Analysis of gender gaps in talent management
The disaggregation of information by sex is an indispensable tool and a first step to use the gender approach in the operation of the company. Its importance lies in the fact that it allows to make visible the differentiated situation of women and men, calculate gender gaps, choose the highest priority actions, plan in an informed way and monitor the impact of the actions.
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Analysis of gender gaps in talent management
To generate and use sex-disaggregated data in the organization on a permanent basis, it is recommended:
- Exploit the available data including the sex variable, especially those that can be taken from administrative records.
- Identify the gaps between women and men, do not only present data on women.
- Cross the sex variable with other relevant characteristics to deepen the analysis.
- Present reports with statistics and indicators disaggregated by sex on a regular and systematic basis, as well as use the results for decision-making.
This tool aims to highlight the importance of adding disaggregation by sex and age in talent management in a company, to identify gender gaps. It is a medium level of complexity and requires a basic understanding of Power BI Desktop.
Tool 12. Data analytics for my company from lens 3 is an introduction to data analysis or data analytics
This guide is focused on a managerial profile and the team of human resources analysts of a company. It explains which graphs allow a better visualization of the distribution by sex and age in various areas of a company. It also explains how to perform the sex ratio calculation using Power BI.
Talent management is a central aspect in companies. Recognizing the importance of having employee growth and retention plans implies having key information about the people who make up teams, such as sociodemographic characteristics and performance. You can seize the opportunity to strategically use that data and learn about retention and performance trends by looking at gender and other demographics.
Deepening the knowledge of the personnel is essential in any analysis of human resources. Having sociodemographic information that characterizes people can enhance the development of growth plans aligned with their profiles and close gender gaps in positions and areas where they exist. For example, a company can detect that there are different proportions according to sex and age, depending on the position, leadership level or area. Analyzing the data, questions such as: are there certain occupations with a greater presence of men in the company? Are women receiving the same training as men? Are leadership positions equal between them and them?
The analysis should be considered from the recruitment and selection phase. Are women and men applying for vacancies equally? Are people of both sexes making progress in the selection processes? Data analysis tools allow a simple and clear visualization of gender gaps within a company, which is the first step in designing strategies to start closing them.
Definitions and key terms
In order to promote a better understanding of this tool, some key terms are presented that will be covered in this guide.
Stacked bar charts:
When wanting to visualize whether within a company there are differences between groups of people by variables such as sex and age, it is significant to reflect on the type of graph that best expresses the distribution. Line, pie, and bar charts are the most common, but they fail to adequately display the composition of groups of people.
In order to be able to see in a simple and easy way distributions by sex and age, the use of bar graphs stacked at 100% is suggested. These types of graphs expose the percentage of the totality of each group and are represented by the percentage of each value against the total amount in each group. This makes it easier to see the relative differences between the quantities. They are useful to reveal the compositions, for example, to analyze if the distribution by sex of the people is equal in the areas of a company or to see the distribution of women and men according to their seniority.
The ratio or proportion of sex (also known as sex ratio) is calculated as the quotient between the number of men (numerator) and the number of women (denominator). The calculation is completed by normalizing the result by 100. How do you read this ratio? If the sex ratio is greater than 100, it means that there is a higher proportion of men than women. If the sex ratio is less than 100, it means that there is a higher proportion of women than men.
The sex ratio is a simple and easy-to-read metric that can be used when it is necessary to synthetically view the distribution of a group of people by gender. It is practical and allows you to see with a single number how balanced a population is in terms of the proportion of women and men that make it up. It can be used in all types of analysis of groups of people within a company, suppliers or clients.
To exemplify the use of this tool, the case of a company with 1,300 male and female employees is presented and two ways of graphing the distribution by sex according to their age are used: stacked bars and 100% stacked bars.
The graph on the right shows that the higher the seniority (measured in years), the higher the proportion of women. Among those who have been here for less than a year, the proportion of men is almost equal to that of women, while among those with more than 5 years of age, 77% are women.
In the graph to the left, it is not at first glance that the proportion of women is constantly growing, group after group. That is why it is recommended to use 100% stacked bars to better visualize these differences.
Graph 1. How to better visualize gender and age distribution with 100% stacked bars
Fuente: Elaboración propia con base en la información del caso de uso
This video details the process of preparing the dashboard that shows different examples of how to visualize disaggregations based on the use case:
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Steps for implementation
- Steps to build a stacked bar chart in Power BI
- Steps for calculating the sex ratio
- Formula for calculating the sex ratio
- Select the fifth available graphic in the “visualizations” tab.
- Once the graph to be used has been selected, to replicate the graphs presented in the video, it is necessary to complete three boxes within the “fields” section: axis, legend and values.
- In the “axis” box, put the variable that we decide to implement to make the division between bars.
- On the other hand, in the “legend” box, the variable that we want to use to perform the division within each bar is inserted. In the case of the example, given the nature of the board presented, the variable “sex” was always used.
- Finally, inside the “values” box, insert the variable that indicates the value that we need the bars to take. In the presented board, we tried to make a simple count of all the observations. For this reason, the count of the variable “generated_id” is used, which is a unique identifier for each of the observations presented in the database.
- The first step in calculating the sex ratio analogous to the one presented in the video is the creation of a measure. To do this, it is necessary to go to the “fields” tab, touch the right button on the name of the loaded database and select the option “new measure”.
- Once the index is created, the calculation of the sex ratio itself will have three steps.
- First, the number of observations in the database that have been identified as male is counted and divided by those that have been identified as female. For this, the “calculate” function is used, followed by the “count” function.
- To differentiate the calculation from the observations of men and the observations of women, it is necessary to filter it with the variable “sex” and the respective value of its categories (in this case it will be M for men and F for women).
- Finally, the calculation is closed by multiplying the division by 100 to normalize the result obtained.
Sex Ratio = (CALCULATE(count(‘Consolidated_base´[generated_id]), ´ Consolidated_base ´[SEX] = “M”)/ CALCULATE (count ‘Consolidated_base‘[generated_id]), ‘Consolidated_base‘[SEX] = “F”))*100
- Consolidated_base is the name of the database
- generated_id is the name of the database field that allows us to count the number of people in our database
- Sex is the name of the database field where the declared sex of the person is specified. It has two values, F=Female, M=Male.