OXFORD

ECONOMICS

BULLETIN of and STATISTICS

~~~

Volume 56 November 1994 No.4 OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 56,4 ( 1 994) 0305-9049

S€’ATIAL DISPARITIES IN THE IMPACT OF THE 1990-92 RECESSION: AN ANALYSIS OF UK

COUNTIES

Jim Taylor and Steve Bradley

INTRODUCTION

The 1990-92 recession in the UK was remarkable for two reasons: its duration and its spatial impact. A short, mild recession was predicted by most forecasting models, including the Treasury’s, when it began. It is the unexpected geographical impact, however, which is the concern of this paper. North-south unemployment differentials diminished rapidly as the economy fell deeper and deeper into recession with the astonishing outcome that by the end of 1992 the unemployment rate in the South East actually rose above that of Scotland for the first time since regional unemployment data became available in 1922 (British Labour Statistics 197 1). Well-established historical relationships between regional and national unemployment rates suddenly collapsed in the summer of 1990. Past experience led us to expect unemploy- ment to rise faster in the north than in the south as the economy moved rapidly into recession during 1990-91. History refused to repeat itself on this occasion; it was the southern regions which experienced the largest increase in unemployment as the national recession set in. This paper attempts to explain why the geographical impact of the

1990-92 recession in the UK was so remarkably different from expectations. It focuses upon the impact of recession on the unemployment rate at the sub- regional level rather than at the regional level since this provides a more useful framework for analysing spatial disparities in the impact of the reces-

367 0 Basil Blackwell Ltd. 1994. Published by Blackwell Publishers, 108 Cowley Road, Oxford OX4 ln, UK & 238 Main Street, Cambridge, MA 021 42. USA.

368 BULLETIN

sion. The availability of unemployment and related labour force data for the counties of England and Wales and the local authority regions of Scotland (the latter are henceforth referred to as counties for convenience) permits a cross-sectional approach to examining the spatial impact of the recession. The purpose of this paper is therefore to identify the factors responsible for the substantial inter-county disparities in the change in unemployment rates during the first two years of the recent recession. Figure 1 shows the extent of these spatial disparities in the impact of the recession at the county level. It is these disparities which this paper aims to explain.

A primary focus of this paper is the interaction between the housing market and the labour market. Particular attention is paid to the way in which spatial disparities in the behaviour of the housing market have led to corresponding spatial disparities in the behaviour of the labour market. Previous research at the macroeconomic level indicates that the surge in house prices during the late 1980s, followed by the sudden reversal (particularly in the south) during 1989-92, had a substantial impact on economic activity in the UK as a whole (Bover, Muellbauer and Murphy 1989; Carruth and Henley 1992). Large changes in house prices had a substantial impact on aggregate consumption spending during the 198Os, one interpretation of this finding being that changes in net (perceived) wealth are important in determining saving from current income. The research reported in the present paper adds a new dimension to this earlier work by examining the effect of the housing market on the spatial impact of the 1990-92 recession.

The remainder of this paper is in three sections. Section I briefly discusses the interaction between the housing market and the labour market and examines the relevance of this interaction to explaining the spatial impact of the recession. Section I1 identifies the factors which might be expected to influence the spatial impact of national business fluctuations. Since the spatial impact of the 1990-92 recession is measured (in this paper) by changes in each GB county’s unemployment rate, this section discusses the range of variables which might be expected to have a significant effect on inter-county differences in the change in the unemployment rate during 1990-92. Section 111 presents the results of the statistical analysis.

I INTERACTION BETWEEN THE HOUSING MARKET AND THE LABOUR MARKET

The importance of the housing market for the performance of the macro- economy has been clearly demonstrated by Bover, Muellbauer and Murphy (1989). They argue that institutional distortions in the housing market associated with owner-occupied housing can have profound effects, under certain circumstances, on macroeconomic activity. In particular, the existence of mortgage interest tax relief and the absence of a capital gains tax on one’s principal residence can have substantial effects on the macroeconomy during 0 Basil BlackweU Ltd. 1994.

AN ANALrSIS OF UK COUNTIES 369

economic upswings, especially when these distortions are accompanied by a rapid growth of financial liquidity and an inelastic supply of housing in the short run (Evans 1989; Muellbauer and Murphy 1991).

