Country

Indicator

Value type

Values present

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

Source: worldbank.org, 19.12.2024

Year: 2020

Country	Value	Value change, %	Rank
Afghanistan	0.38	+1.36%	147
Angola	0.377	+0.463%	148
Albania	0.675	-0.0672%	48
United Arab Emirates	0.7	-0.177%	43
Argentina	0.624	-2.31%	68
Armenia	0.614	-0.526%	76
Antigua & Barbuda	0.641	+4.96%	62
Australia	0.793	-1.58%	16
Austria	0.773	-2.11%	26
Azerbaijan	0.603	-9.26%	81
Burundi	0.426	-0.726%	131
Belgium	0.782	+0.0691%	21
Benin	0.425	+0.861%	132
Burkina Faso	0.407	+1.83%	138
Bangladesh	0.487	+1.3%	114
Bulgaria	0.644	-8.45%	60
Bahrain	0.695	-1.2%	44
Bosnia & Herzegovina	0.602	-5.6%	82
Belarus	0.744		35
Brunei	0.654		54
Bhutan	0.499		110
Botswana	0.457	+0.375%	123
Canada	0.819	+0.0515%	8
Switzerland	0.776	-0.848%	24
Chile	0.677	-1.78%	46
China	0.672	+0.72%	49
Côte d’Ivoire	0.397	+3.09%	141
Cameroon	0.419	+1.15%	134
Congo - Kinshasa	0.387	+0.55%	145
Congo - Brazzaville	0.451	+0.391%	126
Colombia	0.626	+0.493%	66
Comoros	0.444	+1.51%	129
Costa Rica	0.655	+4.86%	53
Cyprus	0.772	+0.0691%	27
Czechia	0.785	-0.769%	19
Germany	0.78	-1.05%	22
Dominica	0.595	-0.316%	84
Denmark	0.782	-1.75%	20
Dominican Republic	0.536	-0.525%	102
Algeria	0.565	+0.841%	94
Ecuador	0.623	-0.235%	71
Egypt	0.529	+0.492%	104
Spain	0.746	-1.63%	34
Estonia	0.822	+0.484%	6
Ethiopia	0.394	-0.221%	142
Finland	0.84	-1.66%	3
Fiji	0.547		99
France	0.791	+1%	18
Micronesia (Federated States of)	0.554	+9.7%	96
Gabon	0.495	+0.291%	111
United Kingdom	0.801	+0.537%	13
Georgia	0.619	-6.86%	73
Ghana	0.471	+1.71%	119
Guinea	0.373	+0.975%	149
Gambia	0.458	+4.59%	122
Greece	0.723	-0.693%	38
Grenada	0.611	+3.54%	78
Guatemala	0.481	+1.13%	116
Guyana	0.531	+0.185%	103
Hong Kong SAR China	0.859	-0.304%	2
Honduras	0.505	+0.145%	109
Croatia	0.75	-1.65%	30
Haiti	0.477	+0.45%	118
Hungary	0.717	-3.13%	39
Indonesia	0.568	+0.427%	92
India	0.505	+1.84%	108
Ireland	0.815	-2.16%	10
Iran	0.624	+0.186%	70
Iraq	0.43	+2.72%	130
Iceland	0.777	+0.723%	23
Israel	0.775	-2.29%	25
Italy	0.747	-2.95%	33
Jamaica	0.576	-1.17%	90
Jordan	0.592	+0.645%	85
Kazakhstan	0.661	-19.7%	51
Kyrgyzstan	0.626	+0.295%	67
Kiribati	0.565	+5.77%	93
St. Kitts & Nevis	0.613	+2.24%	77
South Korea	0.827	-4.26%	4
Kuwait	0.608	+0.699%	80
Laos	0.48	+0.0329%	117
Liberia	0.337	+0.532%	150
St. Lucia	0.642	+2.18%	61
Lithuania	0.766	-2.93%	28
Luxembourg	0.707	-0.355%	41
Latvia	0.765	-4.92%	29
Macao SAR China	0.825	+3.93%	5
Morocco	0.526	+2.09%	106
Moldova	0.617	+0.212%	74
Madagascar	0.422	+1.71%	133
Mexico	0.636	+0.0768%	63
Marshall Islands	0.467	+5.68%	120
North Macedonia	0.58	+4.49%	89
Mali	0.329	-0.976%	152
Malta	0.747	+1.28%	32
Myanmar (Burma)	0.512	+1.54%	107
Montenegro	0.649	+0.195%	56
Mongolia	0.652	-0.484%	55
Mauritania	0.418	+3.1%	135
Malaysia	0.646	-3.47%	58
Niger	0.331	-0.711%	151
Nigeria	0.388	+1.88%	144
Nicaragua	0.536	+0.116%	101
Netherlands	0.817	-0.789%	9
Norway	0.802	+1.37%	12
Nauru	0.552		97
New Zealand	0.801	+0.273%	14
Oman	0.66	-0.614%	52
Pakistan	0.415	+1.44%	136
Panama	0.528	-1.78%	105
Peru	0.616	+1.04%	75
Philippines	0.555	-6.54%	95
Palau	0.633	+3.32%	65
Papua New Guinea	0.445	+1.3%	128
Poland	0.797	-0.541%	15
Portugal	0.792	-1.23%	17
Paraguay	0.546	+0.11%	100
Palestinian Territories	0.62	+1.34%	72
Qatar	0.676	+1.5%	47
Romania	0.624	-1.07%	69
Russia	0.736	-6.45%	36
Rwanda	0.406	+0.762%	139
Saudi Arabia	0.609	-2.93%	79
Sudan	0.4	+0.405%	140
Senegal	0.456	+0.503%	124
Singapore	0.898	-1.4%	1
Solomon Islands	0.445	-2.56%	127
Sierra Leone	0.389	+3.15%	143
El Salvador	0.582	+0.879%	88
Serbia	0.707	-10.3%	40
Slovakia	0.701	-1.87%	42
Slovenia	0.811	-1.43%	11
Sweden	0.821	-0.711%	7
Chad	0.309	+0.429%	153
Togo	0.453	+1.91%	125
Thailand	0.644	-0.639%	59
Timor-Leste	0.491	+0.432%	113
Tonga	0.573	+2.03%	91
Tunisia	0.549	+1.49%	98
Turkey	0.669	+3.39%	50
Tuvalu	0.492	+3.26%	112
Tanzania	0.413	+1.12%	137
Ukraine	0.689	-0.735%	45
Uruguay	0.634	-0.324%	64
United States	0.73	-1.39%	37
Uzbekistan	0.649		57
St. Vincent & Grenadines	0.584	+0.917%	87
Vietnam	0.748	+0.366%	31
Vanuatu	0.484	+2.22%	115
Samoa	0.595	+6.66%	83
Kosovo	0.59	-1%	86
Yemen	0.382	+0.219%	146
South Africa	0.459	+0.61%	121

