Employment by socio-economic classification and sex, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.

Abstract copyright UK Data Service and data collection copyright owner.

This research project aims to provide an in-depth study of the problems encountered in developing quantitative socio-economic indicators for urban and regional policy analysis in Britain. The project consisted of two stages. The data from the first stage of the study are held under SN:3973.
The aims of this part of the study, the second stage, are to address two main objectives, through :
1. The compilation of a meta-database to assess the quality of information sources that are relevant to Local Economic Development (LED), which will provide useful guidelines for future public data compilation practice and will be of use to other researchers in the field.
2. The compilation of an up-to-date database of indicators for LED for the analysis of the following :
what extent the existence of information gaps in public statistical sources affects indicator research on LED;
which particular dimension of LED is most affected;
what other non-public sources of information are available for the measurement of LED;
what are the best data handling techniques to explore the statistical properties of indicators and to carry out preliminary validation analysis of the assembled database;
in what ways could innovative data processing and analysis enhance the interpretation of indicators and maximise the intelligence yielded from the information available;
in what form should indicators be aggregated to gear to the needs of policy-makers;
what are the most appropriate weighting methods to create multivariate indexes;
in what ways do different weighting methods affect the outcome of the final analysis.
Main Topics:

The data file for this study contains 61 variables from the 366 Local Authority Districts in England (before the 1996 local government reorganisation of boundaries). Apart from the LAD (Local Authority District) code and the matching UALAD (Unitary Authority and Local Authority District) code, the other 59 variables are indicators measuring different contributing factors to the process of LED. Eleven key factors were identified, through literature review and a survey of policy-makers, to be important to the process of LED :
locational factors, physical factors, infrastructure, human resources, finance and capital, knowledge and technology, industrial structure, business culture, community image and identity, institutional capacity and quality of life.

Standardisation was applied to the raw data to develop some indicators to enhance interpretation. In some cases, the raw data are expressed as a percentage share of the national sum; in other cases, the case value is expressed as a ratio of the national (English) average value.

Abstract copyright UK Data Service and data collection copyright owner.

This study is comprised by the data collected for a wider project exploring the historical relationship between higher education and the UK economy. The project sought to provide a long-term explanation of the relationships between funding, widening access and socio-economic aspects of higher education. Three main areas were considered:
-The provision of an in-depth historical account and analysis of the numbers and extent of students and staff for the purposes of evaluating the main characteristics of UK higher education development back the 1920s.
-The provision of an in-depth historical account and evaluation of levels and structures of income and expenditure in higher education
-The interpretation of these data with reference to major socio-economic indicators.

Main Topics:

This study is a collation and analysis of statistics on UK higher education which refers to pre-1992 universities and includes all institutions delivering degrees afterwards. The dataset, which gathers historical series on funding and development of universities from the early 1920s, is the result of research into primary and secondary governmental and institutional sources.

Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

Abstract copyright UK Data Service and data collection copyright owner.

The Cohort and Longitudinal Studies Enhancement Resources (CLOSER) project aims to maximise the use, value and impact of longitudinal research. It brings together leading longitudinal studies, the British Library and the UK Data Service, to stimulate interdisciplinary research, develop shared resources, provide training, and share expertise. Resources available from CLOSER include harmonised datasets (developed to facilitate cross-study comparisons) and CLOSER Discovery (a search engine that enables researchers to search and browse questionnaires and data from different longitudinal studies). CLOSER’s website also includes a range of training materials focused on longitudinal studies and data. CLOSER is funded by the Economic and Social Research Council (ESRC) and is based at the UCL Institute of Education.

The CLOSER Work Package 2 provides the harmonisation of socio-economic measures between the National Survey of Health and Development (NSHD), National Child Development Study (NCDS), 1970 British Cohort Study (BCS70), and the Millennium Cohort Study (MCS).

Further information can be found on the CLOSER website. The content of longitudinal studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The Harmonised Socio-Economic Measures in Four Longitudinal Cohort Studies: National Child Development Study provides the harmonised socio-economic measures for the NCDS.

