Abstract

Malawi Conditional Cash Transfer Program (CCT) is a randomized cash transfer intervention targeting young women in Zomba region. The program provides incentives to current schoolgirls and recent dropouts to stay in or return to school. The incentives include average payment of US$10 a month conditional on satisfactory school attendance and direct payment of secondary school fees.

The CCT program started at the beginning of the Malawian school year in January 2008 and continued until November 2009. The impact evaluation study was designed to evaluate the impact of the program on various demographic and health outcomes of its target population, such as nutritional health, sexual behavior, fertility, and subsequent HIV risk.

Baseline data collection was administered from September 2007 to January 2008. The research targeted girls and young women, between the ages of 13 and 22, who were never married. Overall, 3,810 girls and young women were surveyed in the first round. The follow-up survey was carried out from October 2008 to February 2009. The third round was conducted between March and September 2010, after Malawi Conditional Cash Transfer Program was completed. The fourth round started in April 2012 and will continue until September 2012.

Datasets from the baseline round are documented here.

Enumeration Areas (EAs) in the study district of Zomba were selected from the universe of EAs produced by the National Statistics Office of Malawi from the 1998 Census. 176 enumeration areas were randomly sampled out of a total of 550 EAs using three strata: urban areas, rural areas near Zomba Town, and rural areas far from Zomba Town.

Baseline schoolgirls in treatment enumeration areas were randomly assigned to receive either conditional or unconditional transfers, or no transfers at all. A multi-topic questionnaire was administered to the heads of households, where the selected sample respondents resided, as well as to girls and young women.

Geographic coverage

Zomba district.

Zomba district in the Southern region was chosen as the site for this study for several reasons. First, it has a large enough population within a small enough geographic area rendering field work logistics easier and keeping transport costs lower. Zomba is a highly populated district, but distances from the district capital (Zomba Town) are relatively small. Second, characteristic of Southern Malawi, Zomba has a high rate of school dropouts and low educational attainment. Third, unlike many other districts, Zomba has the advantage of having a true urban center as well as rural areas. As the study sample was stratified to get representative samples from urban areas (Zomba town), rural areas near Zomba town, and distant rural areas in the district, we can analyze the heterogeneity of the impacts by urban/rural areas. Finally, while Southern Malawi, which includes Zomba, is poorer, has lower levels of education, and higher rates of HIV than Central and Northern Malawi, these differences are relative considering that Malawi is one of the poorest countries in the world with one of the highest rates of HIV prevalence.

Analysis unit

• Households;
• Girls and young women.

Universe

The survey covers never married girls and young women between the ages of 13 and 22 in Zomba district.

Kind of data

Sample survey data [ssd]

Sampling procedure

First, 176 enumeration areas (EA) were randomly sampled out of a total of 550 EAs using three strata in the study district of Zomba. Each of these 176 EAs were then randomly assigned treatment or control status. The three strata are urban, rural areas near Zomba Town, and rural areas far from Zomba Town. Rural areas were defined as being near if they were within a 16-kilometer radius of Zomba Town. Researchers did not sample any EAs in TA Mbiza due to safety concerns (112 EAs).

Enumeration areas (EAs) in Zomba were selected from the universe of EAs produced by the National Statistics Office of Malawi from the 1998 Census. The sample of EAs was stratified by distance to the nearest township or trading centre. Of the 550 EAs in Zomba, 50 are in Zomba town and an additional 30 are classified as urban (township or trading center), while the remaining 470 are rural (population areas, or PAs). The stratified random sample of 176 EAs consisted of 29 EAs in Zomba town, eight trading centers in Zomba rural, 111 population areas within 16 kilometers of Zomba town, and 28 EAs more than 16 kilometers from Zomba town.

After selecting sample EAs, all households were listed in the 176 sample EAs using a short two-stage listing procedure. The first form, Form A, asked each household the following question: "Are there any never-married girls in this household who are between the ages of 13 and 22?" This form allowed the field teams to quickly identify households with members fitting into the sampling frame, thus significantly reducing the costs of listing. If the answer received on Form A was a "yes", then Form B was filled to list members of the household to collect data on age, marital status, current schooling status, etc.

