Country

Indicator

Value type

Values present

Human Capital Index (HCI), Female (scale 0-1)

Source: worldbank.org, 19.12.2024

Year: 2020

Country	Value	Value change, %	Rank
Afghanistan	0.369	+1.28%	144
Angola	0.356	+0.546%	149
Albania	0.665	+0.309%	48
United Arab Emirates	0.688	-0.0139%	43
Argentina	0.613	-2.3%	68
Armenia	0.603	-0.521%	76
Antigua & Barbuda	0.626	+4.7%	63
Australia	0.784	-1.44%	16
Austria	0.761	-2.37%	27
Azerbaijan	0.587	-9.08%	82
Burundi	0.404	-0.756%	131
Belgium	0.772	+0.0876%	21
Benin	0.401	+0.848%	133
Burkina Faso	0.388	+1.79%	139
Bangladesh	0.478	+1.29%	110
Bulgaria	0.632	-8.15%	58
Bahrain	0.686	-1.19%	44
Bosnia & Herzegovina	0.592	-5.7%	81
Belarus	0.734		34
Brunei	0.645		53
Bhutan	0.475		111
Botswana	0.44	+0.423%	122
Canada	0.81	+0.00318%	7
Switzerland	0.765	-0.821%	24
Chile	0.666	-1.74%	47
China	0.659	+0.697%	49
Côte d’Ivoire	0.378	+2.95%	142
Cameroon	0.397	+1%	136
Congo - Kinshasa	0.368	+0.507%	145
Congo - Brazzaville	0.428	+0.361%	128
Colombia	0.614	+0.34%	67
Comoros	0.413	+1.54%	130
Costa Rica	0.643	+4.21%	54
Cyprus	0.763	+0.0701%	26
Czechia	0.774	-0.952%	19
Germany	0.768	-1.2%	23
Dominica	0.577	-0.658%	86
Denmark	0.774	-1.58%	20
Dominican Republic	0.523	-0.543%	102
Algeria	0.557	+0.801%	92
Ecuador	0.613	-0.245%	69
Egypt	0.514	+0.459%	106
Spain	0.74	-1.43%	30
Estonia	0.811	+0.537%	6
Ethiopia	0.384	-0.36%	141
Finland	0.83	-1.73%	3
Fiji	0.532		97
France	0.781	+1%	18
Micronesia (Federated States of)	0.527	+9.36%	101
Gabon	0.473	+0.271%	113
United Kingdom	0.791	+0.584%	13
Georgia	0.612	-6.78%	71
Ghana	0.458	+1.66%	119
Guinea	0.357	+0.856%	148
Gambia	0.436	+4.69%	124
Greece	0.713	-0.589%	38
Grenada	0.597	+3.23%	78
Guatemala	0.471	+1.08%	115
Guyana	0.516	+0.173%	104
Hong Kong SAR China	0.848	-0.159%	2
Honduras	0.493	+0.134%	109
Croatia	0.739	-1.81%	31
Haiti	0.46	+0.369%	118
Hungary	0.706	-3.14%	39
Indonesia	0.557	+0.769%	93
India	0.5	+1.81%	107
Ireland	0.805	-2.1%	10
Iran	0.612	+0.17%	70
Iraq	0.424	+2.64%	129
Iceland	0.768	+0.621%	22
Israel	0.764	-2.23%	25
Italy	0.738	-2.85%	33
Jamaica	0.562	-1.14%	90
Jordan	0.58	+0.845%	85
Kazakhstan	0.652	-19.2%	51
Kyrgyzstan	0.617	+0.31%	65
Kiribati	0.537	+5.36%	96
St. Kitts & Nevis	0.6	+2.05%	77
South Korea	0.815	-4.47%	5
Kuwait	0.596	+0.678%	79
Laos	0.465	+0.0278%	117
Liberia	0.324	+0.45%	150
St. Lucia	0.627	+1.76%	61
Lithuania	0.756	-2.73%	28
Luxembourg	0.7	-0.301%	40
Latvia	0.751	-4.94%	29
Macao SAR China	0.818	+3.77%	4
Morocco	0.516	+2.14%	105
Moldova	0.607	+0.173%	74
Madagascar	0.404	+1.78%	132
Mexico	0.627	-0.0934%	62
Marshall Islands	0.445	+5.47%	121
North Macedonia	0.573	+4.37%	87
Mali	0.318	-1.02%	151
Malta	0.738	+1.17%	32
Myanmar (Burma)	0.495	+1.48%	108
Montenegro	0.64	+0.269%	55
Mongolia	0.636	-0.509%	56
Mauritania	0.393	+3.14%	138
Malaysia	0.634	-3.25%	57
Niger	0.313	-0.643%	152
Nigeria	0.366	+1.76%	147
Nicaragua	0.527	+0.108%	100
Netherlands	0.807	-1.01%	9
Norway	0.793	+1.26%	12
Nauru	0.531		99
New Zealand	0.791	+0.316%	14
Oman	0.648	-0.6%	52
Pakistan	0.401	+1.21%	135
Panama	0.519	-1.81%	103
Peru	0.605	+1.19%	75
Philippines	0.538	-6.99%	95
Palau	0.616	+3.56%	66
Papua New Guinea	0.429	+1.1%	127
Poland	0.786	-0.681%	15
Portugal	0.781	-1.41%	17
Paraguay	0.531	+0.122%	98
Palestinian Territories	0.607	+1.31%	73
Qatar	0.67	+1.77%	46
Romania	0.611	-1.61%	72
Russia	0.726	-6.48%	36
Rwanda	0.394	+0.777%	137
Saudi Arabia	0.595	-2.58%	80
Sudan	0.385	+0.35%	140
Senegal	0.44	+0.413%	123
Singapore	0.89	-1.14%	1
Solomon Islands	0.433	-2.66%	125
Sierra Leone	0.37	+2.86%	143
El Salvador	0.568	+0.668%	89
Serbia	0.695	-10.2%	41
Slovakia	0.691	-1.76%	42
Slovenia	0.803	-1.34%	11
Sweden	0.81	-0.708%	8
Chad	0.292	+0.256%	153
Togo	0.43	+1.78%	126
Thailand	0.632	-0.634%	59
Timor-Leste	0.473	+0.402%	112
Tonga	0.557	+2.03%	91
Tunisia	0.539	+1.45%	94
Turkey	0.658	+3.79%	50
Tuvalu	0.471	+2.65%	114
Tanzania	0.401	+0.933%	134
Ukraine	0.677	-0.792%	45
Uruguay	0.624	-0.472%	64
United States	0.72	-1.44%	37
Uzbekistan	0.631		60
St. Vincent & Grenadines	0.571	+0.754%	88
Vietnam	0.728	+0.333%	35
Vanuatu	0.468	+2.12%	116
Samoa	0.581	+6.64%	84
Kosovo	0.584	-0.814%	83
Yemen	0.367	+0.0394%	146
South Africa	0.446	+0.566%	120

