In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

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.

Abstract

The rapid and massive dissemination of mobile phones in the developing world is creating new opportunities for the discipline of survey research. The World Bank is interested in leveraging mobile phone technology as a means of direct communication with poor households in the developing world in order to gather rapid feedback on the impact of economic crises and other events on the economy of such households.

The World Bank commissioned Gallup to conduct the Listening to LAC (L2L) pilot program, a research project aimed at testing the feasibility of mobile phone technology as a way of data collection for conducting quick turnaround, self-administered, longitudinal surveys among households in Peru and Honduras.

The project used face-to-face interviews as its benchmark, and included Short Message Service (SMS), Interactive Voice Response (IVR) and Computer Assisted Telephone Interviews (CATI) as test methods of data collection.

The pilot was designed in a way that allowed testing the response rates and the quality of data, while also providing information on the cost of collecting data using mobile phones. Researchers also evaluated if providing incentives affected panel attrition rates. The Honduras design was a test-retest design, which is closely related to the difference-in-difference methodology of experimental evaluation.

The random stratified multistage sampling technique was used to select a nationally representative sample of 1,500 households. During the initial face-to-face interviews, researchers gathered information on the socio-economic characteristics of households and recruited participants for follow-up research. Questions wording was the same in all modes of data collection.

In Honduras, after the initial face-to-face interviews, respondents were exposed to the remaining three methodologies according to a randomized scheme (three rotations, one methodology per week). Panelists in Honduras were surveyed for four and a half months, starting in February 2012.

Geographic coverage

Includes the entire national territory, with the exception of neighborhoods where access of interviewers is extremely difficult, due to lack of transportation infrastructure or for situations that threaten the physical integrity of the interviewers and supervisors (i.e. extremely high crime rate, warfare, etc.)

Analysis unit

• Households

Universe

All the households that exist in the neighborhoods of Honduras, as reported by the 2001 Census. Institutions such as military, religious or educational living quarters are not included in the universe.

Kind of data

Sample survey data [ssd]

Sampling procedure

Honduras did not have an income oversample because the poverty rate is 60 percent, so oversampling 20 percent above the poverty rate would include a large portion of the middle class, which are not the most vulnerable in times of crisis.

The Honduras panel was built on a nationally representative sample of 1,500 households. The sample was drawn by means of a random, stratified, multistage design. The pilot used Gallup World Poll sampling frame.

Census-defined municipalities were classified into five strata according to population size: I. Municipalities with 500,000 to 999,000 inhabitants II. Municipalities with 100,000 to 499,000 inhabitants III. Municipalities with 50,000 to 99,000 inhabitants IV. Municipalities with 10,000 and 49,000 inhabitants V. Municipalities with less than 10,000 inhabitants

Interviews were then proportionally allocated to these five strata according to their share among the country's population.

• The first stage of the design consisted of a random selection of Primary Sampling Units (PSU's) within each of the five strata previously defined.

• In the second stage, in each PSU, one or more Secondary Sampling Units (SSU's) were then selected.

• Once SSU's were selected, interviewers were sent to the field to proceed with the third stage of the sample design, which consisted of selecting households using a systematic "random route" procedure. Interviewers started from the previously selected "random origin" and walked around the block in clockwise direction, selecting every third household on their right hand side. They were also trained to handle vacant, nonresponsive, non-cooperative households, as well as other failed attempts, in a systematic manner.

Mode of data collection

Other [oth]

Research instrument

The following survey instruments were used in the project:

1) Initial face-to-face questionnaire

In Peru, the starting point was the ENAHO (National Household Survey) questionnaire. Step-wise regressions were done to select the set of questions that best predicted consumption. For the purposes of robustness, the regressions were also done with questions that best predicted income, which yielded the same results. A similar procedure was done in Honduras, using the latest household survey deployed by the Honduran Statistics Institute, except that only best predictors of income were chosen, because Honduras did not have a recent consumption aggregate.

The survey gathered information on households' demographics, household infrastructure, employment, remittances, income, accidents, food security, self-perceptions on poverty, Internet access and cellphones use.

2) Monthly questionnaires (SMS, IVR, CATI)

The questionnaires were worded exactly the same way, regardless of the mode, which meant short questions, since SMS is limited to 160 characters. A maximum of 10 questions had to be chosen for the monthly questionnaire. In addition, two questions sought to ensure the validity of the responses by testing if the respondent was a member of the household. Most questions were time-variant and each questionnaire was repeated to observe if answers changed over time. All questions related to variables that strongly affect household welfare and are likely to change in times of crisis.

3) Final face-to-face questionnaire

Gallup conducted face-to-face closing surveys among 700 panelists. The researchers asked about issues the respondets had with mobile phones and coverage during the test. Panelists were also asked what would motivate them to keep on participating in a project like this in the future.

The questionnaires were worded exactly the same way, regardless of the mode, which meant short questions, since SMS is limited to 160 characters, unlike IVR and CATI.

Response rate

In Honduras, 41% of recruited households failed to answer the first round of follow-up surveys. The attrition rate from the initial face-to-face interview to the end of panel study was 50%.

As part of the survey administration process Gallup implemented a number of mechanisms to maximize the response rate and panelist retention. The following strategies were applied to respondents who did not replay first time:

• The surveys were left open for responses for up to 2 weeks after the original transmission of the survey (from original call in the case of IVR and CATI).
• First reminder was sent within 72 hours of first attempt (SMS and IVR).
• Second reminder was sent within 144 hours of first attempt (SMS and IVR).
• Call backs were made within 72 and 144 hours of first attempt (CATI); or
• Up to 2 call backs were made per appointment with respondent (CATI).

Also, in order to minimize non-response, three types of incentives were given. First, households that did not own a mobile phone were provided one for free. Approximately 127 phones were donated in Honduras. Second, all communications between the interviewers and the households were free to the respondents. Finally, households were randomly assigned to one of three incentive levels: one-third of households received US$1 in free airtime for each questionnaire they answered, one-third received US$5 in free airtime, and one-third received no financial incentive (the control group).

Abstract

The main purpose of a Household Income and Expenditure Survey (HIES) was to present high quality and representative national household data on income and expenditure in order to update Consumer Price Index (CPI), improve statistics on National Accounts and measure poverty within the country.

The main objectives of this survey - update the weight of each expenditure item (from COICOP) and obtain weights for the revision of the Consumer Price Index (CPI) for Funafuti - provide data on the household sectors contribution to the National Accounts - design the structure of consumption for food secutiry - To provide information on the nature and distribution of household income, expenditure and food consumption patterns household living standard useful for planning purposes - To provide information on economic activity of men and women to study gender issues - To generate the income distribution for poverty analysis

The 2010 Household Income and Expenditure Survey (HIES) is the third HIES that was conducted by the Central Statistics Division since Tuvalu gained political independence in 1978.

This survey deals mostly with expenditure and income on the cash side and non cash side (gift, home production). Moreover, a lot of information are collected:

at a household level: - goods possession - description of the dwelling - water tank capacity - fruits and vegetables in the garden - livestock

at an individual level: - education level - employment - health

Geographic coverage

National Coverage: Funafuti and /Outer islands.

Analysis unit

• Household level
• Individual level

Universe

The scope of the 2010 Household Income and Expenditure Survey (HIES) was all occupied households in Tuvalu. Households are the sampling unit, defined as a group of people (related or not) who pool their money, and cook and eat together. It is not the physical structure (dwelling) in which people live. HIES covered all persons who were considered to be usual residents of private dwellings (must have been living in Tuvalu for a period of 12-months, or have intention to live in Tuvalu for a period of 12-months in order to be included in the survey). Usual residents who are temporary away are included as well (e.g., for work or a holiday).

All the private household are included in the sampling frame. In each household selected, the current resident are surveyed, and people who are usual resident but are currently away (work, health, holydays reasons, or border student for example. If the household had been residing in Tuvalu for less than one year: - but intend to reside more than 12 months => he is included - do not intend to reside more than 12 months => out of scope.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Tuvalu 2010 Household Income and Expenditure Survey (HIES) outputs breakdowns at the domain level which is Funafuti and Outer Islands. To achieve this, and to match the budget constraint, a third of the households were selected in both domains. It was decided that 33% (one third) sample was sufficient to achieve suitable levels of accuracy for key estimates in the survey. So the sample selection was spread proportionally across all the islands except Niulakita as it was considered too small. The selection method used is the simple random survey, meaning that within each domain households were directly selected from the population frame (which was the updated 2009 household listing). All islands were included in the selection except Niulakita that was excluded due to its remoteness, and size.

