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[Technical Paper] Merge from Develop
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* Bring changes done between Feb-July 2020 in a Private repo to export results for technical paper
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@dianagold
dianagold authored Jan 17, 2021
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6 changes: 6 additions & 0 deletions .gitignore
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# csv files in selected input folders
!08_2pager/081_data/cleaned_*.csv
!08_2pager/081_data/hosted_in_repo/*.csv
!05_working_paper/053_outputs/*correlations.csv
!02_simulation/023_outputs/*.csv
!02_simulation/023_outputs/*/*.csv

# excel for working paper
!05_working_paper/051_rawdata/LPV_*Template.xlsx
!05_working_paper/053_outputs/LPV_*PAPER.xlsx

##################
#Include images used in the 2pager template
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51 changes: 19 additions & 32 deletions 00_documentation/001_technical_note/Technical_Note.md
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1 change: 0 additions & 1 deletion 00_documentation/002_repo_structure/0022_dataset_tables/dyntext_LP.txt
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Expand Up @@ -69,7 +69,6 @@ Documentation of <<dd_display:"`char_value_i5'">>

~~~~
<<dd_display:"`char_i4'">>: <<dd_display:"`char_value_i4'">>
<<dd_display:"`char_i1'">>: <<dd_display:"`char_value_i1'">>
~~~~


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39 changes: 19 additions & 20 deletions 00_documentation/002_repo_structure/0022_dataset_tables/enrollment.md
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Expand Up @@ -10,7 +10,6 @@ Dataset of enrollment. Long in countrycode and year, wide in enrollment definiti

~~~~
sources: Multiple enrollment definitions were combined according to a ranking. Original data from World Bank (country team validation, ANER) and UIS (TNER, NET, GER)
lastsave: 16 Oct 2019 21:57:15 by wb255520
~~~~


Expand All @@ -24,23 +23,23 @@ idvars: countrycode year
valuevars: enrollment_validated_all enrollment_validated_fe enrollment_validated_ma enrollment_validated_flag enrollment_interpolated_all enrollment_interpolated_fe enrollment_interpolated_ma enrollment_interpolated_flag
traitvars: enrollment_source enrollment_definition year_enrollment
. codebook, compact
Variable Obs Unique Mean Min Max Label
----------------------------------------------------------------------------------------------------------------------------------------
countrycode 6293 217 . . . WB country code (3 letters)
year 6293 29 2004 1990 2018 Year
en~dated_all 5771 2908 87.21567 19.10539 100 Validated % of children enrolled in school (using closest year, both genders)
enr~dated_fe 5348 2742 86.29536 15.50506 100 Validated % of children enrolled in school (using closest year, female only)
enr~dated_ma 5348 2743 87.94001 22.14 100.4548 Validated % of children enrolled in school (using closest year, male only)
e~dated_flag 6293 2 .194184 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%)
en~lated_all 5771 3755 87.24924 19.10539 100 Validated % of children enrolled in school (using interpolation, both genders)
enr~lated_fe 4739 2857 87.20877 15.50506 100 Validated % of children enrolled in school (using interpolation, female only)
enr~lated_ma 4739 2858 88.61073 22.14 100.4548 Validated % of children enrolled in school (using interpolation, male only)
e~lated_flag 6293 2 .1978389 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%)
enrollmen~ce 6293 5 . . . The source used for this enrollment value
enrollment~n 6293 5 . . . The definition used for this enrollment value
year_enrol~t 5771 29 2005.041 1990 2018 The year that the enrollment value is from
----------------------------------------------------------------------------------------------------------------------------------------
. codebook, compact
Variable Obs Unique Mean Min Max Label
---------------------------------------------------------------------------------------------------------------------------------------
countrycode 6293 217 . . . WB country code (3 letters)
year 6293 29 2004 1990 2018 Year
en~dated_all 5771 2908 87.21567 19.10539 100 Validated % of children enrolled in school (using closest year, both genders)
enr~dated_fe 5348 2742 86.29536 15.50506 100 Validated % of children enrolled in school (using closest year, female only)
enr~dated_ma 5348 2743 87.94001 22.14 100.4548 Validated % of children enrolled in school (using closest year, male only)
e~dated_flag 6293 2 .194184 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%)
en~lated_all 5771 3755 87.24924 19.10539 100 Validated % of children enrolled in school (using interpolation, both genders)
enr~lated_fe 4739 2857 87.20877 15.50506 100 Validated % of children enrolled in school (using interpolation, female only)
enr~lated_ma 4739 2858 88.61073 22.14 100.4548 Validated % of children enrolled in school (using interpolation, male only)
e~lated_flag 6293 2 .1978389 0 1 Flag for enrollment by gender filled up from aggregate (>=98.5%)
enrollmen~ce 6293 4 . . . The source used for this enrollment value
enrollment~n 6293 6 . . . The definition used for this enrollment value
year_enrol~t 5771 29 2005.041 1990 2018 The year that the enrollment value is from
---------------------------------------------------------------------------------------------------------------------------------------
~~~~
43 changes: 21 additions & 22 deletions 00_documentation/002_repo_structure/0022_dataset_tables/population.md
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Expand Up @@ -10,7 +10,6 @@ Dataset of late primary aged population. Long in countrycode and year, wide in p

