This is the latest instalment of our new weekly series, in which we use the Centre for Cities’ data tools to crunch some of the numbers on Britain’s cities.

Last week in this space we looked at the way the population of Britain’s major cities had changed since the early 1980s. While all sorts of factors drive population change, and there are no doubt a huge variety of local issues at work here, one pattern was very obvious: cities in the south of England have grown quicker than cities in the north.

Three decades is a long time, though, so we promised to break it down a bit and look at how the numbers had changed over shorter time periods.

The answer, annoyingly, is “by less than you’d think”, but hey, in for a penny.

This week, we’re looking at the years from 1981-92, a sort of Greater Thatcher era. Here’s the data, in map form. You can hover over individual cities to get the exact numbers.

The first thing to notice is that, well, this doesn’t look all that different from the map we ran last week when the time frame lasted a generation rather than a decade. In some ways, that isn’t that surprising: in a housing market as sluggish and unresponsive as Britain’s (a problem we may have mentioned before), major population shifts are likely to take decades rather than years.

 

A second point worth highlighting is the rising tide thing. The 1981 census put Britain’s population at around 56.3m; a decade later it was 57.4m. It had barely budged, so not surprisingly, Britain’s cities weren’t growing that quickly either. By 2013, it was up to 64.1m, so, (equally unsurprisingly) at least some of its cities had grown, rather a lot.

Hence the radical difference in what the colour scheme actually means in the two time periods:

 

Nonetheless, the same-i-ness of the two maps points to a depressing, if depressingly obvious, fact: far too many of the British cities that were struggling in the 1980s will likely have kept struggling for rather a lot of the intervening time since.

Here are the 10 fastest growing cities. The map on the left covers trends in the 1980s; the one on the right those all the way up to 2013.

Eight cities pop up in the top 10 on both long and short timeframes. But the Greater Thatcher era top 10 include Hastings and Warrington (which don’t make the cut the longer time frame), and exclude Luton and Crawley (which do).

That said, all of these places are still comfortably near the top of the league table in both timeframes, so we probably shouldn’t draw too many conclusions from differences in the details. The key point is that cities that were growing in the 1980s are likely to have kept growing since.

Here’s the bottom 10.

The interesting one here is Ipswich, which struggled in the 1980s, its population falling by 3.5 per cent between 1981 and 1992. Since then, though, it’s bounced back, and over the years from 1981 to 2013 as a whole it’s up 12.2 per cent. At a guess this is a function of geography: London’s long boom spilling over into anywhere within about an hour’s train ride.

Other than that, once again, it all looks quite familiar. Coventry also struggled in the 1980s, and makes the bottom 10 here (down 5.1 per cent). But it doesn’t do brilliantly over the longer period either (up, but only 3.1 per cent, which is nothing, at a time when national population is up by about five times that), so is less worth talking about.

Meanwhile, the cities of the industrial north east may have thought they were having a bad time in the 80s. As it turned out, things could and did get worse.

The lesson here is probably not very profound, but it’s an important one nonetheless. Population change is a reasonable proxy for “success” in a city: the better a city’s economy, the more likely people are to want to live there, and so the more the population is likely to grow.

But “success” is path dependent: the strong a city economy in the recent past, the more likely it is to be strong today. Many of those cities that were losing people in the 1980s have kept losing people since. All this tells is that a struggling city takes a long time to turn around.