Ethiopia sub-national aggregates, % of population under sever poverty conditions (K > 50%).

Poverty rate at $1.9 a day of Ethiopia slumped by 12.34% from 30.80 % in 2010 to 27.00 % in 2015. Since the 16.18% drop in 1999, poverty rate at $1.9 a day sank by 53.45% in 2015. Population below $1.9 a day is the percentage of the population living on less than $1.9 a day at 2005 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

As of May 2023, 22.8 million people in Ethiopia lacked sufficient food for consumption. The number of inhabitants in the food insecurity situation remained stable compared to the previous month. Furthermore, the prevalence of food insecurity in Ethiopia was measured at 22.25 percent of the population in May 2023. Overall, the number of people with insufficient food consumption in the country fluctuated, peaking at 26.3 percent million individuals in April 2022.

Poverty rate at $3.2 a day of Ethiopia dropped by 8.84% from 71.30 % in 2010 to 65.00 % in 2015. Since the 0.67% upward trend in 1999, poverty rate at $3.2 a day plummeted by 27.37% in 2015. Population below $3.1 a day is the percentage of the population living on less than $3.1 a day at 2005 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

The index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. Critically the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS) and Multi-Indicator Cluster Surveys (MICS) The resources subnational multidimensional poverty data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the severe deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. The global MPI methodology is detailed in Alkire, Kanagaratnam & Suppa (2023)

The coronavirus (COVID-19) pandemic impacted East Africa's poverty level. Extreme poverty rate in the region increased from 33 percent in 2019 to 35 percent in 2021. South Sudan and Brurundi had the highest share of population living on less than 1.90 U.S. dollars per day, 85 percent and 80 percent, respectively.

Poverty ratio at national poverty line of Ethiopia sank by 20.61% from 29.6 % in 2010 to 23.5 % in 2015. Since the 2.86% reduction in 1999, poverty ratio at national poverty line plummeted by 46.83% in 2015. National poverty headcount ratio is the percentage of the population living below the national poverty lines. National estimates are based on population-weighted subgroup estimates from household surveys.

In 2025, around 438.6 million people in Africa were living in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day. The number of poor people on the continent dropped slightly compared to the previous year. Poverty in Africa is expected to decline slightly in the coming years, even in the face of a growing population. The number of inhabitants living below the extreme poverty line would decrease to around 426 million by 2030.

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Ethiopia ET: Increase in Poverty Gap at $3.20: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: % of Poverty Line data was reported at 0.792 % in 2015. This records an increase from the previous number of 0.407 % for 2011. Ethiopia ET: Increase in Poverty Gap at $3.20: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: % of Poverty Line data is updated yearly, averaging 0.418 % from Dec 1999 (Median) to 2015, with 4 observations. The data reached an all-time high of 0.792 % in 2015 and a record low of 0.289 % in 2004. Ethiopia ET: Increase in Poverty Gap at $3.20: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: % of Poverty Line data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank.WDI: Poverty. Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, as a percentage of the $3.20 poverty line. The poverty gap increase due to out-of-pocket health spending is one way to measure how much out-of-pocket health spending pushes people below or further below the poverty line (the difference in the poverty gap due to out-of-pocket health spending being included or excluded from the measure of household welfare). This difference corresponds to the total out-of-pocket health spending for households that are already below the poverty line, to the amount that exceeds the shortfall between the poverty line and total consumption for households that are impoverished by out-of-pocket health spending and to zero for households whose consumption is above the poverty line after accounting for out-of-pocket health spending.; ; World Health Organization and World Bank. 2019. Global Monitoring Report on Financial Protection in Health 2019.; Weighted average;

Poverty gap at $3.2 a day of Ethiopia dropped by 12.29% from 29.3 % in 2010 to 25.7 % in 2015. Since the 15.24% slump in 1999, poverty gap at $3.2 a day plummeted by 38.37% in 2015. Poverty gap at $3.20 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $3.20 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.

Poverty gap at $5.5 a day of Ethiopia slumped by 7.12% from 53.4 % in 2010 to 49.6 % in 2015. Since the 5.75% drop in 1999, poverty gap at $5.5 a day sank by 22.38% in 2015. Poverty gap at $5.50 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $5.50 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.

