This statistic shows the share of disposable income spent on food consumed in 2013, broken down by selected countries. U.S. consumers spent 6.7 percent of their disposable income on food at home, compared to a food expenditure share of 48.1 percent in Pakistan.

Household expenditures on consumer goods appeared to generally be quite stable between January 2019 and February 2020 across all categories. There was, however, a significant change in March 2020 caused by the lockdown implemented by French authorities in response to the coronavirus (COVID-19) crisis. While spending on manufactured goods dropped considerably that month, food expenses increased slightly. At the same time, the share of monthly household expenditure dedicated to food products strongly increased, and represented more than half of household expenditure on consumer goods in April 2020. However, this share had decreased by August 2022, reaching less than 36 percent.

Average food price inflation was highest in low-income countries in 2023 when compared to the rest of the world. When compared to the previous year, food prices were almost 30 percent higher in September and October of 2023 in low-income countries. This figure stood at 3.8 percent for high income countries.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

Food Prices for Nutrition provides indicators on the cost and affordability of a healthy diet (CoAHD) in each country, showing the population’s physical and economic access to sufficient quantities of locally available items for an active and healthy life. It also provides indicators on the cost and affordability of an energy-sufficient diet and of a nutrient-adequate diet. These indicators are explained in detail in the Food Prices for Nutrition DataHub here: https://www.worldbank.org/foodpricesfornutrition.

The database version Food Prices for Nutrition 1.0 contains indicators that were estimated in July 2022, based on 2017 global food retail price data from the International Comparison Program (ICP), when relevant affordability indicators were calculated based on the available Poverty and Inequality Platform (PIP) data from the World Bank expressed in 2011 purchasing power parity terms (PPP). These include indicators measuring the ratio between diet costs and international food poverty lines and indicators measuring the share and volume of the population unable to afford each diet, based on income distributions observed in each country. Countries' income classifications at the aggregate reporting level follow the calendar year of 2020 (the fiscal year of 2022 of the World Bank). The Cost and Affordability of a Healthy Diet indicators reported in the United Nations' State of Food Security and Nutrition in the World 2022 correspond to those in version 1.0.

The database version Food Prices for Nutrition 1.1 updates these aforementioned affordability indicators using the latest international poverty lines and PIP data expressed in 2017 PPP-based dollars.

The database version Food Prices for Nutrition 2.0, estimated in July 2023, uses the latest PIP data expressed in 2017 PPP-based dollars. Countries' income classifications at the aggregate reporting level follow the calendar year of 2021 (the fiscal year of 2023 of the World Bank). The Cost and Affordability of a Healthy Diet indicators reported in the United Nations' State of Food Security and Nutrition in the World 2023 correspond to those in version 2.0.The database version Food Prices for Nutrition 2.1 updates the affordability indicators using the latest PIP data expressed in 2017 PPP-based dollars and population data from the WDI updated in the fall of 2023.

The database version Food Prices for Nutrition 3.0, estimated in July 2024, uses the 2021 global food retail price data from the ICP and updates the methodology of calculating the affordability indicators, including indicators measuring the ratio between diet costs and international food poverty lines and indicators measuring the share and volume of the population unable to afford each diet, and they are based on the latest PIP data expressed in 2017 PPP-based dollars. For the first time, estimates for the prevalence and number of people unable to afford a healthy diet were imputed for countries with missing information based on their regional and global aggregates. Countries' income classifications at the aggregate reporting level follow the calendar year of 2022 standard (the fiscal year of 2024 of the World Bank). The Cost and Affordability of a Healthy Diet indicators reported in the United Nations' State of Food Security and Nutrition in the World 2024 correspond to those in version 3.0.

The database version Food Prices for Nutrition 3.1 updates the affordability indicators using the latest PIP data expressed in 2017 PPP-based dollars and population data from the World Population Prospect by the United Nations Department of Economic and Social Affairs (UN DESA).

This dataset provides numbers and proportions of households involved in primary activities (livestock, fishing, handicraft), by geography (1 sub-national level), sex, age and urbanization, poverty status (2 categories) and food security status (2 categories) for Pacific island countries based on Household Income and Expenditure Surveys (HIES). The table has been compiled as a result of a collaborative project on food security between the Pacific Community (SPC) and the Food and Agriculture Organization of the United Nations (FAO).

Find more Pacific data on PDH.stat.

While the cost of a basic food basket as a share of net minimum wages in Ireland and the United Kingdom is quite modest, such a basket costs more than the minimum wage in Nigeria.