For the second edition (October 2019), revised data and documentation have been deposited.
Main Topics:
The main topics covered are:

• socio-economic measures;
• occupational classification;
  
• social and occupational mobility.
  

The Cohort and Longitudinal Studies Enhancement Resources (CLOSER) project aims to maximise the use, value and impact of longitudinal research. It brings together leading longitudinal studies, the British Library and the UK Data Service, to stimulate interdisciplinary research, develop shared resources, provide training, and share expertise. Resources available from CLOSER include harmonised datasets (developed to facilitate cross-study comparisons) and CLOSER Discovery (a search engine that enables researchers to search and browse questionnaires and data from different longitudinal studies). CLOSER’s website also includes a range of training materials focused on longitudinal studies and data. CLOSER is funded by the Economic and Social Research Council (ESRC) and is based at the UCL Institute of Education.

The CLOSER Work Package 2 provides the harmonisation of socio-economic measures between the National Survey of Health and Development (NSHD), National Child Development Study (NCDS), 1970 British Cohort Study (BCS70), and the Millennium Cohort Study (MCS).

Further information can be found on the CLOSER website. The content of longitudinal studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The Harmonised Socio-Economic Measures in Four Longitudinal Cohort Studies: Millennium Cohort Study provides the harmonised socio-economic measures for the MCS.

For the second edition (October 2019), revised data and documentation have been deposited.

Abstract copyright UK Data Service and data collection copyright owner.

The Cohort and Longitudinal Studies Enhancement Resources (CLOSER) project aims to maximise the use, value and impact of longitudinal research. It brings together leading longitudinal studies, the British Library and the UK Data Service, to stimulate interdisciplinary research, develop shared resources, provide training, and share expertise. Resources available from CLOSER include harmonised datasets (developed to facilitate cross-study comparisons) and CLOSER Discovery (a search engine that enables researchers to search and browse questionnaires and data from different longitudinal studies). CLOSER’s website also includes a range of training materials focused on longitudinal studies and data. CLOSER is funded by the Economic and Social Research Council (ESRC) and is based at the UCL Institute of Education.

The CLOSER Work Package 2 provides the harmonisation of socio-economic measures between the National Survey of Health and Development (NSHD), National Child Development Study (NCDS), 1970 British Cohort Study (BCS70), and the Millennium Cohort Study (MCS).

Further information can be found on the CLOSER website. The content of longitudinal studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The Harmonised Socio-Economic Measures in Four Longitudinal Cohort Studies: 1970 British Cohort Study provides the harmonised socio-economic measures for the BCS70.

For the second edition (October 2019), revised data and documentation have been deposited.

Main Topics:
The main topics covered are:

• socio-economic measures;
• occupational classification;
  
• social and occupational mobility.
  

• Lower Townsend scores represent areas associated with more affluent socioeconomic status.Demographic characteristics of participants in the UK Biobank study subsamples for sex, ethnicity and socio-economic status (Townsend deprivation index).

The Census@Leicester datasets include socio-demographic data from the 2001, 2011, and 2021 Leicester censuses to enable the exploration of recent historical trends. It also includes data from the 2021 census for both Nottingham and Coventry to enable comparisons with other cities.

This online resource that can be used for teaching and research purposes by staff and students and to create a legacy for the Census@Leicester Project.

Abstract copyright UK Data Service and data collection copyright owner.

The aims of this study were to collect data about the development of New Town organisational structure as imposed, purposefully or otherwise, by the Development Corporation and to study the development of organisations and the participation levels of residents.
An associated qualitative dataset, Development of, and Participation in, Political Organizations in New Towns in the UK, is available via Qualidata at the University of Essex.
Main Topics:

Attitudinal/Behavioural Questions
There were two questionnaires for the two samples:
1. Resident sample
2. Leader sample
The questions were largely similar: length of residence in present and previous two addresses, details of previous residence, present tenure, whether considered moving. Sociability, degree of contact with neighbours. Membership of clubs/organisations (present and previous), number of organisations in which respondent was a committee member or which took a stand on public issues and problems. Satisfaction with degree of involvement and changes over the last two years. Whether encouragement given for membership by Development Corporation employees or organisations themselves, body to whom respondent would make suggestions concerning the town, assessment of personal efficacy, political support, attendance at public meetings, discussion with friends, experience of campaign work. Satisfaction with New Town compared to other places of residence.
In addition, the Leader sample were asked about their organisations: type, principal activities, year founded, procedure by which founded, number of members, number generally attending meetings, frequency of meetings, issues and problems considered most important, assessment of organisations effectiveness. Actions organisation has taken, proportional classification of members by occupation.
Background Variables
Age, sex, marital status, number of children/parents/others living in same household. Type of school attended, school-leaving age, further education, employment status, occupation, average family income.

