A summary of the latest employment projections for London produced by four respected organisations. 1) Cambridge Econometrics Employment defined as: Employees + Self-employed + HM Forces Jobs date of publication: Oct-13 projection start year: 2013 projection end year: 2025 2) Experian Economics Employment defined as: Employees + Self-employed date of publication: Jun-14 projection start year: 2013 projection end year: 2031 3) Oxford Economics Forecasting Employment defined as: Employees + Self-employed + HM Forces Jobs + Government supported trainees date of publication: Apr-14 projection start year: 2013 projection end year: 2030 4) UK Commission for Employment & Skills Employment defined as: Employees + Self-employed date of publication: Mar-14 projection start year: 2013 projection end year: 2022

This dataset is about companies in London. It has 42,102 rows. It features 30 columns including city, country, employees, and employee type.

This work looks at in-work poverty in London between 1996 and 2023. It provides an overview of the links between working-age poverty and employment participation at the individual and household levels in the capital. Some key findings include: Poverty has changed. There are now more people in poverty in London who are in a working family than in a workless family. The reverse was true 30 years ago. Insecure forms of work such as part-time work and self-employment are linked to poverty. Ethnic minority workers and those with no educational qualifications are most likely to be working and in poverty. Working families with three or more children have seen their poverty rate increase sharply since the introduction of the two-child benefit cap on Universal Credit. Single parents have a high likelihood of in-work poverty, though the number of people in work and in poverty has also risen sharply among Londoners living as a couple.

Update 29-04-2020: The data is now split into two files based on the variable collection frequency (monthly and yearly). Additional variables added: area size in hectares, number of jobs in the area, number of people living in the area.

Context

I have been inspired by Xavier and his work on Barcelona to explore the city of London! 🇬🇧 💂

Content

The datasets is primarily centered around the housing market of London. However, it contains a lot of additional relevant data: - Monthly average house prices - Yearly number of houses - Yearly number of houses sold - Yearly percentage of households that recycle - Yearly life satisfaction - Yearly median salary of the residents of the area - Yearly mean salary of the residents of the area - Monthly number of crimes committed - Yearly number of jobs - Yearly number of people living in the area - Area size in hectares

The data is split by areas of London called boroughs (a flag exists to identify these), but some of the variables have other geographical UK regions for reference (like England, North East, etc.). There have been no changes made to the data except for melting it into a long format from the original tables.

Acknowledgements

The data has been extracted from London Datastore. It is released under UK Open Government License v2 and v3. The underlining datasets can be found here: https://data.london.gov.uk/dataset/uk-house-price-index https://data.london.gov.uk/dataset/number-and-density-of-dwellings-by-borough https://data.london.gov.uk/dataset/subjective-personal-well-being-borough https://data.london.gov.uk/dataset/household-waste-recycling-rates-borough https://data.london.gov.uk/dataset/earnings-place-residence-borough https://data.london.gov.uk/dataset/recorded_crime_summary https://data.london.gov.uk/dataset/jobs-and-job-density-borough https://data.london.gov.uk/dataset/ons-mid-year-population-estimates-custom-age-tables

Cover photo by Frans Ruiter from Unsplash

Inspiration

The dataset lends itself for extensive exploratory data analysis. It could also be a great supervised learning regression problem to predict house price changes of different boroughs over time.

Table to show all people in work in the UK by their place of residence and place of work (main job). The data helps to show commuting patterns since 2004. Matrix shows where workers commute from (residence) and to (workplace).

Commuting data from the Annual Population Survey 2010 has been presented here using Prezi software.

How does London get to work?

Commuting in London on Prezi.

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

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in London. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In London, the median income for all workers aged 15 years and older, regardless of work hours, was $37,598 for males and $21,979 for females.

These income figures highlight a substantial gender-based income gap in London. Women, regardless of work hours, earn 58 cents for each dollar earned by men. This significant gender pay gap, approximately 42%, underscores concerning gender-based income inequality in the city of London.