Egypt Average Household Expenditure: Percentage Distribution: CO: CR: Food and Beverages data was reported at 34.400 % in 2015. This records a decrease from the previous number of 36.000 % for 2013. Egypt Average Household Expenditure: Percentage Distribution: CO: CR: Food and Beverages data is updated yearly, averaging 39.900 % from Jun 2005 (Median) to 2015, with 5 observations. The data reached an all-time high of 44.500 % in 2005 and a record low of 34.400 % in 2015. Egypt Average Household Expenditure: Percentage Distribution: CO: CR: Food and Beverages data remains active status in CEIC and is reported by Central Agency for Public Mobilization and Statistics. The data is categorized under Global Database’s Egypt – Table EG.H010: Average Household Expenditure.

In December 2024, the inflation rate for food in the European Union (EU) reached 2.4 percent compared to the same month the year prior. Starting in the beginning of 2022, food prices started to rise rapidly. In March 2023, the food inflation rate in the EU reached its peak at 19.19 percent. Since April 2023, the rate started to decrease. Food inflation in Europe One of the main drivers of the increase in consumer prices was the rapid rise in energy prices. In the energy sector, the harmonized index of consumer prices inflation of the EU, a concept to measure and compare inflation internationally, was at 41.1 percent in June 2022, whereas the other categories were all below 10 percent. In Germany, the year-on-year consumer price index development for food and beverages was at 12.33 percent in the year 2023, just a slight dip from the all-time high of 12.51 percent in 2022. By 2024, this had dropped to 1.92 percent. There are a number of ways in which European consumers are trying to save on food costs due to rising prices. The most popular way to deal with the rising food prices is to reduce at-home food waste. An average of about half of consumers in selected European countries stated that this is how they responded to the price increases. Other popular ways were to buy only the essentials or to purchase mostly store brands. Food inflation worldwide In 2022, Europe and Central Asia were the regions with the highest food inflation rates worldwide. The rate of food inflation in those regions was about 18 percent in 2022, which is more than twice as high as it was in the previous year. In Latin America and the Caribbean, the food inflation rate rose from 5.4 to 11.9 percent during the same period. When categorized by income classification, low-income countries have significantly higher food price inflation, as compared to lower-middle-, upper-middle-, and high-income countries. On average, low-income countries had a food price inflation rate of about 30 percent in 2023. The world average rate was at 6.5 percent. Zimbabwe was the country with the highest level of real food inflation worldwide. The southern African country experienced a food inflation of approximately 46 percent in 2024. This was more than two times as high as in any other country in the world.

A major characteristic of life in Western Europe in the second half of the 20th century was the emergence of consumerism. For the generations who had endured the devastation of two world wars, the economic difficulties that accompanied these and the Great Depression, and widespread food shortages across these five decades, the opportunity to spend their newfound income was embraced. In 1950, at the end of the recovery period after the Second World War, almost two-thirds of all income in Western Europe's* most-developed nations was spent on basic essentials, such as food and clothing. While economic recovery ended for most countries by the early 1950s, rapid economic growth did not stop there. Throughout the 1960s and 1970s, household income increased by around three percent per year in the most developed countries and five percent per year across Southern Europe. Food spending In Britain, France, and Germany, the share of income spent on food in 1950 was around 44 percent; this dropped to about 27 percent in 1971, and 13 percent in the 1990s. There were some regional variations, specifically the slower rate of this transition in the south, as 34 and 52 percent of income was spent on food in Spain and Portugal, respectively, in 1971. Clothing spending In Europe's 15 most-developed countries, approximately 16 percent of income was spent on clothing in 1950, but this dropped below seven percent by 1996. This was not only because income rose over this period, but also as quality improved due to advances in manufacturing and synthetic materials, and as clothing became more affordable as much of the production was relocated from Europe to China, Turkey, and other parts of East Asia.

The average for 2021 based on 165 countries was 105.854 index points. The highest value was in South Korea: 208.84 index points and the lowest value was in India: 58.17 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

ObjectiveTo estimate the global and country-level burden of overweight and obesity among pregnant women from 2005 to 2014.MethodsPublicly accessible country-level data were collected from the World Health Organization, the World Bank and the Food and Agricultural Organization. We estimated the number of overweight and obese pregnant women among 184 countries and determined the time-related trend from 2005 to 2014. Based on panel data model, we determined the effects of food energy supply, urbanization, gross national income and female employment on the number of overweight and obese pregnant women.ResultsWe estimated that 38.9 million overweight and obese pregnant women and 14.6 million obese pregnant women existed globally in 2014. In upper middle income countries and lower middle income countries, there were sharp increases in the number of overweight and obese pregnant women. In 2014, the percentage of female with overweight and obesity in India was 21.7%, and India had the largest number of overweight and obese pregnant women (4.3 million), which accounted for 11.1% in the world. In the United States of America, a third of women were obese, and the number of obese pregnant women was 1.1 million. In high income countries, caloric supply and urbanization were positively associated with the number of overweight and obese pregnant women. The percentage of employment in agriculture was inversely associated with the number of overweight and obese pregnant women, but only in upper middle income countries and lower middle income countries.ConclusionThe number of overweight and obese pregnant women has increased in high income and middle income countries. Environmental changes could lead to increased caloric supply and decreased energy expenditure among women. National and local governments should work together to create a healthy food environment.

