Number of workplaces and employees working in industry sectors that operate in the evening or night. The "night time economy" is defined as the following Standard Industrial Classification 2007 (SIC 2007) industries:

• Cultural and leisure activities
• Activities which support night time cultural and leisure activities
• 24-hour health and personal social services
• Activities which support wider social and economic activities

This data is provided at Borough and MSOA level.

This dataset is included in the Greater London Authority's Night Time Observatory. Click here to find out more.

Evening Economy Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2026 to 2032.

Global Evening Economy Market Definition

The term evening economy refers to the economic activity that takes place in the evening after many people finish daytime employment or formal education. These activities include eating and drinking, entertainment, nightlife, etc. The evening economy starts at 6 pm and ends at midnight and there would be sufficient activity to encourage people to stay after work, instead of going home. Key benefits of the evening economy include recreation for people, an increase in employment due to local spending, reduced social exclusion, and increased vitality in towns.

Abstract copyright UK Data Service and data collection copyright owner. This is a mixed methods data collection. This project set out to investigate the rise of lap dancing in the UK and the experiences of women who work in the industry in relation to working conditions and their feelings about work. The project also aimed to explore the role of regulation and governance of the industry and the night time economy in relation to women's experiences as dancers. To do this, the largest English survey so far of dancers/strippers was undertaken (196 responses), and interviews were conducted with 35 dancers across the country and 20 people who worked in clubs as managers, owners, door security, and 'housemums'. A further 15 interviews were conducted with those officials who regulate the industry such as licensing officers and health and safety and police personnel. While 20 clubs across the country were visited during the study, fieldwork was largely concentrated in one city in the North of England and one in the South. Users should note that the UK Data Archive study contains a subset of these data, comprising only the dancers' quantitative survey and seven of the interviews conducted with licensing/health and safety personnel. The project found that many younger women are entering dancing as it offers the benefits of flexible, cash-in-hand work that requires minimal commitment or responsibility. Women generally enjoyed their work and its advantages, although there were regular reports of harassment from customers. Women were using dancing strategically to either further their education or career or position themselves better in the labour market in the future. There were issues raised regarding some clubs' lack of consideration for the welfare of their workers. Most notably, there was evidence of financial exploitation from managers as women would pay high 'house fees' and commission, often earning very little money after a shift. The project found no evidence connecting lap dancing to organised prostitution or trafficking. The project found that lap dancing is a precarious form of work which was ironically enabling women to avoid insecure employment and personal circumstances in the future. Further information about the project and links to publications may be found on the ESRC The Regulatory Dance: Investigating the Structural Integration of Sexual Consumption into the Night Time Economy award webpage and the University of Leeds Social Sciences Institute Regulatory Dance webpage. Main Topics: The interviews with local authority/police personnel covered: health and safety legislation applied to clubs, relations with club owners, licencing, inspections and dancers' working conditions. The dancers' survey covered: demographic details, children, marital status, income, educational background and qualifications, employment history in dancing and other occupations, working conditions in clubs, and attitudes to dancing. Convenience sample Face-to-face interview

Research on the night time economy in the city of London showed that 42 percent of residents were running errands and other every day tasks in the evening. According to the survey, 23 percent were going to work in the evenings and/or at night, and an almost equal share would be socializing in a pub or bar.

The Night Time Commission was set up to provide independent advice to the Mayor as to the sustainable development of London’s night time economy. GLA Opinion Research and Statistics was commissioned to conduct quantitative and qualitative research into the opinions and behaviours of Londoners at night. The following research is outlined below:

• Dec 2017 to Jan 2018 – 6 focus groups (each with 6-8 participants) with Londoners, and 4 face-to-face interviews with night-time workers.
• Complementary moderated discussions on Talk London, the GLA’s online research community
• 26 Feb 2018 to 1 Mar 2018 – representative polling with 1,058 adult Londoners, conducted by YouGov

More detail on the sample and content covered is provided in the report below. This data should be understood alongside concurrent research presented by GLA Economics in the main London at Night report.

