This statistic shows the average price of cellular data per gigabyte in the United States from 2018 to 2023. In 2018, the average price of cellular data was estimated to amount to 4.64 U.S. dollars per GB.

Zimbabwe had the most expensive mobile internet in Africa as of 2023. One gigabyte cost on average ***** U.S. dollars in the African country, the highest worldwide. Overall, the cost of mobile data varied significantly across the continent. South Sudan and The Central African Republic also recorded elevated prices for mobile data, positioning among the ** countries with the highest prices for data globally. By contrast, one gigabyte cost **** U.S. dollars in Malawi, the lowest average price registered in Africa. Determinants for high pricing On average, one gigabyte of mobile internet in Sub-Saharan Africa amounted to **** U.S. dollars in 2023, one of the highest worldwide, according to the source. In Northern Africa, the price for mobile data was far lower, **** U.S. dollars on average. Few factors influence the elevated prices of mobile data in Africa, such as high taxation and the lack of infrastructure. In 2021, around **** percent of the population in Sub-Saharan Africa lived within a range of ** kilometers from fiber networks. Mobile connectivity Over *** million people are estimated to be connected to the mobile internet in Africa as of 2022. The coverage gap has decreased in the continent but remained the highest worldwide in 2022. That year, ** percent of the population in Sub-Saharan Africa lived in areas not covered by a mobile broadband network. Additionally, the adoption of mobile internet is not equitable, as it is more accessible to men than women as well as more spread in urban than rural areas.

One gigabyte of mobile internet in Indonesia cost, on average, 0.28 U.S. dollars per month, in 2023. Data pricing significantly decreased over the period. Out of 59 plans measured, the lowest price observed was 0.02 U.S. dollars per 1GB for a 30 days plan.

One gigabyte for mobile internet in Nigeria cost on average 39 U.S. cents as of August 2023. The country ranked 31st in a list of 237 countries worldwide, from the cheapest to the most expensive for mobile data. In the regional comparison, Nigeria was among the nations with lower costs for mobile data in Africa. Out of 55 plans analyzed in the country, the lowest price observed was 0.13 U.S. dollars per 1GB for a 30 days plan. In the most expensive plan, 1GB cost 1.64 U.S. dollars.

This dataset on Datarooms.org provides a pricing plans comparison of Virtual Data Room (VDR) providers. It includes details on provider names, prices, pricing plans, targeted industries, and free trial offerings.

The Cost of Risk metric shows how much the city spends on handling risks (like insurance, legal expenses, or accident payouts) compared to how much money it collects overall.The performance measure dashboard is available at 5.17 Total Cost of Risk.Additional InformationSource: Peoplesoft and ACFRContact: Laura CalderContact E-Mail: laura.calder@tempe.govData Source Type: ExcelPreparation Method: The total expenses in Fund 2661 (The Risk Management cost center) is divided by the total revenue from Annual Comprehensive Financial Report to calculate the total cost of Risk.Publish Frequency: AnnualPublish Method: ManualData Dictionary (pending update)

An .json file that gives the table of contents for the health plan cost of services required under the No Surprise Act - Price Transparency Reporting.

This statistic shows the monthly cost of unlimited mobile data flat rates for smartphones in selected countries in Europe as of March 2019. According to data provided by Verivox, eight out of the ten countries under consideration had true unlimited flat rates for mobile internet access, whereas the two remaining ones, Poland and Spain, only had options with an upper limit for monthly mobile data usage. Smartphone users in Austria had to pay the most for access to an unlimited data flat rate, at roughly 72 euros per month. Flat rates were cheapest in Poland, the United Kingdom (UK) and the Netherlands.

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

Under the No Surprise Act - Price Transparency Reporting we are providing an .xml file that when downloaded contains the allowed amounts paid to providers outside of the VHP network.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: North, South, Artibonite, Centre, South-East, Grande'Anse, North-East, West, North-West, Market Average

This dataset represents the counts of those individuals who have been determined to have a share of cost (SOC) obligation, which is the monthly amount of medical expenses they must incur before they are eligible to receive Medi-Cal benefits. The dataset includes individuals who have a met or unmet monthly SOC obligation. Individuals who have not met their monthly SOC obligation are not eligible for Medi-Cal. SOC obligations are calculated during the eligibility determination process based on household income.

United Healthcare Transparency in Coverage Dataset

Unlock the power of healthcare pricing transparency with our comprehensive United Healthcare Transparency in Coverage dataset. This invaluable resource provides unparalleled insights into healthcare costs, enabling data-driven decision-making for insurers, employers, researchers, and policymakers.

Key Features:

• Extensive Coverage: Access detailed pricing information for a wide range of medical procedures and services across the United States, covering approximately 76,000 employers.
• Granular Data: Analyze costs at the provider, plan, and employer levels, allowing for in-depth comparisons and trend analysis.
• Massive Scale: Over 400TB of data generated monthly, providing a wealth of information for comprehensive analysis.
• Historical Perspective: Track pricing changes over time to identify patterns and forecast future trends.
• Regular Updates: Stay current with the latest pricing information, ensuring your analyses are always based on the most recent data.

