This technical note describes the methodology used to compute and analyze bread price indices using online price scraping data from major supermarkets in Latin America. The data was collected daily from October 2007 to July 2011 by scraping software that records product prices from retailer websites. Individual product price series were used to calculate daily inflation rates for bread in each country. Gaps in price data for bread, which is regularly stocked, likely indicate supply disruptions that the indices aim to detect in near real-time.
Nowcasting Food Prices in Indonesia with Social Media - Project Overview UN Global Pulse
Pulse Lab Jakarta explored how Twitter data can be used to nowcast food prices in Indonesia. A statistical model was developed to produce daily price indicators for four different food commodities: beef, chicken, onion and chili. When the modeled prices were compared with official food prices, the figures were closely correlated, demonstrating that near real-time social media signals can function as proxy for daily food price statistics.
Crowdsourcing High- Frequency Food Price Data in Rural Indonesia - Project Ov...UN Global Pulse
A feasibility study conducted by Pulse Lab Jakarta, UN World Food Programme, UN Food and Agriculture Organisation, Premise used crowdsourcing to track commodity prices in near real-time in areas where the availability of other data sources was limited. High-resolution and high frequency food price trends were derived from reports generated by “citizen reporters”.
Cite as: UN Global Pulse, “Feasibility Study: Crowdsourcing High- Frequency Food Price Data in Rural Indonesia”, Global Pulse Project Series no. 17, 2015.
Food and nutrition security monitoring and analysis systems finalUN Global Pulse
Executive summary of the United Nations Children’s Fund (UNICEF) and World Food Programme (WFP) research: “Food and Nutrition Security and Analysis Systems: A Review of Five Countries (Indonesia, Madagascar, Malawi, Nepal and Zambia),” conducted as part of UN Global Pulse’s Rapid Impact and Vulnerability Assessment Fund (RIVAF). For more information: http://www.unglobalpulse.org/projects/rapid-impact-and-vulnerability-analysis-fund-rivaf
Analysing Seasonal Mobility Patterns Using Mobile Phone Data - Project Overview UN Global Pulse
Mobile phone data allows for the direct observation of population-scale mobility. In this study, the movements of populations in Senegal in 2013 were quantified using anonymised mobile phone data. Movement patterns among populations groups were extracted and
visualised, which resulted in a series of mobility profiles from different regions of Senegal. These mobility profiles were compared with agricultural cycles and livelihoods of each region.
Cite as: "Analysing Seasonal Mobility Patterns Using Mobile Phone Data", Global Pulse Project Series, no.15, 2015
Data Visualisation and Interactive Mapping to Support Response to Disease Out...UN Global Pulse
From January – May 2015, a typhoid outbreak occurred in Uganda. Pulse Lab Kampala was invited to join the National Task Force in response to the outbreak. In coordination with WHO, and in collaboration with the Ministry of Health, Pulse Lab Kampala produced a series of data visualisations to support the early response to the disease. Visualisations of weekly reports from health centres were produced with interactive maps at district, sub-county and individual health facility level. The visualisations allowed decision making for the allocation of medicine, medical personnel and health centres, as well as targeting training areas.
Cite as: "Data Visualisation and Interactive Mapping to Support Response to Disease Outbreak”, Global Pulse Project Series no. 21, 2015
Mining Citizen Feedback Data for Enhanced Local Government Decision-Making - ...UN Global Pulse
Pulse Lab Jakarta worked with the Nusa Tenggara Barat (NTB) provincial government to explore the contribution of advanced data analytics to local government decision-making by generating insights from a combination of existing complaint systems and passive feedback from citizens on social media.
The results demonstrate the potential utility of (a) near real-time information on public policy issues and their corresponding locations within defined constituencies, (b) enhanced data analysis for prioritisation and rapid response, and (c) deriving insights on different aspects of citizen feedback. The publication of citizen feedback on public-facing dashboards can enhance transparency and help constituents understand how their feedback is processed.
Cite as: UN Global Pulse, “Mining Citizen Feedback Data for Enhanced Local Government Decision-Making”, Global Pulse Project Series no.16, 2015
Using Twitter Data to Analyse Public Sentiment on Fuel Subsidy Policy Reform ...UN Global Pulse
The study analyzed tweets related to fuel subsidy reforms in El Salvador to better understand public sentiment and opinion. It developed a taxonomy of keywords to categorize tweets and found that while household surveys showed satisfaction with the reforms increased over time, tweets expressed more persistent negative views. The research demonstrated social media analysis can provide insights into policy impacts that may differ from surveys. It revealed public dissatisfaction with gas distributors' strikes that likely influenced perceptions more than previously known. The study supported the potential for social media to complement or replace surveys in assessing policy reforms.
Nowcasting Food Prices in Indonesia with Social Media - Project Overview UN Global Pulse
Pulse Lab Jakarta explored how Twitter data can be used to nowcast food prices in Indonesia. A statistical model was developed to produce daily price indicators for four different food commodities: beef, chicken, onion and chili. When the modeled prices were compared with official food prices, the figures were closely correlated, demonstrating that near real-time social media signals can function as proxy for daily food price statistics.
Crowdsourcing High- Frequency Food Price Data in Rural Indonesia - Project Ov...UN Global Pulse
A feasibility study conducted by Pulse Lab Jakarta, UN World Food Programme, UN Food and Agriculture Organisation, Premise used crowdsourcing to track commodity prices in near real-time in areas where the availability of other data sources was limited. High-resolution and high frequency food price trends were derived from reports generated by “citizen reporters”.
Cite as: UN Global Pulse, “Feasibility Study: Crowdsourcing High- Frequency Food Price Data in Rural Indonesia”, Global Pulse Project Series no. 17, 2015.
Food and nutrition security monitoring and analysis systems finalUN Global Pulse
Executive summary of the United Nations Children’s Fund (UNICEF) and World Food Programme (WFP) research: “Food and Nutrition Security and Analysis Systems: A Review of Five Countries (Indonesia, Madagascar, Malawi, Nepal and Zambia),” conducted as part of UN Global Pulse’s Rapid Impact and Vulnerability Assessment Fund (RIVAF). For more information: http://www.unglobalpulse.org/projects/rapid-impact-and-vulnerability-analysis-fund-rivaf
Analysing Seasonal Mobility Patterns Using Mobile Phone Data - Project Overview UN Global Pulse
Mobile phone data allows for the direct observation of population-scale mobility. In this study, the movements of populations in Senegal in 2013 were quantified using anonymised mobile phone data. Movement patterns among populations groups were extracted and
visualised, which resulted in a series of mobility profiles from different regions of Senegal. These mobility profiles were compared with agricultural cycles and livelihoods of each region.
