This dataset provides a range of demographic and socio-economic variables for Registration Sub-Districts (RSDs) in England and Wales, 1851-1911. The measures have mainly been derived from the computerised individual level census enumerators' books (and household schedules for 1911) for England and Wales enhanced under the I-CeM project. I-CeM does not currently include data for 1871, although the project has been able to access a version of the data for that year it does not contain information necessary to calculate many of the variables presented here. Users should therefore beware that 1871 does not contain data for many of the variables. Additional data, for some indicators, has been derived from the tables summarising numbers of births and deaths by year and areas, which were published by the Registrar General in his quarterly, annual and decennial reports of births, deaths and marriages. More information on the data, including overviews of the geographical patterns and changes over time, can be found on the Populations Past – Atlas of Victorian and Edwardian Population website, which provides an interactive mapping facility for these data. The second half of the nineteenth century was a period of major change in the dynamics of the British population. This was a time of transformation from a relatively 'high pressure' demographic regime characterised by medium to high birth and death rates towards a 'low pressure' regime of low birth and death rates, a transformation known as the 'demographic transition'. This transition was not uniform across England and Wales: certain places and social groups appear to have led the declines while others lagged behind. Exploring these geographical patterns can provide insights into the process of change and the influence of economic and geographical factors. This project aimed to utilise the individual-level data of the Integrated Census Microdata (I-CeM) project to calculate age-specific fertility rates both for a range of fine geographical units covering England and Wales and for occupational groups and then to investigate the relationships between these rates and other socioeconomic variables. This was to provide, for the first time, widespread information of the age patterns of fertility which render insight into ‘starting’, ‘spacing’ or ‘stopping’ fertility regulating behaviour. A time series of such measures across geographical and social space is also vital when trying to identify how new forms of behaviour spread through the population. This database contains a variety of measures of fertility, marriage and infant and child mortality, and also a range of socio-economic indicators (related to households, age structure, and social class) for the 2000+ Registration Sub Districts (RSDs) in both England and Wales, for each census year between 1851 and 1871. Most of these data can be mapped using our interactive website www.populationspast.org. This data collection was derived from near complete count individual level census data, from which we have created demographic and socio-economic indicators at a Registration Sub-District level, using a variety of demographic and statistical techniques. For a few variables, birth and death summary data (at Sub-Registration District level) were also used.

Research Hypothesis

This study hypothesizes that soil erosion is driven by a combination of environmental, climatic, and socio-economic factors. It suggests that the interaction of these factors, such as precipitation, land use, and vegetation cover, significantly influences soil erosion patterns across regions and time periods.

Data Overview

The dataset consists of two main components:

Soil Erosion Change Trajectories: Time-series data showing changes in soil erosion over multiple phases, categorized by trends such as increasing, decreasing, or stable erosion levels.

18 Driving Factors: Data on 18 variables, including climatic factors (e.g., precipitation), land use characteristics (e.g., vegetation cover), and socio-economic factors (e.g., population density), that impact soil erosion. These factors were collected through remote sensing, surveys, and publicly available sources.

Findings and Observations

Temporal Trends: Regions with higher precipitation and land disturbance show an increasing trend in soil erosion, while areas with improved land cover exhibit stable or declining erosion. Regional Differences: Erosion levels vary across regions, with some areas showing more severe erosion due to steep slopes and intensive agriculture. Climate and Land Use: Precipitation intensity is a major driver of soil erosion, followed by land use factors like vegetation cover and land management practices. Data Interpretation

Soil Erosion Trajectories: Trajectories show the direction and magnitude of erosion changes, helping to predict future trends and identify high-risk areas. Driving Factors: The analysis helps identify which factors most influence erosion in specific areas, guiding targeted interventions. Data Collection and Usage

The data was collected using remote sensing, field surveys, and climate data, covering a multi-year period. It can be used by researchers and policymakers to identify erosion-prone areas, assess land management practices, and develop predictive models for future erosion under different scenarios.

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demographic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor characteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

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

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country's economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population's susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km.

This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

1. Temporal Coverage of Data: The data collection periods are 2010, 2030, and 2050.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope: The data pertains to the fields of Earth Sciences and Geography.4. Data Volume: The total data volume is approximately 31.5 MB.5. Data Type: Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene): · 7. Field (Feature) Name Explanation:a. Name Explanation: IND: Industrial Value Addedb. Unit of Measurement: Unit: US Dollars (USD)8. Data Source Description:a. Remote Sensing Data:2010 Global Vegetation Index data (Enhanced Vegetation Index, EVI, from MODIS monthly average data) and 2010 Nighttime Light Remote Sensing data (DMSP/OLS)b. Meteorological Data:From the CMCC-CM model in the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC)c. Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agenciesd. Gross Domestic Product Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencese. Other Data:Rivers, roads, settlements, and DEM, sourced from the National Oceanic and Atmospheric Administration (NOAA), Global Risk Data Platform, and Natural Earth9. Data Processing Methods(1) Spatialization of Baseline Industrial Value Added: Using 2010 global EVI vegetation index data and nighttime light remote sensing data, we addressed the oversaturation issue in nighttime light data by constructing an adjusted nighttime light index to obtain the optimal global light data. The EANTIL model was developed using NTL, NTLn, and EVI data, with the following formula:Here, EANTLI represents the adjusted nighttime light index, NTL represents the original nighttime light intensity value, and NTLn represents the normalized nighttime light intensity value. Based on the optimal light index EANTLI and the industrial value-added data from the World Bank, we constructed a regression allocation model to derive industrial value added (I), generating the global 2010 industrial value-added data with the formula:Here, I represents the industrial value added for each grid cell, and Ii represents the industrial value added for each country, EANTLi derived from ArcGIS statistical analysis and the regression allocation model.(2) Spatial Boundaries for Future Industrial Value Added: Using the Logistic-CA-Markov simulation principle and global land use data from 2010 and 2015 (from the European Space Agency), we simulated national land use changes for 2030 and 2050 and extracted urban land data as the spatial boundaries for future industrial value added. To comprehensively characterize the influence of different factors on land use and considering the research scale, we selected elevation, slope, population, GDP, distance to rivers, and distance to roads as land use driving factors. Accuracy validation using global 2015 land use data showed an average accuracy of 91.89%.(3) Estimation of Future Industrial Value Added: Based on machine learning and using the random forest model, we constructed spatialization models for industrial value added under different climate change scenarios: Here, tem represents temperature, prep represents precipitation, GDP represents national economic output, L represents urban land, D represents slope, and P represents population. The random forest model was constructed using factors such as 2010 industrial value added, urban land distribution, elevation, slope, distances to rivers, roads, railways (considering transportation), and settlements (considering noise and environmental pollution from industrial buildings), along with temperature and precipitation as climate scenario data. Except for varying temperature and precipitation values across scenarios, other variables remained constant. The model comprised 100 decision trees, with each iteration randomly selecting 90% of the samples for model construction and using the remaining 10% as test data, achieving a training sample accuracy of 0.94 and a test sample accuracy of 0.81.By analyzing the proportion of industrial value added to GDP (average from 2000 to 2020, data from the World Bank) and projected GDP under future Shared Socioeconomic Pathways (SSPs), we derived future industrial value added for each country under different SSP scenarios. Using these projections, we constructed regression models to allocate future industrial value added proportionally, resulting in spatial distribution data for 2030 and 2050 under different SSP scenarios.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports and Articles):Achievements: Developed a method for downscaling national-scale industrial value-added data by integrating DMSP/OLS nighttime light data, vegetation distribution, and other data. Published the global industrial value-added dataset.

