Country

Indicator

Value type

Values present

Over-age students, primary, female (% of female enrollment)

Source: worldbank.org, 03.09.2025

Year: 2017

Country	Value	Value change, %	Rank
Albania	5.54	-6.19%	65
Andorra	6.78	-2.49%	57
United Arab Emirates	4.95	+12.3%	70
Argentina	6.75	-11.5%	58
Armenia	2.47	-4.77%	97
Antigua & Barbuda	3.16	+6.56%	88
Australia	3.41	+1.05%	83
Austria	12.5	+4.12%	31
Azerbaijan	8.81	+12.8%	46
Burundi	16.6	-38.5%	14
Belgium	4.73	+1.36%	72
Benin	8.67	+20.1%	47
Burkina Faso	16.2	+4.18%	16
Bulgaria	1.18	-17.2%	123
Bahrain	3.08	-5.12%	89
Bosnia & Herzegovina	3.49	+9.41%	82
Belarus	5.26	+5.78%	66
Belize	7.23	-0.25%	53
Bolivia	6.4	-14.6%	60
Brazil	12.6	+3.51%	29
Barbados	1.28	+8.38%	122
Brunei	4.07	+7.92%	78
Bhutan	12.8	-6.27%	28
Canada	0		146
Switzerland	1.12	-3.28%	124
Chile	5.16	-4.57%	67
Côte d’Ivoire	9.87	-0.209%	41
Cameroon	11.2	-16.1%	36
Colombia	13.4	-9.9%	24
Comoros	15.9	-20.5%	17
Costa Rica	9.56	-22.4%	42
Cuba	1.93	-13.4%	107
Cyprus	1.35	-9.68%	120
Czechia	9.09	+1.12%	45
Germany	10.9	-1.09%	38
Djibouti	13.7	-11.9%	23
Denmark	1.61	-9.55%	114
Dominican Republic	9.36	-10.3%	44
Algeria	4.99	-0.0174%	69
Ecuador	2.9	-22.4%	91
Egypt	2.24	-8.3%	100
Eritrea	21.9	+15%	7
Spain	5.69	+1.16%	63
Estonia	2.82	-3.11%	94
Finland	0.954	+6.34%	128
France	2.37	-10%	98
United Kingdom	1.11	-0.534%	126
Georgia	1.72	-24.8%	111
Ghana	16.5	+0.862%	15
Gambia	15.9	+2.79%	18
Greece	1.12	-2.62%	125
Grenada	2.87	+2.92%	93
Guatemala	9.48	-9.85%	43
Hong Kong SAR China	2.26	+3.69%	99
Honduras	10.9	-5.04%	37
Croatia	0.668	+10.9%	135
Hungary	2.22	+0.996%	101
Indonesia	1.63	-0.683%	113
Ireland	0.407	+0.683%	140
Iran	10.5	-13.6%	39
Iceland	0.0193	-63.3%	145
Israel	2.58	+10.3%	95
Italy	3.18	+328%	86
Jamaica	4.63	+0.233%	73
Jordan	0.516	-75.1%	137
Japan	0		146
Kazakhstan	0.279	-76.3%	142
Kyrgyzstan	0.923	-27%	130
Cambodia	13.3	+1.36%	25
Kiribati	3.17	-30.4%	87
South Korea	0.713	-2.21%	133
Kuwait	2.57	+8.83%	96
Laos	11.7	-11.1%	32
Lebanon	5.94	-7.66%	61
Liberia	46.5	-21.6%	1
St. Lucia	1.99	+9.81%	102
Liechtenstein	1.61	+17.4%	115
Sri Lanka	0.948	+9.33%	129
Lesotho	18.6	-3.43%	12
Lithuania	0.728	-1.53%	132
Luxembourg	3.35	+6.33%	84
Latvia	1.83	-0.785%	109
Macao SAR China	3.85	+2.55%	79
Morocco	11.7	-3.56%	33
Monaco	4.5	+201%	74
Moldova	1.49	-2.3%	117
Maldives	1.49	-29.5%	116
Mexico	1.88	-18.1%	108
North Macedonia	1.4	+85.4%	118
Mali	7.22	-34%	54
Malta	0.683	-14.3%	134
Myanmar (Burma)	10.1	+7.16%	40
Montenegro	1.76	-3.02%	110
Mongolia	1.96	-20.5%	105
Mozambique	14.9	+0.43%	21
Mauritania	19.9	+5.83%	8
Mauritius	4.12	-18.9%	77
Malawi	19.2	+2.3%	10
Malaysia	0.196	-1.54%	143
Namibia	12.8	-10.8%	27
Niger	7.76	-1.96%	52
Netherlands	4.93	-6.96%	71
Norway	0		146
Nepal	31.7	+3.33%	3
New Zealand	0.342	-0.327%	141
Oman	1.36	-22%	119
Pakistan	26.5	0%	4
Panama	7.91	-0.836%	51
Peru	5.87	-5.71%	62
Philippines	8.01	-32.3%	49
Poland	0.621	-22.9%	136
Portugal	6.82	-2.69%	55
Palestinian Territories	1.69	+59.7%	112
Qatar	1.99	-7.82%	104
Romania	2.99	+6.72%	90
Russia	2.89	+42,131%	92
Rwanda	25.7	-1.77%	5
Saudi Arabia	4.46	-43.5%	75
Sudan	16.6	+35.8%	13
Senegal	11.4	-4.32%	34
Singapore	0.469	-2.68%	138
Solomon Islands	35.8	-0.596%	2
Sierra Leone	8.61	-12.7%	48
El Salvador	12.6	+2.97%	30
Serbia	0.795	+15.7%	131
São Tomé & Príncipe	11.4	-27.6%	35
Suriname	15.4	-4.78%	19
Slovakia	14.5	-0.993%	22
Slovenia	1.33	+14.7%	121
Sweden	19.7	+1.46%	9
Eswatini	25.3	+0.727%	6
Seychelles	3.27	-7.43%	85
Togo	18.7	-4.7%	11
Tajikistan	0.0594	-19.6%	144
Timor-Leste	15	-6.14%	20
Tunisia	3.51	+5.82%	81
Turkey	1.99	-36.4%	103
Ukraine	7.95	+12.7%	50
Uruguay	5.65	-4.8%	64
United States	5.12	-9.52%	68
Uzbekistan	0.432	-51.4%	139
St. Vincent & Grenadines	1.93	+0.0322%	106
Venezuela	6.59	-0.939%	59
British Virgin Islands	3.64	+14.8%	80
Vietnam	1.01	-15.4%	127
Samoa	6.81	-11.8%	56
South Africa	4.4	-57.2%	76
Zambia	13.1	+2.21%	26