Cite as: "Analysing Seasonal Mobility Patterns Using Mobile Phone Data", Global Pulse Project Series, no.15, 2015
Data Visualisation and Interactive Mapping to Support Response to Disease Out...UN Global Pulse
From January – May 2015, a typhoid outbreak occurred in Uganda. Pulse Lab Kampala was invited to join the National Task Force in response to the outbreak. In coordination with WHO, and in collaboration with the Ministry of Health, Pulse Lab Kampala produced a series of data visualisations to support the early response to the disease. Visualisations of weekly reports from health centres were produced with interactive maps at district, sub-county and individual health facility level. The visualisations allowed decision making for the allocation of medicine, medical personnel and health centres, as well as targeting training areas.
Cite as: "Data Visualisation and Interactive Mapping to Support Response to Disease Outbreak”, Global Pulse Project Series no. 21, 2015
Mining Citizen Feedback Data for Enhanced Local Government Decision-Making - ...UN Global Pulse
Pulse Lab Jakarta worked with the Nusa Tenggara Barat (NTB) provincial government to explore the contribution of advanced data analytics to local government decision-making by generating insights from a combination of existing complaint systems and passive feedback from citizens on social media.
The results demonstrate the potential utility of (a) near real-time information on public policy issues and their corresponding locations within defined constituencies, (b) enhanced data analysis for prioritisation and rapid response, and (c) deriving insights on different aspects of citizen feedback. The publication of citizen feedback on public-facing dashboards can enhance transparency and help constituents understand how their feedback is processed.
Cite as: UN Global Pulse, “Mining Citizen Feedback Data for Enhanced Local Government Decision-Making”, Global Pulse Project Series no.16, 2015
Using Twitter Data to Analyse Public Sentiment on Fuel Subsidy Policy Reform ...UN Global Pulse
The study analyzed tweets related to fuel subsidy reforms in El Salvador to better understand public sentiment and opinion. It developed a taxonomy of keywords to categorize tweets and found that while household surveys showed satisfaction with the reforms increased over time, tweets expressed more persistent negative views. The research demonstrated social media analysis can provide insights into policy impacts that may differ from surveys. It revealed public dissatisfaction with gas distributors' strikes that likely influenced perceptions more than previously known. The study supported the potential for social media to complement or replace surveys in assessing policy reforms.
Using Financial Transaction Data To Measure Economic Resilience To Natural Di...UN Global Pulse
This project explored how financial transaction data can be analysed to better understand the economic resilience of people affected by natural disasters. The project used the Mexican state of Baja California Sur as a case study to assess the impact of Hurricane Odile on livelihoods and economic activities over a period of six months in 2014. The project measured daily Point of Sale transactions and ATM withdrawals at high geospatial resolution to gain insight into the way people prepare for and recover from disaster.
The study revealed that people spent 50% more than usual on items such as food and gasoline in preparation for the hurricane and that recovery time ranged from 2 to 40 days depending on characteristics such as gender or income. Findings suggest that insights from transaction data could be used to target emergency response and to estimate economic loss at local level in the wake of a disaster.
Supporting Forest and Peat Fire Management Using Social Media - Project OverviewUN Global Pulse
A feasibility study was conducted by Pulse Lab Jakarta on the use of real-time information from social media during forest and peat fires haze events to support emergency response management in Indonesia. Specifically, the study sought to explore early signals from Twitter relating to major forest fires or haze events with a view to understanding the relation between communications trends and on-the-ground events. The results of the study demonstrated that Indonesians tweet significantly more about haze during and immediately after major fire events.
Cite as: UN Global Pulse, 'Feasibility Study: Supporting Forest and Peat Fire Management Using Social Media', Global Pulse Project Series, no.10, 2014.
Global Pulse: Mining Indonesian Tweets to Understand Food Price Crises copyUN Global Pulse
Sudden increases in the price of staple foodstuffs like rice can push whole families below the poverty line and cause regional economic instability; these changes can happen rapidly but food price statistics are generally published only monthly or even less frequently.
This project, in collaboration with the Indonesian Ministry of Development Planning, UNICEF and WFP in Indonesia seeks to use social media analysis to provide real-time information from the population that could enable faster responses to food price increases in the form of social protection policies. Global Pulse analysed tweet volumes relevant to food and fuel between March 2011 and April 2013 and found a significant correlation, suggesting that even potential (rather than realised) fuel price rises affect people’s perceptions of food security. Researchers also found a relationship between retrospective official food inflation statistics and the number of tweets referencing food price increases.
http://www.unglobalpulse.org/social-media-social-protection-indonesia
Using Twitter to Measure Global Engagement on Climate Change - Project OverviewUN Global Pulse
Global Pulse developed a real-time social media monitor to measure and explore online discourse about climate change in support of the United Nations Climate Summit in 2014. The publicly accessible monitor analysed tweets in English, Spanish and French on a daily basis to show the volume and content of tweets about climate change across a range of topic areas such as economy and energy. Measuring and visualising public tweets over time created a baseline of engagement, and showed a significant increase in discussions about climate change around the Climate Summit. By providing a tool for comparing interest level between topics and regions, and monitoring the social media impact of climate-related public communications and events, the monitor could be used to measure awareness, support climate policy decision-making and to drive further public engagement.
Cite as: "Using Twitter to Measure Global Engagement on Climate Change', Global Pulse Project Series", no.7, 2014
Analyzing Attitudes Towards Biofuels with Social Media - Project OverviewUN Global Pulse
This project analysed how public perceptions of and attitudes towards biofuels in the UK and Germany evolved other a period of three years, from 2013 to 2015. The project analysed around 350,000 public tweets from the UK and 35,000 tweets from Germany about biofuels to understand whether any changes occurred in the balance between statements for and against the use of biofuels.