Muellbauer and Murphy ( 1991) argue that the housing boom in the mid- 1980s was fuelled by the financial liberalization which occurred in the 1980s. The lifting of credit restrictions on the commercial banks in 1980 was immediately followed in 1981 by the entry of the commercial banks into the mortgage market and then by the Building Societies Act (1986), which gave building societies access to the wholesale money market and permitted them to compete directly with the banks by offering retail banking services. Hence, financial markets were liberalized while the distortions to the housing market were maintained. When these two ingredients were mixed with the spark provided by the housing boom, which was generated by a combination of tax cuts and cheap money, the result proved to be highly explosive. House prices rocketed, particularly in the southern regions because of the initial concentra- tion of the demand shock in the south. The impact on the South East was especially severe because of the inelastic supply of housing resulting from tight planning controls in this region due to greenbelt policy (see Evans 1988, 1989). The house price explosion then spread into the midlands and the north (see Figure 2).

One of the striking features of the housing market is the occasional tendency for overshooting to occur in house prices, which appear to be driven by extrapolative expectations since people tend to expect recent experience of house price changes to continue (Hendry 1984). Moreover, the housing market is periodically subject to speculative frenzy, which occurs when home owners fear that they will miss out on an expected capital gain or will be unable to afford the higher expected price of a suitable house in the future. Sharp fluctuations in the demand for housing therefore result in highly unstable house prices because of the inelastic supply of housing in the short run.

Recent empirical work by Carruth and Henley (1990, 1992) adds further Support to the view that the housing market can have profound effects on the macroeconomy through the effect of changes in housing equity on aggregate consumption spending. According to Carruth and HenW the housing market may affect consumption spending in three main ways: through housing equity withdrawal; through the complementarity between housing consumption and the consumption of household goods; and through a wealth portfolio effect. Housing equity withdrawal occurs when the lending for house purchase exceeds the actual investment in new housing. Home owners can do this by not using the proceeds of house sales to purchase other property; by borrowing more than the difference in price when selling and buying property; and by trading down to cheaper property (Lee and Robinson 1990). Housing equity withdrawal allows households to choose the level of their disposable income since disposable income can be adjusted upwards during periods when the housing market is booming by moving

0 Basil Blackwell Lid. 1994.

370 BULLETIN

house and withdrawing equity for spending purposes. The same outcome can be achieved by re-mortgaging existing property. A further way in which the housing market may affect consumption spending is through the comple- mentarity between housing and household goods. When the housing market is booming, spending on housing will be accompanied by complementary purchases of household goods.

The importance of fluctuations in house prices in explaining regional economic disparities is stressed by Muellbauer and Murphy (1 991), who argue that the sharp increase in the ratio of house prices to wages in the south (compared to the north) during the mid-1 980s exacerbated regional economic disparities because of the importance of this ratio on consumption spending. The same argument can be used to explain spatial differences in the response to the 1990-92 recession: it is the southern regions of the UK which have suffered the largest fall in the house prke/wage ratio since 1988 due to the greater collapse of house prices in the south during the recession. The South East, East Anglia and the South West suffered particularly badly from the loss of housing equity (see Figure 3). This means that home-owners in the south have witnessed a much larger fall in their perceived wealth (which has become negative for many people who bought their homes in the late 1980s) than their counterparts in the north. The direct consequence of this should therefore have been a disproportionate fall in consumption by southern home-owners compared to northern home-owners.

The housing market may also have differential effects on consumption expenditure between regions because of regional disparities in the exposure of households to housing debt (Ash and Bell 1991). Since the average mortgage per household is higher in the southern regions of the UK, particularly in the South East, than in the northern regions, any given increase in mortgage interest rates raises mortgage interest payments to a far greater extent in the south than in the north. This suggests that changes in interest rates will have a greater impact on consumption spending in the south than in the north since regions with relatively low mortgage debt exposure will be less susceptible to fluctuations in the mortgage interest rate.

Although much of a region’s consumption expenditure leaks out of a region in the form of regional imports, it is nevertheless likely that the sharp fall in the house price/wage ratio in the south would lead to a corresponding fall in the demand for local goods and services. To the extent that this has happened, southern labour markets will have been hit more severely than northern labour markets by the collapse of house prices during 1988-92.

