Infant and Child Mortality

Many researchers use infant and child mortality data compiled by the World Bank's World Development Indicators, by UNICEF's State of the World's Children, or, for child mortality rates only, by Ahmad, Lopez, and Inoue (2000). According to Ross (2006), the most transparent is UNICEF's (see page 866).

These international organizations now coordinate in producing infant and child mortality statistics, under the name of The UN Inter-agency Group for Child Mortality Estimation (IGME). See their collection of papers on the child mortality estimation.

Abouharb and Kimball (2007) introduce a dataset on annual infant mortality rates in each country for 1816-2002, by filling as many country-year cells as possible with infant mortality data from a variety of sources (I am not sure if this does not sacrifice the comparability across countries and years). The dataset and the codebook are available at www.prio.no/jpr/datasets (look for the last link for 2007 (vol. 44), no. 6). They avoid using the UN Demographic Yearbooks (which actually do provide annual data in its printed version, but not online) as much as possible. They keep the record on which data source is used, for each country-year observation. It turns out that 41 percent of observations after 1950, mainly developing countries, come from US Census Bureau's International Data Base. I am not sure why we should trust US Census Bureau more than the United Nations.

For poor countries, however, these data may be created by the interpolation of very few data points. See Qian (2015: 303).

Work and Iron Status Evaluation (WISE)

See Duncan Thomas's website for details.

Its consumption expenditure and price data is used by McKelvey (2011) to check the validity of unit value as a proxy for price.


The paneL data on farm labor demand and household composition is used by Lafave and Thomas (2016) to test the neoclassical agricultural household model.

Sample periods

"After a listing survey in late 2001, a population-representative sample of households living in Purworejo kabupaten were interviewed every four months beginning in 2002 and continuing through 2005. A longer-term follow-up was conducted five years after the start of the survey in 2007." (Lafave and Thomas (2016), p. 1926)

See section 4 of Lafave and Thomas (2016) for more detail, including data quality issues.

Indonesian Faimily Life Survey

Indonesian Faimily Life Survey has been conducted four times (1993, 1997, 2000, 2007).

Surveyed community locations are geo-referenced (I'm not sure for which waves, though).

See the BREAD website for a summary description of the data.

Used by Duflo (2001, See Table 1 Panel C and footnote 10) to check the prevalence of grade repetition among primary school pupils.

The 3rd wave in 2000 is used by Maccini and Yang (2009) to estimate the impact of rainfall in the birth year on height, schooling, household expenditures, and earnings in the adulthood.

Used by Alatas et al. (2016) to estimate the AR(1) process of household-level wealth.

MICS (Multiple Indicator Cluster Surveys)

Here's the description of MICS by UNICEF, WHO, The World Bank and UN Population Division (2007) (page 15):

Originally developed by UNICEF to help assess progress towards the goals established by the 1990 World Summit for Children, MICS surveys now serve as a monitoring tool for the MDGs and other international commitments... Three rounds of MICS surveys have been conducted, in 1994-95, 2000-01, and 2005-06. Over 50 countries participated in each round. MICS surveys typically sample 4,000 to 5,000 households, although samples can range up to 15,000 households. The women’s questionnaire includes a module on child mortality which asks how many children a woman has ever borne, when she first gave birth and how many of her children have died or survived.

See UNICEF's website for more details such as the questionnaires and how to download survey.

Ethiopian Rural Household Surveys

A panel household survey conducted by Stefan Dercon and his colleagues in 1989, 1994, 1995, 1997, 1999, 2004, and 2009. The details are available on his website.

Panel data conducted 1989-1995 are downloadable at the above webpage. 1997 can be obtained by contacting IFPRI.

Sample

15 villages with 1477 households in total were surveyed three times, twice in 1994 and once in 1995. Six of the 15 villages were surveyed in 1989 as well. For more detail, see this documentation. In the 1997 survey, 9 additional villages were surveyed.

Variables
Household composition, education, asset ownership, credit, non-food expenditure, off-farm activities, land use, agricultural inputs, crop outputs, livestock ownership, health, health care provision, anthropometrics, food expenditure, female and child activities, etc.

The 1997 survey collects community-level information on electricity and water, sewage and toilet facilities, health services, education, NGO activity, migration, wages, and production and marketing.

The survey in 2004 collects information on interviewees' trust in government, perception on property rights security etc., and is used by Zerfu (2006).

Since 2004, subjective well-being measures are collected, used by Alem and Colmer (2015).

MLSFH (Malawi Longitudinal Study of Family and Health)

https://malawi.pop.upenn.edu

Panel data of up to 4,000 individuals in three regions of rural Malawi (Balaka, Mchinji, and Rumphi), collected in 1998, 2001, 2004, 2006, 2008, 2010 and 2012.