From this researchers could categorize the target population into two main groups: those who were out of school at baseline (baseline dropouts) and those who were in school at baseline (baseline schoolgirls). These two groups comprise the basis of our sampling frame. In each EA, enumerators sampled all eligible dropouts and 75%-100% of all eligible school girls, where the percentage depended on the age of the baseline schoolgirl. This sampling procedure led to a total sample size of 3,810 (in the first round, and 3,805 in follow-up rounds) with an average of 5.1 dropouts and 16.7 schoolgirls per EA.

Mode of data collection

Face-to-face [f2f]

Research instrument

The annual household survey consists of a multi-topic questionnaire administered to the households in which the selected sample respondents reside. The survey consists of two parts: one that is administered to the head of the household and another that is administered to the core respondent - the sampled girl from the target population. The former collects information on the household roster, dwelling characteristics, household assets and durables, shocks and consumption. The core respondent survey provides information about her family background, her education and labor market participation, her health, her dating patterns, sexual behavior, marital expectations, knowledge of HIV/AIDS, her social networks, as well as her own consumption of girl-specific goods (such as soaps, mobile phone airtime, clothing, braids, sodas and alcoholic drinks, etc.).

Abstract

The 2004 Malawi Demographic and Health Survey (MDHS) is a nationally representative survey of 11,698 women age 1549 and 3,261 men age 15-54. The main purpose of the 2004 MDHS is to provide policymakers and programme managers with detailed information on fertility, family planning, childhood and adult mortality, maternal and child health, as well as knowledge of and attitudes related to HIV/AIDS and other sexually transmitted infections (STIs). The 2004 MDHS is designed to provide data to monitor the population and health situation in Malawi as a followup of the 1992 and 2000 MDHS surveys, and the 1996 Malawi Knowledge, Attitudes, and Practices in Health Survey. New features of the 2004 MDHS include the collection of information on use of mosquito nets, domestic violence, anaemia testing of women and children under 5, and HIV testing of adults.

The 2004 MDHS survey was implemented by the National Statistical Office (NSO). The Ministry of Health and Population, the National AIDS Commission (NAC), the National Economic Council, and the Ministry of Gender contributed to the development of the questionnaires for the survey. Most of the funds for the local costs of the survey were provided by multiple donors through the NAC. The United States Agency for International Development (USAID) provided additional funds for the technical assistance through ORC Macro. The Department for International Development (DfID) of the British Government, the United Nations Children's Fund (UNICEF), and the United Nations Population Fund (UNFPA) also provided funds for the survey. The Centers of Disease Control and Prevention provided technical assistance in HIV testing.

The survey used a two-stage sample based on the 1998 Census of Population and Housing and was designed to produce estimates for key indicators for ten large districts in addition to estimates for national, regional, and urban-rural domains. Fieldwork for the 2004 MDHS was carried out by 22 mobile interviewing teams. Data collection commenced on 4 October 2004 and was completed on 31 January 2005.

The principal aim of the 2004 MDHS project was to provide up-to-date information on fertility and childhood mortality levels, nuptiality, fertility preferences, awareness and use of family planning methods, use of maternal and child health services, and knowledge and behaviours related to HIV/AIDS and other sexually transmitted infections. It was designed as a follow-on to the 2000 MDHS survey, a national-level survey of similar scope. The 2004 MDHS survey, unlike the 2000 MDHS, collected blood samples which were later tested for HIV in order to estimate HIV prevalence in Malawi.

In broad terms, the 2004 MDHS survey aimed to: - Assess trends in Malawi's demographic indicators, principally fertility and mortality - Assist in the monitoring and evaluation of Malawi's health, population, and nutrition programmes - Advance survey methodology in Malawi and contribute to national and international databases - Provide national-level estimates of HIV prevalence for women age 15-49 and men age 15-54.