For selection purposes, in the outer island domain, each island was treated as a separate strata and independent samples were selected from each (one third). The strategy used was to list each dwelling on the island by their geographical position and run a systematic skip through the list to achieve the 33% sample. This approach assured that the sample would be spread out across each island as much as possible and thus more representative.

Population and sample counts of dwellings by islands for 2010 HIES Islands: -Nanumea: Population: 123; sample: 41 -Nanumaga: Population: 117; sample: 39 -Niutao: Population: 138; sample: 46 -Nui: Population: 141; sample: 47 -Vaitupu: Population: 298; sample: 100 -Nukufetau: Population: 141; sample: 47 -Nukulaelae: Population: 78; sample: 26 -Funafuti: Population: 791; sample: 254 -TOTAL: Population: 1827; sample: 600.

Mode of data collection

Face-to-face [f2f]

Research instrument

3 forms were used. Each question is writen in English and translated in Tuvaluan on the same version of the questionnaire. The questionnaire was highly based on the previous one (2004 survey).

Household Schedule This questionnaire, to be completed by interviewers, is used to collect information about the household composition, living conditions and is also the main form for collecting expenditure on goods and services purchased infrequently.

• composition of the household and demographic profile of each members
• dwelling information
• dwelling expenditure
• transport expenditure
• education expenditure
• health expenditure
• land and property expenditure
• household furnishing
• home appliances
• cultural and social payments
• holydays/travel costs
• Loans and saving
• clothing
• other major expenditure items

Individual Schedule There will be two individual schedules: - health and education - labor force (individual aged 15 and above) - employment activity and income (individual aged 15 and above): wages and salaries working own business agriculture and livestock fishing income from handicraft income from gambling small scale activies jobs in the last 12 months other income childreen income tobacco and alcohol use other activities seafarer

Diary (one diary per week, on a 2 weeks period, 2 diaries per household were required) The diaries are used to record all household expenditure and consumption over the two week diary keeping period. The diaries are to be filled in by the household members, with the assistance from interviewers when necessary. - All kind of expenses - Home production - food and drink (eaten by the household, given away, sold) - Goods taken from own business (consumed, given away) - Monetary gift (given away, received, winning from gambling) - Non monetary gift (given away, received, winning from gambling).

Cleaning operations

Consistency of the data: - each questionnaire was checked by the supervisor during and after the collection - before data entry, all the questionnaire were coded - the CSPRo data entry system included inconsistency checks which allow the National Statistics Office staff to point some errors and to correct them with imputation estimation from their own knowledge (no time for double entry), 4 data entry operators. 1. presence of all the form for each household 2. consistency of data within the questionnaire

at this stage, all the errors were corrected on the questionnaire and on the data entry system in the meantime.

• after data entry, the extreme amount of each questionnaire where selected in order to check their consistency. at this stage, all the inconsistency were corrected by imputation on CSPRO editing.

Response rate

The final response rates for the survey was very pleasing with an average rate of 97 per cent across all islands selected. The response rates were derived by dividing the number of fully responding households by the number of selected households in scope of the survey which weren't vacant.

Response rates for Tuvalu 2010 Household Income and Expenditure Survey (HIES): - Nanumea 100% - Nanumaga 100% - Niutao 98% - Nui 100% - Vaitupu 99% - Nukufetau 89% - Nukulaelae 100% - Funafuti 96%

As can be seen in the table, four of the islands managed a 100 per cent response, whereas only Nukufetau had a response rate of less than 90 per cent.

Further explanation of response rates can be located in the external resource entitled Tuvalu 2010 HIES Report Table 1.2.

Sampling error estimates

The quality of the results can be found in the report provided in this documentation.

Abstract

The rapid and massive dissemination of mobile phones in the developing world is creating new opportunities for the discipline of survey research. The World Bank is interested in leveraging mobile phone technology as a means of direct communication with poor households in the developing world in order to gather rapid feedback on the impact of economic crises and other events on the economy of such households.

The World Bank commissioned Gallup to conduct the Listening to LAC (L2L) pilot program, a research project aimed at testing the feasibility of mobile phone technology as a way of data collection for conducting quick turnaround, self-administered, longitudinal surveys among households in Peru and Honduras.

The project used face-to-face interviews as its benchmark, and included Short Message Service (SMS), Interactive Voice Response (IVR) and Computer Assisted Telephone Interviews (CATI) as test methods of data collection.

The pilot was designed in a way that allowed testing the response rates and the quality of data, while also providing information on the cost of collecting data using mobile phones. Researchers also evaluated if providing incentives affected panel attrition rates. The Honduras design was a test-retest design, which is closely related to the difference-in-difference methodology of experimental evaluation.

The random stratified multistage sampling technique was used to select a nationally representative sample of 1,500 households. During the initial face-to-face interviews, researchers gathered information on the socio-economic characteristics of households and recruited participants for follow-up research. Questions wording was the same in all modes of data collection.

In Honduras, after the initial face-to-face interviews, respondents were exposed to the remaining three methodologies according to a randomized scheme (three rotations, one methodology per week). Panelists in Honduras were surveyed for four and a half months, starting in February 2012.

In Peru, households were randomly assigned to a communication mode (SMS, IVR, CATI), which stayed constant for all rounds (waves) of the survey.

Geographic coverage

Peru and Honduras - Includes the entire national territory, with the exception of neighborhoods where access of interviewers is extremely difficult, due to lack of transportation infrastructure or for situations that threaten the physical integrity of the interviewers and supervisors (i.e. extremely high crime rate, warfare, etc.)

Analysis unit

• Households

Universe

All the households that exist in the neighborhoods of Honduras, as reported by the 2001 Census. Institutions such as military, religious or educational living quarters are not included in the universe.

Kind of data

Sample survey data [ssd]

Sampling procedure

Honduras

Honduras did not have an income oversample because the poverty rate is 60 percent, so oversampling 20 percent above the poverty rate would include a large portion of the middle class, which are not the most vulnerable in times of crisis.

The Honduras panel was built on a nationally representative sample of 1,500 households. The sample was drawn by means of a random, stratified, multistage design. The pilot used Gallup World Poll sampling frame.

Census-defined municipalities were classified into five strata according to population size: I. Municipalities with 500,000 to 999,000 inhabitants II. Municipalities with 100,000 to 499,000 inhabitants III. Municipalities with 50,000 to 99,000 inhabitants IV. Municipalities with 10,000 and 49,000 inhabitants V. Municipalities with less than 10,000 inhabitants

Interviews were then proportionally allocated to these five strata according to their share among the country's population.

• The first stage of the design consisted of a random selection of Primary Sampling Units (PSU's) within each of the five strata previously defined.

• In the second stage, in each PSU, one or more Secondary Sampling Units (SSU's) were then selected.

• Once SSU's were selected, interviewers were sent to the field to proceed with the third stage of the sample design, which consisted of selecting households using a systematic "random route" procedure. Interviewers started from the previously selected "random origin" and walked around the block in clockwise direction, selecting every third household on their right hand side. They were also trained to handle vacant, nonresponsive, non-cooperative households, as well as other failed attempts, in a systematic manner.

Peru

The Peru panel was built on a nationally representative sample of 1,500 households. The sample was based on the sampling frame for the National Household Survey (ENAHO) conducted by the Peruvian National Statistics Office (INEI) every three months.

In Peru, the sample selection was guided by the following criteria: (i) the sample should be representative nationally, and in urban and rural areas, and (ii) households close to poverty line should be oversampled because policy decisions in time of crises need to be especially mindful of the poor and vulnerable. For the purposes of this project, "close to poverty line" was defined as 40 percent of consumption distribution that symmetrically band the national poverty line: 20 percent above and 20 percent below. In 27 percent of Peruvian households monthly per capita consumption was below the moderate poverty line in 2010 (ENAHO).Those households whose monthly per capita consumption falls between 7 and 47 percent of the national distribution were oversampled.

The L2L sample frame comprises all the panel conglomerados from the fourth trimester of ENAHO 2010, or 281 conglomerados.

