Context

A lot of my friends love the iphone (just like many people across the world), so I thought it would interesting to collect rent and iphone price information for countries around the world and draw comparisons. The data itself was collected manually by search price aggregator sites. I used themacindex[dot]com link for the iphone prices and chose the most expensive iPhone (iPhone 12 Pro Max 512GB) for the comparisons (Go big or go home right?).

The rent is taken from numbeo which I personally use for planning travel. As a price comparison aggregator I feel like Numbeo is an impartial source of raw data. Example link

I chose not to scrape the sites automatically for two main reasons: 1) I am using a laptop with windows and I haven't used scrapy on a windows machine before. 2) I am serving a 14 day quarantine in a country before I start my masters and I am very bored.

For monetary_data.csv the data was gathered on 3rd January 2020 and is reflective of site data on that date. For cost_living.csv the data was gathered manually on 6th January 2020 (UTC) and is reflective of site data on that date.

Content

The monetary_data csv has 4 columns: Country, iphone_price, avg_rent, rent_frac

Country: This column acts as my index for plotting graphs.

iphone_price: This column contains the prices of the iphone model mentioned earlier. From the website, it contains the list price (before taxes and rebates) in USD or local list price in USD equivalent.

avg_rent: This column contains the price of renting a 1 bhk apartment. The value shown is the average of rents for apartments in city center and outside the city center.

rent_fraction: This column contains Numbeo's model calculated fraction of a person's income devoted to rent. For example, a value of 0.20 means that 20% of a person's rent is devoted to rent. I found these values too low in practice (meaning that average monthly expenses were too high to be realistic), so I avoided using that data, but I have included it for completeness sake.

The cost_living csv has 7 columns: Country, Gasoline_per_L, Diesel_per_L, Electricity_per_KWHr, Food_for_2_out, Jeans, and Car (Hatchback)

Note: data obtained from https://www.globalpetrolprices.com/ is licensed under Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0). I have clarified with the owners that my use of this data (the Kaggle DS Survey 2020 competition) does not violate the terms under which it is released.

Country: This column acts as the index for plots

Gasoline_per_L: This data was obtained from globalpetrolprices.com and indicates the Gasoline (Petrol) price per liter in that country.

** Diesel_per_L**: This data was obtained from globalpetrolprices.com and indicates the Diesel price per liter in that country.

Electricity_per_KWHr: This data was obtained from globalpetrolprices.com and indicates the cost of electricity per Kilowatt-hour in that country.

Food_for_2_out: This data was obtained from numbeo and indicates the price for a meal for 2 at a mid-range restaurant in that country.

Jeans: This data was obtained from numbeo and indicates the average price for an ordinary pair of jeans from global manufacturers like Levi's or similar.

Car (Hatckback): This data was obtained from numbeo and indicates the average price of a hatchback car similar to the volkswagen golf.

Inspiration

Is a geographically determined salary a good idea when skills are the same and the projects people work on are basically global in scale? Does PPP (Purchasing Power Parity) make sense when the prices of many common goods are basically the same around the world? However comical, does this data reflect the need to arrive at a better economic model for the world to function on? Especially when this economic model lowers standards of living around the world while benefiting only a select few? Developed countries also suffer from large income disparities across their population.

I am including a fair use statement since the purpose of this dataset is to critic the disparity between location agnostic product pricing and geographically determined salaries.

Fair Use:

Copyright Disclaimer under section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, education and research.

Fair use is a use permitted by copyright statute that might otherwise be infringing.

Non-profit, educational or personal use tips the balance in favor of fair use*

Context

I'm creating a new website in which I need this type of data. I didn't found it easily available as I had to scrape it from an interactive graph, so now I upload it here for everyone

Content

In this dataset you can find real and nominal gold prices since 1791 to 2020. The explanation of the differences between real and nominal prices are:

· Nominal values are the current monetary values. · Real values are adjusted for inflation and show prices/wages at constant prices. · Real values give a better guide to what you can actually buy and the opportunity costs you face.

