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Comparative international educational-attainment series

Source: R/get_attainment.R
get_attainment.Rd

Returns harmonised series of educational attainment for ~111 countries, every 5 years from 1870 to 2010, broken down by sex. The bundled data comes from Lee & Lee (2016), Human capital in the long run (JDE 122, 147–169) — a widely used cross-country dataset that reconstructs attainment back to the 19th century.

Usage

get_attainment(
  level = NULL,
  year = NULL,
  geo_level = c("country"),
  geo = NULL,
  dimension = c("none", "sex"),
  source = NULL,
  wide = FALSE,
  lang = c("en", "pt")
)

Arguments

level

Character vector of education levels. One or more of "primary", "secondary", "tertiary". NULL (default) returns all three.

year

Integer vector or two-element c(min, max) range. NULL for all years (1870–2010 in 5-year steps).

geo_level

Always "country" for this dataset (kept for API consistency with other get_*() functions).

geo

Character vector of ISO 3166-1 alpha-3 country codes (e.g. "BRA", "USA", "ARG"). NULL (default) returns all 111 countries available in Lee & Lee (2016).

dimension

Sex breakdown. One of:

  • "none" (default) — sex totals only (dim_sex = "total");

  • "sex" — break down by sex (male, female), drops the total.

source

Character vector of source keys. NULL returns all available sources (currently only "lee_lee_2016").

wide

Logical. If TRUE, pivots the result to wide form (one column per indicator key). Default FALSE.

lang

One of "en" (default) or "pt". When "pt", factor levels and indicator labels are translated via inst/dict/i18n.yaml. Country names (geo_name) are left in English regardless — they come from the upstream source.

Value

A tibble in the canonical educabr2 long schema (see inst/dict/schema.yaml). Columns: year, geo_level, geo_code, geo_name, level, dim_sex, age_group, indicator, value, unit, source, source_note. geo_level is always "country", unit is always "percent" (0–100), age_group is always "15-64".

Details

The indicator is the cumulative share of the population aged 15–64 that has completed at least the level indicated by level. Lee & Lee publish the data in non-cumulative form ("highest attained level = primary/secondary/tertiary"); the bundled dataset sums the upper categories so that, for any (country, year, sex):

  • level = "primary" ≥ level = "secondary" ≥ level = "tertiary".

This matches the conventional "share of adults who reached at least X" reported in comparative work.

Coverage is comparative-international: ISCED-style primary / secondary / tertiary levels, intentionally distinct from the Brazilian fundamental / medio / superior levels in get_enrollment() and get_schooling() because the underlying definitions differ.

Examples

# Tertiary completion in Brazil over time
get_attainment(level = "tertiary", geo = "BRA")
#> # A tibble: 29 × 12
#>     year geo_level geo_code geo_name level    dim_sex age_group indicator  value
#>    <int> <chr>     <chr>    <chr>    <chr>    <chr>   <chr>     <chr>      <dbl>
#>  1  1870 country   BRA      Brazil   tertiary total   15-64     attainme… 0.0137
#>  2  1875 country   BRA      Brazil   tertiary total   15-64     attainme… 0.0153
#>  3  1880 country   BRA      Brazil   tertiary total   15-64     attainme… 0.0177
#>  4  1885 country   BRA      Brazil   tertiary total   15-64     attainme… 0.0211
#>  5  1890 country   BRA      Brazil   tertiary total   15-64     attainme… 0.0256
#>  6  1895 country   BRA      Brazil   tertiary total   15-64     attainme… 0.0315
#>  7  1900 country   BRA      Brazil   tertiary total   15-64     attainme… 0.109 
#>  8  1905 country   BRA      Brazil   tertiary total   15-64     attainme… 0.160 
#>  9  1910 country   BRA      Brazil   tertiary total   15-64     attainme… 0.219 
#> 10  1915 country   BRA      Brazil   tertiary total   15-64     attainme… 0.275 
#> # ℹ 19 more rows
#> # ℹ 3 more variables: unit <chr>, source <chr>, source_note <chr>

# Primary completion across Latin America, post-1950
get_attainment(level = "primary",
               geo   = c("BRA", "ARG", "CHL", "MEX", "URY"),
               year  = c(1950, 2010))
#> # A tibble: 65 × 12
#>     year geo_level geo_code geo_name  level   dim_sex age_group indicator  value
#>    <int> <chr>     <chr>    <chr>     <chr>   <chr>   <chr>     <chr>      <dbl>
#>  1  1950 country   ARG      Argentina primary total   15-64     attainmen…  45.0
#>  2  1955 country   ARG      Argentina primary total   15-64     attainmen…  49.8
#>  3  1960 country   ARG      Argentina primary total   15-64     attainmen…  57.0
#>  4  1965 country   ARG      Argentina primary total   15-64     attainmen…  62.1
#>  5  1970 country   ARG      Argentina primary total   15-64     attainmen…  69.3
#>  6  1975 country   ARG      Argentina primary total   15-64     attainmen…  78.6
#>  7  1980 country   ARG      Argentina primary total   15-64     attainmen…  86.9
#>  8  1985 country   ARG      Argentina primary total   15-64     attainmen…  96.6
#>  9  1990 country   ARG      Argentina primary total   15-64     attainmen… 105. 
#> 10  1995 country   ARG      Argentina primary total   15-64     attainmen… 112. 
#> # ℹ 55 more rows
#> # ℹ 3 more variables: unit <chr>, source <chr>, source_note <chr>

# Compare male vs female secondary completion in Brazil
get_attainment(level = "secondary", geo = "BRA", dimension = "sex")
#> # A tibble: 58 × 12
#>     year geo_level geo_code geo_name level    dim_sex age_group indicator  value
#>    <int> <chr>     <chr>    <chr>    <chr>    <chr>   <chr>     <chr>      <dbl>
#>  1  1870 country   BRA      Brazil   seconda… female  15-64     attainme… 0.0370
#>  2  1875 country   BRA      Brazil   seconda… female  15-64     attainme… 0.0379
#>  3  1880 country   BRA      Brazil   seconda… female  15-64     attainme… 0.0393
#>  4  1885 country   BRA      Brazil   seconda… female  15-64     attainme… 0.0413
#>  5  1890 country   BRA      Brazil   seconda… female  15-64     attainme… 0.0440
#>  6  1895 country   BRA      Brazil   seconda… female  15-64     attainme… 0.0476
#>  7  1900 country   BRA      Brazil   seconda… female  15-64     attainme… 0.208 
#>  8  1905 country   BRA      Brazil   seconda… female  15-64     attainme… 0.304 
#>  9  1910 country   BRA      Brazil   seconda… female  15-64     attainme… 0.714 
#> 10  1915 country   BRA      Brazil   seconda… female  15-64     attainme… 0.737 
#> # ℹ 48 more rows
#> # ℹ 3 more variables: unit <chr>, source <chr>, source_note <chr>