This dataset provides a comprehensive collection of information about all the latest smartphones available in the market as of the current time.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13571604%2Fb608498b1cf7f70b9a22952566197db6%2FScreenshot%202023-08-02%20003740.png?generation=1690961033930490&alt=media" alt="">

The dataset was created by web scraping reputable online sources to gather accurate and up-to-date information about various smartphone models, their specifications, features, and pricing.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total *** billion users (+***** percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach *** billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like the Americas and Asia.

In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.

Smartphone penetration rate still on the rise

Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.

Smartphone end user sales

In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.

Looking to gain insights into the world of mobile phones? Look no further than our comprehensive dataset, which provides detailed specifications and prices for a wide range of smartphones. With data on everything from screen size and camera quality to battery life and processing power, this dataset is a must-have for anyone interested in the mobile phone market. Whether you're a researcher, a tech enthusiast, or just looking to make an informed purchase, our data will give you the information you need to make smart decisions. So why wait? Download our dataset today and start exploring the world of mobile phones like never before! The prices are in PKR. as the dataset is extracted from Pakistan Mobile market website

Dive into the world of customer insights with the Amazon Mobile Phones Reviews Dataset. This dataset provides comprehensive information on mobile phone reviews available on Amazon, helping businesses, researchers, and analysts unlock the power of consumer feedback.

What Does the Dataset Offer?

The Amazon Mobile Phones Reviews Dataset includes:

• Product Details: Mobile phone names, specifications, and brand information.
• Customer Reviews: Detailed texts highlighting user experiences, likes, and dislikes.
• Ratings: Star ratings provided by customers, reflecting overall satisfaction.
• Sentiment Analysis Potential: The dataset is rich in data points, making it ideal for sentiment analysis and trend tracking.

Use Cases of the Dataset

• Product Improvement: Understand customer expectations and identify areas where mobile phones fall short, enabling businesses to optimize their offerings.
• Market Research: Stay ahead in the competitive landscape by analyzing consumer preferences and emerging trends in the mobile phone industry.
• Competitive Benchmarking: Use the dataset to compare reviews across different brands and identify what makes top products stand out.

Why Choose This Dataset?

Whether you’re a tech company looking to improve product features or a researcher analyzing market trends, the Amazon product review dataset for mobile phones provides the necessary data for meaningful insights. This structured dataset, often available in formats like CSV, makes it easy to integrate with analytics tools for seamless data exploration.

Additional Value for Researchers

The Amazon Mobile Phones Reviews Dataset doesn’t just focus on reviews. It helps researchers uncover sentiment patterns, understand consumer language, and even predict future buying behaviors based on historical data.

For a more detailed analysis, combine this dataset with our broader Amazon product review dataset, which includes reviews across categories for a holistic market view.

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

📱 Global Mobile Reviews Dataset (2025 Edition)

🌍 Research-Based, Web-Scraped Global Review Collection

This dataset presents a curated collection of over 50,000 mobile phone reviews gathered through web scraping, market analysis, and content aggregation from multiple e-commerce and tech review platforms.
It covers eight countries and includes detailed user opinions, ratings, sentiment polarity, and pricing data across leading smartphone brands.

Each record captures customer experience holistically — spanning demographics, verified purchase details, multi-aspect ratings, and currency-adjusted pricing — making this dataset a powerful asset for research, NLP, and analytics.

🎯 Ideal For

• 🧠 Sentiment Analysis & NLP Modeling
• 💬 Text Classification & Review Mining
• 💰 Market Research & Pricing Analytics
• 📊 Consumer Behavior Studies
• 🤖 AI Model Training & Data Science Projects

🧩 Key Highlights

• 50,000+ mobile reviews scraped from top global sources
• Reviews across 8 major countries and multiple platforms
• Demographic data (customer name, age, location)
• Verified purchase flags for reliability
• Detailed product-level sub-ratings
• Pricing in both USD and local currencies
• Multilingual data support and country-specific sentiment distribution
• Professionally cleaned and normalized for research applications

