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

Source: worldbank.org, 19.12.2024

Year: 2020

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 0.411 +1.54% 122
Angola Angola 0.369 +0.642% 145
Albania Albania 0.608 +1.56% 53
United Arab Emirates United Arab Emirates 0.661 -0.997% 37
Argentina Argentina 0.591 -2.6% 60
Armenia Armenia 0.556 -0.404% 78
Antigua & Barbuda Antigua & Barbuda 0.568 +1.5% 73
Australia Australia 0.757 -1.26% 12
Austria Austria 0.733 -3.32% 23
Azerbaijan Azerbaijan 0.568 -7.42% 72
Burundi Burundi 0.37 -0.96% 143
Belgium Belgium 0.749 -0.687% 16
Benin Benin 0.399 +0.801% 130
Burkina Faso Burkina Faso 0.379 +1.09% 139
Bangladesh Bangladesh 0.451 +1.13% 112
Bulgaria Bulgaria 0.598 -8.59% 57
Bahrain Bahrain 0.625 -2.12% 48
Bosnia & Herzegovina Bosnia & Herzegovina 0.569 -7.03% 71
Belarus Belarus 0.667 35
Brunei Brunei 0.609 52
Bhutan Bhutan 0.467 108
Botswana Botswana 0.388 +0.374% 134
Canada Canada 0.785 -0.65% 2
Switzerland Switzerland 0.748 -1.74% 18
Chile Chile 0.638 -2.36% 46
China China 0.647 +1.02% 42
Côte d’Ivoire Côte d’Ivoire 0.382 +2.43% 136
Cameroon Cameroon 0.398 +1.07% 131
Congo - Kinshasa Congo - Kinshasa 0.365 +0.512% 147
Congo - Brazzaville Congo - Brazzaville 0.411 +0.333% 124
Colombia Colombia 0.595 +1.56% 58
Comoros Comoros 0.397 +0.316% 132
Costa Rica Costa Rica 0.615 +4.2% 49
Cyprus Cyprus 0.748 +0.164% 17
Czechia Czechia 0.732 -2.22% 24
Germany Germany 0.736 -2.02% 22
Dominica Dominica 0.518 -1.8% 90
Denmark Denmark 0.738 -2.49% 21
Dominican Republic Dominican Republic 0.484 -1.06% 103
Algeria Algeria 0.515 +0.254% 91
Ecuador Ecuador 0.577 -0.41% 68
Egypt Egypt 0.476 +0.303% 106
Spain Spain 0.717 -0.721% 28
Estonia Estonia 0.746 +0.277% 19
Ethiopia Ethiopia 0.381 -0.338% 137
Finland Finland 0.765 -2.82% 10
Fiji Fiji 0.488 100
France France 0.745 +0.816% 20
Micronesia (Federated States of) Micronesia (Federated States of) 0.491 +8.7% 98
Gabon Gabon 0.442 +0.278% 115
United Kingdom United Kingdom 0.775 +0.934% 7
Georgia Georgia 0.53 -6.4% 84
Ghana Ghana 0.443 +1.32% 114
Guinea Guinea 0.384 +0.145% 135
Gambia Gambia 0.408 +4.29% 126
Greece Greece 0.669 -0.848% 34
Grenada Grenada 0.536 +5.28% 82
Guatemala Guatemala 0.452 +0.902% 111
Guyana Guyana 0.477 +0.186% 105
Hong Kong SAR China Hong Kong SAR China 0.78 -1.93% 6
Honduras Honduras 0.47 +0.173% 107
Croatia Croatia 0.682 -3.54% 32
Haiti Haiti 0.434 +0.399% 118
Hungary Hungary 0.661 -3.08% 38
Indonesia Indonesia 0.524 -0.0526% 87
India India 0.487 +1.79% 101
Ireland Ireland 0.781 -3.07% 4
Iran Iran 0.577 +0.234% 67
Iraq Iraq 0.395 +1.9% 133
Iceland Iceland 0.724 -0.0616% 25
Israel Israel 0.704 -5.55% 29
Italy Italy 0.718 -3.8% 27
Jamaica Jamaica 0.511 -0.486% 92
Jordan Jordan 0.528 +1.47% 85
Kazakhstan Kazakhstan 0.606 -19% 55
