scholarly journals Implementation of marketplace data in the production of Consumer Price Index in Indonesia

Data Science ◽  
2021 ◽  
pp. 1-17
Author(s):  
Muhammad Ghozy Al Haqqoni ◽  
Setia Pramana
Keyword(s):  
Southeast Asia ◽  
Price Index ◽  
Consumer Price Index ◽  
Government Agency ◽  
Digital Economy ◽  
The Internet ◽  
Group Level ◽  
Web Scraping ◽  
Online Marketplaces

Digital Economy in recent years, especially in Southeast Asia, including Indonesia, is growing rapidly. E-commerce is one part of the Digital Economy. BPS-Statistics Indonesia as a Non-ministerial Government Agency responsible directly to the president has conducted an E-commerce Survey in 2019. From this publication, it is concluded that the interest of Indonesian traders using the internet in selling in recent years has increased. So, the urgency of using e-commerce data in its application in official Statistics is increasingly needed. Several studies have carried out the application of e-commerce data in the calculation of The Consumer Price Index (CPI). In this research, e-commerce data is applied with a case study using the data from one of online marketplaces in Indonesia in calculating CPI at city level in Java. The purpose of this study is to compare the marketplace-based CPI data and BPS-Statistics’ survey-based CPI. The data is collected through web scraping techniques and followed by preprocessing data and analyzed descriptively. Web scraper that is built can be used in obtaining data. Commodity-level CPI with marketplace data tends to have relatively large prices which result in higher CPI being compared to BPS-Statistics CPI. Meanwhile, at the expenditure group level, the CPI between the two approaches is broadly similar in general.

E-Learning Business Models

2011 ◽  
pp. 70-94 ◽  
Author(s):  
Sabine Seufert
Keyword(s):  
Business Models ◽  
Digital Economy ◽  
The Internet ◽  
Learning Models ◽  
Web Based ◽  
Education Market ◽  
E Learning ◽  
International Data ◽  
Web Based Education

According to several forecasts given by Gartner Group or International Data Corporation, for example, e-learning as a new buzzword for Web-based education and its commercialization seems to be a growing market in the digital economy. This case study will analyze this new and dynamic e-learning market and the corresponding changes on the education market. A framework of the different education models that have already developed on the e-learning market will be introduced and their benefits and risks discussed. Several cases demonstrate the new e-learning models in action. Therefore, this contribution consists of several smaller cases that can be used for getting an overview of the e-learning market and for a discussion about e-learning as a promising e-commerce application on the Internet.

Web scraping based online consumer price index: The “IPC Online” case

2020 ◽  
Vol 44 (2-3) ◽  
pp. 141-159
Author(s):  
Juan I. Uriarte ◽  
Gonzalo R. Ramírez Muñoz de Toro ◽  
Juan M.C. Larrosa
Keyword(s):  
Price Index ◽  
Consumer Price Index ◽  
Web Scraping ◽  
Online Consumer

scholarly journals Cloud-Based Global Online Marketplaces Review on Trust and Security

2019 ◽  
Vol 13 (04) ◽  
pp. 96
Author(s):  
Osman Ghazali ◽  
Chun Yang Leow ◽  
Shahzad Qaiser ◽  
Nanthini Pattabiraman ◽  
Sathiyaroobaa Vasuthevan ◽  
...  
Keyword(s):  
Information Security ◽  
The Internet ◽  
Technical Requirement ◽  
Tabular Form ◽  
Unauthorized Access ◽  
Building Trust ◽  
Online Marketplace ◽  
Online Marketplaces ◽  
Access Information

Customer disposition to data, nature of the information on site, protection<strong> </strong>concerns, trust, security concerns, and the notoriety of organization efficaciously affect the trust of Internet shoppers in the site. Two noteworthy and basic issues for e-commerce sites and consumers are trust as well as security. A belief that someone is good and honest and will not harm you, or something is safe and reliable is called trust; while security is an attempt to safeguard the data from unauthorized access. Information security is a vital management as well as technical requirement over the internet for effective and secure payment transaction activities. The safety of e-commerce resources from use, destruction, unauthorized access and alteration is known as E-commerce security so there is an urgent need to study its dimensions such as authenticity, integrity, availability, privacy, confidentiality and non-repudiation. This paper reports a review of four popular online marketplaces which are Alibaba, Amazon, eBay andTaoBao as case study on two main criteria namely building trust among users and ensuring security on the platform. Furthermore, we discuss the methods being used by each online marketplace to build trust and their unique way ofimproving the security. Finally, different ways of building trust and technique to ensure the security is presented in a tabular form for each online marketplace.