The indicator "Over-age students, primary, female (% of female enrollment)" serves as a critical measure in understanding the educational dynamics within various societies, reflecting the proportion of female students in primary education who are older than the typical age range for that education level. In essence, it provides insight into issues related to accessibility, social and economic barriers, and the overall effectiveness of educational systems in promoting timely and appropriate learning opportunities for girls. When analyzing this indicator, it becomes evident that it intertwines with various other educational metrics and societal factors that warrant careful exploration.

Understanding the importance of over-age students, particularly in the context of female enrollment, necessitates acknowledging its profound implications on educational equity and outcomes. A high percentage may indicate systemic issues such as early pregnancy, economic strains requiring students to engage in labor, or inadequate schooling infrastructure that fails to accommodate the schooling needs of girls. These factors invariably affect not only the girl’s educational attainment but also the socioeconomic fabric of their communities. For instance, countries with high rates of over-age female students may struggle with gender parity in education outcomes, which can perpetuate cycles of poverty and limit opportunities for women in the workforce.

Looking at the data from 2019, the median value for over-age female students in primary education was recorded at 6.46%. This figure, while seemingly moderate, masks significant disparities across different regions. The top five areas, with up to 31.2% of their female enrollment being over-age students, predominantly come from less affluent countries. For example, Nepal leads with an alarming rate of 31.2%, indicating major barriers that necessitate focus on girls’ education. Factors influencing this figure in Nepal could include socioeconomic pressures, cultural norms, and regional conflicts that disrupt educational access for girls.

Following Nepal, Djibouti recorded a significant 11.09%. Here, historical context plays a role; sociopolitical instability and a lack of resources have had detrimental effects on education systems. The strategies employed in such contexts must prioritize not only access to education but also the socio-cultural barriers women face, thus implementing community outreach programs, financial support for families, and incentives for girls to remain in school. Conversely, regions like Monaco and Kazakhstan, with rates of 1.83% and 0.56% respectively, illustrate lower instances of over-age students that may reflect better education systems, but still warrant a thorough analysis of their educational policies to ensure equity and inclusivity.

The bottom tier of this indicator presents Kazakhstan at 0.56% and Monaco at 1.83%. The low percentages here do not equate to a complete absence of issues related to girls' education. Instead, they hint at a more stable educational system, yet they also suggest that educational policies must continually adapt to newer challenges. This is an essential insight, as low figures can lead to complacency, blinding policymakers to underlying issues that are not immediately apparent within statistical data.

The relationship of the over-age female students indicator with other educational indicators, such as overall enrollment rates, dropout rates, and gender parity indices, is integral. A high occurrence of over-age students suggests a direct correlation to higher dropout rates, as older students may feel out of place or may have conflicting responsibilities outside of school. These dynamics reinforce the urgent need for a comprehensive view when addressing educational challenges, emphasizing how interventions must be multifaceted to promote better educational outcomes.

Various factors can affect the percentage of over-age female students, including economic conditions, cultural attitudes toward female education, quality of schools, and availability of resources. Economic constraints often lead families to prioritize immediate financial benefits over educational aspirations, particularly in regions where economic stability is precarious. Cultural attitudes can significantly sway parents' decisions about sending their daughters to school or allowing them to drop out for domestic duties or early marriage.

To address the challenges associated with over-age students, especially among females, various strategies can be deployed. Governments and educational policymakers must emphasize creating inclusive and flexible schooling systems that cater to the needs of all students, particularly those aged out of the traditional schooling system. Implementing campaigns that promote the importance of education for girls, coupled with practical financial support and community involvement, can reduce dropout rates and encourage timely enrollment.

Furthermore, specialized programs aimed at integrating over-age students back into the education system, through remedial classes or alternative education pathways, can be impactful. Such initiatives can provide tailored support to catch students up to their peers, mitigating feelings of exclusion and stigma that often accompany being an over-age student.

However, there are inherent flaws in relying solely on this indicator to reflect the broader educational climate. The metric only captures a snapshot and does not highlight qualitative aspects of education such as curriculum relevance, teacher performance, or student engagement levels. Moreover, the absence of recent world values adds to the complexity of interpreting this data comprehensively, as trends over time are necessary for understanding the evolution of educational policies and their implications for female students globally.

In conclusion, the "Over-age students, primary, female (% of female enrollment)" indicator encapsulates significant educational challenges and opportunities within global educational frameworks. While it sheds light on existing disparities and systemic barriers facing girls in education, policymakers must pair this data with broader insights and qualitative assessments to craft effective solutions that prioritize educational equity and ensure consistent progress in female enrollment around the world.

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 = 'SE.PRM.OENR.FE.ZS'

# 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 <- 'SE.PRM.OENR.FE.ZS'

# 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

Over-age students, primary, female (% of female enrollment)

Year: 2017

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