We conducted a cross-sectional study of the publicly available 2022 Open Payments data to characterize and quantify sponsored events (available for download at: https://www.cms.gov/priorities/key-initiatives/open-payments/data/dataset-downloads). Data sources We downloaded the 2022 dataset ZIP files from the Open Payments website on June 30th, 2023. We included all records for nurse practitioners, clinical nurse specialists, certified registered nurse anesthetists, and certified nurse-midwives (hereafter advanced practiced registered nurses (APRNs)); and allopathic and osteopathic physicians (hereafter, ‘physicians’). To ensure consistency in provider classification, we linked Payments data to the National Plan and Provider Enumeration System data (June 2023) by National Provider Identifier (NPI) and the National Uniform Claim Committee (NUCC) and excluded individuals with an ambiguous provider type. Event-centric analysis of Open Payments records: Creating an event typology We included only payments classified as “food and beverage” to reliably identify distinct sponsored events. We reasoned that food and beverage would be consumed on the same day in the same place, thus assumed that records for food and beverage associated with the same event would share the date of payment and location. We also assumed that the reported value of a food and beverage payment is the total cost of the hospitality divided by the number of attendees, thus grouped payment records with the same amount, rounded to the nearest dollar. Inferring which Open Payment records relate to the same sponsored event requires analytic decisions regarding the selection and representation of variables that define an event. To understand the impact of these choices, we undertook a sensitivity analysis to explore alternative ways to group Open Payments records for food and beverage, to determine how combination of variables, including date (specific date or within the same calendar week), amount (rounded to nearest dollar), and recipient’s state, affected the identification of sponsored events in the Open Payments data set. We chose to define a sponsored event as a cluster of three or more individual payment records for food and beverage (nature of payment) with the following matching Open Payments record variables: • Submitting applicable manufacturer (name) • Product category or therapeutic area • Name of drug or biological or device or medical supply • Recipient state • Total amount of payment (USD, rounded to nearest dollar) • Date of payment (exact) After examining the distribution of the data, we classified events in terms of size (≥20 attendees as “large” and 3-<20 as “small”) and amount per person. We categorized events <$10 as “coffee”, $10-<$30 as “lunch”, $30-<$150 as “dinner”, and ≥$150 as “banquet”.

Envestnet®| Yodlee®'s Electronic Payment Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

The 2-digit Standard Industrial Classification (SIC) level datasets published alongside the UK industry-to-industry payment flows, 2017 to 2024: experimental data article can be accessed on this page.

The 5-digit SIC level dataset can be accessed on Nomis via the link at the bottom of this page in the 'Important notes and usage information' section.

For each type of payment category among general payments in the Open Payments database, 2013–2018, the mean payment per physician, the number of physicians receiving payments, and the total amount of money transacted.

The new On-Time Payment Policy reporting framework commenced on 1 July 2020. This Late-Payment Policy dataset is no longer being updated.\r \r The new dataset can be accessed from https://www.data.qld.gov.au/dataset/queensland-health-on-time-payment-report

The new On-Time Payment Policy reporting framework commenced on 1 July 2020. This Late-Payment Policy dataset is no longer being updated. The new dataset can be accessed from https://www.data.qld.gov.au/dataset/department-of-housing-public-works-on-time-payment-report

Report on late payments made by the Department of the Premier and Cabinet - the new On-time Payment Policy reporting framework commenced on 1 July 2020. This Late Payment Policy dataset is no longer being updated.

Techsalerator’s Business Technographic Data for Poland: Unlocking Insights into Poland's Technology Landscape

Techsalerator’s Business Technographic Data for Poland offers a detailed and comprehensive dataset crucial for businesses, market analysts, and technology vendors aiming to understand and engage with companies operating within Poland. This dataset provides in-depth insights into the technological landscape, capturing and organizing data related to technology stacks, digital tools, and IT infrastructure utilized by businesses in the country.

Please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

• Company Name: This field lists the names of companies in Poland, enabling technology vendors to target potential clients and allowing analysts to assess technology adoption trends within specific businesses.

• Technology Stack: This field outlines the technologies and software solutions a company uses, such as ERP systems, CRM software, and cloud services. Understanding a company's technology stack is essential for evaluating its digital maturity and operational needs.

• Deployment Status: This field indicates whether the technology is currently deployed, planned for future deployment, or under evaluation. Vendors can use this information to gauge the level of technology adoption and interest among companies in Poland.

