Gender Assessment Data

About the Data

The Global Gender Assessment data set provides insights into how companies across various industries and regions perform on a wide range of gender equity measures. This data set includes scores on strategic actions, leadership, grievance mechanisms, pay equity, and many other factors related to gender based practices within corporate environments.

Collected in 2023, the data allows for comparative evaluation across countries, sectors, and ownership types (e.g., Public, Private, Government). Each record represents a company and its corresponding evaluation across 28 detailed gender related indicators, offering a comprehensive snapshot of corporate gender responsibility worldwide.

The data set can be used by researchers, policy analysts, and advocacy groups to track progress on corporate gender equity, identify gaps, and support data driven decision-making in both public and private sectors.

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Metadata

CSV Name

genderassessment.csv

Data set Characteristics

Multivariate

Subject Area

Equity, Gender, Corporate Governance

Associated Tasks

Comparative Analysis, Score Aggregation, Visualization

Feature Type

Character, Numeric, Factor

Instances

2000 records

Features

30

Has Missing Values?

Yes

Variables

Variable Name	Role	Type	Description	Units	Missing Values
company	ID	Character	Name of the company	–	No
country	Feature	Character	Country where the company is headquartered	–	Yes
region	Feature	Factor	Geographical region (e.g., North America, Europe)	–	Yes
industry	Feature	Factor	Industry sector (e.g., Chemicals, Finance, Mining)	–	Yes
ownership	Feature	Factor	Ownership type (e.g., Public, Private, Government)	–	Yes
year	Feature	Numeric	Year of assessment	Year	No
score	Target	Numeric	Overall gender assessment score	–	Yes
percent_score	Target	Numeric	Overall score expressed as a percentage	%	Yes
strategic_action	Feature	Numeric	Score for strategic gender action	–	Yes
gender_targets	Feature	Numeric	Score for gender-related target setting	–	Yes
gender_due_diligence	Feature	Numeric	Score for due diligence on gender issues	–	Yes
grievance_mechanisms	Feature	Numeric	Availability and quality of grievance mechanisms	–	Yes
stakeholder_engagement	Feature	Numeric	Score for gender-related stakeholder engagement	–	Yes
corrective_action	Feature	Numeric	Corrective measures in place	–	Yes
gender_leadership	Feature	Numeric	Gender representation in leadership roles	–	Yes
development_recruitment	Feature	Numeric	Recruitment and career development opportunities by gender	–	Yes
employee_data_by_sex	Feature	Numeric	Availability of employee data by sex	–	Yes
supply_chain_gender_leadership	Feature	Numeric	Gender representation in supply chain leadership	–	Yes
enabling_environment_union_rights	Feature	Numeric	Union rights and enabling environment for gender equity	–	Yes
gender_procurement	Feature	Numeric	Use of gender-based procurement policies	–	Yes
gender_pay_gap	Feature	Numeric	Disclosure and performance on gender pay equity	%	Yes
carer_leave_paid	Feature	Numeric	Paid leave policies for caregivers	–	Yes
childcare_support	Feature	Numeric	Availability of childcare-related policies	–	Yes
flex_work	Feature	Numeric	Flexible work options	–	Yes
living_wage_supply_chain	Feature	Numeric	Enforcement of living wage in supply chain	–	Yes
health_safety	Feature	Numeric	Workplace health and safety	–	Yes
health_safety_supply_chain	Feature	Numeric	Supply chain health and safety indicators	–	Yes
violence_prevention	Feature	Numeric	Policies and measures to prevent gender-based violence	–	Yes
violence_remediation	Feature	Numeric	Mechanisms for remediation after violence incidents	–	Yes

Key Features of the Data set

Each row represents a single company’s gender assessment in a given year and includes organizational details, overall scores, and performance on various gender-related indicators:

• company – The name of the company assessed for gender equity practices.

• country – The headquarters location of the company, used for geographical grouping.