Disaggregated TIPS: the case for disaggregating inflation-linked bonds into bonds linked to narrower CPI components Slicing and Dicing TIPS

2006 ◽  
Vol 5 (3) ◽  
pp. 325-343 ◽  
Author(s):  
WILLIAM W. JENNINGS
Keyword(s):  
Price Index ◽  
Consumer Price Index ◽  
Medical Needs

Investors generally face inflation-linked obligations – a fact contributing to the popularity of TIPS and other inflation-linked bonds. With TIPS, one characterization of inflation, the Consumer Price Index, applies to all investors. Investors, however, face different inflation. To date, these heterogeneous needs have not been addressed by the inflation-linked marketplace. The paper describes the case for and mechanics of splitting TIPS into disaggregated TIPS matched to components of the Consumer Price Index. Disaggregated TIPS better address the risks of investors' specific real liabilities. A case study highlights disaggregated TIPS applicability to retirees with heavier post-retirement medical needs.

scholarly journals The use of web-scraped data to analyze the dynamics of footwear prices

2021 ◽  
Vol 43 ◽  
pp. 251-269
Author(s):  
Adam Juszczak ◽  
Keyword(s):  
Price Index ◽  
Online Shopping ◽  
Price Change ◽  
Consumer Price Index ◽  
Jel Classification ◽  
Full Data ◽  
Data Set ◽  
Web Scraping ◽  
Price Indexes ◽  
The Difference

Aim/purpose – Web-scraping is a technique used to automatically extract data from websites. After the rise-up of online shopping, it allows the acquisition of information about prices of goods sold by retailers such as supermarkets or internet shops. This study examines the possibility of using web-scrapped data from one clothing store. It aims at comparing known price index formulas being implemented to the web-scraping case and verifying their sensitivity on the choice of data filter type. Design/methodology/approach – The author uses the price data scrapped from one of the biggest online shops in Poland. The data were obtained as part of eCPI (electronic Consumer Price Index) project conducted by the National Bank of Poland. The author decided to select three types of products for this analysis – female ballerinas, male shoes, and male oxfords to compare their prices in over one-year time period. Six price indexes were used for calculation – The Jevons and Dutot indexes with their chain and GEKS (acronym from the names of creators – Gini–Éltető–Köves–Szulc) versions. Apart from the analysis conducted on a full data set, the author introduced filters to remove outliers. Findings – Clothing and footwear are considered one of the most difficult groups of goods to measure price change indexes due to high product churn, which undermines the possibility to use the traditional Jevons and Dutot indexes. However, it is possible to use chained indexes and GEKS indexes instead. Still, these indexes are fairly sensitive to large price changes. As observed in case of both product groups, the results provided by the GEKS and chained versions of indexes were different, which could lead to conclu- sion that even though they are lending promising results, they could be better suited for other COICOP (Classification of Individual Consumption by Purpose) groups. Research implications/limitations – The findings of the paper showed that usage of filters did not significantly reduce the difference between price indexes based on GEKS and chain formulas. Originality/value/contribution – The usage of web-scrapped data is a fairly new topic in the literature. Research on the possibility of using different price indexes provides useful insights for future usage of these data by statistics offices. Keywords: inflation, CPI, web-scraping, online shopping, big data. JEL Classification: C43, C49

scholarly journals Impacts of Earthquakes on Consumer Price Index and Inflation: A Case Study in West Nusa Tenggara Province, Indonesia

2021 ◽  
Vol 1863 (1) ◽  
pp. 012062
Author(s):  
Wahyu Wibowo ◽  
Taly Purwa ◽  
Elya Nabila Abdul Bahri ◽  
Brodjol Sutijo Suprih Ulama ◽  
Regina Niken Wilantari
Keyword(s):  
Price Index ◽  
Consumer Price Index

Prediction Of The Number Of Tourists To Visit Bali Province Using Backpropagation Artificial Neural Network (Case Study: Data 1990-2016)

2020 ◽  
Vol 8 (3) ◽  
pp. 243
Author(s):  
I Gusti Agung Ngurah Panji Palguna ◽  
I Made Widiartha
Keyword(s):  
Price Index ◽  
Good Accuracy ◽  
Consumer Price Index ◽  
Activation Function ◽  
Study Data ◽  
Tourist Destination ◽  
Tourism Sector ◽  
Backpropagation Method ◽  
Case Study Data

Bali Island is the most popular tourist destination in Indonesia. The total number of foreign tourists visiting Indonesia through the entrance of Ngurah Rai Airport reached 40% as of October 2016, with the value of Bali's foreign exchange receipts for Indonesia from the tourism sector amounting to 70 Trillion Rupiah. Minister of Tourism (Menpar) Arief Yahya always uses the password "Bali" in promoting destinations throughout the world. Because in tourism, Bali is a gate that is passed by 40 percent of foreign tourists (tourists) to Indonesia. In support of more accurate decision making, the author makes a system of forecasting numbers of foreign tourists visiting Bali Province by taking a sample of Japan. Factors that are used as input to make predictions include the number of tourists visiting before, the population of the country of origin of foreign tourists, Gross Domestic Product, and the Relative Consumer Price Index of the countries of origin of foreign tourists. In this research, optimization of the activation function, hidden neuron, and learning rate parameters is performed. Forecasting results using the backpropagation method produce a pretty good accuracy with an accuracy of Mean Square Error = 0.0050558, and test data accuracy of MSE = 0.031695. ANN architecture in the training process is then used to calculate predictions of visits by foreign tourists in the testing process

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