The financial and operational success of property development markets depends on a range of socio-economic factors, such as property values, market sentiment and credit conditions. Building project developers' revenue is forecast to slide at a compound annual rate of 3.2% to £35.8 billion over the five years through 2024-25. The economic shock caused by the pandemic had a devastating impact on property development market in 2020-21. Severe supply chain and market disruption caused sentiment to wane and transaction activity fell, while property values initially depreciated and rental fee income stalled. Revenue rebounded in 2021-22, aided by low interest rates, house price inflation and a stronger than anticipated initial economic recovery from the pandemic. Nonetheless, revenue remained below pre-pandemic levels as growth was hindered by a further net deficit on revaluation of assets and lower rental income in office and brick-and-mortar retail markets. The fallout from the pandemic has caused developers to re-align investment towards lower-risk real estate markets which are likely to be more resilient to price shocks. Inaflationary pressures and rising interest rates spurred a further hit to portfolio valuations, discouraging developers from pursuing new developments. Revenue is forecast to grow by 2.5% in the current year, as interest rate cuts spur renewed growth in property values. Revenue is slated to climb at a compound annual rate of 1.3% to reach £38.2 billion over the five years through 2029-30. Following recent interest rate cuts, more stable economic conditions are set to continue to support improved sentiment in the near-term, spurring developers to pursue new ventures. Opportunities for growth are set to be most prominent in high-yield office markets and the technology sector, with growing use of artificial intelligence set to drive demand for the development and construction of data centres. Loosened planning policy is set to drive momentum in residential real estate markets, though more will need to be done for the government to achieve ambitious housebuilding targets.

Camden’s Local Economic Assessment (LEA) describes the position of Camden in the London economy and discusses the economic challenges and opportunities for Camden. The LEA was to provide the Council and its partners with a sound understanding of the borough’s economic position and to inform local decisions on economic development and social policy in the context of significantly reduced public funding. Local authorities are required to prepare an LEA under the Local Democracy, Economic Development and Construction Act 2009.

See also the Central London Local Economic Assessment at http://www.centrallondonforward.gov.uk/work-programme/economy/lea.

This report is a contribution to the Strategic Environmental Assessment (SEA2) conducted by the Department of Trade and Industry (now Department of Energy and Climate Change). SEA2 focuses on the mature areas of the North Sea UK continental shelf which is divided into 3 areas - Northern, Central and Southern North Sea. The socio-economic effects of licensing the SEA2 area are discussed. The scope of the study includes estimates of the reserves which might be discovered and developed, and the related exploration, appraisal, development and decommissioning costs. The possible phasing of these activities through time is also examined. The effects of the development of new fields in extending the lives of existing ones and the implications for the provision of necessary infrastructure onshore are also examined. The employment generated directly and indirectly in the 3 sub-areas is estimated. The distinction is made between employment at the various stages in the exploration, development and production activities. The significance of the employment opportunities provided for the long-term maintenance of a skilled workforce is also considered.

