An empirical analysis of monetary and multidimensional poverty: evidence from a household survey in Pakistan

ABSTRACT

The present study computes multidimensional poverty and compares it with unidimensional estimates of poverty for the district of Mandi Bahuddin in the Punjab province of Pakistan for the years 2010 and 2014. By employing the Foster, Greer and Thorbecke (FGT) and Alkire-Foster Methods, the incidence, severity and depth of unidimensional and multidimensionality poverty was estimated. We found an increase in absolute and relative poverty levels in 2014 as compared to 2010. The multidimensional poverty in terms of household assets has also increased over time. However, the relative proportion of educational and health poverty towards MPI remains higher, thereby calling for a holistic approach to identify multidimensional poverty in the social sector. A comprehensive policy dossier needs to be framed for designing effective poverty alleviation and social welfare programmes in the Punjab, Pakistan.     

Poverty assessment as a multidimensional socio-economic concept: the case of the Rawalpindi region in Pakistan

The definition on poverty has progressed into a multidimensional concept focusing more on socio-economic dimensions than being a mere measure of financial deprivation. Corresponding to this advancement, this study investigated the incidence of multidimensional poverty (MDP) in the Rawalpindi region of Pakistan, taking into account three dimensions, i.e. education, health and housing. Findings revealed an inconsistent declining trend of MDP over time. This inconsistency was mainly attributed to observed fluctuations in deprivation levels of education, health and housing in the region. Over time, education deprivation showed a significant net increase. Notably, the magnitude of MDP in rural areas was higher than in urban areas.     

中沙关系回顾与展望

中国和沙特政治制度不同,但两国发展积极友好的关系没有障碍.目前,沙特是中国在西亚和非洲最大的贸易伙伴,两国经贸合作前景广阔.同时,中沙双方均奉行独立自主的外交政策,拥有良好的政治关系,未来在区域组织和联合国等国际舞台上将有更广泛的合作领域.     

The analysis of policy implementation models in higher education: the case study of Indonesia

This paper aims to analyse and understand the models of policy implementation in Indonesian state universities during the ongoing process of market-driven reforms and how academics respond to these models. The research undertaken was in the form of qualitative study using semi-structured interviews as instruments to collect data. Interviews were conducted with 30 academics in three state universities in Indonesia. The research demonstrated that policy implementation in Indonesian state universities takes largely a dialectical form which takes place both at macro and micro levels, suggesting a blended application of top-down and bottom-up approaches. The results of this study also confirmed that the interaction between policy and local systems plays an important role for the successful implementation of the policy. Indonesian academics perceived that each of these models has its strength and weaknesses particularly in relation to the aspect of collegiality. It is anticipated that the study will both contribute to a better understanding of the dynamics between governments as policy makers and academics as street level bureaucrats in the implementation of a policy, and offer guidance for policy makers in the formulation and enactment of relevant policies in higher education sectors both in Indonesia and other contexts implementing policy implementation.     

Compromise allocation in multivariate stratified surveys with stochastic quadratic cost function

In many real life situations the linear cost function does not approximate the actual cost incurred adequately. The cost of traveling between the units selected in the sample within a stratum is significant, instead of linear cost function. In this paper, we have considered the problem of finding a compromise allocation for a multivariate stratified sample survey with a significant travel cost within strata is formulated as a problem of non-linear stochastic programming with multiple objective functions. The compromise solutions are obtained through Chebyshev approximation technique, D ₁- distance and goal programming. A numerical example is presented to illustrate the computational details of the proposed methods.     

Using the Diffusion of Innovation Concept to Explain the Factors That Contribute to the Adoption Rate of E-journal Publishing

This paper reports on the factors that contribute to the adoption rate of e-journal publishing based upon responses from 82 Malaysian journal publishers. Drawing upon the theory of innovation diffusion, the study examines the role of awareness, three organization variables (publication size, age of the journal, and experience of editors), and attributes of e-journals (relative advantage, compatibility, complexity, trialability and observability) as influences to the rate of e-journal publishing adoption. Findings show that only two attributes—complexity and trialability—emerge as significant contributors to e-journal adoption rates, and all three organization variables—journal's age, publication size, and editorial experience—are significant. Collectively, these five significant variables explained 57.8% of the variance in adoption rate. We discuss the results and implication of the findings with respect to the wider context of e-journal publishing.     

