National Statistical Agencies Research Articles

Multivariate Top-Coding for Statistical Disclosure Limitation.

One of the most challenging problems for national statistical agencies is how to release to the public microdata sets with a large number of attributes while keeping the disclosure risk of sensitive information of data subjects under control. When statistical agencies alter microdata in order to limit the disclosure risk, they need to take into account relationships between the variables to produce a good quality public data set. Hence, Statistical Disclosure Limitation (SDL) methods should not be univariate (treating each variable independently of others), but preferably multivariate, that is, handling several variables at the same time. Statistical agencies are often concerned about disclosure risk associated with the extreme values of numerical variables. Thus, such observations are often top or bottom-coded in the public use files. Top-coding consists of the substitution of extreme observations of the numerical variable by a threshold, for example, by the 99th percentile of the corresponding variable. Bottom coding is defined similarly but applies to the values in the lower tail of the distribution. We argue that a univariate form of top/bottom-coding may not offer adequate protection for some subpopulations which are different in terms of a top-coded variable from other subpopulations or the whole population. In this paper, we propose a multivariate form of top-coding based on clustering the variables into groups according to some metric of closeness between the variables and then forming the rules for the multivariate top-codes using techniques of Association Rule Mining within the clusters of variables obtained on the previous step. Bottom-coding procedures can be defined in a similar way. We illustrate our method on a genuine multivariate data set of realistic size.

Modernization of the poverty monitoring system in the Russian Federation in the context of the evolution of the concept of poverty

The paper analyses the evolutionary stages of the scientific concept of poverty and the main theoretical and methodological approaches to the definition of poverty in the practice of national statistical agencies and international organizations. An assessment of the current state of the Rosstat poverty monitoring system is given, conclusions are drawn about the most promising areas of its development.

Strengthening the use of administrative data to provide gender statistics

The 2030 Agenda for Sustainable Development explicitly recognizes the critical role of gender data in monitoring progress toward all 17 Sustainable Development Goals (SDGs). Yet, gaps in gender data vis-à-vis availability, granularity, timeliness and adherence to international standards, compromise the ability of countries to design gender-responsive policies and meet the commitment enshrined in the SDGs to leave no one behind. Administrative data systems are increasingly recognized as an important source for filling gender data gaps, but there has been limited unpacking of what this means at a system level in terms of 1) prioritization, 2) restructuring data flows to ensure data can be appropriately disaggregated, 3) how data needs can be best supported within current systems structures, and 4) key opportunities for national statistical offices and gender specialists to engage administrative data systems.
 The work aims to identify the specific opportunities, challenges and value of using administrative data more effectively to improve gender-equitable development outcomes; and translate this into practical guidance for national statistical offices and line ministries. While focused primarily on developing countries, the intent is to understand these elements across contexts and levels of system maturity.
 Issues will be explored through literature review, key informant interviews from government and stakeholder groups, and detailed case studies for three countries at different levels of data system maturity. Work will be undertaken by UNICEF with support of the Inter-Agency and Expert Group on Gender Statistics (IAEG-GS). This body, comprising national statistical systems and international agencies, is the coordinating body of the United Nations Global Gender Statistics Programme.
 Guidance developed through this work is anticipated to build on existing initiatives to improve cross-sectoral data for development and ensure we “leave no one behind” as we move forward to achieving the SDGs.

Going Digital Toolkit platform as a monitoring tool of the digital transformation in the Russian Federation

Purpose of the study. OECD «Going Digital Toolkit» is one of the tools for monitoring of the digital transformation. Platform can helps to quantify various areas of digitalization and provides a comprehensive description of the changes in the socio-economic sphere. Despite the Going Digital Toolkit platform has the objective advantages, this tool is practically not used by Russian researchers. The purpose of this study is to analyze the possibility of using «Going Digital Toolkit» platform as a tool for monitoring the development of the digital economy in the Russian Federation. Materials and methods. To achieve the goal of the study, a comparative analysis of the indicators of the Going Digital Toolkit platform was carried out on the OECD countries, European Union countries and the Russian Federation. Research was carried out to: 1) identify the presence of certain indicators in the platform system in case of the analysis the Russian Federation; 2) investigate the relevance of data on indicators presented in the platform system for the Russian Federation; 3) analyze the position of the Russian Federation regarding the OECD and the EU; 4) determine reserves to achieve monitoring integrity based on Russian state statistics and other sources; 5) develop the recommendations for improving the statistics of digital transformation in the Russian Federation. Along with the information published on the Going Digital Toolkit platform, the data of the Federal State Statistics Service (Rosstat) and the data of the Ministry of Digital Development, Telecommunications and Mass Communications of the Russian Federation were used. Results. The position of Russia can be assessed as «lagging» in comparison with the countries of the European Union and the OECD. For most indicators that characterize access and use of the Internet, e-commerce and skills, Russia does not exceed these countries. In some cases, the twofold superiority of the EU and OECD countries over the Russian Federation makes it impossible to make an optimistic forecast to correct the current situation in the near future. However, the position of Russia identified on the basis of the platform’s data does not reflect the situation in real time, which suggests positive dynamics in some of the indicators presented. The issue of using the platform as a tool for monitoring digital transformation in the Russian Federation is debatable. The incompleteness of the information presented in Russia makes the tool impractical, however, the available information, together with the functional advantages of the analytical platform, opens up new opportunities for users. Conclusion. The Going Digital Toolkit platform acts not only as an interactive tool for analyzing numerical values for a number of indicators, but also as a standard of a system of statistical indicators necessary for measuring processes and phenomena of digital transformation. The platform can serve as a guide for national statistical agencies in the development of relevant surveys and improve the methodological foundations of statistics in the field of information and communication technologies.

