About this Dataset

Education Attainment For Ages 25-64 (1950-2015)

This dataset provides detailed information on educational attainment for the population aged 25–64 across 146 countries between 1950 and 2015, based on the Barro-Lee Educational Attainment Dataset.

It includes total, male, and female populations, allowing for gender-disaggregated analysis. The dataset reports both the percentage of population attaining different education levels (from no schooling to tertiary) and the average years of schooling at each level. Additionally, population totals are included to support absolute and relative comparisons across countries and over time.

Researchers and policymakers widely use this dataset to study human capital development, economic growth, gender disparities in education, and long-term global education trends.

Column Definitions

Column	Description
`blcode`	Barro-Lee Country Code
`wbcode`	Three-letter World Bank Country Code
`region_code`	Regional classification code
`country`	Country name
`year`	Year of observation
`sex`	Sex (male, female, or total)
`agefrom`	Starting age of the population cohort
`ageto`	Ending age of the population cohort
`lu`	Percentage of population with no schooling
`lp`	Percentage of population with primary schooling (at least some)
`lpc`	Percentage of population with complete primary schooling
`ls`	Percentage of population with secondary schooling (at least some)
`lsc`	Percentage of population with complete secondary schooling
`lh`	Percentage of population with tertiary schooling (at least some)
`lhc`	Percentage of population with complete tertiary schooling
`yr_sch`	Average years of schooling attained
`yr_sch_pri`	Average years of primary schooling attained
`yr_sch_sec`	Average years of secondary schooling attained
`yr_sch_ter`	Average years of tertiary schooling attained
`pop`	Population in thousands

Tables

Female Population

@barrolee.education_attainment_25_64.female_population

131.31 kB
2,044 rows
20 columns

CREATE TABLE female_population (
  "blcode" BIGINT,
  "country" VARCHAR,
  "year" BIGINT,
  "sex" VARCHAR,
  "agefrom" BIGINT,
  "ageto" BIGINT,
  "lu" DOUBLE,
  "lp" DOUBLE,
  "lpc" DOUBLE,
  "ls" DOUBLE,
  "lsc" DOUBLE,
  "lh" DOUBLE,
  "lhc" DOUBLE,
  "yr_sch" DOUBLE,
  "yr_sch_pri" DOUBLE,
  "yr_sch_sec" DOUBLE,
  "yr_sch_ter" DOUBLE,
  "wbcode" VARCHAR,
  "region_code" VARCHAR,
  "pop" DOUBLE
);

Male Population

@barrolee.education_attainment_25_64.male_population

135.38 kB
2,044 rows
20 columns

CREATE TABLE male_population (
  "blcode" BIGINT,
  "country" VARCHAR,
  "year" BIGINT,
  "sex" VARCHAR,
  "agefrom" BIGINT,
  "ageto" BIGINT,
  "lu" DOUBLE,
  "lp" DOUBLE,
  "lpc" DOUBLE,
  "ls" DOUBLE,
  "lsc" DOUBLE,
  "lh" DOUBLE,
  "lhc" DOUBLE,
  "yr_sch" DOUBLE,
  "yr_sch_pri" DOUBLE,
  "yr_sch_sec" DOUBLE,
  "yr_sch_ter" DOUBLE,
  "wbcode" VARCHAR,
  "region_code" VARCHAR,
  "pop" DOUBLE
);

Total Population

@barrolee.education_attainment_25_64.total_population

134.39 kB
2,044 rows
20 columns

CREATE TABLE total_population (
  "blcode" BIGINT,
  "country" VARCHAR,
  "year" BIGINT,
  "sex" VARCHAR,
  "agefrom" BIGINT,
  "ageto" BIGINT,
  "lu" DOUBLE,
  "lp" DOUBLE,
  "lpc" DOUBLE,
  "ls" DOUBLE,
  "lsc" DOUBLE,
  "lh" DOUBLE,
  "lhc" DOUBLE,
  "yr_sch" DOUBLE,
  "yr_sch_pri" DOUBLE,
  "yr_sch_sec" DOUBLE,
  "yr_sch_ter" DOUBLE,
  "wbcode" VARCHAR,
  "region_code" VARCHAR,
  "pop" DOUBLE
);