Statistical Journal of the IAOS最新文献_第2页

Building trust and facilitating use of data 建立信任和促进数据的使用

Statistical Journal of the IAOS Pub Date : 2024-02-01 DOI: 10.3233/sji-240006

Francesca Perucci, Eric Swanson

引用次数: 0

Analyzing commercial grape farm efficiency in Armavir region (Armenia) by using two-stage empirical approach 采用两阶段实证方法分析亚美尼亚阿尔马维尔地区商业葡萄农场的效率

Statistical Journal of the IAOS Pub Date : 2024-01-23 DOI: 10.3233/sji-230064

H. Asatryan, V. Aleksanyan, Samvel Asatryan, M. Manucharyan

{"title":"Analyzing commercial grape farm efficiency in Armavir region (Armenia) by using two-stage empirical approach","authors":"H. Asatryan, V. Aleksanyan, Samvel Asatryan, M. Manucharyan","doi":"10.3233/sji-230064","DOIUrl":"https://doi.org/10.3233/sji-230064","url":null,"abstract":"The purpose of this paper is to provide an empirical assessment of the economic efficiency of grape-producing farms in Armenia. Upon reviewing various field-related studies the frontier analysis was singled out as a methodological base of this study. More specifically two-stage empirical analysis was performed, which includes the measurement of efficiency levels of grape farms by implementing the DEA technique and then assessing the determinants of obtained efficiency scores by performing Tobit modeling. To obtain necessary data, 365 grape farms from the Armavir region were surveyed. The main findings of this paper suggest that the average efficiency score for grape farms is 0.72, and there is room for improvement in the economic performance of farms with 28%. The main determinants of farm efficiency were cultivated grape varieties, farm size, and selling prices of grapes. The obtained results mainly support the findings of similar studies carried out for various viticulture regions across the world. This study provides some methodology bases for further expansion of similar studies both in terms of including the other Armenian viticulture regions and different years to explore the changes in the efficiency of grape farms over time. This article provides a base of knowledge for policymakers, scholars, researchers, investors, and credit companies for their decision-making processes and other purposes.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"28 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140499035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaboration between national statistical offices and academia: Benefits, conditions, areas of collaboration and practical level experience in countries 国家统计局与学术界之间的合作：各国的优势、条件、合作领域和实践经验

Statistical Journal of the IAOS Pub Date : 2024-01-23 DOI: 10.3233/sji-230117

Charlotte Juul Hansen, Lina Maria Sanchez Cespedes, Leonardo Trujillo Oyola, X. K. Dimakos, Bianca Walsh, Renata Souza Bueno, Amos T. Kabo-Bah, Omar Seidu, Vibeke Oestreich Nielsen

{"title":"Collaboration between national statistical offices and academia: Benefits, conditions, areas of collaboration and practical level experience in countries","authors":"Charlotte Juul Hansen, Lina Maria Sanchez Cespedes, Leonardo Trujillo Oyola, X. K. Dimakos, Bianca Walsh, Renata Souza Bueno, Amos T. Kabo-Bah, Omar Seidu, Vibeke Oestreich Nielsen","doi":"10.3233/sji-230117","DOIUrl":"https://doi.org/10.3233/sji-230117","url":null,"abstract":"National statistical offices (NSOs) and academia benefit from establishing partnerships and collaborating in different ways by bringing together their respective expertise. Collaborative alliances of this nature appear to offer numerous advantages for both the partners and the public and seem to be essential for unlocking opportunities within the evolving data ecosystem. Establishing good and fruitful collaboration between academia and NSOs requires a collaborative environment where each partner can see the benefits of the collaboration and how they could contribute. Different areas of collaboration are presented within four categories: education and learning, research, promotion of data use in society and providing services to each other. The article further discusses the benefits and conditions of a successful partnership. Examples from Brazil, Colombia, Ghana, and Norway showcase practical-level experiences and some lessons learned at the country level.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"37 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140498342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial and demographic distributions of personal insolvency: An opportunity for official statistics 个人破产的空间和人口分布：官方统计的机遇

Statistical Journal of the IAOS Pub Date : 2023-12-15 DOI: 10.3233/sji-230072

Jonas Klingwort, Sven Alexander Brocker, Christian Borgs

引用次数: 0

Unbiased estimation strategies for respondent driven sampling 受访者驱动抽样的无偏估计策略

Statistical Journal of the IAOS Pub Date : 2023-11-20 DOI: 10.3233/sji-230087

P. D. Falorsi, G. Alleva, Francesca Petrarca

引用次数: 0

Hard-to-reach population groups in administrative sources: main challenges and future work 行政来源中难以接触到的人口群体：主要挑战和未来工作