China Holiday Tourism: Labour Day: Number of Tourist: Overnight data was reported at 45,860.000 Person-Time th in 2007. This records an increase from the previous number of 38,840.000 Person-Time th for 2006. China Holiday Tourism: Labour Day: Number of Tourist: Overnight data is updated yearly, averaging 31,310.000 Person-Time th from Dec 2002 (Median) to 2007, with 5 observations. The data reached an all-time high of 45,860.000 Person-Time th in 2007 and a record low of 22,500.000 Person-Time th in 2002. China Holiday Tourism: Labour Day: Number of Tourist: Overnight data remains active status in CEIC and is reported by Ministry of Culture and Tourism. The data is categorized under China Premium Database’s Tourism Sector – Table CN.QFA: Holiday Tourism: Labour Day.

Number of employees finding it convenient for personal life to work in the evening, or at night, or during weekends, by sex, age and economic activity

China Holiday Tourism: Labour Day: Number of Tourist: Key Tourism City: Overnight data was reported at 15,060.000 Person-Time th in 2007. This records an increase from the previous number of 13,790.000 Person-Time th for 2006. China Holiday Tourism: Labour Day: Number of Tourist: Key Tourism City: Overnight data is updated yearly, averaging 12,995.000 Person-Time th from Dec 2002 (Median) to 2007, with 4 observations. The data reached an all-time high of 15,060.000 Person-Time th in 2007 and a record low of 9,140.000 Person-Time th in 2002. China Holiday Tourism: Labour Day: Number of Tourist: Key Tourism City: Overnight data remains active status in CEIC and is reported by Ministry of Culture and Tourism. The data is categorized under China Premium Database’s Tourism Sector – Table CN.QFA: Holiday Tourism: Labour Day.

Pub ePOS Systems Market Outlook

In 2023, the global Pub ePOS systems market size is estimated to be around $5 billion, projected to grow at a compound annual growth rate (CAGR) of 9% to reach approximately $11.2 billion by 2032. The growth of this market is driven by the increasing demand for efficient and integrated point-of-sale solutions in the pub industry, which requires a seamless blend of order management, payment processing, and customer relationship management capabilities. The expansion of pubs globally, coupled with the rising adoption of digital solutions to enhance customer experience and operational efficiency, is significantly contributing to this upward trend.

The burgeoning growth of the Pub ePOS systems market is primarily fueled by the escalating need for modernized and automated solutions in the hospitality sector. With the rapid pace of technological advancements and the ubiquity of smartphones and internet connectivity, pubs are increasingly adopting ePOS systems to streamline their operations, reduce wait times, and deliver superior customer service. These systems not only facilitate faster payment processing but also enable real-time tracking of sales and inventory, thus optimizing resource allocation and minimizing wastage. Furthermore, the integration of analytics within these systems assists pub owners in understanding customer preferences and tailoring their offerings accordingly, thereby augmenting profitability.

Another pivotal growth factor is the increasing consumer preference for contactless transactions, a trend that has been accelerated by the COVID-19 pandemic. The demand for ePOS systems equipped with contactless and mobile payment options is on the rise, as customers seek safer and more hygienic ways to transact. This shift in consumer behavior has compelled pubs to upgrade their existing systems or adopt new ePOS solutions that support contactless payment methods, such as NFC and mobile wallets. Additionally, government regulations and incentives aimed at promoting cashless economies are further propelling the adoption of advanced ePOS systems in the pub industry.

Moreover, the advent of cloud technology has revolutionized the ePOS landscape by offering scalable, flexible, and cost-effective solutions for pubs of all sizes. Cloud-based ePOS systems are gaining traction due to their ease of installation, lower upfront costs, and the ability to access data from anywhere, at any time. These systems provide pub owners with the agility to quickly adapt to changing market conditions, update menus or prices in real-time, and manage multiple locations seamlessly. As the industry continues to embrace digital transformation, the shift towards cloud-based ePOS solutions is expected to significantly contribute to market growth.