I1 FACTORS LIKELY TO INFLUENCE THE SPATIAL IMPACT OF NATIONAL BUSINESS FLUCTUATIONS

It was argued in the previous section that geographical disparities in the impact of the 1990-92 recession are likely to have been influenced by corresponding geographical disparities in changes in house prices. The

0 Basil Blackwell Ltd. 1994.

AN ANALYSIS OF UK COUNTIES

0

-1

Change in Y unemployed 7 r

+ SE EA 8W WM EM YH NW NO HL4 8c

-

4 : i i + i * f * + * + f

c + t

371

Fig. 1. Change in percent unemployed in each GB county by region: 1990(2)- 1992(2)

Annual % change 60 I I

Midlands 60

40 -

30.

20 -

10

-

-

0 .

-20 i I " 1 'I' : ' I ' 1 ; ' 1 ; ' ' : I ' I ; ' ' ' 1 ' ' I I' 1 ; I

1974 1976 1978 1980 1982 1984 1986 1988 1900 1092

Note:

Y H m Yorkshinand Humberside, NW=Nor(b West. NO= NO^ W A = Wales, SC = S c d a d &me: Nationwide Fhdding Society, Quarterly Bulletins

SE-south ~ast, E A - ~ hgl ia , SW = south West, WM = West Midlands, EM - ~ a s t ~idlaods,

Fig. 2. House price inflation in England: 1974-92

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372 BULLETIN

Average male earning8 / houre price8 0

I I I I 1 I 1 I

%E EA SW EM WM YH NW NO WA SC Ni

Note: SE = South East, EA = East Anglia, SW= South west, WM= West Midlands. EM= East Midlands, YH = Yorkshin an4 Hmberside, NW = North West. NO = North. WA = Wales. SC = Scotland, NI = Northun Ireland. Source: Nationwide Building Society. Quarterly Bdldm; Regional Trendr. Hh4SO.

Fig. 3. Ratio of male earnings to house prices in the regions of the UK, 1988 and 1991

impact of national business fluctuations on individual geographical areas, however, is likely to depend upon a range of other factors in addition to those related to the housing market. It is therefore necessary to undertake a multi- variate analysis of the change in the unemployment rate across spatially identifiable labour market areas in order to estimate the specific impact of house price changes on local unemployment rates. The statistical analysis in section I11 below is based upon GB counties. The choice of counties as the relevant spatial unit is dictated by data availability (since county house price data has been published bi-annually by the Halifax Building Society since 1988). Although counties are probably less appropriate than travel-to-work areas for the analysis of spatial unemployment disparities, data availability dictates the use of county level data. Aggregating labour market areas in this way, however, will inevitably lead to a loss of information. The remainder of this section identifies a set of variables which might be expected a priori to influence a county’s responsiveness to national business fluctuations. These include the following:

1. Industry mix

The impact of recession on the demand for goods and services is more severe in some industries than in others. The construction and manufacturing 0 Basil Blackwell Ltd. 1994.

AN ANALYSIS OF UK COUNTIES 373

sectors, for example, have traditionally been more sensitive to national business fluctuations than the service sector. Moreover, construction was par- ticularly badly hit during the 1990-92 recession due to the collapse of the housing market. Given that the industry mix varies substantially between counties, this suggests that a county's industry mix will exert some influence upon its response to national recession. The influence of industry mix can be estimated by including the proportion of each area's employees in each major bdWrial sector as an explanatory variable.

2. Woryorce mix

The sensitivity of local labour markets to national recession may be influenced by an area's workforce mix (Hasluck 1987). This may occur, for instance, because of differences in eligibility for unemployment benefit between different types of workers. People in part-time jobs and self- employed workers are less likely to be eligible for unemployment benefit than full-time employees. Since the proportion of workers in part-time jobs and in self-employment varies substantially between labour market areas, it is necessary to include these variables in the statistical analysis.

3. Competitiveness

The impact of recession on local labour markets can be expected to be greater in those areas which are the least competitive. An area's competitive- ness can be measured in different ways, the simplest of these being its labour Productivity. A potentially more accurate measure of competitiveness is the ratio of earnings to labour productivity since those labour market areas with a lower earnings/productivity ratio are likely to be in a better position to with- stand recession than counties in which this ratio is high.

