Description: For several decades, area-level models have played a critical role in the theory and practice of small area estimation. For an area-level model, we propose a random effects variance estimator that simultaneously (i) improves on the estimation of the related shrinkage factors, (ii) protects empirical best linear unbiased predictors (EBLUP) of the random effects from the common over-shrinkage problem, (iii) avoids complex bias correction in generating strictly positive second-order unbiased mean square error (MSE) estimator either by the Taylor series or single parametric bootstrap method. The idea of achieving multiple desirable properties in an EBLUP method through a suitably devised random effects variance estimator is the first of its kind and holds promise in providing good inferences for random effects under the EBLUP framework. The proposed methodology is also evaluated using a Monte Carlo simulation study and real data analysis. This is a joint work with Prof. Partha Lahiri at the University of Maryland, College Park.
Description: This talk will cover recent developments in the literature on small area estimation in developing countries from a practitioner’s perspective. It will consider the merits of non-traditional sources of auxiliary data, particularly geospatial data. It will also consider different statistical methods for estimation, software packages to carry out estimation, and propose a research agenda to fill knowledge gaps.
Description: This side event of 55th session of the United Nations Statistical Commission, aims to showcase the experiences and lessons learned from the Global Training on Small Area Estimation across three key regions: Latin America and the Caribbean, Asia Pacific, and Africa.
Source: Eurostat (Data extracted on: 25 Jan 2024 )
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Organizer(s): Eurostat
Description: Are you interested in regional statistics and want to learn more about the nomenclature of territorial units for statistics, the so-called NUTS? During this webinar, you will get valuable insight into European regional statistics. How can we compare small EU countries like Luxembourg to Germany, which is the most populous one? Comparing regional data is often more informative compared with country data: it may reveal the disparities or similarities between EU members or even within EU countries themselves. Speakers will give fascinating insights into the lives of the 448 million people living in the EU. They will be covering a variety of topics, from demography to education, labour market, and economy. With the help of interactive visuals, the speakers will show how the EU regions are coping with the green and digital transition. In addition, you can learn how to make your own statistical map for regions.
Description: In unit-level small area models, the response variable corresponds to an individual element within a small area. Unit-level models serve a fundamental role in the field of small area estimation. Predictors based on unit-level models have been demonstrated to be more efficient than predictors based on area-level models, where the response variable is a direct estimator for an area. The seminal work on unit-level small area models is Battese, Harter, and Fuller (1988). This work uses unit-level small area models to predict crop areas at the county level. The model of Battese, Harter, and Fuller (1988) is linear and postulates normal distributions for the random terms. The assumptions of linearity and normality fail to hold in many practical situations. Therefore, research on the unit-level model has expanded in a variety of directions. Extensions of the Battese, Harter, and Fuller (1988) model include lognormal models, zero-inflated models, models with gamma response distributions, models for count data, and methods to incorporate an informative sample design. In this webinar, we discuss the use of the unit-level model for small area estimation, with emphasis on recent developments.
Description: Labor market statistics are of major importance when addressing social and economic challenges. These statistics are typically derived from complex survey data collected on a monthly or quarterly basis. However, when it comes to small geographical areas or domains, survey samples often fail to provide reliable estimates. To address this issue, the Economic Commission for Latin America and the Caribbean (ECLAC) has put forth a standardized approach that utilizes area-level models, taking into account the multinomial nature of occupation statuses. Within a Bayesian framework, we define a both a linkage and a sampling model that enable the simultaneous estimation of a diverse set of indicators of interest, such as unemployment rates, occupation rates, and participation rates. This innovative approach proposed by ECLAC aims to overcome the limitations associated with traditional survey sampling methods, particularly in smaller areas where the precision of estimates may be compromised. To validate the effectiveness of our methodology, we conducted a design-based simulation, which revealed promising results and demonstrated the favorable performance of the proposed approach. Building upon this success, we proceeded to implement the methodology across several countries in Latin America, leveraging the extensive and reliable data repository provided by ECLAC's BADEHOG survey database. By adopting this standardized approach, ECLAC aims to enhance the accuracy and reliability of labor market statistics at both national and subnational levels. The utilization of area-level models and the incorporation of Bayesian techniques allow for more reliable estimations, thus enabling policymakers, researchers, and practitioners to make informed decisions and develop effective strategies to address labor market challenges in Latin America and the Caribbean region.
Title in Spanish: Seminario regional sobre metodologías de estimación en áreas pequeñas y desagregación de datos
Organizer(s): ECLAC Centro Regional de Estudios para el Desarrollo de la Sociedad de la Información
Description: El énfasis de la Agenda 2030 para el desarrollo sostenible en no dejar a nadie atrás y atender particularmente a los más vulnerables hace necesario contar con indicadores suficientemente desagregados según diversas características de la población y escalas geográficas.
