THE HOUSING MARKET AND REGIONAL COMMUTING AND … · market variables, since if commuting is...

I INTRODUCTION

There has been much debate about the connection between housing tenurestructure and the relatively low level of labour mobility in the UK, see Hughesand McCormick (1981, 1987), Minford et al. (1987) and McCormick (1997).These authors have seen the small size of the private rented sector, partly theresult of controls on rents and security of tenure, and restrictions on the mobilityof council tenants as important sources of inefficiency in British labourmarkets. The high degree of persistence in regional unemployment rates com-pared with the US is one symptom of this inefficiency. If regional unemploy-ment rates respond only weakly to demand shocks, regional labour mismatch ishigh which implies both a loss in output and greater vulnerability to inflationarypressures.

Bover et al. (1989) and Muellbauer and Murphy (1991) suggested that theowner-occupied housing market was more intimately involved in accounting forthese inefficiencies than had previously been thought. A body of econometricevidence has now built up to suggest that high relative earnings and employmentopportunities encourage migration to a region while high relative house pricesdiscourage it.1

The most obvious mechanism for this negative effect of house prices onmigration arises through cost of living differentials between regions. If earningsare not deflated by a regional cost of living index which appropriatelyincorporates housing costs, relative house prices will, in part, measure thisomitted cost of living effect. However, there are likely to be further effectsconnected with constraints on credit availability and the risks associated with ahigh level of indebtedness both relative to income and to housing equity.

Typically, house price to income ratios in London and the rest of the SouthEast exceed those in other regions. Mortgage lenders apply ceilings both onloan-to-value and loan-to-income ratios in allocating mortgage loans. Thus,first-time buyers in the South East or considering a move to the South East willbe more likely to be constrained by loan-to-income ceilings and more likely toface cash flow problems if mortgage interest rates rise. When house prices have

420

Scottish Journal of Political Economy, Vol. 45, No. 4, September 1998© Scottish Economic Society 1998. Published by Blackwell Publishers Ltd, 108 Cowley Road, Oxford OX4 1JF, UK and350 Main Street, Malden, MA 02148, USA

T H E H O U S I N G M A R K E T A N D R E G I O N A LC O M M U T I N G A N D M I G R A T I O N C H O I C E S

Gavin Cameron and John Muellbauer*

*Nuffield College, Oxford

1 See Zabalza (1978), Harrigan, Jenkins and McGregor (1986), Mitchell (1989), Jackmanand Savouri (1992a, 1992b), Gabriel et al. (1992), Kingsley and Turner (1993), Potepan(1994), Kiel (1994), Johnes and Hyclak (1994).

risen more strongly in the South East, owner-occupiers in other regions have toborrow relatively more to move to the South East. In contrast, South Eastresidents have an equity cushion which they can use to reduce borrowing orspend on more luxurious housing by moving to other regions.

If one takes a narrow view of the determinants of house prices, i.e., regardsthem as largely determined by local labour and product market conditions, onemight be justified in omitting relative house price effects in migration equations.However, the anomalous or weak earnings and unemployment effects found insuch migration equations, see McCormick (1991), are prima facie evidenceagainst this. The narrow view neglects the supply side, for example the greaterrestrictions on expanding housing supply in London and the South East, andeven more importantly the wealth effects, interest rate effects and speculativeportfolio investment effects in the determination of UK house prices docu-mented in Muellbauer and Murphy (1997).

In the 1980s house price boom, increased portfolio demand for housingcrowded out part of the demand by employees for living space. This is mostlyclearly seen in the South East, which by 1987–8 was showing symptoms ofspeculative frenzy. Thus, in 1987–89 when relative unemployment rates in theSouth East had fallen sharply and relative earnings had experienced strong rises,net regional in-migration into the South East reached record lows of −55,000individuals per annum despite the labour market pressures for higher in-migration. This is an important practical example of the regional labour marketinefficiency discussed above. Moreover, this episode added considerably toinflationary pressure as argued in Bover et al. (1989) and documented ininstitutional detail by Walsh and Brown (1991).

A frequently made point is that regional commuting can help to overcome theblockages the housing market can put in the way of an efficient regionalallocation of labour and jobs. For example, one expects the 1987–89 period tobe one where net commuting to the South East increased to offset the housingmarket constraints. More generally, reflecting the commuting/migrating trade-off, one should expect the housing market variables such as relative houseprices, to have the opposite effect on net regional commuting compared with netregional migration. In contrast, the effects of relative earnings and employmentopportunities should be in the same direction for both net commuting and netmigration.

Gordon (1975), Molho (1982) and Jackman and Savouri (1992a) havepointed out a further implication of the commuting/migrating trade-off on thedeterminants of migration. This results from the fact that fixed costs are muchgreater for migrating, while commuting costs strongly increase with distance.2

Thus the trade-off operates more powerfully for contiguous region migrationthan non-contiguous region migration. If relatively cheap commuting is analternative, the location decision will be more strongly influenced by relativehousing market variables. It will be less strongly influenced by relative labour

© Scottish Economic Society 1998

2 Indeed, taking the utility loss of reduced leisure into account, they increase dispropor-tionately with commuting times and so distance.

HOUSING MARKET, REGIONAL COMMUTING AND MIGRATION 421

market variables, since if commuting is relatively cheap, the decision of whereto live can be made more independently from the decision of where to work.

Jackman and Savouri (1992a) present evidence in favour of this hypothesisby interacting relative house price effects and relative labour market effects witha measure of contiguity between regions. This is the length of boundary sharedby contiguous regions in a model of bilateral migration flows between allbilateral regional alternatives. They show that relative house prices operate morepowerfully and relative vacancies operate more weakly on migration tocontiguous regions.

In the present paper, we provide evidence for the first time on net commutingas well as net migration between British regions in a common framework with amore sophisticated modelling of housing market effects than seen in previouswork. Our data on net commuting are derived from the ratio of employment ona region of employment basis to employment on a region of residence basisusing Census of Employment and Labour Force Survey information for 1983 to1995. The migration data come from the same source as Jackman and Savouri(1992a, 1992b), the National Health Service Central Register.

The plan of the rest of the paper is as follows. Section II provides basic factson commuting and migration between regions in Great Britain and describes ourdata sources. Section III provides empirical results for an econometric model ofnet commuting and Section IV corresponding results for a model of netmigration. Section V draws conclusions.

II SOME BASIC FACTS ON REGIONAL COMMUTING AND MIGRATION

Regional commuting

The 1981 and 1991 Population Censuses provide accurate information on inter-regional commuting. Table 1 summarizes this information by showing, for eachregion, the ratio of employed residents working outside the region (the out-commuters) to employed residents living in the region, and the ratio ofemployees working in the region but resident outside (the in-commuters) to thenumber of employed residents living in the region. It should be no surprise thatthese figures suggest that small regions with many neighbours and goodtransport routes, such as the East Midlands tend to have high inter-regionalcommuting rates. The largest and most populous region, the South East, has thelowest out-commuting rate and one of the lowest in-commuting rates. Theclassic London commuter pattern is largely between the rest of the South Eastand London. However, East Anglia, the East Midlands and the West Midlands,with relatively high out-commuting rates owe these in part to their contiguitywith the South East. It is evident that between 1981 and 1991 there has beensome increase in gross commuting flows, despite the fact that 1991 was a yearof severe recession in the South East.