This dataset provides numbers and proportions of households involved in primary activities (livestock, fishing, handicraft), by geography (1 sub-national level), sex, age and urbanization, poverty status (2 categories) and food security status (2 categories) for Pacific island countries based on Household Income and Expenditure Surveys (HIES). The table has been compiled as a result of a collaborative project on food security between the Pacific Community (SPC) and the Food and Agriculture Organization of the United Nations (FAO).

Find more Pacific data on PDH.stat.

Russians spent approximately 32 percent of their consumer expenditure on food and non-alcoholic drinks in 2023. The share of the food spending marked a decrease from the previous year, when it stood at nearly 33 percent of what households spent per member on a monthly basis.

Abstract

The purpose of the Household Income and Expenditure Survey (HIES) survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in Palau. This information will be used to guide policy makers in framing socio-economic developmental policies and in initiating financial measures for improving economic conditions of the people.

Some more specific outputs from the survey are listed below:

a) To obtain expenditure weights and other useful data for the revision of the consumer price index; b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts; c) To supply basic data needed for policy making in connection with social and economic planning, including producing as many of Palau's National Minimum Development Indicators (NMDI's) as possible; d) To provide data for assessing the impact on household living conditions of existing or proposed economic and social measures, particularly changes in the structure of household expenditures and in household consumption; e) To gather information on poverty lines and incidence of poverty throughout Palau.

Geographic coverage

National Coverage, excluding Sonsorol and Hatohobei. Urban and Rural.

Analysis unit

• Households;
• Individuals.

Universe

All private households and group quarters (people living in Work dormitories, as it is an important aspect of the subject matter focused on in this survey, and not addressed elsewhere).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used was the 2012 Palau census, which provided population figures for everyone living in both private households and group quarters (e.g. worker barracks, school dormitories, prison). The sampling selection was done separately in private dwellings and group quarters.

It is an accepted practice for the Household Income and Expenditure Survey (HIES) to cover all living quarters regarded as private dwellings, and the Palau 2013/14 HIES will follow this recommendation.

For group quarters it is also recommended to exclude the prison, as it is not considered appropriate to include such institutions in a survey such as HIES.

A decision as to whether the remaining group quarters should be included is based on the following criteria:

1) Ease in accessing and covering them in a survey such as HIES 2) Relevance to the subject matter of the survey 3) Whether their impact on the subject matter is mostly covered already

Under these criteria, the following recommendations are made: -School/college dormitories: Will exclude from HIES as these individuals will be covered in the households from which they came (if selected) -Work dormitories: Aim to include in the HIES as they are an important aspect of the subject matter focused on in this survey, and not addressed elsewhere -Live aboard: Will exclude due to the movement of such vehicles, and the minimal impact they may have on such a survey -Convents/religious quarters: Will exclude based on their expected minimum impact on the survey subject matter

NB: Given students in dorms are expected to have a high portion of their income and expenses covered in their original household of origin, and there were no religious group quarters identified during the census, only persons in the prison and living aboard are expected to be excluded from the survey. These people account for 81 out of 2,322 group quarters residents (only 3.6%).

Although the response rates were down in the 2006 HIES, with a smaller more experienced team working over 12 months, it is expected there will be improvements in this area. However, the expected sample loss of 10 per cent was probably too ambitious, and given the actual rate ended up at 287/1,063 = 27 per cent, it is more realistic to assume a sample loss of around 15 per cent with improvements for the 2013/14 HIES. Based on the RSEs presented in 2.3.2, it also appears that the 20 per cent desirable sample produced sound results for the survey, and with higher response rates anticipated, these results from a sample error perspective should improve. It is therefore proposed for the 2013/14 Palau HIES that a sample size of 20 per cent be adopted, which also allows for sample loss of 15 per cent.

In the 2006 Palau HIES, effort was made to design a sample which could produce results for the six domains (stratum). Whilst reasonable results were generated for each of these domains, it was felt that post survey, there was no great use of these results at that level. For the 2013 HIES it is proposed to focus on generating reliable results at the national level, with focus also being place on producing results for the urban/rural split. In the case of Palau, the urban population is considered to consist of the states of Koror and Airai.