Understanding Immunisation Awareness and Sentiment with Social Media - Projec...UN Global Pulse
This multi-country study aims to track and analyse online conversations related to immunisation on social media and mainstream media in India, Kenya, Nigeria and Pakistan. Findings from the study showed that in social media, Nigerian and Pakistani politicians are active and influential in the vaccination debate and the political dimension is often referred to when discussing the failure to eradicate diseases such as polio. However, in Kenya, religious and ideological aspects were more frequently discussed. Twitter activity is primarily driven by sharing of news stories in all countries whereas Facebook focuses on the 'distrust' and 'ideals' categorisation.
Cite as: UN Global Pulse, “Understanding Immunisation Awareness and Sentiment Through Social and Mainstream Media”, Global Pulse Project Series no. 19, 2015.
This report summarizes the 2015 achievements of Pulse Lab Kampala and provides a glimpse into the long-term projects and agenda in the field of big data innovation for development and humanitarian action.
This document summarizes research on using smartphone technology to increase income stability among fish farmers in Senegal. It discusses how fish farming is a major industry in Senegal but many fish farmers live in poverty. The study explores implementing smartphone apps to help farmers track finances and market opportunities. A survey was conducted of students to understand their smartphone use, finding most use social media and financial apps and feel the latter helps manage their money. The document reviews related literature on ICT use in Senegal and income diversification strategies for farmers.
The Effectiveness of Communication Channels for the Uptake of Modern Reproduc...Premier Publishers
Kenya’s population continues to increase with corresponding demand for milk and related products. Despite the emerging Modern Reproductive Technologies (MRTs) for improving dairy and milk production, the uptake of technologies remains relatively low in Kangema sub-county. This study evaluated the effectiveness of communication channels for uptake of MRTs among dairy farmers. It adopted a descriptive research design and employed stratified and systematic probability sampling, in which 108 dairy farmers were interviewed. Data was collected using household questionnaires and focus group discussions. Data was analyzed using SPSS and outcomes presented in tables and graphs. The results established Artificial Insemination (AI), sexed semen, embryo transfer and use of bulls as commonly used technologies. Artificial Insemination was widely used for dairy improvement across Kangema. A lesser percentage of farmers were utilizing sexed semen; however, embryo transfer had not been considered. The common communication channels utilized included; radio, television, veterinary doctors and peer-farmers. Radio was the most effective channel, while social media and internet were least preferred. The Pearson’s chi-square test established a positive association between farmer’s education and monthly income, which influenced access to MRTs. The study recommended radio disseminated reproductive technologies and related best practices, as a factor for increased milk yields.
An overview of a three-year project on ‘Pro-poor responses to wildlife crime’ by IIED principal researcher Dilys Roe.
The presentation was for the Uganda Wildlife Authority Planning Workshop in July 2015.
Few people would start a journey with a map that shows neither where they are nor where they are going. Yet many companies seek to compete without knowing the true cost, and profit, of their products or services, and customers.
Directors often base corporate strategy on misleading information that supports bad decisions. This only helps competitors. Traditional financial information systems measure a company’s performance only in the aggregate.
They may not help to find opportunities to increase competitiveness in the market place.
To create more value and enhance their profitability, organisations in manufacturing and service require accurate information on costs. Activity Based Costing (ABC) can provide it. But organising an effective ABC initiative is not as simple as opening a book and beginning at Chapter One.
MODEL OF MULTIPLE ARTIFICIAL NEURAL NETWORKS ORIENTED ON SALES PREDICTION AND...ijscai
In this paper the authors proposed different Multilayer Perceptron Models (MLP) of artificial neural networks (ANN) suitable for visual merchandising in Global Distribution (GDO) applications involving supermarket product facing. The models are related to the prediction of different attributes concerning
mainly shelf product allocation applying times series forecasting approach. The study highlights the range validity of the sales prediction by analysing different products allocated on a testing shelf. The paper shows the correct procedures able to analyse most guaranteed results, by describing how test and train datasets can be processed. The prediction results are useful in order to design monthly a planogram by taking into
account the shelf allocations, the general sales trend, and the promotion activities. The preliminary correlation analysis provided an innovative key reading of the predicted outputs. The testing has been
performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental
dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as
input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing
dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning
process of the model. The proposed study is developed within a framework of an industry project.
In this paper the authors proposed different Multilayer Perceptron Models (MLP) of artificial neural
networks (ANN) suitable for visual merchandising in Global Distribution (GDO) applications involving
supermarket product facing. The models are related to the prediction of different attributes concerning
mainly shelf product allocation applying times series forecasting approach. The study highlights the range
validity of the sales prediction by analysing different products allocated on a testing shelf. The paper shows
the correct procedures able to analyse most guaranteed results, by describing how test and train datasets
can be processed. The prediction results are useful in order to design monthly a planogram by taking into
account the shelf allocations, the general sales trend, and the promotion activities. The preliminary
correlation analysis provided an innovative key reading of the predicted outputs. The testing has been
performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental
dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as
input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing
dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning
process of the model. The proposed study is developed within a framework of an industry project.
MODEL OF MULTIPLE ARTIFICIAL NEURAL NETWORKS ORIENTED ON SALES PREDICTION AND...ijscai
In this paper the authors proposed different Multilayer Perceptron Models (MLP) of artificial neural networks (ANN) suitable for visual merchandising in Global Distribution (GDO) applications involving supermarket product facing. The models are related to the prediction of different attributes concerning
mainly shelf product allocation applying times series forecasting approach. The study highlights the range
validity of the sales prediction by analysing different products allocated on a testing shelf. The paper shows the correct procedures able to analyse most guaranteed results, by describing how test and train datasets can be processed. The prediction results are useful in order to design monthly a planogram by taking into account the shelf allocations, the general sales trend, and the promotion activities. The preliminary correlation analysis provided an innovative key reading of the predicted outputs. The testing has been
performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning
process of the model. The proposed study is developed within a framework of an industry project.
This document summarizes a master's project that aims to accurately predict sales of 111 weather-sensitive products across 45 Walmart stores using machine learning techniques. The project uses training sales and weather data to build models and evaluates them on held-out test data. Key models tested include stepwise linear regression, K-nearest neighbors, and ensemble methods. Cross-validation is performed and forecast errors are analyzed to develop implications for inventory policies like safety stock levels.