There are three major potential sources for annual data on regional commut-ing. These are the Labour Force Survey (LFS), the New Earnings Survey (NES)and Inland Revenue data. The Labour Force Survey is a place of residence


422 GAVIN CAMERON AND JOHN MUELLBAUER

based household survey but only began to record place of employment in 1992.Table 1 shows data from the Spring 1994 LFS which suggests results fairlycomparable with the 1991 population census.

In principle, the (distinct) 1% samples of national insurance records usedboth by the NES to compute earnings data and the Inland Revenue to computeregional wage and salary information, contain information on the post-code ofthe employer and the employee. The first two digits of the post-code aresufficient to define the region. The NES currently does not collect this informa-tion on the employee for reasons of confidentiality which are hard to justify.The Inland Revenue and ONS appear not to have classified their data in this wayeither.

Despite these unfortunate gaps in our knowledge, the LFS does offer a way ofextracting annual information back to 1984 on net commuting rates by region.LFS Historical Statistics published in 1997 provides estimates of the number ofemployees resident in each region from 1984 to 1996. These use informationfrom 1981 and 1991 Censuses, intervening demographic estimates and applies asophisticated grossing up methodology, which corrects for regionally varyingpatterns of non-response. These data provide a count of the number of personsemployed by region of residence.

The Census of Employment count of regional employment is based on theplace of employment and is a count of the number of jobs rather than thenumber of persons, the latter being lower because of multiple job holding.However, from unpublished information in the LFS, we can estimate theproportion of employees resident in each region with a second job, annuallyback to 1984. It is unlikely that regional commuters have a second job. Thus we


TABLE 1Commuting data

1991 1981 Census 1991 Census 1994Employed LFSresidents In rate Out rate Net rate In rate Out rate Net rate Net rate(000’s) % % % % % % %

GB 23452 2·2 2·2 0·0 2·8 2·8 0·0 0·0NN 1192 1·5 2·1 −0·6 1·9 2·6 −0·7 0·3YH 2009 2·4 2·5 −0·1 3·1 3·2 0·0 0·1EM 1747 3·1 6·3 −3·2 4·2 7·8 −3·6 −4·0EA 912 3·5 3·9 −0·4 4·5 5·3 −0·8 0·5SE 7682 1·8 0·7 1·1 2·3 1·1 1·2 0·9SW 2006 1·9 2·7 −0·7 2·3 3·1 −0·8 −0·5WM 2209 2·6 2·5 0·0 3·3 3·4 0·0 0·2NW 2534 2·3 1·9 0·5 2·9 2·6 0·3 0·5WW 1087 1·7 3·3 −1·6 2·3 4·0 −1·7 −1·7SC 2074 n/a n/a n/a 0·7 1·6 −1·0 −0·2

Notes:Out rate = commuters out divided by employed residents (θ2 in text).In rate = commuters in divided by employed residents (θ1 in text).

Source: Workplace and Travel to Work Survey (Census 1981, 1991).Labour Force Survey (1994).


can convert the Census of Employment regional jobs count into a regional placeof employment based count of persons employed.3

By definition:

(number employed in a region)/ (number of employees resident in a region)= 1 − rate of out-commuting + rate of in-commuting = 1 − θ2 + θ1 (1)

where these rates are defined relative to the number of employed residents.To be precise, if NCE is the number of jobs from the Census of Employment,

PS, is the proportion of employees with second jobs and NLFS is the number ofresident employees from the LFS, the ratio of employment on a place ofemployment basis to employment on a place of residence basis, NR, is given by:

NR = NCE/(1 + PS)NLFS = 1 − θ2 + θ1. (2)

Since the net commuting rate θ1 − θ2 is a small number, typically under 4%,taking logs gives:

θ1 − θ2 Q ln NR (3)

to a very close approximation. In fact, we make one further adjustment to thesedata by subtracting the value of ln NR for Great Britain from the value for theith region. This corrects for any national discrepancies between the LFS andCensus of Employment estimates.4

Figures 1 and 2 plot data from 1983 to 1995 on ln NR respectively, for the


3 The LFS count also includes homeworkers and some workers for employers who do notoperate a PAYE system. Since we have no information on their regional distribution, we omitthese from our analysis.

4 See Spence and Watson (1993) for information on national discrepancies between the twosources.

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

1983 1988 1993

NN

NW

YH

WW

SC

Figure 1. Commuting ratios (lnNRi − lnNRgb) for Northern Regions.



-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

1983 1988 1993

WM

EM

EA

SE

SW

Figure 2. Commuting ratios (lnNRi − lnNRgb) for Southern Regions.

TABLE 2Census and estimated net commuting rates, 1991

Census Estimated% %

GB 0·0 0·0NN −0·7 −2·4YH 0·0 0·4EM −3·6 −3·9EA −0·8 −5·4SE 1·2 2·0SW −0·8 −0·5WM 0·0 −3·5NW 0·3 0·4WW −1·7 −3·5SC −1·0 2·3

Source:Census: Workplace and Travel to Work Survey, 1991.Estimated: Log ratio of employment on census ofemployment basis to LFS employment (see text).

South East and its contiguous regions, and the remaining regions. These datashow a rise in the net commuting rate to the South East in the 1980s broadlycorresponding to the relative economic boom and the rise of relative houseprices in that region. Net commuting rates for contiguous regions show broadlythe reverse pattern, though sampling variability clearly makes some of the yearto year fluctuations erratic in the less populous regions. For 1991, we cancompare our estimates of the net commuting rates θ1 − θ2 with the Censusvalues, see Table 2. The most notable discrepancy is for East Anglia where theCensus figure of −0·8% contrasts with our estimate of −5·4%, reflecting aCensus of Employment estimate substantially below the LFS estimate of


employment. The former excludes home workers and members of the armedforces which are included in the latter. East Anglia has the largest share ofemployment of any region in agriculture, where there are many ‘home workers’,as well as a disproportionate number of members of the armed forces.

Regional migration

This is a topic about which much has been written, see the references above. Wetherefore summarize only some key points. Annual (indeed quarterly) infor-mation back to 1976 is available from the Office of Population Census andSurveys (OPCS) on regional migration flows. These data are based on NationalHealth Service records of patients registering with local doctors and are adjustedfor small lags between moving and registering, see Hornsey (1993) fordiscussion of the data.

Since 1982, an age breakdown of migrants has been available. This suggeststhat generally under 12% of migrants are over retirement age (65 for men, 60for women) while under 20% are aged 15 years or less. Thus migrants consistlargely of working age adults and their dependents. This suggests that dividingmigration flows by the working age population in each region is a sensiblescaling device and produces migration rates comparable5 with the commutingrates shown in Table 1.


5 Note that employment relative to working age population in Great Britain has been in therange 60–70% in the period. Thus migration/working age population is of the same order ofmagnitude as working age migration/employment.

