This data package includes the underlying data to replicate the charts, tables, and calculations presented in The US Revenue Implications of President Trump’s 2025 Tariffs, PIIE Briefing 25-2.

If you use the data, please cite as:

McKibbin, Warwick, and Geoffrey Shuetrim. 2025. The US Revenue Implications of President Trump’s 2025 Tariffs. PIIE Briefing 25-2. Washington: Peterson Institute for International Economics.

This data package includes the underlying data files to replicate the data, tables, and charts presented in Why Trump’s tariff proposals would harm working Americans, PIIE Policy Brief 24-1.

If you use the data, please cite as: Clausing, Kimberly, and Mary E. Lovely. 2024. Why Trump’s tariff proposals would harm working Americans. PIIE Policy Brief 24-1. Washington, DC: Peterson Institute for International Economics.

Determining the Prospective Tariff for a Telecom Company

Project Description

You are an analyst at "Megaline," a federal mobile operator. The company offers two tariff plans to customers: "Smart" and "Ultra." To adjust the advertising budget, the commercial department wants to understand which tariff generates more revenue.

You need to conduct a preliminary analysis of the tariffs on a small sample of customers. You have data on 500 users of "Megaline": who they are, where they are from, which tariff they use, how many calls and messages they sent in 2018. You need to analyze customer behavior and conclude which tariff is better.

Tariff Descriptions

"Smart" Tariff: - Monthly fee: 550 rubles - Included: 500 minutes of calls, 50 messages, and 15 GB of internet traffic - Cost of services beyond the tariff package: 1. Call minute: 3 rubles (Megaline always rounds up minutes and megabytes. If the user talked for just 1 second, it counts as a whole minute); 2. Message: 3 rubles; 3. 1 GB of internet traffic: 200 rubles.

"Ultra" Tariff: - Monthly fee: 1950 rubles - Included: 3000 minutes of calls, 1000 messages, and 30 GB of internet traffic - Cost of services beyond the tariff package: 1. Call minute: 1 ruble; 2. Message: 1 ruble; 3. 1 GB of internet traffic: 150 rubles.

Note: Megaline always rounds up seconds to minutes and megabytes to gigabytes. Each call is rounded up individually: even if it lasted just 1 second, it is counted as 1 minute. For web traffic, separate sessions are not counted. Instead, the total amount for the month is rounded up. If a subscriber uses 1025 megabytes in a month, they are charged for 2 gigabytes.

Project Steps

Step 1: Open the file with data and study the general information File paths: - /datasets/calls.csv - /datasets/internet.csv - /datasets/messages.csv - /datasets/tariffs.csv - /datasets/users.csv

Step 2: Prepare the data - Convert data to the required types; - Find and fix errors in the data, if any. Explain what errors you found and how you fixed them. You will find calls with zero duration in the data. This is not an error: missed calls are indicated by zeros, so they do not need to be deleted.

For each user, calculate: - Number of calls made and minutes spent per month; - Number of messages sent per month; - Amount of internet traffic used per month; - Monthly revenue from each user (subtract the free limit from the total number of calls, messages, and internet traffic; multiply the remainder by the value from the tariff plan; add the corresponding tariff plan's subscription fee).

Step 3: Analyze the data Describe the behavior of the operator's customers based on the sample. How many minutes of calls, how many messages, and how much internet traffic do users of each tariff need per month? Calculate the average, variance, and standard deviation. Create histograms. Describe the distributions.

Step 4: Test hypotheses - The average revenue of users of the "Ultra" and "Smart" tariffs is different; - The average revenue of users from Moscow differs from the revenue of users from other regions. Moscow is written as 'Москва'. You can put it in your value, when check the hypothesis

Set the threshold alpha value yourself.

Explain: - How you formulated the null and alternative hypotheses; - Which criterion you used to test the hypotheses and why.

Step 5: Write a general conclusion

Formatting: Perform the task in Jupyter Notebook. Fill the program code in the cells of type code, and the textual explanations in the cells of type markdown. Apply formatting and headers.

