Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

The supply of labor available in an economy includes people who are employed, those who are unemployed but seeking work, and first-time job-seekers. Not everyone who works is included: unpaid workers, family workers, and students are often omitted, while some countries do not count members of the armed forces. Data on labor and employment are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes.

The total population in Zimbabwe was estimated at 15.2 million people in 2022, according to the latest census figures and projections from Trading Economics. This dataset provides - Zimbabwe Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Techsalerator’s Job Openings Data in Africa offers a comprehensive and insightful dataset designed to provide businesses, recruiters, labor market analysts, and job seekers with a thorough view of employment opportunities across the African continent. This dataset aggregates job postings from a wide range of sources on a daily basis, ensuring that users have access to the most current and extensive collection of job openings available throughout Africa.

Key Features of the Dataset: Broad Coverage:

The dataset aggregates job postings from numerous sources including company career pages, job boards, recruitment agencies, and professional networking sites. This extensive coverage ensures a broad spectrum of job opportunities from multiple channels. Daily Updates:

Job posting data is updated daily, providing real-time insights into the job market. This frequent updating ensures that the dataset reflects the latest job openings and market trends. Sector-Specific Data:

Job postings are categorized by industry sectors such as technology, healthcare, finance, education, manufacturing, and more. This categorization allows users to analyze trends and opportunities within specific industries. Regional Breakdown:

The dataset includes detailed information on job openings across different countries and regions within Africa. This regional breakdown helps users understand job market dynamics and opportunities in various geographic locations. Role and Skill Insights:

The dataset includes information on job roles, required skills, qualifications, and experience levels. This feature assists job seekers in finding opportunities that match their expertise and helps recruiters identify candidates with the desired skill sets. Company Information:

Users can access details about the companies posting job openings, including company names, industries, and locations. This data provides insights into which companies are hiring and where the demand for talent is highest. Historical Data:

The dataset may include historical job posting data, enabling users to perform trend analysis and comparative studies over time. This feature supports understanding changes and developments in the job market. African Countries Covered: Northern Africa: Algeria Egypt Libya Mauritania Morocco Sudan Tunisia Sub-Saharan Africa: West Africa: Benin Burkina Faso Cape Verde Ivory Coast (Côte d'Ivoire) Gambia Ghana Guinea Guinea-Bissau Liberia Mali Niger Nigeria Senegal Sierra Leone Togo Central Africa: Angola Cameroon Central African Republic Chad Congo, Republic of the Congo, Democratic Republic of the Equatorial Guinea Gabon São Tomé and Príncipe East Africa: Burundi Comoros Djibouti Eritrea Eswatini (Swaziland) Ethiopia Kenya Lesotho Malawi Mauritius Rwanda Seychelles Somalia Tanzania Uganda Southern Africa: Botswana Lesotho Namibia South Africa Swaziland (Eswatini) Zimbabwe Benefits of the Dataset: Enhanced Recruitment Strategies: Recruiters and HR professionals can use the dataset to identify hiring trends, understand competitive practices, and refine recruitment strategies based on real-time market insights. Labor Market Analysis: Analysts and policymakers can leverage the dataset to study employment trends, identify skill gaps, and evaluate job market opportunities across different regions and sectors. Job Seeker Support: Job seekers can access a comprehensive and updated list of job openings tailored to their skills and preferred locations, making their job search more efficient and targeted. Strategic Workforce Planning: Companies can gain valuable insights into the availability of talent across Africa, assisting with decisions related to market expansion, office locations, and talent acquisition. Techsalerator’s Job Openings Data in Africa is a critical resource for understanding the diverse and evolving job markets across the continent. By providing up-to-date and detailed information on job postings, it supports effective decision-making for businesses, job seekers, and labor market analysts.

Unemployment Rate in Zimbabwe decreased to 9.10 percent in 2023 from 9.30 percent in 2022. This dataset provides - Zimbabwe Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Zimbabwe is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers. The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters. The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules. The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.

"People Displaced" refers to the number of people living in displacement as of the end of each year.

"New Displacement" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once.

Contains data from IDMC's data portal.

Zimbabwe ZW: Labour Force With Basic Education: % of Total Working-age Population data was reported at 77.168 % in 2011. Zimbabwe ZW: Labour Force With Basic Education: % of Total Working-age Population data is updated yearly, averaging 77.168 % from Dec 2011 (Median) to 2011, with 1 observations. Zimbabwe ZW: Labour Force With Basic Education: % of Total Working-age Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Labour Force. The percentage of the working age population with a basic level of education who are in the labor force. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average;

Zimbabwe ZW: Unemployment with Intermediate Education: % of Total Labour Force data was reported at 19.590 % in 2011. Zimbabwe ZW: Unemployment with Intermediate Education: % of Total Labour Force data is updated yearly, averaging 19.590 % from Dec 2011 (Median) to 2011, with 1 observations. Zimbabwe ZW: Unemployment with Intermediate Education: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank: Employment and Unemployment. The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average;