This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals and health system managers in the Municipality of São Paulo, Brazil. The interviews focused in particular on the primary health care services covering two of the poorest sub-municipal districts, Cidade Tiradentes and Sapopemba. The Unequal Voices project – Vozes Desiguais in Portuguese – aimed to strengthen the evidence base on the politics of accountability for health equity via multi-level case studies of health systems in Brazil and Mozambique. The project examined the trajectories of change in the political context and in patterns of health inequalities in Brazil and Mozambique, and carried out four case studies to compare the operation of different accountability regimes across the two countries and between different areas within each country. The case studies tracked shifts in accountability relationships among managers, providers and citizens and changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. In each country the research team studied one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services. Health inequities - that is, inequalities in health which result from social, economic or political factors and unfairly disadvantage the poor and marginalised - are trapping millions of people in poverty. Unless they are tackled, the effort to fulfill the promise of universal health coverage as part of the fairer world envisaged in the post-2015 Sustainable Development Goals may lead to more waste and unfairness, because new health services and resources will fail to reach the people who need them most. In Mozambique, for example, the gap in infant mortality between the best-performing and worst-performing areas actually increased between 1997 and 2008, despite improvements in health indicators for the country as a whole. However, while many low- and middle-income countries are failing to translate economic growth into better health services for the poorest, some - including Brazil - stand out as having taken determined and effective action. One key factor that differentiates a strong performer like Brazil from a relatively weak performer like Mozambique is accountability politics: the formal and informal relationships of oversight and control that ensure that health system managers and service providers deliver for the poorest rather than excluding them. Since the mid-1990s, Brazil has transformed health policy to try to ensure that the poorest people and places are covered by basic services. This shift was driven by many factors: by a strong social movement calling for the right to health; by political competition as politicians realised that improving health care for the poor won them votes; by changes to health service contracting that changed the incentives for local governments and other providers to ensure that services reached the poor; and by mass participation that ensured citizen voice in decisions on health priority-setting and citizen oversight of services. However, these factors did not work equally well for all groups of citizens, and some - notably the country's indigenous peoples - continue to lag behind the population as a whole in terms of improved health outcomes. This project is designed to address the ESRC-DFID call's key cross-cutting issue of structural inequalities, and its core research question "what political and institutional conditions are associated with effective poverty reduction and development, and what can domestic and external actors do to promote these conditions?", by comparing the dimensions of accountability politics across Brazil and Mozambique and between different areas within each country. As Mozambique and Brazil seek to implement similar policies to improve service delivery, in each country the research team will examine one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services, looking at changes in power relationships among managers, providers and citizens and at changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. As two Portuguese-speaking countries that have increasingly close economic, political and policy links, Brazil and Mozambique are also well-placed to benefit from exchanges of experience and mutual learning of the kind that Brazil is seeking to promote through its South-South Cooperation programmes. The project will support this mutual learning process by working closely with Brazilian and Mozambican organisations that are engaged in efforts to promote social accountability through the use of community scorecards and through strengthening health oversight committees, and link these efforts with wider networks working on participation and health equity across Southern Africa and beyond. This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals and health system managers in the Municipality of São Paulo, Brazil. Interviewee sampling was purposive and made use of snowballing. The interviews focused in particular on the primary health care services covering two of the poorest suprefeituras (sub-municipal districts), Cidade Tiradentes and Sapopemba. The dataset includes a mix of transcripts and summary notes from individual and group interviews. All material is in Portuguese.

Abstract copyright UK Data Service and data collection copyright owner.Following changes in the provision of welfare, school milk and school meals in 1971, studies were set up to assess the possible effects of these changes upon the nutritional state of the population. The aim of the National Study of Health and Growth (NSHG) was to set up an anthropometric system of surveillance on selected growth, nutritional and health characteristics that could identify the effects of the changes in food policy. Height was chosen as the main indicator of nutritional status together with weight and triceps skinfold thickness. The primary aim of the study was to estimate trends in anthropometric measurements for children of the same age. Although changes in rate of growth at a given age may occur over time, the main question to be answered was whether there had been any overall shift in the position of the growth curve. The aim of the first five-year study (subsequently referred to as phase I) was to set up the planned anthropometric system of surveillance. It was originally intended to determine the extent and nature of poor nutrition (both over and under nutrition), to identify selected regional, demographic and socio-economic factors associated with childrens' growth, and to investigate the relationships between weight, height and the consumption of milk and school meals. Main Topics: The new entrants data sets include initial recording and measurements of age, gender, height, weight, triceps skinfold thickness, ethnic origin, school milk consumption and number of siblings for all children. Information on birthweight, past history of respiratory illnesses and hospitalisations, number of siblings and current consumption of school milk and meals and other milk and dairy produce (as supplied by the mother or guardian), is recorded for most new entrant children. Details on household composition, height, weight, birthplace, education and employment of parents/guardians, family income and nutritional knowledge are recorded for each family where available. The subsequent follow-up datasets include data from the yearly follow-ups of the children originally identified in the new entrant data. This includes yearly measurements of height, weight, triceps skinfold thickness, peak expiratory flow and consumption of school milk for each child. Information on consumption of school milk and meals and other milk and dairy produce, health, development and hospitalisations in the past year (as supplied by the parents/guardians) are recorded for each follow-up for each child. Details of family income, family size, employment, domestic accommodation heating and cooking arrangements are recorded for each family unit at each follow-up where available. Standard Measures Height was measured on a specially designed Holtain stadiometer to the last complete 0.5cm as recorded by Tanner et al (1966 - see reference below). Triceps skinfold measurements were taken as recommended by Tanner and Whitehouse (1962 - see reference below) except that the midpoint between the tip of the acromion and olecranon was marked with the arm hanging straight instead of bent. For details see 'Fieldworkers Manual' in Appendix 3 of the User Guide. Respiratory questions were adapted from the MRC questionnaire on chronic bronchitis. References Tanner, J.M. and Whitehouse, R.H. (1962) 'Standards for subcutaneous fat in British children : percentiles for thickness of skinfolds over triceps and below scapula' British Medical Journal, 1, pp.446-450. Tanner, J.M., Whitehouse, R.H. and Takaishi, M. (1966) 'Standards from birth to maturity for height, weight, height velocity, and weight velocity: British children 1965' Archives of Disease in Childhood, 41(219), pp.454-471. Employment Exchange areas were selected by stratified random sampling. Within each area schools were selected by Education Authority as considered to best represent stratum characteristics. Within schools all eligible children were selected (except in two areas in Scotland where only even birthdates were included). All new entrants were eligible for subsequent follow-up each year if they continued to attend any of the schools in the study areas, up to the age of 11 years. Face-to-face interview Postal survey Observation Clinical measurements

