The per capita consumer spending on food and non-alcoholic beverages in Kenya was forecast to continuously decrease between 2024 and 2029 by in total ***** U.S. dollars (-***** percent). After the fifth consecutive decreasing year, the food-related per capita spending is estimated to reach ****** U.S. dollars and therefore a new minimum in 2029. Consumer spending, in this case food-related spending per capita, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group **. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the per capita consumer spending on food and non-alcoholic beverages in countries like Zambia and Tanzania.

This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021

More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx

The real total consumer spending on food and non-alcoholic beverages in Rwanda was forecast to continuously increase between 2024 and 2029 by in total **** million U.S. dollars (***** percent). According to this forecast, in 2029, the real food-related spending will have increased for the fourth consecutive year to *** billion U.S. dollars. Consumer spending, in this case food-related spending, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group **. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.Find more key insights for the real total consumer spending on food and non-alcoholic beverages in countries like Kenya and Seychelles.

Abstract

Rates of overweight, obesity, and chronic diseases such as cardiovascular diseases, hypertension, type 2 diabetes and certain cancers (bowel, lung, prostate and uterine) are on the rise in most sub-saharan Africa (SSA) countries like kenya. These increases can be largely attributed to the shift toward unhealthy diet patterns and increased access to processed foods that are high in fat, sugar, and sodium. The influx of supermarkets in east africa and the replacement of traditional foods for processed foods places this region in a vulnerable position for greater increases in chronic disease rates. Consumer purchasing history from supermarkets can provide valuable insight to food intake over time and the present and future effects on chronic diseases. Purchasing data from supermarkets is available yet underutilized in SSA.

The study aimed to harmonize and increase accessibility to grocery data, use statistical methods to explore purcharing patterns and predict the effects of nutrition on chronic diseases, and inform policy on the various influences on consumer purchases.

A further objective was to examine changes in food purchasing and nutritional composition before, during and after the COVID-19 pandemic restrictions.

Geographic coverage

County coverage: Nairobi

Analysis unit

Supermarket transaction records.

Universe

The survey covers transaction records of individuals who made purchases in supermarkets.

Sampling procedure

The study is a cross-sectional exploratory study with a phased approach employing quantitative secondary data collection from a third-party information management solution provider. The third party provider employs an open integrated point of sale and store information retail system that connects retail touch points and sales channels in several counties in Kenya.

Sampling was conducted after a census of all supermarkets subscribed to the third party system was done. Only those counties with supermarkets subscribed to the platform were sampled. A sample of large, medium sized and small supermarkets were selected to participate in the study. The supermarket sizes were determined as follows; large supermarkets ( supermarkets with a cumulative total of more than 8 branch networks). Medium size supermarkets will be those with 3-8 branch networks in the counties and smaller supermarkets are those with 1-2 branch networks.

Grocery data was received from 2 supermarket chains each with 1 branch.

Sampling deviation

Not Applicable

Mode of data collection

Other [oth]

Research instrument

A standardized form was developed to guide in extration of information from 3rd party information provider for supermarket purchase data. Variables of interest includes supermarket name, supermarket branch, location of supermarket, invoice id, customer id, customer demographics (gender, age), date and time of purchase, product name purchased, unit price per item, number of items purchased, payment method used by customer for purchase etc.

Secondary data collected will not be identifiable as it will be anonymized at the supermarket and client level.

The standardized form is provided as external resources data.

The standardized form is provided as external resources data. V1-V27 the questions are found in the “Study abstraction tool” V28-V30 are generated food classifications (user developed) and are not in any resource V31 the questions are found in the “NOVA-Classification-Reference-Sheet” V32-V59 the questions are found in the “Kenya Food Composition Tables 2018”

Cleaning operations

Not Applicable

Response rate

Not Applicable

Sampling error estimates

Not Applicable

The real total consumer spending on food and non-alcoholic beverages in Mozambique was forecast to continuously increase between 2024 and 2029 by in total *** billion U.S. dollars (+***** percent). After the fifth consecutive increasing year, the real food-related spending is estimated to reach *** billion U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case food-related spending, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group **. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.Find more key insights for the real total consumer spending on food and non-alcoholic beverages in countries like Kenya and Zambia.

