ESCoE Research Seminar

Using electoral registers to quantify immigration in UK local authorities

Presented by Augustin de Coulon (King’s College London and ESCoE)

28 January 2020

Introduction Definitions and Data National trends Regional distribution Estimations Conclusions

Using electoral registers to quantify immigration inUK local authorities

Augustin de Coulon, Richmond Egyei, Jonathan Wadsworth(King’s College Business School, London)

ESCoE28 February, 2020

Using electoral registers

Introduction Definitions and Data National trends Regional distribution Estimations Conclusions

Overview

1 Introduction

2 Definitions and Data

3 Comparisons of national trends

4 Regional distribution

5 Estimations

6 Conclusions

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Measuring immigration is a difficult endeavourCurrently there is no known immigration register in the UKMuch assessment of the level of immigration and associatedinflows and outflows has to be undertaken through the use ofnationally representative surveys (extrapolated to fullpopulations with weights): Labour Force Survey (LFS) /Annual Population Survey (APS) and International PassengerSurvey (IPS)Whilst the respective sample sizes of the surveys does allowestimates of migration at the UK level to be made, theseestimates are subject to margins of uncertaintyLocal area population estimates derived from these surveys arebased on small samples with associated higher margins ofuncertainty.

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What we do

We investigate statistically with the aim of identifyingsimilarities between the migration figures and trends producedfrom three data setsGraphical comparison of trends in EU immigrant estimates.Testing/comparing regional distribution across measuresAssessing directions of discrepancyTesting/isolating LA characteristics related toover/under-estimations

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Methodology

1 We use statistical approaches with the aim of identifyingsimilarities between the migration estimates produced fromthree data sets and how the estimates conform to reality

2 Graphical comparison of national trends in EU nationalsestimates

3 Comparisons of regional distribution/inequality of EU nationals4 Investigation of trends and position of regional EU populations

with panel fixed effect estimations

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Our definitions

Official definition is ’born abroad’ and ’lived for more than 12months in the host country’ (UN)The APS and Census allow use of country of origin,nationality, and years since migrationBut ER only records nationality, for comparison purpose werestrict immigrant to nationality other than BritishFor dual nationality (probably recorded as British in ER, APSand Census)

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Registration and legality

The electoral register is collected by each local authority(electoral registration offices). It is intended to be a list ofeveryone who is eligible to vote. Local authorities have astatutory duty to compile, maintain and update the list on ayearly basis (early December)Every residents is required to respond to the form sent yearly(with threat of £1,000 fine if not)Quote from Wandsworth form: ’Every HH are legally requiredto respond to this letter [..]. We need to know who lives at thisaddress in order to check who is eligible to register to vote’.

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Data used

1 We do not (yet) have access to LA individual registers, only LAaggregates. But we take advantage of the different registersfor local/EU and general elections, for period 2004 to 2017

2 10 percent individual level sample of the 2011 Census3 Labour Force Survey (LFS)/Annual Population Survey (APS)

for the period 2004 to 2017

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Electoral registers, local government electors versusparliamentary electors

Figure: ERR: LA and PC Electors for England

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Figure: LA and PC Electors for WalesUsing electoral registers

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Consistent and steep increase from 2004Difference between the two curves is EU electors (without I, C,M), our measure.Sharp decrease after 2014 reforms (linked to reforms for ERregister from HH to individual electoral register, IER)How this translate when comparing to overall EU figuresdrawn from APS ?

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Figure: LFS vrs ER, EnglandUsing electoral registers

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Figure: LFS vrs ER, WalesUsing electoral registers

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The two curves move extremely close up to 2014The change in ER appears to have led to reductions in EUelectorsCould that be related to EU students?

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Figure: LFS vrs ERR adjusted for students, EnglandUsing electoral registers

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Summary

Adding in EU students (extracted from HESA) accounts forpart of the gapBut probably not the only reason for the reductionReduction of double entries linked to movers (cleaning up theregister?).Not clear why the clearing up of registers affected more theEU nationalsObserve increase since 2015, interesting to check with morerecent figures

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Regional distribution

Does the LA distribution of EU nationals differ across data?We list top 50 LAs by EU nationals size across 3 data sets in2011Usual suspects all there, but the order varies a bitSpearman rank correlation for 2011 (ρER,APS = .879,ρER,Census = ..979, and ρCensus,APS = .880)The rank correlation between APS and ER appear to increaseover time (ρ2004

ER,APS = .862, ρ2014ER,APS = .878 and

ρ2017ER,APS = .911

The raw correlation is high and stable all along (.96)We can see that on decriptive graphs too

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Most of the LAs that featured in the ER list of top 50 LA byEU population size also featured in APS list (i.e. in 2004ranking, we find that 45 LA appeared in both)In 2014, 46 LA appeared in both rankings. In 2017, 44 LAswere found in both rankings

