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README.md
README.md
 
 
company_size_and_turnover.csv
company_size_and_turnover.csv
 
 
credit_score.csv
credit_score.csv
 
 
demand.csv
demand.csv
 
 
earnings.csv
earnings.csv
 
 
expenses.csv
expenses.csv
 
 
expenses_full.csv
expenses_full.csv
 
 
furloughed.csv
furloughed.csv
 
 
gdp.csv
gdp.csv
 
 
growth_rates.csv
growth_rates.csv
 
 
input_output.csv
input_output.csv
 
 
input_output_final.csv
input_output_final.csv
 
 
input_output_intermediate.csv
input_output_intermediate.csv
 
 
input_output_primary.csv
input_output_primary.csv
 
 
keyworker.csv
keyworker.csv
 
 
largecap_count.csv
largecap_count.csv
 
 
largecap_pct_turnover.csv
largecap_pct_turnover.csv
 
 
min_expenses_full.csv
min_expenses_full.csv
 
 
populations.csv
populations.csv
 
 
smallcap_cash.csv
smallcap_cash.csv
 
 
sme_count.csv
sme_count.csv
 
 
sme_rate_payer_vulnerability.csv
sme_rate_payer_vulnerability.csv
 
 
supply.csv
supply.csv
 
 
vulnerability.csv
vulnerability.csv
 
 
wages.csv
wages.csv
 
 
wfh.csv
wfh.csv
 
 
workers.csv
workers.csv
 
 

README.md

Data Schemas

Specifications for all references to regions, sectors and age groups can be found in enums.py

company_size_and_turnover.csv

  • Sector: UK sector
  • min_size: (0 to inf) lower bound of number of employees
  • num_companies: (0 to inf) total number of companies in this sector with employee counts in the min_size bucket
  • num_employees: (0 to inf) total number of employees in this sector for companies with employee counts in the min_size bucket
  • per_turnover: (0 to 100) proportion of turnover share within the sector in percentage terms

credit_score.csv

  • Region: UK region
  • mean: (-inf to inf) mean personal credit score per region
  • stdev: (0 to inf) standard deviation of credit score per region

demand.csv

  • columns: UK sector
  • rows: UK sector
  • values: (0 to 1) demand contribution from row sector to column sector. All rows sum to 1.

earnings.csv

  • Region: UK region
  • earnings: (0 to inf) median personal earnings per region

expenses.csv

  • Region: UK region
  • expenses: (0 to inf): minimum personal expenses per region

expenses_full.csv

  • Region: UK region
  • Sector: UK sector
  • Decile: (one to nine): decile of personal income
  • expenses: (0 to inf): personal expenses per region per sector per decile in normal times

gdp.csv

  • Region: UK Region
  • Sector: UK Sector
  • Age: Age banding
  • gdp: (0 to inf) GDP per region, sector, age group

growth_rates.csv

  • Sector: UK Sector
  • growth_rates: (0 to inf) historic peacetime growth rates per sector

input_output.csv

  • Sector: UK Sector
  • employee_compensation: (0 to inf): mean employee compensation per sector
  • taxes_minus_subsidies: (0 to inf): mean taxes minus subsidies per sector
  • capital_consumption: (0 to inf): mean capital consumption per sector
  • net_operating_surplus: (0 to inf): mean net operating surplus per sector

input_output_final.csv

  • Sector: UK Sector
  • C: consumption
  • K: capital formation
  • E: exports

input_output_intermediate.csv

  • Columns: UK Sector
  • Rows: UK Sector
  • Values: (0 to inf): consumption of products of row sector by column sector

input_output_primary.csv

  • Sector: UK Sector
  • IMPORTS: (-inf to inf)
  • TAXES_PRODUCTS: (-inf to inf)
  • COMPENSATION: (-inf to inf)
  • TAXES_PRODUCTION: (-inf to inf)
  • FIXED_CAPITAL_CONSUMPTION: (-inf to inf)
  • IMPORTS: (-inf to inf)

keyworker.csv

  • Sector: UK Sector
  • keyworker: (0 to 1): fraction workers per sector who still go to work and are unaffected by lockdown

largecap_count.csv

  • Sector: UK Sector
  • largecap_count (0 to inf): number of large-cap companies per sector

largecap_pct_turnover.csv

  • Sector: UK Sector
  • largecap_pct_turnover (0 to 1): fraction of turnover generated by large-cap corporations per sector

min_expenses_full.csv

  • Region: UK region
  • Sector: UK sector
  • Decile: (one to nine): decile of personal income
  • expenses: (0 to inf): minimum personal expenses per region per sector per decile

populations.csv

  • region: UK Region
  • columns: A0, A10, ..., A80 (10 year age bands)
  • values: (0 to inf): population of each region by age group

smallcap_cash.csv

  • Sector: UK Sector
  • smallcap_cash: (0 to inf): number of days of surplus cashflow of cash reserves per sector for small-cap corporations

sme_count.csv

  • Sector: UK Sector
  • sme_count: (0 to inf): number of small and medium enterprises per sector

sme_rate_payer_vulnerability.csv

  • Sector: UK Sector
  • vulnerability: (0 to 100): vulnerability factor (higher = more vulnerable) for sectors which pay rates, and hence will have a higher proportion of companies eligible for new spending government stimulus

supply.csv'

  • columns: UK sector
  • rows: UK sector
  • values: (0 to 1) supply contribution from row sector to column sector. All rows sum to 1.

vulnerability.csv

  • Sector: UK Sector
  • vulnerability: (0 to 1): index representing maximum productivity of each sector under a lockdown situation

wages.csv

  • Sector: UK Sector
  • Age: Lower bound of age band (see src/adapter_covid19/enums.py for definition)
  • wages: (0 to inf): yearly income pre-tax per sector per age band

wfh.csv

  • Sector: UK Sector
  • wfh: (0 to 1): productivity of each sector when working from home

workers.csv

  • Region: UK Region
  • Sector: UK Sector
  • Age: Age banding
  • workers: (0 to inf) Number of workers per region, sector, age group