Analyze Billionaire Wealth Sources

Analyzing the Dataset on Sources of Billionaire Wealth - Case Study

Answered

Objectives

Objectives

This assignment aims to assess students’ theoretical understanding and practical knowledge of concepts covered in independent learning and practical sessions.

Background

The dataset provided in this assignment is created by researchers at the Peterson Institute for International Economics (PIIE) which uses data from the 1996 to 2014 Forbes’ World’s Billionaires lists. The data includes the name, country of citizenship and networth (current US dollars) and other variables of the world’s billionaires.

Using data and analytics, we can use the data to analyse outcomes and look at trends in the numbers, geographical locations, and sources of wealth and/or net worth of the world’s richest people. The dataset provides insights on sources of wealth acquired by the world’s wealthiest, change in gender and age distribution and regions they are based in. Hence, we can learn more about changes in extreme wealth, inequalities of wealth distribution and economic problems.

You are challenged to analyse trends and insights of the world’s wealthiest or any other relevant study using the publicly available dataset. Following are required for the assignment:

Business understanding
Data understanding
Data profiling
Use of data cleaning techniques
Data integration
Data transformation and
Exploratory data analysis

Datasets

You are given 3 datasets scrapped from the dataset created by researchers at the Peterson Institute for International Economics (PIIE). To facilitate the assignment, some modifications are made where appropriate.

This dataset describes the demographics, companies info, sources of wealth:

Wealthiest Demographics: detailed data including demographics;
Company Info: detailed data of the company primarily associated with billionaire’s wealth;
Countrycode: data on countrycodes and country

The below data dictionary provides a description of each column for all datasets.

Wealthiest Demographics Dataset

Name	Description
age	The age of the billionaire
citizenship	Billionaire country of citizenship
gender	Gender identity: female or male
name	Name of the individual or family on the billionaires list
was political	True, if billionaire is linked to a politician, or questionable license
wealth.type	Source of the wealth
worth in billions	Net worth of billionaire, current US dollars in billions
country code	3 digit ISO country code
region	Location classification of the billionaire

Company Info Dataset

Name	Description
category	Broad industry categories
company.name	Company primarily associated with billionaire’s wealth
company.type	Indicates if company was new, acquired or privatized when billionaire or family members were first associated with it
founded	Founding date of the company associated with the billionaire’s wealth
gdp	By country GDP, current US dollars
country code	3 digit country code
name	Name of the individual or family on the billionaires list
industry	The industry labels based on Kaplan and Rauh (2013) which the company primarily associated with billionaire’s wealth is in
relationship	Describes the billionaire’s relationship to their company
sector	The sector which the company primarily associated with billionaire’s wealth is in
year	The year the billionaire or family is listed in the wealthiest list

Countrycode Dataset

Name	Description
citizenship	The status of being a citizen in the country
country code	3 digit country code
region	Location classification of the billionaire

Task – Case Study

What can we learn about the about changes in extreme wealth and inequalities of wealth distribution in United States, Europe and other advanced countries using the dataset on the sources of billionaire wealth? Are the wealth mostly self-made or inherited and can we identify the company and industry from which it comes?

Among self-made billionaires are the individuals, founders, executives, politically connected? Can industries, sectors, regions lead to wealth being generated faster based on the data?

What You Need To Do

Using data and analytics, we can research the data, analyse outcomes and look at trends in the numbers, geographical locations, and sources of wealth and/or net worth of the world’s richest people

You are to explore the data and provide the insights in the numbers, geographical locations, and sources of wealth and/or net worth of the world’s top wealth distribution. Before you can visualize and perform analysis on the data, there is a need to understand the business requirements, understand the data, and perform data cleaning and exploration.

Data Profiling and Data Preparation

Discuss the data quality issues found in the datasets provided.
What method(s) will you use to clean the data? Explain with details.
How will you integrate the datasets?
How will you derive any categorical columns? Document and explain in detail.
What are the imputation methods required? Explain the rationale behind it when you apply.
Apply the data transformation to some columns as appropriate.

Below are some of the fundamental questions about inequality in wealth distribution:

Are the wealthiest distributed equally (geographical locations)?
Are the demographics of the wealthiest equal (median age, citizenship, net worth)?
Which hosts are running a business with multiple listings and where are they?
Find out whether there is any relationship between the wealthiest and:
- Sector of the company primarily linked to the billionaire or family
- Industry primarily linked to the billionaire or family
- Sources of wealth
- Geographical locations
Find out whether there is any similarities/dissimilarities between the billionaires across:
- Regions
- Age group
- Industries
- Sectors
Analyse the growth of the world’s wealthiest on the timeline (any highlight over time e.g. country or region)?