Detailed information about the sampling procedure is available in "Listening to LAC: Using Mobile Phones for High Frequency Data Collection, Final Report" (p. 65-69) and "The World Bank Listening to LAC (L2L) Pilot Project Sample Design for Peru."

Mode of data collection

Other [oth]

Research instrument

The following survey instruments were used in the project:

1) Initial face-to-face questionnaire

In Peru, the starting point was the ENAHO (National Household Survey) questionnaire. Step-wise regressions were done to select the set of questions that best predicted consumption. For the purposes of robustness, the regressions were also done with questions that best predicted income, which yielded the same results. A similar procedure was done in Honduras, using the latest household survey deployed by the Honduran Statistics Institute, except that only best predictors of income were chosen, because Honduras did not have a recent consumption aggregate.

The survey gathered information on households' demographics, household infrastructure, employment, remittances, income, accidents, food security, self-perceptions on poverty, Internet access and cellphones use.

2) Monthly questionnaires (SMS, IVR, CATI)

The questionnaires were worded exactly the same way, regardless of the mode, which meant short questions, since SMS is limited to 160 characters. A maximum of 10 questions had to be chosen for the monthly questionnaire. In addition, two questions sought to ensure the validity of the responses by testing if the respondent was a member of the household. Most questions were time-variant and each questionnaire was repeated to observe if answers changed over time. All questions related to variables that strongly affect household welfare and are likely to change in times of crisis.

3) Final face-to-face questionnaire

Gallup conducted face-to-face closing surveys among 700 panelists. The researchers asked about issues the respondets had with mobile phones and coverage during the test. Panelists were also asked what would motivate them to keep on participating in a project like this in the future.

The questionnaires were worded exactly the same way, regardless of the mode, which meant short questions, since SMS is limited to 160 characters, unlike IVR and CATI.

Response rate

In Honduras, 41% of recruited households failed to answer the first round of follow-up surveys. The attrition rate from the initial face-to-face interview to the end of panel study was 50%.

In Peru, 67 percent of recruited households failed to answer the first round of follow-up surveys. Attrition slightly increased with each wave of the survey (between 1 and 3 percentage points per wave), reaching 75 percent in wave 6.

As part of the survey administration process Gallup implemented a number of mechanisms to maximize the response rate and panelist retention. The following strategies were applied to respondents who did not replay first time:

• The surveys were left open for responses for up to 2 weeks after the original transmission of the survey (from original call in the case of IVR and CATI).
• First reminder was sent within 72 hours of first attempt (SMS and IVR).
• Second reminder was sent within 144 hours of first attempt (SMS and IVR).
• Call backs were made within 72 and 144 hours of first attempt (CATI); or
• Up to 2 call backs were made per appointment with respondent (CATI).

Also, in order to minimize non-response, three types of incentives were given. First, households that did not own a mobile phone were provided one for free. Approximately 127 phones were donated in Honduras, and approximately 200 phones were donated in Peru. Second, all communications between the interviewers and the households were free to the respondents. Finally, households were randomly assigned to one of three incentive levels: one-third of households received US$1 in free airtime for each questionnaire they answered, one-third received US$5 in free

Abstract

The Integrated Household Income and Expenditure Survey with Living Standards Measurement Survey 2002-2003 is one of the biggest national surveys carried out in accordance with an international methodology with technical and financial support from the World Bank and United Nations Development Programme.

Background This survey was developed in response to provide the picture of the current situation of poverty in Mongolia in relation to social and economic indicators and contribute toward implementation and progress on National Millennium Development Goals articulated in the National Millennium Development Report and monitoring of the Economic Growth Support and Poverty Reduction Strategy, as well as toward developing and designing future policies and actions. Also, the survey enriched the national database on poverty and contributed in improving the professional capacity of experts and professionals of the National Statistical Office of Mongolia.

Purpose Since the onset of the transition to a market economy of Mongolia our country the need to study changes in people's living standards in relation to household members' demographic situation, their education, health, employment and household engagement in private enterprises has become extremely important. With that purpose and with the support of the World Bank and the United Nations Development Programme, the National Statistical Office of Mongolia conducted the Integrated Household Income and Expenditure Survey with Living Standards Measurement Survey-like features between 2002 and 2003. In conjunction with LSMS household interviews the NSO also collected a price and a community questionnaire in each selected soum. The latter collected information on the quality of infrastructure, and basic education and health services.

Main importance of the survey is to provide policy makers and decision makers with realistic information about poverty and will become a resource for experts and researchers who are interested in studying poverty as well as social and economic issues of Mongolia.

In July 2003 the Government of Mongolia completed the Economic Growth and Poverty Reduction Strategy Paper in which the Government gave high priority to the fight against poverty. As part of that commitment this paper is a study that intends to monitor poverty and understand its main causes in order to provide policy-makers with useful information to improve pro-poor policies.

Content The Integrated HIES with LSMS design has the peculiarity of being a sub-sample of a larger survey, namely the Household Income and Expenditure Survey 2002. Instead of administering an independent consumption module, the Integrated HIES with LSMS 2002-2003 depends on the HIES 2002 information on household consumption expenditure. This is why the survey is referred as Integrated HIES with LSMS 2002-2003. This survey is the only source of information of income-poverty, and the questionnaire is designed to provide poverty estimates and a set of useful social indicators that can monitor more in general human development, as well as more specific issues on key sectors, such as health, education, and energy. And, the price and social survey, in conjunction with LSMS household interviews, collected information on the quality of infrastructure, and basic education and health services of each selected soum.

HIES - food expenditure and consumption, non-food expenditure, other expense, income LSMS - general information, household roster, housing, education, employment, health, fertility, migration, agriculture, livestock, non-farm enterprises, other souces of income, savings and loans, remittances, durable goods, energy PRICE SURVEY - prices of household consumer goods and services SOCIAL SURVEY - population and households, economy and infrastructure, education, health, agriculture and livestock, and non-agricultural business

Survey results The final report of this survey has main results on key poverty indicators, used internationally, as they relate to various social sectors. Its annexes contain information regarding the consumption structure, poverty lines along with the methodology used, as well as some statistical indicators.

The main contributions of this survey report are: - new poverty estimates based on the latest available household survey, the Integrated HIES with LSMS 2002-2003 - the implementation of appropriate, and internationally accepted, methodologies in the calculation of poverty and its analysis (these methodologies may constitute a reference for the analysis of future surveys) - a 'poverty profile' that describes the main characteristics of poverty

The first section of the report provides information on the Mongolian economic background, and presents the basic poverty measures that are linked to the economic performance to offer an indication of what happened to poverty and inequality in recent years. A second section goes in much more detail in generating and describing the poverty profile, in particular looking at the geographical distribution of poverty, poverty and its correlation with household demographic characteristics, characteristics of the household head, employment, and assets. A final section looks at poverty and social sectors and investigates various aspects of education, health and safety nets. The report contains also a number of useful, but more technical appendixes with information about the HIES-LSMS 2002-2003 (sample design and data quality), on the methodology used to construct the basic welfare indicator, and set the poverty line, some sensitivity analysis, and additional statistical information.

Geographic coverage

The survey is nationally representative and covers the whole of Mongolia.

Analysis unit

• Household (defined as a group of persons who usually live and eat together)
• Household member (defined as members of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)
• Selected soums (for collecting prices of household consumer goods and services and information on quality of infrastructure, basic education, health services and so on)

Universe

The survey covered selected households and all members of the households (usual residents). And the price and social surveys covered all selected soums.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Integrated HIES with LSMS 2002-2003 households are a subset of the household interviewed for the HIES 2002. One third of the HIES 2002 households were contacted again and interviewed on the LSMS topics. The subset was equally distributed among the four quarters.

The HIES 2002, and consequently the Integrated HIES with LSMS 2002-2003, used the 2000 Census as sample frame. 1,248 enumerations areas were part of the sample, which is a two-stage stratified random sample. The strata, or domains of estimation, are four: Ulaanbaatar, Aimag capitals and small towns, Soum centres, and Countryside. At a first stage a number of Primary Sampling Units (PSUs) were selected from each stratum. In the selected PSUs enumerators listed all the households residing in the area, and in a second stage households were randomly selected from the list of households identified in that PSU (10 households were selected in urban areas and 8 households in rural areas).

