I’ve always been struck by how we debate flexibility in the labour market without paying attention to the other problem in the labour market which is that it’s extremely difficult to find out whether you’re really going to like a job until you take it, and then, if you don’t well it’s too late – lots of costs and general angst getting out and moving onto something better. (Will it be better, or just the same old, same old – I’d better stick it out where I am).
So I took the problem down to Troppo labs which came up with Windows on Workplaces which starred (not) at the 20:20 Summit. Hey why pay attention to something new when you can go with the same old same old same old same old Golden Gurus. (See the same article I just linked to.) The basic idea of Windows on Workplaces – quoted from the linked post is as follows:
Say you particularly value some aspect of a job you’re applying for – for instance a good career path, intrinsically rewarding work or flexible family friendly hours. If youre applying from outside the firm youre generally in the dark. . . .
Now firms regularly survey their employees regarding their satisfaction with these things. So it would be good if you could get a peek at their answers. 1 firstly, firms that did badly wouldn’t want to release their information. The second problem is trickier still. Even if you somehow compelled firms to release this data, their survey results cant be readily or reliably compared because they’re not reported against some common standard. I propose . . . not that governments mandate some standard, but rather that they organise and campaign to encourage a standard to emerge. . . . The best firms have an interest in such a standard emerging as it would advantage them in competing to attract employees. And governments are also major employers, so they could establish standards for their own agencies to report against, which, if deftly done might form the kernel around which more widely used standards might emerge.
I’ve never tried to do any empirical work on the extent to which this could improve productivity – though I think it would be large. Now, in the process of completing a major project on estimating the value of open data to the Australian economy, a colleague referenced this paper.
“Match Quality, Worker Productivity, and Worker Mobility: Direct Evidence From Teachers”
by C. Kirabo Jackson. Here’s the abstract. Extremely promising I reckon!
I investigate the importance of the match between teachers and schools for student achievement. I show that teacher effectiveness increases after a move to a different school, and I estimate teacher-school match effects using a mixed-effects estimator. Match quality can “explain away” a quarter of, and is as economically important as, teacher quality. Match quality is negatively correlated with turnover, and increases with experience. This paper provides the first estimates of worker-firm match quality using output data as opposed to inferring productivity from wages or employment durations. Because teacher wages are essentially unrelated to productivity, this is compelling evidence that workers may seek high quality matches for reasons other than higher pay.
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A quick read through that paper leads me to the rather unsurprising and more simple conclusion that teachers that move to nicer schools with less annoying students do a better job (“match effects”). Yet more evidence that people attribute far too much variance in education settings to the individual teachers versus their environment (both management and student).
Thanks Conrad,
This is what the paper says (p. 2):
p 11. is the important one in my books:
On average teachers move to schools where mean school level reading test scores are 0.023? higher, and classes are 0.23 students smaller. Also, teachers move to schools where the percentage of black and low-income students in the school is 2.5 and 3.8 percentage points lower, respectively. Teachers also experience a 7.3 percent pay increase after a move. Teachers are not switching out of large cities into other areas, but they are more likely to switch out of schools in mid-sized cities and towns into rural areas. The patterns here are consistent with teachers leaving low-performing schools that serve low-income ethnic-minority students for higher-achievement schools with fewer low-income students and fewer minority students.
That might be matching, but that definition would presumably satisfy anyone going for a better job (better paid and less stress it appears).
Conrad, you should read the entire paper through. It is not about teachers moving to better schools because the estimates effects are based on comparing different teachers switching across the same schools. Statistically, this is achieved by removing school-level averages from the outcomes of all teachers when comparing their changes in productivity. As such, the match effects are based on different teachers responding differently to switching across the same sets of schools. I recommend reading through the methodology section carefully to get a better idea of what is being estimated.