In 2019, approximately 6 in 10 Ethiopians reported there were times they did not have enough money to buy food that they and their family needed in the past twelve months. This figure had increased from about 4 in 10 Ethiopians suffering from food insecurity in 2012.

This data set is collected by centeral statistic agency of Ethiopia with reference to 2013 and 2015 poverty and inequality indicating variables.The onlly modification made here is new variables are created systematically on the basis of old variables with STATA gen command.This done because poverty and inequality analysis needs further process and creating suitable welfare indicator.

The dataset contains cross-sectional primary data obtained from 384 randomly chosen rural families using semi-structured interviews in order to examine the multidimensional poverty index of the households with its driving factors in the Gurage zone of Ethiopia. The dataset contains all of the essential quantitative information regarding the dimensions and indicators of the multidimensional poverty index. Using Stata software, the variables are correctly coded, labeled, and inserted into the dataset.

Ethiopia ET: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data was reported at 65.000 % in 2015. This records a decrease from the previous number of 71.300 % for 2010. Ethiopia ET: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data is updated yearly, averaging 77.000 % from Dec 1995 (Median) to 2015, with 5 observations. The data reached an all-time high of 89.500 % in 1999 and a record low of 65.000 % in 2015. Ethiopia ET: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank.WDI: Social: Poverty and Inequality. Poverty headcount ratio at $3.65 a day is the percentage of the population living on less than $3.65 a day at 2017 international prices.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Abstract

The World Bank is interested in gauging the views of clients and partners who are either involved in development in Ethiopia or who observe activities related to social and economic development. The World Bank Country Assessment Survey is meant to give the Bank's team that works in Ethiopia, more in-depth insight into how the Bank's work is perceived. This is one tool the Bank uses to assess the views of its critical stakeholders. With this understanding, the World Bank hopes to develop more effective strategies, outreach and programs that support development in Ethiopia. The World Bank commissioned an independent firm to oversee the logistics of this effort in Ethiopia.

The survey was designed to achieve the following objectives: - Assist the World Bank in gaining a better understanding of how stakeholders in Ethiopia perceive the Bank; - Obtain systematic feedback from stakeholders in Ethiopia regarding: · Their views regarding the general environment in Ethiopia; · Their perceived overall value of the World Bank in Ethiopia; · Overall impressions of the World Bank as related to programs, poverty reduction, personal relationships, effectiveness, knowledge base, collaboration, and its day-to-day operation; and · Perceptions of the World Bank's communication and outreach in Ethiopia. - Use data to help inform the Ethiopia country team's strategy.

Geographic coverage

National

Analysis unit

Stakeholder

Universe

Stakeholders of the World Bank in Ethiopia

Kind of data

Sample survey data [ssd]

Sampling procedure

In December 2011, 620 stakeholders of the World Bank in Ethiopia were invited to provide their opinions on the Bank's assistance to the country by participating in a country survey. Participants in the survey were drawn from among the office of the President or Prime Minister; the office of a Minister; the office of a Parliamentarian; employees of a ministry, ministerial department, or implementation agency; consultants/contractors working on World Bank supported projects/programs; project management units (PMUs); local government officials or staff; bilateral or multilateral agencies; private sector organizations; NGOs (including CBOs); the media; independent government institutions; trade unions; academia, research institutes or think tanks; and the judiciary.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The Questionnaire consists of 8 Sections:

1. Background Information: The first section asked respondents for their current position; specialization; familiarity, exposure to, and involvement with the Bank; geographic location; and age.

2. General Issues facing Ethiopia: Respondents were asked to indicate what they thought were the most important development priorities, which areas would contribute most to poverty reduction and economic growth in Ethiopia, whether Ethiopia is headed in the right direction, and whether the economy and standard living has improved in the past five years, as well as rating the extent to which Ethiopia was headed in the right direction in terms of specific development areas.