Chapter 1 introduces statistics and differentiates between descriptive and inferential statistics. It aims to motivate business students to study statistics by presenting applications in business. Some key objectives are to define statistics, discuss its uses in business, and classify data by level of measurement. The chapter also outlines descriptive statistics, inferential statistics, and the different levels of data measurement. It emphasizes that understanding the data level is important for choosing the right analytical techniques.
This document summarizes a business plan for barcoo, a proposed mobile application that allows users to scan product barcodes and access aggregated consumer information about that product. Key points:
- barcoo will use machine learning algorithms to enable mobile phones to recognize 1D barcodes, providing product information like price comparisons, reviews, recipes, and shopping links.
- The target market is German smartphone users, focusing initially on early adopter groups. Revenue will come from mobile advertising and shopping commissions.
- The founders have experience developing mobile technologies and business strategies. An advisory board will provide support.
- An initial funding of €80,000 is requested to launch the beta in early 2009, with break-even
IRJET- Real Time Product Price Monitoring & Analysis Application for E-Commer...IRJET Journal
The document proposes a real-time product price monitoring and analysis application for e-commerce websites. It discusses how e-commerce sites can dynamically adjust prices in response to demand changes. The proposed system would allow users to monitor prices of desired products on different websites and notify them when prices meet a specified threshold. It would generate analytical charts and diagrams to help users make informed purchase decisions. Future work may include an automatic purchasing system and business models to increase monetization for large-scale monitoring. The system aims to help regular buyers obtain products at optimal prices.
The document discusses various methods for constructing price index numbers, including simple and weighted approaches. It provides examples of the simple aggregative method, where the index number equals the sum of current period prices divided by the base period prices. The simple average of price relatives method calculates the index as the sum of price relatives divided by the number of items. Weighted methods assign weights to items based on their importance, with the weighted aggregative method using quantity weights in the calculation.
This document discusses product development management by Group No. 2. It includes an overview of the role of product development in competitiveness, the product development process, organization for product development, tools for efficient product development, and performance measures. It then discusses the product development process, organization for product development, tools for effective product development including understanding customer needs and market research. Finally, it outlines various management accounting tools that can be used for product development such as cost-volume-profit analysis, activity-based costing, budgeting, return on investment analysis, and sensitivity analysis.
Profitable Itemset Mining using WeightsIRJET Journal
This document presents two new algorithms - Profitable Apriori and Profitable FP-Growth - that extend traditional association rule mining algorithms to consider profit and quantity of items. Traditional algorithms like Apriori and FP-Growth are binary and do not account for these factors. The new algorithms incorporate profit per unit and quantity to generate the most profitable itemsets. They are compared based on memory usage, runtime, and number of patterns produced. Both algorithms generate the same profitable patterns, but Profitable FP-Growth uses more memory while Profitable Apriori has a longer runtime due to candidate generation. The algorithms aim to identify truly profitable patterns for effective marketing unlike traditional methods.
Using Financial Transaction Data To Measure Economic Resilience To Natural Di...UN Global Pulse
This project explored how financial transaction data can be analysed to better understand the economic resilience of people affected by natural disasters. The project used the Mexican state of Baja California Sur as a case study to assess the impact of Hurricane Odile on livelihoods and economic activities over a period of six months in 2014. The project measured daily Point of Sale transactions and ATM withdrawals at high geospatial resolution to gain insight into the way people prepare for and recover from disaster.
The study revealed that people spent 50% more than usual on items such as food and gasoline in preparation for the hurricane and that recovery time ranged from 2 to 40 days depending on characteristics such as gender or income. Findings suggest that insights from transaction data could be used to target emergency response and to estimate economic loss at local level in the wake of a disaster.
Supporting Forest and Peat Fire Management Using Social Media - Project OverviewUN Global Pulse
A feasibility study was conducted by Pulse Lab Jakarta on the use of real-time information from social media during forest and peat fires haze events to support emergency response management in Indonesia. Specifically, the study sought to explore early signals from Twitter relating to major forest fires or haze events with a view to understanding the relation between communications trends and on-the-ground events. The results of the study demonstrated that Indonesians tweet significantly more about haze during and immediately after major fire events.
Cite as: UN Global Pulse, 'Feasibility Study: Supporting Forest and Peat Fire Management Using Social Media', Global Pulse Project Series, no.10, 2014.
Global Pulse: Mining Indonesian Tweets to Understand Food Price Crises copyUN Global Pulse
Sudden increases in the price of staple foodstuffs like rice can push whole families below the poverty line and cause regional economic instability; these changes can happen rapidly but food price statistics are generally published only monthly or even less frequently.
This project, in collaboration with the Indonesian Ministry of Development Planning, UNICEF and WFP in Indonesia seeks to use social media analysis to provide real-time information from the population that could enable faster responses to food price increases in the form of social protection policies. Global Pulse analysed tweet volumes relevant to food and fuel between March 2011 and April 2013 and found a significant correlation, suggesting that even potential (rather than realised) fuel price rises affect people’s perceptions of food security. Researchers also found a relationship between retrospective official food inflation statistics and the number of tweets referencing food price increases.
http://www.unglobalpulse.org/social-media-social-protection-indonesia
Using Twitter to Measure Global Engagement on Climate Change - Project OverviewUN Global Pulse
Global Pulse developed a real-time social media monitor to measure and explore online discourse about climate change in support of the United Nations Climate Summit in 2014. The publicly accessible monitor analysed tweets in English, Spanish and French on a daily basis to show the volume and content of tweets about climate change across a range of topic areas such as economy and energy. Measuring and visualising public tweets over time created a baseline of engagement, and showed a significant increase in discussions about climate change around the Climate Summit. By providing a tool for comparing interest level between topics and regions, and monitoring the social media impact of climate-related public communications and events, the monitor could be used to measure awareness, support climate policy decision-making and to drive further public engagement.
Cite as: "Using Twitter to Measure Global Engagement on Climate Change', Global Pulse Project Series", no.7, 2014
Analyzing Attitudes Towards Biofuels with Social Media - Project OverviewUN Global Pulse
This project analysed how public perceptions of and attitudes towards biofuels in the UK and Germany evolved other a period of three years, from 2013 to 2015. The project analysed around 350,000 public tweets from the UK and 35,000 tweets from Germany about biofuels to understand whether any changes occurred in the balance between statements for and against the use of biofuels.