The Human Capital Index (HCI) is an essential metric developed by the World Bank to quantify the contributions of health and education to productivity. Specifically for females, the HCI scores range from 0 to 1, where a higher score signifies greater potential for development and growth in the female population. The significant focus on female HCI stems from the recognition that investing in women’s health and education generates socio-economic benefits that extend not just to individuals, but to communities and economies at large.

The importance of the Female Human Capital Index cannot be overstated. An HCI score close to 1 indicates that females can achieve high productivity levels in their lifetimes, which correlates to better economic outcomes for society. This measure reflects the extent to which girls can be expected to attain essential education milestones and health outcomes that facilitate their participation in the workforce. When women are healthy and well-educated, they contribute effectively to economic growth, leading to a cycle of improved living standards and further opportunities.

The Female HCI is interconnected with various other indicators including economic participation, gender equality, maternal health, educational attainment, and employment rates. For instance, regions with a high female HCI correlate strongly with high female labor force participation rates. This relationship is crucial as it indicates that fostering women's education and health leads to greater engagement in the workforce, which is critical for sustainable economic growth. In contrast, low HCI areas often suffer from high maternal mortality rates and low educational attainment, perpetuating a cycle of poverty and disenfranchisement among women.

Several factors affect the Female Human Capital Index, including cultural attitudes towards women's education and health, government policies related to gender equality, access to healthcare, and economic opportunities. Societal norms can impede the investments required for women's health and education. In some regions, the lack of governmental commitment to policies that support female empowerment can severely restrict women's access to quality education and healthcare, contributing to lower HCI scores.

To improve the Female Human Capital Index, targeted strategies must be implemented. Ensuring equitable access to education for girls, particularly in regions with traditional policies favoring male education, will enhance their capabilities and future opportunities. Moreover, healthcare access must be prioritized to support maternal and child health effectively. Programs promoting women's health education and access to reproductive health services can significantly reduce maternal mortality rates and improve overall health outcomes for women.

Governments and organizations should work to create policies that support women's integration into the workforce. Vocational training programs tailored to women can promote economic empowerment. Additionally, governments must enhance childcare support, flexible working arrangements, and anti-discrimination legislations to nurture a favorable environment for working women.

While there are many benefits tied to improving the Female HCI, certain flaws should be acknowledged. Firstly, the HCI primarily considers education and health but neglects other vital components such as emotional and psychological well-being. Thus, to have a comprehensive view of women's capabilities, additional indices might also need to be considered. Secondly, while achieving higher scores can show progress, it can sometimes mask underlying issues. For instance, countries may score well on the Female HCI while still having significant gender inequality in labor force participation and wages. It is crucial to ensure that improvements in the index correspond with genuine advancements in women's rights and opportunities.

Examining the data from 2020 offers a bird's eye view of the disparities in HCI across different regions. The median value of the Female Human Capital Index stood at 0.6, indicating that globally, women have substantial room for improvement in health and education prospects. The top five performers were led by Singapore with an impressive score of 0.89, followed closely by Hong Kong SAR China (0.85), Finland (0.83), Macao SAR China (0.82), and South Korea (0.81). These regions exemplify effective policies toward gender equality in education and health, showing that investment in women's potential can yield significant dividends.

On the flip side, countries like Chad (0.29), Niger (0.31), Mali (0.32), Liberia (0.32), and Angola (0.36) represent the bottom tier of the HCI rankings. These areas are characterized by high rates of poverty, limited access to education, restricted healthcare services, and socio-cultural constraints that inhibit women's advancement. The stark contrast between the highest and lowest HCI scores serves as a reminder of the urgent need for global efforts to eliminate gender disparities.

In conclusion, enhancing the Female Human Capital Index is vital for building a more equitable and prosperous society. By addressing the factors that contribute to low HCI scores and implementing effective strategies and policies, countries can empower women to fulfill their potential, leading to broad socio-economic improvements. Collectively, this empowers not just women, but their families and communities, creating a ripple effect that contributes to national and global development. While challenges remain, concerted efforts can help bridge the gap, ensuring that all women have the chance to thrive.

Python
R

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'HD.HCI.OVRL.FE'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'HD.HCI.OVRL.FE'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))

Compare

Human Capital Index (HCI), Female (scale 0-1)

Year: 2020

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