Rebecca Coulson is a freelance writer, and was Parliamentary Candidate for the City of Durham at the 2015 General Election.

You’re reaching for the kedgeree, Paul Nuttall has finished the leftovers from last night’s leafy Islington supper, and excitement is building. It’s almost time… That’s right: the provisional results of the 2016 ONS Annual Survey of Hours and Earnings (ASHE) will be published at 9.30 today! In all its Victorian-sounding splendour, its graphs will look as clear this morning as when, Blair-eyed, we saw the series begin in ‘97.

Hailed as counsel for (or against) the UK’s economy, ASHE’s ‘headline measure’ is full-time employees’ median weekly earnings, but it provides breakdowns, too, through filters like gender, occupation, location, and age. As well as helping to assess pay rates in relation to the minimum and living wages, and inform interest adjustments, it’s also relied upon to tell us how we’re progressing (or not) with the gender pay gap. That’d be an easy point for us to settle on, but I’ve written about that here before. So, let’s consider the even bigger picture: why we care about this stuff at all.

In evaluating societal fairness, surely what matters is proportional equality. We should want to know whether people are paid fairly for what they do, rather than how their earnings relate to everyone else’s. That said, of course, what you do or earn has an effect on others, and vice versa – everything combines, influencing our sense of value. But when addressing stats, the line between descriptive and normative can blur: sometimes, we like using the information we have about Britain to promote how we think Britain should be.

Descriptively, we might expect a decent headline showing from today’s ASHE. Last year’s explained, for instance, that, ‘adjusted for inflation, weekly earnings increased by 1.9 per cent compared to 2014’ – the first increase since 2008. This stuff is subtle, however; grading society is hard. And the ONS itself admits that changes to ASHE methodology have ‘result[ed] in a series of discontinues’; it doesn’t include the self-employed (an increasingly significant group); and it suffers from time-lag.

For a quicker view, we can turn to Average Weekly Earnings – the Government’s key short-term indicator. Its latest release reveals that employment has ‘never’ been higher (since 1971, when comparable records began), and that nominal earnings are up 2.3 per cent – or 1.8 per cent in real terms – on a year ago. (Yes, it’s too soon to see the referendum’s full impact, but, either way, we must want positive news now.) And, to findings on earnings, we can add details about how we come together as households, those households’ total income, and how that’s spent.

What about further afield? We may take it for granted, yet the best general indicator of opportunity and wealth is usually the country where you’re born. And, regardless of individual circumstances, and regional inequalities (as described by ASHE), Britons are immensely lucky.

Sure, I might now point out that living standards across the world have boomed over the past 40 years. And then, though you sort of agree, you might claim that progress has been built on, say, oil or greed. I’d reply that, even if you were right, it’d still be good that fewer children die of malnourishment. And you’d call me a consequentialist, because you know I hate that. And off we’d go.

But it seems useful to compare Britain with other countries, which means it’s time for your favourite denim-ed-up girl with the light brown hair. The Gini coefficient remains the usual measure of societal inequality, providing a neat number for world rankings.

To get that number, you simply work out how far a society is from the ‘line of equality’. If you imagine a graph, with the horizontal axis representing the population (ranked by percentage from poorest to richest), and the vertical representing the country’s cumulative income (also in percentage terms), then the diagonal line from bottom left to top right (x=y), which divides the square area enclosed by those axes, is that ‘line of equality’. When a society’s stats draw that line, then its poorest n per cent earn n per cent of the country’s income: good times!

In reality, however, societies’ stats create curves bending somewhere below that line (rather than the poorest 50 per cent earning 50 per cent of the income, they earn, perhaps, 30 per cent). And if you take the area of the space between the equality line and a country’s curve, and divide that space by the area of the whole space below the equality line (the diagonally-cut half of the square), then bingo, the ratio gives us our girl Gini, with results closer to one representing more unequal societies. Sorted.

Or is it? Sometimes, for instance, these graphs ignore redistribution. The original income relation between the richest and the poorest British quintile is 15 times; after tax and transfers, it’s four. And, regardless of data’s accuracy, it being squashed into an overtoned infographic can, unsurprisingly, inhibit nuanced interpretation, as seen with infamously clumsy examples, like the elephant curve, or GDP charts…

Normatively, it’s even trickier. If genuine ‘equality’ is all we want, then why care about anything else than that? If we fixed it so that, say, each household had £1 of disposable income a year, then our curve would be the equality line, and everything would be well, no? Obvious points aside, you might say that £1 would then be all we’d need. And there we’d be again, fighting over the size of the state.

Talking of fighting, the way in which these curves help us determine distributions of wealth (often in unhelpful net terms) leads to those ‘but the top three per cent are 71 per cent better off than the bottom 17 per cent’ conversations. Those ones where you reel off the figures, and I reply that rich people bring in tax and jobs, and that little suggests that increases at the very top affect overall standards. And then you say that that talk feeds the elites, and off we go.

But the word ‘elite’ reminds me of something: isn’t this survey stuff evidence of expertise? Aren’t we doing figures and facts? And, at the heart of the expert-purge, haven’t we discredited number-mongers: the bankers, pollsters, and wannabe-scientist economists? We’re post-all-that, aren’t we? Yet, how do you feed your arguments without number plunder? How can you hate the bankers’ pay, if you don’t know how it relates to yours?

Ok, maybe it’s not the numbers you hate. Maybe, it’s those who manipulate or benefit from them. Sometimes, though, people who denounce experts rely on them to back up their views. And others don’t celebrate progress if it doesn’t fit their narrative. Perhaps the lines are blurred, in more ways than one.

Comparisons are endlessly valuable, when we know what they tell us, and how to use the information. While the Prime Minister plays to both extremes of her extended voter base – those who want capitalism reformed, not just to preclude cartels but also to incorporate progressive thinking; and those who are frustrated by what they see as the exploitation of little losers by elite winners – ensuring we’re asking good questions about these things seems more important than ever. And so does interrogating the answers.