ANALISIS JARINGAN SYARAF TIRUAN METODE BACKPROPOGATION DALAM MEMPREDIKSI KETERSEDIAAN KOMODITAS BERAS BERDASARKAN PROVINSI DI INDONESIA

Food is a major human need that must be completed at any time. This right is one of human rights, stated in article 27 of the 1945 Constitution and in the Rome Declaration (1996). These considerations underlie the issuance of Law No. 7/1996 concerning Food. With these considerations, the Government always considers increasing food security related to increasing domestic production. This research is expected to contribute to the government in order to predict the contribution of rice by province in Indonesia. The data used is data from the National Statistics Agency through the website www.bps.go.id. The data is data on rice / rice production based on provinces in Indonesia in the period of 2010 to 2015. The algorithm used in this study is Artificial Neural Networks with the Backpropagation method. The input (input) variables used are data for 2010 (X1), data for 2011 (X2), data for 2012 (X3), data for 2013 (X4), data for 2014 (X5) and data for 2015 as targets with models training and testing architecture of 4 architectures namely 4-4-1, 4-8-1, 4-16-1, 4-32-1. The resulting output is the best pattern of ANN architecture. The best architectural model is 4-4-1 with 218 days, MSE 0.012728078 and an accuracy rate of 97%. From this model obtained from estimates obtained from provinces in Indonesia.

ANALISIS HUBUNGAN POLA ASUH ORANG TUA DENGAN TINGKAT KEMANDIRIAN PERSONAL HYGIENE PADA ANAK USIA PRA SEKOLAH DI DUSUN CELEGEH DESA BAREBALI LOMBOK TENGAH TAHUN 2019

The role of parents as caregivers and the response from the enviroment is needed for children in the formation of children’s independence. Based on data from the national statistical agency in 2014, the number of pre-school age children in indonesia was 28.022 people. This study aims to study the relationship of parenting parents with the level of personal hygiene independence in pre-school age children in the village of celegeh, barebali village, central lombok.
 This research is a descriptive correlational research with a cross sectional approach. The population in study amounted to 34 people. Sampling is done by using purposive sampling which amounts to 32 people, but in sampling there are exclusion criteria so that the total sample is 30 respondents. Research instruments using questionnaires. Data analysis techniques using the chi-square test α=0,05.
 The results showed that most of the patterns of adoption by respondents were 14 people (46,7%) authoritarian parenting, with 7 independent children (23,3%) and 7 non-independent children (23,3%).
 So it can be concluded there is a relationship between parenting parents with the level of personal hygiene independence in pre-school age children in the village of celegeh, barebali village, central lombok.

Penerapan Algoritma K-Means Clustering untuk Pengelompokan Harga Eceran Beras di Pasar Tradisional Berdasarkan Wilayah Kota

Rice is the main food commodity of the Indonesian people, almost all residents in this country consume rice every day. This causes rice commodities to have a very strategic value, apart from being in control of the lives of many people, it can also be used as a parameter of the country's economic and social stability. This study discusses the application of K-Means Clustering Algorithm for Grouping Retail Prices of Rice in Traditional Markets. The source of this research data is collected based on documents describing the retail price group of rice produced by the National Statistics Agency. The data used in this study are data from 2011-2016 consisting of 33 cities. Data is processed by clustering in 3 clusters, namely high population level clusters, medium and low population level clusters. Centroid data for high population level clusters 10,776, Centroid data for moderate population level clusters 9,436 and Centroid data for low population clusters 8,590. To obtain an assessment based on the grouping of the average retail price of rice in traditional markets in 33 cities with high clusters (C1) of 11 cities namely Padang, Pekanbaru, Tanjung Pinang, Bandar Lampung, Jakarta, Pontianak, Palangkaraya, Banjarmasin, Ternate, Jayapura and Manokwari for medium cluster (C2) as many as 11 cities and for low cluster (C3) as many as 11 cities. From the results of this study can be used as input for the government, especially in cities, so that cities that are included in the high cluster can normalize the retail price of rice in each city area.

