Chris Hanretty Profile picture
Sep 24, 2020 20 tweets 5 min read Read on X
Towns are sexy in UK polisci, aren't they? Let's talk a little bit about the super-sexy Towns Fund gov.uk/government/pub… (1/n)
As @estwebber and @georgegrylls reported this morning, the Perm. Sec at MHCLG gave evidence to the Public Accounts Committee this week thetimes.co.uk/article/robert… (2/n)
Jeremy Pocklington said that ministers had ignored civil service advice concerning the £3.9bn Towns Fund, and had instead applied [ahem!] "their own qualitative assessment" (3/n)
The Towns Fund has previously been the subject of a National Audit Office report (nao.org.uk/report/review-…), which sets out the very detailed scores that civil servants gave to different towns and rank-ordered recommendations (4/n)
(That data's in a horrible, horrible PDF, but I tidied it and put it here: docs.google.com/spreadsheets/d…) (5/n)
Briefly: 541 towns were considered, civil servants made recommendations as to how many towns should be picked in each region, & ranked towns within regions. Each region had a list of high-priority towns, but ministers had discretion over the 60 remaining towns (6/n)
Ministers could have followed the detailed civil service rankings. Here's what the data would have looked like if that had happened. 38 out of 60 funded areas are wholly or partly in Cons held seats (7/n)
But ministers exercised their discretion, and so we get this pattern, with 53 of 61 funded areas in Con-held seats (8/n)
(Technical note: a town (=built up area or subdivision) is in a Conservative seat if the town area overlaps, in whole or in part, with a Westminster seat. Some towns straddle Westminster seats) (9/n)
This association is robust, and highly significant across several different specifications. Here's a logistic regression table for those of you who like that (10/n)
Indeed, because I know how to have a good time, I went so far as to estimate all 4095 models possible using the variables in the NAO data. In *all* models the "Conservative seat" variable is significant (11/n)
If you want, you can estimate a fancier model, where a smoothed spline connects (average) Conservative majority in the seat(s) to funding decisions, controlling for rank within region. For towns on the cut-off, you get a peak at a Conservative majority of +5 to +7% (12/n)
In my judgement, no reasonable analyst of the NAO data could fail to conclude that Conservative areas were advantaged by the process (13/n)
What does that mean politically and legally? (14/n)
Politically, I am not sure how much it means. MPs can and ought to ask more questions about the "qualitative assessment" carried out by ministers... (15/n)
... but the recent attention to standards in public life suggests (cspl.blog.gov.uk/2020/09/22/why…, that the (political/norm-based) maintenance of those standards is slipping (16/n)
Legally, is there a case that Jenrick and Berry wilfully misconducted themselves to such a degree as to amount to an abuse of the public's trust, without reasonable excuse or justification? (17/n)
I don't know enough about the status of the duty to secure value for money in public expenditure, and I don't know what kind of "reasonable excuse or justification" Jenrick and Berry could offer for this pattern (18/n)
But I challenge anyone to look at the data or my code (gist.github.com/chrishanretty/…) and conclude that partisan advantage wasn't a factor here (19/19)
(p.s. I have left out the part of the code that involved wrangling with shapefiles; DM me if you need it)

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More from @chrishanretty

Jun 1, 2023
In response to UCU's Marking and Assessment Boycott (MAB), my employer @RoyalHolloway has decided to implement emergency regulations which in my view seriously call into question the rigour of degrees awarded (1/7)
The regulations include allowing marks for a module to be scaled proportionately (you did 50% of the coursework; that counts for 100%) (2/7)
(cont.) to permit an unlimited number of "allow" outcomes for modules (previously used where sickness or other extenuating circumstances affected students' performance) (3/7)
Read 7 tweets
Nov 20, 2022
Everyone knows the most fun way to watch the World Cup is to support the more democratic nation in each game. So here, thanks to @vdeminstitute data, is your group-by-group rundown! (1/n)
We start in Group A, where the Netherlands is clearly in pole position, and Qatar clearly in last place (2/n) Image
In Group B, England and Wales are in a dead heat (until and unless V-Dem produces estimates of sub-state democracy), with Iran placing last. (3/n) Image
Read 11 tweets
Jul 16, 2022
This use of MRP makes me incredibly anxious. There are considerable technical difficulties here. Begin nerdgasm... (1/15)
MRP works by modelling responses as a function of different demographic and political characteristics, and then making predictions for different voter types (2/15)
It works well when responses can be accurately predicted by these characteristics, or when you have a stupidly large sample size (3/15)
Read 15 tweets
Sep 12, 2020
Strong, strong political reasons to support Osaka in this match #USOpen
Azarenka and Lukashenko have a "friendly" relationship nytimes.com/2020/09/09/spo…
But god-damn, she's started well 😖
Read 5 tweets
Sep 11, 2020
I have some reservations about the claim that Covid-19 divisions now run deeper than Brexit (1/n)
The basis for the claim is that the proportion of mask-wearers who hate, resent or think badly of non-mask wearers (58%) is greater than the proportion of Remainers who think badly of Leavers (33%) (p. 11 of report) (2/n)
First problem with this: in order for something to be divisive, it's got to divide society, and the more evenly it divides society, the more divisive it is. But the (short) report doesn't show what % of the population wear a mask. (3/n)
Read 6 tweets
Sep 9, 2020
Tonight I'll be presenting my #APSA2020 paper, "The voting power of demographic groups". You can find the paper at drive.google.com/file/d/1G-SHiy… and a video presentation at (1/n)
The idea behind the paper is simple: the power of a voter group is the number of seats where the result would have been different had that voter group not voted—or alternately, the number of seats in which that voter group was pivotal (2/n)
The implementation is also simple, in a sense, and relies on multilevel regression and post-stratification (MRP) plus some post-hoc adjustments (3/n)
Read 10 tweets

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