Robust inference in poverty mapping

Small area estimation (SAE) methods are widely used for estimating poverty indicators at finer levels of a country’s geography. Three unit-level SAE techniques – the ELL method (Elbers, Lanjouw, and Lanjouw, 2003), also known as the World Bank method, the Empirical Best Prediction (EBP) method (Molina and Rao, 2010) and the M-Quantile (MQ) method (Tzavidis et al., 2008) have all been used to estimate micro-level FGT poverty indicators (Foster, Greer, and Thorbecke, 1984). These methods vary in terms of their underlying model assumptions particularly differences in consideration of random effects. This thesis provides results from a numerical comparison of the statistical performance of these three methodologies in the context of a realistic simulation scenario based on a recent Bangladesh poverty study. This comparison study shows that the ELL method is the better performer in terms of relative bias but also significantly underestimates the MSEs of its small area poverty estimates when its underlying area homogeneity assumption is violated. A modified MSE estimation method for ELL-type poverty estimates is therefore developed in this thesis. This method is robust to the presence of significant unexplained between-area variability in the income distribution. This ELL-based MSE estimation methodology is based on a separate bootstrap procedure for MSE estimation, where a correction factor is v used to generate cluster-specific random errors that capture the potential between-area variability unaccounted for by the explanatory variables in the ELL regression model

Prevalence of child undernutrition measures and their spatio-demographic inequalities in Bangladesh: an application of multilevel Bayesian modelling

Micro-level statistics on child undernutrition are highly prioritized by stakeholders for measuring and monitoring progress on the sustainable development goals. In this regard district-representative data were collected in the Bangladesh Multiple Indicator Cluster Survey 2019 for identifying localised disparities. However, district-level estimates of undernutrition indicators - stunting, wasting and underweight - remain largely unexplored. This study aims to estimate district-level prevalence of these indicators as well as to explore their disparities at sub-national (division) and district level spatio-demographic domains cross-classified by children sex, age-groups, and place of residence. Bayesian multilevel models are developed at the sex-age-residence-district level, accounting for cross-sectional, spatial and spatio-demographic variations. The detailed domain-level predictions are aggregated to higher aggregation levels, which results in numerically consistent and reasonable estimates when compared to the design-based direct estimates. Spatio-demographic distributions of undernutrition indicators indicate south-western districts have lower vulnerability to undernutrition than north-eastern districts, and indicate significant inequalities within and between administrative hierarchies, attributable to child age and place of residence. These disparities in undernutrition at both aggregated and disaggregated spatio-demographic domains can aid policymakers in the social inclusion of the most vulnerable to meet the sustainable development goals by 2030.Australia National Health and Medical Research Council (NHMRC), APP118472

An Assessment of the Importance of Admission Test for Enrollments in Public Universities of Bangladesh

Public universities in Bangladesh arrange admission test to judge the students’ merit before the enrollment. Academic results of previous examinations (SSC and HSC) are also considered in the admission procedure. There are some disputes regarding the importance of admission test besides the previous academic records. The universities emphasize on the admission test while the government authorities ponder it as a burden for the students. This study has made an attempt to examine the importance of admission test in selection procedure utilizing a particular year admission test database of Shahjalal University of Science and Technology (SUST). Univariate and bivariate analyses along with regression models were used to analyze the data. The results indicate that students with higher score in both SSC and HSC examinations had higher possibility to be eligible for enrollment. However, a vital proportion of applicants with maximum GPA 5.00 in both examinations did not qualify in merit and waiting position. The results also show association and moderate positive correlation of admission test score with SSC and HSC results. Finally, regression analysis indicates that though the contributions of the SSC and HSC results on admission test scores are significant, the variation in admission test scores is not much explained by the previous records. Such findings recommend arranging admission test, besides academic qualification, to select the eligible applicants for enrollment in public universities

Application of ordinal logistic regression analysis in determining risk factors of child malnutrition in Bangladesh

<p>Abstract</p> <p>Background</p> <p>The study attempts to develop an ordinal logistic regression (OLR) model to identify the determinants of child malnutrition instead of developing traditional binary logistic regression (BLR) model using the data of Bangladesh Demographic and Health Survey 2004.</p> <p>Methods</p> <p>Based on weight-for-age anthropometric index (Z-score) child nutrition status is categorized into three groups-severely undernourished (< -3.0), moderately undernourished (-3.0 to -2.01) and nourished (≥-2.0). Since nutrition status is ordinal, an OLR model-proportional odds model (POM) can be developed instead of two separate BLR models to find predictors of both malnutrition and severe malnutrition if the proportional odds assumption satisfies. The assumption is satisfied with low p-value (0.144) due to violation of the assumption for one co-variate. So partial proportional odds model (PPOM) and two BLR models have also been developed to check the applicability of the OLR model. Graphical test has also been adopted for checking the proportional odds assumption.</p> <p>Results</p> <p>All the models determine that age of child, birth interval, mothers' education, maternal nutrition, household wealth status, child feeding index, and incidence of fever, ARI & diarrhoea were the significant predictors of child malnutrition; however, results of PPOM were more precise than those of other models.</p> <p>Conclusion</p> <p>These findings clearly justify that OLR models (POM and PPOM) are appropriate to find predictors of malnutrition instead of BLR models.</p

Modelling the number of antenatal care visits in Bangladesh to determine the risk factors for reduced antenatal care attendance