In more specific terms, the 2004 MDHS survey was designed to:
- Provide data on the family planning and fertility behaviour of the Malawian population and thereby enable policymakers to evaluate and enhance family planning initiatives in the country - Measure changes in fertility and contraceptive prevalence and analyse the factors that affect these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding habits, and important social and economic factors - Examine basic indicators of maternal and child health and welfare in Malawi, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, and use of immunisation services. Particular emphasis was placed on malaria programmes, including malaria prevention activities and treatment of episodes of fever. - Provide levels and patterns of knowledge and behaviour related to the prevention of HIV/AIDS and other sexually transmitted infections - Provide national estimates of HIV prevalence - Measure the level of infant and adult mortality including maternal mortality at the national level - Assess the status of women in the country.

MAIN FINDINGS

Fertility - Fertility Levels and Trends. While there has been a significant decline in fertility in the past two decades from 7.6 children in the early 1980s to 6.0 children per woman in the early 2000s, compared with selected countries in Eastern and Southern Africa, such as Zambia, Tanzania, Mozambique, Kenya, and Uganda, the total fertility rate (TFR) in Malawi is high, lower only than Uganda (6.9).

Family planning - Knowledge of Contraception. Knowledge of family planning is nearly universal, with 97 percent of women age 15-49 and 97 percent of men age 15-54 knowing at least one modern method of family planning. The most widely known modern methods of contraception among all women are injectables (93 percent), the pill and male condom (90 percent each), and female sterilisation (83 percent).

Maternal health - Antenatal Care. There has been little change in the coverage of antenatal care (ANC) from a medical professional since 2000 (93 percent in 2004 compared with 91 percent in 2000). Most women receive ANC from a nurse or a midwife (82 percent), although 10 percent go to a doctor or a clinical officer. A small proportion (2 percent) receives ANC from a traditional birth attendant, and 5 percent do not receive any ANC. Only 8 percent of women initiated ANC before the fourth month of pregnancy, a marginal increase from 7 percent in the 2000 MDHS.

Adult and Maternal Mortality. Comparison of data from the 2000 and 2004 MDHS surveys indicates that mortality for both women and men has remained at the same levels since 1997 (11-12 deaths per 1,000).

Child health - Childhood Mortality. Data from the 2004 MDHS show that for the 2000-2004 period, the infant mortality rate is 76 per 1,000 live births, child mortality is 62 per 1,000, and the under-five mortality rate is 133 per 1,000 live births.

Nutrition - Breastfeeding Practices. Breastfeeding is nearly universal in Malawi. Ninety-eight percent of children are breastfed for some period of time. The median duration of breastfeeding in Malawi in 2004 is 23.2 months, one month shorter than in 2000.

HIV/AIDS - Awareness of AIDS. Knowledge of AIDS among women and men in Malawi is almost universal. This is true across age group, urban-rural residence, marital status, wealth index, and education. Nearly half of women and six in ten men can identify the two most common misconceptions about the transmission of HIV-HIV can be transmitted by mosquito bites, and HIV can be transmitted by supernatural means-and know that a healthy-looking person can have the AIDS virus.

Geographic coverage

The 2004 MDHS is designed to present important characteristics for Malawi as a whole, urban and rural areas separately, and each of ten large districts. These districts are: Blantyre, Kasungu, Machinga, Mangochi, Mzimba, Salima, Tyolo, Zomba, Lilongwe, and Mulanje.

Analysis unit

• Household
• Women age 15-49
• Men age 15-54

Universe

The population covered by the 2004 MDHS is defined as the universe of all women age 15-49 in malawi and all men age 15-54 living in the household.

Kind of data

Sample survey data

Sampling procedure

The primary objective of the 2004 Malawi Demographic and Health Survey (MDHS) is to provide estimates with acceptable precision for important population characteristics such as fertility, contraceptive prevalence, selected health indicators, and infant mortality rates.

Administratively, Malawi is divided into twenty-seven districts. In turn, each district is subdivided into smaller administrative units. In 1998, the National Statistical Office (NSO) carried out a Housing and Population Census. In the census, each administrative unit was sub-divided into enumeration areas (EAs), which is totally classified as urban or rural. For each EA, a sketch map was drawn. The sketch shows the EA boundaries, location of buildings, and other landmarks. The list of EAs serves as the frame for the 2004 MDHS sample.

