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SDG Variables - Detailed Indicators

Overview

This directory contains detailed Sustainable Development Goal (SDG) variables for Bolivia's 339 municipalities. Unlike the aggregated SDG indices in sdg/, this dataset includes the underlying individual indicators that compose each SDG index, providing granular data for in-depth analysis.

Files

sdgVariables.csv

Contains 64 detailed SDG variables plus population and urbanization data for all 339 municipalities.

Variable Dictionary

Variable Name Description
asdf_id Unique spatial identifier for joining datasets
population_2020 Population 2020
urbano_2012 Urbanization rate, 2012 (% of population)

SDG 1: No Poverty

Variable Name Description
sdg1_1_eepr Extreme energy poverty rate, 2016 (% of houses)
sdg1_1_ubn Unsatisfied basic needs, 2012 (% of population)
sdg1_2_mpi Multidimensional poverty index, 2012
sdg1_4_abs Access to the 3 basic services, 2012 (% of households)

SDG 2: Zero Hunger

Variable Name Description
sdg2_2_cmc Chronic malnutrition in children (< 5 years), 2016 (%)
sdg2_2_oww Obesity in women (15-49 years), 2016 (%)
sdg2_4_pual Average area per Production Unit Agriculture and Livestock, 2013 (ha)
sdg2_4_td Tractor density, 2013 (per 1,000 UPAs)

SDG 3: Good Health and Well-being

Variable Name Description
sdg3_1_idca Institutional childbirth coverage, average 2008-2012 (%)
sdg3_2_imr Infant mortality rate (< 1 year), 2016 (per 1,000 live births)
sdg3_2_mrc Children mortality rate (< 5 years), 2016 (per 1,000 live births)
sdg3_3_cdir Chagas disease infestation rate, 2017 (% of households)
sdg3_3_di Dengue incidence, 2018 (per 10,000 population)
sdg3_3_imr Malaria incidence, average 2014-17 (per 1,000 population)
sdg3_3_ti Tuberculosis incidence, 2017 (per 100,000 population)
sdg3_3_hivi HIV incidence, average 2014-17 (per 1,000,000 population)
sdg3_7_afr Adolescent fertility rate (15-19 years), 2012 (births per 1,000 women)

SDG 4: Quality Education

Variable Name Description
sdg4_1_ssdrm Secondary school dropout rate, male, 2017 (% of enrolled)
sdg4_1_ssdrf Secondary school dropout rate, female, 2017 (% of enrolled)
sdg4_4_phe Population with higher education (>= 19 years), 2012 (%)
sdg4_6_lr Literacy rate (>= 15 years), 2012 (%)
sdg4_c_qti Qualified teachers at the initial level, 2016 (%)
sdg4_c_qts Qualified teachers at the secondary level, 2016 (%)

SDG 5: Gender Equality

Variable Name Description
sdg5_1_gpsd Gender parity in school dropouts in secondary school, 2017
sdg5_1_gpyp Gender parity in years of education of young people (25-35 years old), 2012
sdg5_1_gpmpi Gender Parity in the Multidimensional Poverty Index, 2012
sdg5_5_gpop Gender parity in the overall participation rate (>= 10 years), 2012

SDG 6: Clean Water and Sanitation

Variable Name Description
sdg6_1_dwc Drinking water coverage, 2017 (% of population)
sdg6_2_sc Sanitation coverage, 2017 (% of population)
sdg6_3_wwt Wastewater treatment, 2017 (% of wastewater)

SDG 7: Affordable and Clean Energy

Variable Name Description
sdg7_1_ec Electricity coverage, 2012 (% of population)
sdg7_1_rec Residential electricity consumption per capita, 2016 (kWh/person/year)
sdg7_1_cce Clean cooking energy, 2012 (% of households)
sdg7_3_co2epc CO2 emissions per capita by energy, 2016 (tCO2/person/year)

SDG 8: Decent Work and Economic Growth

Variable Name Description
sdg8_4_rem Residential electric meters with zero consumption, 2016 (%)
sdg8_5_oprm Overall participation rate males (>= 10 years), 2012 (%)
sdg8_5_ofrm Overall female participation rate (>= 10 years), 2012 (%)
sdg8_6_mlm Men who do not study or participate in the labor market (15-24 years), 2012 (%)
sdg8_6_wlm Women who do not study or participate in the labor market (15-24 years), 2012 (%)
sdg8_10_dbb Density of bank branches, 2018 (per 100,000 inhabitants)
sdg8_11_idi Index of the degree of intermediation in migration, 2012

