National Statistical System Research Articles

Detection of slums in Rio de Janeiro through satellite images

According to UN-Habitat, more than one billion people live in informal settlements worldwide, of which 200 million living in Africa and another 100 million in Latin America, mainly in countries such as Brazil, Mexico, Colombia, Peru, and Argentina. Rio de Janeiro has 1,074 favelas, representing 22% of the city's total population, making it the Brazilian municipality with the highest percentage of people living in favelas. Ensuring human rights through access to basic services for the populations living in these settlements, through programs and public policies, depends on timely and reliable data. However, despite spending decades establishing their national statistical systems, usually based on data collection directly from individuals, in most countries, the data produced in traditional ways does not portray the dynamics of these populations promptly. As an alternative, we combined free satellite imagery with machine learning and deep learning to identify the area occupied by favelas in the city of Rio de Janeiro. We compared the results of eight distinct segmentation models using the IoU and F1 as metrics. Among the evaluated methods, two stood out for their performance: GradientBoost and XGBoost.

Towards the 4th population census in Ethiopia: Some insights into the feasibility of the Post-Enumeration Survey

While national registry systems are evolving worldwide and, in some cases, replacing reliance on censuses, in countries where well-established population registers are lacking, the population and housing census remains the primary source of detailed data on the number of people, their spatial distribution, age and gender structure, living conditions, and other key socio-economic characteristics. The quality of the census findings is crucial for several reasons, including building public trust in the national statistical system. In many developing countries, conducting a Post-Enumeration Survey appears to be the only feasible way to evaluate the census results. Indeed, the lack or incompleteness of reliable demographic data from alternative sources precludes the use of other methods. This paper discusses some aspects of the feasibility of a Post-Enumeration Survey in Ethiopia. In particular, the paper reports on the main critical issues that emerged from the pilot surveys carried out in the framework of a cooperation project – funded by the Italian Agency for Development Cooperation – aimed at providing methodological support and technical assistance for the preparation of the 4th Ethiopian Population and Housing Census.

Is there a quantitative relationship between democracy and official statistics?

The main purpose of this article is to show the quantitative relationship between political regimes and the quality of the national statistical systems. The data exploratory analysis, usually treated from a qualitative point of view, shows a strong correlation between democracy and official statistics, a thesis confirmed in all continents. The most democratic countries are the ones with the best statistical performance. In fact, among the top 10 democracies, five are also in the top 10 for statistical performance. The correlation between democracy and statistical performance is about 70 percent, although over the years 2016–2022, it has been slightly decreasing. The country’s statistical performance is affected by political regime. The main indicators employed for this analysis are the Democracy Index by The Economist, and the Statistical Performance Indicators by the World Bank. The use of these global indicators, encompassing an entire range of years and countries, is unusual in statistical analysis.

Perceived satisfaction and household fuel transition: evidence from Ugandan households

IntroductionThe dominance of biomass for cooking has implications for the country. For instance, biomass conversion using traditional technologies (three stone) in poorly ventilated kitchens produce smoke that influences the reproductive health outcomes for expectant mother. Beyond health outcomes, uncontrollable harvesting of biomass for cooking contributes to environmental degradation. Unfortunately, biomass in form of firewood, charcoal, plant and animal wastes is widely consumed by more than 94% of Ugandan households for cooking. This paper examined whether perceived satisfaction plays a significant role in household energy transition in Uganda.MethodsThe study adopted a panel data methodology employing an ordered logit model with random effects to estimate the effect of perceived satisfaction on household fuel transition from high to low pollutant cooking fuels in Uganda. The data used was obtained from Uganda Bureau of Statistics (UBoS) which is mandated by an Act of parliament to develop and maintain a national statistics system to ensure collection, analysis and publication of integrated, relevant, reliable and timely statistical information.Findings and discussionOur findings demonstrate that perceived satisfaction influences household fuel transition decisions in Uganda. The study concluded by suggesting policy recommendations for government action.

