Nurses' pay, ambulance times and forgotten female economists

Episode Summary

Executive Summary: This episode of More or Less examines how numbers can mislead or clarify public debate: ambulance times in England improved sharply from December to January but still missed targets; nurse pay in the UK looks middling in Europe yet fares worse versus national averages and has fallen in real terms; the word "data" is debated as plural or singular; women’s early role in economics has been under-remembered; and the ONS’s spreadsheet error showed how fragile statistics can be when data handling goes wrong.

Main Topics: England ambulance performance improves, but targets still missed (Priority: 5/5): January data showed major improvements in call-answer times and ambulance response times after December’s record-bad performance, yet category 1 and 2 targets were still not met. The episode emphasizes that the apparent rebound is partly a comparison with an exceptionally poor prior month, not necessarily a return to acceptable service. Nurses’ pay in the UK compared with Europe (Priority: 5/5): Using OECD data, the program examines gross pay, purchasing power parity, median wages, and relative standing versus national averages. It argues that while UK nurses are not the worst paid in Europe, their pay is low relative to other domestic occupations and has fallen in real terms over the last decade. The grammar battle over "data" (Priority: 4/5): The show debates whether data should be treated as a plural noun or a singular collective noun, prompted by Rishi Sunak’s usage and the Financial Times style change. The discussion covers Latin roots, style guides, and the tension between grammatical tradition and evolving usage. Women in the early history of economics (Priority: 4/5): A historical segment argues that women were more visible and influential in economics in the late 19th and early 20th centuries than commonly remembered, even if they were often excluded from formal academic posts and later erased from the record. ONS spreadsheet error and the fragility of statistics (Priority: 5/5): The episode revisits a major ONS correction to productivity data caused by misaligned spreadsheet columns. It uses the error to illustrate how seemingly small data-handling mistakes can produce dramatic statistical distortions. Spreadsheet autocorrect and hidden data errors (Priority: 3/5): A short interview with Matt Parker explains how Excel can corrupt scientific data, especially in genetics, by auto-converting gene names into dates. The example reinforces the broader theme that tools can introduce systematic errors when users are not careful.

Key Arguments: Ambulance performance improved markedly in January, especially against December’s record lows, but still fell short of NHS targets for the most serious categories. The right comparison for ambulance data is not just month-to-month change but whether performance meets the target and how it compares historically. UK nurses are paid near the European average in absolute gross terms, but not especially well relative to the UK wage distribution. Purchasing power parity is essential when comparing salaries across countries because living costs differ. Gross pay comparisons can be misleading because tax and social-security systems differ across countries; take-home pay structures are not directly comparable. Real nurses’ pay in the UK has fallen over the last decade, unlike in many European countries where it has risen. The word data has a legitimate Latin plural origin, but modern style guides increasingly permit or prefer singular usage. Women were active in the development of economics, especially through empirical, policy-oriented work, but their contributions were often under-credited or erased. The ONS productivity error was caused by a spreadsheet column mismatch, showing that technical handling mistakes can overturn headline statistics. Excel-style autocorrect problems are common enough to affect published research and even force changes in scientific naming conventions.

Data Points: Ambulance call-handler mean wait time: 88 seconds to 9 seconds - England, December 2022 to January 2023 Ambulance call-handler median wait time: 37 seconds to 1 second - England, December 2022 to January 2023 Category 1 ambulance response mean: 11 minutes to 8.5 minutes - England, December 2022 to January 2023 Category 2 ambulance response mean: Over 1.5 hours to 32 minutes - England, December 2022 to January 2023 Category 1 target: 7 minutes - NHS target mentioned for the most serious incidents Category 2 target: 18 minutes - NHS target mentioned for suspected heart attacks and strokes Category 1 incidents change: Nearly 30% drop - January compared with December record high UK nurses' median full-time pay: £37,000 - Compared with UK median wage of £33,000 UK median wage: £33,000 - Used as comparison for nurses’ median pay Nurses' pay relative to UK median: 13% above median - UK median full-time nurses’ pay Nurses' relative pay in EU: About 20% above national average wage - Average across European Union countries Nurses' relative pay in UK: Pretty much exactly the average - Compared with national average wage Real nurses’ pay change in UK since 2010: About -8% - Over the decade since 2010 Real nurses’ pay change in parts of Central and Eastern Europe: 60% to 80% increase - Over the decade since 2010 Real nurses’ pay change in Belgium and the Netherlands: About 10% increase - Over the decade since 2010 Real nurses’ pay change in France: Stayed constant - Over the decade since 2010 ONS productivity revision for UK 2021: 22% growth corrected to -1.8% - Correction after spreadsheet error ONS productivity revision for Canada 2021: 18% growth corrected to -6% - Example of widespread error from the same spreadsheet issue Share of published genetics research with Excel gene-name autocorrect errors: Just over 19% - 2016 review of past 10 years of research Share of published genetics research with Excel gene-name autocorrect errors later: About 30% - 2020 follow-up analysis

Pivotal Quotes: "In future, I'll be using the word datums to avoid arguments." — Presenter: Opening joke framing the singular/plural debate over "data" "The data show that rather than the data show that." — Suzanne Blumson: Financial Times style-guide explanation of the new singular usage policy "The UK's productivity didn't grow by 22%. It fell by 1.8%." — Presenter: Explanation of the ONS spreadsheet correction to productivity data

Implications: Listeners are reminded that statistics need context: targets, baselines, definitions, and methods matter. The episode also shows how language conventions, historical memory, and software quirks can all shape how data is interpreted and trusted.