Abstract

The UNHCR Standardized Expanded Nutrition Surveys (SENS) provide regular nutrition data that plays a key role in delivering effective and timely interventions to ensure good nutritional outcomes among populations affected by forced displacement.

The refugee complex of Dadaab is home to an estimate of 208,000 registered refugees of which the vast majority are Somalis who fled conflict and drought in their home country several decades ago. The Dadaab refugee complex is situated in northeastern Kenya, near the border with Somalia. Dadaab was established in the year 1991 following the beginning of the civil war in Somalia. Somalis were forced to flee as the war worsened, leaving to neighbouring countries including Kenya, Ethiopia and Sudan. Today, Dadaab is home to refugees from many countries in eastern and central Africa, including South Sudan, Burundi, Congo, Ethiopia, Eritrea and Somalia. Somali refugees make up more than 90% of the population. Until early 2017, it consisted of five refugee camps. However, one of the camps, Kambioos, which was also the newest, was closed in March 2017 as refugees began returning to Somalia and the few remaining moved into the other camps. Ifo 2 camp was closed in May 2018 in line with the cam consolidation approach, with refugees either moving to the other camps or being repatriated voluntarily. Refugees live in mud-walled houses with iron sheeting roofs, while some, especially new arrivals, live in tents.

The Standardised Expanded Nutrition Survey (SENS) was conducted between 20 August and 8 September 2018 in the 3 Dadaab refugee camps (Dagahaley, Ifo and Hagadera) to assess the magnitude and severity of malnutrition, assess trends by comparison with previous years and support programmatic decisions.

The weighted prevalence of global acutemalnutrition, the most important indicator, was 8.0% overall, falling within the POOR category (5-9%). However, there was a marked improvement from 9.7% in 2017. Only Ifo camp was within the SERIOUS category (10-14%). The high prevalence of anaemia remains a major concern, as shown by the anaemia prevalence among children which remained above the 40% critical threshold, despite having decreased. Anaemia prevalence among non-pregnant women jumped to 48.9% overall, from 43.6% in 2017 and was above the 40% threshold for all camps. Some improvement was recorded in terms of infant and young chid feeding indicators, although there is still room for improvement. The access to safe drinking water also continued to be satisfactory, while gaps were still observed in terms of sanitation. The duration of the food ration and dietary diversity basically reflect what has been observed in recent surveys.

Geographic coverage

Dadaab Refugee Camps (Ifo, Dagahaley and Hagadera), in Northern Kenya

Analysis unit

Children 0-23 months Children 6-59 months Women 15-49 years Households

Universe

Children 0-59 months Women 15-49 years Refugee households

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage cluster survey with probability proportion to size sampling was employed in this survey. Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology to collect and analyse data on child anthropometry and UNHCR's Standardised Expanded Nutrition Survey (SENS) Guidelines for Refugee Populations was used to guide data collection for other indicators.

The same households sampled by SMART were used in all indicators. Anaemia sample was drawn from the SMART sample size, as recommended by the UNHCR Standardised Expanded Nutrition Survey (SENS) Guidelines. For each of the indicators used, households and individuals were sampled as follows:

Household-level indicators: - WASH: every household - Food Security: every other household - Mosquito net: every other household

Individual-level indicators: - Children 0-59 months: all eligible children in all households were assessed (based on the above calculations) - Women 15-49: all eligible women in every other household were assessed.

The 2-stage cluster sampling method was used to select 30 clusters from each of the 3 camps. At the first stage, a list of blocks was made before the required number were selected using sampling with probability proportional to size (PPS) using ENA softwareIn nearly all cases, a cluster was the equivalent of a block. However, there were exceptions where, for some larger blocks, more than 1 cluster was selected. In this case, the blocks were split further to cater for more than one cluster. In the event that a selected block had more than 250 households, according to SMART guidance, segmentation was done, after which one of the segments was randomly selected to be the cluster.