Segmentation using large language models: A new typology of American neighborhoods

In the United States, recent changes to the National Statistical System have amplified the geographic-demographic resolution trade-off. That is, when working with demographic and economic data from the American Community Survey, as one zooms in geographically one loses resolution demographically due to very large margins of error. In this paper, we present a solution to this problem in the form of an AI based open and reproducible geodemographic classification system for the United States using small area estimates from the American Community Survey (ACS). We employ a partitioning clustering algorithm to a range of socio-economic, demographic, and built environment variables. Our approach utilizes an open source software pipeline that ensures adaptability to future data updates. A key innovation is the integration of GPT4, a state-of-the-art large language model, to generate intuitive cluster descriptions and names. This represents a novel application of natural language processing in geodemographic research and showcases the potential for human-AI collaboration within the geospatial domain.

Establishment of Safety Management Measures for Major Construction Workers through the Association Rule Mining Analysis of the Data on Construction Accidents in Korea

Despite increasing industrial advancements, fatal and severe accidents, such as “Falls”, “Struck-by”, “Hit by an object”, “Be crushed”, and “Caught-in/between” accidents, persist in developed countries, including Korea. Various methods, including risk assessment, monitoring systems, technology improvements, and safety education, are being implemented to reduce accidents. However, only a few studies have revealed the causes of accidents and their interrelationships; these studies are based on limited data. Korea recently published accident data using national statistical systems, including the construction safety management integrated information (CSI), enabling the analyses of major accident types. Here, we selected various representative accident cases to minimize the duplication of the data published from 2019 to 2023 and applied the Material, Method, Machine, or Man (4M) analysis method, a risk assessment technique, to perform an accident-type-based association rule mining (ARM) analysis of the accident factors. Through the ARM analysis, we quantitatively identified complex correlations for major accidents. Based on the 4M factors derived through this analysis, we improved a 2–4 model for accident causation and proposed safety management measures for each construction entity.

La necesidad de la cultura y el aseguramiento de la calidad estadística: experiencia de la AGR

This article describes the process through which the General Auditor's Office of the Republic (AGR), in compliance with a constitutional mandate and aiming to strengthen its capacities, obtained certification for the quality of its statistical operation, which verifies and evaluates the management of territorial comptroller offices. The AGR relied on the Technical Standard for the Quality of the Statistical Process NTC PE 1000:2020, issued by DANE, the leader of the National Statistical System (SEN); the first global standard to establish quality requirements for generating statistics, as well as other statistical guidelines and standards. The challenges faced by the entity in each phase of implementation are detailed. With this experience of integrating the statistical process into its Management System, the AGR makes a methodological contribution that can be replicated by other fiscal control bodies, aiming to promote statistical culture and implement measures to ensure statistical quality.

Address matching using machine learning methods: An application to register-based census

Today, most activities of the statistical offices need to be adapted to the modernization policies of the national statistical system. Therefore, the application of machine learning techniques is mandatory for the main activities of statistical centers. These include important issues such as coding business activities, address matching, prediction of response propensities, and many others. One of the common applications of machine learning methods in official statistics is to match a statistical address to a postal address, in order to establish a link between register-based census and traditional censuses with the aim of providing time series census information. Since there is no unique identifier to directly map the records from different databases, text-based approaches can be applied. In this paper, a novel application of machine learning will be investigated to integrate data sources of governmental records and census, employing text-based learning. Additionally, three new methods of machine learning classification algorithms are proposed. A simulation study has been performed to evaluate the robustness of methods in terms of the degree of duplication and purity of the texts. Due to the limitation of the R programming environment on big data sets, all programming has been successfully implemented on SAS (Statistical analysis system) software.