SDG 9: Industry, Innovation and Infrastructure

Variable Name Description
sdg9_1_routes Number of railways/primary roads entering/leaving the municipality, 2019
sdg9_5_cd Kuaa computers delivered, 2016 (per 100 school-age population, 6-19 years)
sdg9_5_eutf Educational units with technological floors, 2016 (%)
sdg9_c_mnc Fixed and mobile network coverage, 2012 (% of households)
sdg9_c_drb Density of radio bases, 2016 (number of radio bases per 1,000 inhabitants)

SDG 10: Reduced Inequalities

Variable Name Description
sdg10_2_gcye GINI coefficient of years of education, 2012
sdg10_2_iec Inequality in electricity consumption, 2016
sdg10_2_nssp Non-Spanish speaking population (>= 3 years), 2012 (%)

SDG 11: Sustainable Cities and Communities

Variable Name Description
sdg11_1_hocr Overcrowding rate, 2012 (% of households)
sdg11_1_hno Households that do not have a toilet, bathroom or latrine, 2012 (%)
sdg11_2_samt Seats available for mass transit, 2017 (per 1,000 inhabitants)

SDG 13: Climate Action

Variable Name Description
sdg13_1_ccvi Climate change vulnerability Index, 2015
sdg13_2_tco2e Total CO2 emissions per capita, 2016 (tCO2/person/year)
sdg13_2_dra Deforestation rate, average 2016-2018 (% of forest area 2015)

SDG 15: Life on Land

Variable Name Description
sdg15_1_pa Protected areas, 2019 (% of the municipality's land area)
sdg15_5_blr Biodiversity loss rate due to deforestation, average 2016-2018

SDG 16: Peace, Justice and Strong Institutions

Variable Name Description
sdg16_1_rhr Registered homicide rate, average 2015-2017 (per 100,000 inhabitants)
sdg16_6_pbec Programmed budget execution capacity, 2017 (%)
sdg16_9_cr Children registered in the civil registry (< 5 years), 2012 (%)

SDG 17: Partnerships for the Goals

Variable Name Description
sdg17_1_pmtax Proportion of municipal revenues that come from local taxes, 2017 (%)
sdg17_5_pipc Public investment per capita, 2017 (Bs./person)

Usage

This dataset is used for:

  • Detailed SDG Analysis: Examine specific indicators that drive overall SDG performance
  • Policy Targeting: Identify specific areas of intervention (e.g., which health indicators need improvement)
  • Comparative Studies: Compare municipalities on specific metrics rather than aggregated indices
  • Correlation Analysis: Study relationships between specific variables across SDGs
  • Machine Learning: Use granular variables as features for predictive models

Example Code

You can run the examples below in Open In Colab

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load detailed SDG variables
url = "https://raw.githubusercontent.com/quarcs-lab/ds4bolivia/master/sdgVariables/sdgVariables.csv"
df_sdg_vars = pd.read_csv(url)

# Examine poverty indicators
poverty_vars = ['sdg1_1_eepr', 'sdg1_1_ubn', 'sdg1_2_mpi', 'sdg1_4_abs']
df_poverty = df_sdg_vars[['asdf_id'] + poverty_vars]

# Calculate correlation between poverty indicators
corr_matrix = df_poverty[poverty_vars].corr()
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation between SDG 1 Poverty Indicators')
plt.show()

# Identify municipalities with high malnutrition
high_malnutrition = df_sdg_vars[df_sdg_vars['sdg2_2_cmc'] > 30]
print(f"Municipalities with >30% chronic malnutrition: {len(high_malnutrition)}")

# Compare gender parity across indicators
gender_parity_vars = [col for col in df_sdg_vars.columns if 'sdg5' in col]
df_gender = df_sdg_vars[['asdf_id'] + gender_parity_vars]

Data Source

SDG indicators are originally constructed by:

Andersen, L. E., Canelas, S., Gonzales, A., Peñaranda, L. (2020) Atlas municipal de los Objetivos de Desarrollo Sostenible en Bolivia 2020 La Paz: Universidad Privada Boliviana, SDSN Bolivia Available at: https://atlas.sdsnbolivia.org

Relationship to Other Datasets

  • sdg/ - Contains aggregated SDG indices (composite scores)
  • regionNames/ - Administrative names for municipalities
  • datasets/ - Merged datasets including SDG indices and satellite data

Join Key

Use asdf_id to join this dataset with other datasets in the repository.

Notes

  • This dataset provides the granular indicators that compose the SDG indices
  • Some variables have missing values for certain municipalities due to data availability
  • Years of measurement vary by indicator (2012-2019)
  • Units and scales differ across variables - always check the description