All households in the selected clusters were labelled before data collection. At the second stage, the required number of households were selected using systematic random sampling from a list of households. A random number was selected between 1 and the sampling interval, which was calculated by dividing the total number of households in the cluster with the required number of households. The selected number became the first household to be surveyed. Subsequent households were selected by adding the sampling interval until the required number of households were completed. All eligible children below 5 years of age from all selected households were surveyed for the Child Anthropometry and Health, and Infant and Young Child Feeding (IYCF), and WASH. Half of the selected households were selected for the Food Security and Women questionnaire. The survey respondents were the primary caretakers of children below 5 years. Abandoned households were not included in the sampling frame. Absent households or households where children were absent were re-visited before the end of the day. If they were found to be empty, they were recorded as missing and were not replaced. Children who were in health centres at the time of the survey were recorded as absent.

Mode of data collection

Face-to-face [f2f]

Research instrument

1) Children 6-59 months (SENS Modules 1-2): Anthropometric status, oedema, enrolment in selective feeding programmes and blanket feeding programmes (CSB++), immunisation (measles), vitamin A supplementation in last six months, de-worming, morbidity from diarrhoea in past two weeks, haemoglobin assessment. 2) Children 0-23 months (SENS Module 3): Questions on infant and young children feeding practices. 3) Women 15-49 years (SENS Module 2): Pregnancy status, coverage of iron-folic acid pills and post-natal vitamin A supplementation, MUAC measurements for pregnant and lactating women (PLW), and haemoglobin assessment for non-pregnant women. 4) Food Security (SENS Module 4): Access and use of the general food ration (GFR), coping mechanisms when the GFR ran out ahead of time and household food dietary diversity using the food consumption score. 5) WASH (SENS Module 5): Water, sanitation and hygiene- Questions on quality and quantity of drinking water, satisfaction with the drinking water supply, and sanitation facilities

Abstract

The UNHCR Standardized Expanded Nutrition Surveys (SENS) provide regular nutrition data that plays a key role in delivering effective and timely interventions to ensure good nutritional outcomes among populations affected by forced displacement. UNHCR conducted an annual SENS nutrition surveys in Kakuma refugee camp and Kalobeyei Refugee Settlement.

At the time of the survey, the camp was hosting 186,515 refugees originating from 20 countries, comprised of 53.3% (99,320) males and 46.7% (87,195) females. These represented 148,295 from Kakuma and 38,220 from Kalobeyei and originating from 20 nationalities. The number of children under 5 years of age is currently estimated to be 20,468 from Kakuma and 7,576 from Kalobeyei or 15% of the total population. Women of reproductive age were 32,373 from Kakuma and 7,643 from Kalobeyei. According to the United Nations High Commission for Refugees (UNHCR) HIS database (Nov 2018), the main countries of origin are currently South Sudan, 57.8 %, and Somalia, 33.6 %, with the remaining percent originating from various countries in the region including Democratic republic of Congo (6.5%), Ethiopia (5.6%), and Burundi (5.4%) among others

Data collection started on the 27th November of 2019 in Kakuma and 8th of December of 2019 in Kalobeyei settlement. The overall aim of this survey was to assess the general nutrition and health status of refugee population and formulate workable recommendations for appropriate nutritional and public health interventions.

Geographic coverage

Kakuma Refugee Camp and Kalobeyei Refugee Settlement in Turkana County, Kenya

Analysis unit

Children 0-23 months Children 6-59 months Women 15-49 years Households

Universe

Children 0-59 months Women 15-49 years Refugee households

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage cluster survey with probability proportion to size sampling was employed in this survey. Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology to collect and analyse data on child anthropometry and UNHCR's Standardised Expanded Nutrition Survey (SENS) Guidelines for Refugee Populations was used to guide data collection for other indicators.

The same households sampled by SMART were used in all indicators. Anaemia sample was drawn from the SMART sample size, as recommended by the UNHCR Standardised Expanded Nutrition Survey (SENS) Guidelines. For each of the indicators used, households and individuals were sampled as follows:

Household-level indicators: - WASH: every household - Food Security: every other household - Mosquito net: every other household

Individual-level indicators: - Children 0-59 months: all eligible children in all households were assessed (based on the above calculations) - Women 15-49: all eligible women in every other household were assessed.

