Female youth literacy is very high in many of the available 2024 observations, but the same dataset also contains a long lower tail. Using the verified UNESCO Institute for Statistics GALP series, the median across 33 countries and areas is 93.09%, while the lowest and highest values are separated by 66.38 percentage points. The useful story is therefore not a simple ranking. It is the shape of the distribution, the concentration near 100%, the much wider lower range, and the fact that the figures are model-based estimates for a common reference year.
Table of Contents
What the indicator measures
The 2024 series describes the literacy rate of females aged 15 to 24. In the UIS statistical framework, youth literacy refers to the share of people in that age group who can read and write with understanding a short, simple statement related to everyday life. The official series used here is LR.GALP.AG15T24.F and the unit is percent. A value of 95 therefore means an estimated literacy rate of about 95 percent under the indicator definition; it is not a school enrollment rate, graduation rate, or measure of advanced reading proficiency.
The wording of the series matters because these are estimates produced with the Global Age-Specific Literacy Projections Model, commonly shortened to GALP. UIS uses model-based estimates when sufficiently recent observed literacy data are not available for a country. That means “2024” should not be read as if every reporting area carried out an identical literacy survey during 2024. The figures are comparable official estimates, but their underlying evidence can include earlier observations incorporated into a common projection approach.
The distribution across the 33 observations
The verified dataset contains 33 countries and areas with values for 2024. Across those observations, the mean is 87.64 percent and the median is 93.09 percent. The median being about 5.45 percentage points above the mean is a useful clue: a relatively small lower tail pulls the arithmetic average downward. The highest value in this dataset is Macao at 99.99 percent, while Chad is lowest at 33.61 percent. The distance between those two observations is 66.38 percentage points.
Threshold counts make the shape easier to see. Twenty-one of the 33 observations are at or above 90 percent, 14 are at or above 95 percent, and nine reach at least 99 percent. At the other end, six are below 80 percent, five are below 70 percent, and three are below 60 percent. The first quartile is 83.39 percent and the third quartile is 99.03 percent, so the middle half of the observations spans about 15.64 percentage points.
A tightly packed upper group
The five highest observations are Macao at 99.99 percent, Moldova at 99.93 percent, Brazil at 99.68 percent, Thailand at 99.50 percent, and Maldives at 99.26 percent. Venezuela follows at 99.10 percent, with Iran and Tonga both at about 99.09 percent and Chile at 99.03 percent. Once the rate is this close to 100 percent, tiny numerical differences create a ranking even though the practical distance between neighboring observations is very small.
For example, the gap between Macao and Moldova is only 0.06 percentage points, and the gap between Brazil and Thailand is 0.18 points. Those differences should not be presented as decisive evidence that one education system performs meaningfully better than another. Model uncertainty, revisions, and the limits of a percentage near its upper bound all argue for restraint. The more informative conclusion is that a sizeable group of the available observations is clustered very near universal basic literacy among young women.
The middle is not a single uniform group
The median value of 93.09 percent is essentially the value reported for Egypt. Nearby observations include Laos at 94.25 percent, Burundi at 94.73 percent, Puerto Rico at 92.39 percent, Malawi at 91.54 percent, Kenya at 90.53 percent, and Tanzania at 90.02 percent. These are all relatively high percentages, but a rate near 90 percent still differs materially from a rate near 99 percent when translated into the share of a population that is not literate under the indicator definition.
Below 90 percent, the dataset steps down through Uganda at 87.95 percent, the Democratic Republic of the Congo at 86.40 percent, Gambia at 84.92 percent, Angola at 83.39 percent, Sierra Leone at 82.76 percent, Madagascar at 81.47 percent, and Senegal at 79.01 percent. This gradual sequence is important because it shows why a simple high-versus-low split would lose information. There is a substantial middle and lower-middle range before the much larger gaps at the bottom.
The lower tail changes the overall average
The five lowest observations are Mozambique at 68.91 percent, Guinea-Bissau at 65.54 percent, Mali at 56.11 percent, Guinea at 54.28 percent, and Chad at 33.61 percent. Chad is more than 20 percentage points below Guinea, the next-lowest observation. That unusually wide spacing at the bottom contrasts sharply with the tiny gaps among the countries and areas around 99 percent. It is also the main reason the mean sits well below the median.
These low values are descriptive evidence, not an explanation. The dataset does not contain variables for conflict, household income, school access, language of instruction, adult education, rural residence, or data-system capacity. It therefore cannot establish why one country or area has a lower modeled rate than another. Any causal account would need additional sources and a research design that can separate competing explanations. The defensible conclusion here is limited to the size and pattern of the reported differences.
Why mean and median tell different stories
If a reader saw only the 87.64 percent mean, it would be easy to assume that the typical observation lies in the upper 80s. In fact, 21 of 33 observations are above 90 percent and the median is 93.09 percent. The mean is pulled down by a small number of much lower rates. Chad, Guinea, and Mali are all below 60 percent, far from the dense group in the 90s. This is a textbook reason to report both the mean and the median rather than treating one summary statistic as the whole story.