TABLE 3Migration rates

1981 1988 19911991

Working age In Out Net In Out Net In Out NetPopulation rate rate rate rate rate rate rate rate rate

(000’s) % % % % % % % % %

GB 31566 3·2 3·2 0·0 4·2 4·2 0·0 3·6 3·6 0·0NN 1716 2·3 2·8 −0·5 3·0 3·3 −0·3 3·0 3·0 0·0YH 2780 2·6 2·8 −0·2 3·7 3·4 0·3 3·3 3·3 0·0EM 2262 3·7 3·5 0·2 5·2 4·4 0·8 4·3 4·0 0·4EA 1150 5·4 4·3 1·1 7·0 5·5 1·5 5·8 4·8 1·0SE 10146 2·4 2·3 0·1 2·9 3·5 −0·6 2·4 2·9 −0·5SW 2565 4·8 3·9 0·9 6·4 4·9 1·6 5·3 4·4 1·0WM 2942 2·4 2·8 −0·4 3·1 3·4 −0·3 3·0 3·2 −0·2NW 3537 2·2 2·8 −0·6 3·0 3·1 −0·1 2·6 2·9 −0·3WW 1570 3·0 2·8 0·2 4·4 3·3 1·1 3·5 3·2 0·3SC 2898 1·7 1·8 0·0 1·8 2·4 −0·5 2·0 1·7 0·3

Notes:Out rate = migrants out divided by working age population.In rate = migrants in divided by working age population.

Source:Figures are derived from re-registrations recorded at the National Health Service Central Register.


Annual regional migration rates defined in this way for 1981, 1988 and 1991are shown in Table 3. Comparing these with commuting rates for 1981 and 1991shown in Table 1, suggests that the proportion of people who migrate regionallyevery year is substantially below the proportion who commute.

Figures 3 and 4 plot net region (in minus out) migration rates respectively,for the South East and its contiguous regions, and for the remaining regions.These confirm that despite strong relative labour market pressures, there was a


-0.010

-0.005

-

0.005

0.010

0.015

0.020

0.025

0.030

0.035

1981 1986 1991

SE

EA

SW

EM

WM

Figure 4. Net migration rate (net in-migration as proportion of working age population) forSouthern Regions.

-0.010

-0.005

-

0.005

0.010

0.015

1976 1981 1986 1991

NN

NW

YH

WW

SC

Figure 3. Net migration rate (net in-migration as proportion of working age population) forNorthern Regions.


sharp decline in the 1987–89 period in net migration to the South East. This isprima facie. evidence for strong discouragement from high relative houseprices.

Disaggregating the gross flows by age suggests a rise in the proportion ofthose over retirement age in total out migration from the South East from 10·5%in 1982–85 to 11·7% in 1986–88, falling to around 7·5% in 1989–1992. Sincelabour market attachment is weaker for this age group, one expects relativehouse prices to be even more sharply associated with their migration flows.

III AN ECONOMETRIC MODEL OF REGIONAL NET COMMUTING RATES

We showed in Section II that, to a close approximation, the log of NR, theregional ratio of employment on a place of work basis, to employment on aplace of residence basis, is equal to the net commuting rate into a region,θ = θ1 − θ2 where θ1 is the in-commuting rate and θ2 is the out-commuting rate.The measurement of NR was described6 in Section II.

Drawing on the earlier discussions, we assume that commuting patterns toand from a region are dominated by the labour market and housing situations inthe region relative to its contiguous regions. For each region, we constructaverages of the contiguous region variables weighting each region according tonumbers in full-time employment. The variables entering the net commutingrate equation are then defined as the ith regional values minus the average of thecontiguous region values. For example, if ui is the unemployment rate,rcui = ui − contiguous region unemployment rate. Our labour market variablesare the unemployment rate and its first difference, log average earnings for full-time employees, lfte, and its first difference and the proportion of employmentin the production sector, pr. The latter, which enters in lagged form, isinterpreted as a proxy for an out-of-date pattern of employment skills. A regionwith a high value of pr should, other things being equal, have lower rates ofout-commuting and higher rates of in-commuting. However, note that regionalfixed effects control for time-invariant differences between regions in theirintrinsic attractiveness, their labour force characteristics and availability andcost of transport, as well as for the discrepancies highlighted in Table 2.

The housing market variables are the log of second-hand house prices, lhp, ameasure of the interest rate cost of moving, and a measure of downside rate ofreturn risk, rorm; rorm is defined as the three year moving average of rorr,where rorr equals zero if the rate of return is positive and equals the rate ofreturn if this is negative. The rate of return is defined as(0·02 + ∆ ln hpit − abmrt)/(1 − lυrt − 1). In this expression, the 0·02 is taken asthe net imputed rent from owner-occupation, ∆ ln hpi is the rate of capital gainin housing in region i, abmr is the tax adjusted building society mortgageinterest rate divided by 100, and lυr is the average UK loan-to-value ratio for


6 Note that to correct for any common national discrepancies in the LFS and Census ofEmployment measures, we subtracted the log ratio for Great Britain from each of the regionallog-ratios.


building society transactions. Thus, (1 − lυr) represents the proportionate equitystake of an average house buyer, incorporating a gearing factor in the rate ofreturn, see Muellbauer and Murphy (1997).

The interest cost or gain of moving is hard to measure. In principle, both realand nominal interest rates are relevant, the latter because of cash flow problemsand default risk. The costs differ with the length of previous tenure and previoushouse price changes which affect the net equity stake, if any, a potential migrantmay have. Our measure is a crude proxy, the mortgage interest rate weighted bythe ratio of the average mortgage in a region divided by full-time earnings. Wedenote this measure wabmr, the weighted interest rate. The regions with higherhouse prices tend to have higher mortgage to income ratios.

The first version of the model we estimate has the following ‘equilibriumcorrection’ specification:

θit = β0 i + βθit − 1 − β1 ∆rcuit − β2rcuit − 1 + β3 ∆rclfteit

+ β4rclfteit − 1 + β5rcprit − 1 +/−β*6 t ∆rclhpit

+ β*7 trclhpit − 1 − β*8 t ∆rcrormit

−β*9 trcrormit − 1 + β10 ∆rcwabmrit + β11rcwabmrit − 1 + εit (4)

The parameters marked with an asterisk and a time subscript indicate aweighting by the UK proportion of owner-occupiers. Thus β*it = βipooukt − 1. Asowner-occupation becomes more widespread, one expects relative house pricesto matter more, a point confirmed by our estimates for migration below.

To recapitulate, rc denotes the deviation between the region and the averagecontiguous region value of a variable, and the variables and the directions oftheir expected effects are as follows:

u is the unemployment rate: higher unemployment or a rise inunemployment in a region discourages net commuting into the region.

lfte is the log of average full-time earnings in a region: higher earnings or arise in earnings encourages net in-commuting.

pr is the proportion of employment in the production sector: the more oldfashioned is the skill base of workers in a region, the more in-commuting and the less out-commuting takes place.

lhp is the log house price: the higher are prices, the more net in-migrationis discouraged and net in-commuting encouraged. The current rate ofchange effect has an ambiguous sign: it may proxy expected capitalgains which reduce the user cost of housing.7 However, recent capitalgains increase the debt burden of potential in-migrants and the netequity cushion of out-migrants, discouraging net in-migration.

rorm is a downside rate of return risk indicator where a negative valueimplies a downside risk, discouraging in-migration and so encouragingin-commuting. Hence, a negative coefficient is expected.


7 This effect is explored in greater detail in our migration equations below where the qualityand quantity of data permit this.


wabmr is a proxy for the interest rate cost of moving: a higher relative valuediscourages migration to a region and so encourages net in-commuting.