- Full-time workers, aged 15 years and older: In London, among full-time, year-round workers aged 15 years and older, males earned a median income of $53,839, while females earned $41,732, leading to a 22% gender pay gap among full-time workers. This illustrates that women earn 78 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in London.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for London median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in London. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In London, the median income for all workers aged 15 years and older, regardless of work hours, was $50,110 for males and $31,360 for females.

These income figures highlight a substantial gender-based income gap in London. Women, regardless of work hours, earn 63 cents for each dollar earned by men. This significant gender pay gap, approximately 37%, underscores concerning gender-based income inequality in the city of London.

- Full-time workers, aged 15 years and older: In London, among full-time, year-round workers aged 15 years and older, males earned a median income of $70,750, while females earned $49,242, leading to a 30% gender pay gap among full-time workers. This illustrates that women earn 70 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in London.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for London median household income by race. You can refer the same here

The 2014 London Business Survey (LBS) is an innovative survey designed by the Office for National Statistics, on behalf of the London Enterprise Panel and the GLA. The survey collected information from a representative sample of private sector businesses in London in May-July 2014. This dataset contains information on London’s private sector workforce and recruitment by London businesses corresponding with Section 3 of the London Business Survey 2014: Main Findings report. Information is provided on: The number of employees working in London businesses by gender The change in the number of employees compared to 12 months ago, and the outlook for the next 12 months Reasons for a rise or fall in the number of employees Recruitment, including whether London businesses have recruited via Job Centre Plus (JCP), and the perceived suitability of these candidates For statistics on the number of full-time and part-time employees working in London, the ONS’s Business Register and Employment Survey (BRES)is the recommended official source. As with any survey, the 2014 LBS is based on a sample and as such is subject to variability in the results. Care should therefore be taken in interpreting the survey findings. For all estimates, lower and upper limits of 95% confidence intervals are provided in the data files to assist with interpretation. The LBS results represent the population of business units in London. A business unit is defined as a site/workplace, which may also be a head office if the head office is in London. It will be the whole business in the case of businesses which only have one site, or part of the business in the case of multi-site firms. The results are presented by enterprise size band and industry sector.

Presents analysis of nearly one thousand small areas within London showing the number of employees working for businesses with fewer than 250 employees in the UK (small- and medium-sized enterprises, or SMEs) and for businesses with 250 or more employees in the UK (large enterprises). The release includes numbers of SMEs and large firms in these small areas of London. The figures are calculated using data from the Inter-Departmental Business Register (IDBR) for the period 2001 to 2012. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: London Analysis

🇬🇧 United Kingdom English Number and rate of people aged 16 and over, working in London with a flexible working arrangement. This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

The Labour Market Indicators spreadsheet for boroughs and regions will no longer be updated from March 2015. The final version from March 2015 will still be available to download at the bottom of this page. Most of the data is available within datasets elsewhere on the Datastore.

Workforce Jobs
Unemployment
Model based Unemployment for Boroughs
Claimant Count rates for Boroughs and Wards
Employment Rate Trends
Employment rates by Gender, Age and Disability
Number of Self Employed, Full and Part Time Employed
Employment by Occupation
Employment by Industry
Employment, Unemployment, Economic Activity and Inactivity Rates by Disability
Employment by Ethnicity
Economic Inactivity by Gender and Reason
Qualifications of Economically Active, Employed and Unemployed
Qualification levels of working-age population
Apprenticeship Starts and Achievements
Young People Not in Employment, Education or Training (NEET), Borough
19 year olds Qualified to NVQ Level 3
GCE A level examination results of 16-18 year olds
GCSE Results by Pupil Characteristics
People Claiming Out-of-Work Benefits
People Claiming Incapacity Benefit
Children Living in Workless Households
Gross Value Added, and Gross Disposable Household Income
Earnings by place of residence
Earnings by place of work
Business Demographics
Employment projections by sector
Jobs Density
Population Estimates
Population Migration