Detailed Data Points:

For each of the 76,000 employers, the dataset includes: 1. In-network negotiated rates for covered items and services 2. Historical out-of-network allowed amounts and billed charges 3. Cost-sharing information for specific items and services 4. Pricing data for medical procedures and services across providers, plans, and employers

Use Cases

For Insurers: - Benchmark your rates against competitors - Optimize network design and provider contracting - Develop more competitive and cost-effective insurance products

For Employers: - Make informed decisions about health plan offerings - Negotiate better rates with insurers and providers - Implement cost-saving strategies for employee healthcare

For Researchers: - Conduct in-depth studies on healthcare pricing variations - Analyze the impact of policy changes on healthcare costs - Investigate regional differences in healthcare pricing

For Policymakers: - Develop evidence-based healthcare policies - Monitor the effectiveness of price transparency initiatives - Identify areas for potential cost-saving interventions

Data Delivery

Our flexible data delivery options ensure you receive the information you need in the most convenient format:

• Custom Extracts: We can provide targeted datasets focusing on specific regions, procedures, or time periods.
• Regular Reports: Receive scheduled updates tailored to your specific requirements.

Why Choose Our Dataset?

1. Expertise: Our team has extensive experience in healthcare data retrieval and analysis, ensuring high-quality, reliable data.
2. Customization: We can tailor the dataset to meet your specific needs, whether you're interested in particular companies, regions, or procedures.
3. Scalability: Our infrastructure is designed to handle the massive scale of this dataset (400TB+ monthly), allowing us to provide comprehensive coverage without compromise.
4. Support: Our dedicated team is available to assist with data interpretation and technical support.

Harness the power of healthcare pricing transparency to drive your business forward. Contact us today to discuss how our United Healthcare Transparency in Coverage dataset can meet your specific needs and unlock valuable insights for your organization.

This paper investigates the relationship between housing prices and the quality of public schools in the Australian Capital Territory. To disentangle the effects of schools and other neighbourhood characteristics on the value of residential properties, we compare sale prices of homes on either side of high school attendance boundaries. We find that a 5 percent increase in test scores (approximately one standard deviation) is associated with a 3.5 percent increase in house prices. Our result is in line with private school tuition costs, and accords with prior research from Britain and the United States. Estimating the effect of school quality on house prices provides a possible measure of the extent to which parents value better educational outcomes.

Recent developments include: January 2022: IBM and Francisco Partners disclosed the execution of a definitive contract under which Francisco Partners will purchase medical care information and analytics resources from IBM, which are currently part of the IBM Watson Health business., October 2021: Informatica LLC announced an important cloud storage agreement with Google Cloud in October 2021. This collaboration allows Informatica clients to transition to Google Cloud as much as twelve times quicker. Informatica's Google Cloud Marketplace transactable solutions now incorporate Master Data Administration and Data Governance capabilities., Completing a unit of labor with incorrect data costs ten times more estimates than the Harvard Business Review, and finding the correct data for effective tools has never been difficult. A reliable system may be implemented by selecting and deploying intelligent workflow-driven, self-service options tools for data quality with inbuilt quality controls.. Key drivers for this market are: Increasing demand for data quality: Businesses are increasingly recognizing the importance of data quality for decision-making and operational efficiency. This is driving demand for data quality tools that can automate and streamline the data cleansing and validation process.

Growing adoption of cloud-based data quality tools: Cloud-based data quality tools offer several advantages over on-premises solutions, including scalability, flexibility, and cost-effectiveness. This is driving the adoption of cloud-based data quality tools across all industries.

Emergence of AI-powered data quality tools: AI-powered data quality tools can automate many of the tasks involved in data cleansing and validation, making it easier and faster to achieve high-quality data. This is driving the adoption of AI-powered data quality tools across all industries.. Potential restraints include: Data privacy and security concerns: Data privacy and security regulations are becoming increasingly stringent, which can make it difficult for businesses to implement data quality initiatives.

Lack of skilled professionals: There is a shortage of skilled data quality professionals who can implement and manage data quality tools. This can make it difficult for businesses to achieve high-quality data.

Cost of data quality tools: Data quality tools can be expensive, especially for large businesses with complex data environments. This can make it difficult for businesses to justify the investment in data quality tools.. Notable trends are: Adoption of AI-powered data quality tools: AI-powered data quality tools are becoming increasingly popular, as they can automate many of the tasks involved in data cleansing and validation. This makes it easier and faster to achieve high-quality data.

Growth of cloud-based data quality tools: Cloud-based data quality tools are becoming increasingly popular, as they offer several advantages over on-premises solutions, including scalability, flexibility, and cost-effectiveness.

Focus on data privacy and security: Data quality tools are increasingly being used to help businesses comply with data privacy and security regulations. This is driving the development of new data quality tools that can help businesses protect their data..

The graph illustrates the average cost of a lawyer per hour in the United States from 2018 to 2024. The x-axis represents the years from 2018 to 2024, while the y-axis displays the average hourly cost in dollars. Over this seven-year period, the average hourly cost increased from $236.86 in 2018 to a peak of $300.14 in 2024. The data indicates an overall upward trend in hourly lawyer costs over the years, with a large increase in the recent year.

On an annual basis (based on individual Long-Term Care (LTC) facility fiscal year end), California licensed LTC facilities report detailed financial data on facility information, ownership information, patient days & discharges, Balance Sheet, Equity Statement, Cash Flows, Income Statement, Revenue by type and payer, Expense Detail, and Labor Detail. Based on the selected data set, the pivot tables display summarized data on a Profile page and also provides charts on various data items such as Patient Days, Revenue & Expense, and Revenue.

Gold rose to 3,320.86 USD/t.oz on July 1, 2025, up 0.53% from the previous day. Over the past month, Gold's price has fallen 1.80%, but it is still 42.51% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on July of 2025.

Natural gas fell to 3.66 USD/MMBtu on June 30, 2025, down 2.07% from the previous day. Over the past month, Natural gas's price has fallen 0.88%, but it is still 47.76% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on June of 2025.