We have also investigated a more sophisticated form of (4) which incorpor-ates potential influences on overall gross commuting rates as well as theinfluences of relative size of region. Let us write (4) in the form:

θit = β0 i + βθit − 1 + f(xit) + εit. (5)

Now suppose that a decline in housing market turnover nationally causesmigration rates to fall because it takes longer to buy and sell a house. One wouldexpect both in-commuting and out-commuting to rise, implying a multiplicativeincrease in f(xit). We therefore reformulate (5) as:

θit = β0 i + βθit − 1 + λit f(xit) + εit (6)

where λit, which is normalized at unity in 1990, declines with national housingmarket turnover.

Moreover, we also incorporate a relative size of region effect. For example,consider East Anglia, a small region with large and powerful neighbours,particularly the South East. It seems plausible that a given change in relativeconditions has a bigger impact on net commuting to East Anglia relative to itsworkforce or working age population than it would on the net commuting ratefor the South East. To be specific, we formulate λit as

λit = 1 − δ1 (lptran − lptran90)

− δ2 (clwpopit − clwpopSE90) (7)

Here lptran is the log ratio of property market transactions in England andWales excluding right-to-buy sales, to the housing stock, which is expected toreduce commuting rates. clwpop is the log of the sum of working age popula-tions in the contiguous regions: the greater this is, the more one might expect netin-commuting to be affected by relative labour and housing market conditions.

One problem in the data to which we have already alluded is samplingvariability for the smaller regions. This has two aspects: efficiency of estimationand bias. The first we handle by estimating (4) and (6) using generalized leastsquares.8 The bias problem concerns the downward bias on β because ofmeasurement error in θit − 1 and the bias which the presence of fixed effectsinduces when the number of time series observations is small. One way ofdealing with the first is to instrument θit − 1 using the fitted value from a firststage estimation of (4) or (6). This suggested a value of β in the region of 0·4rather than around 0·1 from GLS. Note that β represents not only search costsand other employment adjustment costs relevant for commuting flows, butserially correlated omitted variables such as changes in the transport infra-structure and in relative commuting costs.

We estimate the model given by equation (4) from 1984 to 1995, theinformation on the lagged dependent variable for 1984 coming from the 1983


8 In the ‘seemingly unrelated regressions’ procedure in Hall et al. (1996), Time SeriesProcessor (TSP).


LFS. The results shown in Table 4, column 1, where β is freely estimated,correspond well with the effects anticipated by the economics of commuting andmigration decisions. The change and level of the unemployment rate and ofearnings are highly significant for commuting rates as expected, as is theproportion of employment in the production sector. The housing market effectsare significant too. With only 12 years of data for 10 regions and substantialvariability in the LFS data, even (4) is probably a little too complex. In theevent, we could accept the restrictions β6 = 0, β8 = 0 and β10 = β11 so that thehousing market variables all appear as lagged levels, except for mortgage costswhich appear as a current level. These lags may reflect the fact that the LFS dataare measured in late Spring of each year and the Census of Employment datacorrespond to June of each year. The shorter lags we find in the migrationequation below, may reflect the fact that these are annual flows measured to theend of the year.


TABLE 4Estimates for net commuting rate equation (4), 1984–1995

Column 1 Column 2

dep. Var −1 (β) 0·12 (0·022) 0·4 (— )∆unemployment (−β1) −1·3 (0·052) −1·7 (0·23)unemployment −1 (−β2) −0·24 (0·042) —∆ln earnings −1 (β3) 0·265 (0·020) 0·336 (0·096)ln earnings −1 (β4) 0·184 (0·016) —prop.prod.sector −1 (−β5) 0·50 (0·029) 0·38 (0·18)∆ln house prices (β6) — —ln house prices −1 (β7) 0·051 (0·009) 0·168 (0·018)∆downside risk (−β8) — —downside risk −1 (−β9) −0·089 (0·012) −0·186 (0·026)mortgage cost (β10 = β11) 0·0041 (0·0004) —intercept 0·019 (0·007) 0·022 (0·023)

Notes:Heteroscedastic consistent (White) standard errors are shown in parentheses.

R2 s.e. D.W. R2 s.e D.W.

SE 0·168 0·006 1·67 0·143 0·009 1·73NN 0·035 0·014 2·38 0·050 0·016 2·32NW 0·006 0·009 2·63 0·009 0·010 2·67YH 0·251 0·012 1·08 0·029 0·012 1·27WM 0·493 0·014 1·21 0·514 0·012 1·81EM 0·002 0·017 2·20 0·023 0·020 2·39EA 0·811 0·012 1·63 0·613 0·011 2·23SW 0·540 0·016 1·39 0·531 0·014 2·34WW 0·013 0·016 1·88 0·000 0·018 1·90SC 0·010 0·011 1·93 0·000 0·013 2·69

Notes:Fixed effects relative to the South East corresponding to column 1 are: North−0·03 (0·10), North West −0·006 (0·007), Yorks and Humberside −0·025(0·009), West Midlands −0·026 (0·013), East Midlands −0·070 (0·014), EastAnglia −0·043 (0·013), South West −0·025 (0·012), Wales −0·016 (0·010)and Scotland −0·012 (0·007).


One problem with these estimates, apart from the bias towards zero in theestimate of β, the effect of the lagged dependent variable, results from the manycoefficients in the covariance matrix used in GLS. With 10 regions and only 12time periods, these will be quite imprecisely estimated and the quoted estimatedstandard errors are likely to be too low, even after a degree of freedomcorrection. One can argue that more robust results are obtained when plausiblerestrictions are imposed on the covariance matrix. We therefore repeated theGLS estimation, setting off all-diagonals in the covariance matrix correspondingto non-contiguous regions to zero.

This produced some changes in the parameter estimates, weakening therelative unemployment, earnings and mortgage costs levels effects andstrengthening those of relative house prices. The point estimate of β rises to0·24. Restricting β to 0·4 to overcome the measurement bias has little effect onthe results. Table 4 column 2 shows the results when β = 0·4, β2 = 0, β4 = 0 andβ10 = β11 = 0, i.e., eliminating levels effects in unemployment and earnings andmortgage costs altogether. Given the likely correlation between relative houseprices and earnings, unemployment and mortgage costs, it is not surprising thatthe omission of these levels effects results in substantially bigger relative houseprice effects in column 2 than in column 1. Even with a 10% upward adjustmentin the estimated standard errors for degrees of freedom, all the parameterestimates are still highly significant except for the coefficient on the relativeproportion of workers in the production sector whose significance is nowmarginal.

On this basis, our estimates suggest that rising relative earnings and fallingrelative unemployment encourage in-commuting and high relative house pricesalso encourage in-commuting by discouraging in-migration. However, as far asthe Southern regions are concerned, lower relative house prices in the early1990s, which should have encouraged in-migration, were significantly off-set bya large perceived downside rate of return risk which discouraged in-migrationand so encouraged in-commuting.

Table 4 also shows R-squared, equation standard errors and Durbin-Watsonstatistics for each region. As expected, the standard error is lowest in the mostpopulous region and the negative autocorrelation induced by measurement errorexpected in column 2 is generally reflected in the Durbin-Watson statistics. Thelow R-squared found in some regions is probably also influenced by measure-ment error and suggests that the model has almost no explanatory power for theNorth, the North West the East Midlands, Wales and Scotland.