Used by Thornton (2008) and Delavande and Kohler (2016) for identifying the causes of HIV infection.

Demographic and Health Surveys (DHS)

DHS is a set of cross-country household surveys on health. See Corsi et al (2012) for a succinct overview of the surveys.

The Minnesota Population Center leads the initiative to make the DHS data comparable across countries: see the Integrated DHS website for detail.

HOW TO OBTAIN DATA: Log on to http://www.measuredhs.com and follow the procedure outlined here. (For some surveys, an extra permission of use is needed.)

LIST OF COUNTRIES: available here.

For the following countries, see also separate posts in this blog: Ethiopia, Lesotho, Paraguay, Peru, Sri Lanka, and Zambia.

SAMPLING METHOD: This is the guideline for all DHS surveys. For the actual sampling method used in each survey, see the DHS Final Report for relevant surveys, which can be downloaded at http://www.measuredhs.com/pubs. (Pick the country name and choose "DHS Final Reports" for publication type to search.)

QUESTIONNAIRE: The DHS surveys have evolved over time. There are five phases each of which contains a different set of questions (some of them are consistent across phases). The Model Questionnaire from all the five phases are available here. For the actual questionnaire used in each survey, see the DHS Final Report for relevant surveys, which can be downloaded at http://www.measuredhs.com/pubs. (Pick the country name and choose "DHS Final Reports" for publication type to search.)

The questionnaire consists of the core questionnaire (used in every survey) and optional modules (used in some of the surveys). Among these modules is maternal mortality.

CODEBOOK: For the complete list of variables in the datasets, download DHS Recode Manuals (one for each phase) from here. Beware that it is not easy to map each question in the questionnaire to the variable number in the codebook because the numbering system is very different. But the description of variables in the codebook is sometimes imprecise. Checking exactly what question is asked is therefore essential. Some variables are not available depending on which survey you are looking at. To learn this, see the ".doc" file zipped in the individual recode file for each survey.

Other Documentations:
Fieldwork Manuals

Data Processing Manual (see pp. 7-14 for how dates of birth, marriage, etc. are imputed)

Date imputation takes two stages. First, define the upper and lower bounds of the date. For the date of birth of children of interviewed women, for example, the age of the children, the age at death (if dead), the dates of vaccination, the duration of breastfeeding, etc. are used to narrow the bounds. The fact that two births cannot be less than 7 months apart (allowing for premature births) is also used to narrow the bounds. Then the date is imputed by picking one number randomly within the final bounds.

Description on dataset types (what is household recode, individual recode, etc.)

Description on file types (what is hierarchical or flat file?)

Description on file names (e.g. what does COIR41FL.ZIP stand for?)


What variables are available:

Anthropometry data (height and weight) is now collected for all the surveyed women of child bearing age. In the previous rounds of the surveys (until 1999), such data is collected only for women who gave birth within 3 or 5 years before the survey. (Exception to this is Cote d'Ivoire 1988/89 where all surveyed women were measured for their height and weight.) In the earliest rounds of surveys (until 1989), women's anthropometric data was not collected at all. Deaton (2007) uses the height data from DHS surveys extensively.

Hemoglobin level (for measuring anemia) is collected in the latest rounds of the survey (after 1999). Page 14 of the Model Questionnaire has some description on this. For details, see the following two documents:
http://www.measuredhs.com/pubs/pub_details.cfm?ID=293
http://www.measuredhs.com/pubs/pub_details.cfm?ID=372

Information on children's food intake is available though different versions collect the information in different ways. In DHS I and II surveys, what liquid and food is given during the past 24 hours as complementary diet is collected only for women still breastfeeding (available as variables V409-V414). DHS III surveys collect the same information for all living children born in the past 3 or 5 years (variables M37 or V409-V414 for the last born child). In addition, DHS III surveys ask what liquid and food is given during the past 7 days (variables M40). DHS IV surveys collect the same information only for the last born child living with his/her mother for the last 24 hours (variables M37 or V469) or for the last 7 days (variables M40 or V470).

Information on the treatment for fever and cough (symptoms of malaria and pneumonia, respectively, two of the major killers of children) is not very consistent across different rounds of surveys. First of all, the recall period changed from DHS-II surveys: in DHS-I, mothers were asked whether their child got fever or cough during the last four weeks; in DHS-II, it changed to the last two weeks. Then what treatments were given stopped being asked in DHS-III surveys. In DHS-IV, drugs taken for fever were again asked, but not for cough. To whom advice or treatment was sought keeps being collected, but it is not clear whether health professionals were not absent during the visit.