Understanding Immunisation Awareness and Sentiment with Social Media - Projec...UN Global Pulse
This multi-country study aims to track and analyse online conversations related to immunisation on social media and mainstream media in India, Kenya, Nigeria and Pakistan. Findings from the study showed that in social media, Nigerian and Pakistani politicians are active and influential in the vaccination debate and the political dimension is often referred to when discussing the failure to eradicate diseases such as polio. However, in Kenya, religious and ideological aspects were more frequently discussed. Twitter activity is primarily driven by sharing of news stories in all countries whereas Facebook focuses on the 'distrust' and 'ideals' categorisation.
Cite as: UN Global Pulse, “Understanding Immunisation Awareness and Sentiment Through Social and Mainstream Media”, Global Pulse Project Series no. 19, 2015.
This report summarizes the 2015 achievements of Pulse Lab Kampala and provides a glimpse into the long-term projects and agenda in the field of big data innovation for development and humanitarian action.
This document summarizes research on using smartphone technology to increase income stability among fish farmers in Senegal. It discusses how fish farming is a major industry in Senegal but many fish farmers live in poverty. The study explores implementing smartphone apps to help farmers track finances and market opportunities. A survey was conducted of students to understand their smartphone use, finding most use social media and financial apps and feel the latter helps manage their money. The document reviews related literature on ICT use in Senegal and income diversification strategies for farmers.
The Effectiveness of Communication Channels for the Uptake of Modern Reproduc...Premier Publishers
Kenya’s population continues to increase with corresponding demand for milk and related products. Despite the emerging Modern Reproductive Technologies (MRTs) for improving dairy and milk production, the uptake of technologies remains relatively low in Kangema sub-county. This study evaluated the effectiveness of communication channels for uptake of MRTs among dairy farmers. It adopted a descriptive research design and employed stratified and systematic probability sampling, in which 108 dairy farmers were interviewed. Data was collected using household questionnaires and focus group discussions. Data was analyzed using SPSS and outcomes presented in tables and graphs. The results established Artificial Insemination (AI), sexed semen, embryo transfer and use of bulls as commonly used technologies. Artificial Insemination was widely used for dairy improvement across Kangema. A lesser percentage of farmers were utilizing sexed semen; however, embryo transfer had not been considered. The common communication channels utilized included; radio, television, veterinary doctors and peer-farmers. Radio was the most effective channel, while social media and internet were least preferred. The Pearson’s chi-square test established a positive association between farmer’s education and monthly income, which influenced access to MRTs. The study recommended radio disseminated reproductive technologies and related best practices, as a factor for increased milk yields.
An overview of a three-year project on ‘Pro-poor responses to wildlife crime’ by IIED principal researcher Dilys Roe.
The presentation was for the Uganda Wildlife Authority Planning Workshop in July 2015.
Few people would start a journey with a map that shows neither where they are nor where they are going. Yet many companies seek to compete without knowing the true cost, and profit, of their products or services, and customers.
Directors often base corporate strategy on misleading information that supports bad decisions. This only helps competitors. Traditional financial information systems measure a company’s performance only in the aggregate.
They may not help to find opportunities to increase competitiveness in the market place.
To create more value and enhance their profitability, organisations in manufacturing and service require accurate information on costs. Activity Based Costing (ABC) can provide it. But organising an effective ABC initiative is not as simple as opening a book and beginning at Chapter One.
MODEL OF MULTIPLE ARTIFICIAL NEURAL NETWORKS ORIENTED ON SALES PREDICTION AND...ijscai
In this paper the authors proposed different Multilayer Perceptron Models (MLP) of artificial neural networks (ANN) suitable for visual merchandising in Global Distribution (GDO) applications involving supermarket product facing. The models are related to the prediction of different attributes concerning
mainly shelf product allocation applying times series forecasting approach. The study highlights the range validity of the sales prediction by analysing different products allocated on a testing shelf. The paper shows the correct procedures able to analyse most guaranteed results, by describing how test and train datasets can be processed. The prediction results are useful in order to design monthly a planogram by taking into
account the shelf allocations, the general sales trend, and the promotion activities. The preliminary correlation analysis provided an innovative key reading of the predicted outputs. The testing has been
performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental
dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as
input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing
dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning
process of the model. The proposed study is developed within a framework of an industry project.
In this paper the authors proposed different Multilayer Perceptron Models (MLP) of artificial neural
networks (ANN) suitable for visual merchandising in Global Distribution (GDO) applications involving
supermarket product facing. The models are related to the prediction of different attributes concerning
mainly shelf product allocation applying times series forecasting approach. The study highlights the range
validity of the sales prediction by analysing different products allocated on a testing shelf. The paper shows
the correct procedures able to analyse most guaranteed results, by describing how test and train datasets
can be processed. The prediction results are useful in order to design monthly a planogram by taking into
account the shelf allocations, the general sales trend, and the promotion activities. The preliminary
correlation analysis provided an innovative key reading of the predicted outputs. The testing has been
performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental
dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as
input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing
dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning
process of the model. The proposed study is developed within a framework of an industry project.
MODEL OF MULTIPLE ARTIFICIAL NEURAL NETWORKS ORIENTED ON SALES PREDICTION AND...ijscai
In this paper the authors proposed different Multilayer Perceptron Models (MLP) of artificial neural networks (ANN) suitable for visual merchandising in Global Distribution (GDO) applications involving supermarket product facing. The models are related to the prediction of different attributes concerning
mainly shelf product allocation applying times series forecasting approach. The study highlights the range
validity of the sales prediction by analysing different products allocated on a testing shelf. The paper shows the correct procedures able to analyse most guaranteed results, by describing how test and train datasets can be processed. The prediction results are useful in order to design monthly a planogram by taking into account the shelf allocations, the general sales trend, and the promotion activities. The preliminary correlation analysis provided an innovative key reading of the predicted outputs. The testing has been
performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning
process of the model. The proposed study is developed within a framework of an industry project.
This document summarizes a master's project that aims to accurately predict sales of 111 weather-sensitive products across 45 Walmart stores using machine learning techniques. The project uses training sales and weather data to build models and evaluates them on held-out test data. Key models tested include stepwise linear regression, K-nearest neighbors, and ensemble methods. Cross-validation is performed and forecast errors are analyzed to develop implications for inventory policies like safety stock levels.