The attempt to estimate (6) failed to give sensible convergent results. Inaddition to measurement errors in the data, note that we have no information onvariations in regional commuting costs or the improvement of regional transportlinks. More sophisticated models clearly require better data.

IV AN ECONOMETRIC MODEL OF REGIONAL NET MIGRATION RATES

As discussed in the Introduction, we expect the labour market effects on netmigration rates to be in the same direction as on net commuting rates but the



housing market effects to act in the opposite direction and to have relativelymore powerful effects on migration. Moreover, for contiguous regions to whichcommuting costs are lower, the greater ability to separate place of residencefrom place of work, increases the housing market effects on migration anddiminishes the labour market effects.

In Section II, we defined the net migration rate:

mi* = (NMi/wpopi) (8)

where NM i = net migration to region i and wpopi is the working age populationof region i.

Jackman and Savouri (1992a, 1992b) scale migration flows by dividing bythe national migration rate between regions. This has the advantage ofstandardizing, at least partly, for national variations in migration due, forexample, to varying turnover rates in the labour market and the housing marketand to national changes in tenure structure and in associated mobility rates.

To achieve the same effect but without affecting the general scaling of ourdependent variable, which is comparable to that of the commuting rate, wedefine our dependent variable to be:

mi = mi*/[(M/M90)/(wpop/wpop90) ] (9)

where M is the sum of gross migration inflows for regions of Great Britain andwpop is the GB working age population and the 90 subscript refers to the 1990value.

Our specification for mi is analogous to that for the commuting equation (6)but with a distinction between the contiguous region effects and more generaleffects relative to Great Britain as a whole. While the notation rcxi refers to thedeviation between xi and the average value for the contiguous regions, rxi is thedeviation between xi and the average value for Great Britain.9 To illustrate, theeffect of lagged unemployment is then represented as:

−α2 (ruit − 1 − λ1rcuit − 1) (10)

where λ1 p 0. The contiguous regions are part of the GB average. Thus, (10)allows a weaker relative unemployment effect for contiguous regions.

Analogously, the effect of lagged house prices is then represented as:

−α7 (rlhpit − 1 + λ2rclhpit − 1) (11)

where λ2 p 0 allows a higher relative house price effect for migration to andfrom contiguous regions.

For migration, we have a longer and more accurate span of data than forcommuting, making it possible to estimate a more sophisticated model. We haveadded some further elements, missing from (6), to our equation for the netmigration rate.


9 There is a minor further distinction in that the contiguous region averages all use full-timeemployment weights, while the GB average uses the weight most relevant for that variable.For example, for unemployment this will be regional labour force numbers, including the selfemployed. The differences in weighting are slight.


Our equation for net migration rates corresponding to (8) and (10) has thefollowing form:

mit = α i0 + αmit − 1 − φ1mit − 2

+ λ it[−α1 (∆ruit − λ1 ∆rcuit) − α2 (ruit − 1 − λ1rcuit − 1)

+ α3 (∆rlfteit − 1 − λ1 ∆rclfteit) + α4 (rlfteit − 1 − λ1rclftcit − 1)

+/− α5 (rprit − 1 − λ1rcprit − 1) − φ2 (rlwpopit − 1 − λ1rclwpopit − 1)

− α*6 (∆rlhpit + λ2 ∆rclhpit)

− α*7t (rlhpit − 1 + λ2rclhpit − 1) − φ*3trpretit (rlhpit − 1)

− φ*4 tEt (∆rlhpit + 1 + λ2 ∆rclhpit + 1)

+ φ5 (rlpooit − 1 + λ2rclpooit − 1) + α*8 t (∆rrormit + λ2 ∆rcrormit)

+ α*9 t (rrormit − 1 + λ2rcrormit − 1) − α10 (∆rwabmrit + λ2 ∆rcwabmrit)

− α11 (rwabmrit − 1 + λ2rcwabmrit − 1) ] + φ6 iD8687 + ηit (12)

and λit = 1 + δ1 (lptran − lptran90) − δ2 (clwpopit − clwpopSE90), in contrast to (11)above.

The labour market terms associated with parameters α1 to α11 correspond toterms associated with parameters β1 to β11 in the commuting equation. Asexplained above, they incorporate lower migration responses to contiguousregion labour market relativities and higher responses to contiguous regionhousing market relativities. As in the commuting equation all the house priceeffects are weighted by the UK proportion of owner-occupiers, poouk. Thus,α*it = αi ( pooukt − 1) to denote this weighting.

There are six additional terms not present in the commuting equation. Thefirst term, with parameter −φ1 measures a return migration effect after twoyears. The second term, with parameter −φ2, can be interpreted as a kind ofequilibrium correction term. Net regional migration is an important element inthe change of relative regional populations. In the long run, there is a tendencyfor relative regional population sizes to respond to the same relative labourmarket and housing market forces as act on net migration. This implies thenegative effect shown in the equation. Note also that tendencies towards returnmigration will also be reflected in this variable: thus a region which has had highinflows in the recent past, may have a tendency for these to reverse, other thingsbeing equal. This effect will reflect longer-run return migration beyond thatcaptured in the −φ1mit −2 term.

The third term, with parameter φ*3 t, reflects the larger absolute impact relativehouse prices might be expected to have when the proportion of retired people ishigher.10

The fourth term, with parameter φ*4 t, measures the expected house priceappreciation element in relative user costs. Note that the interest rate elementlargely disappears when taking regional differentials. This is expected to have a


10 Note the absence of the contiguous region effect, which applies much less strongly to theretired who are non-commuters, both in the levels and the interaction effect of the proportionof retired people.


positive effect, offsetting temporarily, the levels of relative house prices. Theseexpectations are derived from a forecasting system of equations for regionalhouse price differentials of a semi-rational form. Thus, it incorporates laggedinformation on interest rates, relative earnings and relative house prices but noton housing supply and demography on which households will be less wellinformed. The model confirms South East leadership with regional relativitieslargely driven by the lagged deviation from the South East and from contiguousregions.

The fifth term, with parameter φ5, measures the impact of relative owner-occupation rates. One reason why these rates have increased in the 1980s hasbeen ‘right-to-buy’ sales to council tenants at discounts as high as 50%. Suchsales have been accompanied by clauses restricting resale within a given numberof years, typically five. This ‘lock-in’ effect should result in lower out-migrationfrom regions with high owner-occupation rates, compared with some baseperiod. Hence a positive effect on net-immigration is predicted.

The last term, φ6 i, measures the impact of a dummy which is +1 in 1988 and−1 in 1987 on migration between the South East and East Anglia. This isprobably the result of the 1986 ‘Big Bang’ reform of financial institutions in theCity of London. This created large salary rises and extra employment in the Cityin 1986 but was followed by rationalization in the sector in 1987. Associatedwith it appears to have been a rise in net migration to the South East in 1986 anda fall in 1987, with the reverse flows taking place in East Anglia. Note that φ6 i

is zero for the other regions.11

We use published data on regional migration from 1981 and unpublished dataprovided by OPCS for 1976–1980. Thus, we can estimate (12) for 1978–1995,again using GLS in the seemingly unrelated regression feature of Hall et al.’sTSP. The results are shown in Table 5. The labour market and housing marketeffects almost all have the expected signs and the significance of the latter isparticularly striking. Column 1 imposes the restriction λ1 = λ2 which is easilyaccepted. Freely estimated values are λ1 = 0·23 (0·085) and λ2 = 0·24 (0·088).The parameter λ = λ1 = λ2 measures the reduced impact of labour marketvariables for contiguous region migration and the increased impact of housingmarket variables, and is significant and of a plausible magnitude.

