scholarly journals Who are and where are the Rwanda’s poorest? A Small Area Estimation Method

2019 ◽  
pp. 245-264
Author(s):  
Mongongo Dosa Pacifique ◽  
Rutagarama Ephrem
Keyword(s):  
Rural Areas ◽  
Small Area ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Middle Income ◽  
Policy Makers ◽  
The Sustainable Development ◽  
Area Estimation ◽  
Development Goals ◽  
Household Heads

As Rwanda is achieving its vision of moving from a low to a middle–income country during the period 2000–2020, its capability of ending poverty along the Sustainable Development Goals’ era (2015–2030) mostly depends on how well the increasing prosperity will be shared among Rwandans along the way up to the 2030 horizon. Knowing those who have not yet benefited enough from the ongoing progress should help Rwanda’s policy makers and other development agencies to serve that purpose. With this perspective, this work has the two major objectives of estimating poverty by sector and studying the relationship between poverty and related variables in Rwanda. We tackle the first objective with the Small Area Estimation method (SAE) and covers the second with the Poisson regression. We find that (1) most of the very poor are located within rural areas, (2) live in larger households and, (3) have female household heads.

scholarly journals District level child nutrition status in Bangladesh: An application of small area estimation method

2019 ◽  
Vol 53 (1) ◽  
pp. 45-61
Author(s):  
Mossamet Kamrun Nesa
Keyword(s):  
Small Area ◽  
Child Nutrition ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Nutrition Status ◽  
Estimation Methods ◽  
Small Scale ◽  
Demographic Health Survey ◽  
Area Estimation ◽  
National Surveys

National level indicators of child undernutrition often hide the real scenario across a country. In order to construct a child nutrition map, accurate estimates of undernutrition are required at very small spatial scales, typically the administrative units of a country or a region within a country. Although comprehensive data on child nutrition are collected in national surveys, the small scale estimates cannot be calculated using the standard estimation methods employed in national surveys, since such methods are designed to produce national or regional level estimates, and assume large samples. Small area estimation method has been widely used to find such micro-level estimates. Due to lack of unit level data, area level small area estimation methods (e.g., Fay-Herriot method) are widely used to calculate small-scale estimates. In Bangladesh, a few works have been done to estimate district level child nutrition status. The Bangladesh Demographic Health Survey covers all districts but district wise sample sizes are very small to get consistent estimates. In this paper, Fay-Herriot Model has been developed to calculate district wise estimates with efficient mean squared error. The Bangladesh Demographic Health Survey 2011 and Population Census 2011 are utilized for this study.

scholarly journals Policy interventions for attraction and retention of female teachers in rural secondary schools

2019 ◽  
Vol 1 (2) ◽  
pp. 50-59
Author(s):  
Isabela M. Kamere ◽  
M I Makatiani ◽  
Arthur Kalanza Nzau
Keyword(s):  
Secondary Schools ◽  
Rural Areas ◽  
Policy Makers ◽  
The Sustainable Development ◽  
Female Teachers ◽  
Policy Interventions ◽  
Life Long Learning ◽  
Key Policy ◽  
Development Goals ◽  
Attraction And Retention

The potential role of female teachers in achieving the Education for all (EFA) and the Sustainable Development Goals, specifically on  ensuring  inclusive and equitable quality education and promoting life-long learning opportunities for all (Goal 4), achieving gender equality and empowering  all women and girls(Goal 5 ) is well documented. Available evidence, however, suggests that attraction and retention of female teachers in secondary schools located in rural areas remains a significant and on-going challenge. In response, policy makers in Kenya have recommended three key policy interventions namely decentralization of teacher recruitment, payment of hardship allowance and provision of housing. A literature search reveals a dearth of information on the perspectives of rural educators on the effectiveness of these interventions. The paper presents findings based on one objective of a broader study which was to: Establish the views of female teachers’ and other stakeholders’ regarding the effectiveness of strategies for attraction and retention of female teachers in Makueni County. This study adopted a mixed methods design. The paper presents findings from the qualitative component of the study. Interviews were used to gather data. Based on their interpretations, the authors provide useful   insights and offer suggestions on how the implementation of these policies could be improved.  