The 2004 Malawi Demographic and Health Survey (MDHS) is a nationally representative survey of 11,698 women age 1549 and 3,261 men age 15-54. The main purpose of the 2004 MDHS is to provide policymakers and programme managers with detailed information on fertility, family planning, childhood and adult mortality, maternal and child health, as well as knowledge of and attitudes related to HIV/AIDS and other sexually transmitted infections (STIs). The 2004 MDHS is designed to provide data to monitor the population and health situation in Malawi as a followup of the 1992 and 2000 MDHS surveys, and the 1996 Malawi Knowledge, Attitudes, and Practices in Health Survey. New features of the 2004 MDHS include the collection of information on use of mosquito nets, domestic violence, anaemia testing of women and children under 5, and HIV testing of adults. The 2004 MDHS survey was implemented by the National Statistical Office (NSO). The Ministry of Health and Population, the National AIDS Commission (NAC), the National Economic Council, and the Ministry of Gender contributed to the development of the questionnaires for the survey. Most of the funds for the local costs of the survey were provided by multiple donors through the NAC. The United States Agency for International Development (USAID) provided additional funds for the technical assistance through ORC Macro. The Department for International Development (DfID) of the British Government, the United Nations Children's Fund (UNICEF), and the United Nations Population Fund (UNFPA) also provided funds for the survey. The Centers of Disease Control and Prevention provided technical assistance in HIV testing. The survey used a two-stage sample based on the 1998 Census of Population and Housing and was designed to produce estimates for key indicators for ten large districts in addition to estimates for national, regional, and urban-rural domains. Fieldwork for the 2004 MDHS was carried out by 22 mobile interviewing teams. Data collection commenced on 4 October 2004 and was completed on 31 January 2005. The principal aim of the 2004 MDHS project was to provide up-to-date information on fertility and childhood mortality levels, nuptiality, fertility preferences, awareness and use of family planning methods, use of maternal and child health services, and knowledge and behaviours related to HIV/AIDS and other sexually transmitted infections. It was designed as a follow-on to the 2000 MDHS survey, a national-level survey of similar scope. The 2004 MDHS survey, unlike the 2000 MDHS, collected blood samples which were later tested for HIV in order to estimate HIV prevalence in Malawi. In broad terms, the 2004 MDHS survey aimed to: Assess trends in Malawi's demographic indicators, principally fertility and mortality Assist in the monitoring and evaluation of Malawi's health, population, and nutrition programmes Advance survey methodology in Malawi and contribute to national and international databases Provide national-level estimates of HIV prevalence for women age 15-49 and men age 15-54. In more specific terms, the 2004 MDHS survey was designed to: Provide data on the family planning and fertility behaviour of the Malawian population and thereby enable policymakers to evaluate and enhance family planning initiatives in the country Measure changes in fertility and contraceptive prevalence and analyse the factors that affect these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding habits, and important social and economic factors Examine basic indicators of maternal and child health and welfare in Malawi, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, and use of immunisation services. Particular emphasis was placed on malaria programmes, including malaria prevention activities and treatment of episodes of fever. Provide levels and patterns of knowledge and behaviour related to the prevention of HIV/AIDS and other sexually transmitted infections Provide national estimates of HIV prevalence Measure the level of infant and adult mortality including maternal mortality at the national level Assess the status of women in the country. MAIN FINDINGS Fertility Fertility Levels and Trends. While there has been a significant decline in fertility in the past two decades from 7.6 children in the early 1980s to 6.0 children per woman in the early 2000s, compared with selected countries in Eastern and Southern Africa, such as Zambia, Tanzania, Mozambique, Kenya, and Uganda, the total fertility rate (TFR) in Malawi is high, lower only than Uganda (6.9). Family planning Knowledge of Contraception. Knowledge of family planning is nearly universal, with 97 percent of women age 15-49 and 97 percent of men age 15-54 knowing at least one modern method of family planning. The most widely known modern methods of contraception among all women are injectables (93 percent), the pill and male condom (90 percent each), and female sterilisation (83 percent). Maternal health Antenatal Care. There has been little change in the coverage of antenatal care (ANC) from a medical professional since 2000 (93 percent in 2004 compared with 91 percent in 2000). Most women receive ANC from a nurse or a midwife (82 percent), although 10 percent go to a doctor or a clinical officer. A small proportion (2 percent) receives ANC from a traditional birth attendant, and 5 percent do not receive any ANC. Only 8 percent of women initiated ANC before the fourth month of pregnancy, a marginal increase from 7 percent in the 2000 MDHS. Adult and Maternal Mortality. Comparison of data from the 2000 and 2004 MDHS surveys indicates that mortality for both women and men has remained at the same levels since 1997 (11-12 deaths per 1,000). Child health Childhood Mortality. Data from the 2004 MDHS show that for the 2000-2004 period, the infant mortality rate is 76 per 1,000 live births, child mortality is 62 per 1,000, and the under-five mortality rate is 133 per 1,000 live births. Nutrition Breastfeeding Practices. Breastfeeding is nearly universal in Malawi. Ninety-eight percent of children are breastfed for some period of time. The median duration of breastfeeding in Malawi in 2004 is 23.2 months, one month shorter than in 2000. HIV/AIDS Awareness of AIDS. Knowledge of AIDS among women and men in Malawi is almost universal. This is true across age group, urban-rural residence, marital status, wealth index, and education. Nearly half of women and six in ten men can identify the two most common misconceptions about the transmission of HIV-HIV can be transmitted by mosquito bites, and HIV can be transmitted by supernatural means-and know that a healthy-looking person can have the AIDS virus.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

The 2003 Santarem dataset consists of 8 interconnected datasets and 1 linking file. The primary unit of analysis is the rural property or lot. Each lot in the sample contains a minimum of 1 household with a mean of 1.33 households per lot in the final sample. Within households, data were collected on subsets of individuals as well as additional properties used by the households in the study. These 2003 Santarem data come from interviews with farm families in an agricultural zone south of the city of Santarem in the Brazilian state of Para. Santarem is a relatively old settlement within the Brazilian Amazon that has experienced waves of regional settlement in the 1930s, mid-century, and the 1970s. The study region is adjacent to the confluence of the Amazon and Tapajos Rivers and the northern terminus of the BR-163 (the Cuiaba-Santarem Highway). BR-163 links intensive agropastoral production (particularly mechanized soybean farming) in the state of Mato Grosso to Santarem, where the multinational corporation Cargill runs a deepwater port (opened in 2003) for loading soybeans onto oceangoing ships. The opening of this port has accelerated the process of urbanization and led to a transformation from a landscape of small family farming to a landscape of mechanized agriculture (description adapted from VanWey, Leah K., and Kara B. Cebulko, 2007, Journal of Marriage and the Family 69: 1257-1270). The discourse on deforestation has focused on the alarming rates of deforestation in the Amazon Basin to the neglect of the dynamic and reciprocal influences between the human population and the environment. Deforestation is a process mediated by human intervention, from the act of clearing to how such a clearing is used and managed over time. It would be helpful to know whether observable rates of forest removal represent a stage in the developmental cycle of households or represents the simple and direct impact of increasing population in these environments. From the point of view of theory and method, it is necessary to develop new approaches that effectively link demographic process to the interactive relationship of population to specific aspects of an environmental matrix. This project addressed multiple scales, from household dynamics to landscape dynamics and has developed methods by which to scale between them. We hypothesize that as households occupy frontier areas past the first generation, they move from a strategy of managing their land under the constraints of available household labor to a strategy that gives greater recognition of the constraints posed by land quality and of the risks to their farm operation coming from external socioeconomic forces and biophysical constraints. In the first generation, the labor available to a household is determined by the size of the household making the initial trip to the frontier (primarily young couples is common in frontier regions) and later by the fertility of these initial migrants. As these initial migrants age and their children enter adulthood (thereby becoming the second generation), labor supply is determined by the reproductive and land use choices of these children. Given the precipitous decline in female fertility, other factors gain salience in the second generation: the suitability of the land for various uses, the availability of off-farm employment and educational opportunities (both locally and those requiring migration), and macroeconomic factors affecting the economic viability of farming. These decisions then directly determine the entries into and exits from the household. This study investigated five basic questions: (1) Does the changing availability of household labor over the household life cycle affect the trajectory of deforestation and land use change in the same way for later generations of Amazonian farmers as for first generation in-migrants? (2) What are the determinants of changing household labor supply? Specifically, what are the biophysical and socioeconomic determinants of entries into and exits from the household through fertility, migration, and marriage? (3) How are the decisions of households regarding land use and labor allocation constrained by soil quality, access to water supplies, interannual drought events (e.g. El Nino type events), and other resource scarcities? (4) Are there notable differences in land use choices made by la

