Women in Derbyshire have been working for free from 30 October and will do so for the rest of the year, an analysis of the gender pay gap shows.
Every year gender equality charity the Fawcett Society calculates when Equal Pay Day will fall – the day that women stop earning for the rest of the year compared to men.
This year it falls on November 20th for the UK as a whole, based on Office for National Statistics (ONS) earnings figures for 2020, which shows an 11.5% gender pay gap for full-time workers.
An analysis of local earning figures by the JPIMedia Data Unit reveals women in full time employment in Derbyshire earn an average of 17.2 per cent less than men.
The average woman earns £14.83 per hour, while men earn £17.90. The figures exclude earnings from overtime.
This means the local Equal Pay Day would fall on 30 October, with women working for free for 63 days - this is equivalent to nine weeks.
The ONS cautions that this year’s figures may be less reliable than previous years, because the coronavirus pandemic has caused difficulties in gathering data.
Last year, there was a 13.9 per cent gender pay gap in Derbyshire. That would have meant women worked for free for 51 days, or just over seven weeks.
Across the UK, the gap has fallen from 13.1% in 2019. The Fawcett Society said it welcomed the drop, but urged against celebrating too early as the coronavirus crisis risks turning the clock back on equality.
Chief executive Sam Smethers said: “There are a number of risks to women’s pay and employment as a result of coronavirus which could turn the clock back for a generation.
“Mothers are more likely to have had their work disrupted due to unequal caring roles and a lack of childcare.
“Men are more likely to have worked under furlough, and to have had their pay topped up.
“The second lockdown looks set to hit women working in hospitality and retail hard while predominantly male-dominated sectors like construction and manufacturing are still at work.”
She added that a quarter of employers were missing from the ONS dataset, which were likely to be the ones hit hardest by the pandemic, while the short-term impact of furlough makes the figures less clear.