scholarly journals Binomial Regression in Small Area Estimation Method for Estimate Proportion of Cultural Indicator

2018 ◽  
Author(s):  
Yudistira Yudistira ◽  
Anang Kurnia ◽  
Agus Mohamad Soleh
Keyword(s):  
Regression Model ◽  
Sample Size ◽  
Small Area ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Alternative Methods ◽  
Area Estimation ◽  
Binomial Regression ◽  
Cultural Indicator ◽  
Sampling Survey

In sampling survey, it was necessary to have sufficient sample size in order to get accurate direct estimator about parameter, but there are many difficulties to fulfill them in practice. Small Area Estimation (SAE) is one of alternative methods to estimate parameter when sample size is not adequate. This method has been widely applied in such variation of model and many fields of research. Our research mainly focused on study how SAE method with binomial regression model is applied to obtained estimate proportion of cultural indicator, especially to estimate proportion of people who appreciate heritages and museums in each regency/city level in West Java Province. Data analysis approach used in our research with resurrected data and variables in order to be compared with previous research. The result later showed that binomial regression model could be used to estimate proportion of cultural indicator in Regency/City in Indonesia with better result than direct estimation method.

Expenditure Per Capita Model with Spatial Small Area Estimation

2021 ◽  
Vol 1 (2) ◽  
pp. 38-47
Author(s):  
Ahmad Risal
Keyword(s):  
Small Area ◽  
Spatial Information ◽  
Small Area Estimation ◽  
Random Effect ◽  
Development Program ◽  
Data Availability ◽  
Estimation Model ◽  
Policy Makers ◽  
Area Estimation ◽  
Direct Estimation

Indonesia is one of many countries around the world that attempt to suffer from high poverty rates. Since, poverty information in a certain area is a point of interest to researchers and policy makers. One problem faced is for the development program to be carried out more effectively and efficiently, it is necessary to have data availability up to the micro-scale. The technique used to reach the goal is Small Area Estimation (SAE). Fay-herriot (FH) model is one method on Small Area Estimation. Since, the SAE techniques require “borrow strength” across neighbor areas so thus Fay-Herriot model approach was developed by integrating spatial information into the model. This method known as Spatial Fay-Herriot Model (SFH) or Spatial Empirical Best Linear Unbiased Prediction (SEBLUP). This study aims to compare MSE of direct estimation, FH, and SFH Model to see which method gives the best result in estimating expenditure. The MSE value of the estimated SFH is smaller than direct estimation and FH, but it does not significant. It means adding spatial information in the small area estimation model does not give a better prediction than the simple small area estimation which is takes account the area as a specific random effect.

scholarly journals A method for small-area estimation of population mortality in settings affected by crises

2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Francesco Checchi ◽  
Adrienne Testa ◽  
Amy Gimma ◽  
Emilie Koum-Besson ◽  
Abdihamid Warsame
Keyword(s):  
Death Rate ◽  
Small Area ◽  
Small Area Estimation ◽  
Descriptive Analysis ◽  
Estimation Method ◽  
Attributable Mortality ◽  
Area Estimation ◽  
Ground Survey ◽  
Population Mortality ◽  
Generic Set