The sample size for children, 6-59 months, was calculated using ENA for SMART software (9th, July 2015) according to UNHCR SENS guidelines (version 2 (2013). The calculation was based on the expected prevalence of global acute malnutrition (GAM) in children, 6-59 months. A precision of 3.5; a design effect (DEFF) of 1.5 for Kakuma and 1 for Kalobeyei; an average household size of 6.6 in Kakuma and 5.2 in Kalobeyei; and, percentage of children under the age of five was estimated at 19.5% in Kakuma and 14.9% in Kalobeyei, using the UNHCR ProGres data, November 2019.

A two-stage cluster survey was conducted using the Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology to collect and analyse data on child anthropometry. Information on other indicators was collected and analysed using UNHCR's Standardised Expanded Nutrition Survey (SENS) Guidelines for Refugee Populations (Version 2 2013) (see www.sens.unhcr.org).

ENA for SMART selects the clusters (blocks), once done a team was sent to a block to label the households numerically with indelible pens. Population density varies across the blocks at Kakuma. If a block contained 100 households or less, all households in the block were marked. If a Block contained more than 100 households then the team walked around the block to identify a path that divided the block into approximately two halves. One portion of the block was selected randomly. In the selected segment of the block the team proceeded to number all households from the first to the last. If there was more than one household in a particular compound, this was indicated at the entrance of the compound (e.g. 2019 SENS HH1). The numbering and labelling was done two days prior to commencement of the survey.

If there was more than one household in a particular compound, this was indicated at the entrance of the compound (e.g. HH1-HH6). The numbering and labelling was done three days prior to commencement of the survey.

Mode of data collection

Face-to-face interview: Mobile

Research instrument

1) Children 6-59 months (SENS Modules 1-2): Anthropometric status, oedema, enrolment in selective feeding programmes and blanket feeding programmes (CSB++), immunisation (measles), vitamin A supplementation in last six months, de-worming, morbidity from diarrhoea in past two weeks, haemoglobin assessment. 2) Children 0-23 months (SENS Module 3): Questions on infant and young children feeding practices. 3) Women 15-49 years (SENS Module 2): Pregnancy status, coverage of iron-folic acid pills and post-natal vitamin A supplementation, MUAC measurements for pregnant and lactating women (PLW), and haemoglobin assessment for non-pregnant women. 4) Food Security (SENS Module 4): Access and use of the general food ration (GFR), coping mechanisms when the GFR ran out ahead of time and household food dietary diversity using the food consumption score.

The per capita consumer spending on food and non-alcoholic beverages in Seychelles was forecast to continuously increase between 2024 and 2029 by in total ******* U.S. dollars (+***** percent). After the fifth consecutive increasing year, the food-related per capita spending is estimated to reach ********* U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case food-related spending per capita, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group **. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the per capita consumer spending on food and non-alcoholic beverages in countries like Tanzania and Kenya.

Abstract

Afrint intensification of food crops agriculture in sub-Saharan Africa Swedish-African Research Network Agricultural development and its relation to food security and poverty alleviation Primary research in nine sub-Saharan African countries. Afrint was in three phases 200I-2016. Afrint I - 2001-2005: The African Food Crisis, the Relevance of Asian Experiences. Afrint II - 2007-2010: The Millennium Development Goals and the African Food Crisis.

Gender gaps and pro-poor agricultural growth in Malawi and Zambia - (Sida). African Urban Agriculture - Kenya and Ghana (Sida, Formas).

Geographic coverage

Sub Saharan Africa, (Ethiopia,Ghana,Kenya,Malawi,Nigeria,Tanzania,Uganda,Zambia) Regions within selected countries

Analysis unit

Household

Universe

Farming Household

Kind of data

Aggregate data [agg]

Sampling procedure

Data collection for the first round of the Afrint project was made in 2002. The data collected as part of the second round are referred to as 2008 data, although in some cases collected in late 2007. From the outset the research team selected five case study countries: Ghana, Kenya,Malawi, Nigeria and Tanzania. Outside francophone Africa, these five countries were ideally suited, in the researchers view, to charting progress in intensification, induced from below by farmers themselves, or state induced, as in the Asian Green Revolution. At the insistence of Sida, to the original five countries, four more were added: Ethiopia, Mozambique, Uganda and Zambia. Unlioriginal five, the three last mentioned countries were deemed less constrained with respect to productive resources in agriculture. Ethiopia on the other hand is peculiar in an African context, with its long history of plough agriculture, and feudal-like social formation. In this project, the heterogeneous sample of countries has proved less cumbersome to work with than one might have expected.