The last phase to finalizing the sample numbers was to adjust the desirable sample numbers, so that they could be easily applied by the HIES team in a practical manner over the course of the 12 month fieldwork. This was achieved by modifying the sample counts (not too much) to enable sample sizes each round would be of a similar size, and workloads for each enumerator were the same size each round. The desirable workload for an enumerator covering the PD population was 10 households, whereas this figure was increased to 14 persons for GQs as it was envisaged the amount of time required to cover a person in a GQ would be significantly less. With this in mind, we wanted to ideally have the PD sample to be divisible by 160 so this would enable an even number of households each round, whilst maintaining a workload of 10 households for interviewers covering these areas. For the GQ sample, given the desirable number of GQs was already 225, and 16x14=224, then a simple reduction of 1 in the GQ sample would result in a nice even workload of 14 persons per round for 1 interviewer. This logic was also applied to the split between urban and rural resulting in 14 workloads in urban and 2 workloads in rural.

Mode of data collection

Face-to-face [f2f]

Research instrument

Developped in English, a questionnaire consisting of four Modules and a Weekly Diary covering 2 weeks was used for the Republic of Palau Household Income and Expenditure Survey (HIES) 2013. Each Module covers distinct but connected portion of the Household.

The Modules are as follows: -Module 1 - Demographic Information: · Demographic Profile · Labor Force Status · Health Status · Communication Status -Module 2 - Household Expenditure: · Housing Characteristics · Housing Tenure Expenditure · Utilities & Communication Details · Utilities & Communication Expenditure · Land & Home Details · Land & Home Expenditure · Household Goods & Assets Details · Household Goods & Assets Expenditures · Vehicles & Accessories Details · Vehicles & Accessories Expenditures · Private Travel Details · Private Travel Expenditures · Household Services Expenditure · Contributions to Special Occasions · Provisions of Financial Support · Loans · Household Assets Insurance & Taxes · Personal Insurance -Module 3 - Individual Expenditures: · Education grants and scholarships · Education Identifications · Education Expenditures · Health Identifications · Health Expenditures · Clothing Identification · Clothing Expenditure · Communication Identification · Communication Expenditures · Luxury Items Identification · Luxury Items Expenditures -Module 4 - Income: · Wages & Salary: In country (current) · Wages & Salary: Overseas (last 12 months) · Wages & Salary: In country (last 12 months) · Income from Non Subsistence Business · Description of Agriculture & Forestry Activities · Income from Agriculture & Forestry Activities · Description of Handicraft & Home Processed Food Activities · Income from Handicraft & Home Processed Food Activities · Description of Livestock & Aquaculture Activities · Income from Livestock & Aquaculture Activities · Description of Fishing & Hunting Activities · Income from Fishing & Hunting Activities · Property Income, Transfer Income & Other Receipts · Remittances & Other Cash Gifts -Weekly Diary - Covering 14 Days (2 weeks): · Daily expenditure of food and non-food items · Payments of service made · Gambling winning and losses · Items received for free · Home produced food and non-food items.

All questionnaires are provided as external resources in this documentation.

Cleaning operations

Program: CSPro 5.1x

Data editing took place at a number of stages throughout the processing, including:

a) Office editing and coding b) During data entry; Error report correction; Secondary editing by Quality Control Officer (QCO) c) Structure checking and completeness

Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Response rate

Some 1,145 households were selected (in private dwellings and workers quarters) to participate in the survey, and the response rate was 75.8% (i.e. 869 households responded). This response rate allows for statistically significant analysis at the national, urban and rural level.

Response rates for private households by State: -Koror: 355 households responded out of 480 selected => 73.9%; -Airai: 119 households responded out of 160 selected => 74.4%; -URBAN: 474 households responded out of 640 selected => 74.1%; -Kayangel: 0 households responded out of 10 selected => 0%; -Ngarchelong: 27 households responded out of 30 selected => 90%; -Ngaraard: 22 households responded

