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

Source: worldbank.org, 19.12.2024

Year: 2020

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 0.356 +1.18% 143
Angola Angola 0.325 +0.486% 149
Albania Albania 0.653 +0.689% 48
United Arab Emirates United Arab Emirates 0.675 +0.201% 44
Argentina Argentina 0.602 -2.33% 68
Armenia Armenia 0.592 -0.514% 76
Antigua & Barbuda Antigua & Barbuda 0.611 +4.46% 64
Australia Australia 0.775 -1.31% 16
Austria Austria 0.749 -2.57% 27
Azerbaijan Azerbaijan 0.57 -8.77% 83
Burundi Burundi 0.38 -0.884% 133
Belgium Belgium 0.762 +0.0934% 21
Benin Benin 0.378 +0.833% 134
Burkina Faso Burkina Faso 0.367 +1.6% 140
Bangladesh Bangladesh 0.468 +1.28% 110
Bulgaria Bulgaria 0.62 -7.87% 57
Bahrain Bahrain 0.676 -1.19% 43
Bosnia & Herzegovina Bosnia & Herzegovina 0.581 -5.88% 81
Belarus Belarus 0.724 34
Brunei Brunei 0.636 52
Bhutan Bhutan 0.451 112
Botswana Botswana 0.412 +0.352% 125
Canada Canada 0.801 -0.0834% 6
Switzerland Switzerland 0.755 -0.783% 24
Chile Chile 0.654 -1.76% 47
China China 0.645 +0.669% 50
Côte d’Ivoire Côte d’Ivoire 0.358 +2.77% 141
Cameroon Cameroon 0.375 +0.833% 136
Congo - Kinshasa Congo - Kinshasa 0.345 +0.225% 146
Congo - Brazzaville Congo - Brazzaville 0.401 +0.142% 129
Colombia Colombia 0.601 +0.171% 69
Comoros Comoros 0.368 +1.31% 139
Costa Rica Costa Rica 0.63 +3.49% 54
Cyprus Cyprus 0.753 +0.0673% 25
Czechia Czechia 0.763 -1.16% 20
Germany Germany 0.755 -1.3% 23
Dominica Dominica 0.558 -1.12% 87
Denmark Denmark 0.765 -1.38% 19
Dominican Republic Dominican Republic 0.508 -0.575% 99
Algeria Algeria 0.548 +0.756% 90
Ecuador Ecuador 0.603 -0.259% 67
Egypt Egypt 0.499 +0.392% 104
Spain Spain 0.735 -1.17% 30
Estonia Estonia 0.799 +0.566% 8
Ethiopia Ethiopia 0.374 -0.501% 137
Finland Finland 0.82 -1.79% 3
Fiji Fiji 0.518 96
France France 0.771 +0.989% 17
Micronesia (Federated States of) Micronesia (Federated States of) 0.488 +9.07% 107
Gabon Gabon 0.447 +0.134% 117
United Kingdom United Kingdom 0.782 +0.674% 13
Georgia Georgia 0.604 -6.77% 66
Ghana Ghana 0.444 +1.55% 118
Guinea Guinea 0.339 +0.616% 148
Gambia Gambia 0.407 +4.65% 127
Greece Greece 0.702 -0.352% 38
Grenada Grenada 0.583 +3.07% 79
Guatemala Guatemala 0.46 +1.02% 111
Guyana Guyana 0.498 +0.099% 105
Hong Kong SAR China Hong Kong SAR China 0.836 +0.0711% 2
Honduras Honduras 0.479 +0.108% 108
Croatia Croatia 0.728 -2.01% 33
Haiti Haiti 0.44 +0.261% 119
Hungary Hungary 0.696 -3.09% 39
Indonesia Indonesia 0.545 +1.1% 91
India India 0.496 +1.77% 106
Ireland Ireland 0.794 -2.1% 11
Iran Iran 0.599 +0.131% 70
Iraq Iraq 0.416 +2.52% 124
Iceland Iceland 0.759 +0.513% 22
Israel Israel 0.752 -2.1% 26
Italy Italy 0.729 -2.75% 32
Jamaica Jamaica 0.543 -1.14% 92
Jordan Jordan 0.568 +1.06% 84
Kazakhstan Kazakhstan 0.644 -18.7% 51
Kyrgyzstan Kyrgyzstan 0.607 +0.312% 65
Kiribati Kiribati 0.502 +5.02% 103
St. Kitts & Nevis St. Kitts & Nevis 0.586 +1.86% 77
South Korea South Korea 0.802 -4.69% 5
Kuwait Kuwait 0.585 +0.655% 78
Laos Laos 0.449 -0.00848% 114
Liberia Liberia 0.308 +0.258% 150