Data Description

Table users (user information): - user_id: unique user identifier - first_name: user's first name - last_name: user's last name - age: user's age (years) - reg_date: date of tariff connection (day, month, year) - churn_date: date of tariff discontinuation (if the value is missing, the tariff was still active at the time of data extraction) - city: user's city of residence - tariff: name of the tariff plan

Table calls (call information): - id: unique call number - call_date: call date - duration: call duration in minutes - user_id: identifier of the user who made the call

Table messages (message information): - id: unique message number - message_date: message date - user_id: identifier of the user who sent the message

Table internet (internet session information): - id: unique session number - mb_used: amount of internet traffic used during the session (in megabytes) - session_date: internet session date - user_id: user identifier

Table tariffs (tariff information): - tariff_name: tariff name - rub_monthly_fee: monthly subscription fee in rubles - minutes_included: number of call minutes included per month - `messages_included...

The Trade in Services Database provides information on annual bilateral services trade flows in Mode 1 (cross-border trade) and Mode 2 (consumption abroad) for 199 countries across a multitude of sectors and years spanning 1985 and 2011. The collection of data on cross-border trade in services is notoriously difficult, in large part due to the intangible nature of services but also the high capacity needed to record such data. This is particularly true for developing countries. This database tries to fill this gap by consolidating multiple sources of bilateral trade data in services using mirror techniques, including the OECD, Eurostat, UN and IMF, as to provide a broader coverage of developed and developing countries over time. Mirror-technique is a method to retrieve export trade flows of a reporter by using information on imports of the partner country. As a word of caution it should be added that the quality of trade data in services is still far away from being comparable to trade data for merchandise goods. Due to the long tradition of tariff revenues, trade data for goods have been collected with quite high quality and accuracy. Due to intangibility and non-storability of services, at-the-border-duties cannot be applied to services, thus having resulted in much weaker compilation practices with considerably less accuracy. Thus, services statistics has ample space for improvement in terms of measurement. In particular with respect to modes 3 and 4, measurement is up to date difficult and incomplete. Ongoing revisions and refinements of the BOP classification work towards solving these issues. As such, the Trade in Services Database should be seen in this light as the best currently available approximation to a comprehensive picture of global trade flows in services.

Daily overview of federal revenue collections such as income tax deposits, customs duties, fees for government service, fines, and loan repayments.

This dataset contains an annual summary of the car parking account from South Lakeland District Council. Please note; figures published in April are interim figures and will be updated later in the year once the final accounts have been audited. These include a breakdown of the income and expenditure on the parking account. SLDC’s car parking account only relates to the off-street car parks that it owns or manages; it does not collect for, or enforce any on-street parking. Expenditure includes cost for the provision and maintenance of designated off street parking places by the local authority. Surplus statement: There are strict rules established by Government that stipulate how surplus parking funds are spent, but there are differences between on and off-street parking Any surpluses from on-street parking and both on and off-street enforcement must be used in accordance with section 55 of the Road Traffic Regulation Act 1984. This means that any income remaining after enforcement costs must be used for transport. Income from off-street parking fees and charges can be for general use by the local authority. The Council only operates off-street car parks and enforcement of them. This authority has to balance the parking needs of residents plus a much-increased transient population visiting the area. We are always looking at ways of improving use of our car parks, such as increasing capacity of underused car parks. There has been no overall increase in tariffs since 2011-12 and lower charges or new tariffs have been established with the aim of making best use of resources and reducing congestion. For example, we have introduced an ‘Early Bird’ tariff in Westmorland Shopping Centre car park saving £3.80 on the daily rate, and set a tariff of £1.20 for all-day parking in a car park in Grange-over-Sands, saving £4.50. Any surplus funds raised from off-street parking facilities after expenditure including car park maintenance and improvement, are used to offset the costs to the Council of providing services to the public, which would otherwise have to be met through Council Tax. Further information about the council’s car parks including location, number of parking bays, number of disabled parking spaces and charges can be found in the Car Parks in South Lakeland dataset.

Explore the World Competitiveness Ranking dataset for 2016, including key indicators such as GDP per capita, fixed telephone tariffs, and pension funding. Discover insights on social cohesion, scientific research, and digital transformation in various countries.