• region – Broader geographical area of the company’s location (e.g., North America, Europe), used for regional comparisons.

• industry – Sector to which the company belongs (e.g., Chemicals,Electronics), used to evaluate industry-wise performance.

• ownership – Type of ownership structure (e.g., Public, Private), used to study differences in scores by governance type.

• year – The year in which the assessment was conducted, allowing for temporal comparisons.

• score – The overall gender assessment score reflecting the company’s total performance.

• percent_score – The overall gender assessment score expressed as a percentage for easier comparison.

Purpose and Use Cases

This data set supports analysis of:

• Gender equity performance across industries, regions, and ownership types.

• Temporal trends in corporate gender assessment scores with a focus on changes over time to track progress or setbacks.

• The influence of gender-related policies and corporate governance structures by examining how internal practices and ownership models affect outcomes.

• The relationship between regulatory environments and gender equity outcomes exploring how regional or national policy contexts align with performance.

Case Study

Objective

Gender equality in the workplace is a critical goal for sustainable and inclusive development. Companies are increasingly being evaluated not only on financial metrics but also on their social and governance performance, including how they support gender equity. This data set offers a standardized assessment of a company’s gender-related policies and practices across the world.

To explore these dynamics, we focus on the following key questions:

What is the average Overall Gender Assessment Score by industry, and which industries have the highest and lowest average scores?

Does ownership type affect gender assessment performance?

By analyzing the score variable across industry and ownership categories, and exploring key gender-related indicators, we aim to identify structural patterns and gaps in corporate gender performance.

Analysis

Loading Libraries

library(diversedata)
library(tidyverse)  # includes ggplot2, readr, dplyr
library(janitor)    # for data cleaning
library(knitr)  # For kable() function
library(car)    # For leveneTest()
library(ggthemes)   # extra ggplot themes
library(ggalt)  # For geom_dumbbell
library(FSA) # For Dunn's test

1. Data Cleaning & Processing

gender_data <- genderassessment |> 
  clean_names() |> 
  drop_na(score) |> 
  mutate(
    industry = as.factor(industry),
    ownership_type = as.factor(ownership)
  )
head(gender_data)

# A tibble: 6 × 30
  company            country region industry ownership  year score percent_score
  <chr>              <chr>   <chr>  <fct>    <chr>     <dbl> <dbl>         <dbl>
1 3M                 United… North… Chemica… Public     2023  11.3            22
2 Asos               United… Europ… Apparel… Public     2023  16.9            32
3 A.P. Moller - Mae… Denmark Europ… Freight… Public     2024  10.9            21
4 ABB                Switze… Europ… Capital… Public     2023  12.8            25
5 AbbVie             United… North… Pharmac… Public     2023  15.4            30
6 Abercrombie & Fit… United… North… Apparel… Public     2023  10              19
# ℹ 22 more variables: strategic_action <dbl>, gender_targets <dbl>,
#   gender_due_diligence <dbl>, grievance_mechanisms <dbl>,
#   stakeholder_engagement <dbl>, corrective_action <dbl>,
#   gender_leadership <dbl>, development_recruitment <dbl>,
#   employee_data_by_sex <dbl>, supply_chain_gender_leadership <dbl>,
#   enabling_environment_union_rights <dbl>, gender_procurement <dbl>,
#   gender_pay_gap <dbl>, carer_leave_paid <dbl>, childcare_support <dbl>, …

2. Exploratory Data Analysis

Average Overall Assessment Score by Industry

industry_stats <- gender_data |> 
  group_by(industry) |> 
  summarise(
    avg_score = mean(score, na.rm = TRUE),
    median_score = median(score, na.rm = TRUE),
    sd_score = sd(score, na.rm = TRUE),
    n = n()
  ) |> 
  arrange(desc(avg_score))

kable(industry_stats, digits = 2, caption = "Average Score by Industry")