The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally and by region and country. Calculated each year by the International Food Policy Research Institute (IFPRI), the GHI highlights successes and failures in hunger reduction and provide insights into the drivers of hunger, and food and nutrition security. The 2014 GHI has been calculated for 120 countries for which data on the three component indicators are available and for which measuring hung er is considered most relevant. The GHI calculation excludes some higher income countries because the prevalence of hunger there is very low. The GHI is only as current as the data for its three component indicators. This year's GHI reflects the most recent available country level data for the three component indicators spanning the period 2009 to 2013. Besides the most recent GHI scores, this dataset also contains the GHI scores for four other reference periods- 1990, 1995, 2000, and 2005. A country's GHI score is calculated by averaging the percentage of the population that is undernourished, the percentage of children youn ger than five years old who are underweight, and the percentage of children dying before the age of five. This calculation results in a 100 point scale on which zero is the best score (no hunger) and 100 the worst, although neither of these extremes is reached in practice. The three component indicators used to calculate the GHI scores draw upon data from the following sources: 1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2014GHI scores. Undernourishment data for the 2014 GHI are for 2011-2013. 2. Child underweight: The "child underweight" component indicator of the GHI scores includes the latest additions to the World Health Organization's (WHO) Global Database on Child Growth and Malnutrition, and additional data from the joint data base by the United Nations Children's Fund (UNICEF), WHO and the World Bank; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey reports; and statistical tables from UNICEF. For the 2014 GHI, data on child underweight are for the latest year for which data are available in the period 2009-2014. 3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2014 GHI scores. For the 2014 GHI, data on child mortality are for 2012. Resources related to 2014 Global Hunger Index

Abstract

The main purpose of a HIES survey 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. These statistics are a requirement for evidence based policy-making in reducing poverty within the country and monitor progress in the national strategic plan "Te Kakeega 3".

The 2015-16 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. With great assitance from the Pacific Community (SPC) experts, the HIES was conducted over a period of 12 months in urban (Funafuti) and rural (4 outer islands) areas. From a total of 1,872 households on Tuvalu, an amount of 38 percent sample of all households in Tuvalu was selected to provide valid response.

Geographic coverage

National Coverage.

Analysis unit

Household and Individual.

Universe

The scope of the 2015/2016 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).

Kind of data

Sample survey data [ssd]

Sampling procedure

Out of the total 1,872 households (HHs) listed in 2015, a sample 706 households which is 38 percent of the the total households were succesfully interviewed for a response rate of 98%.

SAMPLING FRAME: The 2010 (Household Income and Expenditure Survey (HIES) sample was spread over 12 months rounds - one each quarter - and the specifications of the final responding households are summarised below: Tuvalu urban: Selected households: 259 = 217 responded; Tuvalu rural: Selected households: 346 = 324 responded.

In 2010, 605 HHs were selected and 541 sufficiently responded. The 2010 HIES provided solid estimates for expenditure aggregates at the national level (sampling error for national expenditure estimate is 3.1%).

Similarly to the 2010 HIES, private occupied dwellings were the statistical unit for the 2015/2016 HIES. Institutions and vacant dwellings were removed from the sampling frame. Some areas in Tuvalu are very difficult to reach due to the cost of transportation and the remoteness of some islands, which is why they are excluded from the sample selection. The following table presents the distribution of the HHs according to their location (main island or outer islands in each domain) based on the 2012 Population and Housing Census: -Urban - Funafuti: 845 (48%); -Rural - Nanumea: 115 (7%); -Rural - Nanumaga: 116 (7%); -Rural - Niutao: 123 (7%); -Rural - Nui: 138 (8%); -Rural - Vaitupu: 226 (13%); -Rural - Nukufetau: 124 (%); -Rural - Nukulaelae: 67 (%); -Rural - Niulakita: 7 (%); -TOTAL: 1761 (100%).

The 2012 Population and Household Census (PHC) wsa used to select the island to interview, and then in each selected island the HH listing was updated for selection. For budget and logistics reasons the islands of Nui, Nukufetau, Nukulaelae and Niukalita were excluded from the sample selection. In total 19% of the HHs were excluded from the selection but this decision should not affect the HIES outputs as those 19% show similar profile as other HHs who live in the outer islands. This exclusion will be take into consideration in the sampling weight computation in order to cover 100% of the outer island HHs.

SAMPLE SELECTION AND SAMPLE SIZE: A simple random selection was used in each of the selected island (HHs were selected directly from the sampling frame). Based on the findings from the 2010 Tuvalu HIES, the sample in Funafuti has been increased and the one in rural remains stable. Within each rural selected atolls, the allocation of the sample size is proportional to its size (baed on the 2012 population census). The table below shows the number of HHs to survey: Urban - Funafuti: 384; Rural - Vaitupu: 126; Rural - Nanumea: 63; Rural - Niutao: 84; Rural - Nanumaga: 63; TUVALU: 720.

The expected sample size has been increased by one third (361 HHs) with the aim of pre-empting the non contacted HHs (refusals, absence….). The 2015/2016 HIES adopted the standardized HIES methodology and survey instruments for the Pacific Islands region. This approach, developed by the Pacific Community (SPC), has resulted in proven survey forms being used for data collection. It involves collection of data over a 12-month period to account for seasonal changes in income and expenditure patterns, and to keep the field team to a smaller and more qualified group. Their implementation had the objective of producing consistent and high quality data.