St. Lucia St. Lucia 0.611 +1.64% 63
Lithuania Lithuania 0.747 -2.55% 28
Luxembourg Luxembourg 0.693 -0.24% 40
Latvia Latvia 0.738 -4.97% 29
Macao SAR China Macao SAR China 0.81 +3.58% 4
Morocco Morocco 0.505 +2.2% 102
Moldova Moldova 0.596 +0.129% 72
Madagascar Madagascar 0.385 +1.76% 132
Mexico Mexico 0.616 -0.223% 60
Marshall Islands Marshall Islands 0.421 +5.12% 122
North Macedonia North Macedonia 0.566 +4.25% 86
Mali Mali 0.305 -1.11% 151
Malta Malta 0.73 +1.04% 31
Myanmar (Burma) Myanmar (Burma) 0.475 +1.34% 109
Montenegro Montenegro 0.63 +0.342% 55
Mongolia Mongolia 0.619 -0.541% 59
Mauritania Mauritania 0.356 +3.08% 142
Malaysia Malaysia 0.622 -3.08% 56
Niger Niger 0.292 -0.744% 152
Nigeria Nigeria 0.34 +1.43% 147
Nicaragua Nicaragua 0.517 +0.0987% 97
Netherlands Netherlands 0.797 -1.21% 9
Norway Norway 0.785 +1.2% 12
Nauru Nauru 0.506 101
New Zealand New Zealand 0.781 +0.301% 14
Oman Oman 0.634 -0.583% 53
Pakistan Pakistan 0.386 +0.885% 131
Panama Panama 0.506 -1.86% 100
Peru Peru 0.592 +1.28% 74
Philippines Philippines 0.521 -7.46% 95
Palau Palau 0.595 +3.83% 73
Papua New Guinea Papua New Guinea 0.412 +0.888% 126
Poland Poland 0.776 -0.77% 15
Portugal Portugal 0.771 -1.59% 18
Paraguay Paraguay 0.512 +0.11% 98
Palestinian Territories Palestinian Territories 0.592 +1.24% 75
Qatar Qatar 0.663 +2.01% 46
Romania Romania 0.598 -2.12% 71
Russia Russia 0.717 -6.49% 35
Rwanda Rwanda 0.378 +0.648% 135
Saudi Arabia Saudi Arabia 0.582 -2.14% 80
Sudan Sudan 0.369 +0.201% 138
Senegal Senegal 0.423 +0.248% 121
Singapore Singapore 0.881 -0.976% 1
Solomon Islands Solomon Islands 0.42 -2.8% 123
Sierra Leone Sierra Leone 0.351 +2.49% 144
El Salvador El Salvador 0.552 +0.462% 89
Serbia Serbia 0.683 -10.2% 41
Slovakia Slovakia 0.682 -1.73% 42
Slovenia Slovenia 0.795 -1.25% 10
Sweden Sweden 0.799 -0.718% 7
Chad Chad 0.273 -0.104% 153
Togo Togo 0.405 +1.5% 128
Thailand Thailand 0.62 -0.569% 58
Timor-Leste Timor-Leste 0.451 +0.284% 113
Tonga Tonga 0.538 +2.16% 93
Tunisia Tunisia 0.529 +1.42% 94
Turkey Turkey 0.648 +4.23% 49
Tuvalu Tuvalu 0.448 +2.07% 116
Tanzania Tanzania 0.388 +0.677% 130
Ukraine Ukraine 0.664 -0.816% 45
Uruguay Uruguay 0.613 -0.625% 61
United States United States 0.711 -1.5% 36
Uzbekistan Uzbekistan 0.612 62
St. Vincent & Grenadines St. Vincent & Grenadines 0.556 +0.541% 88
Vietnam Vietnam 0.707 +0.276% 37
Vanuatu Vanuatu 0.449 +1.96% 115
Samoa Samoa 0.567 +6.89% 85
Kosovo Kosovo 0.577 -0.637% 82
Yemen Yemen 0.348 -0.357% 145
South Africa South Africa 0.431 +0.539% 120

The Human Capital Index (HCI), specifically focusing on the female demographic, serves as a crucial metric in evaluating the potential of women to contribute to a country’s economic growth and overall development. The index operates on a scale ranging from 0 to 1, where a higher score indicates better outcomes related to education, health, and employment opportunities for women. This variant of the HCI acknowledges that investing in women's health and education is not just a matter of rights but significantly influences the economic landscape of nations.