Average Score by Industry

industry	avg_score	median_score	sd_score	n
Personal & Household Products	14.69	15.60	4.64	20
Electronics	12.63	13.30	5.44	55
Pharmaceuticals & Biotechnology	12.37	12.90	5.80	29
Apparel & Footwear	11.49	10.80	5.98	68
Banks	10.73	11.20	5.80	150
IT Software & Services	10.24	10.25	5.85	34
Retail	9.71	10.45	6.40	18
Insurance	9.60	10.20	5.26	71
Motor Vehicles & Parts	9.54	10.70	6.23	45
Oil & Gas	9.50	10.30	5.93	97
Capital Goods	9.38	9.80	5.99	25
Chemicals	9.23	9.80	4.71	70
Telecommunications	9.09	10.00	5.70	83
Entertainment	9.00	9.00	6.51	2
Payments	9.00	9.00	9.62	2
Food Retailers	8.93	9.10	5.39	76
NA	8.28	8.40	9.58	5
Containers & Packaging	7.53	8.00	4.59	23
Traditional Asset Managers	7.51	8.20	5.32	63
Food Production	7.50	7.10	5.53	206
Development Finance Institutions	7.43	7.60	4.33	15
Utilities	6.95	7.30	5.64	125
Conglomerates	6.94	7.75	6.33	18
Postal & Courier Activities	6.94	6.90	3.35	21
Metals & Mining	6.86	7.35	5.62	114
Construction Materials & Supplies	6.81	6.90	5.33	46
Paper & Forest Products	6.66	6.20	4.04	29
Freight & logistics	6.60	6.50	3.91	33
Waste Management	6.51	7.25	5.70	18
Real Estate	6.38	6.00	5.16	147
Investment Consultants	6.38	5.80	6.22	5
Construction & Engineering	5.64	5.35	5.07	56
Passenger Transport	4.89	2.90	5.21	101
Agricultural Products	4.24	4.25	3.60	32
Alternative Asset Managers	3.63	2.90	3.84	21
Sovereign Wealth Funds	3.18	0.50	4.80	19
Pension Funds	2.97	1.30	3.62	58

Visualization: Average Overall Gender Assessment by Industry Group

gender_data_grouped <- gender_data |> 
  mutate(industry_group = case_when(
    industry %in% c("Apparel & Footwear", "Food Production", "Food Retailers", 
                    "Retail", "Personal & Household Products", "Agricultural Products") ~ "Consumer Goods",
    
    industry %in% c("Banks", "Insurance", "Pension Funds", "Sovereign Wealth Funds", 
                    "Alternative Asset Managers", "Traditional Asset Managers", 
                    "Investment Consultants", "Payments") ~ "Finance",
    
    industry %in% c("Pharmaceuticals & Biotechnology") ~ "Healthcare",
    
    industry %in% c("IT Software & Services", "Telecommunications", "Electronics") ~ "Technology",
    
    industry %in% c("Oil & Gas", "Utilities") ~ "Energy & Utilities",
    
    industry %in% c("Chemicals", "Capital Goods", "Freight & logistics", 
                    "Passenger Transport", "Metals & Mining", "Containers & Packaging", 
                    "Motor Vehicles & Parts", "Postal & Courier Activities", 
                    "Waste Management", "Paper & Forest Products") ~ "Industrials",
    
    industry %in% c("Construction & Engineering", "Construction Materials & Supplies", "Real Estate") ~ "Real Estate",
    
    industry %in% c("Development Finance Institutions") ~ "Public Sector Finance",
    
    industry %in% c("Entertainment") ~ "Entertainment",
    
    TRUE ~ "Other"
  ))

gender_by_group <- ggplot(gender_data_grouped, aes(x = reorder(industry_group, score, FUN = median), y = score)) +
  geom_boxplot(width = 0.5, fill = "skyblue", outlier.alpha = 0.4) +  # width < 1 reduces box width
  coord_flip() +
  labs(
    title = "Overall Average Gender Assessment Score by Industry Group",
    x = "Industry Group", y = "Score"
  ) +
  theme_minimal(base_size = 12)

