Country

Indicator

Value type

Values present

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

Source: worldbank.org, 19.12.2024

Year: 2020

Country	Value	Value change, %	Rank
Afghanistan	0.4	+1.73%	147
Angola	0.362	+0.615%	165
Albania	0.634	+0.888%	50
United Arab Emirates	0.673	-0.442%	43
Argentina	0.602	-2.47%	69
Armenia	0.579	-0.469%	82
Antigua & Barbuda	0.596	+3.02%	73
Australia	0.77	-1.36%	16
Austria	0.747	-2.85%	26
Azerbaijan	0.578	-8.19%	83
Burundi	0.386	-0.874%	152
Belgium	0.76	-0.307%	19
Benin	0.4	+0.842%	146
Burkina Faso	0.384	+1.43%	154
Bangladesh	0.464	+1.2%	122
Bulgaria	0.614	-8.41%	60
Bahrain	0.652	-1.72%	46
Bosnia & Herzegovina	0.58	-6.39%	81
Belarus	0.7		36
Brazil	0.551	+1.06%	91
Brunei	0.626		56
Bhutan	0.475		120
Botswana	0.414	+0.367%	140
Central African Republic	0.292		173
Canada	0.798	-0.321%	5
Switzerland	0.756	-1.3%	20
Chile	0.652	-2.05%	47
China	0.653	+0.928%	45
Côte d’Ivoire	0.38	+2.68%	157
Cameroon	0.397	+1.04%	148
Congo - Kinshasa	0.366	+0.505%	163
Congo - Brazzaville	0.419	+0.342%	139
Colombia	0.604	+0.948%	66
Comoros	0.405	+0.932%	144
Costa Rica	0.629	+4.21%	54
Cyprus	0.756	+0.12%	21
Czechia	0.752	-1.6%	24
Germany	0.751	-1.65%	25
Dominica	0.545	-1.27%	95
Denmark	0.755	-2.04%	22
Dominican Republic	0.503	-0.812%	111
Algeria	0.535	+0.482%	98
Ecuador	0.594	-0.33%	74
Egypt	0.494	+0.378%	114
Spain	0.728	-1.07%	29
Estonia	0.777	+0.398%	12
Ethiopia	0.383	-0.343%	155
Finland	0.796	-2.27%	6
Fiji	0.509		105
France	0.763	+0.896%	18
Micronesia (Federated States of)	0.506	+8.48%	108
Gabon	0.458	+0.268%	124
United Kingdom	0.783	+0.767%	11
Georgia	0.569	-6.59%	85
Ghana	0.45	+1.48%	128
Guinea	0.371	+0.504%	162
Gambia	0.422	+4.53%	136
Greece	0.69	-0.691%	37
Grenada	0.565	+4.41%	87
Guatemala	0.461	+0.985%	123
Guyana	0.495	+0.179%	113
Hong Kong SAR China	0.813	-1.09%	2
Honduras	0.481	+0.149%	118
Croatia	0.71	-2.7%	31
Haiti	0.447	+0.4%	130
Hungary	0.683	-3.12%	40
Indonesia	0.54	+0.387%	96
India	0.494	+1.8%	115
Ireland	0.793	-2.59%	9
Iran	0.593	+0.202%	75
Iraq	0.408	+2.04%	142
Iceland	0.745	+0.249%	27
Israel	0.734	-3.81%	28
Italy	0.728	-3.33%	30
Jamaica	0.535	-0.796%	97
Jordan	0.553	+1.14%	90
Japan	0.805	-4.28%	3
Kazakhstan	0.629	-19.1%	55
Kenya	0.547	+0.842%	93
Kyrgyzstan	0.597	+0.517%	72
Cambodia	0.492	+0.184%	117
Kiribati	0.493	+4.65%	116
St. Kitts & Nevis	0.586	+2.39%	77
South Korea	0.799	-4.29%	4