4. Size structure o f f i rm

A potentially important factor in determining the effect of a major recession on a county's labour market is the extent to which it is able to 'ride the storm'. One factor which may influence this is the size structure of firms since small firms are less likely to be able to withstand the financial pressures of recession than l q e firms. Not only do small firms have greater difficulty obtaining credit, but also have to face higher interest charges than large firms because of the presumed greater risks involved in lending to small firms. The sharp increase in interest rates during 1988-89 proved to be especially harm- ful to the small firm sector. We therefore expect counties with a high propor- tion of small firms to have been more severely affected by the 1990-92 recession than those with a small proportion. The percentage of a county's manufacturing employees in firms with under 20 employees ("AEMP 0-19) is

0 Basil Blackwell Ltd. 1994.

374 BULLETIN

therefore expected to be positively related to the change in unemployment during 1990-92. (Data for non-manufacturing firms were not available.)

5. Housing market efects

The sudden and spectacular reversal of the housing market in 1988 is likely to have had a considerable impact on demand. Carruth and Henley (1992) show that changes in aggregate consumption spending were significantly positively related to changes in house pricess during 1971-89 and argue that the sudden reversal of house prices during 1988-91 is likely to have been a primary influence on the sharp decline in consumer spending in 1990-91. Much of this decrease in consumer spending seems likely to have been induced by a negative wealth effect.

The spatial impact of this negative wealth effect on consumer spending is likely to have been considerably greater in the south than in the north because of the heavy geographical concentration of the collapse of house prices in southern England. Indeed, the north experienced very little house price deflation during 1990-92 compared to the substantial reduction in house prices in the south (see Figure 2). Changes in house prices (AHPRICES) are therefore expected to be negatively related to the change in the unemployment rate across UK counties. In addition, since the impact of house price changes on unemployment will depend upon the proportion of households in a labour market area which are owner-occupied (%OWNER), this suggest that AHPRICES should be weighted by %OWNER when used as an explanatory variable in the regression model.

Regional disparities in owner-occupied households may also have differ- ential effects on consumption expenditure between regions due to regional disparities in the exposure of households to housing mortgage debt through interest rate effects, Estimates of the average mortgage per household by Ash and Bell (1991) indicate substantial regional disparities, this being consider- ably higher in the UK's southern regions than in the northern regions, Any increase in mortgage interest rates will consequently reduce disposable income to a greater extent in southern than in northern regions. A substantial increase in mortgate interest rates (as happened during 1988-90) can there- fore be expected to lead to greater increases in the unemployment rate in regions where %OWNER is high.

III RESULTS

Multiple regression analysis is used to estimate the statistical relationship between the change in the unemployment rate in 54 GB counties (between the second quarter of 1990 and the second quarter of 1992) and the explana- tory variables discussed above. The time period 1990( 2)-92( 2) covers the first two years of the upturn in unemployment after reaching its trough at the end of the late 1980's boom. The availability of house price data for the majority of UK counties from 1988 (from the Halifax Building Society) also 0 Basil Blackwell Ltd. 1994.

A N ANALYSIS OF UK COUNTIES 375

n ~ a n s that the hypotheses relating to the spatial impact of the collapse of the housing market during 1988-89 on local unemployment rates can be tested Using regression analysis. Not all the explanatory variables identified in the above discussion are included in the reported regression equations since a number of these turned out to b: statistically insignificant. Several counties (ten in all) had to be omitted due to an absence of either unemployment data he. for Surrey) or an absence of house price data (i.e. for several Scottish and Welsh counties and the lsle of Wight).

Since the aim of this paper is to estimate the factors responsible for determining spatial disparities in the impact of the national recession, the model can be written as follows:

A U, - A UuK = a, + 1 a, X, + e ,

where AU,=change in Oh unemployment in the i th county, 1990(2) to 1992(2), AU,,,=change in Yo unemployed in the UK, 1990(2) to 1992(2), Xh=explanatory variable j in the ith county, ei=error term, a,), a,=para- meters to be estimated.