Peningkatan Kompetensi dan Skill Petugas Statistik Diskominfo Provinsi Jambi melalui Pelatihan Penggunaan Software Mortpak dan PAST 4.03 serta Platform Canva

The Jambi Province Communication and Information Office (Dinas Kominfo) is an agency who carry out work based on Law Number 23 of 2014 concerning Government Regions, with one of the affairs being the main task and function, namely statistical affairs sectoral. Regional Sectoral Statistics Performance refers to Presidential Regulation Number 39 of 2019 concerning One Indonesian Data (SDI), BPS Perka Number 4 of 2019 concerning the Implementation of Regional Sectoral Statistics. According to Law No. 16 of 1997 concerning Statistics, the types of statistics based on the purpose of their use consist of basic statistics which fully organized by BPS, sectoral statistics carried out by government agencies independently or together with BPS, as well as special statistics organized by institutions, organizations, individuals, and or other elements of society independently or together with BPS. Results Statistics organized by BPS are published in the Official Statistics Gazette (BRS) regularly and transparent so that the public can easily find out and or obtain the required data. Law No. 16/1997 also stipulates the establishment of the Statistical Society Forum as a forum to accommodate the aspirations of the statistical community, which is tasked with providing advice and consideration to BPS. These important points realize the implementation of the National Statistical System reliable and, effective. The role of Sectoral Statistics that must be carried out by the Ministry of Communication and Informatics is related to the provision of data development sector, assisting statistical activities in Other institutions, as well as any performance related to the methodology, education, and training in sectoral statistics. Data collection, presentation, and publication of quality data by the Ministry of Communication and Informatics, cannot be separated from the role of officers statisticians and data collectors. With regard to this Community Service (PPM) Program, the PPM Team In general, it is observed that in carrying out their work, Field Data Collecting Officers and Data Entry Officers often experience difficulties in terms of classification of data for each sector that is a priority for development, especially sectors Environment and Ecology. Entry clerks sometimes only present data individually descriptive in Ms. Excel, without being processed and without further analysis on certain software, in particular related to demographic, ecological, and environmental data. As a solution to the performance problems of Statistics/Data Collection Officers At the Jambi Province Communications and Information Office, upgrading was given in the form of training in the use of Mortpak software and training in data collection for the environmental sector and ecology.

Методологические проблемы формирования и применения интегральных территориальных индексов

The article discusses the problems of improving the methodology for constructing composite territorial indices, as well as solving practical problems for their use in managing the socio- economic development of countries and regions. It is shown that, at their core, composite indices, by aggregating a significant array of diverse information, provide an opportunity for an analytical assessment of the current situation in a region (country), comparison of a territory’s position relative to other regions, and construction of analytical regional ratings. Despite the frequency of use, the questions of the applicability of composite indices, the specifics of the methods for their construction, and the objectivity of the estimates obtained on their basis remain unclear. It is shown that at each stage of calculating index estimates there are a number of problems and assumptions that impose restrictions on their use in statistical and economic analysis. The main problems include poor comprehensiveness of assessments, low comparability of information and lack of unambiguous interpretation of results (especially at the regional level), poor representativeness of information and results for international comparisons, and «incompleteness» of the national statistical system (especially at the municipal level). Lack of unified generally accepted indicators of territorial development and widespread use of expert assessments. The latter often leads to excessive labor intensity and subjectivity of calculations. Particular attention is paid to the issues of modifying the use of integral indices for the purposes of regional policy. The work shows that in the absence of real alternatives to integral indices, on the basis of which series of rating comparisons are then built, it is necessary to continue working on their improvement in order to use them as regional diagnostic tools and factors for making strategic management decisions

Statistics of Industrial Protectionism. Part I: The Formation of Organizational and Institutional Mechanisms

