Data Source: https://www.kaggle.com/datasets/salimwid/technology-company-layoffs-20222023-data?resource=download

Overview/Narrative

The technology industry is an ever-evolving and growing sector that has significantly impacted the global economy. However, the sector has recently been subject to a series of tech layoffs, causing a significant loss of jobs and a decline in the overall economic landscape. This situation has prompted concerns and discussions about the future of the industry, its resilience, and its long-term impact. With so many companies laying off workers, the following research question comes to mind: which tech industry has been most affected by these layoffs? Answering this question is crucial to understand the specific areas that are most affected by the tech layoffs, determine the areas that require more attention and support, and inform policy makers and stakeholders in making better decisions for the future of the tech industry.

In terms of data selection, I found this data set off of Kaggle, which I have included the link to above. I performed a lot of data preperation for this project, including doing research to generalize some industries so that there were a fewer amount of categories when some initial industries were too specific. I also got rid of certain data points that I felt were not relevant to the tech industry or had too few statistics. I also changed the impacted_workforce_percentage variable to be a range instead of a specific number so that I could better represet my data. I decided to utilize three different visualizations to answer my research question, which were a pie chart, bar graph, and scatter plot. I used a pie chart to show how drastic the increasing number of tech company layoffs are, especially within the past month. I used a bar graph to display the tech layoffs in a given industry, and I used a scatter plot to show how affected a specific industry’s workforce percentage was.

Pie Chart-Understanding the Impact of layoffs in the past year

To begin, it is essential to understand the gravity of the situation regarding the recent increase in tech layoffs before diving into industry-specific issues. Thus, it is imperative to demonstrate how drastic the increase of layoffs has been in the past year to prove that this is a pressing issue that demands attention. Below, I have created a pie chart of how many total layoffs there were within my data sample for every month from February 2022 to January 2023.

library(openxlsx)
library(ggplot2)
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
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##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union
techlayoffs = read.xlsx("tech_layoffs.xlsx")

colors <- c("#D85649", "#B0E0E6", "#FFD700", "#90EE90", "#ADD8E6", "#00FFFF", "#FFB6C1", "#DDA0DD", "#FA8072", "#7FFFD4", "#A9A9A9", "#C0C0C0")


plot_ly(techlayoffs, labels = ~reported_month, values = ~total_layoffs, type = "pie", 
        marker = list(colors = colors)) %>%
  layout(title = "Total Layoff Percntage in Tech Industry Monthly")

As we can see, January 2023 has been the month with the most tech layoffs in the past year at 48.9%. From my sample data, 64,991 employees with the tech industry have been laid off within this time. November 2022 came in second with 20% of the layoffs (26,507 employees), and October 2022 in third with 7.67% (10,181 employees). Thus, we now understand how this is an issue that has been especially prominent within the last 5 months.

Bar Chart-Diving into Specific Industry Layoffs

Moving on to industry specific visualizations, I decided to create a bar chart where each bar is a specific tech industry to compare how many total layoffs were performed in the past year. This will give a deeper insight into how specific sectors of the tech industry have been impacted by the layoffs, and allow us to identify which sectors are struggling the most. By comparing the heights of the bars, we can see which industries have experienced the most layoffs, and gain a better understanding of how the tech industry as a whole has been affected.

ggplot(techlayoffs, aes(x = industry, y = total_layoffs, group = 1)) +
  geom_bar(stat = "identity", fill = "#1c67c8") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab("Industry") +
  ylab("Total Number of Layoffs") +
  ggtitle("Tech Layoffs in the past year by Industry")+
  theme(axis.text.x = element_text(angle = 290, hjust = 0))

From this visualization, we see that the Software, E-commerce, and Media were the three tech specific industries with the highest number of layoffs within the past year. Miscellaneous Tech and Fintech also have very notable impacts from the layoffs.

Scatter Plot-Specific Workforce Impacts in Tech Industries

However, this visualization does not take into account how affected the workforce of a specific company was within the given industry. To show this, I created a scatterplot and created intervals of 10% to see if there was at least one company in that industry that had within a certain range of their workforce affected. If there was other valuable data for a company but it did not provide the Impacted Workforce Percentage statistic, this is listed as “Unclear” at the top of the scatterplot.

plot_ly(techlayoffs, x = ~industry, y = ~impacted_workforce_percentage, type = "scatter", mode = "markers") %>% 
  layout(title = "Impacted Workforce Percentage in a specific Tech Industry",
         xaxis = list(title = "Tech Industry"),
         yaxis = list(title = "Impacted Workforce Percentage"))

From this visualization, we can see that although Software is the industry with the most people affected by the layoffs, there was not a company within the sample that had more than 40% of their workforce affected. The same goes for E-commerce, which is the industry with the second most employees affected. We can see that Fintech (financial technology), Health Care, Media, Miscellaneous Tech, and PropTech (property technology) all had at least one company that fully shut down due (in the 99%-100% range means that 100% of operations shut down, 99% is only listed because otherwise this statistic was listed at the bottom of the scatterplot).

From the specific tech industry visualizations we have seen, I believe that Media is the most affected tech industry. This is because they have laid off the third highest amount of employees out of all the industries, and have had companies lose 70-79% or even 100% of their workforce. However, I do believe this data is up to interpretation for what a person wants to classify as “most affected”. It is very arguable that Software E-commerce is the most affected since they had the highest numbers of employees laid off, of Fintech because they have had many companies that impacted large ranges of their workforce. At the same time, I personally believe Media has been projected to be a struggling industry in the news recently, and both visualizations emphasize the different affects on this industry.