3. Overall Attitudes toward the World Bank: Respondents were asked to rate the extent to which the Bank meets Ethiopia's need for knowledge services, the extent to which the Bank encourages the government to see through reforms, and their agreement with various statements regarding the Bank's programs, poverty mission, relationships, and collaborations in Ethiopia. Respondents were also asked to indicate the areas on which it would be most productive for the Bank to focus its resources and research, what the Bank's level of involvement should be, what they felt were the Bank's greatest values and greatest weaknesses in its work, and with which groups the Bank should work more.

4. The Work of the World Bank: Respondents were asked to rate their level of importance and the Bank's level of effectiveness across twenty-two areas in which the Bank was involved, such as helping to reduce poverty and encouraging greater transparency in governance.

5. The Way the World Bank does Business: Respondents were asked to rate the Bank's level of effectiveness in the way it does business, including the Bank's knowledge, personal relationships, collaborations, and poverty mission.

6. Project/Program Related Issues: Respondents were asked to rate their level of agreement with a series of statements regarding the Bank's programs, day-to-day operations, and collaborations in Ethiopia.

7. The Future of the World Bank in Ethiopia: Respondents were asked to rate how significant a role the Bank should play in Ethiopia's development and to indicate what the Bank could to make itself of greater value and to what reasons respondents attributed failed or slow reform efforts.

8. Communication and Outreach: Respondents were asked to indicate where they get information about development issues and the Bank's development activities in Ethiopia, as well as how they prefer to receive information from the Bank. Respondents were also asked to indicate their usage of the Bank's website, PICs, and Development Information Corners, and to evaluate these communication and outreach efforts.

Response rate

A total of 326 stakeholders participated in the country survey (53%).

Background: For decades, health targeted aid in the form of development assistance for health has been an important source of financing health sectors in developing countries. Health sectors in Sub Saharan countries in general and Ethiopia in particular, are even more heavily reliant upon donors. Consequently, a more audible donors support to health sectors was seen during the last four decades, consistent with the donor's response to the global goal of Alma-Ata declaration of "health for all by the year 2000" through primary health care in 1978. Ever since, a massive surge of development assistance for health has followed the out gone of the 2015 United Nations Millennium Declaration Goals in which three out of the eight goals were directly related to health. In spite of the long history of health targeted aid, with an ever increasing volumes, there is an increasing controversy on the extent to which health targeted aid is producing the intended health outcomes in the recipient countries. Despite the vast empirical literatures considering the effect of foreign development aid on economic growth of the recipient countries, systematic evidence that health sector targeted aid improves health outcomes is relatively scarce. The main contribution of this study is, therefore, to present a comprehensive country level, and cross-country evidences on the effect of development assistance for health on health outcomes. Objectives: The overall objective of this study was to analyze the effect of development assistance for health on health outcomes in Ethiopia, and in Sub Saharan Africa. Methods: For the Ethiopian (country level) study, a dynamic time series data analytic approach was employed. A retrospective sample of 36-year observations from 1978 to 2013 was analyzed using an econometric technique - vector error correction model. Beside including time dependency between the variables of interest and allowing for stochastic trends, the model provides valuable information on the existence of long-run and short-run relationships among the variables under study. Furthermore, to estimate the co-integrating relations and the other parameters in the model, the standard procedure of Johansen's approach was used. While development assistance for health expenditure was used as an explanatory variable of interest, life expectancy at birth was used as a dependent variable for the fact that it has long been used with or without mortality measures as health status indicators in the literatures.In the Sub Saharan Africa (cross-country level) study, a dynamic panel data analytic approach was employed using fixed effect, random effect, and the first difference-generalized method of moments estimators in the period confined to the year 1995-2013 over the cross section of 43 SSA countries. While development assistance for health expenditure was used as an explanatory variable of interest here again, infant mortality rate was used for health status measure done for its advantage over other mortality measures in cross-country studies. Results: In Ethiopia, the immediate one and two prior year of development assistance for health was shown to have a significant positive effect on life expectancy at birth. Other things being equal, an increase of development assistance for health expenditure per capita by 1% leads to an improvement in life expectancy at birth by about 0.026 years (P=0.000) in the immediate year following the period, and 0.008 years following the immediate prior two years period (P= 0.025). Similarly, in Sub-Saharan Africa, development assistance for health was found to have a strong negative effect on the reduction of infant mortality rate. The estimates of the study result indicated that during the covered period of study, in the region, a 1% increase in development assistance for health expenditure, which is far less than 10 cents per capita at the mean level, saves the life of two infants per 1000 live births (P=0.000). Conclusion: Contrary to the views of health aid skeptics, this study indicates strong favorable effect of development assistance for health sector in improving health status of people in Sub Saharan Africa in general and the Ethiopia in particular. Recommendations: The policy implication of the current findings is that development assistance for health sector should continue as an interim necessity means. However, domestic health financing system should also be sought, as the targeted countries cannot rely upon external resources continuously for improving the health status of the population. At the same time, the current development assistance stakeholders assumption of targeting facility based primary health care provision should be augmented by a more strong parallel strategy of improving socioeconomic status of the population that promotes sustainable improvement of health status in the targeted countries.