Numerous decisions made by the Ukrainian government in the conditions of a state of war require high-quality data. Among the identified priorities for the recovery of the domestic economy is the development of strategic industries, the implementation of which will necessitate new state decisions, along with statistical data to assess their outcomes. Historical parallels between the situation at the beginning of the 20th century and recent events serve as the basis for the assumption that the answer to the question of "How to accelerate the economic recovery of Ukraine?" should be sought not in the analysis of contemporary policies in developed countries but primarily in the investigation of retrospective mechanisms that facilitated the growth of existing industries and the creation of new ones. The aim of the article is to examine historical facts related to the implementation of industrial protectionism in the challenging conditions of war and post-war recovery in Great Britain. It seeks to identify the organizational and institutional mechanisms of the statistical system that facilitated the implementation and assessment of the outcomes of such decisions. Furthermore, the article aims to formulate conclusions that can be applied to Ukraine. It has been established that the First World War was a pivotal moment in the economic policy of Great Britain, directing it towards protectionism. The catalyst for this shift was the realization that critical national industries were in a precarious state due to the lack of imported supplies of various goods, mainly synthetic dyes and chemical ingredients, upon which the economy and security depended. This prompted the country's parliament and government to regulate industry and implement tools of state aid and protection against foreign competitors. Along with this, it led to organizational and institutional changes in the statistical system, as well as shifts in the directions and methodological approaches to monitoring the obtained results and the actual introduction of statistics related to industrial protectionism. It has been demonstrated that historical parallels during the COVID-19 pandemic are evident in terms of both the identified dependencies on the import of active pharmaceutical ingredients and other substances (two-thirds of production concentrated in China) and the government's actions to address the situation and changes in statistics.
 Drawing from the experience of Great Britain, it is justified that the introduction of measures to develop strategic sectors in Ukraine, accompanied by state incentives and protective tariffs, necessitates the modernization of the organizational and institutional mechanisms of the national statistical system. A component of these processes should be the digitization of functions assigned to the State Statistics Service of Ukraine, which would allow for obtaining high-quality operational data for both decision-making and evaluating their outcomes.

Russian Short-Term Mortality Fluctuations Data Series

The COVID-19 pandemic has revealed significant gaps in the coverage and quality of the existing international and national statistical surveillance systems. The most reliable approach to estimating mortality associated with short-term risk factors or factors fluctuating within calendar years is based on estimating weekly excess mortality. Although this approach is obvious, its application turned out to be problematic due to the lack of reliable data. In response to this challenge, a new Short-Term Mortality Fluctuations (STMF) data series was created in 2020. The Russian Short-Term Mortality Fluctuations Data Series (RuSTMF) is a further development of the STMF idea at the regional level. It contains weekly crude and standardized death rates for the Russian regions and Russia as a whole. The main source for calculating death rates is depersonalized individual data on the deceased provided by the Rosstat. This database provides for the analysis of short- and medium-term changes in mortality in males, females and total population in Russia as a whole and its regions, as well as the estimation of «excess» deaths during short-term sharp increases in mortality due to, for example, heat or cold waves, seasonal influenza epidemics, the Covid-19 pandemic or technogenic catastrophes.

Official Statistics Today: Choosing the Path of Development

Building on the analysis of the current state of state statistics in Russia, the author formulates development priorities for the national statistical system (NSS) amid digitalization and scarcity of resources. The paper explains the NSS focus on the use of records and state registers as the information basis of official statistics. It is proposed to consider Big Data as a source of additional information that requires verification and adjustments when generating official statistics.The article justifies the need to preserve the system of traditional statistical surveys that allows for measuring phenomena and processes in society and the economy, including characteristics that are not registered when events are recorded by authorities. The author emphasizes the relevance of this system when implementing an essential function of state statistical bodies – creating an integrated information and statistical space in the country.