Statistical Journal of the IAOS Pub Date : 2023-11-20 DOI: 10.3233/sji-230074

Donatella Zindato, Maciej Truszczynski

{"title":"Hard-to-reach population groups in administrative sources: main challenges and future work","authors":"Donatella Zindato, Maciej Truszczynski","doi":"10.3233/sji-230074","DOIUrl":"https://doi.org/10.3233/sji-230074","url":null,"abstract":"The paper deals with the concept and the definitions of hard-to-reach groups and the ways of capturing them in administrative sources, providing a detailed discussion of the meaning of hard-to-reach in the context of administrative sources and in relation to the traditional hard-to-count groups in censuses and surveys. The review of country practices shows that hard-to-reach populations in administrative data can be interpreted in different ways and that their definition is dependent on countries’ circumstances, though there are two main reasons for identifying a group as hard-to-reach in administrative sources. One of the interpretations is selecting some groups, typically considered difficult to reach with traditional survey methods (such as homeless, illegal immigrants or indigenous people) and then trying to capture them in registers to overcome the challenges of traditional field collection or to get more complete information. At first glance, administrative data might offer the potential to improve frame coverage for some target populations, but may also lead to other hard-to-reach or “hidden” populations for different population groups. Indeed, another interpretation refers to the incompleteness of registers or linked administrative databases, which makes some groups, such as children or elders, hard-to-reach and hence describe with data, due to time lag in reporting of some events or to other accuracy problems with the source itself. The paper summarizes the experience of national statistical offices in accessing hard-to-reach groups and describes problems and challenges in capturing them. It also proposes further possible work to improve access to hard-to-reach groups using administrative data.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"16 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139254707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning estimation of the resident population 常住人口的机器学习估算

Statistical Journal of the IAOS Pub Date : 2023-11-19 DOI: 10.3233/sji-230090

Violeta Calian, Margherita Zuppardo, Omar Hardarson

引用次数: 0

Web scraping for price statistics in the Philippines 通过网络搜索菲律宾的价格统计数据

Statistical Journal of the IAOS Pub Date : 2023-11-17 DOI: 10.3233/sji-230030

Manuel Leonard F. Albis, Sabrina O. Romasoc, Shushimita G. Pelayo, Bea Andrea C. Gavira, Jazzen Paul J. Asombrado

引用次数: 0

To count or to estimate: A note on compiling population estimates from administrative data 计算还是估算？关于根据行政数据编制人口估计数的说明

Statistical Journal of the IAOS Pub Date : 2023-11-15 DOI: 10.3233/sji-230067

John Dunne, Francesca Kay, Timothy Linehan

{"title":"To count or to estimate: A note on compiling population estimates from administrative data","authors":"John Dunne, Francesca Kay, Timothy Linehan","doi":"10.3233/sji-230067","DOIUrl":"https://doi.org/10.3233/sji-230067","url":null,"abstract":"Like many countries, Ireland has been researching new systems of population estimates compiled using administrative data. Ireland does not have a Central Population Register from which the estimates can be compiled. The primary step in compiling population estimates from administrative data is to first build a Statistical Population Dataset (SPD). Ideally an SPD will have one record for each person in the population containing the relevant attributes. The ideal SPD then allows compilation of statistics by simply counting over records. In practice, the compilation of SPDs is prone to error. These errors can be classified into 4 types of error; overcoverage, undercoverage, domain misclassification and linkage error. Ireland, to date, has investigated 2 different approaches to the compilation of population estimates from administrative data. The first, labeled in this paper as the simple count method, is based on building an SPD which minimises the overall number of individual record errors such that simple counts from the SPD will provide population estimates. The second, labeled in this paper as the estimation method, is based on building an SPD which aims to eliminate all error types bar that of undercoverage and then adjusts counts for undercoverage using Dual System Estimation (DSE) methods to obtain population estimates. This paper explores the advantages and disadvantages of both methods before considering how they could be integrated to eliminate the disadvantages. Many NSIs will be considering similar challenges when compiling annual Census like population estimates and this paper aims to contribute to that discussion.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139271001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classifying respondent comments from the 2021 Canadian Census of Population using machine learning methods1 使用机器学习方法对 2021 年加拿大人口普查受访者的意见进行分类1

Statistical Journal of the IAOS Pub Date : 2023-11-14 DOI: 10.3233/sji-230063

Joanne Yoon

引用次数: 0