Formally, the Afrint sample was drawn in four stages, of which the country selection described above was the first one. The next stage was regions within countries, followed by selection of villages within regions, and with selection of farm households as the last stage. All stages except the final one have been based on purposive sampling. Data collection was sought to be made at all four levels.

The households sampled within these countries were selected with respect to the agricultural potential of the areas in which they reside. The intention was to capture the dynamism in the areas that are 'above average' in terms of ecological and market (infrastructure) endowments but excluding the most extreme cases in this regard. For logistical reasons we could not aim for a sample which is representative in a statistical sense. Instead we aimed at a sample which is illustrative of conditions in the maize-cassava belt, excluding both lowpotential dry and remote areas and extreme outliers at the other end of the scale, i.e. privileged high-potential areas.

Thus we used a four-stage sample design, with purposive sampling at all stages, except the last one, where households were sampled after having made up household lists. When we compare point estimates from the sample with those from other sources, for examples yields for the various crops with FAO statistics, no apparent sample bias has been detected. In addition to household questionnaires we also used village questionnaires. Respondents to village interviews were key persons, like village leaders and extension agents. Investigators were also instructed to conduct focus group interviews with representatives for various segments of the village population, including women farmers.

When going for a second round and a panel in 2008, we went for a balanced panel design, i.e. constructing the 2008 sample so that in itself it would be representative of village populations in 2008. This also involved sampling descendants when a household had been partitioned since 2002. In case of sizeable in-migration to a village, we also provided for sampling from the newly arrived households. The 2002-2008 panel thus is a subset of the two cross sectional samples. In itself this subset is not statistically representative of the village population in any of the two years.

Sampling deviation

20.6 percent

Mode of data collection

Face-to-face [f2f]

Research instrument

Scope of Surey Round I (2001-2005)

Population size and land use Agricultural dynamism: agro-ecology Agricultural dynamism: infrastructure and markets State interventions Markets Farmer organisations Land and land tenure Credit History of intensification (indicators) Labour: Economic constraints and facilitating factors Gender aspects:

Scope of Survey Round II (2007-2010).

Section I Village identification Summary on agro-ecological potential Section II General village characteristics Population size and land use Infrastructure land and land tenure Agricultural dynamism: agro-ecology and environmental problems Cattle Section III General village characteristics Credit Contract farming (commercial) Section IV Staple crops: availability and access to varieties Fertilizer Fertilizer access Agricultural techniques Extension Food security indicators

Section V General village characteristics Population size and land use

Land and land tenure Rural-urban linkages Gender dynamics in relation to crops Food security indicators

Cleaning operations

No editing specification given.

Response rate

79.4 percent

Sampling error estimates

No sampling error estimates given.

Data appraisal

No other forms of appraisal given.

The Consuming Urban Poverty (CUP) project - based at the University of Cape Town’s African Centre for Cities - sought to generate an understanding of the connections between poverty, governance, urban space, and food. CUP research focused on secondary cities in three countries: Kisumu, Kenya; Kitwe, Zambia; and Epworth, Zimbabwe.The research included three quantitative surveys: A retail mapping exercise, a food vendor and retailer survey, and a household survey. Over 2,200 households and 1,200 food retailers were interviewed (between April 2016 and February 2017) in the three secondary cities. In addition, nearly 4,500 traders were mapped as part of a retailer census in these cities. The surveys examined the nature of the urban food system and the experience of food poverty. Qualitative in-depth interviews were also carried out in households across the three cities. A qualitative reverse value chain assessment was also undertaken, which traced five key food items (aligned to the food groups of protein, staple, vegetable, traditional food item and snack food) from the point of consumption to origin (or a point where no further information was available) in each city.

Urban areas in sub-Saharan Africa are growing rapidly. While there has been considerable attention paid to the challenges of African mega-cities, the experiences of smaller urban areas have been relatively neglected. Secondary cities, with populations of less than half a million, are absorbing two-thirds of all urban population growth in Africa. This project focuses on three such cities to build a clearer picture of the dynamics of poverty in these kinds of urban spaces and to provide information and insights which can address poverty reduction.