The explanatory variables X, for which results are reported in Table 1 are defined as follows: AHPRICE = change in house prices (in thousands Of Pounds), 1988( 3)-90( 3), source Halifax Building Society’s bi-annual House Price Index; %OWNER - o/o of houses owner-occupied, source Census Of Population 1991; %EMp 0-19 =% employed in manufacturing firms with under 20 employees, source Business Monitor 2989, PA1003 %AGR + ENRGY = o/o employees in agriculture, forestry, fishing, energy, and water, 1989, source Regional Tjpn& 1991; SCOTTISH 5 Scottish binary var- iable; SCOTTISH = 1 if county is located in Scotland and zero otherwise.

A selection of estimated regression equations is provided in Table 1. In addition to NMing regressions on the change in the total unemployment rate, regressions were also run for males and females separately in order to examine the impact of individual explanatory variables on males and females respectively. All the estimated regression equations indicate that a high Proportion (i.e. around 75 percent) of the inter-county variation in the change in the unemployment rate during the 1990-92 recession is accounted for by just four explanatory variables. Diagnostic tests for mis-specification of functional form and heteroscedasticity did not reveal any problems with the estimated equations.

The regression results support the view that housing market factors had a substantial effect on determining the spatial impact of the 1990-92 recession. This is reflected by the high level of significance of the estimated coefficient on the change in house prices (AHPRICES). A lag of eighteen months on this variable was found to give the best results. The significance of AHPRICES is reinforced by the significantly positive coefficient on %OWNER, which indicates that the impact of recession was greater in those counties with a high rate of home ownership. Weighting AHPRICES by %OWNER has a

I

0 Basil Blackwcll Ltd. 1994.

il w

m

4

o\

m TABLE

1 5 & B. -

Regr

essio

n ana

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of th

e cha

nge in t

he u

nem

ploy

men

t rate

in G

B co

untie

s, 19

9092

. Dep

ende

ni v

aria

ble =

chan

ge in

perc

ent u

nem

ploy

ed

in ea

ch G

B co

unty

(ZW

.2 to

1992

.2); n

= 54

in a

ll eq

uatio

ns

I

P

Chan

ge in

iota

1 une

mpl

oym

ent

Chan

ge in

mal

e une

mpl

oym

ent

Chan

ge in

fem

aIe u

nem

ploy

men

t

. Ex

plan

ator

y var

iabl

es

Eq.

1 E

q.2

Eq.

3 E

q.4

Eq.

5 E

q.6

Eq.

7

Eq.

8 E

q.9

W

W P

Con

stant

AH

PRIC

ES

AHPR

ICES

X O

/oOW

NER

%O

WN

ER

%EM

P 0- 1

9

O/'G

R/ENR

GY

SCO

TTIS

H

R2

- 1.0

73

(0.8

8)

- 0.0

42

(4.3

0)

0.07

2 (4

.13)

0.

050

(3.0

4)

- 0.1

59

(4.0

3)

0.72

- 1.5

36

(1.3

1)

- 0.0

60

(4.2

6)

0.07

8 (4

.61)

0.

050

(3.1

0)

- 0.1

60

(4.0

8)

0.72

1.04

0 (0

.74)

- 0.0

66

(4.9

6)

0.04

2 (2

.04)

0.0

47

(3.0

6)

-0.1

34

- 1.3

09

(2.8

7)

0.76

(3.5

5)

- 1.6

82

- 0.

049

(3.6

6)

(1.W

0.10

7

0.06

1

(2.7

2)

- 0.2

28

(4.1

9)

(4.4

4)

0.7

1

- 2.2

04

(1.3

7)

- 0.

07 1

(3

.68)

0.

1 14

(4

.89)

0.

062

(2.7

6)

- 0.

