Smart phone price index (CPPI) by North American Product Classification System (NAPCS). The table includes annual data for the most recent reference period and the last four periods. Data are available from January 2015. The base period for the index is (2015=100).

Of the top 510 mobile gaming apps worldwide, *** collected user data. As of April 2023, Scrabble GO - New Word Game was the most user data-hungry mobile gaming app with a Data Hunger Index score of ****. The app, rated suitable for players aged nine years and above, collected ** different data points and shared the data with third-party advertisers. Tarbi3ah Baloot was ranked second with a Data Hunger Index rating of **** percent.

This data set is scraped from the phoneDB website.

This dataset is for experimental, study and research purpose.

Mobile data usage is set to explode across the globe, with forecasts expecting monthly data traffic to **************** between 2024 and 2029. This explosion in data use will be driven by a range of shifting consumption habits, not least the adoption of data intensive artificial intelligence and cloud applications.

The research & publication dataset of Mobile Payment, which was indexed by Scopus between 1997 to 2021. The dataset contains 2,180 documents data: authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, correspondence address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.

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.

India Consumer Price Index (CPI): Miscellaneous: Mobile Handset data was reported at 104.200 2012=100 in Oct 2018. This records a decrease from the previous number of 104.700 2012=100 for Sep 2018. India Consumer Price Index (CPI): Miscellaneous: Mobile Handset data is updated monthly, averaging 102.550 2012=100 from Jan 2014 (Median) to Oct 2018, with 58 observations. The data reached an all-time high of 105.100 2012=100 in Mar 2018 and a record low of 101.700 2012=100 in Jan 2016. India Consumer Price Index (CPI): Miscellaneous: Mobile Handset data remains active status in CEIC and is reported by Central Statistics Office. The data is categorized under India Premium Database’s Inflation – Table IN.IA017: Consumer Price Index: 2012=100: Miscellaneous.

Italy: Mobile phone subscribers, in millions: The latest value from 2023 is 78.46 million subscribers, a decline from 78.5 million subscribers in 2022. In comparison, the world average is 54.59 million subscribers, based on data from 156 countries. Historically, the average for Italy from 1960 to 2023 is 37.96 million subscribers. The minimum value, 0 million subscribers, was reached in 1960 while the maximum of 97.19 million subscribers was recorded in 2012.

United States New York Stock Exchange: Index: Dow Jones US Mobile Telecommunications Index data was reported at 422.940 NA in Apr 2025. This records a decrease from the previous number of 443.750 NA for Mar 2025. United States New York Stock Exchange: Index: Dow Jones US Mobile Telecommunications Index data is updated monthly, averaging 324.005 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 443.750 NA in Mar 2025 and a record low of 128.160 NA in Jan 2012. United States New York Stock Exchange: Index: Dow Jones US Mobile Telecommunications Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Dow Jones: Monthly.

Nepal Number Dataset is an index of Nepal contact numbers that are 100% accurate and valid. We always double-check to make sure that this record is correct. So, when you use this number library, you can trust that Nepal contact numbers work. And if you ever get an incorrect number, you get a replacement guarantee. This means that if a phone number doesn’t work, they will give you a new number at no extra cost. Moreover, the Nepal Number Dataset is very reliable. Use it with confidence, knowing that you are following the right steps for a smooth, successful outreach effort. The people on the list have agreed to share their mobile numbers. So, you are not breaking any rules when you use this database. And, getting the customer’s consent makes contacting them more welcoming and effective. Nepal phone data is detailed information about Nepal contact numbers. Trusted sources collect this phone data to ensure its reliability. The sources from which this library comes may include websites, government records, and phone service providers. We verify each source, and you can check the URLs where we got the data. This ensures that the mobile data is accurate and reliable. Also, Nepal phone data providers offer 24/7 support. Also, Nepal phone data follows an opt-in policy. This means that people can share their numbers. This is good because it ensures that people know they are using their information. You won’t get in trouble for using contact details without permission. List to Data helps you to find Nepal contact data for your business. Nepal phone number list is a collection of phone numbers of people living in Nepal. You can sort these contact numbers by gender, age, and relationship status. This means that you can only see the amount that matches your needs. For example, if you want to contact young and single people, you can do so. Also, this contact list follows GDPR rules. Also, the Nepal phone number list helps you remove invalid data. Sometimes, contact numbers may change or stop working. This list checks this and removes those numbers, so you don’t waste time calling people who don’t answer. Using the Nepal phone number list, you reach the right people. Therefore, you get accurate, current information.