This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals, civil society representatives and health system managers in Zambézia Province, Mozambique. The Unequal Voices project – Vozes Desiguais in Portuguese – aimed to strengthen the evidence base on the politics of accountability for health equity via multi-level case studies of health systems in Brazil and Mozambique.The project conducted examined the trajectories of change in the political context and in patterns of health inequalities in Brazil and Mozambique, and carried out four cases studies to compare the operation of different accountability regimes across the two countries and between different areas within each country. The case studies tracked shifts in accountability relationships among managers, providers and citizens and changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. In each country the research team studied one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services. Health inequities - that is, inequalities in health which result from social, economic or political factors and unfairly disadvantage the poor and marginalised - are trapping millions of people in poverty. Unless they are tackled, the effort to fulfill the promise of universal health coverage as part of the fairer world envisaged in the post-2015 Sustainable Development Goals may lead to more waste and unfairness, because new health services and resources will fail to reach the people who need them most. In Mozambique, for example, the gap in infant mortality between the best-performing and worst-performing areas actually increased between 1997 and 2008, despite improvements in health indicators for the country as a whole. However, while many low- and middle-income countries are failing to translate economic growth into better health services for the poorest, some - including Brazil - stand out as having taken determined and effective action. One key factor that differentiates a strong performer like Brazil from a relatively weak performer like Mozambique is accountability politics: the formal and informal relationships of oversight and control that ensure that health system managers and service providers deliver for the poorest rather than excluding them. Since the mid-1990s, Brazil has transformed health policy to try to ensure that the poorest people and places are covered by basic services. This shift was driven by many factors: by a strong social movement calling for the right to health; by political competition as politicians realised that improving health care for the poor won them votes; by changes to health service contracting that changed the incentives for local governments and other providers to ensure that services reached the poor; and by mass participation that ensured citizen voice in decisions on health priority-setting and citizen oversight of services. However, these factors did not work equally well for all groups of citizens, and some - notably the country's indigenous peoples - continue to lag behind the population as a whole in terms of improved health outcomes. This project is designed to address the ESRC-DFID call's key cross-cutting issue of structural inequalities, and its core research question "what political and institutional conditions are associated with effective poverty reduction and development, and what can domestic and external actors do to promote these conditions?", by comparing the dimensions of accountability politics across Brazil and Mozambique and between different areas within each country. As Mozambique and Brazil seek to implement similar policies to improve service delivery, in each country the research team will examine one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services, looking at changes in power relationships among managers, providers and citizens and at changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. As two Portuguese-speaking countries that have increasingly close economic, political and policy links, Brazil and Mozambique are also well-placed to benefit from exchanges of experience and mutual learning of the kind that Brazil is seeking to promote through its South-South Cooperation programmes. The project will support this mutual learning process by working closely with Brazilian and Mozambican organisations that are engaged in efforts to promote social accountability through the use of community scorecards and through strengthening health oversight committees, and link these efforts with wider networks working on participation and health equity across Southern Africa and beyond. This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals, civil society representatives and health system managers in Zambézia Province, Mozambique. Interviewee sampling was purposive and made use of snowballing. The interviewers were part of an N’weti/Kula research team coordinated by Denise Namburete (N’weti) and Cristiano Matsinhe (Kula). The collection includes a mix of transcripts and summary notes from individual and group interviews. All material is in Portuguese.

With the UrbanOccupationsOETR, a European Research Council-funded research project hosted at Koç University 2016-2022, we wanted to highlight the importance of rural economic dynamics to explain differences in long-term regional economic development in the late Ottoman Empire. We provide an Excel dataset on the crop-specific agricultural mix and land area of an Ottoman region, Bursa, in the 1840s. This dataset is the result of a new geosampling methodology we devised, representing a key development in the agricultural and overall economic history of Southeast Europe and the Middle East.

The 1840s serve as a good period to choose for base years mainly due to three main factors to sample economic data on a regional scale. First, due to Tanzimat reforms (planned and only partially accomplished transformation of the Ottoman central administration in the mid-nineteenth century), the 1840s marked a watershed of bureaucratical information gathering. Especially, the temettuat registers were created as a by-product to realize a drastic change in tax collection. With at least in its first iteration, the unsuccessful abolishment of tax-farming by the Tanzimat decree in 1839, the Ottoman central administration aimed to transform the existing indirect and communal taxation with direct and individual modalities. To accomplish this goal, the administration had to survey the tax base, which was in disguise due to centuries-long tax farming practices. The temettuat registers were conducted in the core regions of the empire with the main exception of the imperial capital, Istanbul. Second, the 1840s correspond to the last period before the beginning of drastic territorial losses, primarily in Southeast Europe, which triggered in size and frequency unprecedented waves of emigration and immigration between the core territories of the empire both in Southeast Europe as well as in Anatolia, which continued until the official demise or the implosion of the empire. Third and lastly, the 1840s serves as a very suitable point to assess the dynamics of pre-industrial and ancienne regime agricultural dynamics due to the lack of modern means of mechanization, irrigation, and fertilization combined with extremely rudimentary transport facilities.

The temettuat surveys are invaluable resources for they provide agricultural asset- / crop-type specific agricultural mix information with cultivation area per household. However, extracting their detailed information requires a team and years. To overcome this, we developed a sampling strategy that selected five locations per subdistrict using the Analytical Hierarchy Process (AHP), considering factors of agricultural suitability (85% weight), connectivity to historical roads (within a 500-meter to the closest road or to the Danube, 15% weight, justified by its impact on suitability), and subdistrict population size (chosen villages must represent at least 5% of the subdistrict's total population).