📦 Brands Covered

🌐 Countries Represented

🧾 Example Record

📈 Research & Analytical Applications

• Sentiment Mining: Detect sentiment polarity in real-world review text
• Cross-Country Analysis: Compare satisfaction trends by region and currency
• Price–Rating Studies: Explore pricing elasticity and value perception
• Demographic Insights: Link sentiment to user age and verified purchase behavior
• Market Comparison: Understand brand trust and perception across regions

🧪 Data Collection & Research Approach

This dataset was compiled through an extensive research process combining web scraping, content aggregation, and analytical validation from multiple open and public review sources including:

• E-commerce platforms (e.g., Amazon, Flipkart, BestBuy, eBay)
• Tech review forums and discussion threads
• Mobile product feedback portals and blogs

Data was then: - Filtered for quality and consistency
- Mapped with real-world pricing and currency exchange rates
- Manually validated for sentiment balance and linguistic variation

⚠️ Note: All data is collected from publicly available review information and anonymized for research and educational use only.
No private or personally identifiable data was used or retained.

🧩 Research Summary

The dataset provides a multi-dimensional representation of the modern mobile ecosystem — integrating global pricing, sentiment trends, and demographic diversity to aid data scientists, researchers, and AI practitioners in building better understanding of customer perspectives.

China is leading the ranking by number of smartphone users, recording ****** million users. Following closely behind is India with ****** million users, while Seychelles is trailing the ranking with **** million users, resulting in a difference of ****** million users to the ranking leader, China. Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

As of June 7, 2015, Sierra Leone had reported more than 12,900 cases of Ebola Virus Disease (EVD), and over 3,900 deaths since the outbreak began. The Government of Sierra Leone, with support from the World Bank Group, has been conducting mobile phone surveys with the aim of capturing the key socio-economic effects of the virus. Three rounds of data collection have been conducted, in November 2014, January-February 2015, and May 2015. The survey was given to household heads for whom cell phone numbers were recorded during the nationally representative Labor Force Survey conducted in July and August 2014. Overall, 66 percent of the 4,199 households sampled in that survey had cell phones, although this coverage was uneven across the country, with higher levels in urban areas (82 percent) than rural areas (43 percent). Of those with cell phones, 51 percent were surveyed in all three rounds, and 79 percent were reached in at least one round. The main focus of the data collection was to capture impacts of EVD on labor market indicators, agricultural production, food security, migration, and utilization of non-Ebola essential health services.

https://1000logos.net/wp-content/uploads/2018/09/35-Best-Cell-Phone-Company-Brands-and-logos.png" alt="Cell Phone Company Brands">

Hey there, phone fans! This dataset dives into the hottest-selling mobile phones, giving you the lowdown on the Top 120 models that ruled the market. We've got data on:

Manufacturer: The big names behind the phones, like Apple, Samsung, and whoever else is cooking up the latest tech (you know, these Tony Starks). Model: The specific phone name, because let's face it, not all iPhones are created equal (looking at you, iPhone 3G). Form Factor: We're talking about the phone's overall shape and style, whether it's a classic Bar, a sleek Touchscreen, or Keyboard bar phone. Year: The year these phones hit the shelves, so you can see how trends changed over time. Units Sold (Million): This is the big one - how many millions of people actually bought these phones?

So, if you're curious about the cell phone hall of fame, or just want to see how phone technology has evolved over the years, this dataset is your one-stop shop.

This competition involves advertisement data provided by BuzzCity Pte. Ltd. BuzzCity is a global mobile advertising network that has millions of consumers around the world on mobile phones and devices. In Q1 2012, over 45 billion ad banners were delivered across the BuzzCity network consisting of more than 10,000 publisher sites which reach an average of over 300 million unique users per month. The number of smartphones active on the network has also grown significantly. Smartphones now account for more than 32% phones that are served advertisements across the BuzzCity network.

TechCorner Mobile Purchase & Engagement Data (2024-2025)

Context

TechCorner Mobile Sales & Customer Insights is a real-world dataset capturing 10 months of mobile phone sales transactions from a retail shop in Bangladesh. This dataset was designed to analyze customer location, buying behavior, and the impact of Facebook marketing efforts.