How Much Does the Food Insecurity Experience Scale Overlap with Poor Food Consumption and Monetary Poverty? Evidence from West Africa

Abstract The Food Insecurity Experience Scale (FIES), which combines three food-access dimensions into a single indicator, is rapidly being incorporated into national statistical systems. However, there is no prediction about how one of the incorporated dimensions—subjective experiences associated with food insecurity—overlaps with poor food consumption. Using data from West Africa, this study illustrates that in 4 out of 10 countries, there is a similar prevalence of food insecurity according to the FIES among segments of the population that are likely undernourished and segments that are likely not undernourished. And in 5 out of 10 countries, there is a relatively large prevalence of food insecurity according to the FIES in the segments of the population that are least likely to be undernourished. Combined, the results offer guidance to policymakers when choosing food-access indicators and illustrate the importance of using the FIES along with other food-access measures.

Breastfeeding and infant care as 'sexed' care work: reconsideration of the three Rs to enable women's rights, economic empowerment, nutrition and health.

Women's lifelong health and nutrition status is intricately related to their reproductive history, including the number and spacing of their pregnancies and births, and for how long and how intensively they breastfeed their children. In turn, women's reproductive biology is closely linked to their social roles and situation, including regarding economic disadvantage and disproportionate unpaid work. Recognizing, as well as reducing and redistributing women's care and domestic work (known as the 'Three Rs'), is an established framework for addressing women's inequitable unpaid care work. However, the care work of breastfeeding presents a dilemma, and is even a divisive issue, for advocates of women's empowerment, because reducing breastfeeding and replacing it with commercial milk formula risks harming women's and children's health. It is therefore necessary for the interaction between women's reproductive biology and infant care role to be recognized in order to support women's human rights and enable governments to implement economic, employment and other policies to empower women. In this paper, we argue that breastfeeding-like childbirth-is reproductive work that should not be reduced and cannot sensibly be directly redistributed to fathers or others. Rather, we contend that the Three Rs agenda should be reconceptualized to isolate breastfeeding as 'sexed' care work that should be supported rather than reduced with action taken to avoid undermining breastfeeding. This means that initiatives toward gender equality should be assessed against their impact on women's ability to breastfeed. With this reconceptualization, adjustments are also needed to key global economic institutions and national statistical systems to appropriately recognize the value of this work. Additional structural supports such as maternity protection and childcare are needed to ensure that childbearing and breastfeeding do not disadvantage women amidst efforts to reduce gender pay gaps and gender economic inequality. Distinct policy interventions are also required to facilitate fathers' engagement in enabling and supporting breastfeeding through sharing the other unpaid care work associated with parents' time-consuming care responsibilities, for both infants and young children and related household work.

Official Statistics and Big Data Processing with Artificial Intelligence: Capacity Indicators for Public Sector Organizations

Efficient monitoring and achievement of the Sustainable Development Goals (SDGs) has increased the need for a variety of data and statistics. The massive increase in data gathering through social networks, traditional business systems, and Internet of Things (IoT)-based sensor devices raises real questions regarding the capacity of national statistical systems (NSS) for utilizing big data sources. Further, in this current era, big data is captured through sensor-based systems in public sector organizations. To gauge the capacity of public sector institutions in this regard, this work provides an indicator to monitor the processing capacity of the public sector organizations within the country (Pakistan). Some of the indicators related to measuring the capacity of the NSS were captured through a census-based survey. At the same time, convex logistic principal component analysis was used to develop scores and relative capacity indicators. The findings show that most organizations hesitate to disseminate data due to concerns about data privacy and that public sector organizations’ IT personnel are unable to deal with big data sources to generate official statistics. Artificial intelligence (AI) techniques can be used to overcome these challenges, such as automating data processing, improving data privacy and security, and enhancing the capabilities of IT human resources. This research helps to design capacity-building initiatives for public sector organizations in weak dimensions, focusing on leveraging AI to enhance the production of quality and reliable statistics.

Citizen Science: What is in it for the Official Statistics Community?