Sampling deviation

For budget and logistics reasons the islands of Nui, Nukufetau, Nukulaelae and Niukalita were excluded from the sample selection. In total 19% of the HHs were excluded from the selection but this decision should not affect the HIES outputs as those 19% show similar profile as other HHs who live in the outer islands. This exclusion will be take into consideration in the sampling weight computation in order to cover 100% of the outer island HHs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey contain 4 modules and 2 Diaries (1 diary for each of the two weeks that a household was enumerated). The purpose of a Diary is to record all the daily expenses and incomes of a Household as shown by its topics below; - DIARY
The Diary module contains questions such as "What did your Household buy Today (Food and Non-Food Items)?", "Payments for Services made Today", "Food, Non-Food and Services Received for Free", "Home-Produced Items Today", "Overflow Sheet for Items Bought This Week", "Overflow Sheet for Services Paid for This Week", "Overflow Sheet for Items Received for Free this Week", and an "Overflow Sheet for Home-Produced Items This Week".

The 4 modules are detailed below; - MODULE 1 - DEMOGRAPHIC INFORMATION The module contains individual demograhic questions on their Demographic Profiles, Labour Force status (Activities), Education status, Health status, Communication status and questions on "Household members that have left the household". - MODULE 2 - HOUSEHOLD EXPENDITURE The module contains household expenditure questions the housing characteristics, Housing tenure expenditures, Utilities and Communication, Land, Household goods and assets, Vehicles and accessories, Private Travel details, Household services expenditures, Cash contributions, Provisions of Financial support, Household asset insurance and taxes and questions on Personal insurance. - MODULE 3 - INDIVIDUAL EXPENDITURE This module contains individual expenditure questions on Education, Health, Clothing, Communication, Luxury Items, Alcohol, Kava and Tobacco, and Deprivation questions. - MODULE 4 - HOUSEHOLD & INDIVIDUAL INCOME
This module contains household and individual questions on their income, on topics such as Wages and Salary, Agricultural and Forestry Activities, Fishing, Gathering and Hunting Activities, Livestock and Aquaculture Activities, Handicraft/Home-processed Food Activities, Income from Non-subsistence Business, Property income, transfer income & other Receipts, and Remmitances and other Cash gifts.

Depending on the information being collected, a recall period (ranging from the last 7 days to the last 12 months) is applied to various sections of the questionnaire. The forms were completed by face-to-face interview, usually with the HH head providing most of the information, with other household (HH) members being interviewed when necessary. The interviews took place over a 2-week period such that the HH diary, which is completed by the HH on a daily basis for 2 weeks, can be monitored while the module interviews take place. The HH diary collects information on the HH's daily expenditure on goods and services; and the harvest, capture, collection or slaughter of primary produce (fruit, vegetables and animals) by intended purpose (home consumption, sale or to give away). The income and expenditure data from the modules and the diary are concatenated (ensuring that double counting does not occur), annualised, and extrapolated to form the income and expenditure aggregates presented herein.

Cleaning operations

The survey procedure and enumeration team structure allowed for in-round data entry, which gives the field staff the opportunity to correct the data by manual review and by using the entry system-generated error messages. This process was designed to improve data quality. The data entry system used system-controlled entry, interactive coding and validity and consistency checks. Despite the validity and consistency checks put in place, the data still required cleaning. The cleaning was a 2-stage process, which included manual cleaning while referencing the questionnaire, whereas the second stage involved computer-assisted code verification and, in some cases, imputation. Once the data were clean, verified and consistent, they were recoded to form a final aggregated database, consisting of: 1. Person level record - characteristics of every HH member, including activity

Abstract

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation.

The survey's main objectives are: - To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials. - To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands. - To measure mean household and per-capita expenditure for various expenditure items along with socio-economic correlates. - To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation. - To define mean household and per-capita income from different sources. - To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependent on the results of this survey. - To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas. - To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure. - To study the relationships between demographic, geographical, housing characteristics of households and their income and expenditure for commodities and services. - To provide data necessary for national accounts especially in compiling inputs and outputs tables. - To identify consumers behavior changes among socio-economic groups in urban and rural areas. - To identify per capita food consumption and its main components of calories, proteins and fats according to its sources and the levels of expenditure in both urban and rural areas. - To identify the value of expenditure for food according to sources, either from household production or not, in addition to household expenditure for non-food commodities and services. - To identify distribution of households according to the possession of some appliances and equipment such as (cars, satellites, mobiles ...) in urban and rural areas. - To identify the percentage distribution of income recipients according to some background variables such as housing conditions, size of household and characteristics of head of household.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

• Household/families
• Individuals

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2008/2009 HIECS is a two-stage stratified cluster sample, approximately self-weighted, of nearly 48000 household in urban and rural areas. The main elements of the sampling design are described below.