This dataset supports the publication: Armstrong, J., Ortega, A., Blainey, S., Preston, J., Thompson, D., Squicciarini, G., & Ntotsios, E. (2019). Noise reduction for ballasted track: a comparative socio-economic assessment. International Journal of Transport Development and Integration, 3(1), 15-29. https://doi.org/10.2495/tdi-v3-n1-15-29

The Social Sciences and Humanities Research and Development Activities industry’s revenue is expected to contract at a compound annual rate of 4.3% over the five years through 2024 to €3.6 billion. This includes a forecast drop of 2.8% in 2024. Revenue is far lower than in the Natural Science and Engineering Research and Development Activities industry, since scientific innovation, engineering techniques and healthcare developments are prioritised over research in societal change, economics, political science and cultural progress. Spending by businesses on R&D for these subjects is significantly lower, with most money instead coming from publicly backed organisations, such as think tanks, political organisations and cultural centres. That being said, Belgium, Germany, Malta and Sweden all rely more heavily on business-funded R&D, whereas Greece, Luxembourg, Slovakia and Estonia all depend more on government funding, finds Eurostat. Horizon Europe and the Humanities in the European Research Area (HERA) have made significant funding contributions since 2020 to progress in R&D, which has boosted revenue. Revenue is slated to climb at a compound annual rate of 3.5% over the five years through 2029 to €4.3 billion. Horizon Europe funding will continue to boost revenue for R&D activities and businesses are expected to allocate more funding to R&D as the value of research in humanities and social sciences becomes clearer. HERA will continue to encourage collaboration on R&D in Europe through the Collaboration of Humanities and Social Sciences in Europe programme, which includes countries like Spain, the UK, Estonia, Poland and Croatia.

This research produced evidence on the issue of minority ethnic teacher retention in England in 2019/20 academic year. Focusing on the perspectives of 24 minority ethnic teachers from different demographics and professional backgrounds, we investigated in interviews why minority ethnic teachers leave schools that employ high numbers of minority ethnic staff and enrol students from similar backgrounds, and what should be done to support their retention.

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute of Statistics (RSIS), the Federal Institute of Statistics (FOS) and the Agency for Statistics of BiH (BHAS), carried out a Living Standards Measurement Survey (LSMS).

The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:

1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs.

2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.

3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and provided funding for a further three years of data collection for a panel survey, known as the Household Survey Panel Series (HSPS) – and more popularly known as Living in BiH (LiBiH). Birks Sinclair & Associates Ltd. in cooperation with the Independent Bureau for Humanitarian Issues (IBHI) were responsible for the management of the HSPS with technical advice and support provided by the Institute for Social and Economic Research (ISER), University of Essex, UK.

The panel survey provides longitudinal data through re-interviewing approximately half the LSMS respondents for three years following the LSMS, in the autumns of 2002 and 2003 and the winter of 2004. The LSMS constitutes Wave 1 of the panel survey so there are four years of panel data available for analysis. For the purposes of this documentation we are using the following convention to describe the different rounds of the panel survey: - Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel - Wave 2 Second interview of 50% of LSMS respondents in Autumn/Winter 2002 - Wave 3 Third interview with sub-sample respondents in Autumn/Winter 2003 - Wave 4 Fourth interview with sub-sample respondents in Winter 2004

The panel data allows the analysis of key transitions and events over this period such as labour market or geographical mobility and observations on the consequent outcomes for the well-being of individuals and households in the survey. The panel data provides information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty and movements in an out of poverty are experienced by different types of households and individuals over the four year period. Most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within BiH at a time of social reform and rapid change.

In order to develop base line (2004) data on poverty, incomes and socio-economic conditions, and to begin to monitor and evaluate the implementation of the BiH MTDS, EPPU commissioned this modified fourth round of the LiBiH Panel Survey.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Kind of data

Sample survey data [ssd]

Sampling procedure

The Wave 4 sample comprised of 2882 households interviewed at Wave 3 (1309 in the RS and 1573 in FBiH). As at previous waves, sample households could not be replaced with any other households.

Panel design

Eligibility for inclusion

The household and household membership definitions assume the same standard definitions used at Wave 3. While the sample membership, status and eligibility for interview are as follows: i) All members of households interviewed at Wave 3 have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview, i.e. younger than 15 years. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules

The panel design provides that sample members who move from their previous wave address must be traced and followed to their new address for interview. In some cases the whole household will move together but in other cases an individual member may move away from their previous wave household and form a new "split-off" household of their own. All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This method has the benefits of maintaining the maximum number of respondents within the panel and being relatively straightforward to implement in the field.