Chapter 1 introduces statistics and differentiates between descriptive and inferential statistics. It aims to motivate business students to study statistics by presenting applications in business. Some key objectives are to define statistics, discuss its uses in business, and classify data by level of measurement. The chapter also outlines descriptive statistics, inferential statistics, and the different levels of data measurement. It emphasizes that understanding the data level is important for choosing the right analytical techniques.
This document summarizes a business plan for barcoo, a proposed mobile application that allows users to scan product barcodes and access aggregated consumer information about that product. Key points:
- barcoo will use machine learning algorithms to enable mobile phones to recognize 1D barcodes, providing product information like price comparisons, reviews, recipes, and shopping links.
- The target market is German smartphone users, focusing initially on early adopter groups. Revenue will come from mobile advertising and shopping commissions.
- The founders have experience developing mobile technologies and business strategies. An advisory board will provide support.
- An initial funding of €80,000 is requested to launch the beta in early 2009, with break-even
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The document discusses various methods for constructing price index numbers, including simple and weighted approaches. It provides examples of the simple aggregative method, where the index number equals the sum of current period prices divided by the base period prices. The simple average of price relatives method calculates the index as the sum of price relatives divided by the number of items. Weighted methods assign weights to items based on their importance, with the weighted aggregative method using quantity weights in the calculation.
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Global ozone meter market size is projected to grow at a steady pace over the forecast years. Ozone is identified as a hazardous gas which must be monitored and measured at regular intervals. Click : https://bit.ly/2CTnvYB
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IRJET- An Implementation of Diary Food Products using Android ApplicationIRJET Journal
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This document discusses strategies to improve the profitability of Vengo vending machines. It addresses cartridge manipulation and product shelf life, patron loyalty, and price sensitivity. For cartridge manipulation, it recommends replacing unpopular products with popular ones to reduce restocking frequency. For patron loyalty, it suggests tracking frequent buyers but not over-focusing on any one buyer unless they contribute significantly month over month. It also proposes a patron appreciation program to recognize and retain loyal customers. Models and analyses in the appendices provide details on implementation.
This document discusses using machine learning techniques to predict stock market prices. It begins with an introduction to existing stock prediction methods like fundamental and technical analysis. The proposed system would use machine learning models to analyze historical stock price data and sentiment analysis of news articles to predict future stock prices, volatility, and market trends. The methodology section outlines different models, including using only historical prices, classifying sentiment of news, and aspect-based sentiment analysis. Features like stock price volatility, momentum, and index momentum would be used. The conclusion states that accurately predicting the complex stock market requires considering various factors.
Industrial cost accounting is a systematic process of recording, classifying, analyzing, and allocating costs associated with industrial operations. It provides key benefits like informed decision making, cost control, budgeting and regulatory compliance. The food industry utilizes various cost accounting techniques and tracks costs like raw materials, labor, marketing and production. Emerging technologies are improving areas like data analytics, automation, supply chain visibility and cost optimization.
A production - Inventory model with JIT setup cost incorporating inflation an...IJMER
A production inventory model with Just-In-Time (JIT) set-up cost has been developed in which inflation and time value of money are considered under an imperfect production process. The demand rate is considered to be a function of advertisement cost and selling price. Unit production cost is considered incorporating several features like energy and labour cost, raw material cost and development cost of the manufacturing system. Development cost is assumed to be a function of reliability parameter.
Considering these phenomena, an analytic expression is obtained for the total profit of the model. The model provides an analytical solution to maximize the total profit function.A numerical example is presented to illustrate the model along with graphical analysis. Sensitivity analysis has been carried out to identify the most sensitive parameters of the model.
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In 2016-2017, Pulse Lab Kampala worked with various UN agencies and development partners in Uganda and the region to test, explore and develop 17 innovation projects. The Lab also furthered the development of tools and technologies that leverage data sources from radio content, social media, mobile phones and satellite imagery, and created technology toolkits. These toolkits can enhance decision-making by providing real-time situational awareness for project and policy implementation.
The 2018 Annual Report details exploratory research conducted by the Pulse Labs and presents solutions that were mainstreamed with partners.
It summarized the adoption of the first UN Principles for Personal Data Protection and Privacy, and showcases Global Pulse's contributions to develop standards and national strategies for the ethical and privacy protective use of big data and artificial intelligence.
Finally, the report highlights Global Pulse's engagement with the data innovation ecosystem through capacity building, collaborative research, and responsible data partnerships.
Risks, Harms and Benefits Assessment Tool (Updated as of Jan 2019)UN Global Pulse
The Data Innovation Risk Assessment Tool is an initial assessment of potential risks for data use that includes seven guiding checkpoints to understand: the "Data Type" involved in the data analytics process, the "Risks and Harms" of data use, the mode and legitimacy of "Data Access", the "Data Use", the adequacy of "Data Security", the adequate level of "Communication and Transparency" and the due diligence on engagement of "Third Parties". The Assessment contains guiding comments for each checkpoint and its questions are grounded in the key international data privacy and data protection principles and concepts such as Purpose Specification, Purpose Compatibility, Data Minimization, Consent Legitimacy, Lawfulness and Fairness of data access and use.
2015 was an eventful year for Pulse Lab Jakarta. The broader data innovation ecosystem within which the Lab operates has grown from a specialist network to include a broader range of public, social, and private sector actors who are interested in exploring insights from new data sources as well as learning how data innovation can complement existing datasets and operations. This report provides an overview of the work of Pulse Lab Jakarta in 2015, including the foundation blocks that will lead to an impactful 2016.
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This toolkit provides the methodology for focusing the data-gathering power of existing communities, increasing their capacity to work together and building awareness of the potential of the data created by this work. It aims to help citizens identify and articulate their own problems using the supplementing data in their communities.
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Banking on Fintech: Financial inclusion for micro enterprises in IndonesiaUN Global Pulse
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in Indonesia research was conducted by Pulse Lab Jakarta,
with the support of the Department of Foreign Affairs and Trade
(DFAT) Australia and the Indonesia Fintech Association (AFTECH). It presents successful practices from early adopters and attempts to translate them into opportunities for other unbanked populations.