Our geosampling methodology of the 1840s tax registers (temettuat) is based on contemporary Ottoman population registers. With this geosampling method, we aim to estimate the regional (district (sancak) and subdistrict (kaza)) level total area of cultivation and shares of the agricultural mix for key products. We are using two mid-nineteenth-century datasets: Ottoman tax (TMT) (temettuat) surveys for agricultural asset / crop type and cultivation area and the population (nüfus) (NFS) registers for population-based sampling. Connectivity is based on a detailed and provenly accurate 1940s German military map of Turkey, Deutsche Heereskarte (DHK). The agricultural suitability raster is an amalgamation of the Land Capability Classification (LCC) encapsulating the variables of soil quality and quantity and the Digital Elevation Model (DEM) based on Shuttle Radar Topography Mission with 30-meter-resolution and comprising elevation, slope, and ruggedness data.

In the end, a geosampling initiative was undertaken across six regions in Southeast Europe and Anatolia, namely Ankara, Bursa, Plovdiv, Ruse, Manisa, and Edirne, covering a total of 277 locations with 17,675 households. Our project team entered the economic data from those records into a Microsoft Access database. We employed a specially crafted data entry template to systematically organize the tax survey data into multiple categories.

After geosampling locations, our objective extended to deriving estimates for the total cultivated area within each subdistrict and regions. To achieve this goal, it was imperative that the data undergoes coding the cultivation areas into a standardized and comparable land-use scheme. We adopted the Corine Land Cover (CLC) nomenclature from the European Union's Earth Observation Programme (Copernicus), established in 1985 and regularly updated. Our study followed the revised guidelines issued by the European Environment Agency on 10.05.2019. Despite its primary design for contemporary land cover analysis, CLC nomenclature proved well-suited for accurately representing the agricultural tax data and the historical context of the 1845 Ottoman tax surveys.

In our analysis, we coded micro-level cultivated land entries associated with individual households, using CLC's highest detail level. Successfully, every cultivated land entry was coded into the third level of detail in CLC, encompassing sub-categories such as 2.1 – “Arable land”, 2.2 – “Permanent crops”, 2.3 – “Pastures”, and 2.4 – “Heterogeneous agricultural areas”—all falling under the overarching category of 2 - Agricultural areas. Additionally, we coded entries related to 3.1 - “Forest” and 3.2 – “Shrub and/or herbaceous vegetation associations”, falling under the primary category of 3 – “Forest and seminatural areas.”

Finally, cultivation area expressed in Ottoman measurement units like dönüm (1/9,2 of a hectare) are converted into hectares to ensure consistency and ease of spatiotemporal comparison.

We provide the geosample data of the Bursa region, positioned in Western Anatolia, renowned for its historical and economic significance, large and cosmopolitan population, and diverse geophysical characteristics. This data covers all the geosampled households, the individuals residing in them, and their CLC-coded agricultural assets / crops with quantity / cultivation area.

The Bursa region comprised 591 geolocated settlements in 12 subdistricts in 1840. Notably, the city of Bursa, serving as the major urban center and regional capital, was intentionally left out of the sample. Additionally, the subdistrict of Pazarköy, with its 14 settlements, was excluded. Despite being initially part of the Bursa region in population registers, it became attached to the northern neighboring Kocaili district in 1845. Consequently, out of the 576 remaining settlements, we geosampled 55 populated places from 11 subdistricts, covering 3547 households, representing 12% of the total households in the region, totaling 30,518. The variables of the tax surveys of the geosampled locations were read, extracted, and entered into the customized Microsoft Access database.

In the Bursa region, there are a total of 13,344 entries for agricultural assets coded with CLC across all 55 sampled populated areas. Our dataset includes all these entries and covers 3,325 households (out of the total of 3,547 sampled households) that owned these assets. This allows for a comprehensive analysis of the agricultural mix and land area at a detailed level.

The tax survey data was transcribed in Turkish using modern Turkish spelling and punctuation to keep the nuances of the original source. That said, because the original register information is largely presented in a standardized fashion and grouped under detailed variables, the data can easily be translated into other languages and coded into specific coding schemes.

The categories and descriptions of the variables of the geosample dataset for the Bursa region are as follows:

The “richness index” represents the level of economical wellbeing a country certain area in 2010. Regions with higher income per capita and low poverty rate and more access to market are wealthier and are therefore better able to prepare for and respond to adversity. The index results from the second cluster of the Principal Component Analysis preformed among 9 potential variables. The analysis identifies four dominant variables, namely “GDPppp per capita”, “agriculture share GDP per agriculture sector worker”, “poverty rate” and “market accessibility”, assigning weights of 0.33, 0.26, 0.25 and 0.16, respectively. Before to perform the analysis all variables were log transformed (except the “agriculture share GDP per agriculture sector worker”) to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1; inverse method was applied for the “poverty rate” and “market accessibility”) in order to be comparable. The 0.5 arc-minute grid total GDPppp is based on the night time light satellite imagery of NOAA (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161) and adjusted to national total as recorded by International Monetary Fund for 2010. The “GDPppp per capita” was calculated dividing the total GDPppp by the population in each pixel. Further, a focal statistic ran to determine mean values within 10 km. This had a smoothing effect and represents some of the extended influence of intense economic activity for the local people. Country based data for “agriculture share GDP per agriculture sector worker” were calculated from GDPppp (data from International Monetary Fund) fraction from agriculture activity (measured by World Bank) divided by the number of worker in the agriculture sector (data from World Bank). The tabular data represents the average of the period 2008-2012 and were linked by country unit to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The first administrative level data for the “poverty rate” were estimated by NOAA for 2003 using nighttime lights satellite imagery. Tabular data were linked by first administrative unit to the first administrative boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The 0.5 arc-minute grid “market accessibility” measures the travel distance in minutes to large cities (with population greater than 50,000 people). This dataset was developed by the European Commission and the World Bank to represent access to markets, schools, hospitals, etc.. The dataset capture the connectivity and the concentration of economic activity (in 2000). Markets may be important for a variety of reasons, including their abilities to spread risk and increase incomes. Markets are a means of linking people both spatially and over time. That is, they allow shocks (and risks) to be spread over wider areas. In particular, markets should make households less vulnerable to (localized) covariate shocks. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-05-15

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

Richness index (2010)