Citizen science data are an example of a non-traditional data source that is starting to be used in the monitoring of the United Nations (UN) Sustainable Development Goals (SDGs) and for national monitoring by National Statistical Systems (NSSs). However, little is known about how the official statistics community views citizen science data, including the opportunities and the challenges, apart from some selected examples in the literature. To fill this gap, this paper presents the results from a survey of NSS representatives globally to understand the key factors in the readiness of national data ecosystems to leverage citizen science data for official monitoring and reporting, and assesses the current awareness and perceptions of NSSs regarding the potential use of these data. The results showed that less than 20% of respondents had direct experience with citizen science data, but almost 50% felt that citizen science data could provide data for SDG and national indicators where there are significant data gaps, listing SDGs 1, 5, and 6 as key areas where citizen science could contribute. The main perceived impediments to the use of citizen science data were lack of awareness, lack of human capacity, and lack of methodological guidance, and several different kinds of quality issues were raised by the respondents, including accuracy, reliability, and the need for appropriate statistical procedures, among many others. The survey was then used as a starting point to identify case studies of successful examples of the use of citizen science data, with follow-up interviews used to collect detailed information from different countries. Finally, the paper provides concrete recommendations targeted at NSSs on how they can use citizen science data for official monitoring.

The Implementation Of Satu Data Bencana Indonesia (SDBI) For Sustainable Development

The likelihood of disaster statistics is not the same across all countries and ups and downs over time. As country with the potential disasters more than 3.000 events every year, disaster data and statistics play an important role in reducing disaster risks and building resilience through estimating the risks, hazards and vulnerability. Therefore, the National Disaster Management Agency (BNPB) and the Central Statistical Agency (BPS) jointly developed Satu Data Bencana Indonesia (SDBI) as the official sectoral statistical domain within the National Statistical System to Disaster Statistical Framework (DRFS) established by UN ESCAP and was designed adhering to the principles of Satu Data Indonesia. Based on the background described above, research needs to be carried out as learning material to increase knowledge about intergovernmental-disaster data and its implementation for decision-makers. The method used is qualitative with a descriptive exploratory approach: data collection by observation, in-depth interviews, and documentation; data analysis techniques by data collection, data reduction, and data presentation. Several issues in implementing the platform of SDBI as regulated in the National Disaster Management Agency (BNPB) Regulations Number 1 of 2023 concerning Satu Data Bencana are weak coordination, non-standardized methodology, and poor communication. The conclusion from this study is that the methodological practices and procedures of disaster management for data collection and management should be shared with agencies involved in the technical working group to ensure coordination. The needs to form technical working group consisting of BPS, BNPB and BIG to minimize problems in implementing Satu Data Bencana Indonesia cannot be delayed to ensure the sustainability of nations ‘development.

STATISTICAL ASSESSMENT OF CHANGES IN THE DEVELOPMENT OF AGRICULTURE, FORESTRY AND FISHERIES NETWORK IN THE REPUBLIC AND CHANGES IN ITS TENDENCIES

In this article, the preliminary results of the republic's agriculture, forestry and fishery network at the end of 2022 are statistically analyzed by regions and economic categories. Methodology for collecting statistical data on the agricultural sector, developing a reliable and comprehensive national agricultural statistics system, increasing the availability of information on trends and market prices in agriculture. Conclusions and suggestions were made regarding increasing the scope of information on the productivity of farmers and agricultural holdings, developing the organizational and methodological bases of agricultural monitoring.

Comparing Labor Productivity of Vietnam to Some Asian Countries

In Vietnam, social labor productivity is an indicator of the National Statistical Indicator System (specified in the Law on Statistics), which is calculated by the average GDP per employed worker per year. Research results show that in the 2016-2020 period, labor productivity of the whole economy has improved markedly, with an average increase of 5.78%/year in the 2016-2020 period. However, Vietnam’s labor productivity still has internal limitations that have not been overcome. In comparison with selected Asian countries, Vietnam’s labor productivity has a low absolute value despite its relatively high growth since 1991. In most of the sectors compared, it is basically at the lowest level. As a result, proposing solutions to improve and enhance labor productivity aimed at promoting sustainable economic growth in Vietnam in the coming time is an urgent issue for the development of the country.