It should be noted that non-response case of households once selected for the survey exerts unfavorable influence on the representativeness of the survey. Therefore an enumerator should take every step to avoid that. To obtain true and timely survey results a proper agreement should be reached with a selected household before a survey starts. One of the main reasons of non-response is that an enumerator doesn't meet with the household members who are able to give the required information. An enumerator should visit a household at least 3 times within the given period to take the questionnaire.

Another common reason is that a household refuses to participate in the survey. In this case an enumerator should explain the purpose of the survey again, explain that the private data will be kept strictly confidential according to the corresponding law. If necessary an enumerator can ask local statistical division or local administration for the help. However this practice is very seldom.

If there is no possibility to take the questionnaires from the selected households due to weather conditions or disasters, reserved households with numbers 11, 12, 13 respectively from the list provided by the NSO should replace the omitted ones. However the reasons of replacements are to be declared in detail on the form.

Sampling deviation

At the planning stage the time lag between the HIES and LSMS interviews was expected to be relatively short. However, for various reasons it is on average of about 9 months, and for some households more than one year. Households interviewed in the first and second quarter of 2002 were generally re-interviewed in March and April 2003, while households of the third and fourth quarter of 2002 were re-interviewed in May, June and July of 2003. The considerable time lag between HIES and LSMS interviews was the main responsible for a considerable loss of households in the LSMS sample, households that could not be easily relocated and therefore re-interviewed. Due also to some incomplete questionnaires, the number of households that were used for the final poverty analysis is 3,308.

Mode of data collection

Face-to-face [f2f]

Research instrument

A

Abstract

In 2010, the EU-SILC instrument covered 32 countries, that is, all EU Member States plus Iceland, Turkey, Norway, Switzerland and Croatia. EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labor, education and health observations only apply to persons aged 16 and over. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

The 6th version of the 2010 Cross-Sectional User Database as released in July 2015 is documented here.

Geographic coverage

The survey covers following countries: Austria; Belgium; Bulgaria; Croatia; Cyprus; Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Spain; Ireland; Italy; Latvia; Lithuania; Luxembourg; Hungary; Malta; Netherlands; Poland; Portugal; Romania; Slovenia; Slovakia; Sweden; United Kingdom; Iceland; Norway; Turkey; Switzerland

Small parts of the national territory amounting to no more than 2% of the national population and the national territories listed below may be excluded from EU-SILC: France - French Overseas Departments and territories; Netherlands - The West Frisian Islands with the exception of Texel; Ireland - All offshore islands with the exception of Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia; United kingdom - Scotland north of the Caledonian Canal, the Scilly Islands.

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the crosssectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Related Materials.

Mode of data collection

Mixed

This data collection is part of a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals.

There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. These include age, sex, race, ethnic origin, marital status, household relationship, education, and veteran status. Limited data are provided on housing unit characteristics such as units in structure, tenure, access, and complete kitchen facilities. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, and participation in various cash and noncash benefit programs for each month of the four- month reference period. Data for employed persons include number of hours and weeks worked, earnings, and weeks without a job. Nonworkers are classified as unemployed or not in the labor force. In addition to providing income data associated with labor force activity, the core questions cover nearly 50 other types of income. Core data also include postsecondary school attendance, public or private subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to link individuals to the core files.

1. The Wave 1 Topical Module covers recipiency and employment history.

2. The Wave 2 Topical Module includes work disability, education and training, marital, migration, and fertility histories, and household relationships.

3. The Wave 3 Topical Module covers medical expenses and utilization of health care, work-related expenses and child support, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earning accounts, rental properties, stocks and mutual fund shares, mortgages, and other assets.

4. The Wave 4 Topical Module covers work schedule, taxes, child care, and annual income and retirement accounts.

5. Data in the Wave 5 Topical Module describe child support agreements, school enrollment and financing, support for non-household members, adult and child disability, and employer-provided health benefits.

6. The Wave 6 Topical Module covers medical expenses and utilization of health care, work related expenses, child support paid and child care poverty, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earning accounts, rental properties, stock and mutual fund shares, mortgages, and other financial investments.

7. The Wave 7 Topical Module covers informal caregiving, children's well-being, and annual income and retirement accounts.

8. The Wave 8 Topical Module and Wave 8 Welfare Reform Topical Module cover child support agreements, support for nonhousehold members, adult disability, child disability, adult well-being, and welfare reform.

9. The Wave 9 Topical Module covers medical expenses and utilization of heath care (adults and children), work related expenses, child support paid and child care poverty, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earnings accounts, rental properties, stocks and mutual fund shares mortgages, and other financial investments

As of 2024, around **** million people in South Africa are living in extreme poverty, with the poverty threshold at **** U.S. dollars daily. This means that ******* more people were pushed into poverty compared to 2023. Moreover, the headcount was forecast to increase in the coming years. By 2030, over **** million South Africans will live on a maximum of **** U.S. dollars per day. Who is considered poor domestically? Poverty is measured using several matrices. For example, local authorities tend to rely on the national poverty line, assessed based on consumer price indices (CPI) of a basket of goods of food and non-food components. In 2023, the domestic poverty line in South Africa stood at ***** South African rand per month (around ***** U.S. dollars per month). According to a survey, social inequality and poverty worried a significant share of the South African respondents. As of September 2024, some ** percent of the respondents reported that they were worried about the state of poverty and unequal income distribution in the country.   Eastern Cape residents received more grants South Africa’s labor market has struggled to absorb the country’s population. In 2023, almost a third of the economically active population was unemployed. Local authorities employ relief assistance and social grants in an attempt to reduce poverty and assist poor individuals. In 2023, almost ** percent of South African households received state support, with the majority share benefiting in the Eastern Cape.

This data collection is part of a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. These include age, sex, race, ethnic origin, marital status, household relationship, education, and veteran status. Limited data are provided on housing unit characteristics such as units in structure, tenure, access, and complete kitchen facilities. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, and participation in various cash and noncash benefit programs for each month of the four- month reference period. Data for employed persons include number of hours and weeks worked, earnings, and weeks without a job. Nonworkers are classified as unemployed or not in the labor force. In addition to providing income data associated with labor force activity, the core questions cover nearly 50 other types of income. Core data also include postsecondary school attendance, public or private subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to link individuals to the core files. The Wave 1 Topical Module covers recipiency and employment history. The Wave 2 Topical Module includes work disability, education and training, marital, migration, and fertility histories, and household relationships. The Wave 3 Topical Module covers medical expenses and utilization of health care, work-related expenses and child support, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earning accounts, rental properties, stocks and mutual fund shares, mortgages, and other assets. The Wave 4 Topical Module covers work schedule, taxes, child care, and annual income and retirement accounts. Data in the Wave 5 Topical Module describe child support agreements, school enrollment and financing, support for non-household members, adult and child disability, and employer-provided health benefits. The Wave 6 Topical Module covers medical expenses and utilization of health care, work related expenses, child support paid and child care poverty, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earning accounts, rental properties, stock and mutual fund shares, mortgages, and other financial investments. The Wave 7 Topical Module covers informal caregiving, children's well-being, and annual income and retirement accounts. The Wave 8 Topical Module and Wave 8 Welfare Reform Topical Module cover child support agreements, support for nonhousehold members, adult disability, child disability, adult well-being, and welfare reform. The Wave 9 Topical Module covers medical expenses and utilization of heath care (adults and children), work related expenses, child support paid and child care poverty, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earnings accounts, rental properties, stocks and mutual fund shares mortgages, and other financial investments (Source: downloaded from ICPSR 7/13/10)

As of 2022, the poverty rate was projected at **** percent in Egypt. This was nearly *** percentage points less than the year before. Overall, from 2018 onwards, the poverty rate dropped to **** percent in 2019, before increasing again to about ** percent in 2020. Since 2020, projected poverty rates have followed a declining trend. They are expected to decrease further in 2023. The outbreak of the coronavirus (COVID-19) pandemic contributed to the increase of the poverty rate in 2020. Adjusted national poverty lines National poverty lines are calculated based on consumption patterns of households in the country and are therefore adjustable over the years. Egypt’s national poverty line stood at ****** Egyptian pounds (comparable to ****** U.S. dollars) annually as of 2019/2020. This was an increase from ***** Egyptian pounds (****** U.S. dollars) ten years prior. In November 2016, the Central Bank of Egypt (CBE) declared that it fully floated the Egyptian pound, causing the currency devaluation.   Poverty more prevalent among larger households Poverty rates in the country were higher in households with more individuals. In households with *** or more members, the rate was as high as **** percent in 2019/2020. On the other hand, the poverty rate was significantly lower among households with *** to ***** members. Moreover, Rural Egypt had a higher share of population considered poor compared to Urban Egypt. In fact, in its rural areas in Upper Egypt, the poverty rate reached nearly ** percent.   

