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  undt-2013-099.pdf

Score: 826272.7 - www.un.org/en/internalj...dt/judgments/undt-2013-099.pdf

  Kazakhstan’s Labour Ministry urges changes into methods for calculating minimum subsistence level

Kazakhstan’s Labour Ministry urges changes into methods for calculating minimum subsistence level Skip to main content ILO Advancing social justice, promoting decent work ILO is a specialized agency of the United Nations Русский Countries Topics Sectors Search ilo.org Search ilo.org Menu Home About the ILO Newsroom Meetings and events Publications Research Labour standards Statistics and databases Contact Us Eastern Europe and Central Asia About the office Staff Vacancies Areas of work Employment Enterprises development Gender equality International Labour Standards Occupational safety and health (OSH) Social protection Workers' and Employers' organizations Countries covered Armenia Azerbaijan Belarus Georgia Kazakhstan Kyrgyzstan Russia Tajikistan Turkmenistan Uzbekistan News room Information resources Library Publications Video Links Newsletters Press about us Projects Events ILO home Regions and countries Eastern Europe and Central Asia News room Kazakhstan’s Labour Ministry urges changes into methods for calculating minimum ... Kazakhstan’s Labour Ministry urges changes into methods for calculating minimum subsistence level Kazakhstan’s Ministry of Labour and Social Protection in December will submit to the government its proposals to change methods for calculating a minimum subsistence level in the republic, statistics agency chief Alikhan Smailov said on October 15. (...) Since 2006, when Kazakhstan for the last time revised the calculation methods, the structure of households’ spending has changed and the share of non-food expenditures has increased, the statistics agency chief said.

Score: 825772.9 - https://www.ilo.org/moscow/new...WCMS_481320/lang--en/index.htm

  Information Notice No.1

This meeting will be organized as part of the Conference of European Statisticians’ work programme for 2022, within the context of the High-Level Group for the Modernisation of Official Statistics. 3. In particular, the expert meeting will explore the following themes: • Identifying new methods that can improve the quality and efficiency of editing and imputation; • Investigating statistical quality risks arising from using new methods and data sources, and ways to address them; • Developing approaches for standardizing and implementing statistical data editing functionalities; and • Facilitating the sharing of experiences, ideas and tools for modernizing statistical data editing and imputation processes. 4. (...) The participants at the previous meeting on statistical data editing in 2020 expressed their interest in examining the following topics in this meeting: • Machine Learning /Artificial Intelligence for editing and imputation; • Modernisation of data editing and statistical production; • Use of administrative data for editing and imputation; • Quality in the context of statistical data editing; • Imputation (for ‘Covid-19 mass nonresponse‘) and variance; • Use of Deep Learning on Big Data synchronized with editing and imputation; • Statistical data editing and pre-processing of new digital data for statistical purposes; • Infrastructure needed for open source statistical data editing packages; • Selection bias and statistical data editing; • Missing prices and outlying price data; and • New and emerging methods. 2 6. (...) They could also highlight the expected impact that new methods might have on a statistical agency, including how they contribute to standardizing concepts, terminology, methods, data structures and improving the quality of its data products.

Score: 825689.8 - https://unece.org/sites/defaul...2022-02/SDE%202022%20INF.1.pdf

  WHO | Global Health Estimates

Due to changes in data and some methods, latest estimates are not comparable to previously-released WHO estimates. Statistics Life expectancy, 2000–2016 Child mortality, 1990–2016 Child causes of death, 2000–2017 Adult mortality, 2000–2016 Causes of death, 2000–2016 DALYs, 2000–2016 Projections 2016-2060 – Previous estimates and projections Publications World Health Statistics Levels & trends in child mortality Report UN Inter-agency Group for Child Mortality Estimation Methods WHO methods and data sources for global causes of death, 2000–2016 WHO methods and data sources for life tables 1990–2016 pdf, 4.50Mb WHO methods for life expectancy and healthy life expectancy 1990-2015 pdf, 4.50Mb WHO-MCEE methods and data sources for child causes of death 2000–2016 pdf, 1.35Mb WHO methods and data sources for global burden of disease estimates, 2000–2015 pdf, 747kb – More methodology documents Multimedia 10 facts on the state of global health NEW Life expectancy, 2000–2016 Life expectancy, healthy life expectancy (HALE) and life tables Causes of death, 2000–2016 Cause-specific mortality estimates Contact us Please send us your comment or question by e-mail. Related WHO activities Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) Global Health Observatory Health statistics and health information systems Environmental burden of disease Burden of foodborne diseases Global Infobase WHO-Choice You are here: Health statistics and information systems Global Health Estimates Regions Africa Americas Eastern Mediterranean Europe South-East Asia Western Pacific About us Careers Library Procurement Publications Frequently asked questions Contact us Subscribe to our newsletters Privacy Legal Notice © 2022 WHO