Core Indicators

Number of London residents of working age in employment
Employment rate
Number of male London residents of working age in employment
Male employment rate
Number of female London residents of working age in employment
Female employment rate
Workforce jobs
Jobs density
Number of London residents of working age who are economically inactive
Economic inactivity rate
Number of London residents aged 16+ who are unemployed (model based)
Proportion of London residents aged 16+ who are unemployed (model based)
Claimant unemployment
Claimant Count as a proportion of the working age population
Incidence of skill gaps (Numbers and rates)
GCSE (5+ A*–C) attainment including English and Maths
Number of working age people in London with no qualifications
Proportion of working age people in London with no qualifications
Number of working age people in London with Level 4+ qualifications
Proportion of working age people in London with Level 4+ qualifications
Number of people of working age claiming out of work benefits
Proportion of the working age population who claim out of work benefits
Number of young people aged 16-18 who are not in

This dataset is about books. It has 1 row and is filtered where the book is Police and people in London : the PSI report. It features 7 columns including author, publication date, language, and book publisher.

Employment (workplace) by industry from the Business register and employment survey (BRES). This data excludes self-employed but includes proprietors
Employment = employees + working proprietors. Working Proprietors are sole traders, sole proprietors, partners and directors. This does not apply to registered charities.
Numbers have all been rounded to the nearest 100
Before the BRES first existed in 2009, the ABI collected employment data by industry. The two surveys are not directly comparable. The BRES is a business survey which collects both employment and financial information. Only employment information for the location of an employees workplace is available from Nomis
The BRES is based on a sample of approximately 80,000 businesses and is used to provide an estimate of the number of employees.
The difference between the estimate and its true value is known as the sampling error. The actual sampling error for any estimate is unknown but we can estimate, from the sample, a typical error, known as the standard error. This provides a means of assessing the precision of the estimate; the lower the standard error, the more confident we can be the estimate is close to the true value. NOMIS website article

This dataset excludes farm based agriculture data contained in SIC class 0100.

Data and charts accompanying the 'Business Register Employment Survey 2010: London' publication

The ABI was replaced by the Business Register and Employment Survey (BRES) from 2009 onwards, therefore this dataset will no longer be updated.

More on ONS website

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by method used to travel to work (2001 specification) and by distance travelled to work. The estimates are as at Census Day, 21 March 2021.

_As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Due to methodological changes the ‘mainly work at or from home: any workplace type’ category has a population of zero. Please use the transport_to_workplace_12a classification instead. Read more about this quality notice._

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Method used to travel to workplace

A person's place of work and their method of travel to work. This is the 2001 method of producing travel to work variables.

"Work mainly from home" applies to someone who indicated their place of work as their home address and travelled to work by driving a car or van, for example visiting clients.

Distance travelled to work

The distance, in kilometres, between a person's residential postcode and their workplace postcode measured in a straight line. A distance travelled of 0.1km indicates that the workplace postcode is the same as the residential postcode. Distances over 1200km are treated as invalid, and an imputed or estimated value is added.

“Work mainly at or from home” is made up of those that ticked either the "Mainly work at or from home" box for the address of workplace question, or the “Work mainly at or from home” box for the method of travel to work question.

Distance is calculated as the straight line distance between the enumeration postcode and the workplace postcode.

Combine this variable with “Economic activity status” to identify those in employment at the time of the census.

2021 Census Topic Summary Labour Market & Travel To Work 4th tranche of 2021 Census 'Topic Summary' tables: Labour Market & Travel to Work. These are a set of univariate tables for the topic that provide data for Camden and sub areas (where available) and provide comparator data for London boroughs, Greater London and England & Wales. Cautionary note. 2021 Census took place during COVID-19 restrictions and some people usually resident in Camden were living elsewhere at the time and are not included in the statistics. This particularly affects the labour market and travel to work statistics as many people were working from home, or not able to work. ONS advises to 'take care when using these statistics for planning purposes'.

Dataset - Working class white people-England-London in the news

Percentage of respondents in work earning less than the London Living Wage (LLW) taken from the ONS Annual Survey of Hours and Earning (ASHE). Data is provided by whether the employee is male or female and works full-time or part-time. Data is also provided by borough. Also includes employees earning below the UK Living Wage by region outside London.

The minimum wage levels in each year are stated in the table.

This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

About this dataset

Context

The dataset tabulates the London population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of London. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 6,399 (61.51% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the London population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in London is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the London is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for London Population by Age. You can refer the same here