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.

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.

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.

📱 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.

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

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

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).

The mobile_addiction_data.csv file is a synthetic yet realistic dataset designed to model global patterns of mobile phone usage and behavioral addiction. It includes data for 3,000 individuals across 10 countries, capturing 35 variables per user. These variables encompass a wide range of information, including demographics (such as age, gender, income, and education), daily smartphone behaviors (like screen time, app usage, phone unlocks), lifestyle habits (sleep duration, physical activity), and self-reported mental health indicators (stress, anxiety, depression). The dataset also includes user-reported addiction levels, the presence of screen-time control tools, and indicators of tech engagement like data usage and push notifications. This dataset is ideal for exploratory data analysis, behavioral research, and building machine learning models related to digital addiction, mental health, and mobile technology usage patterns.

Success.ai’s Phone Number Data offers direct access to over 50 million verified phone numbers for professionals worldwide, extracted from our expansive collection of 170 million profiles. This robust dataset includes work emails and key decision-maker profiles, making it an essential resource for companies aiming to enhance their communication strategies and outreach efficiency. Whether you're launching targeted marketing campaigns, setting up sales calls, or conducting market research, our phone number data ensures you're connected to the right professionals at the right time.

Why Choose Success.ai’s Phone Number Data?

Direct Communication: Reach out directly to professionals with verified phone numbers and work emails, ensuring your message gets to the right person without delay. Global Coverage: Our data spans across continents, providing phone numbers for professionals in North America, Europe, APAC, and emerging markets. Continuously Updated: We regularly refresh our dataset to maintain accuracy and relevance, reflecting changes like promotions, company moves, or industry shifts. Comprehensive Data Points:

Verified Phone Numbers: Direct lines and mobile numbers of professionals across various industries. Work Emails: Reliable email addresses to complement phone communications. Professional Profiles: Decision-makers’ profiles including job titles, company details, and industry information. Flexible Delivery and Integration: Success.ai offers this dataset in various formats suitable for seamless integration into your CRM or sales platform. Whether you prefer API access for real-time data retrieval or static files for periodic updates, we tailor the delivery to meet your operational needs.

Competitive Pricing with Best Price Guarantee: We provide this essential data at the most competitive prices in the industry, ensuring you receive the best value for your investment. Our best price guarantee means you can trust that you are getting the highest quality data at the lowest possible cost.

Targeted Applications for Phone Number Data:

Sales and Telemarketing: Enhance your telemarketing campaigns by reaching out directly to potential customers, bypassing gatekeepers. Market Research: Conduct surveys and research directly with industry professionals to gather insights that can shape your business strategy. Event Promotion: Invite prospects to webinars, conferences, and seminars directly through personal calls or SMS. Customer Support: Improve customer service by integrating accurate contact information into your support systems. Quality Assurance and Compliance:

Data Accuracy: Our data is verified for accuracy to ensure over 99% deliverability rates. Compliance: Fully compliant with GDPR and other international data protection regulations, allowing you to use the data with confidence globally. Customization and Support:

Tailored Data Solutions: Customize the data according to geographic, industry-specific, or job role filters to match your unique business needs. Dedicated Support: Our team is on hand to assist with data integration, usage, and any questions you may have. Start with Success.ai Today: Engage with Success.ai to leverage our Phone Number Data and connect with global professionals effectively. Schedule a consultation or request a sample through our dedicated client portal and begin transforming your outreach and communication strategies today.

Remember, with Success.ai, you don’t just buy data; you invest in a partnership that grows with your business needs, backed by our commitment to quality and affordability.

"Mobile phone usage is a global phenomenon, with billions of people worldwide using smartphones for communication, entertainment, and information. Average daily screen time varies across countries, with some nations spending over 5 hours per day on their devices."

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.

Unlock the power of direct engagement with our comprehensive dataset of 34 million verified global phone numbers. This dataset is curated for businesses and data-driven teams looking to enhance customer acquisition, power targeted outreach, enrich CRM records, and fuel B2C growth at scale.

Whether you're running SMS marketing campaigns, telemarketing, building a mobile app user base, or performing identity validation, this dataset offers a scalable, compliant foundation to reach real users worldwide.

🔍 What’s Included: ✅ 34,000,000+ mobile and landline numbers

🌍 Global coverage, including high volumes from the US, UK, Canada, Europe, and emerging markets

🧹 Clean, structured format (CSV/JSON/SQL) for easy integration

📱 Includes carrier, country code, line type, and location data (where available)

🧠 Ideal Use Cases: B2C & D2C marketing campaigns

SMS and voice call outreach

Lead generation & prospecting

Mobile app user acquisition

Identity verification & enrichment

Market analysis and segmentation

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales. 2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Ethiopia Socioeconomic Survey (ESS) 2018-2019 and Ethiopia COVID-19 High Frequency Phone Survey of Households (HFPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

1. HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.
2. HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.
3. HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.
4. target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

1. YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.
2. H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59.
   The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License
Creative Commons Attribution 4.0 International.

Related datasets

• Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612
• Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

This dataset contains detailed specifications and official launch prices of various mobile phone models from different companies. It provides insights into smartphone hardware, pricing trends, and brand competitiveness across multiple countries. The dataset includes key features such as RAM, camera specifications, battery capacity, processor details, and screen size.

One important aspect of this dataset is the pricing information. The recorded prices represent the official launch prices of the mobile phones at the time they were first introduced in the market. Prices vary based on the country and the launch period, meaning older models reflect their original launch prices, while newer models include their most recent launch prices. This makes the dataset valuable for studying price trends over time and comparing smartphone affordability across different regions.

Features:

• Company Name: The brand or manufacturer of the mobile phone.
• Model Name: The specific model of the smartphone.
• Mobile Weight: The weight of the mobile phone (in grams).
• RAM: The amount of Random Access Memory (RAM) in the device (in GB).
• Front Camera: The resolution of the front (selfie) camera (in MP).
• Back Camera: The resolution of the primary rear camera (in MP).
• Processor: The chipset or processor used in the device.
• Battery Capacity: The battery size of the smartphone (in mAh).
• Screen Size: The display size of the smartphone (in inches).
• Launched Price: (Pakistan, India, China, USA, Dubai): The official launch price of the mobile in the respective country at the time of its release. Prices vary based on the year the mobile was launched.
• Launched Year: The year the mobile phone was officially launched.

This statistic shows a ranking of the estimated worldwide number of mobile cellular subscriptions per 100 inhabitants in 2020, differentiated by country. Included are only subscriptions that also allow voice communication over the Public Switched Telephone Network (PSTN). Pure data and M2M (machine-to-machine) connections are excluded.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 more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

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.

acquired from six different smartphones under diverse real-world conditions.## 📱 Devices Used

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Malawi Integrated Household Panel Survey (IHPS) 2019 and Malawi High-Frequency Phone Survey on COVID-19 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Nigeria General Household Survey, Panel (GHS-Panel) 2018-2019 and Nigeria COVID-19 National Longitudinal Phone Survey (COVID-19 NLPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.