gender_by_group

Comparison of Average Overall Gender Assessment Scores Between 2023 vs 2024 by Region

region_wide <- gender_data |> 
  filter(year %in% c(2023, 2024)) |>  # Ensure only these years are included
  group_by(region, year) |> 
  summarise(avg_score = mean(score, na.rm = TRUE)) |> 
  pivot_wider(
    names_from = year, 
    values_from = avg_score,
    names_prefix = ""  # Removes automatic "year" prefix
  ) |> 
  ungroup()

`summarise()` has grouped output by 'region'. You can override using the
`.groups` argument.

ggplot(region_wide, aes(x = `2023`, xend = `2024`, y = reorder(region, `2024`))) +
  geom_dumbbell(
    size = 1.2,  # thinner connecting line
    colour = "#a3c4dc",       # light blue line
    colour_x = "#87CEEB",     # skyblue for 2023
    colour_xend = "#00008B"   # dark blue for 2024
  ) +
  # Dummy points to create legend
  geom_point(aes(x = `2023`, color = "2023"), size = 2) +
  geom_point(aes(x = `2024`, color = "2024"), size = 2) +
  scale_color_manual(
    name = "Year",
    values = c("2023" = "#87CEEB", "2024" = "#00008B")
  ) +
  labs(
    title = "Change in Average Score by Region (2023 to 2024)",
    x = "Average Score", y = "Region"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold"),
    axis.title.y = element_blank(),
    legend.position = "bottom"
  )

Warning: Using the `size` aesthetic with geom_segment was deprecated in ggplot2 3.4.0.
ℹ Please use the `linewidth` aesthetic instead.

3. Gender Assessment Score by Ownership Type

In addition to industry-based analysis, we examine whether ownership structure affects the overall gender performance scores. The following box plot illustrates how scores vary by ownership category

filtered_data <- gender_data |>
  subset(!is.na(score) & !is.na(ownership))

ggplot(filtered_data, aes(x = ownership, y = score, fill = ownership)) +
  geom_boxplot() +
  scale_fill_manual(values = c(
    "Public" = "#1f77b4",     # blue
    "Private" = "#6baed6",    # lighter blue
    "Government" = "#08306b"  # dark blue
  )) +
  labs(title = "Score by Ownership Type", x = "Ownership", y = "Score") +
  theme_minimal()

Hypothesis Testing: Gender Assessment Scores by Ownership Type

We aim to test whether companies with different ownership types have significantly different mean gender assessment scores.

Hypotheses

• Null Hypothesis (H₀):
  The mean score is the same across all ownership groups.
  \[ \mu_{\text{public}} = \mu_{\text{Private}} = \mu_{\text{Government}} \]

• Alternative Hypothesis (Hₐ):
  Not all group means are equal.
  

Assumption checks for ANOVA

Before applying ANOVA to compare gender assessment scores across ownership types, it is essential to verify that the assumptions underlying the test are met.

1. Independence of Observations

• Observations within and across groups must be independent: This assumption is satisfied by the data set design, provided that each company appears only once. This assumption is generally satisfied by the data set design, provided that each company appears only once and that there is no clustering of related firms or repeated measurements. Since our data set includes one record per company without repeated measures, we consider the independence assumption to be reasonably met.

2. Normality of Residuals

ANOVA assumes that the residuals are normally distributed. We assess this using the Shapiro-Wilk test:

anova_model <- aov(score ~ ownership, data = gender_data)
shapiro.test(residuals(anova_model))

    Shapiro-Wilk normality test

data:  residuals(anova_model)
W = 0.98844, p-value = 1.423e-11

Inference

Since the p-value is less than 0.05, we reject the null hypothesis of normality. The residuals are not normally distributed, violating the normality assumption for ANOVA.