Abstract

The objectives of the Smallholder Household Survey in Mozambique were to: • Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Mozambique according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Geographic coverage

National coverage

Analysis unit

Households and individual household members

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens; AND 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

The CGAP smallholder household survey in Mozambique is a nationally-representative survey with a target sample size of 3,000 smallholder households. The sample was designed to provide reliable survey estimates at the national level and for the following regions: 1. North region, comprised of the provinces of Niassa, Cabo Delgado, and Nampula; 2. Centre region, comprised of Zambezia, Tete, Maica, and Sofala, Manica; and 3. South region, consisting of Inhambane, Maputo Province, Maputo City and Gaza.

Sampling Frame

The sampling frame for the smallholder household survey is the 2009-2010 Census of Agriculture and Livestock (Censo Agro-Pecuário, CAP II) conducted by the Mozambique National Statistical Office (INE) and based on the 2007 Census of Population and Housing (2007 RGPH). CAP II is a large sample that was designed to be representative at the district level and its sample of enumeration areas (EAs) is considered as the "master sample" for the national agricultural surveys. EAs with less than 15 agricultural households (mostly in urban areas) were excluded from the sampling frame for CAP II.

Sample Allocation and Selection

In order to take non-response into account, the target sample size was increased to 3,158 households assuming a household non-response rate of 5% observed in similar national households. The total sample size was first allocated to the three regions based on the number of agricultural households. Within each region, the resulting sample was further distributed proportionally to urban and rural areas.

The sample for the smallholder survey is a stratified multistage sample. Stratification was achieved by separating urban and rural areas within each region. Since the CAP II master sample that was used as the sampling frame for the survey is stratified by district, rural and urban areas, the rural strata of the individual districts for the CAP II master sample were collapsed up to the province level, and the same for the urban strata within each province. However, the district was still used as a sorting variable in order to provide implicit stratification by district.

At the first sampling stage the CAP II sample EAs were selected systematically with PPS within each district, rural and urban stratum, where the measure of size was the number of agricultural households in the census frame. In general if the EAs are selected with PPS at the first sampling stage, a subsample of EAs would be selected with equal probability within each stratum. However, in the case of the smallholder survey, the district strata were collapsed to the province level (separately for the rural and urban strata). Within each province the weights in CAP II vary by district, rural/urban stratum, by a factor of Mdh/ndh, where Mdh is the total number of agricultural households in the CAP II sampling frame for stratum (rural/urban) h in district d (from the RGPH 2007), and ndh is the number of sample EAs selected for CAP II in stratum h of district d.

Therefore in order to stabilize the weights within the rural and urban stratum of each province for the smallholder survey, the subsample of EAs included in the smallholder sample were selected within each stratum with probability proportional to the measure Mdh/ndh.

A household listing operation was carried out in all selected EAs to identify smallholder households and to provide a frame for the selection of 15 households per selected EA at the third stage. Households were selected in each EA with equal probability. In each selected household, the household questionnaire was administered to the head of the household, the spouse or any knowledgeable adult household member. The multiple respondent questionnaire was administered to all adult members in each selected household. In addition, in each selected household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Building on secondary research on the smallholder sector and discussions with stakeholders, the design process for the survey instrument began. This process involved defining the end goal of the research by: • Drawing from existing survey instruments; • Considering the objectives and needs of the project; • Accounting for stakeholder interests and feedback; • Learning from the ongoing financial diaries in country; and, • Building from a series of focus groups conducted early on in the study.

Using this foundation, a framework for the survey instrument was developed to share with stakeholders and capture all the necessary elements of a smallholder household. The framework consisted of five main subject areas: (i) demographics, (ii) household economics, (iii) agricultural practices, (iv) mobile phones, and (v) financial services.

In order to capture the complexity inside smallholder households, the smallholder household survey was divided into three questionnaires: the Household questionnaire, the Multiple Respondent questionnaire and the Single respondent questionnaire.

The household questionnaire collected information on: • Basic household members’ individual characteristics (age, gender, education attainment, schooling status, relationship with the household head) • Whether each household member contributes to the household income or participates in the household’s agricultural activities. This information was later used to identify all household members eligible for the other two questionnaires.
• Household assets and dwelling characteristics

Both the Multiple and Single Respondent questionnaires collected different information on: • Agricultural practices: farm information such as size, crop types, livestock, decision-making, farming associations and markets • Household economics: employment, income, expenses, shocks, borrowing and saving habits, and investments

In addition, the Single respondent questionnaire collected information on: • Mobile phones: attitudes toward phones, usage, access, ownership, desire and importance • Financial services: attitudes towards financial products and services such as banking and mobile money, including ownership, usage, access and importance.

Before the start of fieldwork, all three questionnaires were pretested in all languages to make sure that the questions were clear and could be understood by respondents. The pretest took place 19 - 24 June 2015 in Maputo, Mozambique and 17 - 20 July 2015 in Ihambane, Nampula and Tete, Mozambique. In total, the pretest covered 79 households. At the end of the pretest, debriefing sessions were held with the pretest field staff and the questionnaires were modified based on the observations from the pretest. Following the finalization of questionnaires, a script was developed to support data collection on smart phones. The script was tested and validated before its use in the field.

Cleaning operations

During data collection, InterMedia received a weekly partial SPSS data file from the field which was analyzed for quality control and used to provide timely feedback to field staff while they were still on the ground. The partial data files were also used to check and validate the structure of the data file. The full data file was also checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible.

Response rate

The user guide includes household and individual response rates for the CGAP smallholder household survey in Mozambique. A total of 3,041 households were selected for the sample, of which 2,782 were found to be occupied during data collection. Of these, 2,574 were successfully interviewed, yielding a household response rate of 92.5 percent.

In the interviewed households 5,502 eligible household members were identified for individual interviews. Completed interviews were conducted for 4,456 yielding a response rate of 81.0 percent for the Multiple Respondent questionnaire.

Among the 2,574 selected for the Single Respondent questionnaire, 2,209 were successfully interviewed corresponding to a response rate of 85.8 percent.

Sampling error estimates

The sample design for the

Abstract

Over the past decade, Albania has been undergoing a transition toward a market economy and a more open society. It has faced severe internal and external challenges, such as lack of basic infrastructure, rapid collapse of output and inflation rise after the collapse of the communist regime, turmoil during the 1997 pyramid crisis, and social and economic instability because of the 1999 Kosovo crisis. Despite these shocks, Albanian economy has recovered from a very low income level through a sustained growth during the past few years, even though it remains one of the poorest countries in Europe, with GDP per capita at around 1,300$. Based on the Living Standard Measurement Study (LSMS) 2002 survey data (wave 1, henceforth), for the first time in Albania INSTAT has computed an absolute poverty line on a nationally representative poverty survey at household level. Based on this welfare measure, one quarter (25.4 percent) of the Albanian population, or close to 790,000 individuals, were defined as poor in 2002. The distribution of poverty is also disproportionately rural, as 68 percent of the poor are in rural areas, against 32 percent in urban areas (as compared to a total urban population well over 40 percent). These estimates are quite sensitive to the choice of the poverty line, as there are a large number of households clustered around the poverty line. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood. The availability of a nationally representative survey is crucial as the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) -the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS) - drew attention, once again, to the need for accurately measuring household welfare according to well-accepted standards, and for monitoring these trends on a regular basis. This target is well-achieved by drawing information over time on a panel component of LSMS 2002 households, namely the Albanian Panel Survey (APS), conducted in 2003 and 2004. An increasing attention to the policies aimed at achieving the Millennium Development Goals (MDGs) is paid by the National Parliament of Albania, recently witnessed by the resolution approved in July 2003, where it pushes “… the total commitment of both state structures and civil society to achieve the MDGs in Albania by 2015”. The path towards a sustained growth is constantly monitored through the National Reports on Progress toward Achieving the MDGs, which involves a close collaboration of the UN with the national institutions, led by the National Strategy for Social and Economic Development (NSSED) Department of the Ministry of Finance. Also, in the process leading to the Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyse on a regular basis information it needs to inform policy-makers. In its first phase (2001-2006), this monitoring system will include the following data collection instruments:

(i) Population and Housing Census (ii) Living Standards Measurement Surveys every 3 years (iii) Annual panel surveys.