The Evening Economy plays a crucial role in the expansion of the pub ePOS systems market, as it encompasses the activities and services that occur during the evening and night-time hours, often centered around leisure and hospitality. Pubs, being a significant part of the evening economy, are increasingly investing in advanced ePOS systems to cater to the growing demand for efficient service during peak hours. These systems help manage high volumes of transactions, streamline operations, and enhance customer experiences, which are essential for thriving in the competitive evening economy. As more people seek entertainment and dining options after work hours, the integration of sophisticated ePOS solutions enables pubs to offer seamless service, attract more patrons, and ultimately boost their revenue.

In terms of regional outlook, North America currently dominates the pub ePOS systems market, attributed to the high concentration of pubs and the rapid adoption of advanced technologies in the region. Europe is also a significant market, driven by the rich pub culture and the increasing trend towards digital payment solutions. Meanwhile, the Asia Pacific region is anticipated to witness the fastest growth, supported by the expanding hospitality sector and the growing popularity of pubs in countries like India and China. The increasing urbanization and rising disposable incomes in these regions are further fueling the demand for sophisticated ePOS systems, creating lucrative opportunities for market players.

Component Analysis

The component segment of the pub ePOS systems market is broadly categorized into software, hardware, and services. Software solutions are an integral part of ePOS systems, providing

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

A vision-based patient monitoring system (VBPMS), Oxevision, has been introduced in approximately half of National Health Service (NHS) mental health trusts in England. A VBPMS is an assistive tool that supports patient safety by enabling non-contact physiological and physical monitoring. The system aims to help staff deliver safer, higher-quality and more efficient care. This paper summarises the potential health economic impact of using a VBPMS to support clinical practice in two inpatient settings: acute mental health and older adult mental health services. The economic model used a cost calculator approach to evaluate the potential impact of introducing a VBPMS into clinical practice, compared with clinical practice without a VBPMS. The analysis captured the cost differences in night-time observations, one-to-one continuous observations, self-harm incidents, and bedroom falls at night, including those resulting in A&E visits and emergency service callouts. The analysis is based on before and after studies conducted at five mental health NHS trusts, including acute mental health and older adult mental health services. Our findings indicate that the use of a VBPMS results in more efficient night-time observations and reductions in one-to-one observations, self-harm incidents, bedroom falls at night, and A&E visits and emergency service callouts from night-time falls. Substantial staff time in acute mental health and older adult mental health services is spent performing night-time observations, one-to-one observations, and managing incidents. The use of a VBPMS could lead to cost savings and a positive return on investment for NHS mental health trusts. The results do not incorporate all of the potential benefits associated with the use of a VBPMS, such as reductions in medication and length of hospital stay, plus the potential to avoid adverse events which would otherwise have a detrimental impact on a patient’s quality of life.

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

The benchmark interest rate in Argentina was last recorded at 29 percent. This dataset provides the latest reported value for - Argentina Money Market Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Specific Parameters of the "new nighttime light data set".

Abstract

Statistics South Africa provides data on international tourism based on secondary data obtained from the South African Department of Home Affairs. The information from this data used by stakeholders to measure and understand international tourism in South Africa. Detailed information about national domestic tourism is limited, however and there was a need to collect more detailed information on domestic tourism in order to better understand and measure the contribution of the tourism industry towards the national economy. The Domestic Tourism Survey (DTS) is aimed at addressing this need by collecting accurate statistics on the travel behaviour and expenditure of South African residents travelling within and outside the borders of South Africa. Such information is crucial in determining the contribution of tourism to the South African economy as well as helping with planning, marketing, policy formulation and regulation of tourism-related activities.

This survey provides data on domestic tourism activity from the beginning of February 2008 until the end of August 2008. In addition to the basic demographic information collected in the majority of household surveys conducted by Statistics South Africa, the DTS covers five areas specifically related to travel and expenditure patterns. These include trips taken by the household, domestic day trips by the respondent and/or other household members, domestic day trips by other household members (without the respondent), domestic overnight trips by the respondent and/or other household members, domestic overnight trips by other household members (without the respondent).