The primary goal was to identify whether customers are from the local area (Rangamati Sadar, Inside Rangamati) or completely outside Rangamati. Since TechCorner operates a Facebook page, the dataset also includes insights into whether Facebook marketing is effectively reaching potential buyers.

Additionally, the dataset helps in determining: ✔ How many customers are new vs. returning buyers ✔ If customers are followers of the shop’s Facebook page ✔ Whether a customer was recommended by an existing buyer

This dataset is valuable for:

Retail sales analysis to understand product demand fluctuations.

Marketing impact measurement (Facebook engagement vs. actual purchase behavior).

Customer segmentation (local vs. non-local buyers, social media influence, word-of-mouth impact).

Sales trend analysis based on preferred phone models and price ranges.

With a realistic, non-uniform distribution of daily sales and some intentional missing values, this dataset reflects actual retail business conditions rather than artificially smooth AI-generated data.

Marketing & Customer Queries

Does he/she Come from Facebook Page? → Whether the customer came from a Facebook page (Yes/No). Used to analyze Facebook marketing reach.

Does he/she Followed Our Page? → Whether the customer is already a follower of the shop’s Facebook page (Yes/No). Helps measure brand loyalty and organic engagement.

Did he/she buy any mobile before? → Whether the customer is a repeat buyer (Yes/No). Determines the percentage of returning customers.

Did he/she hear of our shop before? → Whether the customer knew about the shop before purchasing (Yes/No). Identifies the impact of referrals or previous marketing efforts.

Was this customer recommended by an old customer? → Whether an existing customer referred them to the shop (Yes/No). Helps evaluate the effectiveness of word-of-mouth marketing.

Acknowledgements

This dataset is derived from real-world mobile sales transactions recorded at TechCorner, a retail shop in Bangladesh. It accurately reflects customer purchasing behavior, pricing trends, and the effectiveness of Facebook marketing in driving sales. Special appreciation to TechCorner for providing comprehensive insights into daily sales patterns, customer demographics, and market dynamics.

This dataset can be used for:

📊 Predictive modeling of sales trends based on customer demographics and marketing channels. 📈 Marketing effectiveness analysis (impact of Facebook promotions vs. organic sales). 🔍 Clustering customers based on purchasing habits (new vs. returning buyers, Facebook users vs. walk-ins). 📌 Understanding demand for different smartphone brands in a local retail market. 🚀 Analyzing how word-of-mouth recommendations influence new customer acquisition.

💡 Can you build a model to predict if a customer is likely to return? 💬 How effective is Facebook in driving actual sales compared to walk-ins? 🔍 Can we cluster customers based on behavior and brand preferences?

In the third quarter of 2025, Samsung shipped approximately **** million smartphones, marking an increase compared to both the previous quarter and the same period in the prior year. The company’s strong sales performance consistently positions Samsung as the world’s leading smartphone vendor, ahead of Apple. Samsung smartphone sales – how many phones does Samsung sell? Global smartphone sales reached over *** billion units during 2024. While the global smartphone market is led by Samsung and Apple, Xiaomi has gained ground following the decline of Huawei. Together, these three companies hold more than ** percent of the global smartphone market share.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Individuals who own a mobile telephone (%)





  Dataset Description

This dataset provides country-level data for the indicator "5.b.1 Individuals who own a mobile telephone (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is presented in a wide format, where each row… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/mobile-phone-ownership-for-african-countries.

Dataset Card: Synthetic Mobile Network Performance

Dataset Description This dataset contains synthetically generated mobile signal measurements designed to mirror real-world data in the UK. The data represents geolocated signal quality metrics from mobile devices, capturing a range of environmental and temporal conditions over several months in 2025. All data has been anonymized, aggregated, and processed to protect user privacy. The synthetic dataset has undergone pre-processing… See the full description on the dataset page: https://huggingface.co/datasets/joefee/cell-service-data.