Penerapan Clustering dalam Mengelompokkan Jumlah Kunjungan Wisatawan Mancanegara Dengan Metode K-Means

The Indonesian tourism sector currently contributes approximately 4% of the total economy. In 2019, the Indonesian Government wants to increase this figure to double to 8% of PDB (Produk Domestik Bruto), a target that implies that within the next 4 years, the number of visitors needs to be doubled to approximately 20 million tourists. This study discusses the Application of Clustering in Grouping the Number of Foreign Tourist Visits by Nationality and Month of Arrival by the K-Means Method. The source of this research data was collected based on data on the number of foreign tourist visits produced by the National Statistics Agency. K-Means clustering is one of the data mining techniques that gives a description of an item's cluster. The purpose of this study is to classify the number of foreign tourists in Indonesia. The results of this study are grouping the number of foreign tourist visits grouped by two clusters (high and low), high clusters of 4 countries and low clusters of 87 countries. Countries that are included in the lower clusters can be used for the Government of Indonesia in terms of improving existing facilities in tourist attractions so that visiting tourists will increase in the future.

Algoritma K-Medoids untuk Mengelompokkan Desa yang Memiliki Fasilitas Sekolah di Indonesia

School facilities are learning facilities and infrastructure. Study rooms, study rooms, sports halls, prayer rooms, arts rooms and sports equipment. Means of learning to read textbooks, reading books, school laboratory tools and facilities and various other learning media. This study discusses the application of the K-Medoids method in grouping villages that have school facilities based on the province and education level. Data sources used from the National Statistics Agency (BPS). This study uses data mining techniques in data processing using the k-medoids clustering method. The k-medoid method is part of a fairly efficient grouping of partitions in small datasets and looks for the most representative points. The advantages of this method can overcome the shortcomings of the k-means method that is sensitive to outliers. Another advantage of this method is that the results of the grouping process do not match the entry sequence of the dataset. Grouping k-medoid method can be applied to the percentage of facilities based on the province, so that provincial grouping can be determined based on the data. From the grouping data, 3 clusters were obtained, namely a low cluster of 15 provinces, a moderate cluster of 16 provinces and a high cluster of 3 provinces from the percentage of school facilities in each province. It is hoped that this research can provide information to the government about data collection of school facilities in Indonesia which discusses examiners in the provision of school facilities in Indonesia.

Penerapan Data Mining Dalam Mengelompokkanm Jumlah Usaha Berdasarkan Provinsi Menggunakan K-Means Clustering

Industrial revolution 4.0 is an era where technology in the IT field is growing very rapidly, it can also be said a new era for entrepreneurs in Indonesia that must be a golden opportunity to improve business performance and opportunities for millennials to enter the business or business world. In this era people are expected to be able to compete especially in the business world. This study discusses the Application of Data Mining in Grouping the Number of Enterprises by Province Using K-Means Clustering. The source of this research data is collected based on the information documents of the number of businesses in Indonesia produced by the National Statistics Agency. The data used in this study are provincial data consisting of 34 provinces. Data will be processed by clustering in 3 clusters, namely cluster of high number of businesses, cluster of medium number of businesses and cluster of low number of businesses. The results obtained from the assessment process are based on the index of the number of businesses with 4 provinces, the number of high businesses, namely North Sumatra, West Java, Central Java, and East Java, 13 Provinces, the number of medium enterprises and 17 other provinces, including low business numbers. This can be used as input to the community, especially in provinces where the number of businesses is low so they can compete in the business world and input for the government to provide facilities and infrastructure to support entrepreneurs in Indonesia.

ANN: Prediction of Per Capita Income Rural Community on Poverty Line Based on Province

The problem of poverty is a fundamental problem that is of concern to every country. The Indonesian state has established a poverty reduction program as the main program. Poverty occurs in urban and rural communities. This research raises the problem of poverty in rural communities. The contribution of research to the government is to predict the per capita income of rural communities according to the poverty line based on the future provinces. The data used is data from the National Statistics Agency. These data are 2015 semester 1 data up to 2018 semester 1. The algorithm for its completion uses the artificial neural network backpropagation method. Input data is 2015 data for the 1 to 2017 semester 2. The training and testing architecture model is 4, namely 6-2-1, 6-3-1, 6-2-3-1, and 6-3-2-1. Target data is 2018 semester 1 data. The best architectural model is 6-2-1 with 79 epoch, MSE 0,004801 and 100% accuracy rate. From this model, a prediction of rural income per capita in the poverty line is based on the provinces of each province in Indonesia.

