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

Source: worldbank.org, 19.12.2024

Year: 2020

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 0.427 +1.6% 125
Angola Angola 0.397 +0.512% 140
Albania Albania 0.62 +0.935% 52
United Arab Emirates United Arab Emirates 0.673 -1.58% 38
Argentina Argentina 0.604 -2.47% 60
Armenia Armenia 0.567 -0.413% 79
Antigua & Barbuda Antigua & Barbuda 0.581 +1.71% 73
Australia Australia 0.768 -1.39% 12
Austria Austria 0.746 -3.16% 23
Azerbaijan Azerbaijan 0.584 -7.77% 71
Burundi Burundi 0.392 -0.878% 143
Belgium Belgium 0.76 -0.836% 16
Benin Benin 0.427 +0.826% 127
Burkina Faso Burkina Faso 0.399 +1.18% 138
Bangladesh Bangladesh 0.461 +1.24% 114
Bulgaria Bulgaria 0.612 -8.61% 57
Bahrain Bahrain 0.635 -2.15% 49
Bosnia & Herzegovina Bosnia & Herzegovina 0.578 -6.93% 74
Belarus Belarus 0.679 36
Brunei Brunei 0.619 53
Bhutan Bhutan 0.49 107
Botswana Botswana 0.412 +0.308% 133
Canada Canada 0.795 -0.652% 3
Switzerland Switzerland 0.759 -1.69% 17
Chile Chile 0.652 -2.38% 44
China China 0.66 +1.05% 42
Côte d’Ivoire Côte d’Ivoire 0.403 +2.58% 135
Cameroon Cameroon 0.422 +1.24% 129
Congo - Kinshasa Congo - Kinshasa 0.387 +0.591% 145
Congo - Brazzaville Congo - Brazzaville 0.434 +0.405% 122
Colombia Colombia 0.61 +1.77% 58
Comoros Comoros 0.432 +0.266% 123
Costa Rica Costa Rica 0.625 +4.34% 50
Cyprus Cyprus 0.76 +0.154% 15
Czechia Czechia 0.744 -2.15% 24
Germany Germany 0.751 -1.99% 21
Dominica Dominica 0.535 -1.56% 89
Denmark Denmark 0.747 -2.59% 22
Dominican Republic Dominican Republic 0.498 -1.17% 102
Algeria Algeria 0.522 +0.286% 93
Ecuador Ecuador 0.588 -0.401% 68
Egypt Egypt 0.492 +0.334% 106
Spain Spain 0.724 -0.906% 28
Estonia Estonia 0.762 +0.05% 14
Ethiopia Ethiopia 0.393 -0.154% 142
Finland Finland 0.776 -2.78% 10
Fiji Fiji 0.502 100
France France 0.755 +0.773% 20
Micronesia (Federated States of) Micronesia (Federated States of) 0.518 +9.01% 94
Gabon Gabon 0.466 +0.327% 111
United Kingdom United Kingdom 0.784 +1.04% 8
Georgia Georgia 0.538 -6.63% 87
Ghana Ghana 0.456 +1.38% 117
Guinea Guinea 0.404 +0.278% 134
Gambia Gambia 0.431 +4.13% 124
Greece Greece 0.681 -1.07% 35
Grenada Grenada 0.549 +5.51% 82
Guatemala Guatemala 0.463 +0.946% 113
Guyana Guyana 0.494 +0.19% 104
Hong Kong SAR China Hong Kong SAR China 0.793 -1.91% 4
Honduras Honduras 0.483 +0.182% 108
Croatia Croatia 0.694 -3.4% 31
Haiti Haiti 0.453 +0.502% 119
Hungary Hungary 0.672 -3.24% 41
Indonesia Indonesia 0.536 -0.209% 88
India India 0.492 +1.83% 105
Ireland Ireland 0.792 -3.18% 5
Iran Iran 0.59 +0.252% 67
Iraq Iraq 0.402 +1.97% 136
Iceland Iceland 0.731 -0.2% 26
Israel Israel 0.721 -5.2% 29
Italy Italy 0.728 -3.74% 27
Jamaica Jamaica 0.527 -0.5% 91
Jordan Jordan 0.543 +1.45% 85
Kazakhstan Kazakhstan 0.614 -19.5% 55