~~~~
sources: World Bank staff estimates using the World Bank's total population and age distributions of the United Nations Population Division's World Population Prospects.
lastsave: 16 Oct 2019 21:57:14 by wb255520
~~~~


Expand All @@ -24,25 +23,25 @@ idvars: countrycode year_population
valuevars: population_fe_10 population_fe_primary population_fe_9plus population_ma_10 population_ma_primary population_ma_9plus population_all_10 population_all_primary population_all_9plus population_fe_1014 population_ma_1014 population_all_1014 population_source
traitvars: population_source
. codebook, compact
Variable Obs Unique Mean Min Max Label
----------------------------------------------------------------------------------------------------------------------------------------
countrycode 13237 217 . . . WB country code (3 letters)
year_popul~n 13237 61 2020 1990 2050 Year of population
populat~e_10 11795 6667 320676.7 479 1.33e+07 Female population aged 10 (WB API)
po~e_primary 10514 7901 2282059 3477 7.53e+07 Female population primary age, country specific (WB API)
popu~e_9plus 11722 8056 1192931 967 5.14e+07 Female population aged 9 to end of primary, country specific (WB API)
populat~a_10 11795 6671 339959 492 1.42e+07 Male population aged 10 (WB API)
po~a_primary 10514 7939 2419477 3858 8.08e+07 Male population primary age, country specific (WB API)
popu~a_9plus 11722 8043 1259789 1007 5.52e+07 Male population aged 9 to end of primary, country specific (WB API)
populat~l_10 11795 7305 660635.7 971 2.75e+07 Total population aged 10 (WB API)
po~l_primary 10514 8655 4701535 7335 1.56e+08 Total population primary age, country specific (WB API)
popu~l_9plus 11722 8815 2452720 1974 1.07e+08 Total population aged 9 to end of primary, country specific (WB API)
popul~e_1014 11792 8026 1582075 2300 6.21e+07 Female population between ages 10 to 14 (WB API)
popul~a_1014 11792 8055 1676621 2300 6.72e+07 Male population between ages 10 to 14 (WB API)
popul~l_1014 11792 8758 3258696 4600 1.29e+08 Total population between ages 10 to 14 (WB API)
population~e 13237 1 . . . The source used for population variables
----------------------------------------------------------------------------------------------------------------------------------------
. codebook, compact
Variable Obs Unique Mean Min Max Label
---------------------------------------------------------------------------------------------------------------------------------------
countrycode 13237 217 . . . WB country code (3 letters)
year_popul~n 13237 61 2020 1990 2050 Year of population
populat~e_10 11795 6667 320676.7 479 1.33e+07 Female population aged 10 (WB API)
po~e_primary 10514 7901 2282059 3477 7.53e+07 Female population primary age, country specific (WB API)
popu~e_9plus 11722 8056 1192931 967 5.14e+07 Female population aged 9 to end of primary, country specific (WB API)
populat~a_10 11795 6671 339959 492 1.42e+07 Male population aged 10 (WB API)
po~a_primary 10514 7939 2419477 3858 8.08e+07 Male population primary age, country specific (WB API)
popu~a_9plus 11722 8043 1259789 1007 5.52e+07 Male population aged 9 to end of primary, country specific (WB API)
populat~l_10 11795 7305 660635.7 971 2.75e+07 Total population aged 10 (WB API)
po~l_primary 10514 8655 4701535 7335 1.56e+08 Total population primary age, country specific (WB API)
popu~l_9plus 11722 8815 2452720 1974 1.07e+08 Total population aged 9 to end of primary, country specific (WB API)
popul~e_1014 11792 8026 1582075 2300 6.21e+07 Female population between ages 10 to 14 (WB API)
popul~a_1014 11792 8055 1676621 2300 6.72e+07 Male population between ages 10 to 14 (WB API)
popul~l_1014 11792 8758 3258696 4600 1.29e+08 Total population between ages 10 to 14 (WB API)
population~e 13237 1 . . . The source used for population variables
---------------------------------------------------------------------------------------------------------------------------------------
~~~~
53 changes: 29 additions & 24 deletions 00_documentation/002_repo_structure/0022_dataset_tables/proficiency.md
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Expand Up @@ -10,39 +10,44 @@ Dataset of proficiency. One country may have multiple or no observations at all.

~~~~
sources: Compilation of proficiency measures from 3 sources: CLO (Country Level Outcomes from GLAD), National Learning Assessment (from UIS), HAD (Harmonized Assessment Database)
lastsave: 16 Oct 2019 21:57:14 by wb255520
~~~~