• Sample Size: It has been deemed important to retain the same sample size of the previous two HIECS rounds. Thus, a sample of about 48000 households has been considered. The justification of maintaining the sample size at this level is to have estimates with levels of precision similar to those of the previous two rounds: therefore trend analysis with the previous two surveys will not be distorted by substantial changes in sampling errors from round to another. In addition, this relatively large national sample implies proportional samples of reasonable sizes for smaller governorates. Nonetheless, oversampling has been introduced to raise the sample size of small governorates to about 1000 households. As a result, reasonably precise estimates could be extracted for those governorates. The oversampling has resulted in a slight increase in the national sample to 48658 households.

• Cluster size: An important lesson learned from the previous two HIECS rounds is that the cluster size applied in both surveys is found to be too large to yield an accepted design effect estimates. The cluster size was 40 households in the 2004-2005 round, descending from 80 households in the 1999-2000 round. The estimates of the design effect (deft) for most survey measures of the latest round were extraordinary large. As a result, the cluster size was decreased to only 19 households (20 households in urban governorates to account for anticipated non-response in those governorate. In view of past experience non-response is almost nil in rural governorates).

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document that is provided as an external resources in both Arabic and English.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires were used: 1- Expenditure and consumption questionnaire 2- Diary questionnaire for expenditure and consumption 3- Income questionnaire

Cleaning operations

Harmonized Data - The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets. - The harmonization process starts with cleaning all raw data files received from the Statistical Office. - Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/compute/recode/rename/format/label harmonized variables. - A post-harmonization cleaning process is run on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

For the total sample, the response rate was 96.3% (93.95% in urban areas and 98.4% in rural areas).

Sampling error estimates

The sampling error of major survey estimates has been derived using the Ultimate Cluster Method as applied in the CENVAR Module of the Integrated Microcomputer Processing System (IMPS) Package. In addition to the estimate of sampling error, the output includes estimates of coefficient of variation, design effect (DEFF) and 95% confidence intervals.

Data appraisal

The precision of survey results depends to a large extent on how the survey has been prepared for. As such, it was deemed crucial to exert much effort and to take necessary actions towards rigorous preparation for the present survey. The preparatory activities, extended over 3 months, included forming Technical Committee. The Committee has set up the general framework of survey implementation such as:

1- Applying the recent international recommendations of different concepts and definitions of income and expenditure considering maintaining the consistency with the previous surveys in order to compare and study the changes in pertinent indicators.

2- Evaluating the quality of data in all different Implementation stages to avoid or minimize errors to the lowest extent possible through: - Implementing field editing after finishing data collection for households in governorates to avoid any errors in suitable time. - Setting up a program for the Survey Technical Committee Members and survey staff for visiting field work in all governorates (each 15 days) to solve any problem in the proper time. - Re-interviewing a sample of households by Quality Control Department and examining the differences with the original responses. - For the purpose of quality assurance, tables were generated for each survey round where internal consistency checks were performed to study the plausibility of mean household expenditure on major expenditure commodity groups and its variability over major geographic regions.