Poverty cannot be understood or addressed by focusing on poor individuals or households alone. Rather it needs to be understood as having many intersecting drivers operating at a range of scales, from the individual, to the neighbourhood, to the city and beyond. Nor can it be understood or addressed by focusing on governance, infrastructure or economic growth, alone. The challenge of this project is to understand the dynamic connections between poverty, governance and urban spaces. We argue that the study of food is a powerful lens to understand these connections. As Carolyn Steel writes, "In order to understand cities properly, we need to look at them through food". The project therefore asks the central question: What does the urban food system in three secondary cities in Africa reveal about the dynamics of urban poverty and its governance, and what are the lessons for generic poverty reduction?

There are significant gaps in knowledge about African urban growth and urban poverty. This project therefore consolidates existing survey and census data to understand patterns and trends of urbanization and poverty in the three case study countries and cities. Because there are data gaps, we will also use remote sensing to generate new data on the spread of urban areas. This information provides the basis for general statements to be made about urban poverty, and for poverty reduction strategies generated in the project to be assessed against a broader representation of poverty.

The project turns its focus to food as a way to understand the connections between poverty, governance and urban space. It will conduct a survey in each of three cities to assess how many households, and what kinds of households and individuals, are unable to get enough safe and nutritious food. Poor nutrition is an important indicator and driver of poverty. Most work on food poverty has focused on the household scale alone. This project argues that if food poverty, and poverty more generally, is to be addressed, it will be necessary to take a broader view and look at the food system. The food system in these cities is shifting rapidly as the supermarket sector increases and the flows of food become more global. This project assesses these changes by mapping the food retail environment, interviewing key people involved in the food system and analyses policy in order to test the impact of a changing food system on food poverty, and what appropriate governance responses might be.

The project therefore scans the globe for useful precedents in addressing urban poverty through strategic planning of, and interventions in the urban food system. Throughout the project the focus will be on working with local governments, NGOs and civil society organisations to generate local solutions that are adaptable to multiple contexts.

The outputs from this project are designed to have both practical and academic impacts. Policy impact will be generated by policy briefs and city reports that support the workshops to be held with municipal officials and policy makers. These will be translated into popular media resources to raise public awareness. Reports addressing urbanization, poverty and governance at a wider scale will be produced. These will be disseminated at major urban events and included in university curricula. Peer-reviewed academic publications will be produced in order to influence academic debates.

Abstract

In a marked departure from its historical past, Ethiopia has been registering robust economic growth and remarkable social and human development over the past two decades. The country has witnessed one of the fastest growing non-oil and non-mineral economies in the world. Ethiopia economic growth has been higher than the growth rates in most African countries and overtook Kenya as East Africa's largest economy in 2017 (IMF 2017). Given this growth path and having recognized the role that growth plays in poverty reduction, the government of Ethiopia has put a strong poverty and welfare monitoring system to monitor progress in poverty reduction on a continuous basis. To this effect, the Government launched Household Income Consumption & Expenditure (HICE) Survey and Welfare Monitoring System (WMS) in 1995/96 and made poverty analysis to be an integral part of the overall Monitoring and Evaluation (M&E) System as part of its endeavor to address the poverty reduction agenda.

So far, the HICES was conducted five times: in 1995/96, 1999/2000, 2004/05, 2010/11 and 2015/16 and have been used as the main official data source for tracking poverty and welfare, informing the policy making body and helping the Government of Ethiopia design and implement a series of poverty reduction strategies and programs since the beginning of 2000s and subsequently. The national development programs and plans such as the Sustainable Development and Poverty Reduction Program (2001/02-2004/5); the Plan for Accelerated and Sustained Development to End Poverty (2005/06-2009/10) and the First Growth and Transformation Plan (2010/11-2014/15) as well as the Second Growth and Transformation Plan (2015/16 - 2019/20) which has already entered in to its third year of implementation (2017/18) have all been informed by the poverty outcomes generated from these surveys.

