Trends and Socioeconomic Gradients in Adult Mortality Around the Developing World 1991-2009
Benin, Burkina Faso, Bolivia, Brazil, Cameroon, Congo, Dem. Rep., Dominican Republic, Ethiopia, Gabon, Guinea, Guatemala, Haiti, Indonesia, Jorda..., 1991 - 2009
Get Microdata
Reference ID
WLD_2011_TSGAM_v01_M
Producer(s)
Damien de Walque and Deon Filmer
Metadata
Created on
Jul 22, 2011
Last modified
Apr 26, 2021
Page views
52627
Downloads
66
Identification
Survey ID Number
WLD_2011_TSGAM_v01_M
Title
Trends and Socioeconomic Gradients in Adult Mortality Around the Developing World 1991-2009
Country/Economy
| Name | Country code |
|---|---|
| Benin | BEN |
| Burkina Faso | BFA |
| Bolivia | BOL |
| Brazil | BRA |
| Cameroon | CMR |
| Congo, Dem. Rep. | COD |
| Dominican Republic | DOM |
| Ethiopia | ETH |
| Gabon | GAB |
| Guinea | GIN |
| Guatemala | GTM |
| Haiti | HTI |
| Indonesia | IDN |
| Jordan | JOR |
| Kenya | KEN |
| Cambodia | KHM |
| Liberia | LBR |
| Lesotho | LSO |
| Morocco | MAR |
| Madagascar | MDG |
| Mali | MLI |
| Mozambique | MOZ |
| Mauritania | MRT |
| Malawi | MWI |
| Namibia | NAM |
| Niger | NER |
| Nigeria | NGA |
| Nepal | NPL |
| Peru | PER |
| Philippines | PHL |
| Rwanda | RWA |
| Senegal | SEN |
| Sierra Leone | SLE |
| Eswatini | SWZ |
| Chad | TCD |
| Togo | TGO |
| Tanzania | TZA |
| Uganda | UGA |
| Yemen, Rep. | YEM |
| South Africa | ZAF |
| Zambia | ZMB |
| Zimbabwe | ZWE |
Abstract
The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15-49.
The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.
This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.
The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.
This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Country
Version
Version Date
2011-06
Scope
Keywords
| Keyword |
|---|
| Demographics |
| Health Monitoring and Evaluation |
| Population Policies |
| Early Child and Children's Health |
Coverage
Geographic Coverage
We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere).
The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as "representative" of the rest of the world, or even the rest of the developing world.
The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as "representative" of the rest of the world, or even the rest of the developing world.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Damien de Walque and Deon Filmer | World Bank |
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 1991 | 2009 |
Data Collection Mode
Face-to-face [f2f]
Data Collection Notes
The authors combine data from 84 Demographic and Health Surveys from 46 countries.
Data Processing
Data Editing
In the course of carrying out this study, the authors created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data.
The naming conventions for the adult mortality-related are as follows. Variables are named:
GGG_MC_AAAA
GGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:
All - All
Fem - Female
Mal - Male
Rur - Rural
Urb - Urban
Rurm - Rural/Male
Urbm - Urban/Male
Rurf - Rural/Female
Urbf - Urban/Female
Noed - No education
Pri - Some or completed primary only
Sec - At least some secondary education
Noedm - No education/Male
Prim - Some or completed primary only/Male
Secm - At least some secondary education/Male
Noedf - No education/Female
Prif - Some or completed primary only/Female
Secf - At least some secondary education/Female
Rch - Rural as child
Uch - Urban as child
Rchm - Rural as child/Male
Uchm - Urban as child/Male
Rchf - Rural as child/Female
Uchf - Urban as child/Female
Edltp - Less than primary schooling
Edpom - Primary or more schooling
Edltpm - Less than primary schooling/Male
Edpomm - Primary or more schooling/Male
Edltpf - Less than primary schooling/Female
Edpomf - Primary or more schooling/Female
Edltpu - Less than primary schooling/Urban
Edpomu - Primary or more schooling/Urban
Edltpr - Less than primary schooling/Rural
Edpomr - Primary or more schooling/Rural
Edltpmu - Less than primary schooling/Male/Urban
Edpommu - Primary or more schooling/Male/Urban
Edltpmr - Less than primary schooling/Male/Rural
Edpommr - Primary or more schooling/Male/Rural
Edltpfu - Less than primary schooling/Female/Urban
Edpomfu - Primary or more schooling/Female/Urban
Edltpfr - Less than primary schooling/Female/Rural
Edpomfr - Primary or more schooling/Female/Rural
M refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value "n") or whether it is a mortality estimate (in which case M takes on the value "m").
C refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value "u") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value "a").
AAAA refers to the age group that the mortality estimate is calculated for. It takes on the values:
1554 - Ages 15-54
1524 - Ages 15-24
2534 - Ages 25-34
3544 - Ages 35-44
4554 - Ages 45-54
Other variables that are in the databases are:
period - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 … 2000-04)
svycountry - Name of country for DHS countries
ccode3 - Country code
u5mr - Under-5 mortality (from World Development Indicators)
cname - Country name
gdppc - GDP per capita (constant 2000 US$) (from World Development Indicators)
gdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators)
pop - Population (from World Development Indicators)
hivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010)
region - Region
The naming conventions for the adult mortality-related are as follows. Variables are named:
GGG_MC_AAAA
GGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:
All - All
Fem - Female
Mal - Male
Rur - Rural
Urb - Urban
Rurm - Rural/Male
Urbm - Urban/Male
Rurf - Rural/Female
Urbf - Urban/Female
Noed - No education
Pri - Some or completed primary only
Sec - At least some secondary education
Noedm - No education/Male
Prim - Some or completed primary only/Male
Secm - At least some secondary education/Male
Noedf - No education/Female
Prif - Some or completed primary only/Female
Secf - At least some secondary education/Female
Rch - Rural as child
Uch - Urban as child
Rchm - Rural as child/Male
Uchm - Urban as child/Male
Rchf - Rural as child/Female
Uchf - Urban as child/Female
Edltp - Less than primary schooling
Edpom - Primary or more schooling
Edltpm - Less than primary schooling/Male
Edpomm - Primary or more schooling/Male
Edltpf - Less than primary schooling/Female
Edpomf - Primary or more schooling/Female
Edltpu - Less than primary schooling/Urban
Edpomu - Primary or more schooling/Urban
Edltpr - Less than primary schooling/Rural
Edpomr - Primary or more schooling/Rural
Edltpmu - Less than primary schooling/Male/Urban
Edpommu - Primary or more schooling/Male/Urban
Edltpmr - Less than primary schooling/Male/Rural
Edpommr - Primary or more schooling/Male/Rural
Edltpfu - Less than primary schooling/Female/Urban
Edpomfu - Primary or more schooling/Female/Urban
Edltpfr - Less than primary schooling/Female/Rural
Edpomfr - Primary or more schooling/Female/Rural
M refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value "n") or whether it is a mortality estimate (in which case M takes on the value "m").
C refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value "u") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value "a").
AAAA refers to the age group that the mortality estimate is calculated for. It takes on the values:
1554 - Ages 15-54
1524 - Ages 15-24
2534 - Ages 25-34
3544 - Ages 35-44
4554 - Ages 45-54
Other variables that are in the databases are:
period - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 … 2000-04)
svycountry - Name of country for DHS countries
ccode3 - Country code
u5mr - Under-5 mortality (from World Development Indicators)
cname - Country name
gdppc - GDP per capita (constant 2000 US$) (from World Development Indicators)
gdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators)
pop - Population (from World Development Indicators)
hivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010)
region - Region
Access policy
Contacts
Citation requirements
"Trends and Socioeconomic Gradients in Adult Mortality Around the Developing World", Damien de Walque and Deon Filmer, World Bank Policy Research Working Paper 5716, June 2011. Electronic dataset Ref. WLD_2011_TSGAM_v01_M downloaded from [URL] on [date].
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Metadata production
DDI Document ID
DDI_WLD_2011_TSGAM_v02_M_v01_WBDG
Date of Metadata Production
2011-07-21
DDI Document version
DDI Document - Version 02 - (04/21/21)
This version is identical to DDI_WLD_2011_TSGAM_v01_M_v01_WBDG but country field has been updated to capture all the countries covered by survey.
This version is identical to DDI_WLD_2011_TSGAM_v01_M_v01_WBDG but country field has been updated to capture all the countries covered by survey.