Abstract Background Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and conflict resolution. Methods We describe here a ‘small-area estimation’ method to circumvent these data gaps and quantify both total and excess (i.e. crisis-attributable) death rates and tolls, both overall and for granular geographic (e.g. district) and time (e.g. month) strata. The method is based on analysis of data previously collected by national and humanitarian actors, including ground survey observations of mortality, displacement-adjusted population denominators and datasets of variables that may predict the death rate. We describe the six sequential steps required for the method’s implementation and illustrate its recent application in Somalia, South Sudan and northeast Nigeria, based on a generic set of analysis scripts. Results Descriptive analysis of ground survey data reveals informative patterns, e.g. concerning the contribution of injuries to overall mortality, or household net migration. Despite some data sparsity, for each crisis that we have applied the method to thus far, available predictor data allow the specification of reasonably predictive mixed effects models of crude and under 5 years death rate, validated using cross-validation. Assumptions about values of the predictors in the absence of a crisis provide counterfactual and excess mortality estimates. Conclusions The method enables retrospective estimation of crisis-attributable mortality with considerable geographic and period stratification, and can therefore contribute to better understanding and historical memorialisation of the public health effects of crises. We discuss key limitations and areas for further development.

ESTIMASI ANGKA PARTISIPASI KASAR PERGURUAN TINGGI PROVINSI PAPUA MELALUI SMALL AREA ESTIMATION

2020 ◽  
Vol 2019 (1) ◽  
pp. 104-109
Author(s):  
Budi Subandriyo
Keyword(s):  
Sustainable Development ◽  
Small Area ◽  
Sustainable Development Goals ◽  
Small Area Estimation ◽  
Auxiliary Variable ◽  
Best Linear Unbiased Predictor ◽  
Area Estimation ◽  
Direct Estimation ◽  
Development Goals ◽  
Best Linear Unbiased

Angka Partisipasi Kasar (APK) m0erupakan salah satu indikator statistik yang digunakan untuk melihat besarnya tingkat partisipasi pendidikan pada suatu wilayah. Besar atau kecilnya nilai APK perguruan tinggi menunjukkan seberapa mudah penduduk untuk mengakses Pendidikan di perguruan tinggi. Hal ini sesuai dengan tujuan pembangunan berkelanjutan (Sustainable Development Goals) yang memiliki program untuk terus meningkatkan kesempatan belajar, salah satunya pendidikan di perguruan tinggi. Oleh karena itu, diperlukan upaya peningkatan akses pendidikan di universitas dan perguruan tinggi melalui penyediaan data APK-PT yang akurat. Apabila dilihat berdasarkan daerah tingkat provinsi, Provinsi Papua merupakan provinsi dengan APK-PT dua terbawah di antara provinsi lainnya yaitu sebesar 19,03 persen. Akan tetapi, ketersediaan data APK-PT hingga tingkar kabupaten atau kota masih belum tersedia karena kurangnya ukuran sampel. Salah satu upaya untuk mengoptimalkan sampel yang tersedia dan menghasil estimasi APK-PT di tingkat kabupaten/kota yaitu dengan menggunakan metode Small Area Estimation (SAE) berbasis area level. Pada penelitian ini digunakan data Survei Sosial Ekonomi Nasional (SUSENAS) 2018 untuk memperoleh estimasi langsung (direct estimation) APK-PT dan Potensi Desa (PODES) 2018 di Provinsi Papua sebagai variabel penyerta (auxiliary variable) dalam pemodelan SAE. Metode SAE yang digunakan adalah Empirical Best Linear Unbiased Predictor – Fay Herriot (EBLUP-FH) dan EBLUP benchmarking seperti EBLUP Difference Benchmarking (EBLUP-DB), EBLUP You-Rao Benchmarking (EBLUP-YR), dan EBLUP Augmented Bencharking (EBLUP-AB). Berdasarkan hasil penelitian disimpulkan bahwa penggunaan estimasi SAE yang cocok pada data APK-PT di Provinsi Papua adalah model EBLUP Augmented Benchmarking dengan nilai rata-rata MSE terendah yaitu sebesar 22,06 persen.

scholarly journals Identification of Disaggregated Hotspots of Child Morbidity in Bangladesh: An Application of Small Area Estimation Method

