Country

Indicator

Value type

Values present

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

Source: worldbank.org, 19.12.2024

Year: 2020

Country	Value	Value change, %	Rank
Afghanistan	0.413	+1.8%	149
Angola	0.385	+0.502%	164
Albania	0.643	+0.4%	50
United Arab Emirates	0.684	-0.634%	43
Argentina	0.612	-2.45%	69
Armenia	0.589	-0.474%	82
Antigua & Barbuda	0.607	+3.26%	71
Australia	0.778	-1.51%	16
Austria	0.757	-2.68%	26
Azerbaijan	0.591	-8.51%	80
Burundi	0.406	-0.82%	153
Belgium	0.769	-0.317%	19
Benin	0.422	+0.89%	145
Burkina Faso	0.402	+1.55%	154
Bangladesh	0.472	+1.25%	125
Bulgaria	0.625	-8.64%	60
Bahrain	0.66	-1.73%	47
Bosnia & Herzegovina	0.588	-6.23%	83
Belarus	0.71		36
Brazil	0.556	+0.927%	95
Brunei	0.634		58
Bhutan	0.499		118
Botswana	0.431	+0.327%	141
Central African Republic	0.321		173
Canada	0.806	-0.288%	5
Switzerland	0.766	-1.28%	20
Chile	0.662	-2.04%	46
China	0.665	+0.957%	45
Côte d’Ivoire	0.398	+2.86%	156
Cameroon	0.418	+1.17%	147
Congo - Kinshasa	0.385	+0.573%	165
Congo - Brazzaville	0.44	+0.403%	137
Colombia	0.616	+1.2%	66
Comoros	0.434	+0.903%	140
Costa Rica	0.638	+4.62%	56
Cyprus	0.764	+0.117%	21
Czechia	0.762	-1.49%	25
Germany	0.762	-1.64%	23
Dominica	0.559	-1.14%	94
Denmark	0.762	-2.21%	24
Dominican Republic	0.515	-0.8%	112
Algeria	0.542	+0.516%	101
Ecuador	0.603	-0.312%	76
Egypt	0.508	+0.412%	116
Spain	0.734	-1.2%	30
Estonia	0.789	+0.29%	12
Ethiopia	0.392	-0.214%	158
Finland	0.805	-2.26%	7
Fiji	0.521		108
France	0.771	+0.934%	18
Micronesia (Federated States of)	0.529	+8.76%	103
Gabon	0.478	+0.316%	123
United Kingdom	0.791	+0.796%	11
Georgia	0.576	-6.72%	86
Ghana	0.462	+1.54%	132
Guinea	0.387	+0.613%	163
Gambia	0.442	+4.42%	135
Greece	0.7	-0.859%	38
Grenada	0.577	+4.66%	85
Guatemala	0.47	+1.03%	128
Guyana	0.51	+0.181%	115
Hong Kong SAR China	0.823	-1.12%	2
Honduras	0.491	+0.165%	121
Croatia	0.719	-2.55%	32
Haiti	0.463	+0.479%	131
Hungary	0.692	-3.27%	39
Indonesia	0.55	+0.166%	96
India	0.498	+1.84%	119
Ireland	0.801	-2.66%	9
Iran	0.604	+0.219%	75
Iraq	0.414	+2.14%	148
Iceland	0.751	+0.256%	27
Israel	0.745	-3.75%	28
Italy	0.735	-3.32%	29
Jamaica	0.548	-0.808%	97
Jordan	0.564	+1.11%	90
Japan	0.813	-4.2%	3
Kazakhstan	0.635	-19.6%	57
Kenya	0.565	+0.914%	89
Kyrgyzstan	0.605	+0.554%	72
Cambodia	0.507	+0.198%	117
Kiribati	0.517	+4.83%	110
St. Kitts & Nevis	0.598	+2.53%	77
South Korea	0.809	-4.22%	4
Kuwait	0.572	-0.297%	88
Laos	0.47	-0.0196%	127
Lebanon	0.524	-1.83%	107
Liberia	0.332	+0.565%	171
St. Lucia	0.615	+2.5%	68
Sri Lanka	0.604	+0.843%	74
Lesotho	0.423	+0.447%	144
Lithuania	0.715	-3.1%	34
Luxembourg	0.691	-0.954%	40
Latvia	0.719	-4.3%	31
Macao SAR China	0.801	+4.4%	8
Morocco	0.514	+2.29%	113
Moldova	0.593	+0.496%	79
Madagascar	0.409	+1.61%	151
Mexico	0.621	+0.34%	62
Marshall Islands	0.441	+4.95%	136
North Macedonia	0.563	+4.23%	91
Mali	0.329	-0.973%	172
Malta	0.716	+0.157%	33
Myanmar (Burma)	0.494	+1.3%	120
Montenegro	0.641	+1.61%	54
Mongolia	0.63	-0.501%	59
Mozambique	0.377	+1.98%	168
Mauritania	0.406	+2.92%	152
Mauritius	0.64	-0.201%	55
Malawi	0.429	+0.649%	143
Malaysia	0.621	-3.71%	63
Namibia	0.465	+0.277%	129
Niger	0.333	-0.724%	170
Nigeria	0.381	+1.8%	166
Nicaragua	0.516	+0.163%	111
Netherlands	0.8	-1.38%	10
Norway	0.778	+0.386%	15
Nepal	0.519	+1.34%	109
Nauru	0.527		104
New Zealand	0.783	+0.463%	13
Oman	0.621	-0.52%	64
Pakistan	0.419	+1.86%	146
Panama	0.511	-2.36%	114
Peru	0.616	+1.73%	67
Philippines	0.531	-5.62%	102
Palau	0.605	+3.76%	73
Papua New Guinea	0.444	+1.19%	134
Poland	0.763	-0.82%	22
Portugal	0.778	-1.84%	17
Paraguay	0.542	+0.119%	100
Palestinian Territories	0.591	+1.48%	81
Qatar	0.643	+0.211%	51
Romania	0.596	-1.3%	78
Russia	0.69	-6.5%	41
Rwanda	0.392	+0.708%	159
Saudi Arabia	0.587	-1.16%	84
Sudan	0.391	+0.445%	160
Senegal	0.435	-0.174%	139
Singapore	0.886	-1.15%	1
Solomon Islands	0.43	-2.56%	142
Sierra Leone	0.379	+2.96%	167
El Salvador	0.56	+0.354%	93
Serbia	0.687	-11.1%	42
South Sudan	0.334	+0.503%	169
Slovakia	0.673	-2.36%	44
Slovenia	0.781	-1.89%	14
Sweden	0.805	-0.908%	6
Eswatini	0.39	+0.76%	161
Seychelles	0.657	+0.388%	49
Chad	0.317	+0.477%	174
Togo	0.452	+2.79%	133
Thailand	0.62	-1.34%	65
Tajikistan	0.525	-5.56%	105
Timor-Leste	0.472	+0.316%	124
Tonga	0.545	+2.51%	98
Trinidad & Tobago	0.624	+0.167%	61
Tunisia	0.525	+1.42%	106
Turkey	0.658	+3.35%	48
Tuvalu	0.464	+2%	130
Tanzania	0.401	+1.1%	155
Uganda	0.396	+0.587%	157
Ukraine	0.643	-1.79%	52
Uruguay	0.608	-0.536%	70
United States	0.711	-1.56%	35
Uzbekistan	0.641		53
St. Vincent & Grenadines	0.545	-0.248%	99
Vietnam	0.708	+0.436%	37
Vanuatu	0.47	+2.34%	126
Samoa	0.56	+5.15%	92
Kosovo	0.573	-0.428%	87
Yemen	0.388	+0.221%	162
South Africa	0.437	+0.746%	138
Zambia	0.41	+1.61%	150
Zimbabwe	0.488	+1.24%	122