Changed distributions over time:ER: in the 50 LAs with the highest count of EU citizens,accounts for 61.8 percent of all UK EU citizens, in 2004In 2014 and 2017, the figure is 56.4 and 56.0 percentrespectivelyFor APS the same account for 63.8 percent in 2004, 56.0 in2014, and 53.9 percent in 2017

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Table: EU Population Geographic Inequality among the Three Data Sets

Inequality measures APS Electoral Register CensusRelative mean deviation 0.463 0.462 0.437Coefficient of variation 1.433 1.442 1.365Standard deviation of logs 1.169 1.138 1.084Gini coefficient 0.621 0.602 0.576Mehran measure 0.777 0.738 0.712Piesch measure 0.543 0.534 0.508Kakwani measure 0.316 0.301 0.276Theil index (GE(a), a = 1) 0.607 0.670 0.608Mean Log Deviation (GE(a), a = 0) 0.664 0.686 0.616Entropy index (GE(a), a = -1) 1.303 1.226 1.113Half (Coeff.Var. squared) (GE(a), a = 2) 1.023 1.037 0.928

Source: 2011 APS, ER and Census data sets

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Consistent with the earlier findings, closer correspondence btwER and the Census than the APSAPS tends tend to show greater inequality in 2011 over arange of measuresnot the case when one use measures reliant on regionalweigting of pop size (GE), except when a=-1ER may be better at capturing local EU area stocks than theAPS

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Changes over time: APS=red, ER=blue

Figure: Geographic inequality over time

The line plot show the relationship between the Geographic inequalityover time as derived from Gini index estimates from the Electoral rollregister and the APS/LFS . : Authors’ computation from the Electoralroll register and the APS/LFS data set

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Differences in LA figures

APSit = α+ βERit +347∑n=2

νiDi + εit

Unit of observation is LAi for year t = 2004− 2017;

APS : EU Annual Population Survey, ER: EU Electoral register electors

Di local authority dummies (fixed effects)

εit : cluster-robust standard error to adjust for within-cluster correlation aswell as heteroskedasticty. The clustering dimension is regions.

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No control LA intercepts +Year dum. + Post 2014(1) (2) (3) (4)

ER 1.250∗∗∗ 1.426∗∗∗ 1.347∗∗∗ 1.059∗∗∗

(0.006) (0.015) (0.018) (0.041)Constant 157.8∗∗∗ 1582.018∗∗∗ 1953.776∗∗∗ 2779.21∗∗∗

(34.44) (518.049) (493.319) (2376.586)Post2014 411.33∗∗∗

(131.559)ER x Post2014 0.329∗∗∗

(0.032)LA dummies X X Xyear dummies X XAdjusted R2 0.897 0.934 0.942 0.936Observations 4858 4858 4858 4858

Robust standard errors in parentheses (white). Bradford is the referenceomitted LA. Mean dep value= 4007 (6282), ∗ p < .1, ∗∗ p < .05, ∗∗∗ p < .01.

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ER slope statistically significant different from 1, Pval is zeroup to four decimal points, means that discrepancy increasedover the periodconstant different from 0, overall APS overstates EU migrantsby a factor of 158Does not account of size and unequal dispersion of EUmigrantsAdding LA dummies relax the constraintsAdding year dummies removes time shocks. A number ofsignificant and negative dummies (e.g. Aylesbury, Basingstoke,Bedford, Brent, Bristol, etc..)Adding a post-year 2014 structural break, allows to account forthe changes in ER collection. Now ER slope is 1 (Pval = 0.15)

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Figure: Extracting LA dummies

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Explaining different intercepts, OLS

(1) (2) (3)

POPCen11 -0.035∗∗∗ -0.0112 -0.0200∗∗∗

(0.007) (0.008) (0.006)

Constant 57.45 59.30 -2366.3∗∗∗

(110.3) (199.2) (190.5)Rural/urban X

Regions dummies XAdjusted R2 0.06 0.19 0.48Observations 347 347 347

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East Midlands (England) 2493.7(212.4)

East of England 2250.1***(204.3)

North East (England) 2788.9***(291.9)

North West (England) 2662.3***(206.4)

South East (England) 2158.7***(191.7)

South West (England) 2467.7***(212.9)

Wales 2617.9***(242.7)

West Midlands (England) 2620.1***(219.5)

Yorkshire and The Humber 3058.6***(240.7)

Notes: Rural/urban never significant, except urban with major cities (others are: mainly rural, urban

with cities, urban with minor cities and urban with significant rural)

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Conclusions

ER appears to offer complementary and useful information onregional figures and trendsER appears reasonably close to 2011 figures from decennialcensusRegional dispersion probably better measured through ER thanAPSNationality is not immigration, but quite closeSome evidence that APS in London over-estimate EUnationals and/or EU nationals do not register in the ER asmuch in London

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Next steps

We need access to electors EU nationality by LAGetting years of entry into ER would improve the comparisonstooAccess to EU settlement scheme data by LA (HO). This wouldadd a fourth source of data for the year 2019/20

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�ank you !

B augustin.decoulon@kcl.ac.uk

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