Kyrgyzstan Kyrgyzstan 0.587 +0.821% 70
Kiribati Kiribati 0.48 +4.18% 110
St. Kitts & Nevis St. Kitts & Nevis 0.59 +3.03% 66
South Korea South Korea 0.799 -4.01% 2
Kuwait Kuwait 0.547 -1.08% 83
Laos Laos 0.464 -0.0815% 112
Liberia Liberia 0.329 +0.598% 152
St. Lucia St. Lucia 0.596 +3.31% 64
Lithuania Lithuania 0.672 -3.16% 40
Luxembourg Luxembourg 0.681 -1.57% 34
Latvia Latvia 0.682 -3.62% 33
Macao SAR China Macao SAR China 0.782 +4.86% 9
Morocco Morocco 0.505 +2.46% 98
Moldova Moldova 0.572 +0.754% 76
Madagascar Madagascar 0.4 +1.53% 137
Mexico Mexico 0.609 +0.658% 59
Marshall Islands Marshall Islands 0.425 +4.41% 128
North Macedonia North Macedonia 0.55 +3.89% 81
Mali Mali 0.331 -0.928% 151
Malta Malta 0.692 -0.736% 32
Myanmar (Burma) Myanmar (Burma) 0.481 +1.08% 109
Montenegro Montenegro 0.636 +2.89% 48
Mongolia Mongolia 0.614 -0.496% 56
Mauritania Mauritania 0.398 +2.72% 139
Malaysia Malaysia 0.602 -3.98% 62
Niger Niger 0.337 -0.896% 150
Nigeria Nigeria 0.377 +1.76% 148
Nicaragua Nicaragua 0.5 +0.191% 101
Netherlands Netherlands 0.786 -1.91% 7
Norway Norway 0.758 -0.635% 19
Nauru Nauru 0.505 99
New Zealand New Zealand 0.772 +0.694% 11
Oman Oman 0.594 -0.44% 65
Pakistan Pakistan 0.427 +2.24% 126
Panama Panama 0.498 -2.89% 103
Peru Peru 0.621 +2.38% 51
Philippines Philippines 0.512 -4.72% 96
Palau Palau 0.582 +3.57% 72
Papua New Guinea Papua New Guinea 0.447 +1.13% 120
Poland Poland 0.735 -1.11% 25
Portugal Portugal 0.768 -2.25% 13
Paraguay Paraguay 0.543 +0.119% 84
Palestinian Territories Palestinian Territories 0.57 +1.62% 78
Qatar Qatar 0.617 -1.22% 54
Romania Romania 0.572 -1.57% 77
Russia Russia 0.648 -6.69% 46
Rwanda Rwanda 0.38 +0.637% 147
Saudi Arabia Saudi Arabia 0.574 +0.298% 75
Sudan Sudan 0.386 +0.46% 146
Senegal Senegal 0.419 -0.612% 131
Singapore Singapore 0.878 -0.965% 1
Solomon Islands Solomon Islands 0.42 -2.26% 130
Sierra Leone Sierra Leone 0.375 +2.8% 149
El Salvador El Salvador 0.542 -0.00902% 86
Serbia Serbia 0.673 -11.8% 39
Slovakia Slovakia 0.65 -2.69% 45
Slovenia Slovenia 0.758 -2.29% 18
Sweden Sweden 0.792 -1.21% 6
Chad Chad 0.328 +0.491% 153
Togo Togo 0.46 +3.81% 115
Thailand Thailand 0.601 -1.94% 63
Timor-Leste Timor-Leste 0.456 +0.167% 118
Tonga Tonga 0.526 +3.01% 92
Tunisia Tunisia 0.506 +1.43% 97
Turkey Turkey 0.652 +3.51% 43
Tuvalu Tuvalu 0.445 +1.35% 121
Tanzania Tanzania 0.392 +1.13% 144
Ukraine Ukraine 0.603 -2.69% 61
Uruguay Uruguay 0.588 -0.782% 69
United States United States 0.695 -1.7% 30
Uzbekistan Uzbekistan 0.637 47
St. Vincent & Grenadines St. Vincent & Grenadines 0.514 -1.47% 95
Vietnam Vietnam 0.676 +0.362% 37
Vanuatu Vanuatu 0.46 +2.35% 116
Samoa Samoa 0.533 +3.69% 90
Kosovo Kosovo 0.559 -0.174% 80
Yemen Yemen 0.395 +0.232% 141
South Africa South Africa 0.418 +0.846% 132