Score: 825354.3 - https://www.who.int/healthinfo/global_burden_disease/en/

  方法创新 | FAO | 联合国粮食及 农业组织

FAO Statistics Division works to strengthen the statistical processes throughout the Organization by developing improved methods and their implementation in the corporate Statistical Working System. FAO Statistical Working System FAO Statistical Working System is a corporate platform used in FAO for the processing and storage of statistical datasets, providing the framework needed to use the same methods, standards, classifications and approaches within statistical processes. Within the FAO Statistical Working System, the methodological innovation work focuses on progressively integrates the corporate statistical processes with the aim to improve the quality of FAO statistical production, methods and outputs.

Score: 824926.8 - https://www.fao.org/food-agric.../methodological-innovation/zh/

  Seasonal Adjustment, Index of Industrial Production

Seasonal Adjustment, Index of Industrial Production Seasonal Adjustment of National Index Data at International Level Shyam Upadhyaya, Shohreh Mirzaei Yeganeh United Nations Industrial Development Organization (UNIDO), Vienna, Austria unido.org/statistics Overview What and why Basic concepts Costs and risks Methods Software UNIDO experience Recommendation unido.org/statistics 2 Seasonally adjusted and original series - Industrial Production Index unido.org/statistics Seasonally adjusted and original series - Industrial Production Index unido.org/statistics IIP percentage change QII 2011 to QI 2011 QII 2012 to QI 2012 Cameroon Russia Cameroon Russia -17.17% -15.48% -6.86% -1.39% Original -0.66% -8.51% 8.43% -0.66% SA unido.org/statistics Why seasonally adjust? (...) In general, other random disruptions and unusual movements that are readily understandable in economic terms (for example the consequences of economic policy, large scale orders or strikes) will also continue to be visible unido.org/statistics Seasonal Adjustment the Seasonally Adjusted results do not show “normal” and repeating events, they provide an estimate for what is new in the series which is the ultimate goal of Seasonal Adjustment unido.org/statistics Costs and Risks Seasonal Adjustment is time consuming, significant computer/human resources must be dedicated to this task Inappropriate or low-quality Seasonal Adjustment can generate misleading results and increase the probability of false signals (credibility effects) The presence of residual seasonality, as well as over-smoothing, are concrete risks which could negatively affect the interpretation of Seasonally Adjusted data unido.org/statistics Seasonal adjustment methods Model based method TRAMO/SEATS Filter based method X12-ARIMA unido.org/statistics TRAMO/ SEATS TRAMO (Time Series Regression with ARIMA Noise, Missing Observations and Outliers) and SEATS (Signal Extraction in ARIMA Time Series) developed by Victor Gómez and Agustin Maravall at Bank of Spain. (...) The method and software used should be explicitly mentioned in the metadata accompanying the series. unido.org/statistics Countries with no SA experience are encouraged to compile, maintain and update their national calendars or, as a minimal alternative, to supply an historical list of public holidays including, whenever possible, information on compensation holidays.

Score: 824926.8 - https://www.unido.org/sites/de.../files/2014-07/UNIDO-SA_0.pptx

  STATISTICAL COMMISSION and

Further, given the difficulty of accurately measuring migration, data integration could include methods to reconcile different migration figures as derived from different sources, for example “triangulating” (assuring validity through the use of more than one method to collect data on the same topic) results from different data sources to develop estimates for “hard-to-count” migration populations, such as irregular migrants or emigrants. 3. (...) The Task Force will carry out the following activities: (a) Review of methods used or under study by countries to integrate administrative sources to measure migration, including a list of potential administrative sources; page 3 (b) Review of methods used or under study by countries to combine administrative and non-administrative data sources to measure migration, including statistical modeling; (c) Review of methods used or under study by countries to reconcile migration figures derived from different data sources for improving final migration estimates; (d) Development of guidelines, based on practical examples, on how data integration can be used to improve the measurement of immigration, emigration and net migration; (e) Development of guidelines on the production of metadata/documentation for integrated migration statistics; (f) Description of good practices for improving communication between statistical agencies and the actual producers of administrative data related to migration. (...) METHOD OF WORK 13. The Task Force will primarily work via email, wiki workspace, and telephone conferences.

