China Government Revenue: Tax: Year to Date: Tariffs data was reported at 48.300 RMB bn in Mar 2025. This records an increase from the previous number of 31.600 RMB bn for Feb 2025. China Government Revenue: Tax: Year to Date: Tariffs data is updated monthly, averaging 128.732 RMB bn from Jan 2007 (Median) to Mar 2025, with 211 observations. The data reached an all-time high of 299.785 RMB bn in Dec 2017 and a record low of 12.274 RMB bn in Jan 2007. China Government Revenue: Tax: Year to Date: Tariffs data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under China Premium Database’s Government and Public Finance – Table CN.FA: Government Revenue: Tax.

Graph and download economic data for Federal government current tax receipts: Taxes on production and imports: Customs duties (B235RC1Q027SBEA) from Q1 1959 to Q2 2025 about receipts, imports, tax, federal, production, government, GDP, and USA.

According to estimates, President Trump's proposals to impose universal tariffs as well as tariffs on Chinese, Canadian, and Mexican imports would considerably increase the average tariff rate. If Trump's proposals go into effect, it is estimated that the average tariff rate of all imports would almost triple, marking the highest rate in the United States since 1969.

Russia Federal Government Revenue: Oil & Gas: Export Tariffs data was reported at 47.300 RUB bn in Mar 2025. This records a decrease from the previous number of 69.100 RUB bn for Feb 2025. Russia Federal Government Revenue: Oil & Gas: Export Tariffs data is updated monthly, averaging 132.400 RUB bn from Jan 2018 (Median) to Mar 2025, with 87 observations. The data reached an all-time high of 348.200 RUB bn in Apr 2022 and a record low of -69.000 RUB bn in Jul 2024. Russia Federal Government Revenue: Oil & Gas: Export Tariffs data remains active status in CEIC and is reported by Ministry of Finance of the Russian Federation. The data is categorized under Russia Premium Database’s Government and Public Finance – Table RU.FB006: Federal Government Revenue and Expenditure: General.

In the United States, the revenue from customs duty amounted to 80 billion U.S. dollars in 2023. The forecast predicts a slight increase in customs duty revenue to 97 billion U.S. dollars in 2024, and an increase over the next decade to 96 billion U.S. dollars by 2034.

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.

Venezuela Central Government: Revenue: Current: Non Tax: Non Oil: Rates data was reported at 211,283.265 VEF th in Sep 2010. This records an increase from the previous number of 206,176.565 VEF th for Aug 2010. Venezuela Central Government: Revenue: Current: Non Tax: Non Oil: Rates data is updated monthly, averaging 40,334.464 VEF th from Jan 1998 (Median) to Sep 2010, with 153 observations. The data reached an all-time high of 218,220.063 VEF th in Oct 2007 and a record low of 12,326.345 VEF th in Feb 1998. Venezuela Central Government: Revenue: Current: Non Tax: Non Oil: Rates data remains active status in CEIC and is reported by Ministry of Economy, Finance and Public Banking. The data is categorized under Global Database’s Venezuela – Table VE.F001: Central Government: Revenue and Expenditure: Monthly.

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...

Russia Federal Government Revenue: Oil & Gas: Export Tariffs: Petroleum Products data was reported at 0.200 RUB bn in Mar 2025. This records a decrease from the previous number of 0.600 RUB bn for Feb 2025. Russia Federal Government Revenue: Oil & Gas: Export Tariffs: Petroleum Products data is updated monthly, averaging 24.800 RUB bn from Jan 2018 (Median) to Mar 2025, with 87 observations. The data reached an all-time high of 75.300 RUB bn in Dec 2018 and a record low of -3.300 RUB bn in Mar 2024. Russia Federal Government Revenue: Oil & Gas: Export Tariffs: Petroleum Products data remains active status in CEIC and is reported by Ministry of Finance of the Russian Federation. The data is categorized under Russia Premium Database’s Government and Public Finance – Table RU.FB006: Federal Government Revenue and Expenditure: General.

China Govt Revenue: YoY: Year to Date: Tax: Tariffs data was reported at -13.800 % in Mar 2020. This records an increase from the previous number of -14.300 % for Feb 2020. China Govt Revenue: YoY: Year to Date: Tax: Tariffs data is updated monthly, averaging 5.200 % from Feb 2008 (Median) to Mar 2020, with 141 observations. The data reached an all-time high of 60.100 % in Feb 2011 and a record low of -35.900 % in Jan 2013. China Govt Revenue: YoY: Year to Date: Tax: Tariffs data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under China Premium Database’s Government and Public Finance – Table CN.FA: Government Revenue: Tax: Period on Period Change.

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.