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

The 2000 Malawi Demographic and Health Survey (MDHS) is a nationally representative sample survey covering 14,213 households, 13,220 women age 15-49, and 3,092 men age 15-54. The 2000 MDHS is similar, but much expanded in size and scope, to the 1992 MDHS. The survey was designed to provide information on fertility trends, family planning knowledge and use, early childhood mortality, various indicators of maternal and child health and nutrition, HIV/AIDS, adult and maternal mortality, and malaria control programme indicators. Unlike earlier surveys in Malawi, the 2000 MDHS sample was sufficiently large to allow for estimates of certain indicators to be produced for 11 districts in addition to estimates for national, regional, and urban-rural domains. Twenty-two mobile survey teams, trained and supervised by the National Statistical Office, conducted the survey from July to November 2000. The principal aim of the 2000 MDHS project is to provide up-to-date information on fertility and childhood mortality levels, nuptiality, fertility preferences, awareness and use of family planning methods, use of maternal and child health services, and knowledge and behaviours related to HIV/AIDS and other sexually transmitted infections. It was designed as a follow-on to the 1992 MDHS survey, a national-level survey of similar scope. The 2000 MDHS survey also strived to collect data that would be comparable to those collected under the international Multiple Indicator Cluster Survey (MICS), sponsored by UNICEF. In broad terms, the 2000 MDHS survey aimed to : Assess trends in Malawi's demographic indicators-principally, fertility and mortality Assist in the evaluation of Malawi's health, population, and nutrition programmes Advance survey methodology in Malawi and contribute to national and international databases. In more specific terms, the 2000 MDHS survey was designed to provide data on the family planning and fertility behaviour of the Malawian population and to thereby enable policymakers to evaluate and enhance family planning initiatives in the country. Measure changes in fertility and contraceptive prevalence and at the same time, study the factors that affect these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding habits, and important social and economic factors. Examine basic indicators of maternal and child health and welfare in Malawi, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, and use of immunisation services. A particular emphasis was placed on the area of malaria programmes, including prevention activities and treatment of episodes of fever. Describe levels and patterns of knowledge and behaviour related to the prevention of HIV/AIDS and other sexually transmitted infections. Measure the level of adult and maternal mortality at the national level. Assess the status of women in the country. SUMMARY OF FINDINGS FERTILITY Fertility Decline. The 2000 MDHS data indicate that there has been a modest decline in fertility since the 1992 MDHS. Large Fertility Differentials. Fertility levels remain high in Malawi, especially in rural parts of the country. The total fertility rate among rural women is 6.7 births per woman compared with 4.5 births in urban areas. Childbearing at Young Ages. One-third of adolescent females (age 15-19) have either already had a child or are currently pregnant. FAMILY PLANNING Increasing Use of Contraception. A principle cause of the fertility decline in Malawi is the steady increase in contraceptive use over the last decade. Changing Method Mix. Currently, the most widely used methods among married women are injectable contraceptives (16 percent), female sterilisation (5 percent), and the pill (3 percent). Source of Family Planning Methods. The survey results show that government-run facilities remain the major source for contraceptives in Malawi-providing family planning methods to 68 percent of the current users. CHILD HEALTH AND SURVIVAL Progress in Reducing Early Childhood Mortality. The 2000 MDHS data indicate that mortality of children under age 5 has declined since the early 1990s. Childhood Vaccination Coverage Declines. The 2000 MDHS results show that 70 percent of children age 12-23 months are fully vaccinated. Improved Breastfeeding Practices. The 2000 MDHS results show that exclusive breast-feeding of children under 4 months of age has increased to 63 percent from only 3 percent in the 1992 MDHS. Nutritional Status of Children. The results show no appreciable change in the nutritional status of children in Malawi since 1992; still, nearly half (49 percent) of the children under age five are chronically malnourished or stunted in their growth. MALARIA CONTROL PROGRAMME INDICATORS Bednets. The use of insecticide-treated bednets (mosquito nets) is a primary health intervention proven to reduce malaria transmission. Treatment of Fever in Children Under Age Five. The survey found that 42 percent of children under age five had a fever in the two weeks preceding the survey. WOMEN'S HEALTH Maternal Health Care. The survey findings indicate that use of antenatal services remains high in Malawi. Constraints to Use of Health Services. Women in the 2000 MDHS were asked whether certain circumstances constrain their access to and use of health services for themselves. Rising Maternal Mortality. The survey collected data allowing measurement of maternal mortality. For the period 1994-2000, the maternal mortality ratio was estimated at 1,120 maternal deaths per 100,000 live births. This represents a rise from 620 maternal deaths per 100,000 estimated from the 1992 MDHS for the period 1986-1992. HIV/AIDS Impact of the Epidemic on Adult Mortality. All-cause mortality has risen by 76 percent among men and 74 percent among women age 15-49 during the 1990s. The age patterns of the increase are consistent with causes related to HIV/AIDS. Improved Knowledge of AIDS Prevention Methods. The 2000 MDHS results indicate that practical AIDS prevention knowledge has improved since the 1996 MKAPH survey. Condom Use. One of the main objectives of the National AIDS Control Programme is to encourage consistent and correct use of condoms, especially in high-risk sexual encounters. The HIV-testing Experience. The 2000 MDHS data show that 9 percent of women and 15 percent of men have been tested for HIV. However, more than 70 percent of both men and women, while not yet tested, said that they would like to be tested.

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 HIECS 2010/2011 is the tenth Household Income, Expenditure and Consumption Survey that was carried out in 2010/2011, among a long series of similar surveys that started back in 1955. The survey main objectives are:

• To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials.

• To measure average household and per-capita expenditure for various expenditure items along with socio-economic correlates.

• To Measure the change in living standards and expenditure patterns and behavior for the individuals and households in the panel sample, previously surveyed in 2008/2009, for the first time during 12 months representing the survey period.

• To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation.

• 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 define average 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.

• 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 nutrition components and the levels of expenditure in both urban and rural areas.

• To identify the value of expenditure for food according to its 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 equipments such as (cars, satellites, mobiles ,…etc) in urban and rural areas that enables measuring household wealth index.

• To identify the percentage distribution of income earners according to some background variables such as housing conditions, size of household and characteristics of head of household.

Compared to previous surveys, the current survey experienced certain peculiarities, among which :

1- The total sample of the current survey (26.5 thousand households) is divided into two sections:

a- A new sample of 16.5 thousand households. This sample was used to study the geographic differences between urban governorates, urban and rural areas, and frontier governorates as well as other discrepancies related to households characteristics and household size, head of the household's education status, etc.

b- A panel sample with 2008/2009 survey data of around 10 thousand households was selected to accurately study the changes that may have occurred in the households' living standards over the period between the two surveys and over time in the future since CAPMAS will continue to collect panel data for HIECS in the coming years.

2- The number of enumeration area segments is reduced from 2526 in the previous survey to 1000 segments for the new sample, with decreasing the number of households selected from each segment to be (16/18) households instead of (19/20) in the previous survey.

3- Some additional questions that showed to be important based on previous surveys results, were added, such as:

a- Collect the expenditure data on education and health on the person level and not on the household level to enable assessing the real level of average expenditure on those services based on the number of beneficiaries.

b- The extent of health services provided to monitor the level of services available in the Egyptian society.

c- Smoking patterns and behaviors (tobacco types- consumption level- quantities purchased and their values).

d- Counting the number of household members younger than 18 years of age registered in ration cards.

e- Add more details to social security pensions data (for adults, children, scholarships, families of civilian martyrs due to military actions) to match new systems of social security.

f- Duration of usage and current value of durable goods aiming at estimating the service cost of personal consumption, as in the case of imputed rents.

4- Quality control procedures especially for fieldwork, are increased, to ensure data accuracy and avoid any errors in suitable time, as well as taking all the necessary measures to guarantee that mistakes are not repeated, with the application of the principle of reward and punishment. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality. Data is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

National

Analysis unit

1- Household/family

2- Individual/Person

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 sample of HIECS, 2010-2011 is a self-weighted two-stage stratified cluster sample, of around 26500 households. The main elements of the sampling design are described in the following:

1- Sample Size It has been deemed important to collect a smaller sample size (around 26.5 thousand households) compared to previous rounds due to the convergence in the time period over which the survey is conducted to be every two years instead of five years because of its importance. The sample has been proportionally distributed on the governorate level between urban and rural areas, in order to make the sample representative even for small governorates. Thus, a sample of about 26500 households has been considered, and was distributed between urban and rural with the percentages of 47.1 % and 52.9, respectively. This sample is divided into two parts: a- A new sample of 16.5 thousand households selected from main enumeration areas. b- A panel sample with 2008/2009 survey data of around 10 thousand households.