The Human Capital Index (HCI) measures the economic value of human capital within a country, with a specific focus on how well countries are optimizing their talent through education, health, and essential skills. When we examine the HCI specifically for females, expressed on a scale from 0 to 1, it provides critical insights into gender disparities in human development. The upper bound score represents the ideal scenario, whereas lower scores indicate barriers to education, health, and other factors affecting women's potential contributions to society and the economy.

The importance of the HCI for females cannot be overstated. As nations strive for sustainable development and economic resilience, ensuring that women are fully part of the labor force equates to a more knowledgeable, skilled, and healthier workforce. The upward trend of female participation often correlates with improved societal outcomes, including reduced poverty rates, lower child mortality, and enhanced economic growth, demonstrating that women's empowerment is not simply a matter of equity but a vital driver of development.

In 2020, the median Human Capital Index score for females was recorded at 0.61, a figure that reflects the uneven playing field faced by women across the globe. This score underscores that while many countries have progressed, there remains a significant gap in the realization of human capital potential for women, particularly in myriad developing regions. Those countries that prioritize investments in education and health for women generally reflect higher HCI values, showcasing the interconnectedness between educational attainment, health services, and economic productivity.

The top five areas with the best HCI for females in 2020 were Singapore (0.9), Hong Kong SAR China (0.86), Finland (0.84), South Korea (0.83), and Macao SAR China (0.83). These countries stand as exemplars in addressing gender-specific disparities and promoting women's education and health. For instance, Singapore’s extensive emphasis on equitable access to education and robust health services underpins its high HCI score, positioning its female population for enhanced labor market participation and productivity. Finland, recognized for its comprehensive social policies, resonates with values that support female empowerment, signifying a holistic approach to improving female human capital.

Conversely, the bottom five areas highlighted a stark contrast in their HCI scores, namely Chad (0.31), Mali (0.33), Niger (0.33), Liberia (0.34), and Guinea (0.37). These areas struggle with substantial barriers, including inadequate educational infrastructure, limited healthcare access, and prevailing socio-cultural norms that inhibit women's opportunities for personal and professional growth. The low HCI scores of these countries reflect systemic challenges; for example, in some regions, girls are still not afforded equal opportunities to pursue education, leading to higher rates of early marriage and lower economic empowerment, which in turn perpetuate cycles of poverty and disenfranchisement.

The HCI for females relates directly to other indicators such as Gender Inequality Index, Educational Attainment, and Economic Participation and Opportunity indices. Improvement in one can stimulate advancements in the others—greater educational attainment can lead to higher economic participation, while enhanced economic conditions can improve access to health and education. Therefore, policies aimed at integrating women's health and education within broader economic frameworks can catalyze improvements in HCI and related indicators.

Several factors influence the HCI for females, including socioeconomic status, cultural norms surrounding gender roles, governmental policies on education and healthcare, and overall investment in social services. For example, countries with strong governmental support programs for families, comprehensive health care systems, and policies that promote gender equality often see a more favorable HCI for females. Additionally, countries that have integrated gender perspectives into their national development strategies tend to witness enhanced human capital for women.

To improve HCI for females globally, a multi-pronged strategy is essential. Investment in inclusive education systems tailored to eliminate barriers faced by teenage girls, such as school fees, safety concerns, or cultural pressures, can help close gender gaps. Enhancing access to healthcare services, ensuring reproductive health initiatives, and providing mental health resources are crucial for fostering a healthy talent pool. Furthermore, advocating societal change that promotes gender equality and dismantles entrenched stereotypes will positively affect women's participation rates in various sectors.

While the data surrounding women's HCI is significant, there are inherent flaws in how these indexes are constructed. For instance, the reliance on statistics can overlook nuanced social factors that inhibit female advancement. Additionally, purely quantitative aspects may not fully capture the quality of education or healthcare women experience. Moreover, due to cultural contextual differences, not all strategies proven effective in one region will produce similar results elsewhere without adaptation. Therefore, countries must interpret and address these indicators with a lens that appreciates both universal challenges and local realities.

In conclusion, the Human Capital Index for females serves as a critical metric for understanding the broader implications of gender inequality on social and economic development. By recognizing the correlation between female empowerment and human capital potential, countries can forge robust strategies to enhance women's HCI scores. Collectively, through persistent efforts and deliberate policies, we can close the gender gap and maximize the potential of human capital in all its diversity.

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.UB.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.UB.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, Upper Bound (scale 0-1)

Year: 2020

Acquire full dataset with Python or R from source