Kuwait	0.563	-0.296%	88
Laos	0.457	-0.0329%	125
Lebanon	0.515	-1.81%	104
Liberia	0.319	+0.455%	168
St. Lucia	0.603	+2.37%	68
Sri Lanka	0.598	+0.835%	71
Lesotho	0.4	+0.477%	145
Lithuania	0.706	-2.84%	34
Luxembourg	0.686	-0.899%	39
Latvia	0.707	-4.3%	33
Macao SAR China	0.796	+4.24%	7
Morocco	0.504	+2.35%	110
Moldova	0.584	+0.411%	79
Madagascar	0.392	+1.69%	150
Mexico	0.613	+0.211%	61
Marshall Islands	0.423	+4.71%	135
North Macedonia	0.557	+4.1%	89
Mali	0.318	-1.01%	169
Malta	0.709	+0.107%	32
Myanmar (Burma)	0.478	+1.23%	119
Montenegro	0.633	+1.66%	51
Mongolia	0.614	-0.472%	59
Mozambique	0.362	+1.87%	166
Mauritania	0.382	+2.96%	156
Mauritius	0.622	-0.226%	58
Malawi	0.413	+0.53%	141
Malaysia	0.611	-3.41%	62
Namibia	0.446	+0.305%	131
Niger	0.316	-0.737%	170
Nigeria	0.361	+1.65%	167
Nicaragua	0.508	+0.141%	107
Netherlands	0.79	-1.63%	10
Norway	0.771	+0.327%	15
Nepal	0.505	+1.29%	109
Nauru	0.508		106
New Zealand	0.776	+0.612%	13
Oman	0.608	-0.502%	64
Pakistan	0.406	+1.69%	143
Panama	0.502	-2.4%	112
Peru	0.605	+1.78%	65
Philippines	0.516	-6.02%	103
Palau	0.588	+3.67%	76
Papua New Guinea	0.429	+1.04%	133
Poland	0.753	-0.938%	23
Portugal	0.769	-1.85%	17
Paraguay	0.528	+0.128%	101
Palestinian Territories	0.58	+1.45%	80
Qatar	0.638	+0.464%	49
Romania	0.584	-1.72%	78
Russia	0.681	-6.52%	41
Rwanda	0.38	+0.701%	158
Saudi Arabia	0.576	-0.751%	84
Sudan	0.377	+0.374%	159
Senegal	0.42	-0.226%	137
Singapore	0.879	-0.897%	1
Solomon Islands	0.42	-2.55%	138
Sierra Leone	0.363	+2.67%	164
El Salvador	0.546	+0.219%	94
Serbia	0.677	-10.9%	42
South Sudan	0.307	+0.199%	171
Slovakia	0.665	-2.21%	44
Slovenia	0.775	-1.74%	14
Sweden	0.795	-0.891%	8
Eswatini	0.373	+0.801%	161
Seychelles	0.633	+0.352%	52
Chad	0.3	+0.252%	172
Togo	0.432	+2.63%	132
Thailand	0.609	-1.22%	63
Tajikistan	0.504	-7.01%	110
Timor-Leste	0.454	+0.279%	127
Tonga	0.531	+2.53%	100
Trinidad & Tobago	0.603	+0.193%	67
Tunisia	0.517	+1.41%	102
Turkey	0.649	+3.83%	48
Tuvalu	0.448	+1.6%	129
Tanzania	0.39	+0.925%	151
Uganda	0.384	+0.537%	153
Ukraine	0.631	-1.71%	53
Uruguay	0.599	-0.599%	70
United States	0.702	-1.67%	35
Uzbekistan	0.623		57
St. Vincent & Grenadines	0.533	-0.471%	99
Vietnam	0.69	+0.397%	38
Vanuatu	0.455	+2.23%	126
Samoa	0.548	+4.98%	92
Kosovo	0.567	-0.304%	86
Yemen	0.373	+0.0398%	160
South Africa	0.425	+0.687%	134
Zambia	0.397	+1.47%	149
Zimbabwe	0.467	+1.23%	121