Goal 2End hunger, achieve food security and improved nutrition and promote sustainable agricultureTarget 2.1: By 2030, end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year roundIndicator 2.1.1: Prevalence of undernourishmentSN_ITK_DEFC: Prevalence of undernourishment (%)SN_ITK_DEFCN: Number of undernourish people (millions)Indicator 2.1.2: Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)AG_PRD_FIESMS: Prevalence of moderate or severe food insecurity in the adult population (%)AG_PRD_FIESMSN: Total population in moderate or severe food insecurity (thousands of people)AG_PRD_FIESS: Prevalence of severe food insecurity in the adult population (%)AG_PRD_FIESSN: Total population in severe food insecurity (thousands of people)Target 2.2: By 2030, end all forms of malnutrition, including achieving, by 2025, the internationally agreed targets on stunting and wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women and older personsIndicator 2.2.1: Prevalence of stunting (height for age SH_STA_STNT: Proportion of children moderately or severely stunted (%)SH_STA_STNTN: Children moderately or severely stunted (thousands)+2 or SH_STA_WAST: Proportion of children moderately or severely wasted (%)SH_STA_WASTN: Children moderately or severely wasted (thousands)SN_STA_OVWGT: Proportion of children moderately or severely overweight (%)SN_STA_OVWGTN: Children moderately or severely overweight (thousands)Indicator 2.2.3: Prevalence of anaemia in women aged 15 to 49 years, by pregnancy status (percentage)SH_STA_ANEM: Proportion of women aged 15-49 years with anaemia (%)SH_STA_ANEM_PREG: Proportion of women aged 15-49 years with anaemia, pregnant (%)SH_STA_ANEM_NPRG: Proportion of women aged 15-49 years with anaemia, non-pregnant (%)Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists and fishers, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employmentIndicator 2.3.1: Volume of production per labour unit by classes of farming/pastoral/forestry enterprise sizePD_AGR_SSFP: Productivity of small-scale food producers (agricultural output per labour day, PPP) (constant 2011 international $)PD_AGR_LSFP: Productivity of large-scale food producers (agricultural output per labour day, PPP) (constant 2011 international $)Indicator 2.3.2: Average income of small-scale food producers, by sex and indigenous statusSI_AGR_SSFP: Average income of small-scale food producers, PPP (constant 2011 international $)SI_AGR_LSFP: Average income of large-scale food producers, PPP (constant 2011 international $)Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil qualityIndicator 2.4.1: Proportion of agricultural area under productive and sustainable agricultureTarget 2.5: By 2020, maintain the genetic diversity of seeds, cultivated plants and farmed and domesticated animals and their related wild species, including through soundly managed and diversified seed and plant banks at the national, regional and international levels, and promote access to and fair and equitable sharing of benefits arising from the utilization of genetic resources and associated traditional knowledge, as internationally agreedIndicator 2.5.1: Number of plant and animal genetic resources for food and agriculture secured in either medium- or long-term conservation facilitiesER_GRF_ANIMRCNTN: Number of local breeds for which sufficient genetic resources are stored for reconstitutionER_GRF_PLNTSTOR: Plant breeds for which sufficient genetic resources are stored (number)Indicator 2.5.2: Proportion of local breeds classified as being at risk of extinctionER_RSK_LBREDS: Proportion of local breeds classified as being at risk as a share of local breeds with known level of extinction risk (%)Target 2.a: Increase investment, including through enhanced international cooperation, in rural infrastructure, agricultural research and extension services, technology development and plant and livestock gene banks in order to enhance agricultural productive capacity in developing countries, in particular least developed countriesIndicator 2.a.1: The agriculture orientation index for government expendituresAG_PRD_ORTIND: Agriculture orientation index for government expendituresAG_PRD_AGVAS: Agriculture value added share of GDP (%)AG_XPD_AGSGB: Agriculture share of Government Expenditure (%)Indicator 2.a.2: Total official flows (official development assistance plus other official flows) to the agriculture sectorDC_TOF_AGRL: Total official flows (disbursements) for agriculture, by recipient countries (millions of constant 2018 United States dollars)Target 2.b: Correct and prevent trade restrictions and distortions in world agricultural markets, including through the parallel elimination of all forms of agricultural export subsidies and all export measures with equivalent effect, in accordance with the mandate of the Doha Development RoundIndicator 2.b.1: Agricultural export subsidiesAG_PRD_XSUBDY: Agricultural export subsidies (millions of current United States dollars)Target 2.c: Adopt measures to ensure the proper functioning of food commodity markets and their derivatives and facilitate timely access to market information, including on food reserves, in order to help limit extreme food price volatilityIndicator 2.c.1: Indicator of food price anomaliesAG_FPA_COMM: Indicator of Food Price Anomalies (IFPA), by type of productAG_FPA_CFPI: Consumer Food Price IndexAG_FPA_HMFP: Proportion of countries recording abnormally high or moderately high food prices, according to the Indicator of Food Price Anomalies (%)

Abstract Brazil's Conditional Cash Transfer program, Bolsa Família Program - PBF (Family Allowance), represents a major increase in household income in the country. Households allocate these resources among several consumption categories. This work aimed to identify the impact of the PBF on the expenditure value of each category and on the share of expenditure. Data from the household budget survey - POF, carried out in 2002-2003 and 2008-2009, were used. The following methodologies were combined: Propensity Score Matching (PSM), Differences-in-Differences (DD), and Seemingly Unrelated Regressions (SUR). The results indicate that the largest impact of the PBF was on food and housing, both in absolute and relative terms. However, the effect on food was surprisingly negative. On the other hand, the impact on housing was positive. It was possible to conclude that directing income to women does not affect the consumption of tobacco. The impact of the PBF is also larger in households where women have the autonomy to decide how to allocate the PBF.