• Industry Sector: This field specifies the industry in which the company operates, such as finance, manufacturing, or IT services. Knowing the industry helps vendors tailor their products to sector-specific demands and emerging trends in Poland.

• Geographic Location: This field identifies the company's headquarters or primary operations within Poland. Geographic information aids in regional analysis and understanding localized technology adoption patterns across the country.

Top 5 Technology Trends in Poland

• Fintech Innovations: Poland’s financial sector is embracing technological advancements with a strong focus on fintech solutions, including digital banking, blockchain applications, and payment processing technologies.

• E-commerce Growth: The rise of e-commerce in Poland is driven by advancements in online retail platforms, digital payment solutions, and logistics technologies, enabling businesses to reach broader audiences and enhance customer experiences.

• Cloud Computing Adoption: Cloud-based solutions are increasingly popular among Polish companies, offering scalable and cost-effective alternatives to traditional IT infrastructure. This trend is notable across various sectors including finance, healthcare, and education.

• Cybersecurity Enhancements: With growing digital transactions, there is a heightened emphasis on cybersecurity in Poland. Businesses are investing in advanced security measures, including threat detection systems, data encryption, and secure communication protocols.

• Smart Manufacturing: The manufacturing sector in Poland is adopting Industry 4.0 technologies, including IoT devices, automation, and data analytics to optimize production processes and improve operational efficiency.

Top 5 Companies with Notable Technographic Data in Poland

• PKO Bank Polski: As one of Poland's largest banks, PKO Bank Polski is at the forefront of fintech innovation, with investments in digital banking platforms, mobile apps, and advanced cybersecurity solutions.

• CD Projekt Red: Renowned for its video game development, CD Projekt Red is leveraging cutting-edge technologies in game design, cloud computing, and digital distribution to enhance gaming experiences worldwide.

• Orange Polska: A major telecommunications provider, Orange Polska is driving connectivity improvements through investments in 5G technology, fiber-optic networks, and digital services.

• Grupa Lotos: A key player in the energy sector, Grupa Lotos is focusing on integrating digital technologies into its operations, including advanced data analytics and IoT solutions for better resource management.

• Allegro: A leading e-commerce platform, Allegro is utilizing advanced technology solutions in its online marketplace, including AI-driven recommendation systems, cloud services, and robust payment processing infrastructure.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for Poland, please contact info@techsalerator.com with your specific needs. Techsalerator offers customized quotes based on the required number of data fields and records, with datasets available for delivery within 24 hours. Ongoing access options can also be arranged upon request.

Included Data Fields

• Company Name
• Technology Stack
• Deployment Status
• Industry Sector
• Geographic Location
• IT Infrastr...

This attachment contains data linked to the research article titled "Dynamic Heterogeneous Panel Analysis of Financial Market Disciplinary Effects on Fiscal Balance".The dataset contains cyclically adjusted primary balance, long-term interest rate, interest payment as a share of revenue, effective borrowing cost, lagged public debt as a share of GDP, fiscal rule index, VXO index, EMU dummy, and partial sums of positive and negative changes in the long-term interest rate, interest payment, effective borrowing cost, and strucural primary balance.

In financial year 2024, almost 164 billion digital payments were recorded across India. This was a significant increase compared to the previous three years.  Variety of digital payments  The total value of digital payments included large-scale interbank payments, such as Real Time Gross Settlement (RTGS) or National Electronic Funds Transfer (NEFT), as well as payments used by individuals, such as credit and debit cards. India’s mobile payment system, Unified Payments Interface (UPI), recorded strong gains, both in numbers and in value, since 2015. Thereby, it comes as no surprise that international key players, such as Google Pay or Amazon Pay, entered the market. Nevertheless, the most used app in 2021 was domestic app PhonePe.  COVID-19 effects  Since the beginning of the COVID-19 pandemic in India the number of digital payment transactions continued to grow. This was also true for the various methods of credit and debit transfers, including mobile payments through UPI. Nevertheless, the value of card payments and of large value credit transfers, such as RTGS, decreased considerably in financial year 2021.

Report on late payments made by the Department of Justice and Attorney-General. \r \r The new On-Time Payment Policy reporting framework commenced on 1 July 2020. This Late-Payment Policy data set is no longer being updated.

The debit card penetration in the United Kingdom was forecast to continuously decrease between 2024 and 2029 by in total 0.2 percentage points. According to this forecast, in 2029, the debit card penetration will have decreased for the eighth consecutive year to 94.82 percent. The penetration rate refers to the share of the total population who use debit cards.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).