Paternity leave trends & patterns

The goal of this project was to obtain parental leave trends and patterns around the business world using crowdsourced information from 1601 companies across different industries. During the exploratory data analysis, I found that the average might be misleading because of the right-skewed distribution of the data. Therefore, I focused on the analysis from the perspective of the ILO's recommendations for parental leave.

The results show that although significant progress has been made in parental policies by companies, there is still much to be done to achieve more gender equality at work.

About this project

In this project, I worked as a Data Visualization Specialist at an online business journal. My role was to create charts, visuals, and infographics as supporting content for articles.

This particular article focused on Parental Leave Policies across the business world, and this impactful visual has been created as supporting content, to mark Women’s History Month in the United States.

My aim as a Data Visualization Specialist was to make readers of this article understand the content without having to read the article itself. As they say, “A picture is worth a thousand words”.

The Dataset:

The dataset (provided by Maven Analytics) contains a table in CSV format with 1,601 records, one for each company.

• Each record contains the company's name & industry:sub-industry, as well as crowdsourced information on the paid & unpaid weeks off they offer as part of both their maternity & paternity leave policies (when available)
• Tags: Business, Human Resources, Leave Policies
• Data Structure: Single Table
• Number of Records: 1,601
• Number of Fields: 6

Approach:

The idea I had was to create infographics for the users to easily understand the data.

The idea was to make readers of this article understand the content without having to read the article itself, using impactful visuals, charts, and infographics.

Step 1: Data Cleaning & Aggregation

• CountBlank function was used in MS Excel to ascertain missing data (if any).
• The data was quite clean. What was needed was to manipulate the data to fit the brief.

Step 2: Data Manipulation

• Missing Data: Some of the fields (especially “Paid Paternity Leave” and “Unpaid Paternity Leave” contained missing data (N/A).
• This type of missing data is termed Missing At Random (MAR): there is randomness in missing values within the male (paternity) and female (maternity) subgroups.
• The causality of the demography leading to missing data cannot be proved in most cases, as we do not have an exhaustive set of variables affecting the decision to fill the survey.

a.Mean: used when distribution is near normal.

b.Median: used in case of significant presence of outliers in the data.

c.Mode: used for categorical variable.

As a user-reported data, where users report conflicting information, consensus numbers (if any) or the median are shown.

So, initially, I used the median numbers for “Paid Paternity Leave” and “Unpaid Paternity Leave” fields to replace the “N/A”s (using CTRL + H).

Median for “Paid Paternity Leave” = 6.

Median for “Unpaid Paternity Leave” = 6.

But this approach distorted the distribution.

After a second look at the distribution, I concluded that since “N/A” means “no information has been reported”, then the companies had zero leave policy. In other words, instead of using the “median” approach, “N/A”s were replaced with “0”.

Step 3: ETL

Step 4: Data Transformation and Field Categorization in Power Query

Used split column by delimiter to extract the sub-industry from the industry column.

Created a new fact table for the industry column called Industry_Fact.

Step 5: Data Modelling

Connect Industry_Fact to Parental_Leave table using industry column.

Step 6: DAX creation (Paid Maternity Leave, Unpaid Maternity Leave, Total Maternity Leave, Paid Paternity Leave, and Unpaid Paternity Leave, Total Maternity Leave, Companies meeting standard maternity paid, Companies meeting standard maternity paid, %Maternity paid, %Paternity paid )

Step 7: Analyses and Dashboard Design

Challenges:

The major challenge was in trying to understand how best to deal with "N/A" values and to create minimal charts and visuals that will adequately tell the story.

1. On average, companies offer 10.9 weeks of paid maternity leave and 1.3 weeks of paid paternity leave. Paid paternity leave average is far less than the recommended standard of 12 weeks.

2. Only 789 out of the 1,601 companies meet the minimum standard of 12 weeks of maternity leave, while only 107 companies meet the minimum standard for paternity leave of 8 weeks.

3. The percentage of paid leave for employees, 53 Companies do not have data for paid Maternity Leave. 97% of companies offer paid maternity leave. 1344 companies do not have data on paid paternity leave. only 16% of companies offer any paid Paternity leave.

Recommendations:

· Infant heath is best supported by exclusive breastfeeding for six months (WHO recommendations based on research evidence). The best way to support this is to provide at least six months of paid maternity leave.

· Where this is not possible, a minimum of 18 weeks of paid maternity leave should be provided (consistent with current ILO recommendations).

· Fathers should be provided with paid paternity leave of adequate length to support bonding with the infant, establish a role for the father in the care of the child, and support children’s health development and gender equality. There is substantial evidence that paternity leave increases a father’s involvement, reduces gender inequality, and benefits both infant and maternal health.

· Total paid parental leave (maternity, paternity and parental) should be long enough to ensure access to all preventive care and to ensure high-quality infant care at least until the age at which affordable, quality non-parental care is available. This should be at least 6 months and, in many settings, should total 9–12 months.

·Paid parental leave should be structured to better cover the informal sector.