Pulse Lab Jakarta, in collaboration with the Government of Indonesia, developed ‘Haze Gazer,’ a crisis analysis tool that provides real-time situational information from various data sources to enhance disaster management efforts. The prototype uses advanced data analysis of sources including: satellite imagery, information on population density and distribution from government databases, citizen-generated data and real-time data from social media. The capability afforded by the tool can
enhance disaster risk management efforts to protect vulnerable populations as well as the environment.
Cite as: UN Global Pulse, “Haze Gazer: A crisis analysis tool,” Tool Series, no. 2, 2016.
Building Proxy Indicators of National Wellbeing with Postal Data - Project Ov...UN Global Pulse
This study investigated using data from international postal flows and other global networks as proxy indicators for national socioeconomic metrics. Electronic postal records from 2010-2014 involving 187 countries were analyzed. Connectivity measures from these networks were strongly correlated with indicators like GDP, HDI, and poverty rate. Combining these network data into a multiplex model further improved correlations and generated multidimensional connectivity indicators. This demonstrated new approaches for approximating standard socioeconomic benchmarks in a global, real-time manner using alternative data sources like postal and digital network flows.
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Using Big data Analytics for Improved Public Transport UN Global Pulse
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Cite as: UN Global Pulse, ‘Using Big Data Analytics for Improved
Public Transport,’ Project Series, no. 25, 2017.
Pulse Lab Jakarta developed Translator Gator, a people-powered language game that creates dictionaries for recognising sustainable development-related conversations in Indonesia. The game builds taxonomies, i.e. sets of relevant keywords, by incentivising players to translate words from English into different Indonesian languages, including Bahasa Indonesia, Jawa, Sunda, Minang, Bugis and Melayu.
Cite as: UN Global Pulse, 'Translator Gator: Crowdsourcing
Translation of Development Keywords in Indonesia’, Tool
Series no. 4, 2017.
Big Data for Financial Inclusion, Examining the Customer Journey - Project Ov...UN Global Pulse
Pulse Lab Jakarta collaborated with the UNCDF Shaping Inclusive Finance Transformations (SHIFT) programme to undertake an
analysis of financial services usage, particularly among women in the ASEAN region. The project analysed customer savings and loan data from four Financial Service Providers (FSPs) in Cambodia to understand the factors that affect savings and loans mobilisation, as well as how usage of these products explains economic issues in Cambodia.
Cite as: UN Global Pulse, 'Big Data for Financial Inclusion, Examining The Customer Journey', Project Series, no. 27, 2017.
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This project used data from Twitter to monitor protection issues and the safe access to asylum of migrants and refugees in Europe. In collaboration with the UN High Commissioner for Refugees (UNHCR), Global Pulse created taxonomies that were used to explore interactions among refugees and between them and service providers, as well as xenophobic sentiment of host communities towards the displaced populations. Specifically, the study focused on how refugees and migrants were perceived in reaction to a series of terrorist attacks that took place in Europe in 2016. The results were used to develop a standardized information product to improve UNHCR’s ability to monitor and analyse relevant social media feeds in near real-time.
Cite as: UN Global Pulse, “Understanding Movement and Perceptions of Migrants and Refugees with Social Media,” Project Series, no. 28, 2017.
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1. Technical
Note
Bread
price
index:
a
pilot
case
Roberto
Rigobon
MIT
&
NBER
November
2011
Introduction
This
note
presents
the
methodology
used
by
PriceStats
for
the
computation
and
analysis
of
the
bread
indices
used
in
its
joint
research
project
with
the
United
Nations’
Global
Pulse
Initiative.1
The
objective
of
this
partnership
was
to
investigate
and
show
how
scraping
online
prices
could
provide
real-‐time
insights
on
price
dynamics,
focusing
on
the
case
of
bread.
We
first
provide
a
short
description
of
the
data
gathering
process,
before
discussing
the
actual
computation
of
the
indices.
Data
and
Data
collection
PriceStats
received
the
data
from
the
Billion
Prices
Project
(BPP)
at
MIT.
The
data
is
collected
using
ascraping
software
that
records,
on
a
daily
basis,
the
price
of
all
goods
sold
or
advertised
online.
This
is
done
following
a
3-‐step
methodology:
1. The
software
identifies
and
downloads
all
public
web
pages
where
product
and
price
information
appear,
for
each
retailer
at
a
given
time
of
the
day.
These
pages
are
individually
retrieved
using
the
same
URL
or
web-‐address
every
day;
2. It
analyzes
the
underlying
code
and
locates
each
piece
of
information
that
needs
to
be
collected.
This
is
performed
on
the
basis
of
custom
characters
in
the
code
that
allow
the
software
to
match
the
format
used
on
a
particular
page
by
a
specific
supermarket
in
order
to
identify
the
location
as
well
as
the
starting
and
end
points
of
the
price
information.
For
example,
prices
are
often
preceded
by
a
dollar
sign
and
followed
by
two
decimals:
in
such
a
case
this
series
of
characters
is
the
‘marker
used
by
the
scraping
software
to
identify
and
record
the
price
every
day;
3. It
stores
the
scraped
variables
in
a
database
containing
one
observation
per
product-‐
day.
Along
with
the
price
and
product
characteristics,
retailers
show
an
ID
for
each
product
in
the
page's
code
(typically
not
visible
when
the
page
is
displayed
to
the
customer),
which
allows
us
to
uniquely
identify
each
product
over
time.
1
This methods white paper arose from an on-going series of collaborative research projects conducted by
the United Nations Global Pulse in 2011. Global Pulse is an innovation initiative of the Executive Office of
the UN Secretary-General, which seeks to harness the opportunities in digital data to strengthen evidence-
based decision-making. This research was designed to better understand where digital data can add value
to existing policy analysis, and to contribute to future applications of digital data to global development.
This project was conducted in collaboration with PriceStats and the Billion Prices Project at MIT. For
more information on this project or the other projects in this series, please visit:
http://www.unglobalpulse.org/research.
Technical
Note:
“Bread
Price
Index:
A
Pilot
Case”
1
2. The
retailers
included
in
this
pilot
study
are
the
biggest
supermarkets
in
Argentina,
Brazil,
Chile,
Colombia,
Uruguay,
and
Venezuela.