Penerapan Data Mining Dalam Clustering Produksi Daging Sapi Di Indonesia Menggunakan Algoritma K-Means

Cows are animals that are found in Indonesia, cattle provide many benefits for humans ranging from milk that is rich in nutrients to meat as a source of high animal protein for humans. Beef production in Indonesia is not sufficient to meet domestic needs, so the government needs to ask for other meat and most still need beef to meet the daily protein needs of the community. The discussion of this study about the Application of Data Mining in the Cluster of Beef Production in Indonesia Using the K-Means Algorithm. The data source of this study was collected based on documents about beef production produced by the National Statistics Agency. The data used in this study are data from 2009-2016 consisting of 34 provinces. This study clustered in 3 groups, namely medium and low. The results of this study were 9 provinces included in the high group, 3 provinces included in the middle group and 22 provinces entered the low group.

Model Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Produksi Telur Ayam Petelur Berdasarkan Provinsi Di Indonesia

Improving human resources cannot be achieved without adequate nutrition. To educate, strengthen and improve the achievements of Indonesian people, much depends on fulfilling good nutrition, especially animal protein such as meat, milk and eggs (Anonymous, 1990).Eggs are one product that can meet some of the nutritional needs of the community. These livestock products also have the potential to be developed optimally, because in addition to the price that is relatively cheap compared to other animal proteins, the business is also relatively easy and even though it is cultivated in small-scale businesses it can increase income and expand employment opportunities (Anonymous, 1994). The data used is data from the National Statistics Agency through the website www.bps.go.id. The data is data on the number of egg production of laying hens based on the provinces in 2010 to 2017. The algorithms used in this study are Artificial Neural Networks with the Backpropogation method. The input variables used are data for 2010 (X1), data for 2011 (X2), data for 2012 (X3), data for 2013 (X4), data for 2014 (X5) data for 2015 (X6) and data in 2016 (X7) with a training and testing architecture model of 4 architectures namely 7-4-1, 7-8-1, 7-16-1dan 7-32-1. Target data is taken from 2017 data. The output produced is the best pattern of ANN architecture. The best architectural model is 32-1 with MSE 0.0082336 and an accuracy rate of 96.88%. From this model, the prediction of egg production of laying hens is based on the province of each province in Indonesia.

Cigarette consumption estimates for 71 countries from 1970 to 2015: systematic collection of comparable data to facilitate quasi-experimental evaluations of national and global tobacco control interventions

ObjectivesTo collect, appraise, select, and report the best available national estimates of cigarette consumption since 1970.DesignSystematic collection of comparable data.Setting and population71 of 214 countries for which searches for national...

The structure of emergent prefabricated housing industries: a comparative case study of Australia and Sweden

Prefabricated housing is a disruptive innovation struggling to take hold in a traditional complex product system (CoPS). It is quicker to produce and has improved environmental performance compared to traditional housing. CoPS have more dense and complex network connections than commodity industries, making disruptive innovation more difficult. Effective relational capabilities can achieve the coordination necessary to address this challenge. The prefabricated housing industry needs to develop a structure that drives these capabilities. Using a case study methodology, the structure of the industry is examined in two contrasting countries, namely Australia and Sweden, as they represent an early and late stage of industry emergence, respectively. A new framework is proposed for this purpose, enabling a repeatable, orderly and comprehensive disaggregation of industry structure to examine the latent drivers of relational capability. The main empirical contribution is to (1) describe an industry that is yet to be formally recognized by national statistical agencies and (2) show how the adoption of prefabricated housing can be accelerated. The study found that younger industries need a focused industry association with diverse membership to act as an effective system integrator.