The 2004 MDHS is designed to present important characteristics for Malawi as a whole, urban and rural areas separately, and each of ten large districts. These districts are: Blantyre, Kasungu, Machinga, Mangochi, Mzimba, Salima, Tyolo, Zomba, Lilongwe, and Mulanje. In the interest of presenting estimates for the remaining 17 districts in Malawi in as much breakdown as possible, these districts are grouped as follows: - Group 1: The rest of the Northern region (Chitipa, Karonga, Rumphi, Likoma, and Nkhata Bay) - Group 2: Dowa, Dedza, and Nkhotakota - Group 3: Mchinji and Ntchisi - Group 4: Mwanza, Chikwawa, and Nsanje - Group 5: Phalombe and Chiradzulu - Group 6: Balaka and Ntcheu

SAMPLE ALOCATION

The target sample for the 2004 MDHS sample is about 15,140 households. Based on the level of non-response found in the 2000 MDHS, approximately 13,000 women with completed interviews are expected to be obtained. A sample of households will be selected from each EA, and all women age 15 to 49 identified in these households were interviewed. One in every three sampled households was selected for the male survey and HIV testing. All men age 15-54 in these households are eligible for individual interview. The selected households will be distributed in 522 EAs, 64 in the urban and 458 in the rural areas.

SAMPLE SELECTION

The 2004 MDHS sample will be selected using a stratified two-stage cluster design. In each domain, the clusters are selected with a probability proportional to household size

Abstract

The Schooling Income and Health Risk (SIHR) project is a randomized evaluation of a conditional and unconditional cash transfer intervention targeting young women in Malawi that provided incentives (in the form of school fees and cash transfers) to current schoolgirls and recent dropouts to stay in or return to school. The program, known as the Zomba Cash Transfer Program (ZCTP), took place in Zomba, Malawi during 2008 and 2009. The incentives include average payment of US$10 a month conditional on satisfactory school attendance and direct payment of secondary school fees.

The SIHR project was specifically designed to answer a number of important questions about cash transfer programs for which there is little prior evidence. First, almost all information about the impacts of these programs come from Latin America, where income levels are much higher and institutional capacity is vastly superior compared with many poor countries in Sub-Saharan Africa. Second, the evidence base to effectively choose program design parameters (such as conditionality, transfer size, and the specific identity of the program beneficiary within households) is limited. Third, evidence on final outcomes, such as learning, labor market outcomes, and HIV risk is lacking. Finally, long term evaluations of cash transfer programs are rare - mainly because the control groups in these evaluations are treated after a short period of time.

The data collection effort includes household surveys, individual quantitative and qualitative interviews, academic assessments, Voluntary Counseling and Testing, earky childhood development assessments, school surveys, market surveys, community surveys, and health facility assessments.

The datasets from the fourth round of the impact evaluation are documented here.

Geographic coverage

Zomba district.

Zomba district in the Southern region was chosen as the site for this study for several reasons. First, it has a large enough population within a small enough geographic area rendering field work logistics easier and keeping transport costs lower. Zomba is a highly populated district, but distances from the district capital (Zomba Town) are relatively small. Second, characteristic of Southern Malawi, Zomba has a high rate of school dropouts and low educational attainment. Third, unlike many other districts, Zomba has the advantage of having a true urban center as well as rural areas. As the study sample was stratified to get representative samples from urban areas (Zomba town), rural areas near Zomba town, and distant rural areas in the district, researchers can analyze the heterogeneity of the impacts by urban/rural areas. Finally, while Southern Malawi, which includes Zomba, is poorer, has lower levels of education, and higher rates of HIV than Central and Northern Malawi, these differences are relative considering that Malawi is one of the poorest countries in the world with one of the highest rates of HIV prevalence.

Analysis unit

• Households;
• 13-22 year-nold ever-married girls and young women at the baseline;
• Partners of the women recruited at baseline;
• Children of the women recruited at baseline, with those aged 3-4 years old being administered development assessments.