2019 ◽  
Author(s):  
Sumonkanti Das ◽  
Bappi Kumar ◽  
Luthful Alahi Kawsar
Keyword(s):  
Small Area ◽  
Census Data ◽  
Small Area Estimation ◽  
Random Effect ◽  
National Level ◽  
Estimation Method ◽  
Response Variable ◽  
Area Estimation ◽  
Child Morbidity ◽  
Administrative Units

AbstractAcute respiratory infection (ARI) and diarrhoea are two major causes of child morbidity and mortality in Bangladesh. National and regional level prevalence of ARI and diarrhoea are calculated from nationwide surveys; however, prevalence at micro-level administrative units (say, district and sub-district) is not possible due to lack of sufficient data. In such case, small area estimation (SAE) methods can be applied by combining a survey data with a census data. Using a SAE method for dichotomous response variable, this study aims to estimate the proportions of under-5 children experienced with ARI and diarrhoea separately as well as either ARI or diarrhoea within a period of two-week preceding the survey. The ARI and diarrhoea information extracted from Bangladesh Demographic and Health Survey 2011 are used to develop a random effect logistic model for each of the indicators, and then the prevalence is estimated adapting the World Bank SAE approach for the dichotomous response variable using the 5% data of the Census 2011. The estimated prevalence of each indicator significantly varied by district and sub-district (1.4-11.3% for diarrhoea, 2.2-11.8% for ARI and 4.3-16.5% for ARI/diarrhoea at sub-district level). In a number of districts and sub-district, the proportions are found double the national level. District and sub-district levels spatial distributions of the indicators might help the policy makers to identify the vulnerable disaggregated and remote hotspots. Particularly, aid industries can provide effective interventions at the highly vulnerable spots to overcome the gaps between micro and macro level administrative units.

scholarly journals Small Area Estimation untuk Estimasi Angka Kematian Bayi di Indonesia menggunakan Metode Empirical Best Linear Unbiased Prediction

2021 ◽  
Vol 10 (2) ◽  
pp. 171
Author(s):  
Nadra Yudelsa Ratu ◽  
Easbi Ikhsan
Keyword(s):  
Sustainable Development ◽  
Small Area ◽  
Sustainable Development Goals ◽  
Small Area Estimation ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Area Estimation ◽  
Development Goals ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Angka Kematian Bayi (AKB) adalah jumlah kematian bayi usia di bawah satu tahun untuk setiap 1000 kelahiran bayi lahir hidup dalam kurun waktu satu tahun. IMR merupakan indikator penting dari status kesehatan dari masyarakat dalam suatu daerah. Hal ini sejalan dengan Sustainable Development Goals (SDG’s) yang ke tiga yaitu memastikan kehidupan yang sehat dan mendukung kesejahteraan bagi semua untuk semua usia. AKB dihasilkan melalui estimasi langsung dari Survei Demografi dan Kesehatan Indonesia (SDKI). Akan tetapi, dalam SDKI 2017, AKB hanya bisa menghasilkan indikator pada level nasional. Hal ini disebabkan estimasi langsung dari AKB di beberapa provinsi memiliki nilai Relatif Standard Error (RSE) yang besar dan ukuran sampel yang tidak mencukupi. Dalam jurnal ini, kami mempelajari Small Area Estimation (SAE) menggunakan metode Empirical Best Linear Unbiased Prediction (EBLUP) level area untuk mengatasi keterbatasan estimasi AKB di level provinsi. SAE dilakukan dengan meminjam kekuatan beberapa variabel dari data Potensi Desa (PODES) yang berkorelasi kuat dengan AKB tingkat provinsi di Indonesia. Hasil penelitian menunjukkan bahwa SAE menggunakan metode EBLUP memiliki nilai RSE yang lebih kecil dibandingkan estimasi langsung dari SDKI. Sehingga, dapat dikatakan bahwa SAE menggunakan metode EBLUP baik untuk memperkirakan AKB level provinsi di Indonesia pada tahun 2017.

Sign in / Sign up

Export Citation Format

Share Document