This Subnational Human Development Index Database contains for the period 1990-2017 for 1625 regions within 161 countries the national and subnational values of the Subnational Human Development Index (SHDI), for the three dimension indices on the basis of which the SHDI is constructed – education, health and standard of living --, and for the four indicators needed to create the dimension indices -- expected years of schooling, mean years of schooling, life expectancy and gross national income per capita.

Subnational HDI

The Global Gridded Relative Deprivation Index (GRDI), Version 1.10 (GRDIv1.10) data set characterizes relative levels of multidimensional deprivation and poverty at 30 arc-seconds spatial resolution (~1 km at the equator) ranging from 0 (lowest) to 100 (highest). GRDIv1.10 is built from sociodemographic and satellite data inputs from five global spatial layers that were spatially harmonized, indexed, and aggregated into an index. These include Subnational Human Development Index (SHDI) and Infant Mortality Rate (IMR) data, gridded Child Dependency Ratios (CDR), nighttime lights intensity, and proportion of the grid cell that is built-up. This version corrects some issues with GRDI Version 1 (GRDIv1) by using a less restrictive uninhabited areas mask based on populated areas instead of built-up areas, improved processing of the child dependency ratio data, and removal of the trend in nighttime light input. In addition, a different set of weights is used to combine the five input layers in the final index that dampens the urban-bias exhibited by the previous version of the index, no matter the development level of the country. To provide a global gridded relative deprivation index characterizing the levels of multidimensional deprivation in each pixel at ~1 km resolution.

In this product, WWHGD visualizes tabular data found in version 4.0 of the Subnational Human Development Index (SHDI) created by Global Data Lab (GDL). It is a time lapse of SHDI values in Cameroon from 1990 to 2018. The SHDI combines Education, Health, and Standard of Living Indices gathered from statistical offices to create an aggregate development score.

BackgroundEducation and health are both constituents of human capital that enable people to earn higher wages and enhance people’s capabilities. Human capabilities may lead to fulfilling lives by enabling people to achieve a valuable combination of human functionings—i.e., what people are able to do or be as a result of their capabilities. A better understanding of how these different human capabilities are produced together could point to opportunities to help jointly reduce the wide disparities in health and education across populations.Methods and findingsWe use nationally and regionally representative individual-level data from Demographic and Health Surveys (DHS) for 55 low- and middle-income countries (LMICs) to examine patterns in human capabilities at the national and regional levels, between 2000 and 2017 (N = 1,657,194 children under age 5). We graphically analyze human capabilities, separately for each country, and propose a novel child-based Human Development Index (HDI) based on under-five survival, maternal educational attainment, and measures of a child’s household wealth. We normalize the range of each component using data on the minimum and maximum values across countries (for national comparisons) or first-level administrative units within countries (for subnational comparisons). The scores that can be generated by the child-based HDI range from 0 to 1.We find considerable heterogeneity in child health across countries as well as within countries. At the national level, the child-based HDI ranged from 0.140 in Niger (with mean across first-level administrative units = 0.277 and standard deviation [SD] 0.114) to 0.755 in Albania (with mean across first-level administrative units = 0.603 and SD 0.089). There are improvements over time overall between the 2000s and 2010s, although this is not the case for all countries included in our study. In Cambodia, Malawi, and Nigeria, for instance, under-five survival improved over time at most levels of maternal education and wealth. In contrast, in the Philippines, we found relatively few changes in under-five survival across the development spectrum and over time. In these countries, the persistent location of geographical areas of poor child health across both the development spectrum and time may indicate within-country poverty traps.Limitations of our study include its descriptive nature, lack of information beyond first- and second-level administrative units, and limited generalizability beyond the countries analyzed.ConclusionsThis study maps patterns and trends in human capabilities and is among the first, to our knowledge, to introduce a child-based HDI at the national and subnational level. Areas of chronic deprivation may indicate within-country poverty traps and require alternative policy approaches to improving child health in low-resource settings.

Ranked bottom and top five states, HLI, 2016.

The datasets comprise a compilation of air pollution and economic data for the city of New Delhi from publicly available data by Indian government, as well as FIRECOUNT data made available by NASA for academic purposes.

Pollution data is 24h data, processed from data taken from Central Control Room for Air Quality Management: https://app.cpcbccr.com/ccr/

FIRECOUNT Data is processed from the VIIRS 375 m (S-NPP) 24h data made available by NASA: https://firms.modaps.eosdis.nasa.gov/active_fire/#firms-txt

Air Pollution and Weather Data were downloaded for 24 hr periods from CCR website for each of the 5 monitoring stations for 5 pollution parameters for the years 2012 to 2021. Gaps in the data were filled with "NaN" for Python processing. No other modification was done to the data. The data for Sep-Dec periods for each year was taken for the regression analysis.

Economic Data was taken from Ministry of Statistics, for the city of New Delhi. No changes were made to the data. The data for the city of New Delhi was taken as the same value for the entire year, for every year, and merged with all other data.

FIRECOUNT data was taken from "Active Fire Data" by NASA. For every day (Sep-Dec), for each year (2012-2021), number of values of fire were counted and recorded. These values correspond to the number of field fires in that 24 hr period in the region of interest. This data was merged with all other data.

Subnational HDI data was taken from Global Data Lab site, for the city of New Delhi. The value for every year was taken as constant (for every day) for that year.

All these datasets were merged to create 5 files of data for 5 monitoring stations. For regression analysis, all the datasets are used in the merged files.

Agarwal, Arti (2022), “Data for: The Economic Cost of Air Pollution Due to Stubble Burning: Evidence from Delhi”, Mendeley Data, V1, doi: 10.17632/yxzxvxtvpr.1

Nightlights (NTL) have been widely used as a proxy for economic activity, despite known limitations in accuracy and comparability, particularly with outdated Defense Meteorological Satellite Program (DMSP) data. The emergence of newer and more precise Visible Infrared Imaging Radiometer Suite (VIIRS) data offers potential, yet challenges persist due to temporal and spatial disparities between the two datasets. Addressing this, we employ a novel harmonized NTL dataset (VIIRS + DMSP), which provides the longest and most consistent database available to date. We evaluate the association between newly available harmonized NTL data and various indicators of economic activity at the subnational level across 34 countries in sub-Saharan Africa from 2004 to 2019. Specifically, we analyze the accuracy of the new NTL data in predicting socio-economic outcomes obtained from two sources: 1) nationally representative surveys, i.e., the household Wealth Index published by Demographic and Health Surveys, and 2) indicators derived from administrative records such as the gridded Human Development Index and Gross Domestic Product per capita. Our findings suggest that even after controlling for population density, the harmonized NTL remain a strong predictor of the wealth index. However, while urban areas show a notable association between harmonized NTL and the wealth index, this relationship is less pronounced in rural areas. Furthermore, we observe that NTL can also significantly explain variations in both GDP per capita and HDI at subnational levels.