Abstract

Ethiopia is one of those countries that suffer the hardest hits of poverty. Persistent war and drought and inappropriate policies are presumed to enhance the extent of poverty in the country. According to the report on Poverty Situation in Ethiopia which was based on the 1995/96 Household Income, Consumption and Expenditure Survey and the 1996 Welfare Monitoring Survey 45.5 percent of the total population are found to live below the poverty line. The report has also revealed the disparity among urban-rural residents in which 47.5 percent of the urban population. Hence, the issue of poverty reduction would necessarily be an agenda of higher priority for the government and policy markers.

As in the case of a number of Africa countries that undertook the Social Dimensions of Adjustment (SDA) program, the issue of welfare monitoring in Ethiopia arose as part of the Economic Reform Program (ERP) currently being undertaken in the country. The ERP specifically and strongly underlies the effects of the reform program on poverty and the analytical capacity of the government to monitor such effects. To this end, the government has set up a Welfare Monitoring System (WMS) by mid 1994.

In view of the wider context of the problem, the establishment of the WMS is envisaged to consist of the following major elements: - establish an information system that provides a continuous picture of the poverty scenario in the country; - indicate the impact of reform programs on the level of household welfare; - establish follow-up procedures on the various programs and activates targeted towards poverty alleviation; and - conduct regular statistical survey to assess, in particular, the efficiency of targeted programs.

In order to fulfill the data needs to monitor households' socioeconomic welfare and the ongoing economic reforms, the Central Statistical Authority (CSA) has been conducting Welfare Monitoring Surveys starting from 1996. Reports of the 1996 and 1998 Welfare Monitoring Surveys have also been disseminated.

The Welfare Monitoring Survey (WMS) 2000, like the previous ones, focuses on wide range of socioeconomic indicators, which are vital inputs in the process of monitoring and evaluation of policies, particularly in poverty reduction strategies. The report is presented in two volumes. Volume I presents results based on individual data base and Volume II presents the findings based on household database. Proxy estimate of households' domestic expenditure obtained by recall interview (with reference periods of 7 days and a month prior to the data of interview) is used to classify households (on quintile basis) for the purpose of tabulating the results.

Objectives of the Welfare Monitoring System The WMS which involves various ministries and the Central Statistical Authority (CSA) is established with the following objectives: - provide baseline data on existing poverty situation and establish a system of information gathering on relevant key indicators; - identify poor and vulnerable group that could be the focus of targeted intervention programs; - undertake periodic surveys and researches to evaluate targeted programs; - assess the short and medium term effects of macroeconomic and sectorial policies and programs on the poor; - produce conclusive reports and suggestions needed for due attention by the government and concerned implementing agencies.

Geographic coverage

The WMS 2000 covered the population in sedentary areas of the country on a sample basis excluding the non-sedentary population in Afar and Somalia Regional States. That is, the survey covered the population in sedentary areas of the nine Regional States and two administrative regions, each of which is composed of rural and urban parts.

Analysis unit

• Households
• Individual (including adult women aged 15 and above, children aged 5 and below)

Universe

The survey covered households in the selected samples except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WMS 2000 covered both the urban and the sedentary rural parts of the country. The survey has not covered six zones in Somalia Regional State and two zones in Afar Regional State that are inhabited mainly by nomadic population. For the purpose of the survey, the country was divided into three categories. That is, the rural parts of the country and the urban areas that were divided into two broad categories taking into account sizes of their population.