For variables V414 and V414A-D (what foods did the baby eat during the last 24 hours) in the DHS II surveys, even though the Recode Manual suggests that V414 is used only if V414-D is not collected, Burkina Faso's 1992 survey and Niger's 1992 survey use both. For both cases, it seems V414 refers to another food category in addition to V414A and V414D (in the questionnaire, three types of foods were asked about: "bouillie", "Autre aliment specialement préparé pour l'enfant", and "Plat familial").

Data on schooling for all the members of surveyed households is available in the household schedule. See variables HV106-HV110, HV121-HV129.

Data on infrastructure at the household level is available: access to electricity (HV206), telephone (HV221), and tap water (HV231).

Self-reported ethnicity of women is available (variable V131) for some surveys including the Rwanda 1992 survey.

References for understanding the dataset further

In order to understand the medical background for each variable, read Model Questionnaires, which also explain the purpose of each question. Do have a look at the earlier versions of the questionnaire because the newest version often describes the purpose of questions newly added only.

For further medical background for child health history questions (immunization, treatment of diarrhea, etc.), Gareth Jones et al. (2003) "How Many Child Deaths Can We Prevent This Year?" The Lancet, 362: 65-71 is extremely useful.

I have created this document to help researchers to match subnational districts in Sub-Saharan African countries between different rounds of DHS surveys.

Lubotsky and Wittenberg (2006, Review of Economics & Statistics) proposes how to make the best of asset ownership variables in the DHS data to estimate the impact of household wealth.

Papers using this dataset include:

Vogl (2016) to demonstrate how the relationship between fertility and income/education flips over the decades:

• "[T]he associations of fertility with parental durable goods ownership and paternal education flipped from positive to negative in Africa and in rural Asia [between the 1986-94 and 2006-11 periods]; they were negative throughout in Latin America." (page 367)
• "Among birth cohorts of the 1940s and 50s, most countries show positive associations between the number of ... siblings and educational attainment. Among cohorts of the 1980s, most countries show the opposite." (page 367)

Oster (2007) to measure sexual behaviour and knowledge on HIV in African countries.

Oster (2005) to measure sex ratios at birth in African countries.

Young (2005) for the data on fertility and education. In some footnotes of this paper, the author reports a couple of peculiarities found in the data (see footnotes 35 and 58).

Dow et al. (1999) use Malawi 1992, Tanzania 1994, Zambia 1992, and Zimbabwe 1994 to investigate the effect of tetanus vaccine on birth weight.

Pitt (1997) use the datasets for 14 Sub-Saharan African countries to investigate the determinants of child mortality.

Used by demographers to investigate the determinants of immunization uptakes. See Desai and Alva (1998) and Gage et al. (1997).

Thomas et al. (1991) use Brazil 1986 survey to investigate how parental education affects child height, by using the information on the frequency for mothers to listen to the radio, watch television, and read a newspaper.

Other papers using the DHS surveys can be found here.

IHME Development Assistance for Health Database 1990-2008

The Institute for Health Metrics and Evaluation (IHME) has compiled the cross-country panel data on foreign aid to the health sector. See Ravishankar et al. (2009) for detail. In particular, this dataset contains aid not only from governments but also from the Global Fund and private philanthropy.

PETS/QSDS Data Portal

PETS (Public Expenditure Tracking Surveys) and QSDS (Quantitative Service Delivery Surveys) are the two major surveys by the World Bank on public service delivery in education and health. PETS/QSDS Data Portal disseminates survey instruments and results.


See Reinikka and Svensson (2004) for more detail on the PETS surveys.


(HT: Mounir Karadja)

National Family and Health Survey (NFHS)

The National Family and Health Surveys (NFHS) are India's Demographic and Health Surveys.

Used by Oster (2006) to investigate the effect of access to health facilities on gender inequality in child health investments.

Jayachandran and Kuziemko (2011) use the 1992, 1998, and 2005 waves to show that breastfeeding duration is shorter for girls because the mother wants to have a son (and thus stops breastfeeding in order to get pregnant again).

Kenyan Life Panel Survey (KLPS)

Quote from Baird, Hicks, Miguel, and Kremer:

A midterm round (KLPS-1) was conducted in 2003-05. 

The Kenyan Life Panel Survey (KLPS-2) was collected during 2007-2009, and tracked a representative sample of approximately 7,500 respondents who had been enrolled in primary school grades 2-7 in the 75 PSDP schools at baseline in 1998.

See the survey webpage for more detail.

INDEPTH

http://www.indepth-network.org/

ENAHO

The ENANO (Encuesta Nacional de Hogares) is an annual household survey conducted by the Instituto Nacional de Estadistica e Informacion, Peru's national statistical agency.

The 2002 and 2003 surveys are used by Hunt (2007) to investigate whether crime victims are more likely to pay bribes.