2020 Bhowmik et al. The existence of excess zeros in the distribution of antenatal care (ANC) visits in Bangladesh raises the research question of whether there are two separate generating processes in taking ANC and the frequency of ANC. Thus the main objective of this study is to identify a proper count regression model for the number of ANC visits by pregnant women in Bangladesh covering the issues of overdispersion, zero-inflation, and intra-cluster correlation with an additional objective of determining risk factors for ANC use and its frequency. The data have been extracted from the nationally representative 2014 Bangladesh Demographic and Health Survey, where 22% of the total 4493 women did not take any ANC during pregnancy. Since these zero ANC visits can be either structural or sampling zeros, two-part zero-inflated and hurdle regression models are investigated along with the standard onepart count regression models. Correlation among response values has been accounted for by incorporating cluster-specific random effects in the models. The hurdle negative binomial regression model with cluster-specific random intercepts in both the zero and the count part is found to be the best model according to various diagnostic tools including likelihood ratio and uniformity tests. The results show that women who have poor education, live in poor households, have less access to mass media, or belong to the Sylhet and Chittagong regions are less likely to use ANC and also have fewer ANC visits. Additionally, women who live in rural areas, depend on family members\u27 decisions to take health care, and have unintended pregnancies had fewer ANC visits. The findings recommend taking both cluster-specific random effects and overdispersion and zero-inflation into account in modelling the ANC data of Bangladesh. Moreover, safe motherhood programmes still need to pay particular attention to disadvantaged and vulnerable subgroups of women

On exploring and ranking risk factors of child malnutrition in Bangladesh using multiple classification analysis

BACKGROUND: Logistic regression analysis is widely used to explore the determinants of child malnutrition status mainly for nominal response variable and non-linear relationship of interval-scale anthropometric measure with nominal-scale predictors. Multiple classification analysis relaxes the linearity assumption and additionally prioritizes the predictors. Main objective of the study is to show how does multiple classification analysis perform like linear and logistic regression analyses for exploring and ranking the determinants of child malnutrition. METHODS: Anthropometric data of under-5 children are extracted from the 2011 Bangladesh Demographic and Health Survey. The analysis is carried out considering several socio-economic, demographic and environmental explanatory variables. The Height-for-age Z-score is used as the anthropometric measure from which malnutrition status (stunting: below −2.0 Z-score) is identified. RESULTS: The fitted multiple classification analysis models show similar results as linear and logistic models. Children age, birth weight and birth interval; mother’s education and nutrition status; household economic status and family size; residential place and regional settings are observed as the significant predictors of both Height-for-age Z-score and stunting. Child, household, and mother level variables have been ranked as the first three significant groups of predictors by multiple classification analysis. CONCLUSIONS: Detecting and ranking the determinants of child malnutrition through Multiple classification analysis might help the policy makers in priority-based decision-making. TRIAL REGISTRATION: “Retrospectively registered

Robust mean-squared error estimation for poverty estimates based on the method of Elbers, Lanjouw and Lanjouw

The method of Elbers, Lanjouw and Lanjouw (ELL) is the small area estimation method developed by the World Bank for poverty mapping and is widely used in developing countries. However, it has been criticized because of its assumption of negligible between-area variability when used to calculate small area poverty estimates. In particular, the mean-squared errors (MSEs) of these estimates are significantly underestimated when this between-area variability cannot be adequately explained by the model covariates. A method of MSE estimation for ELL-type estimates is proposed which is robust to significant unexplained between-area variability. Simulation results show that the method proposed performs better than standard ELL MSE estimators when the area homogeneity assumption is violated. An application to a Bangladesh poverty mapping study provides some empirical evidence for this robustness

Predicting the Finite Population Distribution Function under a Multilevel Model

Chambers and Dunstan proposed a model-based predictor of the population distribution function that makes use of auxiliary population information under a general sampling design. Subsequently, Rao, Kovar, and Mantel proposed design-based ratio and difference predictors of the population distribution function that also use this auxiliary information. Both predictors (CD and RKM) assume a single level model for the target population. In this article we develop predictors of the finite population distribution function for a population that follows a multilevel model. These new predictors use the same smearing approach underpinning the CD predictor. We compare our new predictors with the CD and RKM predictors via design-based simulation, and show that they perform better than these single level predictors when there is significant intra-cluster correlation. The performances of these new two level predictors are also examined via an empirical study based on data from a large-scale UK business survey aimed at estimating the distribution of hourly pay rates. AMS subject classification: Primary 62G30, Secondary 62G3

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Not AvailableThe demand for district level statistics has increased tremendously in Bangladesh due to existence of decentralised approach to governance and service provision. The Bangladesh Demographic Health Surveys (BDHS) provide a wide range of invaluable data at the national and divisional level but they cannot be used directly to produce reliable district-level estimates due to insufficient sample sizes. The small area estimation (SAE) technique overcomes the sample size challenges and can produce reliable estimates at the district level. This paper uses SAE approach to generate model-based district-level estimates of diarrhoea prevalence among under-5 children in Bangladesh by linking data from the 2014 BDHS and the 2011 Population Census. The diagnostics measures show that the model-based estimates are precise and representative when compared to the direct survey estimates. Spatial distribution of the precise estimates of diarrhoea prevalence reveals significant inequality at district-level (ranged 1.1-13.4%) with particular emphasis in the coastal and north-eastern districts. Findings of the study might be useful for designing effective policies, interventions and strengthening local-level governance.Not Availabl