Example of real vs nominal:

· If you receive an 8% increase in your wages from £100 to £108, this is the nominal increase. · However, if inflation is 2%, then the real increase in wages is (8-2%) 6%. · The real wage is a better guide to how your living standards changes. It shows what you are actually able to buy with the extra increase in wages. · If wages increased 80%, but inflation was also 80%, the real increase in wages would be 0% – in effect, despite the monetary increase in wages of 80%, the amount of goods and services you could buy would be the same.

Hope this dataset is useful for you! Any questions or answers do not hesitate in contact me.

This dataset contains the updated 2024 data from the Jobs and Salaries in Data Science dataset. The information is sourced from ai-jobs.net/salaries/2024/.

About Dataset

work_year: The year in which the data was recorded. This field indicates the temporal context of the data, important for understanding salary trends over time.

job_title: The specific title of the job role, like 'Data Scientist', 'Data Engineer', or 'Data Analyst'. This column is crucial for understanding the salary distribution across various specialized roles within the data field.

job_category: A classification of the job role into broader categories for easier analysis. This might include areas like 'Data Analysis', 'Machine Learning', 'Data Engineering', etc.

salary_currency: The currency in which the salary is paid, such as USD, EUR, etc. This is important for currency conversion and understanding the actual value of the salary in a global context.

salary: The annual gross salary of the role in the local currency. This raw salary figure is key for direct regional salary comparisons.

salary_in_usd: The annual gross salary converted to United States Dollars (USD). This uniform currency conversion aids in global salary comparisons and analyses.

employee_residence: The country of residence of the employee. This data point can be used to explore geographical salary differences and cost-of-living variations.

experience_level: Classifies the professional experience level of the employee. Common categories might include 'Entry-level', 'Mid-level', 'Senior', and 'Executive', providing insight into how experience influences salary in data-related roles.

employment_type: Specifies the type of employment, such as 'Full-time', 'Part-time', 'Contract', etc. This helps in analyzing how different employment arrangements affect salary structures.

work_setting: The work setting or environment, like 'Remote', 'In-person', or 'Hybrid'. This column reflects the impact of work settings on salary levels in the data industry.

company_location: The country where the company is located. It helps in analyzing how the location of the company affects salary structures.

company_size: The size of the employer company, often categorized into small (S), medium (M), and large (L) sizes. This allows for analysis of how company size influences salary.

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work_year: The year in which the data was recorded. This field indicates the temporal context of the data, important for understanding salary trends over time.

job_title: The specific title of the job role, like 'Data Scientist', 'Data Engineer', or 'Data Analyst'. This column is crucial for understanding the salary distribution across various specialized roles within the data field.

job_category: A classification of the job role into broader categories for easier analysis. This might include areas like 'Data Analysis', 'Machine Learning', 'Data Engineering', etc.

salary_currency: The currency in which the salary is paid, such as USD, EUR, etc. This is important for currency conversion and understanding the actual value of the salary in a global context.

salary: The annual gross salary of the role in the local currency. This raw salary figure is key for direct regional salary comparisons.

salary_in_usd: The annual gross salary converted to United States Dollars (USD). This uniform currency conversion aids in global salary comparisons and analyses.

employee_residence: The country of residence of the employee. This data point can be used to explore geographical salary differences and cost-of-living variations.

experience_level: Classifies the professional experience level of the employee. Common categories might include 'Entry-level', 'Mid-level', 'Senior', and 'Executive', providing insight into how experience influences salary in data-related roles.

employment_type: Specifies the type of employment, such as 'Full-time', 'Part-time', 'Contract', etc. This helps in analyzing how different employment arrangements affect salary structures.

work_setting: The work setting or environment, like 'Remote', 'In-person', or 'Hybrid'. This column reflects the impact of work settings on salary levels in the data industry.

company_location: The country where the company is located. It helps in analyzing how the location of the company affects salary structures.

company_size: The size of the employer company, often categorized into small (S), medium (M), and large (L) sizes. This allows for analysis of how company size influences salary.