Public and Private Production in a Mixed Delivery System: Regulation, Competition and Costs

Academics and policy makers are increasingly shifting the debate concerning the best form of public service provision beyond the traditional dilemma between pure public and pure private delivery modes, because, among other reasons, there is a growing body of evidence that casts doubt on the existence of systematic cost savings from privatization, while any competition seems to be eroded over time. In this paper we compare the relative merits of public and private delivery within a mixed delivery system. We study the role played by ownership, transaction costs, and competition on local public service delivery within the same jurisdiction. Using a stochastic cost frontier, we analyze the public-private urban bus system in the Barcelona Metropolitan Area. Our results suggest that private firms tendering the service have higher delivery costs than those incurred by the public firm, especially when transaction costs are taken into account. Tenders, therefore, do not help to reduce delivery costs. Our results suggest that under a mixed delivery scheme, which permits the co-existence of public and private production, the metropolitan government and the regulator can use private delivery to contain costs in the public firm and, at the same time, benefit from the greater flexibility of private firms for dealing with events not provided for under contract.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

ARMA modeling of time series based on rational approximation of spectral density function

This study is concerned with Autoregressive Moving Average (ARMA) models of time series. ARMA models form a subclass of the class of general linear models which represents stationary time series, a phenomenon encountered most often in practice by engineers, scientists and economists. It is always desirable to employ models which use parameters parsimoniously. Parsimony will be achieved by ARMA models because it has only finite number of parameters. Even though the discussion is primarily concerned with stationary time series, later we will take up the case of homogeneous non stationary time series which can be transformed to stationary time series. Time series models, obtained with the help of the present and past data is used for forecasting future values. Physical science as well as social science take benefits of forecasting models. The role of forecasting cuts across all fields of management-—finance, marketing, production, business economics, as also in signal process, communication engineering, chemical processes, electronics etc. This high applicability of time series is the motivation to this study.

Analysis of Some Stochastic Inventory Models with Pooling/ Retrial of Customers

In this thesis we have presented several inventory models of utility. Of these inventory with retrial of unsatisfied demands and inventory with postponed work are quite recently introduced concepts, the latt~~ being introduced for the first time. Inventory with service time is relatively new with a handful of research work reported. The di lficuity encoLlntered in inventory with service, unlike the queueing process, is that even the simplest case needs a 2-dimensional process for its description. Only in certain specific cases we can introduce generating function • to solve for the system state distribution. However numerical procedures can be developed for solving these problem.

Farm-level performance of genetically modified cotton - A frontier analysis of cotton production in Maharashtra

In this paper, the yield increases resulting from the cultivation of Bt cotton in Maharashtra, India, are analysed. The study relies on commercial farm, rather than trial, data and is among the first of its kind to be based on real farm and market conditions. Findings show that since its commercial release in 2002, Bt cotton has had a significant positive impact on yields and on the economic performance of cotton growers in Maharashtra. This difference remains even after controlling for different soil and insecticide inputs in the production of Bt cotton. There is also significant spatial and temporal variation in this 'benefit', and much depends upon where production is taking place and on the season.

Estimating the stochastic discount factor without a utility function

Using the Pricing Equation in a panel-data framework, we construct a novel consistent estimator of the stochastic discount factor (SDF) which relies on the fact that its logarithm is the serial-correlation ìcommon featureîin every asset return of the economy. Our estimator is a simple function of asset returns, does not depend on any parametric function representing preferences, is suitable for testing di§erent preference speciÖcations or investigating intertemporal substitution puzzles, and can be a basis to construct an estimator of the risk-free rate. For post-war data, our estimator is close to unity most of the time, yielding an average annual real discount rate of 2.46%. In formal testing, we cannot reject standard preference speciÖcations used in the literature and estimates of the relative risk-aversion coe¢ cient are between 1 and 2, and statistically equal to unity. Using our SDF estimator, we found little signs of the equity-premium puzzle for the U.S.

Lima beans production and economic revenue as function of organic and mineral fertilization

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Measurements of inclusive W plus jets production rates as a function of jet transverse momentum in p(p)over-bar collisions root s=1.96 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Ratios of dijet production cross sections as a function of the absolute difference in rapidity between jets in proton-proton collisions at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Stochastic scheduling - A flow shop no wait application and solutions

Minimizing the makespan of a flow-shop no-wait (FSNW) schedule where the processing times are randomly distributed is an important NP-Complete Combinatorial Optimization Problem. In spite of this, it can be found only in very few papers in the literature. By considering the Start Interval Concept, this problem can be formulated, in a practical way, in function of the probability of the success in preserve FSNW constraints for all tasks execution. With this formulation, for the particular case with 3 machines, this paper presents different heuristics solutions: by integrating local optimization steps with insertion procedures and by using genetic algorithms for search the solution space. Computational results and performance evaluations are commented. Copyright (C) 1998 IFAC.

Broiler breeder behavior and egg production as function of environmental temperature

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

NUTRITIONAL STATUS AND DRY MASS PRODUCTION OF ALFALFA IN FUNCTION OF POTASSIUM RATES IN TWO SOILS

Potassium fertilization is very important to alfalfa crop in terms of yield, quality and persistence of forage, especially on soils naturally poor K. Thus, to assess the effects of K fertilization in alfalfa production and nutritional status, was carried out an experiment in a greenhouse using samples of a Dystrophic Oxisol medium texture (LV) (0.6 mmol(c) dm(-3) K) and a Dystrophic Ultisol sandy/medium texture (PVA) (2.2 mmol(c) dm(-3) K). A completely randomized design in a factorial arrangement 6 x 2 (six K rates and two soils) was used, with four replications. The K rates used were: 0, 25, 50, 100, 150 and 200 mg kg(-1) K. Potassium fertilization increased K content in soil and shoots. Dry matter production was increased with the K addition. However, in the PVA, this occurred only in the second cut. In LV, potassium fertilization increased N concentration in alfalfa shoots in both cuts. Plants with K concentration around 10 g kg(-1) had typical symptoms of this nutrient deficiency. The K critical levels of K in soil and shoots were 1.8 mmolc dm(-3) and 16.7 g kg(-1), respectively.

Dry matter production of Tanzania grass as a function of agrometeorological variables

The objective of this work was to develop and validate linear regression models to estimate the production of dry matter by Tanzania grass (Megathyrsus maximus, cultivar Tanzania) as a function of agrometeorological variables. For this purpose, data on the growth of this forage grass from 2000 to 2005, under dry-field conditions in Sao Carlos, SP, Brazil, were correlated to the following climatic parameters: minimum and mean temperatures, degree-days, and potential and actual evapotranspiration. Simple linear regressions were performed between agrometeorological variables (independent) and the dry matter accumulation rate (dependent). The estimates were validated with independent data obtained in Sao Carlos and Piracicaba, SP, Brazil. The best statistical results in the development and validation of the models were obtained with the agrometeorological parameters that consider thermal and water availability effects together, such as actual evapotranspiration, accumulation of degree-days corrected by water availability, and the climatic growth index, based on average temperature, solar radiation, and water availability. These variables can be used in simulations and models to predict the production of Tanzania grass.