Education and Earnings for Poverty Reduction : Short-Term Evidence of Pro-Poor Growth from the Mexican Oportunidades Program

Education, as an indispensable component of human capital, has been acknowledged to play a critical role in economic growth, which is theoretically elaborated by human capital theory and empirically confirmed by evidence from different parts of the world. The educational impact on growth is especially valuable and meaningful when it is for the sake of poverty reduction and pro-poorness of growth. The paper re-explores the precious link between human capital development and poverty reduction by investigating the causal effect of education accumulation on earnings enhancement for anti-poverty and pro-poor growth. The analysis takes the evidence from a well-known conditional cash transfer (CCT) program — Oportunidades in Mexico. Aiming at alleviating poverty and promoting a better future by investing in human capital for children and youth in poverty, this CCT program has been recognized producing significant outcomes. The study investigates a short-term impact of education on earnings of the economically disadvantaged youth, taking the data of both the program’s treated and untreated youth from urban areas in Mexico from 2002 to 2004. Two econometric techniques, i.e. difference-in-differences and difference-in-differences propensity score matching approach are applied for estimation. The empirical analysis first identifies that youth who under the program’s schooling intervention possess an advantage in educational attainment over their non-intervention peers; with this identification of education discrepancy as a prerequisite, further results then present that earnings of the education advantaged youth increase at a higher rate about 20 percent than earnings of their education disadvantaged peers over the two years. This result indicates a confirmation that education accumulation for the economically disadvantaged young has a positive impact on their earnings enhancement and thus inferring a contribution to poverty reduction and pro-poorness of growth.

QoS evaluation of Bandwidth Schedulers in IPTV Networks Offered SRD Fluid Video Traffic

Internet protocol TV (IPTV) is predicted to be the key technology winner in the future. Efforts to accelerate the deployment of IPTV centralized model which is combined of VHO, encoders, controller, access network and Home network. Regardless of whether the network is delivering live TV, VOD, or Time-shift TV, all content and network traffic resulting from subscriber requests must traverse the entire network from the super-headend all the way to each subscriber's Set-Top Box (STB). IPTV services require very stringent QoS guarantees When IPTV traffic shares the network resources with other traffic like data and voice, how to ensure their QoS and efficiently utilize the network resources is a key and challenging issue. For QoS measured in the network-centric terms of delay jitter, packet losses and bounds on delay. The main focus of this thesis is on the optimized bandwidth allocation and smooth data transmission. The proposed traffic model for smooth delivering video service IPTV network with its QoS performance evaluation. According to Maglaris et al [5] first, analyze the coding bit rate of a single video source. Various statistical quantities are derived from bit rate data collected with a conditional replenishment inter frame coding scheme. Two correlated Markov process models (one in discrete time and one in continuous time) are shown to fit the experimental data and are used to model the input rates of several independent sources into a statistical multiplexer. Preventive control mechanism which is to be including CAC, traffic policing used for traffic control. QoS has been evaluated of common bandwidth scheduler( FIFO) by use fluid models with Markovian queuing method and analysis the result by using simulator and analytically, Which is measured the performance of the packet loss, overflow and mean waiting time among the network users.

A finite sample improvement of the fixed effects estimator : applied to technical infficiency

The FE ('fixed effects') estimator of technical inefficiency performs poorly when N ('number of firms') is large and T ('number of time observations') is small. We propose estimators of both the firm effects and the inefficiencies, which have small sample gains compared to the traditional FE estimator. The estimators are based on nonparametric kernel regression of unordered variables, which includes the FE estimator as a special case. In terms of global conditional MSE ('mean square error') criterions, it is proved that there are kernel estimators which are efficient to the FE estimators of firm effects and inefficiencies, in finite samples. Monte Carlo simulations supports our theoretical findings and in an empirical example it is shown how the traditional FE estimator and the proposed kernel FE estimator lead to very different conclusions about inefficiency of Indonesian rice farmers.

Growth, migration and unemployment across Swedish municipalities

Fundamental questions in economics are why some regions are richer than others, why their economic growth rates vary, whether their growth tends to converge and the key factors that contribute to the variations. These questions have not yet been fully addressed, but changes in the local tax base are clearly influenced by the average income growth rate, net migration rate, and changes in unemployment rates. Thus, the main aim of this paper is to explore in depth the interactive effects of these factors (and local policy variables) in Swedish municipalities, by estimating a proposed three-equation system. Our main finding is that increases in local public expenditures and income taxes have negative effects on subsequent local income growth. In addition, our results support the conditional convergence hypothesis, i.e. that average income tends to grow more rapidly in relatively poor local jurisdictions than in initially “richer” jurisdictions, conditional on the other explanatory variables.