3. Homogeneity of Variances

Another key assumption is that the variance of scores is roughly equal across the ownership types. We use Levene’s Test to assess this:

leveneTest(score ~ ownership, data = gender_data)

Warning in leveneTest.default(y = y, group = group, ...): group coerced to
factor.

Levene's Test for Homogeneity of Variance (center = median)
        Df F value    Pr(>F)    
group    2  25.558 1.097e-11 ***
      1993                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Inference: As the p-value is well below 0.05, we reject the null hypothesis of equal variances. The assumption of homogeneity of variance is violated.

Since both the normality of residuals and homogeneity of variance assumptions are violated, a non-parametric alternative to ANOVA is more appropriate.

4. Kruskal-Wallis test

To evaluate whether the score differs across different types of business ownership, we use the Kruskal–Wallis test.

Hypotheses

• Null Hypothesis (H₀):
  The median score is the same across all ownership groups.
  \[ \hat{\mu}_{\text{public}} = \hat{\mu}_{\text{Private}} = \hat{\mu}_{\text{Government}} \]

• Alternative Hypothesis (Hₐ):
  Not all group medians are equal.
  

Assumptions of the Kruskal-Wallis Test

• Ordinal or Continuous Dependent Variable: The variable score is numeric, satisfying this assumption.

• Independent Groups: Each business (or observation) belongs to only one ownership type. This ensures group independence.

• Independent Observations: Each row in the data set represents a unique business entity, so the observations are independent.

kruskal.test(score ~ ownership, data = gender_data)

    Kruskal-Wallis rank sum test

data:  score by ownership
Kruskal-Wallis chi-squared = 517.7, df = 2, p-value < 2.2e-16

• The p-value is small (< 0.05), meaning we reject the null hypothesis (H₀) that all ownership groups have the same median score.
• There are statistically significant differences in median score between at least two ownership types.

Since the test indicates that not all ownership groups are equal, we need to conduct a post-hoc test to identify which specific groups differ.

5. Dunn’s Post-Hoc Test (with Bonferroni Correction)

To determine which ownership groups differ, we conduct Dunn’s test with Bonferroni adjustment for multiple comparisons.

# Run Dunn's test with Bonferroni correction
dunnTest(score ~ ownership, data = gender_data, method = "bonferroni")

Warning: ownership was coerced to a factor.

Warning: Some rows deleted from 'x' and 'g' because missing data.

Dunn (1964) Kruskal-Wallis multiple comparison

  p-values adjusted with the Bonferroni method.

            Comparison          Z      P.unadj        P.adj
1 Government - Private  -4.232728 2.308732e-05 6.926197e-05
2  Government - Public -18.867414 2.113945e-79 6.341836e-79
3     Private - Public -16.052311 5.507422e-58 1.652227e-57

Inference

Pairwise comparisons using Dunn’s test (with Bonferroni correction) indicate statistically significant differences in gender assessment scores across all ownership types:

• Government-owned companies have significantly lower scores than both Private and Public companies.

• Private companies also score significantly lower than Public companies.

Discussion

This analysis highlights notable disparities in gender assessment performance across industries, ownership types, and regions. Industries demonstrated varying levels of engagement with gender equity, with the Personal & Household Products sector achieving the highest average scores. In contrast, the Pension Funds industry consistently lagged behind, pointing to potential structural or cultural barriers that may impede progress in specific sectors.

Ownership structure played a critical role in performance. Government-owned companies significantly outperformed both privately held and publicly traded firms. In contrast, publicly traded companies, despite their visibility and access to resources, showed relatively poor performance.

Despite the insights provided, this analysis faces some limitations. While average scores are useful for summarizing overall trends, they can hide meaningful differences within subgroup like firm size or revenue.

Attribution

Data sourced from World Benchmarking Alliance, available under a Creative Commons Attribution 4.0 International License. Original data set: 2024 Gender Assessment data set.