ANALISIS JARINGAN SYARAF TIRUAN METODE BACKPROPOGATION DALAM MEMPREDIKSI KETERSEDIAAN KOMODITAS BERAS BERDASARKAN PROVINSI DI INDONESIA

Food is a major human need that must be completed at any time. This right is one of human rights, stated in article 27 of the 1945 Constitution and in the Rome Declaration (1996). These considerations underlie the issuance of Law No. 7/1996 concerning Food. With these considerations, the Government always considers increasing food security related to increasing domestic production. This research is expected to contribute to the government in order to predict the contribution of rice by province in Indonesia. The data used is data from the National Statistics Agency through the website www.bps.go.id. The data is data on rice / rice production based on provinces in Indonesia in the period of 2010 to 2015. The algorithm used in this study is Artificial Neural Networks with the Backpropagation method. The input (input) variables used are data for 2010 (X1), data for 2011 (X2), data for 2012 (X3), data for 2013 (X4), data for 2014 (X5) and data for 2015 as targets with models training and testing architecture of 4 architectures namely 4-4-1, 4-8-1, 4-16-1, 4-32-1. The resulting output is the best pattern of ANN architecture. The best architectural model is 4-4-1 with 218 days, MSE 0.012728078 and an accuracy rate of 97%. From this model obtained from estimates obtained from provinces in Indonesia.

Measuring poverty and income distribution in Southeast Asia

The paper surveys the extensive research on the measurement of poverty and inequality in Southeast Asia over the last five decades. It examines the work carried out by both independent scholars and international agencies including the World Bank and the Asian Development Bank. The paper argues that the household surveys carried out by national statistical agencies across the region, and used by most researchers, are often flawed. These flaws have led to rather misleading results both for individual countries and for cross‐country comparisons. Further problems have been found in the data on purchasing power parities (PPP) which have been used by the World Bank to estimate poverty both in Southeast Asia and in other developing countries. The paper also points out that estimates of poverty and inequality are often politically sensitive, and argues that a more accurate understanding of the estimation problems will only emerge if governments support rather than suppress open debate over measurement problems.

TECHNOLOGICAL PROGRESS AND HUMAN DEVELOPMENT: EVIDENCE FROM INDONESIA

This paper examined direct and indirect impact of technological progres on human development, with income disparities and povertyas moderating variables. Except data on technical progress, all data were collected from National Statistics Agency. A path model of analysis was empolyed to examine direct and indirect impacts.There were four paths to be analysed: direct impact (Path-1: P 41 ) and indirect impacts : (Path-2: P 43 x P 31 , Path-3: P 43 x P 32 x P 21 and Path-4 : P 42 x P 21 ). Four hypothesis had been tested.The results showed that impact of technological progress on human development were negative, both direct and indirect.

International Mobility of Students in Italy and the UK: Does it Pay Off and for Whom?

International student mobility is the most recognised element of Erasmus+, a major EU policy. Not enough is known about the causal effect of studying abroad on labour market outcomes. This is because most of the existing studies dismiss selection bias: the different composition of students opting and not opting for studying abroad. The purpose of this paper is to answer the following three questions, whilst accounting for selection bias. First, does international student mobility (ISM) have an effect on labour market outcomes? Second, do the returns to ISM vary between two countries with contrasting labour market and education systems?Third, do the returns to ISM differ according to the socio-economic background of the students? Results are compared between Italy and the UK using Italian Institute of National Statistics and UK Higher Education Statistics Agency graduate survey data. Using propensity score matching, the returns to study-related stays abroad are estimated on a set of labour market outcomes around six to twelve months and three years after graduation for undergraduates (UK and Italy) and postgraduates (Italy only).Results indicate that mobility is positively associated with some outcome variables under scrutiny. Mobile graduates seem to benefit from better employment chances than non-mobile graduates. Returns to ISM tend to be slightly higher among graduates in Italy. Mobility seems to matter most for uptake and completion of further post-graduate studies in Italy. It is the especially the socially disadvantaged mobile who opt for further education after graduation.

Forecasting the Price of Indonesia’s Rice Using Hybrid Artificial Neural Network and Autoregressive Integrated Moving Average (Hybrid NNs-ARIMAX) with Exogenous Variables

As a primary food, rice has a special attention by the Indonesian Government. The variability and trend of rice price become its main concern. Based on the data obtained from Indonesian national statistics agency, it shows that there is an increasing trend toward the retail price of rice in traditional markets. The price of rice has uniqueness in the process of determining it. Many variables have influenced the price and it is highly regulated. In order to help the decision maker to determine the price, they somehow need a clear insight of future trend of its price changing regarding to several influencing variable. Thus, an appropriate forecasting should be conducted. This research includes rice harvest area, rice production, rice consumption, season as independent variables and use combination of Artificial Neural Network and ARIMAX to forecast the price of rice in in several Indonesian provinces. The result shows that the combination model gives better result than ANN model. The average of decreasing MAPE about 1.21% for ANN and Hybrid NNs-ARIMA, and 0.23% for ANN and Hybrid NNs-ARIMAX. The results of this research are expected to help the Ministry of Agriculture and the National Logistics Agency in making decisions and policies of national rice price.