2- Cluster size The cluster size in the previous survey has been decreased compared to older surveys since large cluster sizes previously used were found to be too large to yield accepted design effect estimates (DEFT). As a result, it has been decided to use a cluster size of only 16 households (that was increased to 18 households in urban governorates and Giza, in addition to urban areas in Helwan and 6th of October, to account for anticipated non-response in those governorates: in view of past experience indicating that non-response may almost be nil in rural governorates). While the cluster size for the panel sample was 4 households.

3- Core Sample The core sample is the master sample of any household sample required to be pulled for the purpose of studying the properties of individuals and families. It is a large sample and distributed on urban and rural areas of all governorates. It is a representative sample for the individual characteristics of the Egyptian society. This sample was implemented in January 2010 and its size reached more than 1 million household (1004800 household) selected from 5024 enumeration areas distributed on all governorates (urban/rural) proportionally with the sample size (the enumeration area

The National Association of County and City Health Officials (NACCHO) used a stratified random sampling design for the 2014 Forces of Change Survey. A representative sample was used instead of a complete census design to minimize survey burden on local health departments (LHD) while enabling the calculation of both national- and state-level estimates. LHDs were stratified by two variables: state and size of the population served. For stratification by size of population served, three categories were used: small (less than 50,000 people served), medium (50,000-499,999 people served), and large (500,000 or more people served). Because LHDs with large population sizes represent a relatively small portion of all LHDs, these LHDs were oversampled to ensure a sufficient number of responses for the analysis. Two states (Hawaii and Rhode Island) were excluded from the study because they had no LHDs. In addition, some states did not have any LHDs in a particular size category, resulting in a total of 122 strata. The sampling plan was designed to select a minimum of 33 percent of the LHDs in a given stratum and at least two LHDs per stratum whenever possible. Once the sampling plan was finalized, NACCHO drew a random sample of the specified size from within each stratum. In some centralized states, two or more LHDs had the same person listed as the contact person. To minimize response burden, no more than two LHDs with the same contact person were kept in the sample. However, two contacts in Alabama received three surveys each because additional contacts in their state were not available. When LHDs with a common contact person were dropped from sample, or when contact information was not available, a replacement was drawn. Overall, a sample of 952 LHDs was selected of which 648 responded to the survey. The National Association of County and City Health Officials' (NACCHO) Forces of Change Survey is an evolution of NACCHO's Job Losses and Program Cuts Surveys (also known as the Economic Surveillance Surveys) which measured the impact of the economic recession on local health departments' (LHD) budgets, staff, and programs. The Forces of Change Survey continues to measure changes in LHD budgets, staff, and programs and assess more broadly the impact of forces affecting change in LHDs, such as health reform and accreditation. More specifically, the survey collected information about LHD staffing levels, workforce reductions, and changes in budget sizes; provided services or functions; changes in the level of service delivery; billing for clinical services; efforts to help people enroll in health insurance from exchanges under the Affordable Care Act; awareness of and involvement in the State Innovation Models Initiative; participation in the Public Health Accreditation Board's national accreditation program for LHDs; and whether LHDs are part of a combined health and human services agency. The collection is comprised of the public-use version (Restricted-Use Level 1) of the Forces of Change 2014 dataset, and includes 133 variables for 648 cases, with demographic variables related to LHD budgets, governance type, and number of employees. The National Association of County and City Health Officials (NACCHO) administered the questionnaire using Qualtrics, an online survey administration tool. On January 15, 2014, the designated primary contact of every local health department (LHD) in the sample received an invitation via e-mail from NACCHO's president to participate in the survey. The survey link was sent via Qualtrics on January 23, 2014. After the initial invitation, the potential participants received up to five reminder e-mails. In addition, NACCHO made reminder calls to people who had yet to complete the survey, targeting states with low response rates. Some state associations of county and city health officials (SACCHOs) assisted by encouraging their members to take part in the survey. NACCHO generated national statistics using estimation weights to account for sampling and non-response. All data were self-reported; NACCHO did not independently verify the data provided by LHDs. A detailed description of survey methodology is available on NACCHO's Forces of Change webpage. The National Association of County and City Health Officials' (NACCHO) Forces of Change Survey was developed as an evolution to NACCHO's Job Losses and Program Cuts surveys, which measured the impact of the economic recession on local health departments' (LHD) budgets, staff, and programs. The Forces of Change Survey measured changes in LHD budgets, staff, and programs and assessed more broadly the impact of forces affecting change in LHDs (such as health reform). Beginning in 2014, NACCHO began conducting the Forces of Change survey yearly in years that the National Profile Study of Local Health Departments was not fielded. web-based surveyAdditional information about this study is available on the Forces of Change website.The restricted-use da...

This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals and health system managers in Maputo, Mozambique. The Unequal Voices project – Vozes Desiguais in Portuguese – aimed to strengthen the evidence base on the politics of accountability for health equity via multi-level case studies of health systems in Brazil and Mozambique. The project conducted examined the trajectories of change in the political context and in patterns of health inequalities in Brazil and Mozambique, and carried out four cases studies to compare the operation of different accountability regimes across the two countries and between different areas within each country. The case studies tracked shifts in accountability relationships among managers, providers and citizens and changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. In each country the research team studied one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services.Health inequities - that is, inequalities in health which result from social, economic or political factors and unfairly disadvantage the poor and marginalised - are trapping millions of people in poverty. Unless they are tackled, the effort to fulfill the promise of universal health coverage as part of the fairer world envisaged in the post-2015 Sustainable Development Goals may lead to more waste and unfairness, because new health services and resources will fail to reach the people who need them most. In Mozambique, for example, the gap in infant mortality between the best-performing and worst-performing areas actually increased between 1997 and 2008, despite improvements in health indicators for the country as a whole. However, while many low- and middle-income countries are failing to translate economic growth into better health services for the poorest, some - including Brazil - stand out as having taken determined and effective action. One key factor that differentiates a strong performer like Brazil from a relatively weak performer like Mozambique is accountability politics: the formal and informal relationships of oversight and control that ensure that health system managers and service providers deliver for the poorest rather than excluding them. Since the mid-1990s, Brazil has transformed health policy to try to ensure that the poorest people and places are covered by basic services. This shift was driven by many factors: by a strong social movement calling for the right to health; by political competition as politicians realised that improving health care for the poor won them votes; by changes to health service contracting that changed the incentives for local governments and other providers to ensure that services reached the poor; and by mass participation that ensured citizen voice in decisions on health priority-setting and citizen oversight of services. However, these factors did not work equally well for all groups of citizens, and some - notably the country's indigenous peoples - continue to lag behind the population as a whole in terms of improved health outcomes. This project is designed to address the ESRC-DFID call's key cross-cutting issue of structural inequalities, and its core research question "what political and institutional conditions are associated with effective poverty reduction and development, and what can domestic and external actors do to promote these conditions?", by comparing the dimensions of accountability politics across Brazil and Mozambique and between different areas within each country. As Mozambique and Brazil seek to implement similar policies to improve service delivery, in each country the research team will examine one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services, looking at changes in power relationships among managers, providers and citizens and at changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. As two Portuguese-speaking countries that have increasingly close economic, political and policy links, Brazil and Mozambique are also well-placed to benefit from exchanges of experience and mutual learning of the kind that Brazil is seeking to promote through its South-South Cooperation programmes. The project will support this mutual learning process by working closely with Brazilian and Mozambican organisations that are engaged in efforts to promote social accountability through the use of community scorecards and through strengthening health oversight committees, and link these efforts with wider networks working on participation and health equity across Southern Africa and beyond. This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals and health system managers in Maputo, Mozambique. Interviewee sampling was purposive and made use of snowballing. The interviewers were part of an N’weti/Kula research team coordinated by Denise Namburete (N’weti) and Cristiano Matsinhe (Kula). The collection includes a mix of transcripts and summary notes from individual and group interviews. All material are in Portuguese.