229

(4.2

3)

0.71

1.31

7 (0

.68)

- 0.0

79

0.06

4 (2

.28)

0.

057

(2.7

0)

- 0.

194

(3.7

1)

- 1.

789

(2.8

5)

0.75

(4.3

5)

- 0.3

85

- 0.7

84

(0.5

1)

(1.0

7)

- 0.0

35

(5.7

0)

- 0.0

49

0.02

8 0.

034

(2.6

2)

(3.1

8)

0.03

0 0.

031

(2.9

6)

(3.0

3)

- 0.0

69

- 0.0

7 1

(2.8

0)

(2.8

5)

(5.5

5)

0.72

0.7

1

1.56

1

(7.9

7)

W

-0.0

55

S (6

.84)

f;; 2

0.03

0 (2

.99)

- 0.

052

(2.1

6)

- 0.

869

0.73

(3

.77)

Not

es.

1. (

)=ab

solu

te va

lue

of t-r

atio

. 2.

Tes

ts fo

r mis-

spec

ifica

tion o

f fun

ctio

nal f

orm

and

for h

eter

osce

dast

icity

did n

ot re

veal

any p

robl

ems.

AN ANALYSIS OF UK COUNTIES 377 negligible effect on the results. The statistical evidence supports the conten- tion that the spatial impact of the recession was strongly influenced by spatial disparities in the magnitude of the collapse of the housing market.

Inclusion of a Scottish binary variable into the estimated regression equation leads to a sharp reduction in the magnitude of the coefficient O n '/&WNER (though the coefficients on the other explanatory variables mnain virtually unchanged). This interaction between %OWNER and SCOTTISH arises because of the far lower proportion of home owners in Scotland. The regression results do not enable us to unscramble the separate effects of %OWNER and SCOTTISH on the change in unemployment. It might be noted, however, that o/oOWNER loses significance entirely when SCOTTISH is introduced into the female regression equation.

The results reported in Table 1 also indicate that the housing market Was not alone in determining the spatial impact of the 1990-92 recession. m0 other variables, both industry-related, consistently had a high level of signifi- cance. These are the size structure of firms (o/oEMP 0-19) and a measure Of the industry mix of counties (%AGR/ENRGY ). As expected, counties with a

proportion of small manufacturing firms were more severely affected by the recession than counties with a low proportion; and the recession had a less severe impact on counties with a high dependence on primary sector activities. Inclusion of other measures of a county's industry mix, however, Were found not to add to the explanatory power of the model. In addition, no evidence could be found to support the contention that either labour costs or labour productivity had my effect on the dependent variable. The failure to find any competitiveness or industry mix effects, however, may be due to the inadequacy of the proxy variables used to measure the effect of these two factors. It is also possible that the size structure of firms will capture some competitiveness effects. Various other variables, which might a pion' have been expected to influence a county's sensitivity to national recessions, were included in the regression analysis (e.g. the per cent of workers who are part- time, the occupational mix, the female/male employment ratio, the earnings/ labour productivity ratio, and population density) but none of these additional variables improved the explanatory power of the model.

The importance of three of the explanatory variables (AHPRICES, %OWNER and %EMP 0-1 9) in determining the sensitivity of individual counties to the 1990-92 recession can be seen from Table 2. This shows that counties located in the three southern regions of the UK (i.e. the South East, East Anglia and the South West) suffered from the collapse of the housing market to a far greater extent than the northern regions. Counties that have traditionally been sensitive to national recessions, such as Merseyside, Tyne and Wear, and Strathclyde) benefitted substantially from the absence of the collapse in house prices that occurred in the southern counties. Differences between counties in their dependence on small firms reinforce the north-south disparity in the effect of the collapse of the housing market on local unemployment rates. Greater London, East Sussex and Cornwall were

Q Basil Blackwell Ltd. 1994.