Kind of data

Sample survey data [ssd]

Sampling procedure

First, 176 enumeration areas (EA) were randomly sampled out of a total of 550 EAs using three strata in the study district of Zomba. Each of these 176 EAs were then randomly assigned treatment or control status. The three strata are urban, rural areas near Zomba Town, and rural areas far from Zomba Town. Rural areas were defined as being near if they were within a 16-kilometer radius of Zomba Town. Researchers did not sample any EAs in TA Mbiza due to safety concerns (112 EAs).

Enumeration areas (EAs) in Zomba were selected from the universe of EAs produced by the National Statistics Office of Malawi from the 1998 Census. The sample of EAs was stratified by distance to the nearest township or trading centre. Of the 550 EAs in Zomba, 50 are in Zomba town and an additional 30 are classified as urban (township or trading center), while the remaining 470 are rural (population areas, or PAs). The stratified random sample of 176 EAs consisted of 29 EAs in Zomba town, eight trading centers in Zomba rural, 111 population areas within 16 kilometers of Zomba town, and 28 EAs more than 16 kilometers from Zomba town.

After selecting sample EAs, all households were listed in the 176 sample EAs using a short two-stage listing procedure. The first form, Form A, asked each household the following question: “Are there any never-married girls in this household who are between the ages of 13 and 22?” This form allowed the field teams to quickly identify households with members fitting into the sampling frame, thus significantly reducing the costs of listing. If the answer received on Form A was a “yes”, then Form B was filled to list members of the household to collect data on age, marital status, current schooling status, etc.

From this researchers could categorize the target population into two main groups: those who were out of school at baseline (baseline dropouts) and those who were in school at baseline (baseline schoolgirls). These two groups comprise the basis of our sampling frame. In each EA, enumerators sampled all eligible dropouts and approximately two-thirds of all eligible school girls, where the sampling percentage depended on the age and location of the baseline schoolgirl. This sampling procedure led to a total sample size of 3,796 with an average of 5.1 dropouts and 16.7 schoolgirls per EA.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household survey consists of a multi-topic questionnaire administered to the households in which the selected sample respondents reside.

The survey consists of four parts: one that is administered to the head of the household; another that is administered to a core respondent - a sampled girl from the target population; another part is administered to the core respondent's partner; finally, assessments for early childhood development are administered to children of the core respondents who were aged 3-4 years old at the time of data collection.

The first part of the survey collects information on the household roster, dwelling characteristics, household assets and durables, shocks, deaths and consumption. The core respondent survey provides information about her family background, her education and labor market participation, her health, her children's health, her dating patterns, sexual behavior, marital expectations, knowledge of HIV/AIDS, as well as her own consumption of girl-specific goods (such as soaps, mobile phone airtime, clothing, braids, sodas and alcoholic drinks, etc.). The partner's survey provides information on the partner's education and labor market participation, health, dating patterns, sexual behavior, and marital expectations. Finally, children of the core respondent who were 3-4 years old at the time of data collection are administered two separate developmental assessments (the Malawi Developmental Assessment Tool and the Strengths and Difficulties Questionnaire).

Much of the information gathered in the fourth round is similar to that collected in the previous rounds, but there is a significant portion of distinct and new information pertinent to Round 4.

Models A and C specify the logarithm of district under five population, and models B and D specify a spline of the log of under five population. Models A and B are for all districts in Malawi except Likoma, while Models C and D are for all districts except Likoma and Zomba districts.