Category I: Including rural area of 44 zones in 7 regions, 5 special weredas in SNNPR and rural areas of Gambella, Harari, Addis Ababa and Dire Dawa regions each of which are survey domains (reporting levels).The regions that constitute the 44 zones are Tigray, Afar, Amhara, Oromiya, Somalia, Benishangul_Gumuz, and SNNPR. All in all 54 basic rural domains including total rural (country level) are defined for the survey.

Category II: Comprises if all regional capitals and five other urban centers. Each urban center in this category is the survey domain (reporting level) for which separate survey results for major survey characteristics are reported.

Category III: Urban centers in the country other than those under category II are grouped to this category. There are four domains (reporting levels) in this category: Tigray other urabn, Amhara other urban, Oromiya other urban and SNNPR other urban. Eleven additional domains other than those reporting levels defined in Category II and Category III, can be constructed by combining basic domains from these two categories. These domains are: 1) Tigray urban, 2) Afar urban, 3) Amhara urban, 4) Oromiya urban, 5) Somalia urban, 6) Beneshangul-Gumuz urban, 7) SNNPR urban, 8) Gambella urban, 9) Harari urban, 10) Addis Ababa urban and 11) Dire Dawa urban

In addition to the above urban and rural domains, survey results can also be reported at regional and country levels by aggregating the corresponding survey results for urban and rural areas.

Definition of the survey domains was based on both technical and resource considerations. More specifically, sample size for the domains were determined to enable provision of major indicators with reasonable precision subject to the resources that were available for the survey.

Selection Scheme and Sample Size in Each Category a) Category I: A stratified two-stage sample design was used to select the sample in which the primary sampling units (PSUs) were EAs. Sample enumeration area (EAs) from each domain were selected using systematic sampling that is probability proportional to size, size being number of households obtained from 1994 population and housing census. A total of 1450 EAs were selected form the rural parts of the country. Within each sample EA a fresh list of households was prepared at the beginning of the survey's fieldwork and for the administration of the survey questionnaire 12 households per sample EA for rural areas were systematically selected.

b) Category II: In this category also, a stratified two-stage sample design was used to select the sample. In this category a strata constitutes all the Regional State Capitals and the five Major Urban Centers in the country. The primary sampling units (PSUs) are the EA's in the Regional State Capitals and the five Major Urban Centers and excludes the special EAs (non-conventional households). Sample enumeration areas (EAs) from each strata were selected using systematical sampling that is probability proportional to size, size being number of households obtained from the 1994 population and housing census. A total of 373 EAs were selected from this domain of study. Within each sample EAs a fresh list of households was prepared at the beginning of the survey's field work and for the administration of the questionnaire 16 households per sample EA were systematically selected.

c) Category III: Three-stage stratified sample design was adopted to select the sample from domains in category III. The PSUs were other urban centers selected using systematic sampling that is probability proportional to size; size being number if households obtained from the 1994 population and housing census. The secondary sampling units (SSUs) were EAs which were selected using systematic sampling that is probability proportional to size; size being number of households obtained from the 1994 population and housing census. A total of 169 EA's selected from the sample of other urban centers and was determined by proportional allocation to their size of households from the 1994 census. Ultimately, 16 households within each of the sample EAs were selected systematically from a fresh list of households prepared at the beginning of the survey's fieldwork for the administration of the survey questionnaire.

Note: Distribution of EAs and households covered in the survey by domain (reporting levels) and category are given in Table II.1 and Table II.2 of 2000 Welfare Monitoring Survey report which is provided in this documentation.

Mode of data collection

Face-to-face [f2f]

Research instrument

Basically there were two types of questionnaires; one referring to individual household members and the other pertaining to households in general. 1. Individual level questionnaires were used to collect basic population characteristics, health, education, on nutritional status of the children (anthropometric measurements) and immunization. 2. Household-based questionnaires included modules on housing amenities, accessibility of basic facilities such as food market, post office and telephone, possession of household asset and schedule on living standard indicators with respect to basic