The Human Capital Index (HCI) is a nuanced metric designed to measure the potential productivity of individuals in a given country, reflecting their ability to contribute to economic growth and societal development. The index is scaled between 0 and 1, where a score closer to 1 signifies that a country has invested sufficiently in its human capital, resulting in higher expected productivity of future generations. In a rapidly globalizing world, understanding the HCI is imperative as it relates to various aspects such as economic performance, social stability, and overall quality of life.

The importance of the Human Capital Index cannot be overstated. It serves as a diagnostic tool for policymakers to gauge their country’s education and health systems. By revealing disparities in human capital, the HCI pushes countries to focus on essential areas such as health care, education accessibility, and overall well-being. This focus can lead to enhanced job opportunities, increased productivity, and ultimately sustainable economic growth. Therefore, investing in human capital becomes indispensable for nations aiming to enhance their competitive advantage on the global stage.

When it comes to relations to other indicators, the HCI connects directly with dimensions like Gross Domestic Product (GDP), income inequality, and employment rates. Countries scoring high on the HCI tend to have robust economies, characterized by higher GDPs and lower rates of unemployment. For example, nations like Singapore and Hong Kong, which boast some of the highest HCI scores globally, also report thriving economies with diverse industries and high incomes. Conversely, nations with lower HCI scores, such as the Central African Republic and Chad, often experience systemic challenges like high unemployment rates and lower economic growth, creating a vicious cycle of poverty and underdevelopment.

A range of factors affects a country’s Human Capital Index. Education quality and accessibility play a starring role. Nations investing heavily in equitable education systems usually see increased HCI scores. Health care availability and quality further amplify the potential of human capital. Improved health outcomes ensure a more productive workforce. Additionally, socio-economic conditions, cultural attitudes towards education, governmental policies focused on health and education, and even societal stability can influence HCI. Countries grappling with conflicts or economic instability often witness declines in human capital due to disrupted education and health services.

In terms of strategies and solutions to improve the HCI, governments and organizations should prioritize multi-faceted approaches. Formulating educational policies focusing on accessible, equitable, and high-quality education can greatly raise HCI scores. Initiatives might include investing in vocational education, modernizing curricula, and providing incentives for teachers in underserved areas. Additionally, enhancing health care services, especially maternal and child health programs, can provide a significant boost to HCI rankings. Embracing technology for distance learning can also play an important role, especially in regions where traditional education infrastructures are lacking.

Moreover, partnerships between government, private sectors, and non-profit organizations can mobilize resources effectively. Such collaborations can foster an environment where talent is nurtured, enabling local communities to flourish. International cooperation can also yield substantial benefits in knowledge transfer and resource allocation for countries striving to improve their HCI.

However, it is crucial to understand the flaws within the HCI framework. One notable limitation is that the index may not capture the qualitative aspects of human capital comprehensively. For instance, informal education, which plays a significant role in skill development in many regions, is often overlooked. Furthermore, the HCI does not account for the socio-political context, meaning countries might achieve high scores while still dealing with underlying issues such as corruption or inequality. Also, the use of median values can mask disparities between different regions or demographic groups within a country. These facets raise concerns regarding the index’s ability to truly reflect the depth of human capital.

In 2020, the worldwide median value for the HCI was recorded at 0.56. This figure indicates that, on average, human capital is still under-leveraging its potential across the globe. The leading areas in human capital development were Singapore with a remarkable score of 0.88, followed by Hong Kong SAR China (0.81), Japan (0.80), South Korea (0.80), and Canada (0.80). These countries highlight the positive outcomes of robust investments in education and health sectors. In stark contrast, the bottom-ranked nations were the Central African Republic (0.29), Chad (0.30), South Sudan (0.31), Niger (0.32), and Mali (0.32). The limited scores from these countries underscore the urgent need for systemic reforms in education and health to improve the prospects of human capital.

To summarize, the Human Capital Index offers invaluable insights into the potential productivity of populations around the world. Through strategically designed policies and sustained investments, nations can enhance their HCI, promoting not just economic growth but also equity and opportunity for future generations. Despite its limitations, the HCI remains a pivotal tool for assessment, urging countries to prioritize human capital development as the cornerstone of progress.

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'

# 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'

# 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))

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

Year: 2020

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