BackgroundMother-to-child transmission of syphilis remains high especially in the WHO AFRO region with a prevalence of 1.62%, resulting in a congenital syphilis rate of 1,119 per 100,000 live births. Elimination efforts can be supported by an understanding of the spatial and temporal changes in disease over time, which can identify priority areas for targeted interventions aimed at reducing transmission.MethodsWe collated routine surveillance data from health facilities and covariate data from demographic and health surveys conducted in Malawi between 2014 and 2022. We fitted a Bayesian hierarchical mixed model with spatial and temporally structured random effects to model the district-level monthly counts of maternal syphilis notifications as a function of individual- and district-level predictors. We then generated district-level spatiotemporally explicit risk profiles to estimate the effect of individual- and district-level covariates on maternal syphilis notifications and to identify hotspot areas.ResultsOverall, the national prevalence of maternal syphilis increased from 0.28% (95% CI: 0.27–0.29%) in 2014 to peaking in 2021 at 1.92% (95% CI: 1.89–1.96%). Between 2020 and 2022, there was a decline in prevalence, with the most significant decline seen in Zomba District (1.40, 95% CI: 1.12–1.66%). In regression models, a one percentage point increase in district-level antenatal HIV prevalence was associated with increased maternal syphilis (prevalence ratio [PR]: 1.15, 95% credible interval [CrI]: 1.10–1.21). There was also an increased prevalence of maternal syphilis associated with an increased district-level mean number of sex partners (PR: 1.05, 95% CrI: 0.80–1.37). The number of districts with a high prevalence of maternal syphilis also increased between 2014 and 2022, especially in the southern region, where most had a high probability (approaching 100%) of having high maternal syphilis (defined as relative risk >1 compared to the standard population of women aged 15–49 years) in 2022.ConclusionMaternal syphilis prevalence in Malawi shows an increasing upward trend, with an estimated six times relative increase between 2014 and 2022 (0.28% to 1.73%) and strong associations with higher district-level HIV prevalence. Controlling syphilis depends on reaching vulnerable populations at the sub-national level, which may be disproportionately affected. Our findings support the move to integrate the elimination of mother-to-child transmission (EMTCT) of syphilis programs with existing prevention of mother-to-child transmission (PMTCT) of HIV programs.

A crude version of the INFORM risk-framework is applied to Enumeration Areas (which is unofficial, but is deeper than admin-3), in Southern Malawi. This is done specifically for area selection regarding the ECHO2 project in 3 TA's: Mwambo (Zomba district), Makhwira (Chikwawa district) and Ndamera (Nsanje district).

Scope

Enumeration areas are retrieved from http://www.masdap.mw/layers/geonode%3Aeas_bnd. These are used, because we want to prioritize on a deeper level than Traditional Authority (admin-3) level, and there are no other official boundaries available.

The dataset in principle data for the whole of Malawi, but contains 4 filters, which can be applied, which are the following:

• Filter_south: this filters out only the South of Malawi, for which the drough and flood analysis has been carried out (see details below).
• Filter_district: contains all EA's from the 3 pre-identified districts Zomba, Chikwawa and Nsanje.
• Filter_TA: contains all EA's from the 3 pre-identified TAs Mwambo, Makhwira and Ndamera.
• Filter_GVH: there are also 44 Group Village Heads pre-identified for the project. As these GVH's are points on a map, all EA's are selected here which have a GVH within their boundaries or very close to their boundaries.

INFORM risk-framework

The INFORM framework (http://www.inform-index.org/) is applied to assess risk per community, which is considered the main criteria for prioritization within the project.

Because of low data availability we apply a crude version for now, with only some important indicators of the framework actually used. Since we feel that these indicators (see below) still constitute together a current good assessment of risk, and we want to stimulate the use and acceptance of the INFORM-framework, we choose to use it anyway.

The INFORM risk-score consists of 3 main components: hazards, vulnerability and coping capacity.

• Hazard: For hazard we focus - in line with the ECHO2 project - on floods and droughts only. Analysis has been carried out (see more details below), to determine flood and drought risk on a scale from 0-10 with a resolution of 250meter grid cells. This has subsequently been aggregated to Enumeration Areas, by taking a population-weighted average. Thereby taking into account where people actually live within the Enumeration Areas. (Population data source: Worldpop: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00155)

• Vulnerability: Vulnerability is operationalized here through poverty incidence. Poverty rate (living below $1.25/day) is retrieved from Worldpop (http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00157) and again transformed from a 1km resolution grid to Enumeration Areas through a population-weighted average.