About the **15 variables** in this dataset:
About the **21 variables** in this dataset:

~~~~
The variables belong to the following variable classifications:
idvars valuevars traitvars
idvars: countrycode year idgrade test nla_code subject
valuevars: nonprof_all se_nonprof_all nonprof_ma se_nonprof_ma nonprof_fe se_nonprof_fe
valuevars: nonprof_all se_nonprof_all nonprof_ma se_nonprof_ma nonprof_fe se_nonprof_fe fgt1_all fgt1_fe fgt1_ma fgt2_all fgt2_fe fgt2_ma
traitvars: min_proficiency_threshold source_assessment surveyid
. codebook, compact
Variable Obs Unique Mean Min Max Label
----------------------------------------------------------------------------------------------------------------------------------------
countrycode 697 146 . . . WB country code (3 letters)
year 697 20 2009.898 1996 2017 Year of assessment
idgrade 697 4 4.308465 3 6 Grade ID
test 697 7 . . . Assessment
nla_code 697 22 . . . Reference code for NLA in markdown documentation
subject 697 3 . . . Subject
nonprof_all 697 697 30.44718 .2252221 99.89659 % pupils below minimum proficiency (all)
se_nonprof~l 559 559 .9498702 .1218972 3.419903 SE of pupils below minimum proficiency (all)
nonprof_ma 559 559 24.92177 .1586974 97.96137 % pupils below minimum proficiency (ma)
se_nonprof~a 559 559 1.199997 .1287481 3.848194 SE of pupils below minimum proficiency (ma)
nonprof_fe 559 559 22.02045 .1284599 97.83222 % pupils below minimum proficiency (fe)
se_nonprof~e 559 559 1.142743 .1141958 3.566816 SE of pupils below minimum proficiency (fe)
min_profic~d 694 18 . . . Minimum Proficiency Threshold (assessment-specific)
source_ass~t 697 3 . . . Source of assessment data
surveyid 697 503 . . . SurveyID (countrycode_year_assessment)
----------------------------------------------------------------------------------------------------------------------------------------
. codebook, compact
Variable Obs Unique Mean Min Max Label
---------------------------------------------------------------------------------------------------------------------------------------
countrycode 697 146 . . . WB country code (3 letters)
year 697 20 2009.898 1996 2017 Year of assessment
idgrade 697 4 4.308465 3 6 Grade ID
test 697 7 . . . Assessment
nla_code 697 22 . . . Reference code for NLA in markdown documentation
subject 697 3 . . . Subject
nonprof_all 697 697 30.44595 .2252221 99.89659 % pupils below minimum proficiency (all)
se_nonprof~l 559 559 1.05663 .1218972 3.419903 SE of pupils below minimum proficiency (all)
nonprof_ma 561 561 25.08579 .1586974 97.96137 % pupils below minimum proficiency (ma)
se_nonprof~a 559 559 1.297817 .1287481 3.848194 SE of pupils below minimum proficiency (ma)
nonprof_fe 561 561 22.18714 .1284599 97.83222 % pupils below minimum proficiency (fe)
se_nonprof~e 559 559 1.245255 .1141958 4.098772 SE of pupils below minimum proficiency (fe)
fgt1_all 559 559 .1708391 .0308948 .7537994 Avg gap to minimum proficiency (all, FGT1)
fgt1_fe 559 559 .1641004 .0257019 .7643428 Avg gap to minimum proficiency (fe, FGT1)
fgt1_ma 559 559 .1757896 .0298228 .7457443 Avg gap to minimum proficiency (ma, FGT1)
fgt2_all 559 559 .0610742 .001687 .6159182 Avg gap squared to minimum proficiency (all, FGT2)
fgt2_fe 559 559 .0572421 .0011683 .6308874 Avg gap squared to minimum proficiency (fe, FGT2)
fgt2_ma 559 559 .0639653 .0017091 .6044815 Avg gap squared to minimum proficiency (ma, FGT2)
min_profic~d 694 18 . . . Minimum Proficiency Threshold (assessment-specific)
source_ass~t 697 3 . . . Source of assessment data
surveyid 697 503 . . . SurveyID (countrycode_year_assessment)
---------------------------------------------------------------------------------------------------------------------------------------
~~~~
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