Turning to the labour market effects first and contrasting them with those inthe commuting equation, the ∆ unemployment effect is small and insignificant inthe migration equation, so that we can accept α1 = α2, though it was large andsignificant in the commuting equation. This makes sense since one expects


11 We also investigated the increased impact one might expect from relative house prices inthe period after financial liberalization. Financial liberalization, reflected in much easier accessto mortgage loans in the 1980s, brought substantial rises in loan-to-value ratios so loweringcash deposits required for home buying. Restricted access to credit in earlier years at givenhouse price differentials, should have made it harder for households to overcome thesedifferentials. We interacted relative log house prices with 1-lυrf, where lυrf is the UK loan-to-value ratio for first-time buyers thus measuring the proportion of the price of a home which afirst-time buyer has to put up as a deposit. We expected a negative effect to reflect the greaterimpact of relative house prices before the early 1980s, but found an insignificant positiveeffect.


migration to respond to more long-term employment opportunities. However,for earnings the rate of change effect, α3, exceeds the level effect, α4.

The lagged proportion of employment in the production sector, a crude proxyfor an old-fashioned skill base of the workforce, has a small negative effect,instead of the large positive effect in the commuting equation. One might havethought that, other things being equal, a lack of modern skills among the work-


TABLE 5Estimates for net migration rate equation (12), 1978–1995

Column 1 Column 2 Column 3

mig −1 (α) 0·24 (0·0033) 0·26 (0·031) 0·51 (0·057)mig −2 (−φ1) −0·09 (0·0032) −0·07 (0·031) −0·10 (0·053)∆unemployment (−α1) — — −0·154 (0·039)unemployment −1 (−α2) −0·023 (0·0049) −0·022 (0·0051) 0·008 (0·015)∆ln earnings (α3) 0·050 (0·0044) 0·047 (0·0035) −0·022 (0·015)ln earnings −1 (α4) 0·024 (0·0031) 0·020 (0·0031) −0·0016 (0·0007)prop.prod. sector −1 (α5) −0·051 (0·0044) −0·038 (0·0066) 0·089 (0·027)∆ln house prices (−α6) −0·015 (0·0065) −0·014 (0·0012) —ln house prices −1 (−α7) −0·015 (0·0065) −0·014 (0·0012) —∆ downside risk (α8) 0·012 (0·0011) 0·0083 (0·0016) —downside risk −1 (a9) 0·021 (0·0015) 0·018 (0·0021) —∆ mortgage cost (−α10) — — —mortgage cost −1 (−α11) — — —w.age pop −1 (−φ1) −0·0011 (0·00012) −0·0009 (0·00019) −0·024 (0·004)p.ret × lnhp −1 (−φ2) −0·077 (0·014) −0·061 (0·013) —(1 − lυrf ) × lnhp −1 (−φ3) — — —Et ∆lnhpt + 1 (φ4) 0·025 (0·0017) 0·022 (0·0016) —prop. owner occup (φ5) 0·0019 (0·00055) 0·0041 (0·00093) —D8687SE φ6SE 0·00062 (0·00018) 0·00062 (0·00009) 0·0019 (0·0005)D8687EA φ6EA −0·0049 (0·00036) −0·0043 (0·00035) −0·0112 (0·0001)housing turnover −2·16 (0·14) −2·96 (0·39) —interaction effect (−d1)contiguity shift effect −λ1 −0·23 (0·017) −0·18 (0·017) −0·69 (0·111)intercept −0·0026 (0·00033) −0·0028 (0·00048) −0·0021 (0·0017)

Notes:Standard errors are shown in parenthesis. See Figures 5–7 for graphs of the key variables for the SouthEast.

R2 s.e.10 −2 D.W. R2 s.e.10 −2 D.W. R2 s.e.10 −2 D.W

NN 0·639 0·123 1·72 0·562 0·139 1·80 0·651 0·113 1·55NW 0·871 0·071 2·30 0·872 0·074 2·53 0·490 0·141 2·37YH 0·605 0·102 1·94 0·607 0·109 2·32 0·650 0·100 1·57WM 0·750 0·063 1·68 0·803 0·056 2·67 0·550 0·075 1·77EM 0·831 0·086 1·27 0·863 0·096 1·36 0·180 0·147 1·48EA 0·776 0·227 1·48 0·686 0·266 1·39 0·660 0·281 1·37SE 0·861 0·069 1·20 0·875 0·070 1·24 0·723 0·099 0·91SW 0·818 0·134 1·64 0·751 0·150 1·76 0·767 0·162 1·06WW 0·807 0·100 1·86 0·789 0·112 1·99 0·500 0·160 1·26SC 0·895 0·092 2·13 0·884 0·092 1·83 0·720 0·153 1·87

Notes:Fixed effects relative to the South East corresponding to column 1: North −0·0005 (0·0005), North West0·0000 (0·0005), Yorks and Humberside 0·0016 (0·0005), West Midlands 0·0015 (0·0005), EastMidlands 0·0060 (0·0005), East Anglia 0·0061 (0·0006), Wales 0·0027 (0·0005), and Scotland 0·0028(0·0005).




-0.1

-0.05

0

0.05

0.1

0.15

0.2

1978 1983 1988 1993

Unemployment

Full-time earnings

Production workers

Figure 5. Unemployment, full-time earnings, and share of production workers in total, forSouth East relative to Great Britain.

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

1978 1983 1988 1993

Downside risk

Mortgage cost

Relative house prices

Relative owner-occupation

Figure 6. Downside risk, mortgage cost, relative house prices and relative owner-occupationrate for south east.

force in a region would result both in higher in-commuting and higher in-migration. However, we know from the Labour Force Survey for 1994 thatemployees who commute between regions earn, on the average 62% more thanthe average earnings of employed residents in the region. Commuters are likelyto be a selective group of highly skilled people. If regional migrants are lessselective and if a high proportion of employment in the production sector alsosignals more limited long-term employment opportunities for the mass of thework force, the negative impact on net migration is explicable.

The relative house price effects, including the interactions with the proportionof people over the retirement age, and the offsetting effects of expected houseprice appreciation operate in the expected direction and are strongly significant.As expected, recent experience of a negative rate of return in housingdiscourages in-migration.

A high rate of owner occupation in a region, by discouraging out-migration,increases net in-migration. One interpretation is as the lock-in effect associatedwith right-to-buy sales to existing council tenants, though the rate of owner-occupation is not an ideal proxy for this concept.

The multiplicative λit effect responds strongly to the rate of turnover in thehousing market. Note that though our net migration rates are scaled by the totalgross migration rate between regions in Great Britain, this is likely to beinsufficient to correct net migration for variations in national economicconditions. Many moves, even long-distance ones have little to do withfluctuations in economic activity. But our λit interaction effect applies only to theeffect of variations in relative economic conditions between regions. Housingmarket turnover is likely to have a bigger impact on these relative economicfactors than on the gross migration rate in Great Britain. The relative regional



-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

1978 1983 1988 1993

Forecast

Actual

Figure 7. Forecast and actual change in log house prices for South East relative toGreat Britain.

size component of λit reflected in the parameter δ2 was always insignificant andis omitted in the Table 5 results.