Score: 824800.6 - https://unece.org/DAM/stats/do...R_Data_Integration_Updated.pdf

  Statistics

Statistics Statistics Asia-Pacific Stats Café Series: Statistics that Leave No One Behind: Inclusive Data Charter Asia-Pacific Stats Café Series: Statistics that Leave No One Behind: Inclusive Data Charter KBOONPRI Thu, 07/01/2021 - 10:02 Event attendance type Open meeting Event Type Other Events Event Background This Stats Café brought together experts from the Inclusive Data Charter network, including international and regional organizations and national governments, to discuss experiences in producing and using inclusive and disaggregated statistics to progress the Leave No One Behind commitment. (...) Renice Akinyi Bunde Statistician, Governance, Peace and Security Statistics, Kenya National Bureau of Statistics Know More Moderator Petra Nahmias Chief, Population and Social Statistics Section, Statistics Division, ESCAP Know More Petra Nahmias is Chief of the Population and Social Statistics Section at UNESCAP, having recently taken up this role. She previously led the statistics team at UNHCR, working on a wide variety of statistical and demographic issues related to forced displacement and statelessness.

Score: 823761.2 - https://www.unescap.org/taxonomy/term/176/feed

  Slide 1

Adjustments shown using less well- known methods Age misreporting Age misreporting (45+)  New method(s) based on:  Basic statistic: cmRx(T1,T2) computed using two censuses (at T1 and T2) and intercensal deaths between T1 and T2  A standard pattern of age misreporting  Alternative techniques to estimate magnitude of age misreporting Statistic: cmRx(T1,T2)  From previous studies (Dechter-Preston, Del Popolo, Preston-Condran-Himes) using (a) two census at T1 and T2 and intercensal deaths in (T1,T2) Behavior of key statistic cmRx(T1,T2) under different conditions  Main problems:  Unequal census completeness leads to statistic’s behavior that mimics age over(under)statement  Intercensal migration leads to statistic’s behavior that mimic age over(under)statement  Conditions :  Adjusted for relative completeness of census enumeration  Closed to migration (or adjusted for it) Age patterns and levels of age misreporting  Main idea:  Detect problem with statistic  Reconstruct true population (matrix)  Age pattern of age misreporting  Level of age misreporting  From previous studies  India (Bhat)  Latin America (Ortega)  US: Medicare records (Preston et al)  We use Costa Rica 2002 matching study (census-voting register) and estimate standard patterns of  Population age misreporting  Probability of over(under) stating age at age x  Conditional probability of over(under) stating age by 1-10 years given over(under) statement at age x  The above is referred to as “standard pattern of age misstatement”  Generates a “standard matrix” of population transfers across ages Main results from Costa Rica study  Gender differences in age misreporting: marginal  Age differences in prob. of misreporting: large  Overstatement overwhelms under statement Age patterns of age misreporting Outcome  Matrix of net “age transfers” is a standard pattern of age misreporting that we assume prevails in all countries  Observed patterns produced by identical standard but different levels of age misreporting (age specific probability of misreporting)  Standard death and population patterns of age misreporting are identical Strategy  Estimate model predicting prob of age net overstatement as a function of age  Estimate negative binomial model for conditional probability of overestimation  Generate the Costa Rican standard of age net overstatement  Allow shifts in levels of net overstatement: the shifts or magnitude of age misreporting are estimated from data Identification conditions  We can estimate both LEVELS of net overstatement of ages at death and population  BUT:  Cannot identify simultaneously population over and under statement, only net overstatement  Must assume age patterns of over (under) statement of ages at death and population are identical  Must assume that standard is appropriate for observed population METHODS TO ESTIMATE MAGNITUDE OF AGE MISREPORTING DEATH AND POPULATION  Brute force iterative procedure :  plausible but time consuming  Inverse regression based on regression models estimated in simulated population. (...) Our is a generalization  Main ingredients  5 population profiles (see Appendix 1 for definition)  Patterns of errors of census/death completeness  Patterns of age over-reporting  Age dependent completeness  Total of up to 94500 different simulated populations  Measurement of error of main parameter: relative completeness of death registration  Adult mortality adjustments  Relative completeness  Methods: Bennett Horiuchi, Bennet-Preston, Preston Hill, Brass-Hill, Brass-Martin etc...A suite of 8-12 methods (depending on how one counts them). (...) Estimate Bennet-Horiuchi  III.Adjust cmRx(T1,T2) function using (I)  IV Estimate lebvel of age misreporting using optimal (regression based method)  V. Adjust mortality rates and construction life tables from age 5 on Uncertainty  Evaluation study produces  Metapopulation====== error distributions of each candidate method under different conditions violating assumption  Can attach probability (of error) measure to each candidate method  Can use them explicitly in estimation thus generating bounds of uncertainty of target parameters THANK YOU