The Human Capital Index (HCI) is a compelling metric designed to measure the potential productivity of a country's population based on health and education. Operating on a scale from 0 to 1, the HCI offers a quantitative framework that signifies how well a country enhances its human capital, which is essential for economic growth and sustainability. An HCI score of 0 indicates that a country's population holds no productive capabilities, while a score of 1 reflects maximum potential. The upper bound score of the HCI represents the best attainable scenario, paving the way for comprehensive assessment and benchmarking across different regions and nations.

The importance of the HCI cannot be overstated. It is increasingly becoming a vital tool for policymakers, economists, non-profit organizations, and educational institutions alike. As economies evolve and the labor market becomes more competitive, investing in human capital leads to better economic outcomes, influencing overall productivity, innovation, and growth rates. Moreover, a higher HCI correlates with better health indicators and educational outcomes, linking the well-being of citizens with their capacity to contribute positively to society and the economy.

In examining the relationships between the HCI and other indicators, we find that it is closely tied to economic performance, social stability, and access to resources. For example, nations with high HCI scores like Singapore (0.89), Hong Kong (0.82), Japan (0.81), South Korea (0.81), and Canada (0.81) tend to experience robust economic growth, higher levels of productivity, and greater innovation. These countries often demonstrate significant investments in education, healthcare, and training programs, which are core components assessed by the HCI. In contrast, countries at the lower end of the spectrum, such as Chad (0.32), the Central African Republic (0.32), Mali (0.33), Liberia (0.33), and Niger (0.33), often grapple with stagnated economic development, widespread poverty, and poor health outcomes, limiting their human capital potential.

Several factors affect the Human Capital Index. Access to quality education stands out as a primary determinant; nations that invest heavily in their educational infrastructures—such as school accessibility, quality of teaching, and curriculum adequacy—tend to score higher on the HCI. Furthermore, healthcare accessibility is crucial, encompassing maternal and child health, nutrition, and healthcare services' availability and quality. Socioeconomic factors, including income levels, culture, and governmental policies, play a significant role too. Political stability and effective governance often foster environments that enable the flourishing of human capital through sound management and strategic resource allocation.

To improve HCI scores, countries must implement comprehensive strategies that focus on enhancing both health and education systems. These may involve increasing public spending on education and health, improving infrastructures, and ensuring that marginalized communities have access to essential services. Adult education and lifelong learning initiatives also play a crucial role; investing in the current workforce ensures that individuals remain competitive in a rapidly changing job market. Countries can also look towards adopting innovative educational models and incorporating technology to enhance learning experiences and outcomes.

In addressing flaws and challenges associated with the Human Capital Index, it is essential to consider that while the HCI provides valuable insights, it may not capture all dimensions of human capital effectively. For instance, cultural factors, informal education, and the impact of social networks on human capital development are not adequately reflected in the index. Furthermore, the variability in data collection methods and standards across countries can lead to discrepancies and reduce the comparability of results. Hence, while the HCI is a useful tool, it should be complemented by other qualitative assessments and localized metrics to gain a more nuanced understanding of human capital development.

In summary, the Human Capital Index serves as a crucial barometer for gauging a nation's investment in its people, interlinking education, health, and economic performance. The stark contrast between countries with high scores, like Singapore and those with markedly lower scores, such as Chad, highlights the disparities in human capital development across the globe. Addressing the multifaceted challenges and leveraging strategic investments in health and education can lead to significant improvements in HCI scores, fostering economic resilience and social stability.

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'

# 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'

# 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), Upper Bound (scale 0-1)

Year: 2020

Acquire full dataset with Python or R from source