Russia Federal Government Revenue: Oil & Gas: Export Tariffs: Crude Oil data was reported at 0.000 RUB bn in Mar 2025. This stayed constant from the previous number of 0.000 RUB bn for Feb 2025. Russia Federal Government Revenue: Oil & Gas: Export Tariffs: Crude Oil data is updated monthly, averaging 43.500 RUB bn from Jan 2018 (Median) to Mar 2025, with 87 observations. The data reached an all-time high of 175.200 RUB bn in Dec 2018 and a record low of -5.700 RUB bn in Feb 2024. Russia Federal Government Revenue: Oil & Gas: Export Tariffs: Crude Oil data remains active status in CEIC and is reported by Ministry of Finance of the Russian Federation. The data is categorized under Russia Premium Database’s Government and Public Finance – Table RU.FB006: Federal Government Revenue and Expenditure: General.

Ecuador GG: Revenue: Non Oil: Tax: Tariffs data was reported at 88.351 USD mn in Jan 2025. This records a decrease from the previous number of 104.466 USD mn for Dec 2024. Ecuador GG: Revenue: Non Oil: Tax: Tariffs data is updated monthly, averaging 112.917 USD mn from Jan 2013 (Median) to Jan 2025, with 145 observations. The data reached an all-time high of 194.288 USD mn in Jul 2015 and a record low of 40.971 USD mn in Apr 2020. Ecuador GG: Revenue: Non Oil: Tax: Tariffs data remains active status in CEIC and is reported by Ministry of Economy and Finance. The data is categorized under Global Database’s Ecuador – Table EC.F001: General Government: Revenue and Expenditure.

Russia Federal Government Revenue: Year to Date: Administrative Payments & Charges data was reported at 11.642 RUB bn in Feb 2025. This records an increase from the previous number of 4.910 RUB bn for Jan 2025. Russia Federal Government Revenue: Year to Date: Administrative Payments & Charges data is updated monthly, averaging 7.944 RUB bn from Jan 2005 (Median) to Feb 2025, with 242 observations. The data reached an all-time high of 66.615 RUB bn in Dec 2024 and a record low of 0.000 RUB bn in Jan 2005. Russia Federal Government Revenue: Year to Date: Administrative Payments & Charges data remains active status in CEIC and is reported by Federal Treasury. The data is categorized under Russia Premium Database’s Government and Public Finance – Table RU.FB001: Federal Government Revenue: ytd.

China Govt Revenue: Tax: Contract data was reported at 41.500 RMB bn in Nov 2018. This records an increase from the previous number of 33.100 RMB bn for Oct 2018. China Govt Revenue: Tax: Contract data is updated monthly, averaging 33.350 RMB bn from Aug 2008 (Median) to Nov 2018, with 72 observations. The data reached an all-time high of 59.700 RMB bn in Mar 2018 and a record low of 9.624 RMB bn in Aug 2008. China Govt Revenue: Tax: Contract data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under China Premium Database’s Government and Public Finance – Table CN.FA: Government Revenue: Tax.

The Monthly Treasury Statement (MTS) dataset provides information on receipts and outlays of the Federal Government.

Panama Central Government Revenue: CR: Non-Tax: Rates & Rights data was reported at 70,334.045 PAB th in Sep 2018. This records a decrease from the previous number of 82,867.833 PAB th for Aug 2018. Panama Central Government Revenue: CR: Non-Tax: Rates & Rights data is updated monthly, averaging 35,801.523 PAB th from Jan 1995 (Median) to Sep 2018, with 285 observations. The data reached an all-time high of 168,870.250 PAB th in Apr 2014 and a record low of 3,881.000 PAB th in Jan 2003. Panama Central Government Revenue: CR: Non-Tax: Rates & Rights data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Panama – Table PA.F001: Central Government Revenue.

Key information about Estonia Monthly Earnings

• Estonia Monthly Earnings stood at 2,205 USD in Sep 2024, compared with the previous figure of 2,217 USD in Aug 2024
• Estonia Monthly Earnings data is updated monthly, available from Jan 2000 to Sep 2024, with an average number of 1,177 USD
• The data reached the an all-time high of 2,274 USD in Jun 2024 and a record low of 267 USD in Sep 2000

CEIC converts Monthly Earnings into USD. Statistics Estonia provides Gross Monthly Average Wages and Salaries in EUR. Federal Reserve Board average market exchange rate is used for currency conversions. Monthly Earnings cover employees with an employment contract, a service contract and working under the Public Service Act. Monthly Earnings prior to January 2021 are based on old methodology.


Further information about Estonia Monthly Earnings

• In the latest reports, Estonia Population reached 1 million people in Dec 2023
• Unemployment Rate of Estonia dropped to 5 % in Mar 2023
• The country's Labour Force Participation Rate dropped to 67 % in Sep 2024

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 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.