Mixed ranked set sampling design

The main focus of agricultural, ecological and environmental studies is to develop well designed, cost-effective and efficient sampling designs. Ranked set sampling (RSS) is one method that leads to accomplish such objectives by incorporating expert knowledge to its advantage. In this paper, we propose an efficient sampling scheme, named mixed RSS (MxRSS), for estimation of the population mean and median. The MxRSS scheme is a suitable mixture of both simple random sampling (SRS) and RSS schemes. The MxRSS scheme provides an unbiased estimator of the population mean, and its variance is always less than the variance of sample mean based on SRS. For both symmetric and asymmetric populations, the mean and median estimators based on SRS, partial RSS (PRSS) and MxRSS schemes are compared. It turns out that the mean and median estimates under MxRSS scheme are more precise than those based on SRS scheme. Moreover, when estimating the mean of symmetric and some asymmetric populations, the mean estimates under MxRSS scheme are found to be more efficient than the mean estimates with PRSS scheme. An application to real data is also provided to illustrate the implementation of the proposed sampling scheme.     

Improved best linear unbiased estimators for the simple linear regression model using double ranked set sampling schemes

ABSTRACT

In this paper, we consider the best linear unbiased estimators (BLUEs) based on double ranked set sampling (DRSS) and ordered DRSS (ODRSS) schemes for the simple linear regression model with replicated observations. We assume three symmetric distributions for the random error term, i.e., normal, Laplace and some scale contaminated normal distributions. The proposed BLUEs under DRSS (BLUEs-DRSS) and ODRSS (BLUEs-ODRSS) are compared with the BLUEs based on ordered simple random sampling (OSRS), ranked set sampling (RSS), and ordered RSS (ORSS) schemes. These estimators are compared in terms of relative efficiency (RE), RE of determinant (RED), and RE of trace (RET). It is found that the BLUEs-ODRSS are uniformly better than the BLUEs based on OSRS, RSS, ORSS, and DRSS schemes. We also compare the estimators based on imperfect RSS (IRSS) schemes. It is worth mentioning here that the BLUEs under ordered imperfect DRSS (OIDRSS) are better than their counterparts based on IRSS, ordered IRSS (OIRSS), and imperfect DRSS (IDRSS) methods. Moreover, for sensitivity analysis of the BLUEs, we calculate REs and REDs of the BLUEs under the assumption of normality when in fact the parent distribution follows a non normal symmetric distribution. It turns out that even under violation of normality assumptions, BLUEs of the intercept and the slope parameters are found to be unbiased with equal REs under each sampling scheme. It is also observed that the BLUEs under ODRSS are more efficient than the existing BLUEs.     

Assessing Schools’ Academic Performance Using a Belief Structure

A lot of attention has been given to education as it is the most practical way of improving social mobility. In order to be progressive and resilient, there has to be an overall transformation of society as evidenced in the annual budget allocations which has educational implications to it. Therefore, schools will come to term that they are under the watching eyes of interested parties who want them to perform academically. Thus, this will lead the stakeholders of education to make assessment and comparison between schools. However, the process of assessing schools is not an easy task as it involves many attributes. The problem of aggregating a large number of attributes will be a challenge if we use the traditional Multi Criteria Decision Making (MCDM) method. Hence, in this paper an innovative approach called Evidential Reasoning (ER) will be used to assess school performance in a multilevel or hierarchical setting which involved indirect measurement of quality by using standardized examination results, rather than directly measuring the quality of the processes unfolding within the schools. The approach is different from most conventional decision making modeling methods in that it employs a belief structure to represent an assessment as a distribution. By using this method, we are able to assess and rank the schools involved by giving an appropriate weight and merit point for each attribute and grade, respectively. For easy comprehension of the method, a numerical example was given on how to utilize the proposed method in order to obtain a single performance figure for school ranking purposes. Finally, by comparing the school ranking reveals that there is little similarity with the normal practice currently adopted.     

Estimation of the distribution function under hybrid ranked set sampling

Recently, a hybrid ranked set sampling (HRSS) scheme has been proposed in the literature. The HRSS scheme encompasses several existing ranked set sampling (RSS) schemes, and it is a cost-effective alternative to the classical RSS and double RSS schemes. In this paper, we propose an improved estimator for estimating the cumulative distribution function (CDF) using HRSS. It is shown, both theoretically and numerically, that the CDF estimator under HRSS scheme is unbiased and its variance is always less than the variance of the CDF estimator with simple random sampling (SRS). An unbiased estimator of the variance of CDF estimator using HRSS is also derived. Using Monte Carlo simulations, we also study the performances of the proposed and existing CDF estimators under both perfect and imperfect rankings. It turns out that the proposed CDF estimator is by far a superior alternative to the existing CDF estimators with SRS, RSS and L-RSS schemes. For a practical application, a real data set is considered on the bilirubin level of babies in neonatal intensive care.