Prices
were
collected
on
a
daily
basis
between
October
2007
and
July
2011,
with
different
starting
dates
for
each
supermarket.
On
average,
each
supermarket
offers
12,000
products
everyday.
The
collection
process
focused
exclusively
on
bread.
Bread
indices
The
computation
of
price
changes
is
based
on
consecutive
data
points,
i.e.
it
relies
on
data
available
for
days
T
and
T-‐1
(or
T
and
T+1)
with
no
interruption
in
the
series.
The
collection
of
high-‐frequency
price
information
of
every
single
product
sold
in
each
supermarket
produces
a
great
number
of
data
points.
At
the
same
time,
it
also
leads
to
many
gaps
in
individual
price
series.
In
most
cases
these
gaps
occur
when
the
scraping
software
fails
or
when
individual
items
are
temporarily
out
of
stock.
Scraping
failures
are
typically
resolved
in
a
few
days
by
the
PriceStats
scraping
team,
but
seasonal
products
can
create
missing
values
that
last
several
months.
Some
products
are
temporarily
discontinued
from
stores
while
being
substituted
for
another
item—for
example
the
case
of
iPad
1
and
2.
Others
are
strictly
seasonal
(such
as
Christmas
trees);
and
others
may
be
temporarily
out
of
stock
while
the
store
is
being
revamped
or
reorganized.
It
is
also
possible
that
the
observation
is
missing
because
the
product
is
being
reclassified
and
the
scraping
simply
fails.
All
these
reasons
will
produce
a
“missing”
observation
that
is
not
the
reflection
of
a
supply
disruption.
The
standard
treatment
suggested
by
the
literature
is
to
fill
missing
prices
with
the
last
recorded
price
available
for
each
product.
Fortunately,
bread
(and
for
that
matter
all
staple
foods)
is
regularly
sold
in
supermarkets
and
the
gaps
tend
to
be
very
short
or
non-‐existent.
Bread
is
not
seasonal;
it
is
never
“reclassified”,
and
the
scrape
jobs
are
stable
and
rarely
fail.
Therefore,
a
missing
observation
is
very
likely
indicative
of
a
supply
problem,
especially
if
the
missing
observation
lasts
for
a
significant
period
of
time.
This
is
important
because
our
methodology
relies
on
using
these
gaps
and
price
hikes
as
the
detection
mechanism
for
the
supply
disruption.
We
subsequently
calculated
a
daily
inflation
rate
of
bread
for
each
country,
as
follows:
1. We
estimated
the
average
daily
inflation
rate
for
each
of
the
bread
products
in
our
data,
by
country;
2. We
then
constructed
an
index
aggregating
those
daily
changes,
by
country.
We
treated
every
bread
product
equally
and
took
a
simple
geometric
average
of
their
daily
price
inflations;
3. All
the
inflation
indices
were
normalized
to
1
on
the
first
day
data
was
available.
The
results
are
shown
below:
Technical
Note:
“Bread
Price
Index:
A
Pilot
Case”
2
6.
Preliminary
observations
As
Figures
1—6
show,
bread
inflation
follows
very
different
trajectories
in
these
6
countries.
In
Argentina
(Figure
1),
the
price
index
rose
from
1
in
October
2007
to
more
than
2
in
August
2011.
This
represents
an
overall
inflation
of
more
than
100
percent
over
the
period—way
above
that
of
international
wheat
prices.
Venezuela
(Figure
6)
is
the
second
country
in
terms
of
bread
inflation
in
our
sample,
with
a
70
percent
increase
between
April
2008
and
August
2011.
Although
the
overall
inflation
is
close
to
Argentina’s,
it
is
interesting
to
notice
that
prices
in
Venezuela
tend
to
rise
in
steps
or
jumps,
rather
than
more
gradually
as
in
the
Argentinean
case.
In
other
words,
changes
in
bread
price
in
Venezuela
seem
to
happen
concomitantly
across
bread
types,
creating
these
sharp
discontinuities.
These
differences
may
reflect
more
effective
price
controls
in
Venezuela
than
in
Argentina—although
any
greater
effectiveness
would
clearly
be
short
lived.
Beyond
these
differences,
the
rate
of
bread
inflation
in
both
places
is
extremely
large.
The
Argentinean
annualized
rate
is
20.31
percent,
while
in
Venezuela
it
is
17.26
percent.
The
other
four
countries
(Brazil,
Figure
2;
Chile,
Figure
3;
Colombia,
Figure
4,
Uruguay,
Figure
5)
in
the
sample
exhibit
a
very
different
behavior.
In
contrast
to
the
close
to
monotonous
increase
in
prices
observed
in
Argentina
and
Venezuela,
the
price
of
bread
in
these
4
countries
tended
to
fluctuate,
reflecting
somewhat
the
behavior
of
wheat
prices.
The
patterns
of
bread
inflation
in
Argentina
and
Venezuela—way
above
that
of
wheat—suggest
that
larger
inflation
pressures
are
at
play,
while
in
the
other
four
countries
the
factors
affecting
the
price
of
bread
seem
to
be
more
directly
related
to
wheat
prices.
We
were
not
yet
able
to
further
test
this
hypothesis
formally
because
of
the
length
of
the
data
series.
In
summary,
these
differences
in
price
behavior
are
interesting
because
bread
is
mostly
wheat
plus
energy.
Still,
the
stochastic
processes
in
these
countries
seem
very
different.
Some
reasons
could
be
explored,
for
which
longer
data
series
are
needed.
Two
possible
explanations
could
be:
(i)
some
of
these
countries
are
exporters
of
wheat,
while
others
are
importers;
(ii)
and
some
of
these
countries
face
very
high
inflationary
pressures—mostly
from
excessive
monetary
and
fiscal
expansions—that
has
led
them
to
resort
to
price
controls
to
temporarily
mitigate
the
inflationary
pressure
on
staple
products,
thereby
masking
the
underlying
bread
inflation.
Further
research
is
needed
to
identify
which
of
these
effects
may
be
at
play.
What
this
tool
shows
is
the
potential
offered
by
online
marketplaces
to
scrape
prices
in
real
time
and
construct
highly
reactive
inflation
measures
that
complement
other
existing
sources
of
data.
Technical
Note:
“Bread
Price
Index:
A
Pilot
Case”
6