Essays on growth and environment

This thesis consists of a summary and four self-contained papers. Paper [I] Following the 1987 report by The World Commission on Environment and Development, the genuine saving has come to play a key role in the context of sustainable development, and the World Bank regularly publishes numbers for genuine saving on a national basis. However, these numbers are typically calculated as if the tax system is non-distortionary. This paper presents an analogue to genuine saving in a second best economy, where the government raises revenue by means of distortionary taxation. We show how the social cost of public debt, which depends on the marginal excess burden, ought to be reflected in the genuine saving. We also illustrate by presenting calculations for Greece, Japan, Portugal, U.K., U.S. and OECD average, showing that the numbers published by the World Bank are likely to be biased and may even give incorrect information as to whether the economy is locally sustainable. Paper [II] This paper examines the relationships among per capita CO2 emissions, per capita GDP and international trade based on panel data spanning the period 1960-2008 for 150 countries. A distinction is also made between OECD and Non-OECD countries to capture the differences of this relationship between developed and developing economies. We apply panel unit root and cointegration tests, and estimate a panel error correction model. The results from the error correction model suggest that there are long-term relationships between the variables for the whole sample and for Non-OECD countries. Finally, Granger causality tests show that there is bi-directional short-term causality between per capita GDP and international trade for the whole sample and between per capita GDP and CO2 emissions for OECD countries. Paper [III] Fundamental questions in economics are why some regions are richer than others, why their growth rates differ, whether their growth rates tend to converge, and what key factors contribute to explain economic growth. This paper deals with the average income growth, net migration, and changes in unemployment rates at the municipal level in Sweden. The aim is to explore in depth the effects of possible underlying determinants with a particular focus on local policy variables. The analysis is based on a three-equation model. Our results show, among other things, that increases in the local public expenditure and income taxe rate have negative effects on subsequent income income growth. In addition, the results show conditional convergence, i.e. that the average income among the municipal residents tends to grow more rapidly in relatively poor local jurisdictions than in initially “richer” jurisdictions, conditional on the other explanatory variables. Paper [IV] This paper explores the relationship between income growth and income inequality using data at the municipal level in Sweden for the period 1992-2007. We estimate a fixed effects panel data growth model, where the within-municipality income inequality is one of the explanatory variables. Different inequality measures (Gini coefficient, top income shares, and measures of inequality in the lower and upper part of the income distribution) are examined. We find a positive and significant relationship between income growth and income inequality measured as the Gini coefficient and top income shares, respectively. In addition, while inequality in the upper part of the income distribution is positively associated with the income growth rate, inequality in the lower part of the income distribution seems to be negatively related to the income growth. Our findings also suggest that increased income inequality enhances growth more in municipalities with a high level of average income than in municipalities with a low level of average income.

Fitting spatial models in the R package: hglm

We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM) with spatially correlated random effects. A CAR family for conditional autoregressive random effects was implemented. Eigen decomposition of the matrix describing the spatial structure (e.g. the neighborhood matrix) was used to transform the CAR random effectsinto an independent, but heteroscedastic, gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR model.This gives a computationally efficient algorithm for moderately sized problems (e.g. n<5000).

Optimization of cable cycles : a trade-off between reliability and cost

This paper elaborates the routing of cable cycle through available routes in a building in order to link a set of devices, in a most reasonable way. Despite of the similarities to other NP-hard routing problems, the only goal is not only to minimize the cost (length of the cycle) but also to increase the reliability of the path (in case of a cable cut) which is assessed by a risk factor. Since there is often a trade-off between the risk and length factors, a criterion for ranking candidates and deciding the most reasonable solution is defined. A set of techniques is proposed to perform an efficient and exact search among candidates. A novel graph is introduced to reduce the search-space, and navigate the search toward feasible and desirable solutions. Moreover, admissible heuristic length estimation helps to early detection of partial cycles which lead to unreasonable solutions. The results show that the method provides solutions which are both technically and financially reasonable. Furthermore, it is proved that the proposed techniques are very efficient in reducing the computational time of the search to a reasonable amount.

Towards Qualitative Approaches to Bayesian Evidential Reasoning

A crucial aspect of evidential reasoning in crime investigation involves comparing the support that evidence provides for alternative hypotheses. Recent work in forensic statistics has shown how Bayesian Networks (BNs) can be employed for this purpose. However, the specification of BNs requires conditional probability tables describing the uncertain processes under evaluation. When these processes are poorly understood, it is necessary to rely on subjective probabilities provided by experts. Accurate probabilities of this type are normally hard to acquire from experts. Recent work in qualitative reasoning has developed methods to perform probabilistic reasoning using coarser representations. However, the latter types of approaches are too imprecise to compare the likelihood of alternative hypotheses. This paper examines this shortcoming of the qualitative approaches when applied to the aforementioned problem, and identifies and integrates techniques to refine them.

