Country

Indicator

Value type

Values present

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

Source: worldbank.org, 19.12.2024

Year: 2020

Country	Value	Value change, %	Rank
Afghanistan	0.395	+1.46%	122
Angola	0.331	+0.492%	147
Albania	0.595	+2.16%	53
United Arab Emirates	0.648	-0.44%	36
Argentina	0.578	-2.62%	59
Armenia	0.544	-0.4%	77
Antigua & Barbuda	0.554	+1.38%	72
Australia	0.746	-1.15%	12
Austria	0.72	-3.5%	23
Azerbaijan	0.55	-7.19%	74
Burundi	0.347	-1.2%	143
Belgium	0.738	-0.577%	16
Benin	0.372	+0.746%	132
Burkina Faso	0.357	+0.836%	138
Bangladesh	0.442	+1.03%	109
Bulgaria	0.584	-8.55%	57
Bahrain	0.614	-2.1%	48
Bosnia & Herzegovina	0.558	-7.16%	70
Belarus	0.654		35
Brunei	0.6		51
Bhutan	0.443		108
Botswana	0.356	+0.351%	140
Canada	0.776	-0.574%	2
Switzerland	0.737	-1.67%	17
Chile	0.623	-2.45%	46
China	0.633	+0.986%	42
Côte d’Ivoire	0.361	+2.2%	137
Cameroon	0.374	+0.843%	131
Congo - Kinshasa	0.339	+0.214%	145
Congo - Brazzaville	0.382	+0.0461%	128
Colombia	0.579	+1.33%	58
Comoros	0.347	-0.00792%	144
Costa Rica	0.605	+4%	49
Cyprus	0.736	+0.172%	18
Czechia	0.72	-2.26%	24
Germany	0.721	-2.02%	22
Dominica	0.5	-2.01%	91
Denmark	0.727	-2.45%	21
Dominican Republic	0.467	-0.95%	102
Algeria	0.507	+0.214%	87
Ecuador	0.565	-0.42%	65
Egypt	0.459	+0.232%	103
Spain	0.71	-0.511%	27
Estonia	0.729	+0.465%	20
Ethiopia	0.369	-0.548%	133
Finland	0.753	-2.89%	10
Fiji	0.473		100
France	0.734	+0.824%	19
Micronesia (Federated States of)	0.451	+8.42%	107
Gabon	0.415	+0.114%	116
United Kingdom	0.765	+0.873%	8
Georgia	0.521	-6.26%	84
Ghana	0.428	+1.17%	113
Guinea	0.362	-0.12%	135
Gambia	0.378	+4.24%	130
Greece	0.657	-0.535%	34
Grenada	0.522	+4.95%	82
Guatemala	0.439	+0.823%	111
Guyana	0.456	+0.106%	105
Hong Kong SAR China	0.768	-2.04%	6
Honduras	0.454	+0.127%	106
Croatia	0.67	-3.68%	32
Haiti	0.413	+0.189%	118
Hungary	0.648	-3.01%	37
Indonesia	0.512	+0.103%	85
India	0.482	+1.76%	96
Ireland	0.769	-2.96%	5
Iran	0.562	+0.179%	67
Iraq	0.388	+1.79%	126
Iceland	0.715	+0.0546%	25
Israel	0.689	-5.79%	29
Italy	0.708	-3.85%	28
Jamaica	0.49	-0.526%	93
Jordan	0.511	+1.38%	86
Kazakhstan	0.597	-18.6%	52
Kyrgyzstan	0.567	+0.576%	64
Kiribati	0.421	+3.53%	114
St. Kitts & Nevis	0.556	+2.7%	71
South Korea	0.769	-4.15%	3
Kuwait	0.522	-1.02%	83
Laos	0.431	-0.175%	112
Liberia	0.296	+0.126%	151
St. Lucia	0.56	+2.91%	69
Lithuania	0.648	-2.58%	38
Luxembourg	0.665	-1.38%	33
Latvia	0.646	-3.69%	39
Macao SAR China	0.765	+4.5%	7
Morocco	0.481	+2.66%	97
Moldova	0.551	+0.63%	73
Madagascar	0.361	+1.63%	136
Mexico	0.59	+0.513%	56
Marshall Islands	0.381	+3.66%	129
North Macedonia	0.535	+3.8%	81
Mali	0.305	-1.09%	150
Malta	0.675	-0.834%	30
Myanmar (Burma)	0.44	+0.823%	110
Montenegro	0.614	+3.03%	47
Mongolia	0.572	-0.3%	62
Mauritania	0.331	+2.5%	148
Malaysia	0.578	-3.38%	60
Niger	0.295	-0.869%	152
Nigeria	0.328	+1.18%	149
Nicaragua	0.479	+0.167%	98
Netherlands	0.76	-2.58%	9
Norway	0.742	-0.621%	14
Nauru	0.458		104
New Zealand	0.751	+1.09%	11
Oman	0.562	-0.367%	66
Pakistan	0.394	+2.07%	124
Panama	0.471	-3.09%	101
Peru	0.59	+2.36%	55
Philippines	0.476	-5.84%	99
Palau	0.539	+3.05%	79
Papua New Guinea	0.41	+0.56%	119
Poland	0.71	-1.23%	26
Portugal	0.744	-2.35%	13
Paraguay	0.503	+0.0905%	90
Palestinian Territories	0.538	+1.46%	80
Qatar	0.604	-0.256%	50
Romania	0.547	-2.02%	75
Russia	0.631	-6.44%	43
Rwanda	0.349	+0.5%	142
Saudi Arabia	0.545	+1.96%	76
Sudan	0.352	+0.174%	141
Senegal	0.384	-0.815%	127
Singapore	0.86	-0.372%	1
Solomon Islands	0.395	-2.56%	123
Sierra Leone	0.335	+2.05%	146
El Salvador	0.505	-0.514%	89
Serbia	0.644	-11.3%	40
Slovakia	0.63	-2.75%	44
Slovenia	0.741	-1.88%	15
Sweden	0.769	-0.996%	4
Chad	0.286	-0.0683%	153
Togo	0.407	+3.18%	120
Thailand	0.569	-1.88%	63
Timor-Leste	0.413	+0.0157%	117
Tonga	0.491	+3.11%	92
Tunisia	0.486	+1.57%	95
Turkey	0.628	+4.2%	45
Tuvalu	0.405	+0.464%	121
Tanzania	0.364	+0.598%	134
Ukraine	0.575	-2.43%	61
Uruguay	0.562	-0.674%	68
United States	0.673	-2.1%	31
Uzbekistan	0.592		54
St. Vincent & Grenadines	0.488	-1.71%	94
Vietnam	0.633	+0.232%	41
Vanuatu	0.421	+2.16%	115
Samoa	0.505	+3.38%	88
Kosovo	0.542	+0.0645%	78
Yemen	0.356	-0.422%	139
South Africa	0.393	+0.723%	125