The reported standard errors for parameter estimates and for equations areasymptotic. Moreover, we have tested and imposed a number of parameterrestrictions on the model. On both accounts, therefore, the standard errors are alittle optimistic (note, for example, a t-ratio of 23 for the relative house pricelevels effect!). But even a 20% upward adjustment of the standard errors wouldhave left almost all the estimated parameters still very significant.

Lagrange multiplier tests for residual autocorrelation suggested potentialproblems in the South East, reflected in the Durbin-Watson of 1·20 for thatregion. There is also evidence of serial correlation in its contiguous regions,East Midlands and East Anglia. This suggests that our specification could beimproved further.

In column 2, we report the results of estimating the net migration equation upto 1990, thus omitting the years when the Southern part of the country sufferedits worst recession since the 1930s. The unemployment, earnings, and thevarious house price effects are quite stable over the two samples. There arelarger differences in the three parameters, λ, δ1 and φ5, indicating furtherevidence of a specification error. One interpretation of φ5 is as a ‘lock-in’ effectarising from right-to-buy sales of council houses. However, the owner-occupation rate represents a mix of other influences as well. For example, giventhe decline in the construction of social housing, regions with above averagelong-term in-migration are likely to experience above average increases in theirowner-occupation rates. Omitted long-term influences on migration maytherefore be reflected in the owner-occupation rate. The failure to distinguishthese may account for the parameter instability in φ5 and may be contributing toresidual autocorrelation in the South East.

Another potential cause of difficulties is the Poll Tax introduced in Scotlandin 1988, in England and Wales in 1989 and replaced by the Council Tax in1992. Some among the younger, more mobile population, tried to avoidpayment of the Poll Tax by avoiding being registered on the electoral registerand may similarly have avoided the NHS register. This could have led to anundercounting of in-migration into regions, such as the South East, where in-migration of young people tends to be positive. Indeed, this hypothesis couldaccount for part of the fall of net in-migration into the South East in 1989–91which our model cannot explain.

The shifts in the parameter estimates of λ and δ1 may also indicate a problem.Our measure of the housing market turnover rate may not be the mostappropriate. If δ1 is restricted to the full sample value, the estimate of λ issimilar to the full sample estimate.

Generally speaking, the really robust effects are the levels effects ofunemployment, earnings and relative house prices. These always come throughvery strongly irrespective of how the rest of the equation is specified andwhether or not relative house prices are weighted by the rate of owner-occupation. However, the omission of all the housing effects from the modelresults in an insignificant and perversely signed unemployment levels effects,



though the change in the unemployment rate still has a negative effect on netmigration rates. The level earnings effect is significantly negative, a perverseresult. These results are reported in column 3 of Table 5.

Enthusiasts for general equilibrium interpretations, seeing only the results incolumn 3, might be tempted to conclude that migration is driving both earningsand unemployment, so that simultaneous equation bias has grossly contaminatedthese estimates. Given the evidence of columns 1 and 2, that would clearly bewrong. We suspect that many of the apparent anomalies in earlier studies oflabour market effects on migration, which McCormick (1991) discusses, havetheir cause in the omission of relevant housing effects.

It is worth making two more points regarding the house price effects onmigration. It has often been argued that house prices represent incomeexpectations so that their labour market and consumption consequences may bedominated by these expectations effects, see Spencer (1989) and Pagano (1990).Because migration should respond to earnings expectations, there should on thiscount be a positive response of net in-migration to higher relative house prices.A second argument in the same direction arises from the possibility that anexogenous in-migration shock could raise house prices in a region. Yetempirically we find a strong negative response. Therefore, there are twopossibilities. One is that the earnings expectations effect and the endogeneitybias are rather weak relative to the other roles of house prices. The other is thatwe have biased house price effects and the discouraged migration effect is evenstronger than our estimates indicate.

The housing market effects, omitted from many earlier migration studies,including those as recent as Pissarides and McMaster (1990), clearly haveextremely important implications not only on explaining variations in rates ofnet migration, but in obtaining sensible earnings and unemployment effects onmigration.

V CONCLUSIONS

We have developed econometric models for net regional commuting andmigration in Britain using a common framework. The results support thehypothesis that migration responds strongly to relative earnings and relativeemployment prospects, as measured by the unemployment rate. However, theevidence is for extremely important housing market effects. High relative houseprices discourage net migration to a region, though expected house price rises,by reducing the user cost of housing, can provide a temporary offset. Further-more, recent experience of negative returns in the housing market acts as astrong disincentive against net migration to a region. As owner-occupation hasrisen, the evidence is that the influence of relative house prices on net migrationrates has risen also.

Though our data on net commuting is for a shorter period and suffers fromsampling variability, the same basic set of forces operate. As expected, therelative labour market factors are even more powerful than on migration. Butbecause of the commuting/migration trade-off, relative housing market



conditions also have quite significant effects on commuting patterns. They act inthe opposite direction from their effects on migration patterns.

More research needs to be done on inter-regional commuting patterns. Muchbetter data than we have been able to use could be collected as an administrativeby-product of the national insurance and taxation systems. It is a matter forserious regret that these data are not being routinely produced.

The commuting/migration trade-off also implies a different response ofmigration to contiguous region relativities than to relativities with more distantregions. As Gordon (1975), Molho (1982) and Jackman and Savouri (1992a)have noted, labour market relativities should have weaker effects and housingmarket relativities stronger effects for contiguous region migration decisions.Our findings support this conjecture.

Thus, for contiguous regions, commuting can act as a significant safety valveto offset the pressures the housing market can exert on the operation of regionallabour markets. Of course, commuting comes at a cost and a sharply increasingone with time and distance.

The UK recession of the 1990s, heavily concentrated in the South, reducedNorth-South unemployment differentials to the lowest levels seen since at leastthe 1920s, for example, temporarily raising the unemployment rate in Londonabove that of Scotland.

At the time of writing, there are signs of the traditional North-South divide12

re-emerging, with higher unemployment rates, lower earnings and lower houseprices in the North. The relative appreciation of house prices in the South Eastof the last four years is playing an increasingly important part in this processand is likely to put pressures of the kind experienced in the second half of the1980s on the allocation of labour between regions and on pay. One aspect ofthis, as in the 1980s, is that the demand for living space by employees iscrowded out by portfolio demand for houses. Long distance commuting isunlikely to be sufficient to offset these regional imbalances. These are seriousproblems for UK entry into the Eurozone given the likely downward pressure oninterest rates coming from this source.

To clarify the nature of the problems our paper addresses and to illustrate thescope for improvements, we now consider policy reform in two areas, policytowards the private rented sector and the Council Tax.

The market rented sector in the UK has, after many years of rent and tenurecontrols and fiscal bias against landlords and tenants, fallen to what is surely asub-optimal size, as low as 7% of dwellings (excluding social housing) in 1990,though a slight recovery has since occurred. Various functional arguments infavour of a larger market rented sector are set out in McLennan (1994).Research by Hughes and McCormick (1981, 1987) and others suggests that ofall tenure groups, tenants in the private rented sector have the highest mobilityrates, even after controlling for age and other characteristics. A larger marketrented sector permits greater labour mobility because transactions costs arelower which allows it to act, as a more efficient buffer stock for fluctuations in



12 See Gordon (1990) and McCormick (1997).

regional mobility. When landlords are institutional investors, portfolio drivenincreases in regional house prices will not translate into the crowding out ofliving space for employees which can occur in owner-occupied housing. Alarger institutional market rented sector would make less likely the kind ofpressure on the labour market which came from the South East housing marketin the late 1980s.