On Qualitative Probabilities for Legal Reasoning about Evidence

A crucial aspect of evidential reasoning in crime investigation involves comparing the support that evidence provides for alternative hypotheses. Recent work in forensic statistics has shown how Bayesian Networks (BNs) can be employed for this purpose. However, the specification of BNs requires conditional probability tables describing the uncertain processes under evaluation. When these processes are poorly understood, it is necessary to rely on subjective probabilities provided by experts. Accurate probabilities of this type are normally hard to acquire from experts. Recent work in qualitative reasoning has developed methods to perform probabilistic reasoning using coarser representations. However, the latter types of approaches are too imprecise to compare the likelihood of alternative hypotheses. This paper examines this shortcoming of the qualitative approaches when applied to the aforementioned problem, and identifies and integrates techniques to refine them.

Quantified Interference: Information Theory and Information Flow

The paper investigates which of Shannon’s measures (entropy, conditional entropy, mutual information) is the right one for the task of quantifying information flow in a programming language. We examine earlier relevant contributions from Denning, McLean and Gray and we propose and motivate a specific quantitative definition of information flow. We prove results relating equivalence relations, interference of program variables, independence of random variables and the flow of confidential information. Finally, we show how, in our setting, Shannon’s Perfect Secrecy theorem provides a sufficient condition to determine whether a program leaks confidential information.

Are price limits on futures markets that cool? evidence from the brazilian merchantile & futures exchange

This paper investigates the impact of price limits on the Brazil- ian future markets using high frequency data. The aim is to identify whether there is a cool-off or a magnet effect. For that purpose, we examine a tick-by-tick data set that includes all contracts on the São Paulo stock index futures traded on the Brazilian Mercantile and Futures Exchange from January 1997 to December 1999. Our main finding is that price limits drive back prices as they approach the lower limit. There is a strong cool-off effect of the lower limit on the conditional mean, whereas the upper limit seems to entail a weak magnet effect on the conditional variance. We then build a trading strategy that accounts for the cool-off effect so as to demonstrate that the latter has not only statistical, but also economic signifi- cance. The resulting Sharpe ratio indeed is way superior to the buy-and-hold benchmarks we consider.

Yet another reason to tax goods

The optimal taxation of goods, labor and capital income is considered in a two period model where: i) private information changes through time; ii) savings are not observed, and; iii) savings a§ect preferences conditional on the realization of types. The simultaneous appearance of these three elements cause optimal commodity taxes to depend on o§-equilibrium savings. As a consequence, separability no longer su¢ ces for the uniform taxation prescription of Atkinson and Stiglitz (AS) to obtain. If preferences are homothetic AS is partially restored: taxes are uniform within periods, however, future consumption is taxed at a higher rate than current consumption.

Estimating the term structure of volatility and fixed income derivative pricing

Estimating the parameters of the instantaneous spot interest rate process is of crucial importance for pricing fixed income derivative securities. This paper presents an estimation for the parameters of the Gaussian interest rate model for pricing fixed income derivatives based on the term structure of volatility. We estimate the term structure of volatility for US treasury rates for the period 1983 - 1995, based on a history of yield curves. We estimate both conditional and first differences term structures of volatility and subsequently estimate the implied parameters of the Gaussian model with non-linear least squares estimation. Results for bond options illustrate the effects of differing parameters in pricing.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise based upon data from a well known survey is also presented. Overall, theoretical and empirical results show promise for the feasible bias-corrected average forecast.

Redistribution with ex-ante unobserved choice

Introducing dynamics to Mirrlees' (1971) optimal taxation model creates a whole new set of issues that are only starting to be investigated in the literature. When choices are made before one's realizing her productivity incentive constraints ought to be defined as a function of more complex strategies than in the static case. 80 far, all work has assumed that these choices are observable and can be contracted upon by the government. Here we investigate choices that : i) are not observed, andj ii) affect preferences conditional Oil the realization of types. In the simplest possible model where a non-trivial filtration is incorporated we show how these two characteristics make it necessary for IC constraints to be defined in terms of strategies rather than pure announcements. Tax prescriptions are derived, and it is shown that they bear some resemblance to classic optimal taxation results. We are able to show that in the most 'natural' cases return on capital ought to be taxed . However, we also show that the uniform taxation prescription of Atkinson and Stiglitz fails to hold, in general.