The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (APS 2003, 2004 and 2006), drawing heavily on the 2001 census information. Here our target is to illustrate the main characteristics of the APS 2004 data with reference to the LSMS. The survey work was undertaken by the Living Standards Unit of INSTAT, with the technical assistance of the World Bank.

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE DESIGN

Panel sample, with LSMS 2002 and 2004 The APS 2004 collects information on 1,797 valid observations at household level and 7,476 at individual level. The sample of the second and third waves of the panel (APS) has been selected from the LSMS 2002 in order to be representative of Albanian households and individuals at national level. The LSMS 2002 differs from the APS 2003 and 2004 in that the former is designed to be representative at regional level (Mountain, Central, Coastal and Tirana) as well as for urban and rural domains, while the latter are for last domains only (urban and rural) LSMS 2002 sample design The LSMS is based on a probability sample of housing units (HUs) within the 16 strata of the sampling frame. It is divided in three regions: Coastal, Central, and Mountain Area. In addition, urban areas of Tirana are also considered as a separate region/stratum. The three regions are further stratified in major cities (the most important cities in the region), other urban (other cities in the region), and rural. The city of Tirana and its suburbs have been implicitly stratified to improve the efficiency of the sample design. Each stratum has been divided in Enumeration Area (EA), in accordance with the 2001 Census data, and each Primary Sampling Unit (PSU) selected with probabilities proportional to the number of occupied HUs in the EA. Every EA includes occupied and unoccupied HUs. Occupied rather than total units have been used because of the large number of empty dwellings registered in the Census data. The Housing Unit, defined as the space occupied by one household, is taken as sampling unit because is more permanent and easier to identify compared to the household. 10 EAs for each major city (75 for Tirana) and 65 EAs for each rural region -with the exception of the mountain area which is over-represented (75 EAs)- are selected. 8 households, plus 4 eventual substitutes, have been systematically selected in each EAs. As the LSMS consists of 450 EAs, total sample size is 3,600 households.

(b) STRATIFICATION

The panel component selected from the LSMS is designed to provide a nationally representative sample of households and individuals within Albania. It consists of roughly half of the households in the 2002 LSMS, interviewed both in 2003 and 2004. Contrarily to what done for the LSMS, no over-sampling in the Mountain Area has been performed for the panel survey. The sample is designed to minimize the variability in households' selection probabilities. It ensures national representativeness by matching the sample distribution across strata with the population distribution drawn from 2001 Census data. In Table 3 the ex-ante sampling scheme of the 2003-2004 APS is shown. Compared to the LSMS design, statistical precision has improved. Under equal stratum population variances hypothesis, sample design effects are expected to be around 1.02, compared to the 1.28 of the LSMS sample. Moreover, further precision is obtained by keeping all 450 EAs of LSMS in the panel sample, thus reducing the eventual bias due to clustering because of new design. Finally, the panel survey has a number of peculiar features that should be considered when using the data. The sample is designed to focus on individuals, who have been also traced when moving from the original household to a new one. This possibility represents the only way a household can enter the panel sample if it has not been already interviewed in the wave 1 (or in wave 2 for the APS 2004). If an original survey member (OSM) moves to a new household, his/her old and new household -and their members- are both included in the panel sample. Though a moved OSM will be present in the roster of both sampled households, he/she is a valid member only in the new one. In the old household he/she is considered as "moved away", hence not a valid member. This might generate some confusion. Three modalities exist to classify an individual in the third wave. First, when he/she is an OSM, that is a respondent interviewed both in wave 1 and 2. Second, when he is a re-joiner from 2002, that is an OSM not interviewed in 2003 (i.e. because temporarily absent) who returns in 2004. Third, when he/she is a new member, whenever he/she is a newborn of an original household, a member joined by an OSM or a person who co-resides with an original survey household. So, the APS is an indefinite life panel study, without replacement by drawing new sample units. From wave 2, only individuals aged 15 years and over are considered valid members, hence eligible for the interview. Individuals moved out of Albania are not accounted as valid for this survey year, though they are still eligible for future waves.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

A first data cleaning took place in Albania and implemented by INSTAT in collaboration with ISER and Government of Albania consultants. The cleaning process has involved following activities: 1. defining data checking routines and writing the syntax code of the cleaning programs; 2. generating lists of outliers and inconsistencies for each module to be checked against paper questionnaires; During the first few days, data cleaning operators have been working on the Export Procedure of the Data Entry Program to check if data export succeeded and to finalize the English version of the dictionaries and error messages. Some changes were made to the Export Procedure due to a problem on the “Agriculturea2” file conversion and to the

Abstract

EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Longitudinal data is limited to income information and a limited set of critical qualitative, non-monetary variables of deprivation, aimed at identifying the incidence and dynamic processes of persistence of poverty and social exclusion among subgroups in the population. The longitudinal component is also more limited in sample size compared to the primary, cross-sectional component. Furthermore, for any given set of individuals, microlevel changes are followed up only for a limited duration, such as a period of four years.

For both the cross-sectional and longitudinal components, all household and personal data are linkable. Furthermore, modules providing updated information in the field of social exclusion is included starting from 2005.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labour, education and health observations only apply to persons 16 and older. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

This is the 5th release of 2010 Longitudinal user database as published by EUROSTAT in September 2014.

Geographic coverage

National

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the cross-sectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Mode of data collection

Mixed

The share of people considered vulnerable due to a lack of access to health services in Mexico amounted to more than one third of the country's population in 2022. In that year, it was estimated that 39.1 percent of the Mexican population suffered vulnerabilities for this reason. The situation has worsened since 2016, when only 15.5 percent of the population were considered to be facing a lack of access to health services.

Abstract

South African policymakers are endeavouring to ensure that the poor have better access to financial services. However, a lack of understanding of the financial needs of poor households impedes a broad strategy to attend to this need.
The Financial Diaries study addresses this knowledge gap by examining financial management in rural and urban households. The study is a year-long household survey based on fortnightly interviews in Diepsloot (Gauteng), Langa (Western Cape) and Lugangeni (Eastern Cape). In total, 160 households were involved in this pioneering study which promises to offer important insights into how poor people manage their money as well as the context in which poor people make financial decisions. The study paints a rich picture of the texture of financial markets in townships, highlighting the prevalence of informal financial products, the role of survivalist business and the contribution made by social grants. The Financial Diaries dataset includes highly detailed, daily cash flow data on income, expenditure and financial flows on both a household and individual basis.

Geographic coverage

Langa in Cape Town, Diepsloot in Johannesburg and Lugangeni, a rural village in the Eastern Cape

Analysis unit

Units of analysis in the Financial Diaries Study 2003-2004 include households and individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

To create the sampling frame for the Financial Diaries, the researchers echoed the method used in the Rutherford (2002) and Ruthven (2002), a participatory wealth ranking (PWR). Within South Africa, the participatory wealth ranking method is used by the Small Enterprise Foundation (SEF), a prominent NGO microlender based in the rural Limpopo Province. Simanowitz (1999) compared the PWR method to the Visual Indicator of Poverty (VIP) and found that the VIP test was seen to be at best 70% consistent with the PWR tests. At times one third of the list of households that were defined as the poorest by the VIP test was actually some of the richest according to the PWR. The PWR method was also implicitly assessed in van der Ruit, May and Roberts (2001) by comparing it to the Principle Components Analysis (PCA) used by CGAP as a means to assess client poverty. They found that three quarters of those defined as poor by the PCA were also defined as poor by the PWR. We closely followed the SEF manual to conduct our wealth rankings, and consulted with SEF on adapting the method to urban areas.

The first step is to consult with community leaders and ask how they would divide their community. Within each type of areas, representative neighbourhoods of about 100 households each were randomly chosen. Townships in South Africa are organised by street - with each street or zone having its own street committee. The street committees are meant to know everyone on their street and to serve as stewards of all activity within the street. Each street committee in each area was invited to a central meeting and asked to map their area and give a roster of household names. Following the mapping, each area was visited and the maps and rosters were checked by going door to door with the street committee.