Zimbabwe ZW: Employment In Services: Modeled ILO Estimate: Male: % of Male Employment data was reported at 23.529 % in 2017. This records a decrease from the previous number of 24.418 % for 2016. Zimbabwe ZW: Employment In Services: Modeled ILO Estimate: Male: % of Male Employment data is updated yearly, averaging 25.076 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 30.287 % in 1999 and a record low of 17.625 % in 2008. Zimbabwe ZW: Employment In Services: Modeled ILO Estimate: Male: % of Male Employment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Employment and Unemployment. Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

Zimbabwe ZW: Unemployment with Advance Education: Male: % of Male Labour Force data was reported at 6.720 % in 2011. Zimbabwe ZW: Unemployment with Advance Education: Male: % of Male Labour Force data is updated yearly, averaging 6.720 % from Dec 2011 (Median) to 2011, with 1 observations. Zimbabwe ZW: Unemployment with Advance Education: Male: % of Male Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank: Employment and Unemployment. The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average;

This layer contains information about the recurrence of food insecurity calculated during the Integrated Context Analysis (ICA) run in Zimbabwe in 2015. Data source: ZIMVAC 2009-2013, Fewsnet 2009-2013. The key indicator used for the analysis was the recurrence of food insecurity among 20% or above of the population. The 20% threshold was set in order to represent 1 or more out of 5 households/people from the total district population as food insecure.

Original dataset title: ICA Zimbabwe, 2015 - Recurrence of Food Insecurity, 2009-2013

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster. B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low. • Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed. • Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents. • Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group)

o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta. • Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta. • It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Data here cover child labor, gender issues, refugees, and asylum seekers. Children in many countries work long hours, often combining studying with work for pay. The data on their paid work are from household surveys conducted by the International Labour Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. Gender disparities are measured using a compilation of data on key topics such as education, health, labor force participation, and political participation. Data on refugees are from the United Nations High Commissioner for Refugees complemented by statistics on Palestinian refugees under the mandate of the United Nations Relief and Works Agency.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Economic growth is central to economic development. When national income grows, real people benefit. While there is no known formula for stimulating economic growth, data can help policy-makers better understand their countries' economic situations and guide any work toward improvement. Data here covers measures of economic growth, such as gross domestic product (GDP) and gross national income (GNI). It also includes indicators representing factors known to be relevant to economic growth, such as capital stock, employment, investment, savings, consumption, government spending, imports, and exports.

Zimbabwe ZW: Employment To Population Ratio: Modeled ILO Estimate: Aged 15-24 data was reported at 65.360 % in 2017. This records an increase from the previous number of 65.108 % for 2016. Zimbabwe ZW: Employment To Population Ratio: Modeled ILO Estimate: Aged 15-24 data is updated yearly, averaging 65.108 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 68.800 % in 2004 and a record low of 46.036 % in 1997. Zimbabwe ZW: Employment To Population Ratio: Modeled ILO Estimate: Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank: Employment and Unemployment. Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Zimbabwe ZW: Employment To Population Ratio: Modeled ILO Estimate: Aged 15+: Male data was reported at 83.453 % in 2017. This records an increase from the previous number of 83.192 % for 2016. Zimbabwe ZW: Employment To Population Ratio: Modeled ILO Estimate: Aged 15+: Male data is updated yearly, averaging 82.639 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 84.369 % in 2004 and a record low of 72.830 % in 1999. Zimbabwe ZW: Employment To Population Ratio: Modeled ILO Estimate: Aged 15+: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank: Employment and Unemployment. Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

The Gross Domestic Product (GDP) in Zimbabwe expanded 4.50 percent in the fourth quarter of 2023 over the same quarter of the previous year. This dataset provides - Zimbabwe GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Money Supply M1 in Zimbabwe decreased to 66512430.68 ZWL Thousands in January from 79259813.10 ZWL Thousands in December of 2024. This dataset provides - Zimbabwe Money Supply M1 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

Since 2014, UNHCR has undertaken a comprehensive revision of the framework for monitoring UNHCR Livelihoods and Economic Inclusion programs. Since 2017, mobile data collection (survey) tools have been rolled out globally, including in Zimbabwe. The participating operations conducted a household survey to a sample of beneficiaries of each livelihoods project implemented by UNHCR and its partner. The dataset consists of baseline (103 observations) and endline data (89 observations) from the same sample beneficiaries, in order to compare before and after the project implementation and thus to measure the impact.

Geographic coverage

Harare Tongogara

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size for this dataset is: Baseline data : 103 Endline data : 89 Total : 192

The sampling was conducted by each participating operation based on general sampling guidance provided as the following;

• At least 100 randomly selected beneficiaries for each project
• Representativeness of sub-groups (gender, camp, etc.) should be kept as much as possible
• Baseline and endline beneficiaries should be the same

Sampling deviation

Some operations may deviate from the sampling guidance due to local constraints such as logistical and security obstacles.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire used to collect the survey consists of five sections: Partner Information, General Information on Beneficiary, Access to Agricultural Production Enabled and Enhanced, Access to Self-Employment/ Business Facilitated, and Access to Wage Employment Facilitated.

Cleaning operations

The dataset presented here has undergone light checking, cleaning, harmonisation of localised information, and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, and grouping values of select variables). Empty values can occur for several reasons (e.g. no occurrence of agricultural interventions among the beneficiaries will result in empty variables for the agricultural module).

Response rate

Information not available