Definition of 'out-of-scope'

It is important to maintain movers within the sample to maintain sample sizes and reduce attrition and also for substantive research on patterns of geographical mobility and migration. The rules for determining when a respondent is 'out-of-scope' are:

i. Movers out of the country altogether i.e. outside BiH This category of mover is clear. Sample members moving to another country outside BiH will be out-of-scope for that year of the survey and ineligible for interview.

ii. Movers between entities Respondents moving between entities are followed for interview. Personal details of "movers" are passed between the statistical institutes and an interviewer assigned in that entity.

iii. Movers into institutions Although institutional addresses were not included in the original LSMS sample, Wave 4 individuals who have subsequently moved into some institutions are followed. The definitions for which institutions are included are found in the Supervisor Instructions.

iv. Movers into the district of Brcko
Are followed for interview. When coding, Brcko is treated as the entity from which the household moved.

Feed-forward

Details of the address at which respondents were found in the previous wave, together with a listing of household members found in each household at the last wave were fed-forward as the starting point for Wave 4 fieldwork. This "feed-forward" data also includes key variables required for correctly identifying individual sample members and includes the following: - For each household: Household ID (IDD); Full address details and phone number - For each Original Sample Member: Name; Person number (ID); unique personal identifier (LID); Sex; Date of birth

The sample details are held in an Access database and in order to ensure the confidentiality of respondents, personal details, names and addresses are held separately from the survey data collected during fieldwork. The IDD, LID and ID are the key linking variables between the two databases i.e. the name and address database and the survey database.

Mode of data collection

Face-to-face [f2f]

Research instrument

Approximately 70% of the questionnaire was based on the Wave 3 questionnaire, carrying forward core measures in order to measure change over time. However in order to develop base line (2004) data on poverty, incomes and socio-economic conditions, and to begin to monitor and evaluate the implementation of the BiHDS the Wave 4 questionnaire additionally contained the Wave 1 Consumption module and a few other LSMS items to allow direct comparability with the Wave 1 data.

Cleaning operations

Dat entry

As at previous waves, CSPro was the chosen data entry software. The CSPro program consists of two main features intended to reduce the number of keying errors and to reduce the editing required following data entry:
- Data entry screens that included all skip patterns. - Range checks for each question (allowing three exceptions for inappropriate, don't know and missing codes).

The Wave 4 data entry program had similar checks to the Wave 3 program - and DE staff were instructed to clear all anomalies with SIG fieldwork members. The program was tested prior to the commencement of data entry. Twelve data entry staff were employed in each Field Office, as all had worked on previous waves training was not undertaken.

Editing

Instructions for editing were provided in the Supervisors Instructions. At Wave 4 supervisors were asked to take more time to edit every questionnaire returned by their interviewers. The SIG Fieldwork Managers examined every Control Form.

Response rate

The level of cases that were unable to be traced is extremely low as are the whole household refusal or non-contact rates. In total, 9128 individuals (including children) were enumerated within the sample households at Wave 4, 5019 individuals in the FBiH and 4109 in the RS. Within in the 2875 eligible households, 7603 individuals aged 15 or over were eligible for interview with 7116 (93.6%) being successfully interviewed. Within co-operating households (where there was at least one interview) the interview rate was

This MAED Data Note contains historic annual data on electricity demand (2018 to 2023) segregated by sector (industry, services, households). Additionally, historic social and economic data on population (total, growth, urban-rural split, household size), GDP (total, sectoral split, growth) and electrification are included within this data note. This historic data can be used to create base year energy intensitities used as a foundation for energy demand modelling using the Model for the Analysis of Energy Demand (MAED) simulation software. Two illustrative scenarios are developed alongside a baseline demand projection from 2018 to 2050.

This work was supported by the Climate Compatible Growth Programme (#CCG) of the UK's Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government's official policies.

Abstract copyright UK Data Service and data collection copyright owner.

Wellbeing in Developing Countries is a series of studies which aim to develop a conceptual and methodological approach to understanding the social and cultural construction of wellbeing in developing countries. The Wellbeing in Developing Countries Research Group (WeD), based at the University of Bath, drew on knowledge and expertise from three different departments (Economics and International Development, Social and Policy Sciences and Psychology) as well as a network of overseas contacts. The international, interdisciplinary team formed a major programme of comparative research, focused on six communities in each of four countries: Ethiopia, Thailand, Peru and Bangladesh. All sites within the countries have been given anonymous site names, with the exception of Ethiopia where the team chose to follow an alternative locally agreed procedure on anonymisation. Data can be matched across studies using the HOUSEKEY (Site code and household number).