Global B2B Mobile Phone Number Database | 100M+ Verified Contacts | 95% Accuracy Forager.ai provides the world’s most reliable mobile phone number data for businesses that refuse to compromise on quality. With 100 million+ professionally verified mobile numbers refreshed every 3 weeks, our database ensures 95% accuracy – so your teams never waste time on dead-end leads.

Why Our Data Wins ✅ Accuracy You Can Trust 95% of mobile numbers are verified against live carrier records and tied to current job roles. Say goodbye to “disconnected number” voicemails.

✅ Depth Beyond Digits Each contact includes 150+ data points:

Direct mobile numbers

Current job title, company, and department

Full career history + education background

Location data + LinkedIn profiles

Company size, industry, and revenue

✅ Freshness Guaranteed Bi-weekly updates combat job-hopping and role changes – critical for sales teams targeting decision-makers.

✅ Ethically Sourced & Compliant First-party collected data with full GDPR/CCPA compliance.

Who Uses This Data?

Sales Teams: Cold-call C-suite prospects with verified mobile numbers.

Marketers: Run hyper-personalized SMS/WhatsApp campaigns.

Recruiters: Source passive candidates with up-to-date contact intel.

Data Vendors: License premium datasets to enhance your product.

Tech Platforms: Power your SaaS tools via API with enterprise-grade B2B data.

Flexible Delivery, Instant Results

API (REST): Real-time integration for CRMs, dialers, or marketing stacks

CSV/JSON: Campaign-ready files.

PostgreSQL: Custom databases for large-scale enrichment

Compliance: Full audit trails + opt-out management

Why Forager.ai? → Proven ROI: Clients see 62% higher connect rates vs. industry averages (request case studies). → No Guesswork: Test-drive free samples before committing. → Scalable Pricing: Pay per record, license datasets, or get unlimited API access.

B2B Mobile Phone Data | Verified Contact Database | Sales Prospecting Lists | CRM Enrichment | Recruitment Phone Numbers | Marketing Automation | Phone Number Datasets | GDPR-Compliant Leads | Direct Dial Contacts | Decision-Maker Data

Need Proof? Contact us to see why Fortune 500 companies and startups alike trust Forager.ai for mission-critical outreach.

Abstract

The Global Findex 2025 reveals how mobile technology is equipping more adults around the world to own and use financial accounts to save formally, access credit, make and receive digital payments, and pursue opportunities. Including the inaugural Global Findex Digital Connectivity Tracker, this fifth edition of Global Findex presents new insights on the interactions among mobile phone ownership, internet use, and financial inclusion.

The Global Findex is the world’s most comprehensive database on digital and financial inclusion. It is also the only global source of comparable demand-side data, allowing cross-country analysis of how adults access and use mobile phones, the internet, and financial accounts to reach digital information and resources, save, borrow, make payments, and manage their financial health. Data for the Global Findex 2025 were collected from nationally representative surveys of about 145,000 adults in 141 economies. The latest edition follows the 2011, 2014, 2017, and 2021 editions and includes new series measuring mobile phone ownership and internet use, digital safety, and frequency of transactions using financial services.

The Global Findex 2025 is an indispensable resource for policy makers in the fields of digital connectivity and financial inclusion, as well as for practitioners, researchers, and development professionals.

Geographic coverage

National Coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most low- and middle-income economies, Global Findex data were collected through face-to-face interviews. In these economies, an area frame design was used for interviewing. In most high-income economies, telephone surveys were used. In 2024, face-to-face interviews were again conducted in 22 economies after phone-based surveys had been employed in 2021 as a result of mobility restrictions related to COVID-19. In addition, an abridged form of the questionnaire was administered by phone to survey participants in Algeria, China, the Islamic Republic of Iran, Libya, Mauritius, and Ukraine because of economy-specific restrictions. In just one economy, Singapore, did the interviewing mode change from face to face in 2021 to phone based in 2024.

In economies in which face-to-face surveys were conducted, the first stage of sampling was the identification of primary sampling units. These units were then stratified by population size, geography, or both and clustered through one or more stages of sampling. Where population information was available, sample selection was based on probabilities proportional to population size; otherwise, simple random sampling was used. Random route procedures were used to select sampled households. Unless an outright refusal occurred, interviewers made up to three attempts to survey each sampled household. To increase the probability of contact and completion, attempts were made at different times of the day and, where possible, on different days. If an interview could not be completed at a household that was initially part of the sample, a simple substitution method was used to select a replacement household for inclusion.