ObjectiveThe objective of this study was to identify factors influencing the development of China-ASEAN trade- from the total economic volume of both sides, distance, the population size of ASEAN countries, the construction of a free trade area, and the signing of the Belt and Road initiative, resource endowment per capita, the exchange rate between RMB and ASEAN countries, and the land area of ASEAN countries—to develop a conceptual framework for China-ASEAN trade potential.Study designThis study uses panel data from 2001 to 2021 that is evenly distributed among 10 ASEAN countries to serve as the dataset. Firstly, the unit roots are checked and the cointegration relationships are examined, focusing on the heterogeneity test. Based on the classical trade gravity model, the innovative trade gravity model with key influencing factors is constructed. On the basis of the classical trade gravity model, an innovative trade gravity model of key influencing factors is constructed. The trade potential model is used to calculate the direct trade potential coefficient between China and ASEAN countries, which points out the direction for the sustainability of bilateral trade.ResultsThis study finds that among the factors affecting China-ASEAN bilateral trade, the total economic output of both sides, distance, population size of ASEAN countries, the construction of the FTA, and the signing of the Belt and Road Initiative all have a positive impact on bilateral trade. Three influencing factors, namely per capita resource endowment, exchange rate between RMB and ASEAN countries, and the size of ASEAN countries, have a negative impact on bilateral trade, but to a lesser extent. The trade potential between China and Vietnam falls into the category of potential re-modelling, indicating that both sides are currently utilizing their trade potential to the greatest extent possible, that trade growth space is limited, and that new trade opportunities must be discovered. The trade potential index between China and nine ASEAN countries, excluding Vietnam, is in the potential-exploiting category, indicating that the potential has not been fully utilized by both sides and that there is still room for growth in the scale of trade between the two countries.ConclusionWith the shift of the world’s economic center of gravity in the direction of Asia following COVID-19, China and ASEAN countries should seize the opportunity to strengthen their comprehensive strength and economic aggregates and further develop China’s constructive role in the regional organization. The signing of the Belt and Road Initiative and the construction of a free trade zone has had a positive effect on the development of bilateral trade. Propose that: positive trade factors should continue to be strengthened, trade barriers should be removed, and new dynamics of bilateral trade growth should be enhanced.

The Italian Labour Force Survey is the main source of statistical information on the Italian labor market. The information gathered from the population constitutes the basis on which official estimations of employment and unemployment are calculated, as well as information on the main job’s issues –occupation, the sector of economic activity, hours worked, contracts’ type and duration, training. The survey data are used to analyze a number of individual, family and social factors too, such as the increasing labor mobility, changing professions, the growth in female participation, etc.., which determine the difference in labor participation of the population. Starting from the first quarter of 2021, the indications of European Regulation 1700/2019 have been transposed, which concern in particular the changes in the definitions of family and employee, and a new questionnaire has been adopted (see notes). The questionnaire is divided into several sections. In particular, in addition to the first socio-demographic information, the first section covers the employment status during the interview’s week, dealing with questions about the type of work, hours worked, reasons for not working. The second section – reserved for employed people – covers the main job, investigating, in particular, the position in the profession, the industry in which he works, the company he works for, the type of contract, working full-time or part-time and reasons for his selection, working hours, overtime hours, shift work, night and weekend work, job transfer, salary, job satisfaction. The third section – always reserved for employed people – concerns the secondary work (if any). It’s exclusively addressed to respondents who carry out another activity compared to the main one and only detects certain information such as the type of activity, type of contract, occupation, the economic sector he works in, hours worked. The fourth section – for unemployed people – collects information about previous work experiences: last work, type of contract, occupation, economic sector, the reasons for the interruption of work. The fifth section deals with the job search. It investigates the reason for seeking a job, the actions put in place to look for it, the channels used to look for and the type of work sought. The sixth section deals with self-perceived employment conditions, and retirement. The seventh section concerns employment services and employment agencies, and investigates their use by the respondents: quantity of contacts, reason for contact, services required. The eighth section concerns education and training: degree obtained, course of study currently attended, professional training. The last section focuses on the self-perception of the employment status, compared to the previous year. 129,313 individuals, 58,499 families. Two-stage stratified random sample Computer-Assisted Telephone Interviewing (CATI) Computer-Assisted Personal Interviewing (CAPI)

The Italian Labour Force Survey is the main source of statistical information on the Italian labor market. The information gathered from the population constitutes the basis on which official estimations of employment and unemployment are calculated, as well as information on the main job’s issues –occupation, the sector of economic activity, hours worked, contracts’ type and duration, training. The survey data are used to analyze a number of individual, family and social factors too, such as the increasing labor mobility, changing professions, the growth in female participation, etc.., which determine the difference in labor participation of the population. Starting from the first quarter of 2021, the indications of European Regulation 1700/2019 have been transposed, which concern in particular the changes in the definitions of family and employee, and a new questionnaire has been adopted (see notes). The questionnaire is divided into several sections. In particular, in addition to the first socio-demographic information, the first section covers the employment status during the interview’s week, dealing with questions about the type of work, hours worked, reasons for not working. The second section – reserved for employed people – covers the main job, investigating, in particular, the position in the profession, the industry in which he works, the company he works for, the type of contract, working full-time or part-time and reasons for his selection, working hours, overtime hours, shift work, night and weekend work, job transfer, salary, job satisfaction. The third section – always reserved for employed people – concerns the secondary work (if any). It’s exclusively addressed to respondents who carry out another activity compared to the main one and only detects certain information such as the type of activity, type of contract, occupation, the economic sector he works in, hours worked. The fourth section – for unemployed people – collects information about previous work experiences: last work, type of contract, occupation, economic sector, the reasons for the interruption of work. The fifth section deals with the job search. It investigates the reason for seeking a job, the actions put in place to look for it, the channels used to look for and the type of work sought. The sixth section deals with self-perceived employment conditions, and retirement. The seventh section concerns employment services and employment agencies, and investigates their use by the respondents: quantity of contacts, reason for contact, services required. The eighth section concerns education and training: degree obtained, course of study currently attended, professional training. The last section focuses on the self-perception of the employment status, compared to the previous year. 120,351 individuals, 52,387 families. Two-stage stratified random sample Computer-Assisted Telephone Interviewing (CATI) Computer-Assisted Personal Interviewing (CAPI)