Impact of Layoffs on the Individuals


This blog has been created as part of the Study of Computation Social Science. The objective is to understand how to we capture, analyse and comprehend the social data using AI / ML techniques. 

Introduction

A layoff is the temporary or permanent termination of employment by an employer, often due to financial or restructuring reasons, and not due to any fault of the employee. During a layoff, an employer may terminate the employment of one or more employees, who are then eligible for severance pay or other benefits. 

Background

Great Resignation

  • According to the U.S. Bureau of Labor Statistics, over 47 million workers in the country quit their jobs in 2021
  • Flexibility is one big contributing factor to why many working professionals now choose to join the movement in post-pandemic times 
  • In 2021, over 47 million US workers quit their jobs as businesses adopted remote work. Flexibility is a major factor in post-pandemic job choices. 
  • Around 40% of employees left their jobs as they were dissatisfied with traditional employers' lack of job security, work-life balance, and learning opportunities. 
  • Due to this there was Over hiring during the pandemic
  • Part of the rise in layoffs is due to correcting the hiring of too many people. During the height of the pandemic, the use of technology grew significantly as everything moved online. 

 Layoff's Trend

  • Consumer demand and profits boomed for online services through the pandemic, and leaders were told to grow at all costs. Amazon doubled its workforce throughout the pandemic to from 798,000 workers in 2019 to 1.6 million in 2021 
  • And after the Great Resignation and quiet quitting rocked the market, the new era of “loud layoffs” is having an outsized impact on how people feel about their jobs. 
  • In November, 11% of HR leaders said they’re planning layoffs in response to economic volatility, down from 16% in October, according to Gartner data 
  • When employees were laid off or terminated, the likelihood that their direct colleagues would quit was 7.7% higher than if those employees had remained 
  • The increased likelihood began on day one of the peer's layoff or firing and increased sharply 45 to 105 days after the other worker's termination, with a peak at around 75 days. 

Our Hypothesis

  • The recent layoffs in the technological space seem to have a multi-fold impact across the tech industry. There are three broad categories of the workforce – 
    • Beginner (up to 28 years) 
    • Mid-career (28 years to 40 years) 
    • Senior (40 years plus)
  • On these three categories of professionals, our hypothesis is that mid-career individuals might have a major impact considering the following factors – 
    • Financial Stability: Significant reduction in income, EMIs might be in progress 
    • Setting up Family: Support wife and kids. Ageing Parents 
    • Opportunity to switch jobs: The trend of layoffs follows all over, so getting a job is not easy. Also, changing the career to some other domain may not be easy 
    • Social Impact: Losing a sense of identity and community. Losing colleagues and the daily routine of going to work can be difficult to adjust to and can lead to feelings of isolation. 
    • Emotional Impact: Losing a job can be a blow to their self-esteem, as it can feel like a rejection of their skills and abilities. It can also lead to feelings of uncertainty about the future, and anxiety about finding a new job. 
    • Financial Stability Setting up Family: Support wife and kids. Aging Parents. 

Problem Statement

“Investigate the impact of layoffs on mid-career individuals, with a focus on the psychological, financial, and professional consequences of job loss.”

Our Approach

  • Choose to manually collect data from the posts made by individuals about layoffs on LinkedIn 
  • Reasoning behind choosing the data from LinkedIn 
    • Twitter: predominantly news and discourse on layoffs, less personal experiences shared, less meaningful data for study 
    • Layoffs.fyi : daily and company level stats on layoffs, also contains company wise lists of people looking for opportunities, Data used to gather statistics 
    • Reddit: Anonymous posts and news about layoffs, lacks demographic information about affected individuals, data is not reliable 
    • Blind: Trusted information but anonymous data. Cannot gather demographic information 
  • Drive the insights from the posts as 
    • Emotions 
    • Impact 
    • Inferences 
    • Compared the data across different geographic, job profiles and experience levels 
    • Used EmoRoBERTa and BertTweet ML model to extract the insights 

Findings

Word Cloud from Data

Heatmaps

Age Group wise Emotion Distribution














Country wise Emotion Distribution



















Job Function wise Emotion Distribution

BERT Tweet Model for grouping of emotions

Analysis Summary

Age Group

0-5 years

More than 50% of people are being admiration and optimistic regarding the layoffs They are most expressive among other experience group Asking for more care during tough times

6-10 years

40% people are showing 92% gratitude regarding layoffs They are aspirational about layoffs

10-15 years

30% people are showing 65% admiration towards the opportunities they got as part of the company Remaining people are being neutral about layoffs

15+ years

Most of the people are feeling saddest about the layoffs among all other experience groups

Demographic

Indians

  • 51% of Indians are feeling admiration and gratitude towards their company amid layoffs
  • 22% of Indians are feeling approval and disappointment towards their company
  • Intensity of gratitude is less in Indians
  • Indians are expressing more joyous than other countries regarding working for an organisation

USA

  • 28% of Americans are feeling admiration
  • 30% of Americans are having realisation, disappointment

 Job Function

Majority of posts on layoffs are being posted by software engineers, then next comes human resources

Software engineers

  • 27% of software engineers are in admiration about the layoffs and company
  • 15% of them are disappointed
  • 21% are feeling gratitude

Human Resources

  • 20% of HR's are feeling admiration and gratitude regarding layoffs
  • 30% are feeling neutral, joy and disappointment of layoffs

Limitations & Ethics

Ethical Clearance

  • All personally identifiable information will be masked for the study.
  • To adhere to LinkedIn platform guidelines, data will not be collected using any programmatic means. All data will be curated manually using accounts of real people.
  • To avoid traceability, company names will be masked. Demographic information will be grouped to study the patterns.
  • No re-identifiable information will be published in public domain. Data access will be restricted to students who are conducting the study and evaluators only.
  • All the inferences are made on aggregated data and the original transcripts of Linkedin posts are not used for analysis beyond forming inferences

Limitations/Bias

  • Data source bias
    • Usage of LinkedIn data than user surveys, interviews because of time constraints of project
  • Population bias
    • Only people connected on the social media up to 3rd person view were covered
    • Considered only India and USA
  • Normative bias
    • LinkedIn posts are more formal
    • Less intense response
    • Folks wanted to keep their profile clean.
  • Data Collection
    • Used anonymized and fresh profile for most of the data
  • Algorithm
    • sentiment-analysis --> arpanghoshal/EmoRoBERTa
    • sentiment-analysis --> finiteautomata/bertweet-base-sentiment-analysis
  • Only gathered 200+ posts through LinkedIn
    • Algorithm could accept only 512 characters
  • 15+ years of experience age group is not very active on LinkedIn

Team

  • Bharat Subhash Surana
  • Manisha Kallepalle
  • Somana Sarath Kumar
  • Sri Varshini
  • Umesh Udayaprakash

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