered, vary with the employee’s aversion to effort? Does more “sincerity ” necessarily translate into more employee development? Does more training in fact induce the employee to work harder? In theory the answer could go either way. On the one hand, an employer may wish to leverage the use of such programs to motivate a lazy employee to work harder. Conversely, especially because effort is unobservable, one can argue that she may be better off rewarding a sincere employee with more development opportunities. This work reaches a definite and perhaps unpredictable conclusion. We find that there is an inverse relationship between the optimal quantity of the training program and increased aversion to effort for both a relatively lazy and a relatively sincere employee. This is also true regardless of whether the program is relatively cheap or relatively expensive for the employer to offer. Perhaps surprisingly, there is no qualitative change in the comparative statics results, if the employer can monitor or observe effort

Assessment of foetal cardiac function by myocardial tissue doppler in foetal growth restriction

Background: One of the consequences of IUGR is the development of cardiac diastolic dysfunction in fetuses. Tissue doppler in echocardiography is a new technique to detect myocardial tissue function and can act as a useful tool in the identification of this complication. Hence we decided to undertake this study to assess the utility of myocardial tissue doppler in detecting foetal cardiac dysfunction in IUGR. It was a prospective case control study in a tertiary care teaching hospital.Methods: Foetal cardiac function in the third trimester of pregnancy was evaluated with the help of myocardial tissue doppler and compared between IUGR and normal growth babies and correlated with vessel doppler findings and neonatal outcomes.Results: There were sixty two IUGR and fifty eight normal growth babies in the study. In babies with IUGR, particularly the ones with severe IUGR, abnormal vessel doppler and adverse neonatal outcomes, right ventricular MPI was found to be significantly lower. However, the variable had a poor sensitivity (40%) in detecting fetuses at risk for poor neonatal outcomes.Conclusions: Myocardial tissue doppler shows right sided cardiac dysfunction in IUGR babies in comparison to normal growth babies It is however not a sensitive indicator of adverse perinatal outcome in IUGR babies

Automatic Irrigation Model Powered by Smart Rain Prediction Device in India

This paper presents a simple rain prediction device-based automatic irrigation management algorithm using a combination of weather parameters and soil moisture measurements for the water balance required for a crop at each condition during its growing phase that will reduce farmer intervention for irrigation and avoid unnecessary irrigation by predicting the rainfall before starting the motor for irrigating the field. This device is powered by various technologies like deep learning to classify clouds responsible for rain, machine learning models to predict rainfall based on atmospheric parameters and the Internet of Things (IoT) using different sensors to collect data from the field. This algorithm is very appropriate for farmers who are in remote locations and are not able to use the internet and WIFI due to its unavailability. The device will be attached to the motor, will take the data from sensors and will do the rain prediction at device level only and will switch ON/OFF the motor based on the soil moisture value and rain prediction without any human intervention.

Automatic Irrigation Model Powered by Smart Rain Prediction Device in India

This paper presents a simple rain prediction device-based automatic irrigation management algorithm using a combination of weather parameters and soil moisture measurements for the water balance required for a crop at each condition during its growing phase that will reduce farmer intervention for irrigation and avoid unnecessary irrigation by predicting the rainfall before starting the motor for irrigating the field. This device is powered by various technologies like deep learning to classify clouds responsible for rain, machine learning models to predict rainfall based on atmospheric parameters and the Internet of Things (IoT) using different sensors to collect data from the field. This algorithm is very appropriate for farmers who are in remote locations and are not able to use the internet and WIFI due to its unavailability. The device will be attached to the motor, will take the data from sensors and will do the rain prediction at device level only and will switch ON/OFF the motor based on the soil moisture value and rain prediction without any human intervention.