Geographic coverage

National coverage

Analysis unit

The units of analysis in the Domestic Tourism Survey are households and individuals

Universe

The target population of the survey consists of all private households and residents in workers’ hostels in the nine provinces of South Africa. The survey does not cover other collective living quarters such as students’ hostels, oldage homes, hospitals, prisons and military barracks and is therefore only representative of non-institutionalised and non-military persons in South Africa.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the DTS 2008 was based on a master sample (MS). The master sample used a two-stage, a stratified design with probability–proportional-to-size (PPS) sampling of PSUs from within strata, and systematic sampling of dwelling units (DUs) from the sampled primary sampling units (PSUs). A self-weighting design at provincial level was used and MS stratification was divided into two levels. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2001 data were summarised at PSU level. The following variables were used for secondary stratification; household size, education, occupancy status, gender, industry and income. Census enumeration areas (EAs) as delineated for Census 2001 formed the basis of the PSUs. The following additional rules were used: • Where possible, PSU sizes were kept between 100 and 500 dwelling units (DUs); • EAs with fewer than 25 DUs were excluded; • EAs with between 26 and 99 DUs were pooled to form larger PSUs and the criteria used was same settlement type; • Virtual splits were applied to large PSUs: 500 to 999 split into two; 1 000 to 1 499 split into three; and 1 500 plus split into four PSUs; and • Informal PSUs were segmented. A Randomised Probability Proportional to Size (RPPS) systematic sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3 080 PSUs were selected. In each selected PSU a systematic sample of dwelling units was drawn. The number of DUs selected per PSU varies from PSU to PSU and depends on the Inverse Sampling Ratios (ISR) of each PSU.

Mode of data collection

Face-to-face [f2f]

Research instrument

The DTS 2008 questionnaire collected data on the following topics:

Cover page Household information, response details, field staff information, result codes, etc. Flap: Demographic information (name, sex, age, population group, etc.) and basic tourism information Section 1: Information on trips taken by respondent and other household members in the past six months and barriers for not taking trips. Section 2: Day trip taken by the respondent in the past six months prior to the survey interview, destination, means of transport, purpose of trip, activities on the trip, and expenditure Section 3: Day trip taken by other household members without the respondent in the past six months prior to the survey interview, destination, means of transport, purpose of trip, activities on the trip, and expenditure. Section 4: Domestic overnight trips taken(inside South Africa), taken by the respondent in the past sixmonths, prior to the survey interview, destination, means of transport, purpose of trip, activities on the trip, and expenditure. Section 5: Domestic overnight trips taken by the other household member in the past six months prior to the survey interview, destination, means of transport, purpose of trip, activities on the trip, and expenditure. Section 6: Outbound overnight trips outside South Africa, taken by the respondent in the past six months, prior to the survey interview, destination, means of transport, purpose of trip, activities on the trip, and expenditure. Section 7: Outbound overnight trips outside South Africa, taken by the respondent in the past six months, prior to the survey interview, destination, means of transport, purpose of trip, activities on the trip, and expenditure. Section 8: Column number of the responding person, and the language used during the interview. All sections: Comprehensive coverage of domestic and foreign trips undertaken

Note: The DTS questionnaire had two sections for the domestic overnight trips taken by the respondent and other household members without the respondent. Thus section 4 (domestic overnight trips by the respondent) and section 5 (domestic overnight trips by other household members without the respondent). The purpose of this split was to collect as many domestic overnight trips as possible from the households.

Data appraisal

Caution must be exercised when interpreting the results of the DTS at low levels of dis-aggregation. Revisions to the DTS data sets based on the new population estimates involved benchmarking at national level in terms of age, sex and population group while at provincial level, benchmarking was by population group only. The sample and reporting are based on the provincial boundaries as defined in December 2005.