Kyrgyzstan Kyrgyzstan 0.578 +0.719% 66
Kiribati Kiribati 0.453 +3.89% 110
St. Kitts & Nevis St. Kitts & Nevis 0.574 +2.99% 70
South Korea South Korea 0.784 -4.11% 3
Kuwait Kuwait 0.535 -1.05% 83
Laos Laos 0.448 -0.106% 113
Liberia Liberia 0.314 +0.464% 152
St. Lucia St. Lucia 0.579 +2.99% 64
Lithuania Lithuania 0.66 -2.87% 39
Luxembourg Luxembourg 0.673 -1.47% 33
Latvia Latvia 0.665 -3.65% 36
Macao SAR China Macao SAR China 0.774 +4.69% 8
Morocco Morocco 0.494 +2.57% 97
Moldova Moldova 0.563 +0.72% 75
Madagascar Madagascar 0.381 +1.62% 138
Mexico Mexico 0.6 +0.535% 56
Marshall Islands Marshall Islands 0.405 +4.1% 128
North Macedonia North Macedonia 0.543 +3.87% 81
Mali Mali 0.319 -0.989% 150
Malta Malta 0.683 -0.787% 31
Myanmar (Burma) Myanmar (Burma) 0.461 +1% 109
Montenegro Montenegro 0.626 +2.97% 47
Mongolia Mongolia 0.594 -0.407% 59
Mauritania Mauritania 0.371 +2.77% 142
Malaysia Malaysia 0.59 -3.65% 61
Niger Niger 0.318 -0.809% 151
Nigeria Nigeria 0.355 +1.58% 149
Nicaragua Nicaragua 0.49 +0.18% 99
Netherlands Netherlands 0.773 -2.24% 9
Norway Norway 0.75 -0.585% 14
Nauru Nauru 0.483 104
New Zealand New Zealand 0.761 +0.917% 11
Oman Oman 0.578 -0.403% 65
Pakistan Pakistan 0.411 +2.19% 123
Panama Panama 0.486 -2.96% 102
Peru Peru 0.606 +2.37% 54
Philippines Philippines 0.495 -5.27% 96
Palau Palau 0.563 +3.38% 74
Papua New Guinea Papua New Guinea 0.429 +0.824% 120
Poland Poland 0.722 -1.2% 26
Portugal Portugal 0.756 -2.27% 13
Paraguay Paraguay 0.526 +0.13% 86
Palestinian Territories Palestinian Territories 0.555 +1.57% 79
Qatar Qatar 0.61 -0.751% 51
Romania Romania 0.559 -1.77% 77
Russia Russia 0.639 -6.55% 45
Rwanda Rwanda 0.366 +0.637% 146
Saudi Arabia Saudi Arabia 0.56 +1.07% 76
Sudan Sudan 0.37 +0.384% 144
Senegal Senegal 0.402 -0.666% 129
Singapore Singapore 0.869 -0.676% 1
Solomon Islands Solomon Islands 0.408 -2.39% 125
Sierra Leone Sierra Leone 0.356 +2.48% 148
El Salvador El Salvador 0.524 -0.249% 88
Serbia Serbia 0.659 -11.6% 40
Slovakia Slovakia 0.64 -2.69% 44
Slovenia Slovenia 0.75 -2.11% 15
Sweden Sweden 0.78 -1.12% 5
Chad Chad 0.308 +0.293% 153
Togo Togo 0.434 +3.56% 119
Thailand Thailand 0.586 -1.82% 63
Timor-Leste Timor-Leste 0.437 +0.16% 117
Tonga Tonga 0.509 +3.09% 93
Tunisia Tunisia 0.497 +1.49% 95
Turkey Turkey 0.64 +3.85% 43
Tuvalu Tuvalu 0.426 +0.867% 121
Tanzania Tanzania 0.379 +0.912% 140
Ukraine Ukraine 0.589 -2.51% 62
Uruguay Uruguay 0.575 -0.731% 69
United States United States 0.684 -1.89% 30
Uzbekistan Uzbekistan 0.614 50
St. Vincent & Grenadines St. Vincent & Grenadines 0.501 -1.62% 94
Vietnam Vietnam 0.655 +0.322% 41
Vanuatu Vanuatu 0.442 +2.3% 116
Samoa Samoa 0.52 +3.57% 89
Kosovo Kosovo 0.551 -0.0757% 80
Yemen Yemen 0.378 +0.0341% 141
South Africa South Africa 0.406 +0.78% 127