All-Transactions House Price Index for Mobile, AL (MSA) was 313.36000 Index 1995 Q1=100 in April of 2025, according to the United States Federal Reserve. Historically, All-Transactions House Price Index for Mobile, AL (MSA) reached a record high of 313.36000 in April of 2025 and a record low of 66.92000 in January of 1985. Trading Economics provides the current actual value, an historical data chart and related indicators for All-Transactions House Price Index for Mobile, AL (MSA) - last updated from the United States Federal Reserve on November of 2025.

Abstract

In Chad, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey will be a sub-sample of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) in Chad.

This has the advantage of conducting cost effectively welfare analysis without collecting new consumption data. The 30 minutes questionnaires covered many modules, including knowledge, behavior, access to services, food security, employment, safety nets, shocks, coping, etc. Data collection is planned for four months (four rounds) and the questionnaire is designed with core modules and rotating modules.

The main objectives of the survey are to: • Identify type of households directly or indirectly affected by the pandemic; • Identify the main channels by which the pandemic affects households; • Provide relevant data on income and socioeconomic indicators to assess the welfare impact of the pandemic.

Geographic coverage

National coverage, including Ndjamena (Capital city), other urban and rural

Analysis unit

• Households
• Individuals

Universe

The survey covered only households of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (ECOSIT 4) which excluded populations in prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Chad COVID-19 impact monitoring survey is a high frequency Computer Assisted Telephone Interview (CATI). The survey’s sample was drawn from the Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) which was conducted in 2018-2019. ECOSIT 4 is a survey with a sample size of 7,493 household’s representative at national, regional and by urban/rural. During the survey, each household was asked to provide a phone number of at least one member or a non-household member (e.g. friends or neighbors) so that they can be contacted for follow-up questions. The sampling of the high frequency survey aimed at having representative estimates by national and area of residence: Ndjamena (capital city), other urban and rural area. The minimum sample size was 2,000 for which 1,748 households (87.5%) were successfully interviewed at the national level. To account for non-response and attrition and given that this survey was the first experience of INSEED, 2,833households were initially selected, among them 1,832 households have been reached. The 1,748 households represent the final sample and will be contacted for the next three rounds of the survey.

Sampling deviation

None

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire is in French and has been administrated in French and local languages. The length of an interview varies between 20 and 30 minutes. The questionnaires consisted of the following sections: 1- Household Roster 2- Knowledge of COVID-19 3- Behavior and Social Distancing 4- Access to Basic Services 5- Employment and Income 6- Prices and Food Security 7- Other Impacts of COVID-19 8- Income Loss 9- Coping/Shocks 10- Social Safety Nets 11- Fragility 12. Gender based Violence (for the fourth wave) 13. Vaccine (for the fourth wave)

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the INSEED with the support of the WB team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Response rate

The minimum sample expected is 2,000 households covering Ndjamena, other urban and rural areas. Overall, the survey has been completed for 1,748 households that is about 87.5 % of the expected minimal sample size at the national level. This provide reliable estimates at national and area of residence level.

In 2025, the monthly mobile data traffic consumed per smartphone in India, Nepal, and Bhutan reached almost 35 gigabytes (GB), the highest figure of any region. This figure is forecast to almost double by the end of the decade, driven by the adoption of data intensive activities such as 4K streaming and mobile gaming.