Geographic coverage

• National Coverage
• Rural and Urban
• Sedentary / Pastoralist areas
• All sub-cities of Addis Ababa, all zones of the Affar, Amhara, Benishangul Gumuz, Dire Dawa Administration, Gambella, Harari, Oromiya, Southern Nations, Nationalities And Peoples’ Region (SNNPR), Somali, and Tigray.

Analysis unit

• Household
• Individual

Universe

The survey covered all household members.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2015/16 HCE survey covered all rural and urban areas of the country. Unlike previous surveys all non-sedentary areas in Afar and Somali regional states are also covered by this survey. A stratified random sampling technique was employed to draw representative sample. The country was first stratified into nine regional states and two city administrations. Then each regional state was further stratified into three broad categories namely, rural, major urban centers and other urban area categories. However, Harari region and Dire Dawa City Administration were stratified into rural and urban categories, while Addis Ababa has only urban category, but stratified by Sub-City. Therefore, each category of a specific region, in most cases, was considered to be a survey domain or reporting level for which the major findings of the survey are reported. Accordingly, the 2015/16 HICE and Welfare Monitoring Surveys have 49 reporting levels.

In the first two categories, namely the rural and major urban, a two-stage stratified sampling technique was implemented whereby the Enumeration Areas (EAs) were considered as a Primary Sampling Unit (PSU) and the households were considered as the Secondary Sampling Unit (SSU). The EAs were selected using the Probability Proportional to Size (PPS); size being the number of households obtained from the 2007 Population and Housing Census, while the sample households were systematically selected from a fresh list of households within the EA made during the survey period.

On the other hand, for the other urban category, a three-stage stratified sampling technique was carried out. In this case, the urban centers, EAs and households were used as a PSU, SSU and the Tertiary Sampling Unit (TSU), respectively. Here, the PSUs and SSUs were selected using the PPS, while the selection of households follow the same approach as described earlier.

The 2015/16 HCE survey sampled 30,255 households in urban and rural areas of the country. Of which a total of 864 EAs and 10,368 households (12 households per EA) were selected to represent rural areas and a total of 1,242 EAs and 19,872 sample households (16 households per EA) were selected for urban domains, specifically, 744 EAs and 11,904 households and 498 EAs and 7,968 households to represent major urban and other urban areas, respectively.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

As per the change to a new method of data collection using digital instrument (CAPI), undoubtedly the survey questionnaire has to redesign. Accordingly, based on the pilot exercise a new draft questionnaire has been developed and distributed to main data users (Governmental and research institute). As a result comments, suggestions, feedbacks and valuable ideas were forwarded from respective users, and the CSA has used these useful inputs to refine and finalize the survey questionnaire.

More details on the questionnaire are provided as external resources.

Cleaning operations

Data editing, coding and cleaning: The primary stage data cleaning task wasn’t carried out manually, rather unlike previous surveys, it was directly carried out by subject matter experts in collaboration with computer programmer. At this stage various editing and coding activities has been conducted. Such as: - Assigning of new codes for newly appeared commodities, especially in the area of prepared food items (particularly out of home consumption), and industrial products. - Correction of measuring unit errors. - Converting of local measuring units to metric equivalents; - Correction of misplaced observations, data being collected using improper modules; and other similar editorial activities has been carried out.

Consistency, Imputation, Validation and Estimation: Data validation and cleaning activity was carried out by subject matter specialists and data processing programmers. The data cleaning and validity checking activities were carried out at commodity, household and visit levels and has been done systematically. Various type of edit specification documents were prepared by subject matter specialists and used for the purpose. Data consistency and validation activities have passed through various phases and steps, such as: - Imputation of missing observations (either value or quantity) using the available results of the price survey that was collected at the time of the survey from a nearby market places; - Validity and consistency of quantity and value of consumption items were checked at a visit level based on internal and/or external price data; - Conversion of consumption of own production into value equivalent using the observed quantities and the external price survey data; - Estimation of calorie contents for out of home consumption; - merging of first and second visit data to produce weekly data sets; - Estimation of annual consumption-expenditure based on weekly data; - Conversion of 3 months reference period data to annual; - Comparison of the household expenditure was made on durable goods that were collected at different reference periods (3 and 12 months) in order to decide whether to utilize the 3 or 12 month's data for the analysis, and similar validation techniques have been carried out.