Understanding the importance of the Female HCI is imperative. First, it underlines the essential role women play in societal development. By encapsulating indicators like educational attainment, survival rates, and economic participation, the index highlights the necessity of fostering environments where women can thrive. Countries with a robust female workforce experience growth that transcends mere economic metrics, fostering stable communities, improved healthcare, and better education systems. Therefore, achieving a higher HCI not only benefits women but has a cascading positive effect on society as a whole.

The relationship between the Female HCI and other developmental indicators can be profound. For instance, regions with enhanced female education generally report lower levels of poverty and better health outcomes. This shows a direct correlation where increased female literacy leads to elevated family income levels and improved child health. Moreover, nations that succeed in bolstering the Female HCI often see higher GDP growth rates, as women's participation in the workforce contributes vitally to overall productivity.

Several factors influence the Female HCI. One significant contributor is educational access. In many parts of the world, cultural norms and socio-economic barriers hinder girls and women from accessing quality education. Without this foundational support, the potential for women to meaningfully engage in the economy diminishes. Furthermore, health services play a critical role, as access to healthcare directly impacts women’s working capacity and overall life expectancy. Factors like maternal mortality rates and child health also significantly affect this index.

To improve the Female HCI, concerted strategies must be adopted. Educational initiatives, particularly focused on girls' education, are paramount. This could involve governmental policies that encourage female enrollment in schools and promote STEM fields among women. Additionally, health programs that aim to improve maternal and reproductive health can directly contribute to higher HCI scores. Workplace policies that promote gender equality, such as parental leave and child care support, also play a crucial role in enabling women to participate fully in the economy.

Moreover, financial inclusion initiatives can empower women by providing them access to loans and economic resources necessary for starting businesses or investing in education. Community awareness programs aimed at changing traditional perceptions about women’s roles can further boost female participation in the workforce and in education.

However, the Female HCI is not without its flaws. One major critique is that the index relies heavily on existing statistical data, which may not always provide an accurate picture of women’s realities on the ground. In regions suffering from conflict or instability, data gathering can be severely hampered, leading to potential misrepresentation of the actual conditions that women face. Furthermore, while the index aims to encapsulate a variety of aspects, it may overlook qualitative factors that contribute to women's empowerment and societal roles.

Analyzing the data from 2020 further illuminates the disparities in the Female HCI across the globe. The median value stands at 0.59, suggesting that, on average, women across various countries experience moderate conditions concerning human capital development. The top five areas—Singapore (0.88), Hong Kong SAR China (0.84), Finland (0.82), Macao SAR China (0.81), and South Korea (0.80)—highlight instances of exceptional female human capital enhancement. These regions have made commendable investments in education, healthcare, and gender equality initiatives, allowing women to access a broad spectrum of opportunities.

Conversely, the bottom five areas—Chad (0.27), Niger (0.29), Mali (0.31), Liberia (0.31), and Angola (0.33)—represent stark contrasts. With HCI scores significantly lower than the median, these countries face extensive challenges in promoting women's health, education, and economic empowerment. The low scores indicate barriers that could range from inadequate educational infrastructure and high rates of maternal mortality to sociocultural norms that restrict women's rights and opportunities.

Globally, values of human capital are intertwined, highlighting a universal need for progress in these areas. The disparities in the Female HCI reflect broader issues of inequality that countries must address to enhance overall human capital. Efforts to close the gaps require collective action from governments, institutions, and communities. Ensuring that women have access to quality education and career development opportunities is not just a moral imperative—it is a strategic necessity that can drive diverse societal benefits and foster sustainable economic growth.

In conclusion, the Female Human Capital Index stands as a powerful lens through which we can assess women's potential contributions to society. Addressing the challenges reflected in this index is essential for achieving broader developmental goals and realizing the benefits of an inclusive workforce. With targeted strategies and a commitment to gender equality, nations can unlock the full potential of their female populations, fostering comprehensive social and economic growth.

                    
# 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.LB.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.LB.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))