The Human Capital Index (HCI) is a crucial metric that assesses the potential productivity of individuals, particularly focusing on the health and education outcomes that contribute to an individual's ability to thrive in the workforce. Specifically, when looking at the HCI for males, the scale ranges from 0 to 1, with higher values indicating better human capital development. The index aims to quantify the contribution of human capital to economic growth and development, making it a pivotal tool for policymakers and economists alike.

The importance of the HCI cannot be overstated. It serves as a direct indicator of a country’s future productivity levels and economic potential. Countries with higher HCI values generally display better educational access, health outcomes, and a more skilled workforce. In essence, the HCI serves as a predictive measure of a nation's economic trajectory. For instance, the median value of the HCI for males in the latest report from 2020 stands at 0.56, indicating that globally, there is a significant gap in male human capital development that needs to be addressed.

The data from 2020 reveals striking disparities among different regions. The top five areas—Singapore (0.87), Canada (0.79), South Korea (0.78), Ireland (0.78), and Sweden (0.78)—demonstrate how investment in education and healthcare can propel a nation towards higher productivity and development. For instance, Singapore, leading with a value of 0.87, showcases its strong educational systems and robust healthcare policies that align with its rapid economic growth. In contrast, the bottom five areas, which include Chad (0.31), Liberia (0.31), Niger (0.32), Mali (0.32), and Nigeria (0.36), highlight a critical need for investment in human capital. These lower values indicate significant challenges related to educational access, health crises, and socioeconomic barriers that hinder male populations from maximizing their potential.

The relationship between the HCI and other economic indicators, such as GDP per capita, employment rates, and income inequality, is profound. Generally, countries with higher HCI values tend to have higher GDP per capita and lower unemployment rates. This correlation indicates that human capital development is intrinsically linked to economic prosperity. Furthermore, as nations work to improve their HCI, they often see progress in reducing income inequality—where access to education and healthcare becomes more equitable, thereby empowering previously disadvantaged groups within the male population.

Various factors affect the HCI, including socio-economic status, government policies, cultural attitudes towards education and health, and the availability of resources. For instance, countries that prioritize education through government-funded programs and incentivize healthcare accessibility tend to score higher on the HCI. Conversely, political instability, corruption, and lack of infrastructure in regions like Chad and Nigeria contribute to their lower HCI figures. This dynamic reveals how interconnected these elements are in shaping the human capital landscape.

To improve HCI, especially for males in underperforming regions, a multipronged approach is essential. Key strategies may include enhancing access to quality education, implementing comprehensive health programs targeting male populations, providing vocational training, and promoting policies that create resilient economic opportunities. Governments need to implement systematic reforms that not only focus on immediate educational attainments but also encourage lifelong learning as a societal norm.

Solutions must also address the cultural and social barriers that may inhibit male participation in education and health programs. Programs focused on community engagement, mentorship, and role modeling can help shift perceptions around education and health, promoting their importance in securing a better future.

Despite its relevance, there are flaws associated with the HCI. The index does not account for the qualitative aspects of education or healthcare; merely measuring years of schooling or basic health metrics can be misleading. For instance, a country may have a high number of educated males, but the quality of that education may be subpar, which would not translate into actual productivity or skill sets relevant for the job market. Additionally, the HCI does not differentiate between varying educational outcomes or health conditions that can affect different populations within male demographics.

Moreover, there is a growing concern that the HCI may not fully address gender disparities, particularly as the focus remains mostly on male outcomes in some analyses. A balanced approach that evaluates both males and females while recognizing their unique challenges is warranted for a comprehensive understanding of human capital development.

In conclusion, the Human Capital Index for males is a pivotal indicator that underscores the importance of education and health in economic development and productivity. The stark contrasts between the top and bottom areas emphasize the urgent need for targeted interventions and policies that can foster human capital growth. Addressing the flaws in measurement and execution will ultimately lead to more effective strategies that empower male populations across the globe, creating a healthier, more educated workforce that drives 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.MA'

# 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.MA'

# 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))