w 4

TABL

E 2

00

Estim

ated

$ect

of AH

PRIC

ES, %

OW

NER

and

%EM

P 0-

19 o

n th

e spa

tial i

mpa

ct of

the

lW

92

rece

ssio

n

e

W

y1

P

Coun

ty

Chan

ge in

%

Esti

mat

ed co

mbi

ned

Esti

mat

ed ef

ect o

f sue

un

empl

oyed

in co

unty

G

ect of c

hang

e in

hous

e str

uctu

re of

Ch

ange

in %

m

inus

chan

ge in

'x

pric

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d X, o

wne

r m

anuf

actu

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irm

un

empl

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un

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in U

K oc

cupa

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n ch

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in

on ch

ange

in %

I m

.2-1

992.

2 19

922-

1992

.2

% u

nem

ploy

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unem

ploy

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SOU

TH E

AST

Be

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re

Berk

shire

Bu

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Ea

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ssex

Es

sex

Gre

ater

Lon

don

Ham

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re

Her

tford

shire

K

ent

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Su

rrey

W

est S

usse

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AN

GL

lA

Cam

brid

gesh

ire

Norfolk

Suffo

lk

Avo

n C

ornw

all

Dev

on

SO

UTH

WES

T

5.5

4.5

5.1

6.6

6.3

5.7 5.2

5.6

5.8

4.2

4.8 - 4.3

3.9

3.7

4.9

5.2

4.7

1.6

0.6 1.2

2.7 2.4

1.8

1.3

1.7

1.9

0.3

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- 0.

4 0.

0 - 0.

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1.3

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0.8

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So

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Her

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1 in

Tab

le 1

.

AN ANALYSIS OF UK COUNTIES 381

particularly hard hit because of their high dependence on small manufactur- ing firms,

CONCLUSION

This paper has shown that unusual and unexpected events occurred in the UK hbour market during the 1990-92 recession. In particular, regional unemployment disparities declined when they were expected to widen.

Recent research suggests that one of the primary reasons for the remark- able narrowing of unemployment rates between regions can be found in the interaction between the housing market and the labour market. The collapse Of house prices in the south, while house prices in the north held up, led to more severe recessionary conditions in the south compared to the north than would otherwise have been the case. This is supported by statistical evidence which shows that the counties with the largest fall in house prices and the highest percentage of home Owners subsequently experienced the largest increase in their unemployment rate. The size structure of firms was also found to be a statistically significant explanatory variable. Counties with a high proportion of their manufacturing workforce employed in small firms (under 20 workers) experienced a substantially higher increase in unemploy- ment than counties which were not SO heavily dependent upon small firms, This result is consistent with the fact that small firms are less resilient to Prolonged recession than large firms.

The question which now dominates the discussion of the future behaviour Of regional labour markets is whether traditional regional disparities will re- emerge during the remainder of the recession and the subsequent Upturn. This is an important question since future trends in regional unemployment disparities have implications for the drawing of the UK’s assisted area map, which has traditionally been heavily influenced by regional unemployment disparities. The unprecedented reduct ion of regional unemployment disparities during such a Severe recession means that it is going to be far more difficult to distinguish between areas in need of assisted area status over the longer term and those which are not. It is crucially important that the delineation of assisted areas should be based not on current unemployment rates but on those which we expect to exist over the medium term. The Question which must be addressed is whether the spatial impact Of the 1990-92 recession has been a one-off event that is unlikely to be repeated in the forseeable future. Will the traditional spatial structure Of unemployment in the UK reassert itself in the next business upturn?

The Management School, Lancaster University

did not repeat itself on this occasion.

Dare of Receipt of Final Manuscript: January 1994

Q Basil Blackwell Ltd. 1994.

382 BULLETIN

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Muellbauer, J. and Murphy, A. (1991). ‘Regional Economic Disparities: The Role of Housing’, in Bowen, A. and Mayhew, K. (eds.). Reducing Regional Inequalities, Kegan Page, London.

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