Description: UNSD conducted a capacity building training workshop on the Degree of Urbanisation(DEGURBA) Methodology for national statistics offices and geospatial institutions of countries in Central Asia. The workshop was organized in cooperation with the European Commission Joint Research Center (EC-JRC) and UN-Habitat and included the participation of statisticians and geospatial specialists from Azerbaijan, Kazakhstan, Kyrgyzstan, Tajikistan and Uzbekistan, as well as experts from UNSD, ESCAP, EC-JRC and UN-Habitat. As national Statistical Offices use different national criteria to define cities, urban and rural areas, the Degree of Urbanisation methodology was developed by the European Commission, UN-Habitat, FAO and other partners as a way to harmonize these definitions and promote international and regional data comparability, particularly in the context of reporting on the Sustainable Development Goals (SDGs) and on the New Urban Agenda (NUA). DEGURBA was endorsed by the Statistical Commission at its 51stSession in 2020. Through theoretical and practical sessions the participants were trained on 1) the need for this methodology 2) theory and concepts 3) data inputs, data preparation and technical capacity 4) applying the methodology, reviewing and evaluating the results and5) its applications for reporting of data and SDG indicators by degree of urbanization. The participants also exchanged experiences/challenges and learned from each other. Finally, this capacity building workshop further emphasized and provided practical knowledge on the importance of integrating census data with geospatial information.
Description: The launch of the new eLearning course on Small Area Estimation (SAE) was an event to remember. UNSD, UN ECLAC Statistics Division, and UNFPA gathered a prestigious panel of experts to discuss how Small Area Estimation (SAE) methods can support SDGs and countries, and how the eLearning course is an essential tool for disseminating knowledge and making information more accessible. The panel discussion featured insights from internationally renowned professors, as well as experiences from countries and international organizations, all of which inspired the audience and highlighted how much still needs to be done. The launch of this eLearning course marks a milestone in the journey on SAE, which started with the launch of the Toolkit on SAE on the Wiki platform requested by IAEG-SDG one year ago. With over 220 people attending the online event, it's clear that the topic of SAE is highly popular. More information on the event page.
Source: World Bank (Data extracted on: 03 Feb 2023 )
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Organizer(s): World Bank
Description: Advancing the World Bank’s twin goals of ending extreme poverty and boosting shared prosperity requires knowing who the poor are and where they live. By reaching the most granular levels of geographic aggregation, policymakers can significantly improve outcomes for the world’s poorest. However, survey data alone is not sufficient for targeting at the lowest levels. This is where small area estimation becomes necessary. The publication of Guidelines to Small Area Estimation for Poverty Mapping caps more than two decades of poverty mapping experience at the World Bank since the launch of an innovative method combining census and survey data to study the spatial dimensions of poverty. The guidelines build upon the lessons learned from experience and seek to guide readers on the best methods available for a variety of data landscapes.
Description: Model-based small area estimation approaches are in great demand by institutions interested in reliable statistics at disaggregated levels. Such tools overcome estimation challenges using small or incomplete survey data, in part, by combining data from multiple sources. The course starts with an overview of important small area estimation concepts. Area-level models are the main focus of the course, as proven to be of great practical use. Hierarchical Bayes inference is adopted. Software programs are provided for the model fit, validation, prediction, and comparison, with model specification scrips in R STAN. Illustrations use public-use data from the U.S. Census Bureau, for state-level poverty rate estimation research. Learning outcomes to be covered Learn how to conduct area-level model-based small area estimation analyses using freely available software.
Target Audience: The course is intended for statisticians interested in hierarchical Bayes model-based small area estimation.
Topics:
Original webpage was deleted, archived version from the Internet Archive (not a UN service): Link
IASS Webinar: Hukum Chandra prize 2022: Small area estimation: a novel approach on estimation of mean squared prediction error of small-area predictors
Description: As many countries have turned their attention to the estimation of poverty at the subnational level, this webinar aims to discuss practical aspects of poverty mapping.