• Lack of Coping capacity: Coping capacity is measured through traveltimes to various facilities, namely traveltime to nearest hospistal, traveltime to nearest trading centre and traveltime to nearest secondary school. Together these are all proxies of being near/far to facilities, and thereby an indicator of having higher/lower coping capacity. See https://510.global/developing-and-field-testing-a-remoteness-indicator-in-malawi/ for more information on how these traveltimes were calculated and validated.

Use

All features are stored in a CSV, but can easily be joined to the geographic shapefile to make maps on EACODE.

Flood and Drought calculations

Drought layer

The drought risk map was created by analyzing rainfall data in the past 20 years using standard precipitation index (SPI) , which is a widely used index in drought analysis. Based on SPI6 values for the period October-march, which is the main rainy season in Malawi. Each pixel is classified to drought or no drought for each year based on SPI6 values, drought year if SPI value for a pixel is less than -1. Next, relative frequency is calculated, the number of times drought has occurred in the considered 20 year period. This frequency is then converted to probability of drought occurrence in a given year. We validated our analysis by comparing NDVI values for the drought year against long term average values.

Flood layer

To identify flood moments in Malawi Landsat imagery was studied (1984-2017). Floods were clearly evidenced in 9 dates. For the clearest and most representative layers the mNDWI (modified Normalized Water Index) was calculated. The index mNDWI (McFeeters 1996; Xu 2006) for Landsat bands is calculated as follows: (b2GREEN-b7MIRSWIR/b2GREEN+b7MIRSWIR). In this variation of the index the higher values are the wettest. A threshold was applied to the mNDWI to separate flood from non-flood or water from non-water pixels. The resulting layers were aggregated and the final stretched from 0-10, where 0 are the pixels where no flood is expected while pixels with 10 are where most frequent flood has been evidenced and therefore expected. The largest flood was observed in 2015, as the scenes were cloudy the flood extent was manually interpreted from several scenes. The evidenced flood dates are: 29 Feb. 1988 low flood, 19 march 1989, 17 march 1997, Feb 1998, March 1999 low flood, 2001 since February 16 until end of April, 2007 17 February since early Feb., 2008 Feb. medium flood, 2015 January – March. The water bodies in this layer are not represented and have a value of 0 like the rest of land where flood is absent.

Abstract

The Integrated Household Survey is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO) roughly every 5 years to monitor and evaluate the changing conditions of Malawian households. The IHS data have, among other insights, provided benchmark poverty and vulnerability indicators to foster evidence-based policy formulation and monitor the progress of meeting the Millennium Development Goals (MDGs), the goals listed as part of the Malawi Growth and Development Strategy (MGDS) and now the Sustainable Development Goals (SDGs).

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Children under 5 years
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop

Universe

Members of the following households are not eligible for inclusion in the survey: • All people who live outside the selected EAs, whether in urban or rural areas. • All residents of dwellings other than private dwellings, such as prisons, hospitals and army barracks. • Members of the Malawian armed forces who reside within a military base. (If such individuals reside in private dwellings off the base, however, they should be included among the households eligible for random selection for the survey.) • Non-Malawian diplomats, diplomatic staff, and members of their households. (However, note that non-Malawian residents who are not diplomats or diplomatic staff and are resident in private dwellings are eligible for inclusion in the survey. The survey is not restricted to Malawian citizens alone.) • Non-Malawian tourists and others on vacation in Malawi.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IHS4 sampling frame is based on the listing information and cartography from the 2008 Malawi Population and Housing Census (PHC); includes the three major regions of Malawi, namely North, Center and South; and is stratified into rural and urban strata. The urban strata include the four major urban areas: Lilongwe City, Blantyre City, Mzuzu City, and the Municipality of Zomba. All other areas are considered as rural areas, and each of the 27 districts were considered as a separate sub-stratum as part of the main rural stratum. This is the first round of the survey to include the island district of Likoma in the sampling frame. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS4 strata are composed of 32 districts in Malawi.

A stratified two-stage sample design was used for the IHS4.