Social cohesion, The image abroad of your country encourages business development, Scientific articles published by origin of author, International Telecommunication Union, World Telecommunication/ICT Indicators database, Data reproduced with the kind permission of ITU, National sources, Fixed telephone tariffs, GDP (PPP) per capita, Overall, Exports of goods - growth, Pension funding is adequately addressed for the future, Companies are very good at using big data and analytics to support decision-making, Gross fixed capital formation - real growth, Economic Performance, Scientific research legislation, Percentage of GDP, Health infrastructure meets the needs of society, Estimates based on preliminary data for the most recent year., Singapore: including re-exports., Value, Laws relating to scientific research do encourage innovation, % of GDP, Gross Domestic Product (GDP), Health Infrastructure, Digital transformation in companies is generally well understood, Industrial disputes, EE, Female / male ratio, State ownership of enterprises, Total expenditure on R&D (%), Score, Colombia, Estimates for the most recent year., Percentage change, based on US$ values, Number of listed domestic companies, Tax evasion is not a threat to your economy, Scientific articles, Tax evasion, % change, Use of big data and analytics, National sources, Disposable Income, Equal opportunity, Listed domestic companies, Government budget surplus/deficit (%), Pension funding, US$ per capita at purchasing power parity, Estimates; US$ per capita at purchasing power parity, Image abroad or branding, Equal opportunity legislation in your economy encourages economic development, Number, Article counts are from a selection of journals, books, and conference proceedings in S&E from Scopus. Articles are classified by their year of publication and are assigned to a region/country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractional-count basis. The sum of the countries/economies may not add to the world total because of rounding. Some publications have incomplete address information for coauthored publications in the Scopus database. The unassigned category count is the sum of fractional counts for publications that cannot be assigned to a country or economy. Hong Kong: research output items by the higher education institutions funded by the University Grants Committee only., State ownership of enterprises is not a threat to business activities, Protectionism does not impair the conduct of your business, Digital transformation in companies, Total final energy consumption per capita, Social cohesion is high, Rank, MTOE per capita, Percentage change, based on constant prices, US$ billions, National sources, World Trade Organization Statistics database, Rank, Score, Value, World Rankings

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Mongolia, Netherlands, New Zealand, Norway, Oman, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom, Venezuela

Follow data.kapsarc.org for timely data to advance energy economics research.

This summary table shows, for Budget Receipts, the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for various types of receipts (i.e. individual income tax, corporate income tax, etc.). The Budget Outlays section of the table shows the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for functions of the federal government. The table also shows the amounts for the budget/surplus deficit categorized as listed above. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

The United States recorded a government budget deficit of 291000 USD Million in July of 2025. This dataset provides - United States Government Budget Value - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table represents the breakdown of tax refunds by recipient (individual vs business) and type (check vs electronic funds transfer). Tax refunds are also represented as withdrawals in the Deposits and Withdrawals of Operating Cash table. All figures are rounded to the nearest million. As of February 14, 2023, Table VI Income Tax Refunds Issued was renamed to Table V Income Tax Refunds Issued within the published report.

This table shows the receipts and outlays of the United States Government by month for the current fiscal year, up to and including the current accounting month. The table also shows the total receipts and outlays for the current fiscal year-to-date and the comparable prior fiscal year-to-date. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This table represents the breakdown of taxes that are received by the federal government. Federal taxes received are represented as deposits in the Deposits and Withdrawals of Operating Cash table. All figures are rounded to the nearest million.

This table shows the gross receipts, refunds and net receipts for the current month, the current fiscal year-to-date and the prior fiscal year-to-date for the various receipts of the federal government. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This table shows the gross outlays, applicable receipts and net outlays for the current month, current fiscal year-to-date and prior fiscal year-to-date by various agency programs accounted for in the budget of the federal government. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This table is a subsidiary table for Means of Financing the Deficit or Disposition of Surplus by the U.S. Government providing a detailed view of the Change in Excess of Liabilities. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This table represents the breakdown of total public debt outstanding as it relates to the statutory debt limit. All figures are rounded to the nearest million.

This table is a subsidiary table for Means of Financing the Deficit or Disposition of Surplus by the U.S. Government providing a detailed view of federal funds and trust funds that are invested in Government Account Series (GAS) securities. Federal funds include general funds, special funds, and revolving funds (public enterprise revolving funds, intragovernmental revolving funds, and credit financing accounts). A trust fund is a type of account, designated by law, for receipts or offsetting receipts dedicated to specific purposes and the expenditure of these receipts. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This table represents the issues and redemption of marketable and nonmarketable securities. All figures are rounded to the nearest million.

Get data on the daily cash and debt operations of the U.S. Treasury, including cash balance, deposits, and withdrawals; income tax refunds; and debt transactions.

This table represents deposits and withdrawals from the Treasury General Account. A summary of changes to the Treasury General Account can be found in the Operating Cash Balance table. All figures are rounded to the nearest million.