The research raises fundamental questions both for the academic study of development, and for the policy community. The WeD arrived at the following definition of wellbeing through their research: "Wellbeing is a state of being with others, where human needs are met, where one can act meaningfully to pursue one's goals, and where one enjoys a satisfactory quality of life".

Further information about the project can be found on the WeD website and the ESRC Award webpage.

Wellbeing in Developing Countries: Quality of Life, 2004-2005 comprises the Quality of Life (QoL) Survey which was carried out in each of the four countries. The QoL was administered to approx 370 men and women in each country (approximately 60 per research site) in the local language by a team of interviewers selected by each of the country teams. The majority of respondents also completed the other surveys that form part of this research project and are available from the UKDA under GN 33394. The remainder were sampled proportionately according to age, socio-economic status, ethnicity and religion. For each country there is one data file at the individual level.


Main Topics:

Topics covered include:

• positive and negative affect Scale (PANAS)
• goals
• satisfaction with goal attainment
• values (both individual and group)
• satisfaction with life scale (SWLS)

Building contractors and developers depend on various socio-economic factors, including property values, underlying sentiment in the housing market, the degree of optimism among downstream businesses and credit conditions. All of these drivers typically track in line with economic sentiment, with recent economic shocks spurring a difficult period for building contractors and developers. Nonetheless, the enduring need for building services, particularly to tackle housing shortages across the continent, ensures a strong foundation of work. Revenue is forecast to grow at a compound annual rate of 2.3% to reach €1.3 trillion over the five years through 2025. Operational and supply chain disruption caused by the pandemic reversed the fortunes of building contractors and developers in 2020, as on-site activity tumbled and downstream clients either cancelled, froze or scaled back investment plans. Aided by the release of pent-up demand and supportive government policy, building construction output rebounded in 2021. Excess demand for key raw materials led to extended lead times during this period, while input costs recorded a further surge as a result of the effects of rapidly climbing energy prices following Russia’s invasion of Ukraine. Soaring construction costs and the impact of interest rate hikes on both the housing market and investor sentiment led to a renewed slowdown in building construction activity across the continent. However, falling inflation and the start of an interest rate cutting cycle have spurred signs of a recovery in new work volumes, supporting anticipated revenue growth of 2.3% in 2025. Revenue is forecast to increase at a compound annual rate of 6.7% to €1.7 trillion over the five years through 2030. Activity is set to remain sluggish in the medium term, as weak economic growth and uncertainty surrounding the impact of the volatile global tariff environment on inflation and borrowing costs continue to weigh on investor sentiment. Contractors and developers will increasingly rely on public sector support, including measures to boost the supply of new housing, as countries seek to tackle severe housing shortages. Meanwhile, the introduction of more stringent sustainability requirements will drive demand for energy retrofits.

This datasets contains the anonymised results of a survey of well owners in Kisumu, Kenya. Data includes information on the amount of water abstracted daily from the well and ways in which this water was used and handled, information on other sources of water (e.g. piped utility water and rainwater) and how this is used, and the assets and services that the well owner has access to. Answers from questions to assess food poverty are also included. The surveys were carried out during February and March 2014 and include data from 51 well owners. The data were collected as part of the Groundwater2030 project, which aims to reduce the health problems that result from consumption of contaminated groundwater in urban areas of Africa. The project was co-ordinated by the University of Southampton, with partners at the University of Surrey, the Victoria Institute of Research on Environment and Development (VIRED) International, and the Jaramogi Oginga Odinga University of Science and Technology. The project was funded by the Natural Environment Research Council and the Department for International Development as part of the Unlocking the Potential of Groundwater for the Poor (UPGro) programme. Full details about this dataset can be found at https://doi.org/10.5285/4ca855a3-752c-4492-8e26-3438652dd35c