Respondents were randomly selected within sampled households. Each eligible household member (that is, all those ages 15 or older) was listed, and a handheld survey device randomly selected the household member to be interviewed. For paper surveys, the Kish grid method was used to select the respondent. In economies in which cultural restrictions dictated gender matching, respondents were randomly selected from among all eligible adults of the interviewer’s gender.

In economies in which Global Findex surveys have traditionally been phone based, respondent selection followed the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies in which mobile phone and landline penetration is high, a dual sampling frame was used.

The same procedure for respondent selection was applied to economies in which phone-based interviews were being conducted for the first time. Dual-frame (landline and mobile phone) random digit dialing was used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digit dialing was used in economies with limited or no landline presence (less than 20 percent). For landline respondents in economies in which mobile phone or landline penetration is 80 percent or higher, respondents were selected randomly by using either the next-birthday method or the household enumeration method, which involves listing all eligible household members and randomly selecting one to participate. For mobile phone respondents in these economies or in economies in which mobile phone or landline penetration is less than 80 percent, no further selection was performed. At least three attempts were made to reach the randomly selected person in each household, spread over different days and times of day.

Research instrument

The English version of the questionnaire is provided for download.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in: Klapper, Leora, Dorothe Singer, Laura Starita, and Alexandra Norris. 2025. The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9.

General This dataset contains real-world measurement data for data-based modeling of a differential-drive robot. The dataset is especially tailored for data-based modeling using Extended Dynamic Mode Decomposition (EDMD) for control-affine systems. It contains predecessor and successor pose data of the wheeled mobile robot (i.e., its position in the plane of an inertial frame of reference as well as its orientation w.r.t. the x-axis) when constant control inputs are applied to the robot, which is done for two different realizations of the differential-drive robot. In the first realization, a desired constant translational and rotational velocity is sent to the robot (kinematic realization), while in the second realization, the robot's control actions are desired translational and rotational accelerations (second-order robot). A total of three different datasets are provided, two for the kinematic mobile robot and one for the second-order robot. The second, smaller dataset for the kinematic mobile robot shall indicate the data-efficiency of the EDMD approach. For each of the three datasets, three raw data files with predecessor pose data (X_i.dat) and three raw data files of successor pose data (Y_i.dat) are provided, where the number i from the set {0,1,2} corresponds to the predecessor and successor data and indicates the applied control basis u_i. In addition to zero control (i=0), the EDMD approach requires data for the differential-drive mobile robot for two linearly independent constant control vectors over a predefined sampling time. Further information about the chosen control bases and the sampling times can be found in the readme files associated with the dataset directories. Notably, the dataset for the second-order robot realization additionally contains approximative velocity data as well as the exact times at which the pose measurement of the external motion capture system has been received. This additional time information is provided to facilitate the smoothing of the velocity data. File Setup The following files and directories are provided. kinematic_dataset1 This directory contains raw data files containing the predecessor and successor pose data for the first sampling of the kinematic mobile robot. Each line consists of [x-position, y-position, orientation]. The chosen constant control vectors read u0=[0 m/s, 0 rad/s], u1=[0.2 m/s, 0.6 rad/s], and u2=[0.2 m/s, -0.4 rad/s] and the sampling time is 0.1 seconds. kinematic_dataset2 This directory contains raw data files containing the predecessor and successor pose data for the second sampling of the kinematic mobile robot. Each line consists of [x-position, y-position, orientation]. The chosen constant control vectors read u0=[0 m/s, 0 rad/s], u1=[0.2 m/s, 0.6 rad/s], and u2=[0.2 m/s, -0.4 rad/s] and the sampling time is 0.05 seconds. secondorder_dataset This directory contains raw data files containing the predecessor and successor pose data for the sampling of the second-order mobile robot. Each line consists of [x-position, y-position, orientation, v (translational velocity), omega (angular velocity)]. The chosen constant control vectors read u0=[0 m/^2s, 0 rad/s^2], u1=[0.2 m/s^2, 0 rad/s^2], and u2=[0 m/s^2, 0.5 rad/s^2] and the sampling time is 0.05s. Note that additional time instances of the measured data are provided in the respective first column. This might facilitate the necessary smoothing of the translational and angular velocities. ProcessVisualizeKinematic.m This is a minimal MATLAB file which can be used to process and visualize the recorded data for the kinematic mobile robot. Further information can be found in the comments of the file. ProcessVisualizeSecondorder.m This is a minimal MATLAB file which can be used to process and visualize the recorded data for the second-order mobile robot. Further information can be found in the comments of the file.