Note: Detailed sample design information is presented in the "Fourth Integrated Household Survey 2016-2017, Basic Information Document" document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

HOUSEHOLD QUESTIONNAIRE The Household Questionnaire is a multi-topic survey instrument and is near-identical to the content and organization of the IHS3. It encompasses economic activities, demographics, welfare and other sectoral information of households. It covers a wide range of topics, dealing with the dynamics of poverty (consumption, cash and non-cash income, savings, assets, food security, health and education, vulnerability and social protection). Although the IHS4 household questionnaire covers a wide variety of topics in detail it intentionally excludes in-depth information on topics covered in other surveys that are part of the NSO’s statistical plan (such as maternal and child health issues covered at length in the Malawi Demographic and Health Survey).

AGRICULTURE QUESTIONNAIRE All IHS4 households that are identified as being involved in agricultural or livestock activities were administered the agriculture questionnaire, which is primarily modelled after the IHS3 counterpart. The modules are expanding on the agricultural content of the IHS3, IHS2, AISS, and other regional agricultural surveys, while remaining consistent with the NACAL topical coverage and methodology. The development of the agriculture questionnaire was done with input from the aforementioned stakeholders who provided input on the household questionnaire as well as outside researchers involved in research and policy discussions pertaining to the Malawian agriculture. The agriculture questionnaire allows, among other things, for extensive agricultural productivity analysis through the diligent estimation of land areas, both owned and cultivated, labor and non-labor input use and expenditures, and production figures for main crops, and livestock. Although one of the major foci of the agriculture data collection effort was to produce smallholder production estimates for major crops, it is also possible to disaggregate the data by gender and main geographical regions. The IHS4 cross-sectional households supply information on the last completed rainy season (2014/2015 or 2015/2016) and the last completed dry season (2015 or 2016) depending on the timing of their interview.

FISHERIES QUESTIONNAIRE The design of the IHS4 fishery questionnaire is identical to the questionnaire designed for IHS3. The IHS3 fisheries questionnaire was informed by the design and piloting of a fishery questionnaire by the World Fish Center (WFC), which was supported by the LSMS-ISA project for the purpose of assembling a fishery questionnaire that could be integrated into multi-topic household-surveys. The WFC piloted the draft instrument in November 2009 in the Lower Shire region, and the NSO team considered the revised draft in designing the IHS4 fishery questionnaire.

COMMUNITY QUESTIONNAIRE The content of the IHS4 Community Questionnaire follows the content of the IHS3 & IHPS Community Questionnaires. A “community” is defined as the village or urban location surrounding the enumeration area selected for inclusion in the sample and which most residents recognize as being their community. The IHS4 community questionnaire was administered to each community associated with the 780 cross-sectional EAs. Identical to the IHS3 approach, to a group of several knowledgeable residents such as the village headman, the headmaster of the local school, the agricultural field assistant, religious leaders, local merchants, health workers and long-term knowledgeable residents. The instrument gathers information on a range of community characteristics, including religious and ethnic background, physical infrastructure, access to public services, economic activities, communal resource management, organization and governance, investment projects, and local retail price information for essential goods and services.

Cleaning operations

DATA ENTRY PLATFORM To ensure data quality and timely availability of data, the IHS4 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHS4, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Samsung Galaxy Tab S2 tablet computer. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. In Survey Solutions, Headquarters can then see the location of the dwellings plotted on a map of Malawi to better enable supervision from afar – checking both the number of interviews performed and the fact that the sample households lie within EA boundaries. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

DATA MANAGEMENT The IHS4 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHS4 Interviews were collected in “sample” mode (assignments generated from headquarters) as opposed to “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in IHS3 and IHPS. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (NSO management) assigned work to supervisors based on their regions of coverage. Supervisors then made assignments to the enumerators linked to their Supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHS4 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to STATA for other consistency checks, data cleaning, and analysis.

DATA CLEANING The data cleaning process was done in several stages over the course of field work and through preliminary analysis. The first stage of data cleaning was conducted in the field by the field based field teams utilizing errors generated with the Survey Solutions application. For questions that