The Human Capital Index (HCI) is a critical measure that helps assess the potential productivity of the next generation of workers in terms of their health and education. Focusing on males specifically, the Upper Bound of the HCI, which ranges from 0 to 1, provides insights into the extent to which a male population can expect to achieve a high level of expected productivity based on various factors. The HCI takes into account elements such as years of schooling, quality of education, and health outcomes – all pivotal in shaping a man's ability to contribute effectively to the economy. The importance of this index cannot be overstated, as it directly correlates with economic growth, social stability, and societal progress. In the latest data from 2020, the median value of the HCI for males was 0.57. This figure indicates that, on average, males across the globe are projected to attain around 57% of their potential productivity based on the education and health factors measured by the index. A crucial aspect of the HCI is its ability to facilitate comparisons between different countries and regions, thus providing insights into socio-economic disparities. When analyzing the top five areas regarding male HCI, Singapore stands out significantly with a score of 0.88. This high value reflects robust educational systems and excellent health care that significantly bolster the productivity of its male population. South Korea, with an HCI of 0.8, also demonstrates a strong commitment to both education and health, showcasing its effective policies that prioritize human capital development. Canada, Hong Kong SAR China, and Ireland, all with HCI values of around 0.79, further attest to this trend, revealing a universal recognition of the importance of investing in education and health as cornerstones of economic growth. These regions have structured systems that ensure not only educational attainment but also the quality of education, leading to highly skilled and adaptable workforces. Conversely, the HCI scores for the bottom five areas depict a stark and urgent need for improvement. Countries such as Chad, Liberia, Mali, Niger, and Sierra Leone, each with HCI values around 0.33 to 0.38, reflect systemic challenges that inhibit their male populations from reaching their full economic potential. Issues such as inadequate access to quality education, high rates of child mortality, and limited healthcare resources contribute significantly to these low figures. These challenges call for targeted interventions that can uplift the human capital in these regions to higher productivity levels. It’s essential to understand the relationships between the Human Capital Index and other socio-economic indicators. The HCI is interconnected with factors such as GDP per capita, literacy rates, and employment rates. Higher HCI values typically align with higher GDP per capita, as a well-educated and healthy workforce contributes positively to economic output. Furthermore, nations with higher HCI values often showcase better literacy rates, reflecting an effective educational structure and an emphasis on lifelong learning and skill development. Multiple factors significantly affect HCI, particularly for males. Educational access and quality are paramount; significant focus is needed on creating equitable systems that provide all boys, regardless of socio-economic background, the opportunity to succeed academically. Health factors, including nutrition and child health outcomes, also play a vital role, as poor health can severely curtail educational attainment and productivity. Economic stability is another critical factor; in areas where economies struggle, education and health services can suffer, impacting the HCI directly. To enhance the Human Capital Index, several strategies and solutions can be implemented. Strengthening educational systems to ensure quality schooling and universally available educational resources is imperative. Investment in teacher training, curriculum development, and technology can bridge gaps in education. Health services must also prioritize preventive care and access to nutrition, creating environments that foster physical and mental development. Collaborations between governments, non-profit organizations, and community groups can drive initiatives aimed at raising awareness and resources toward enhancing human capital. While the Human Capital Index provides significant insights regarding male productivity potential, it is not devoid of flaws. One limitation arises from its reliance on existing data, which can be outdated or inaccurately reported, especially in regions with less stringent data collection methodologies. Furthermore, the index largely focuses on males while neglecting a comprehensive view that includes female counterparts, which is critical considering the growing participation of women in various fields. Consequently, this lack of a holistic approach may distort the understanding of human capital as a whole. In conclusion, the Human Capital Index, particularly when focused on males, is instrumental in diagnosing economic and social disparities on a global scale. The latest figures from 2020 reveal a median value of 0.57, hinting at both the potential for improvement and the immediate need for action in lower-ranking regions. By exploring strategies that fortify education and health, countries can aspire to elevate the HCI, ultimately leading to enriched economic landscapes and societal well-being.

                    
# 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.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.UB.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))