The Human Capital Index (HCI) has emerged as a significant metric in assessing the potential productivity of individuals within a particular region, specifically focusing on the health, education, and survival of the population. When evaluating the HCI for males, the lower bound score ranges from 0 to 1, where a higher score indicates a stronger foundation for human capital development. This score is crucial as it reflects the resources dedicated to education, the healthcare system's effectiveness, and the overall quality of life crucial for male populations in various regions.

The latest data available from 2020 shows a median value of 0.54 for male human capital, which serves as a benchmark for evaluating regional performance against a broader scale. This indicates that, on average, a little over half of the potential productivity of males is being realized through the positive impact of health and education systems. In simple terms, a score of 0.54 illustrates that while there are regions performing adequately in terms of human capital, many others fall significantly behind, underscoring a global disparity in the development of human resources.

Notably, the analysis of top-performing countries reveals a stark contrast to those at the bottom of the list. Countries such as Singapore, Canada, South Korea, Sweden, and Ireland stand out with scores like 0.86, 0.78, and 0.77 respectively. Such high scores indicate not just the effective utilization of human resources, but also extensive investments in educational infrastructure, public health, and social equity. For instance, Singapore, with its highly developed economy, prioritizes education and workforce development significantly, resulting in a high HCI. In contrast, this investment leads to a productive economy that can adapt to technological advancements and global competition.

Conversely, the bottom of the index paints a disheartening picture. Countries such as Chad and Niger, both scoring 0.29, highlight critical issues in educational access and healthcare facilities. These nations often grapple with systemic challenges like poverty, conflict, and under-resourced infrastructures that hinder not only male human capital but the overall development of their populations. The scores in Chad, Niger, Liberia, Mali, and Nigeria, all below 0.35, signify a dire need for renewed focus on policies aimed at uplifting educational standards and healthcare services, which are vital for improving human capital.

The importance of the Human Capital Index, especially for males, cannot be overstated. A higher HCI is often associated with better economic outcomes, improved societal stability, and enhanced workforce productivity. It plays a pivotal role in determining the labor market's health and, consequently, the economic viability of a nation. Furthermore, a strong male HCI can have broader implications, including reducing crime rates, increasing civic participation, and fostering innovation, as individuals are better equipped to contribute positively to their communities and economies.

The relations between the HCI and other indicators are evident, particularly in relation to economic performance and social development. For example, nations with higher HCI scores typically exhibit lower rates of unemployment and higher GDP per capita. This correlation underlines the importance of education and health in shaping economic trajectories. Social indicators, such as gender equality and anti-poverty measures, also resonate with HCI outcomes; regions investing in these areas tend to report higher human capital scores, showcasing the interconnectedness of social policies and human development.

Several factors contribute to the variations seen in the HCI, including government policies, socio-economic conditions, cultural values, and the level of investment in public services. Regions with stable governments and progressive policies on education and health typically demonstrate higher scores. Additionally, economic stability allows for consistent investments in human capital development initiatives, creating a virtuous cycle of improvement. Conversely, regions mired in conflict, poverty, and poor governance see stagnation, as these factors severely stifle potential growth.

To improve male human capital, strategies must encompass a multitude of factors. Investment in education must be prioritized, with initiatives aimed at broadening access, improving quality, and adapting to future workforce needs. Additionally, enhancing healthcare access and focusing on preventative measures are paramount, particularly in regions struggling with public health issues. Investment in vocational training and skills development can also significantly enhance the employability of the male population, ensuring alignment with market demands.

While the HCI presents invaluable insights into human capital development, it is not without flaws. The simplified numerical score may not capture the nuanced social and economic factors that affect human capital. Additionally, it does not account for regional disparities within countries, leading to potential misunderstandings about national performance as a whole. Furthermore, over-reliance on quantitative measures without qualitative assessments may overlook critical cultural and local context-related factors essential for a comprehensive understanding.

In conclusion, the Human Capital Index is crucial in framing discussions around educational and health policies, particularly when focused on the male segment of the population. The disparities revealed by the latest data from 2020, where top performers like Singapore and Canada significantly outshine nations such as Chad and Niger, demand urgent attention and action. While challenges remain, through targeted strategies encompassing education, healthcare, and socio-economic reforms, improvements in male human capital can foster broader economic and social benefits, enhancing overall quality of life and productivity worldwide.

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.LB.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.LB.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))

Compare

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

Year: 2020

Acquire full dataset with Python or R from source