Global Layoffs(2020–2022): My First Data Analytics Project
Background
Considering the recent layoffs in big tech companies, I got curious. Is this isolated to companies with funding difficulties in a specific country or spread across other countries? To solve the riddle, I went on Kaggle.com and found that tens of thousands of people have lost jobs across 55 countries, 98 companies and 27 industries in the past two years since COVID-19 was declared a pandemic by the World Health Organisation.
About the Dataset
As stated above, the dataset was obtained from Kaggle.com https://www.kaggle.com/datasets/swaptr/layoffs-2022. It reports the layoffs across 55 countries and 27 industries from 2020 to 2022. The data has nine columns: company(name of layoff company), location(location of the company headquarters), industry(industry of the company), total laid_off(number of employees laid off), percentage laid_off(percentage of employees laid off), date(date of layoff), stage(stage of company funding), country(the country company resides), fund_raised(fund raised by the company in Million $).
Data Wrangling(Cleaning) Process
Before working on any data downloaded in a version other than xlsx, the first step is to convert it by saving it as a new document. I “save as” instead of “save” directly because keeping the raw data safe and untouched is essential. After converting, it is time to clean the data and prepare it for uninterrupted analysis.
I cleaned the data following the steps below:
1. Converted funds_raised to Currency $
2. 1 duplicate found and removed.
3. Added a new column: Year, and extracted from the date column
4. Added a new column: Month, and extracted from the date column
5. Added a new column: Month in words, and extracted from the date column
6. Added a new column: Fund raised in million (to have the accurate total on the pivot table)
7. Found unique values of funding stage
8. Found unique values of countries to know the number of countries we have: 55
9. Found unique values of companies to know the number of companies we have: 98
10. Found unique values of companies to know the number of industries we have: 27
11. Turned data into a table and named it Layoff_Table.
12. And finally, summarised with pivot table.
Data Analysis
To confirm my assumptions that global layoffs are isolated to companies with funding difficulties in a specific country, as stated in the background, I found answers to the following questions:
· Layoff by Countries
· Top 10 Layoff Companies
· Layoff by Month
· Layoff by Year
· Layoff by Industry
· Funding by Month
· Layoff by Stage
Layoff by Countries
Represented as “Layoff by Countries” on the dashboard. On the pivot table, I inserted the country column into the row tab and total_laid_off in the value tab(where the number of layoffs is summed up automatically) and finally filtered the top 10 countries.
The highest layoff was in the United States, five times more than the second country, India. I used a bar chart to visualize the data to see clearly the number of layoffs and differences across the top 10 countries.
Top 10 Layoff Companies
Represented as “Top 10 Layoff Companies” on the dashboard. On the pivot table, I inserted the company column into the row tab and total_laid_off in the value tab(where the number of layoffs is summed up automatically) and finally filtered the top 10 companies.
In relation to the Layoff by Countries above, of the top 10 companies with the highest layoff, the first eight are all located in the USA.
Layoff by Month
On the pivot table, I inserted the month in words column into the row tab and total_laid_off in the value tab(where the number of layoffs is summed up automatically).
I used a line chart to visualize layoffs by month to show the dismissal across the twelve months.
Layoff by Year
On the pivot table, I inserted the Year column into the row tab and total_laid_off in the value tab(where the number of layoffs is summed up automatically).
I used a doughnut chart to visualize layoffs by year because there are only three values.
Layoff by Industry
On the pivot table, I inserted the Industry column into the row tab and total_laid_off in the value tab(where the number of layoffs is summed up automatically) and finally filtered the top 10 industries.
I used a column chart to visualize layoffs by industry and picked out the top ten industries to see which industries laid off the highest number of staff.
Funding by Month
On the pivot table, I inserted the Month in words column into the row tab and the fund raised in the value tab(where the number of layoffs is summed up automatically).
I used a line chart to show the time series and the differences across the twelve months.
Layoff by Stage
On the pivot table, I inserted the Stage column into the row tab and total_laid_off in the value tab(where the number of layoffs is summed up automatically) and filtered the top 5 stages with the highest layoff.
I used a column bar to visualize my data because it has more than four values, and it makes the data easier to interpret.
Data Visualisation
To visualize the data, I drew a template(as seen below), which guided me in picking suitable charts for the data analyzed above.
All data analyzed can be viewed at a glance on this dashboard and understood without looking at the raw data. The dashboard below is my first. Your comments and recommendations will be greatly appreciated.
Insight
As stated in the introduction, we understand that layoffs have been rampant in tech companies in the US. However, in this dataset, unlike our understanding, tech companies like Meta and Amazon were categorized as Consumer and Retail, respectively. So that raises questions like which companies are seen as “tech companies” is “tech” an industry? There is no one size fits all answer to these questions. Some experts believe tech is an industry, while others believe all living companies use technology today. Hence tech is not an industry. It serves every industry.
On the first look at the data, I assumed companies with less funding would automatically lay off more staff, simple logic, less money, and fewer people. However, to my surprise, listed companies (IPO) with the highest funding laid off the highest number of people. If the value of the top five stages is represented in percentage, IPO accounts for 72.72% of the total funding and 52.31% of the total layoff.
Based on research, listed companies (IPOs) often face external pressure and are more volatile because of the stock market. Companies are obligated to create value and profit for the stockholders, and because their report sheet is public, the pressure and structural changes that occur when they go public make them more susceptible to cutting funds, hence cutting staff.
We see from Layoff by Year above that layoffs have been significantly affected by the COVID-19 pandemic in 2020 and geopolitical issues leading to supply chain disruptions, high inflation, high-interest rates and low spending in 2022. The year is not over, as we still hear news of big tech companies sending staff home to cut spending and increase profit for shareholders and, for some, to keep the company running.
As seen in Layoff by Month, the layoff was highest in November, which is a result of the layoffs in 2022, accounting for 20.93% of the total since March 2020. Although we see a steep in November compared to April to June in Funding by Month, contrary to our understanding, of the total funding received in November (2020 to 2022) — — 9.74%, 9.69% of it was obtained in November 2022. Hence, we see there is no direct correlation between funding and high layoff in November 2022.
“November has been the worst month so far in 2022,” said Roger Lee, who runs the site layoffs.fyi, and has been tracking tech firings since the start of the pandemic.
While each company is facing its own challenges as we expect a recession to loom next year, most big layoffs in November 2022 have been because companies are cutting off business units that no longer fit the present world or don’t earn as expected. Whether or not there will be a recession next year is still debatable. Still, with inflation all over the globe at decades high, consumers and the market confidence level is low, disrupting the market order.
Recommendations
Companies should, for future purposes, build a system that better takes care of laid-off employees, not just financially but mentally. Getting an email or letter telling you you just lost your job after a night’s sleep or during the night is a nightmare no one wants to experience. It is traumatic, to say the least.
For employees, having multiple plan Bs when the market is booming never fails. Not everyone can smell doom loom; even if we can predict downturns, we don’t predict losing our jobs, so having multiple plan Bs and emergency funds will go a long way.
Conclusions
As the data suggests, the companies’ funding stage and how much funds were raised are in no way correlated with the number of staff laid off. Also, unlike I assumed before analysing, the layoff is not particular to the US but spread worldwide.