The rapid and massive dissemination of mobile phones in the developing world is creating new opportunities for the discipline of survey research. The World Bank is interested in leveraging mobile phone technology as a means of direct communication with poor households in the developing world in order to gather rapid feedback on the impact of economic crises and other events on the economy of such households. The World Bank commissioned Gallup to conduct the Listening to LAC (L2L) pilot program, a research project aimed at testing the feasibility of mobile phone technology as a way of data collection for conducting quick turnaround, self-administered, longitudinal surveys among households in Peru and Honduras. The project used face-to-face interviews as its benchmark, and included Short Message Service (SMS), Interactive Voice Response (IVR) and Computer Assisted Telephone Interviews (CATI) as test methods of data collection. The pilot was designed in a way that allowed testing the response rates and the quality of data, while also providing information on the cost of collecting data using mobile phones. Researchers also evaluated if providing incentives affected panel attrition rates. The random stratified multistage sampling technique was used to select a nationally representative sample of 1,500 households. During the initial face-to-face interviews, researchers gathered information on the socio-economic characteristics of households and recruited participants for follow-up research. Questions wording was the same in all modes of data collection. In Peru, households were randomly assigned to a communication mode (SMS, IVR, CATI), which stayed constant for all rounds (waves) of the survey.

Abstract

The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform policy. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets Kenyan nationals and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques.

The data set contains information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority are randomly selected. The samples cover urban and rural areas and are designed to be representative of the population of Kenya using cell phones. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing.

The data is uploaded in three files. The first is the hh file, which contains household level information. The ‘hhid’, uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the ‘adult_id’. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the ‘child_id’.

The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 4,061 Kenyan households Wave 2: July 16 to September 18, 2020; 4,492 Kenyan households Wave 3: September 28 to December 2, 2020; 4,979 Kenyan households Wave 4: January 15 to March 25, 2021; 4,892 Kenyan households Wave 5: March 29 to June 13, 2021; 5,854 Kenyan households Wave 6: July 14 to November 3, 2021; 5,765 Kenyan households Wave 7: November 15, 2021, to March 31, 2022; 5,633 Kenyan households Wave 8: May 31 to July 8, 2022: 4,550 Kenyan households

The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Household, Individual

Sampling procedure

The COVID-19 RRPS with Kenyan households has two samples. The first sample consists of households that were part of the 2015/16 KIHBS CAPI pilot and provided a phone number. The 2015/16 KIHBS CAPI pilot is representative at the national level stratified by county and place of residence (urban and rural areas). At least one valid phone number was obtained for 9,007 households and all of them were included in the COVID-19 RRPS sample. The target respondent was the primary male or female household member from the 2015/16 KIHBS CAPI pilot. The second sample consists of households selected using the Random Digit Dialing method. A list of random mobile phone numbers was created using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The initial sampling frame therefore consisted of 92,999,970 randomly ordered phone numbers assigned to three networks: Safaricom, Airtel and Telkom. An introductory text message was sent to 5,000 randomly selected numbers to determine if numbers were in operation. Out of these, 4,075 were found to be active and formed the final sampling frame. There was no stratification and individuals that were called were asked about the households they live in. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was administered in English and is provided as a resource in pdf format. Additionally, questionnaires for each wave are also provided in Excel format coded for SCTO. The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/