Though controls on rent and security of tenure are no more, the privaterented sector is still subject to two types of tax discrimination. The first isthat landlords, unlike owner occupiers, are subject to capital gains tax. Thesecond arises through Council Tax and is explained below. It can be arguedthat policy should go beyond creating a level playing field and for a timefavour the market rented sector to counteract the long-lasting effects of pastdistortions.

Five arguments can be made against the current Council Tax system. By notbeing linked to current market values, Council Tax fails to give importantstabilization benefits both at regional and national levels. Since house prices riserelative to incomes in typical consumer-led upswings, the higher tax revenuecollected as they do so could substantially dampen these cycles particularly atthe regional level where feedbacks via spending often increase regionalimbalances. Secondly, the tax raises revenue, £9·8b in 1996, which, as afraction of national income is only one third of that raised by the old DomesticRates. There are strong general efficiency arguments in favour of propertytaxes, and insufficient use is being made of this part of the tax base. Thirdly, thetax is in most respects highly regressive: those in a £1m house pay only twicethe amount paid by those living in a £70,000 house and the top priced authority,Westminster, has the lowest tax rate in the country. Fourthly, this regressivenessis linked to a serious inefficiency in the use of housing and land resources. Notethat by converting a house in the form of three flats into a single familyresidence, the Council Tax bill is substantially reduced. Thus the form ofCouncil Tax encourages this kind of conversion at a time when the trend istowards smaller households requiring smaller units, and also discourages thebuilding of smaller units. Moreover, since rental units tend to be dispropor-tionately smaller, the Council Tax effectively taxes the rental sector moreheavily. Finally, Council Tax is subject to the potential difficulty of a mis-matchbetween cash-flows and property values which impinges on the elderly andincreases resistance against higher tax rates.

There are thus four key points to reforming Council Tax:

Revalue every two years, or index annually to local house price indiceswith full revaluation every 5 years. If the current banded system isretained, as few as eight bands is far too crude.

Tax rates should be approximately proportional to house value minussay £20,000, with local authorities restricted to a range from a floor,say, of 0·6% to a ceiling of 1% (thus avoiding the Westminsterproblem).



To make effective the automatic stabilizing role of the role, ratesupport grants should be cut when higher house prices raise the taxbase, so that councils do not merely spend the extra revenue.

Finally, the option should be offered (following long practice in manyparts of North America), for the over-65’s to build up a Council Taxcharge with accumulated interest, registered annually at the LandRegistry, and settled on sale of the dwelling or on death of thesurviving resident spouse. Efficient financial markets will readily allowlocal authorities to convert these claims into cash.

The research in this paper has highlighted problems arising from theinteraction of labour and housing markets in Great Britain. The proposedreforms should improve the automatic stabilizers at national and regional levels,reduce crowding out by wealthy investors of housing for employees, and assistin the regeneration of the private rented sector, thereby reducing mobilitybarriers.

ACKNOWLEDGEMENTS

The authors are grateful for support to the ESRC under grant numberR000237500 ‘Modelling Non-Stationarity in Economic Time Series’. Com-ments from Jurgen Doornik, Andrew Glyn, Bronwyn Hall, David Hendry, PaulRuud and Neil Shephard are gratefully acknowledged but responsibility forerrors lies with the authors.

DATA APPENDIX

Descriptions of variables

mit Internal migration: recorded net movements between the standardregions of Great Britain, scaled as described in the text. Figures arederived from re-registrations recorded at the NHS Central Register.Source : Population Trends and OPCS.

θit Regional commuting: the log ratio of employment on a census ofemployment basis to employment on a Labour Force Survey basis,the latter adjusted for second jobs.

uit Regional unemployment rate. Source : Employment Gazette, variousissues.

lfteit Log average weekly earnings of full-time employees. Source : NewEarnings Survey.

prit Share of production workers in total employment. Source : Office forNational Statistics, Earnings and Employment Division.

lhpit Log mix-adjusted second-hand house prices. Source : Department ofEnvironment, Transport and the Regions.

lpooit Log percentage of owner-occupiers. Source : Department ofEnvironment, Transport and the Regions.



rrormit ‘Downside risk’ variable. Defined as equal to the log change in houseprices minus the mortgage interest rate, adjusted for gearing (that is,the loan to value ratio) and for imputed rent. When this is negative,there is a downside risk; otherwise the variable is set to zero. Thevariable enters as a three year moving average.

wabmrit ‘Mortgage cost’ variable. Defined as the mortgage interest rateadjusted for the size of mortgages in the region relative to populationand earnings.

∆rlhp1hit Expected change in house prices over the next year. Source : Authors’calculations.

lυrit Loan to value ratio for all purchasers from building societies. Source :Housing Finance.

lυrfit Loan to value ratio for first-time buyers from building societies,excluding ‘right-to-buy’ purchases. Source : Department ofEnvironment, Transport and the Regions.

wpopit Working age population. Source : OPCS.



TABLE A1Summary statistics for South East variables

Variable name Mean Std. deviation Minimum Maximum

mse −0·002 0·002 −0·005 0·001θse 0·033 0·005 0·022 0·041rcuse −0·022 0·009 −0·032 −0·008ruse −0·025 0·009 −0·036 −0·012∆rcuse 0·000 0·004 −0·009 0·006∆ruse 0·000 0·004 −0·006 0·006crlftese 0·179 0·040 0·113 0·225∆crlftese 0·006 0·009 −0·009 0·022rlftese 0·110 0·028 0·065 0·144∆rlftese 0·004 0·006 −0·005 0·015rcprse −0·114 0·006 −0·128 −0·106rprse −0·070 0·005 −0·083 −0·064rclhpse 0·335 0·086 0·256 0·536∆rclhpse 0·004 0·052 −0·127 0·084rlhpse 0·264 0·072 0·194 0·411∆rlhpse 0·003 0·039 −0·083 0·060rlpoose 0·042 0·001 0·040 0·042rclpoose 0·042 0·001 0·041 0·043rrormse −0·024 0·039 −0·121 0·003rcrormse −0·014 0·031 −0·088 0·019∆rrormse −0·001 0·023 −0·041 0·049∆rcrormse 0·000 0·020 −0·028 0·057wabmrse 0·029 0·006 0·014 0·038cwabmrse 0·002 0·002 −0·003 0·004∆wabmrse −0·001 0·005 0·009 0·007∆cwabmrse −0·001 0·001 −0·002 0·001∆rlhplhse −0·001 0·025 −0·059 0·032∆lrhplhse 0·002 0·014 −0·014 0·034

Notes:See text for description of variables. Housing market variables in this table are notweighted by the proportion of owner-occupiers in the UK. Sample period is 1977 to 1995.

Notes : The following convention for variable names is used in the paper. Forexample, uit is the regional unemployment rate, ruit is the regional unemploy-ment rate minus the GB unemployment rate, and rcuit is the regionalunemployment rate minus the unemployment rate of the contiguous regions.Contiguous region variables are calculated using employment weights.

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Date of receipt of final manuscript: May/June 1998.



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