Response rate

The response rate for the HCE 2015 is 99.9%. Out of the 19,872 sample households, only 11 households were not covered by the survey.

The real per capita consumer spending on food and non-alcoholic beverages in Rwanda was forecast to continuously decrease between 2024 and 2029 by in total **** U.S. dollars (-**** percent). After the eighth consecutive decreasing year, the real food-related per capita spending is estimated to reach ****** U.S. dollars and therefore a new minimum in 2029. Consumer spending, in this case food-related spending per capita, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group **. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.Find more key insights for the real per capita consumer spending on food and non-alcoholic beverages in countries like Kenya and Mozambique.

The real per capita consumer spending on food and non-alcoholic beverages in Zambia was forecast to continuously increase between 2024 and 2029 by in total **** U.S. dollars (+***** percent). According to this forecast, in 2029, the real food-related per capita spending will have increased for the seventh consecutive year to ****** U.S. dollars. Consumer spending, in this case food-related spending per capita, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group **. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.Find more key insights for the real per capita consumer spending on food and non-alcoholic beverages in countries like Kenya and Uganda.

The total consumer spending on food and non-alcoholic beverages in Zimbabwe was forecast to continuously increase between 2024 and 2029 by in total * billion U.S. dollars (+**** percent). After the ******* consecutive increasing year, the food-related spending is estimated to reach **** billion U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case food-related spending, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group **. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period. The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the total consumer spending on food and non-alcoholic beverages in countries like Uganda and Kenya.

As of 2023, roughly 265,000 visitors from the United States arrived in Kenya, the largest number among all other countries. Over 201,000 visitors came from Uganda, while close to 158,000 were from Tanzania, both neighboring nations. Overall, Kenya recorded over 1.9 million international visitor arrivals that year. This shows a steady post-pandemic recovery since 2020, with the number almost reaching pre-pandemic levels recorded in 2019. Changes in airport movements Due to COVID-19 restrictions, passenger flight landings in Kenya decreased severely in the second quarter of 2020. In May 2020, the traffic in the country’s main airports reduced to less than 200 flights, only a few coming from abroad. Thus, 2020 turned out to record one of the lowest performances in the annual number of flight landings in Kenya. However, by the end of that year, signs of recovery were already spotted. The rebounding trend followed through 2021: Passenger flight landings increased by 40 percent compared to 2020, although the number remained 37.5 percent lower than the pre-coronavirus level. Touristic destination for international visitors Being the home of Mount Kenya, the second-highest mountain in Africa, Kenya attracts many tourists with its rich nature and wildlife. In 2021, nearly 300,000 international visitors chose the country as a holiday destination. This corresponded to roughly 35 percent of the total inbound visits. In 2019, previous to the COVID-19 pandemic, direct earnings from international tourism generated around 2.6 billion U.S. dollars. Inbound visitors spent mostly on accommodation, air passenger transport, and food and beverage services.

Kenya's inflation rate decreased to 3.6 percent in September 2024. The indicator measure price variations in goods and services compared to the same month one year earlier. In September 2023, inflation in Kenya stood at 6.8 percent. The current country’s Consumer Price Index (CPI) increased by 0.26 percent compared to the previous month, mainly due to higher prices in food and transport. For 2023, projections indicated that the annual average inflation for Kenya would be above above the rate projected in other African countries, such as South Africa and Tanzania, at almost eight percent. Inflation accelerated by food prices Kenyans feel the inflation effects mostly on groceries since food and non-alcoholic beverages account for roughly one-third of the household expenditure. As of May 2022, the category recorded a surge in prices of 12.4 percent. Furthermore, a deep look into food prices variations in Kenya reveals that vegetables, tubers, plantains, cooking bananas, and pulses were 20 percent more expensive in 2020, compared to 2019. Fruits and nuts recorded an inflation rate of 18.2 percent in the same period. Impacts on food security Fluctuating food prices in Kenya also depend on the varying domestic agricultural output. Particularly, when weather conditions are unfavorable, crop outputs are affected and, consequently, food prices increase - a scenario contributing to the persistence of food insecurity. For instance, around eight million people lacked sufficient food for consumption in Kenya as of November 2021.