The 2001 survey is used by Dell (2010) to measure household consumption as living standards, net of transfers and normalized to Lima metropolitan prices; ethnicity (the language spoken in the household);

HIV Prevalence Rates

Cross-country data
The UNAIDS/WHO Report on the Global HIV/AIDS Epidemic annually provides data on national adult HIV prevalence rates (the percentage of the adult population living with HIV) for odd-numbered years between 1997 and 2005.

Used by Iqbal and Zorn (2010).


Sub-national data
Several DHS surveys conducted after 2000 offer HIV prevalence rates at the sub-national level.

To learn which country has such data, visit the DHS publication search webpage and select "HIV Fact Sheet" in the "Publication Type" field. Many African countries plus Ukraine, Dominican Republic, Cambodia, and Haiti are available as of April 2009.

These HIV Fact Sheets, however, do not provide all the available data. You need to browse the DHS Final Report for each survey, which can be done by visiting this webpage in the DHS website and clicking the country of your interest. Once you download the Final Report, look for the chapter on HIV prevalence, which includes a table of HIV prevalence rates by sub-national regions and by sex.

U.S. Census HIV/AIDS Surveillance Database also provides the sub-national HIV prevalence rates for selected countries, but for pregnant women only. This data is used by Oster (2007).

China Health and Nutritional Survey (CHNS)

According to Qian (2008) (see page 1278), the 1981 round of the China Health and Nutritional Survey (CHNS) reports local enforcement of the one child policy.

DHS Maternal Mortality Module

As part of the DHS survey, surveyed women (age 15-49) are asked about the mortality of their brothers and sisters. It is originally designed to obtain maternal mortality estimates (hence the name of the module). But it can also be used to obtain adult mortality in general.

See pages 112-113 of Stanton et al. (2000) for a brief history of maternal mortality modules in the DHS surveys.

Note that not every DHS survey includes this module. To see which DHS survey contains this module, use the Survey Search page by checking the box for Maternal Mortality in "Select Survey Characteristics" (at the bottom of the page).

The model questionnaire for the maternal mortality module is available here. For the actual questionnaire used in each survey, see the end of the DHS Final Report for the survey of concern.

There is a statistics literature on whether this module can provide unbiased estimates of adult mortality. See the following papers:

Trussell, James and German Rodriguez, "A Note on the Sisterhood Estimator of Maternal
Mortality," Studies in Family Planning, 1990, 21 (6), 344-346.

Hill, K. and J. Trussell (1991) “Further developments in indirect mortality estimation”. Population Studies, 45, 455-472.

Gakidou, E., M. Hogan, A.D. Lopez (2004) “Adult mortality: Time for a reappraisal”. International Journal of Epidemiology, 33, 710-717.

Gakidou, Emmanuela and Gary King, "Death by Survey: Estimating Adult Mortality without Selection Bias from Sibling Survival Data," Demography, 2006, 43 (3), 569-585.

Economics papers using this dataset include Emily Oster (2008) "Routes of Infection: Exports and HIV Incidence in Sub-Saharan Africa." (See also her accompanying paper.)

ICRISAT Village Level Studies (VLS) Panel Data

Village Level Studies by ICRISAT is "one of the first major panel surveys using a household framework in a developing country" (Strauss and Duncan (1995), page 1892).

ICRISAT provides the list of journal articles using the dataset. Examples include Behrman and Deolalikar (1987, JPE).

Townsend (1994, Econometrica) uses household data from three villages in India (Aurepalle, Kanzara, and Shirapur: see this map on the ICRISAT website for their locations) for the 10 year period between 1975 and 1984.

Fafchamps (1993, Econometrica) and Udry (1996, JPE) use household data from six villages (Woure, Silgey, Kolbila, Ouonon, Koho, and Sayero) in Burkina Faso for the three year period of 1981 to 1983.

Cross-country IQ data

R. Lynn and T. Vanhanen (2006) IQ and global inequality (Augusta, GA: Washington Summit) compile a cross-country data on IQ.

See section 2 of Eppig et al. (2010) for the list of papers validating this data.

Used by Eppig et al. (2010) (see also an article from The Economist magazine) to show its correlation with the disability-adjusted life years lost due to 28 infectious diseases.

Malawi Diffusion and Ideational Change Project (MDICP)

MDICP is a joint panel data collection effort by University of Pennsylvania and Malawi College of Medicine. Data was collected in 1998, 2001, and 2004.

Thornton (2008) uses this sample of households to evaluate the impact of giving monetary incentives on learning their HIV status (after tests were offered for free).

SEDLAC

SEDLAC (Socioeconomic database for Latin America and the Caribbean) provides cross-country data for Latin American countries based on household surveys.