Title in Arabic: دورة متقدمة في أساليب تقدير المناطق الصغيرة
Organizer(s): AITRS ESCWA UNFPA
Description: زاد الطلب، من القطاع العام والقطاع الخاص، على التقديرات الموثوقة للمجالات الصغيرة إلى حدٍ كبير في جميع أنحاء العالم. وهذا يرجع إلى إستخدامها المتزايد في صياغة السياسات والبرامج وتخصيص الأموال الحكومية والتخطيط الإقليمي. تقدير المنطقة الصغيرة هو مجال بحث في الإحصاءات الرسمية والإحصاءات المسحية ذات أهمية عملية كبيرة للمعاهد الإحصائية الوطنية والمنظمات ذات الصلة. على الرغم من التطورات السريعة في المنهجية والبرمجيات، وسوف يستفيد الباحثون والمستخدمون من وجود إرشادات عملية تساعد في عملية تقدير المجالات الصغيرة. تقدير المناطق الصغيرة كان ولا يزال في الغالب مجالًاً خصبًا جدًا للبحث الأكاديمي في الإحصاءات الرسمية مع مساهمات نظرية وتطبيقية مهمة. في العقد الماضي وبشكل متزايد تم الإعتراف والإدراك لدى العديد من المعاهد الإحصائية الوطنية والمنظمات الأخرى في جميع أنحاء العالم بأهمية إنتاج إحصاءات المجالات الصغيرة. ومن الأمثلة على ذلك، مكتب الإحصاء الأمريكي، والبنك الدولي، ومكتب المملكة المتحدة للإحصاءات الوطنية، والمكتب الإحصائي لإيطاليا، وغيرها. هذا الإهتمام ساهم في تكوين مجموعات بحثية في علم تقدير المناطق الصغيرة من خلال تطوير منهجيات وأدوات حسابية جديدة متاحة للإستخدام العام.
Description: Within its recently initiated Webinar Series on Statistical Experience Sharing, SESRIC will organise a webinar on ‘Measurement Methods for SDG 11 and the New Urban Agenda in the OIC Countries’ 31 May 2021 in collaboration with the United Nations Human Settlements Programme (UN-HABITAT) and United Nations Economic and Social Commission for Western Asia (UN-ESCWA) with the participation of official statisticians working in National Statistical Offices (NSOs) of the OIC countries. The main objective of the Webinar is to introduce to the participants the SDG 11 (Making Cities and Human Settlements Inclusive, Safe, Resilient and Sustainable) and the New Urban Agenda (NUA) monitoring frameworks, their measurement approaches as well as tools to aid in the computation of the relevant indicators. The Webinar aims to achieve the following related to SDG 11 indicators: Introduce the SDG 11 and the NUA targets, indicators and monitoring framework; Share experiences in urban reporting and dissemination of urban indicators; Introduce the Global Urban Monitoring framework, the urban observatory model and the National Sample of Cities concept as useful tools for the urban agenda monitoring; and Discuss challenges on urban monitoring at national and local levels in the OIC countries. During the Webinar, SESRIC will brief the participants on the outcomes of the questionnaire on the human settlements / population indicators to monitor and report on SDG 11. The Webinar will be conducted through a video conferencing platform by following synchronous learning and instruction approaches designed in line with the virtual training solutions undertaken by SESRIC in order to better serve the Centre’s training activities and keep participants motivated and engaged during this time of global crisis due to COVID-19. Webinar Documents: Concept Note and Agenda (English) (Arabic) (French) Questionnaire on Human Settlements / Population Indicators to Monitor and Report on SDG 11 (English) (Arabic) (French)
Description: تُستخدم المسوحات بالعينات التي ترعاها الحكومات والتي تجريها المعاهد الإحصائية على نطاق واسع لإنتاج تقديرات للمجاميع والمعدلات والنسب على المستوى الكلي في المجتمعات المنتهية الحقيقية. في السنوات الأخيرة أصبح الاهتمام منصباً على توفير تقديرات أو مؤشرات على مستوى أكثر تفصيلاً، أي للمجموعات الجزيئة من المجتمع الكلي، والتي لم يكن مخططاً لها في تصميم المسح الأصلي، دون تكبد تكاليف إضافية بسبب زيادة حجم عينة المسح، والذي هو هدف تقدير المناطق أو المجالات أو المساحات الصغيرة. على سبيل المثال معدلات البطالة للمجموعات السكانية الجغرافية الإجتماعية الديموغرافية. في الأدبيات، يعبر عادة على التقسيمات الفرعية التي من أجلها تكون البيانات الإحصائية (أو التقديرات) مطلوبة بالمناطق أو المجالات، بغض النظر عما إذا كانت بالفعل تتوافق مع مناطق جغرافية أو مجموعات فرعية إجتماعية إقتصادية، أو تقاطعات لكليهما. وتهدف الورشة الى : * التعريف باشكاليلت تقدير المناطق الصغيرة * التعرف على التقديرات المباشرة - تقدير المناطق المعتمدة على تصميم المسح * التعرف على التقديرات غير المباشر - التقديرات المركبة