Two references groups were then selected from the street committee and senior members of the community with between four and eight people in each reference group. Each reference group was first asked to indicate how they define a poor household versus those that are well off. This discussion had a dual purpose. First, it relayed information about what each community believes is rich or poor. Second, it started the reference group thinking about which households belong under which heading.

Following this discussion, each reference group then ranked each household in the neighbourhood according to their perceived wealth. The SEF methodology of wealth ranking is de-normalised in that reference groups are invited to put households into as many different wealth piles as they feel in appropriate. Only households that are known by both reference groups were kept in the sample.

The SEF guidelines were used to assign a score to each household in a particular pile. The scores were created by dividing 100 by the number of piles multiplied by the level of the pile. This means that if the poorest pile was number 1, then every household in the pile was assigned a score of 100, representing 100% poverty. If the wealthiest pile was pile number 6, then every household in that pile received a score of 16.7 and every household in pile 5 received a score of 33.3. An average score for both reference groups was taken for the distribution.

One way of assessing how good the results are is to analyse how consistent the rankings were between the two reference groups. According to the SEF methodology, a result is consistent if the scores between the two reference groups have no more than a 25 points difference. A result is inconsistent if the difference between the scores is between 26 and 50 points while a result is unreliable is the difference between the scores is above 50 points. SEF uses both consistent and inconsistent rankings, as long as they use the average across two reference groups - this would mean that 91% of the sample could be used. However, because only used two reference groups were used, only the consistent household for the final sample selection was considered.

To test this further,the number of times that the reference groups put a household in the exact same category was counted. The extent of agreement at either end of the wealth spectrum between the two reference groups was also assessed. This result would be unbiased by how many categories the reference groups put households into.

Following the example used in India and Bangladesh, the sample was divided into three different wealth categories depending on the household's overall score. Making a distinction between three different categories of wealth allowed the following of a similar ranking of wealth to Bangladesh and India, but also it kept the sample from being over-stratified. A sample of 60 households each was then drawn randomly from each area. To draw the sample based on a proportion representation of each wealth ranking within the population would likely leave the sample lacking in wealthier households of some rankings to draw conclusions. Therefore the researchers drew equally from each ranking.

Mode of data collection

Face-to-face [f2f]

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling

UNITS IDENTIFIED: - Dwellings: No - Vacant units: Yes - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: Any independent premises within the total installation that has been equipped to lodge persons and permits them to reside there for many reasons (they are watchpersons or guards of an industry for example). - Group quarters: Those places, buildings and houses in which the sick, police, prisoners for various crimes, young or children delinquents, workers, students, religious persons, the elderly or other groups that carry out or live together under the same roof. These places, buildings or houses in which groups of persons live without family ties between them, or that is, who being NON FAMILY groups, have been designated by the government, by a private company or other institution, to resolve problems or social necessities like health, discipline, security, social adaptation, work in places far from the family dwelling, old age, being orphaned, poverty, study or religious life, etc.

Universe

All live individuals at midnight June 25, 1995

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics and Censuses

SAMPLE DESIGN: Systematic sample of every 10th household with a random start, drawn by the Minnesota Population Center

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 435,728

Mode of data collection

Face-to-face [f2f]

Research instrument

A single enumeration form requested information on the dwelling and household, and a second enumeration form requested information of the individuals.

Abstract

The rapid and massive dissemination of mobile phones in the developing world is creating new opportunities for the discipline of survey research. The World Bank is interested in leveraging mobile phone technology as a means of direct communication with poor households in the developing world in order to gather rapid feedback on the impact of economic crises and other events on the economy of such households.

The World Bank commissioned Gallup to conduct the Listening to LAC (L2L) pilot program, a research project aimed at testing the feasibility of mobile phone technology as a way of data collection for conducting quick turnaround, self-administered, longitudinal surveys among households in Peru and Honduras.

The project used face-to-face interviews as its benchmark, and included Short Message Service (SMS), Interactive Voice Response (IVR) and Computer Assisted Telephone Interviews (CATI) as test methods of data collection.

The pilot was designed in a way that allowed testing the response rates and the quality of data, while also providing information on the cost of collecting data using mobile phones. Researchers also evaluated if providing incentives affected panel attrition rates.

The random stratified multistage sampling technique was used to select a nationally representative sample of 1,500 households. During the initial face-to-face interviews, researchers gathered information on the socio-economic characteristics of households and recruited participants for follow-up research. Questions wording was the same in all modes of data collection.

In Peru, households were randomly assigned to a communication mode (SMS, IVR, CATI), which stayed constant for all rounds (waves) of the survey.

Geographic coverage

Includes the entire national territory, with the exception of neighborhoods where access of interviewers is extremely difficult, due to lack of transportation infrastructure or for situations that threaten the physical integrity of the interviewers and supervisors (i.e. extremely high crime rate, warfare, etc.)

Analysis unit

• Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The Peru panel was built on a nationally representative sample of 1,500 households. The sample was based on the sampling frame for the National Household Survey (ENAHO) conducted by the Peruvian National Statistics Office (INEI) every three months.

In Peru, the sample selection was guided by the following criteria: (i) the sample should be representative nationally, and in urban and rural areas, and (ii) households close to poverty line should be oversampled because policy decisions in time of crises need to be especially mindful of the poor and vulnerable. For the purposes of this project, "close to poverty line" was defined as 40 percent of consumption distribution that symmetrically band the national poverty line: 20 percent above and 20 percent below. In 27 percent of Peruvian households monthly per capita consumption was below the moderate poverty line in 2010 (ENAHO).Those households whose monthly per capita consumption falls between 7 and 47 percent of the national distribution were oversampled.

The L2L sample frame comprises all the panel conglomerados from the fourth trimester of ENAHO 2010, or 281 conglomerados.

Detailed information about the sampling procedure is available in "Listening to LAC: Using Mobile Phones for High Frequency Data Collection, Final Report" (p. 65-69) and "The World Bank Listening to LAC (L2L) Pilot Project Sample Design for Peru."

Sampling deviation

A number of restive communities in Peru did not allow Gallup's interviewers to enter the area. Where possible, these were replaced following INEI's standard methodology. When confronted with a problem in a particular location, INEI moves to the next "Centro Poblado" in the same "Conglomerado."

Mode of data collection

Other [oth]

Research instrument

The following survey instruments were used in the project:

1) Initial face-to-face questionnaire

In Peru, the starting point was the ENAHO (National Household Survey) questionnaire. Step-wise regressions were done to select the set of questions that best predicted consumption. For the purposes of robustness, the regressions were also done with questions that best predicted income, which yielded the same results.

The survey gathered information on households' demographics, household infrastructure, employment, remittances, income, accidents, food security, self-perceptions on poverty, Internet access and cellphones use.

2) Monthly questionnaires (SMS, IVR, CATI)

The questionnaires were worded exactly the same way, regardless of the mode, which meant short questions, since SMS is limited to 160 characters. A maximum of 10 questions had to be chosen for the monthly questionnaire. In addition, two questions sought to ensure the validity of the responses by testing if the respondent was a member of the household. Most questions were time-variant and each questionnaire was repeated to observe if answers changed over time. All questions related to variables that strongly affect household welfare and are likely to change in times of crisis.

A maximum of 10 questions was chosen for the monthly questionnaire. In addition, two questions sought to ensure the validity of the responses by testing if the respondent was a member of the household. To accomplish this, the first two questions in each monthly questionnaire asked the respondent for their gender and year of birth, and the answers were compared to the household roster obtained during the face-to-face interview.

3) Final face-to-face questionnaire

Gallup conducted face-to-face closing surveys among 700 panelists. The researchers asked about issues the respondets had with mobile phones and coverage during the test. Panelists were also asked what would motivate them to keep on participating in a project like this in the future.

Response rate

In Peru, 67 percent of recruited households failed to answer the first round of follow-up surveys. Attrition slightly increased with each wave of the survey (between 1 and 3 percentage points per wave), reaching 75 percent in wave 6.

As part of the survey administration process Gallup implemented a number of mechanisms to maximize the response rate and panelist retention. The following strategies were applied to respondents who did not replay first time:

• The surveys were left open for responses for up to 2 weeks after the original transmission of the survey (from original call in the case of IVR and CATI).
• First reminder was sent within 72 hours of first attempt (SMS and IVR).
• Second reminder was sent within 144 hours of first attempt (SMS and IVR).
• Call backs were made within 72 and 144 hours of first attempt (CATI); or
• Up to 2 call backs were made per appointment with respondent (CATI).