Thailand Market Concentration: Mobile data was reported at 3,373.000 Index in Sep 2018. This records an increase from the previous number of 3,363.000 Index for Jun 2018. Thailand Market Concentration: Mobile data is updated quarterly, averaging 3,461.000 Index from Mar 2002 (Median) to Sep 2018, with 67 observations. The data reached an all-time high of 5,382.000 Index in Mar 2002 and a record low of 3,336.000 Index in Mar 2012. Thailand Market Concentration: Mobile data remains active status in CEIC and is reported by Office of The National Broadcasting and Telecommunications Commission. The data is categorized under Global Database’s Thailand – Table TH.TB006: Telecommunication Statistics: Office of The National Broadcasting and Telecommunications Commission .

About this dataset

Context

The dataset illustrates the median household income in Mobile, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Mobile decreased by $1,596 (3.19%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 4 years and declined for 7 years.

https://i.neilsberg.com/ch/mobile-al-median-household-income-trend.jpeg" alt="Mobile, AL median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mobile median household income. You can refer the same here

Smart phone price index (CPPI) by North American Product Classification System (NAPCS). The table includes annual data for the most recent reference period and the last four periods. Data are available from January 2015. The base period for the index is (2015=100).

The file combines data from various sources that provide insight into indicators that can be used to characterize broadband connectivity in Montenegro, Malaysia, and Thailand. Indicators for changes in the number and annual growth of the rural population, indicators showing Internet penetration and use of social networks, number of fixed broadband subscriptions, number of secured Internet servers, and number of fixed and mobile phone subscribers are covered. In addition, some other data that can be used to characterize the DESI index in the broader context of digitization (for example, infrastructure, affordability, 2G-5G coverage, Internet connection speeds, etc.) are given.

We introduce the Stanford Streaming MAR dataset. The dataset contains 23 different objects of interest, divided to four categories: Books, CD covers, DVD covers and Common Objects. We first record one video for each object where the object is in a static position while the camera is moving. These videos are recorded with a hand-held mobile phone with different amounts of camera motion, glare, blur, zoom, rotation and perspective changes. Each video is 100 frames long, recorded at 30 fps with resolution 640 x 480. For each video, we provide a clean database image (no background noise) for the corresponding object of interest. We also provide 5 more videos for moving objects recorded with a moving camera. These videos help to study the effect of background clutter when there is a relative motion between the object and the background. Finally, we record 4 videos that contain multiple objects from the dataset. Each video is 200 frames long and contains 3 objects of interest where the camera captures them one after the other. We provide the ground-truth localization information for 14 videos, where we manually define a bounding quadrilateral around the object of interest in each video frame. This localization information is used in the calculation of the Jaccard index. 1. Static single object: 1.a. Books: Automata Theory, Computer Architecture, OpenCV, Wang Book. 1.b. CD Covers: Barry White, Chris Brown, Janet Jackson, Rascal Flatts, Sheryl Crow. 1.c. DVD Covers: Finding Nemo, Monsters Inc, Mummy Returns, Private Ryan, Rush Hour, Shrek, Titanic, Toy Story. 1.d. Common Objects: Bleach, Glade, Oreo, Polish, Tide, Tuna. 2. Moving object, moving camera: Barry White Moving, Chris Brown Moving, Titanic Moving, Titanic Moving - Second, Toy Story Moving. 3. Multiple objects: 3.a. Multiple Objects 1: Polish, Wang Book, Monsters Inc. 3.b. Multiple Objects 2: OpenCV, Barry White, Titanic. 3.c. Multiple Objects 3: Monsters Inc, Toy Story, Titanic. 3.d. Multiple Objects 4: Wang Book, Barry White, OpenCV.

This deposit contains the processed data from the Mobile scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the index of all related datasets for more details on the paper, and see the included README for details on this dataset.

The mobile payment reseach & publication dataset, which was indexed by Scopus from 1997 to 2019. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.