Score: 823507.1 - https://www.un.org/en/developm...mber-2016-modified_Palloni.pdf

  Estrategia global para el mejoramiento de las estadísticas agropecuarias y rurales | FAO | Organizac

Significant results have been achieved: i) agricultural statistical methods have been completely upgraded and endorsed by FAO, ii) Strategic Plans for Agricultural and Rural Statistics (SPARS)have been prepared in almost 40 countries, iii) a fast-track model of technical assistance has been successfully implemented and iv) tangible progress has been made in countries’ overall capacity through regional training programmes. (...) Support will include: i) assistance in the design of strategic plans for agricultural statistics; ii) training in agricultural statistics and provision of scholarships; and iii) technical assistance and training on tools, methodologies for data collection, data analysis and dissemination.     Resources Action Plan Phase 2   Guidelines and Handbooks Training material: Technical Report on Improving the Use of GPS, GIS and Remote Sensing in Setting Up Master Sampling Frames Technical Report on Reconciling Data from Agricultural Censuses and Surveys Literature Review on Reconciling Data from Agricultural Censuses and Surveys Technical Report on Cost – Effectiveness of  Remote Sensing for Agricultural Statistics in Developing and Emerging Economies Technical Report on Developing More Efficient and Accurate Methods for the Use of Remote Sensing in Agricultural Statistics Technical Report on the Integrated Survey Framework Case-Studies on the measurement of productivity and efficiency in agriculture Pilot tests of an international definition of urban – rural territories - Summary report Methodology for definition and spatial delimitation of rural areas Master Sampling Frames - The field experiments conducted in Nepal Master Sampling Frames (MSF) for fishery and aquaculture statistics Gaps and Methodological Approach: A Critical Analysis of Methods for Surveys of Fisheries and Aquaculture Identifying the Most Appropriate Sampling Frame for Specific Landscape Types A Literature Review and Key Agri/Environmental Indicators Field Test Report on Agri-environmental Indicators (AEls): towards a Suistainable Agriculture How to Include the Wood fuel Supplementary Module into Existing Surveys and Derive Woodfuel Indicators Developing a Wood fuel Survey Module for Incorporation into Existing Household Surveys and Censuses in Developing Countries Improving the Methodology for Using Administrative Data in an Agricultural Statistic System Improving Methods for Estimating Livestock Production and Productivity - Methodological Report Improving Methods for Estimating Livestock Production and Productivity - Literature Review A Literature Review on Frameworks and Methods for Measuring and Monitoring Sustainable Agriculture Methodology for Estimation of Crop Area and Crop Yield under Mixed and Continuous Cropping A Review of Literature Related to Master Sampling Frames for Fisheries and Aquaculture Surveys Master Sampling Frames for Agriculture - Supplement on selected country experiences Productivity and Efficiency Measurement in Agriculture - Literature Review and Gaps Analysis The Social Dimension of Rural Statistics A Minimum Set of Environmental Indicators for Improving Rural Statistics Literature Review Report and Proposal for an International Framework for Farm Typologies Information on Land in the Context of Agricultural Statistics National Statistics Related to Woodfuel and International recommendations Agricultural Cost of Production - Country field test and Desk-study reports Sex-Disaggregated Data and Gender Indicators in Agriculture- A Review of data Gaps and Good Practices Improving methods for using existing land cover databases and classification methods - A literature review Linking Area and List Frames in Agricultural Surveys Measuring vegetable crops area and production: Technical report on a pilot survey in two districts of Ghana- Final report Field Test Report on the Estimation of Crop Yields and Post-Harvest Losses in Ghana Measuring inadequate employment in Kenya: Field test report for  Decent Work within an agricultural context in developing countries share mail live_help x SÍGUENOS EN Organigrama de la FAO Oficinas en el mundo Oficina Regional para África Oficina Regional para Asia y el Pacífico Oficina Regional para Europa y Asia Central Oficina Regional para América Latina y el Caribe Oficina Regional para el Cercano Oriente y África del Norte Oficinas subregionales Empleo Contacto Términos y condiciones Alerta de estafa Reportar mala conducta Descarga nuestra App © FAO 2022

Score: 823410.7 - https://www.fao.org/food-agric...evelopment/global-strategy/es/