Also, in order to minimize non-response, three types of incentives were given. First, households that did not own a mobile phone were provided one for free. Approximately 200 phones were donated in Peru. Second, all communications between the interviewers and the households were free to the respondents. Finally, households were randomly assigned to one of three incentive levels: one-third of households received US$1 in free airtime for each questionnaire they answered, one-third received US$5 in free airtime, and one-third received no financial incentive (the control group).

Abstract

The main objective of the 2019 Chattogram for Low Income Area Gender, Inclusion, and Poverty (CITY) study is to collect primary data from male and female residents in slum and non-slum poor neighborhoods in Chattogram, the second largest city of Bangladesh, and build the evidence base about their constraints to access more and better jobs. The CITY survey was designed to shed light on poverty, economic empowerment, and livelihood in urban areas of Bangladesh as well as to identify key constraints and solutions for low-income women trying to obtain better jobs.

A broad array of information was collected on issues related to women's economic empowerment, ranging from demographic and socioeconomic characteristics to detailed work history, time use, attitudes about work, and perceptions of work. The key feature of this survey is to collect economic data directly from the main household members, generally the main couples, unlike traditional surveys which only interviewed the heads of households (who tend to be men in most cases); thus, failed to gather valuable information from the female population.

Geographic coverage

Poor areas of slum & non-slum areas of Chattogram, the second largest city of Bangladesh.

Analysis unit

Household, individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The CITY 2019 survey was designed using a two-stage sampling strategy. The major features include the following steps:

FIRST STAGE: The primary sampling units (PSUs) in the first stage were selected using a probability proportional to size (PPS) methods. Using the 2011 census sampling frame, low-income PSUs were defined as non-slum census enumeration areas (EAs) using the 2011 Bangladesh Poverty Map. Three strata were used for sampling the low-income EAs. These strata were defined based on the poverty head-count ratios. The first stratum encompasses EAs with a poverty headcount ratio less than 10%; the second stratum between 11% and 14%; and the third stratum, those exceeding 15%. Overall, 22 low-income EAs were selected in the Chattogram City Corporation (CC).

Slums were defined as informal settlements that were listed in the Bangladesh Bureau of Statistics' slum census from 2013/14. This census was used as sampling frame of the slum areas. Based on the sizes of the slums, three strata were used for sampling purposes. This time the strata were based on the size of the slums. The first stratum comprises slums of 50 to 75 households; the second 76 to 99 households; and the third, more than 100 households. Small slums with fewer than 50 households were not included in the sampling frame. Overall, 18 slums were included as a part of the survey.

SECOND STAGE: The second stage of the selection process in each of the EAs began with a listing exercise. For very large EAs, a smaller section was delineated for the listing. The second level of stratification are defined as follows:

i) Households with both working-age male and female members; ii) Households with only a working-age female; iii) Households with only a working-age male.

Households were randomly selected from each stratum with the predetermined ratio of 16:3:1. Overall, data was collected from 805 households (1289 individuals - 580 in slum and 709 in non-slum areas).

Sampling deviation

For EAs where the ratio was unable to be attained due to absence of households in certain strata, households from the first category to arrive at a final number of 20 per EA.

Mode of data collection

Computer Assisted Personal Interview [capi]

Response rate

77%

Abstract

The 2018 Dhaka Low Income Area Gender, Inclusion, and Poverty (DIGNITY) survey attempts to fill in the data and knowledge gaps on women's economic empowerment in urban areas, specifically the factors that constrain women in slums and low-income neighborhoods from engaging in the labor market and supplying their labor to wage earning or self-employment. While an array of national-level datasets has collected a wide spectrum of information, they rarely comprise all of the information needed to study the drivers of Female Labor Force Participation (FLFP). This data gap is being filled by the primary data collection of the specialized DIGNITY survey; it is representative of poor urban areas and is specifically designed to address these limitations. The DIGNITY survey collected information from 1,300 urban households living in poor areas of Dhaka in 2018 on a range of issues that affect FLFP as identified through the literature. These range from household composition and demographic characteristics to socioeconomic characteristics such as detailed employment history and income (including locational data and travel details); and from technical and educational attributes to issues of time use, migration history, and attitudes and perceptions.

The DIGNITY survey was designed to shed light on poverty, economic empowerment, and livelihood in urban areas of Bangladesh. It has two main modules: the traditional household module (in which the head of household is interviewed on basic information about the household); and the individual module, in which two respondents from each household are interviewed individually. In the second module, two persons - one male and one female from each household, usually the main couple, are selected for the interview. The survey team deployed one male and one female interviewer for each household, so that the gender of the interviewers matched that of the respondents. Collecting economic data directly from a female and male household member, rather than just the head of the household (who tend to be men in most cases), was a key feature of the DIGNITY survey.

Geographic coverage

The DIGNITY survey is representative of low-income areas and slums of the Dhaka City Corporations (North and South, from here on referred to as Dhaka CCs), and an additional low-income site from the Greater Dhaka Statistical Metropolitan Area (SMA).

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure followed a two-stage stratification design. The major features include the following steps (they are discussed in more detail in a copy of the study's report and the sampling document located in "External Resources"):

FIRST STAGE: Selection of the PSUs

Low-income primary sampling units (PSUs) were defined as nonslum census enumeration areas (EAs), in which the small-sample area estimate of the poverty rate is higher than 8 percent (using the 2011 Bangladesh Poverty Map). The sampling frame for these low-income areas in the Dhaka City Corporations (CCs) and Greater Dhaka is based on the population census of 2011. For the Dhaka CCs, all low-income census EAs formed the sampling frame. In the Greater Dhaka area, the frame was formed by all low-income census EAs in specific thanas (i.e. administrative unit in Bangladesh) where World Bank project were located.

Three strata were used for sampling the low-income EAs. These strata were defined based on the poverty head-count ratios. The first stratum encompasses EAs with a poverty headcount ratio between 8 and 10 percent; the second stratum between 11 and 14 percent; and the third stratum, those exceeding 15 percent.

Slums were defined as informal settlements that were listed in the Bangladesh Bureau of Statistics' slum census from 2013/14. This census was used as sampling frame of the slum areas. Only slums in the Dhaka City Corporations are included. Again, three strata were used to sample the slums. This time the strata were based on the size of the slums. The first stratum comprises slums of 50 to 75 households; the second 76 to 99 households; and the third, 100 or more households. Small slums with fewer than 50 households were not included in the sampling frame. Very small slums were included in the low-income neighborhood selection if they are in a low-income area.

Altogether, the DIGNITY survey collected data from 67 PSUs.

SECOND STAGE: Selection of the Households

In each sampled PSU a complete listing of households was done to form the frame for the second stage of sampling: the selection of households. When the number of households in a PSU was very large, smaller sections of the neighborhood were identified, and one section was randomly selected to be listed. The listing data collected information on the demographics of the household to determine whether a household fell into one of the three categories that were used to stratify the household sample:

i) households with both working-age male and female members; ii) households with only a working-age female; iii) households with only a working-age male.

Households were selected from each stratum with the predetermined ratio of 16:3:1. In some cases there were not enough households in categories (ii) and (iii) to stick to this ratio; in this case all of the households in the category were sampled, and additional households were selected from the first category to bring the total number of households sampled in each PSU to 20.

The total sample consisted of 1,300 households (2,378 individuals).

Sampling deviation

The sampling for 1300 households was planned after the listing exercise. During the field work, about 115 households (8.8 percent) could not be interviewed due to household refusal or absence. These households were replaced with reserved households in the sample.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires for the survey were developed by the World Bank, with assistance from the survey firm, DATA. Comments were incorporated following the pilot tests and practice session/pretest.

Cleaning operations

Collected data was entered into a computer by using the customized MS Access data input software developed by Data Analysis and Technical Assistance (DATA). Once data entry was completed, two different techniques were employed to check consistency and validity of data as follows:

1. Five (5%) percent of the filled-in questionnaire was checked against entered data to measure the transmission error or typos, and;
2. A logical consistency checking technique was employed to identify inconsistencies using SPSS and or STATA software.