The quartiles add another layer. One quarter of the observations are at or below 83.39 percent, while one quarter are at or above 99.03 percent. The upper quarter is compressed against the 100 percent ceiling, whereas the lower quarter spreads across a much wider range. Percentage indicators with a natural upper bound often show this kind of asymmetry. That observation describes the numerical shape only; it does not prove any specific statistical mechanism or policy effect.
How to interpret a GALP estimate
UIS documentation explains that GALP can produce age- and sex-specific literacy estimates when recent observed data are unavailable. The model uses the latest available observations as a base and projects the literacy experience of new cohorts entering older age groups while accounting for changing population composition. This makes the series more useful for a common reference year, but it also means a modeled 2024 estimate is not identical in nature to a direct measurement collected from every individual country during that year.
That distinction matters most when differences are small. A ranking difference of a few tenths of a percentage point near 100 percent should be treated as a fine numerical ordering, not as a strong substantive divide. By contrast, the difference between rates in the 90s and rates in the 50s or 30s is too large to be explained by rounding alone. Readers can therefore use broad bands and the overall distribution more confidently than very narrow rank positions.
This is not a complete world ranking
The most important coverage limitation is that the verified file includes 33 countries and areas, not every country in the world. Missing places were not converted to zero and are not part of the calculations. Statements such as “highest in the world” or “lowest in the world” would therefore be unsupported. The precise wording is “highest among the 33 observations in this dataset” or “lowest among the available 2024 observations used here.” The same limitation prevents a defensible global or regional average from being calculated from this subset.
The list also includes areas such as Macao and Puerto Rico alongside sovereign states. For that reason, “countries and areas” or “reporting areas” is more accurate than assuming every row has the same political status. The statistical comparison is valid at the level of the indicator values, but it does not erase differences in administrative status, data systems, or population size. Those contextual dimensions require separate information.
What can and cannot be learned from one rate
This series can answer a focused set of questions: how the 2024 modeled female youth literacy rates compare across the 33 available observations, where the center of the distribution lies, how wide the spread is, and which observations fall into higher or lower bands. It cannot directly answer how large the gender gap is, because a corresponding male series is not included here. It also cannot show whether a country improved over time, because only one reference year is being analyzed.
Literacy under this indicator should not be confused with advanced comprehension, digital literacy, or the quality of schooling. A rate near 100 percent does not mean every young woman has identical skills, and a low population-level rate should never be used to stereotype individuals. The statistic is a broad population indicator designed to summarize basic literacy status for a defined age and sex group. More detailed skill assessments measure different concepts.
Using the data in a careful comparison
For policy or research work, the first step is to match the denominator. Female literacy among people aged 15 to 24 should be compared with the same age-sex definition, not with adult literacy for ages 15 and over or with a combined-sex youth rate unless the difference is made explicit. The next step is to distinguish modeled estimates from direct observations. A third step, if population impact is important, is to combine the rate with an official count of women aged 15 to 24 for the same year and geographic definition.
Time-series work needs another safeguard: use the same indicator code and preferably data extracted from the same release or version. Model revisions can update historical estimates when new information becomes available. Mixing an old download with a newer database extract can make a revision look like a real-world change. Reproducible analysis should record the indicator code, extraction date, reference year, and source release so that later updates can be identified.
Source and scope
The values come from the UNESCO Institute for Statistics Data API series LR.GALP.AG15T24.F. UIS metadata defines youth literacy for ages 15 to 24 and explains its treatment of literacy measurement and model-based estimates. The UIS Data Browser glossary provides the general definition of literacy and literacy rate, while the SDG 4 indicator metadata provides the methodological framework. No missing geography was filled with a zero in this analysis.
In summary, the available 2024 estimates show two features at once: a large concentration of observations in the 90s, including nine at or above 99 percent, and a much wider lower tail extending to 33.61 percent. The median of 93.09 percent is therefore more representative of the center than the mean of 87.64 percent, but neither number captures the entire 66.38-point spread. The result is best read as a comparison of these 33 verified observations, not as a complete ranking of the world or an explanation of the causes behind the differences.
Frequently Asked Questions
What is the median female youth literacy rate in the 2024 dataset?
The median across the 33 available countries and areas is 93.09%. The mean is lower at 87.64% because a small number of much lower observations pull the average down.
Which observations are highest and lowest?
Macao is highest at 99.99% and Chad is lowest at 33.61% among the 33 observations used here. These are not claims about a complete world ranking.
Are GALP estimates the same as direct survey observations?
They measure the same literacy concept, but the evidence is not identical. UIS can use GALP to estimate age- and sex-specific literacy rates when recent observed data are not available.
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