Residential mobility, housing tenure and the labour

market in Britain

René Böheim and Mark TaylorInstitute for Social and Economic Research

and Institute for Labour ResearchUniversity of Essex

Colchester, Essex CO4 3SQemail: rene@essex.ac.uk, taylm@essex.ac.uk

15/07/99

Abstract: Using data for 1991 to 1997 from the British Household Panel Survey (BHPS),this research investigates the reasons to move house and the extent and determinants ofhouse moves. In particular, we examine the relationships between labour market dynamicsand residential mobility. Panel data allow the study of the sequence of household moves andindividual labour market status changes, enabling unique analysis of the relationshipbetween residential and job mobility. Our findings suggest that the unemployed are morelikely to move than employees. This supports the classical economic hypothesis thatindividuals move to escape unemployment, and suggests that the unemployed are notimmobile. A desire to move motivated by employment reasons has the single largestpositive impact on the probability of moving between regions.

JEL Classification: J61; J64; R21; R23

Keywords: Migration; Housing; Residential mobility; Unemployment; Job tenure; Jobmobility; Panel data.

Acknowledgements: The support of the ESRC, the University of Essex and theLeverhulme Trust is gratefully acknowledged. Thanks to Alison Booth, Nick Buck, StephenJenkins, Anna Vignoles and participants at the European Society for Population EconomicsConference, Turin, June 1999, and Education and Employment Economics Group AnnualConference, Swansea, July 1999 for helpful discussions and comments on earlier drafts ofthe paper. This work derives from an Institute for Labour Research programme on “LabourMarket Dynamics in a Changing Environment” funded by the Leverhulme Trust.

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Non-technical summary

Housing and labour markets are closely linked. Individuals need to live within easy reach oftheir work place. For those in employment, commuting costs, transportation infrastructureand journey time restrict the choice of residence. Furthermore, job and employer changesoften require house moves, and housing demand will therefore reflect patterns inemployment turnover and labour market trends.

Using data for 1991 to 1997 from the British Household Panel Survey (BHPS), this researchinvestigates the reasons to move house, whether or not moves take place, and why they doso. In particular, we examine the relationships between labour market dynamics andresidential mobility, addressing the decision of moving house conditional on the actual jobstatus and on the preferences to move. Panel data allow the study of the sequence ofhousehold moves and individual labour market status changes. They provide importantinformation on the events associated with each change. Previous studies have almostexclusively used Census data or data from cross-sectional surveys to study residentialmobility, and “the general lack of longitudinal data is a major vacuum in understandingBritish migration....” (Coleman and Salt, 1992, p.400). This paper contributes uniquely tothe literature by using panel data to directly examine the links between housing tenure andresidential and job mobility in Britain in the 1990s.

The paper is essentially empirical; we estimate reduced form equations rather than presentstructured modelling. We develop a simple framework in which to consider the migrationdecision at the household level. Following models of rural-urban migration developed byTodaro (1969) and Harris and Todaro (1970), we assume that the decision to migrate is achoice variable determined by expected utility flows.

The findings show that the BHPS provides representative data for Britain on housing tenureand labour market status in Britain in the 1990s. Although 44% of individuals express apreference for moving at each wave, approximately 10% of individuals actually move houseevery year. This proportion has remained relatively constant across the 1990s. Two-thirds ofmovers remain in the same Local Authority district.

Results from multivariate analysis suggest that the unemployed are more likely to move thanemployees. This supports the classical economic hypothesis that individuals move to escapeunemployment, and suggests that the unemployed are not immobile. Further, a desire tomove motivated by employment reasons has the single largest effect on the probability ofmoving between regions. Mortgage holders are found to have low levels of labour marketand residential mobility relative to those in other housing tenures. This finding possiblyreflects the general situation of the housing market in the 1990s, with low mobility ingeneral and a large number of low income house purchasers who became caught in thenegative equity trap. Private renters are found to have the most residential mobility.

This evidence suggests that employment plays a major role in motivating residentialmobility, particularly between regions. A desire to move motivated by employment relatedreasons and current personal unemployment increase the probability of moving. In addition,policies promoting home ownership might not be the most efficient or effective mechanismfor promoting residential and labour market mobility in Britain in the 1990s.

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Introduction

“Frequently investment goes where there are skilled people wanting work. But there mustbe some mobility of labour. If people are not willing to move as their fathers did theeconomy can not thrive.” (Margaret Thatcher, 1980).

“We can not ignore the price that unemployment today is exacting. I know theseproblems. I grew up in the thirties with an unemployed father. He didn’t riot. He got onhis bike and looked for work and he kept looking until he found it.” (Norman Tebbit,1981).

Housing and labour markets are closely linked. Individuals need to live within easy reach of

their work place. For those in employment, commuting costs, transportation infrastructure

and journey time restrict the choice of residence. Furthermore, job and employer changes

often require house moves, and housing demand will therefore reflect patterns in

employment turnover and labour market trends. This relationship between the housing and

labour markets is increasingly important given the recent focus by successive governments

in Britain and across Europe on labour market flexibility.1 A flexible labour market, one

which reacts swiftly and efficiently to demand shocks, requires individuals to be able to

locate to areas of high labour demand. For this to happen successfully requires those seeking

work to be able to (i) identify areas of high labour demand, and (ii) move to such areas. The

residential mobility of labour market participants is therefore an important policy

consideration.

Using data for 1991 to 1997 from the British Household Panel Survey (BHPS), this research

investigates the reasons to move house and the extent and determinants of house moves. In

particular, we examine the relationships between labour market dynamics and residential

mobility in Britain. Panel data allow the study of the sequence of household moves and

individual labour market status changes. They provide important information on the events

1 Labour market rigidities are often seen as the striking contrast between European and US labour markets.Generally, the large share of long-term unemployed is taken as evidence that transitions between labour marketstates are less frequent in Europe.

4

associated with each change. Previous studies have almost exclusively used Census data or

data from cross-sectional surveys to study residential mobility, and “the general lack of

longitudinal data is a major vacuum in understanding British migration....” (Coleman and

Salt, 1992, p. 400). This paper contributes uniquely to the literature by using panel data to

directly examine the links between housing tenure and residential and job mobility in Britain

in the 1990s.2

Each Briton is estimated to make between seven and eleven residential moves during their

lifetime, although only one or two of these are likely to be between regions (Rees, 1979). In

comparison with the United States, mobility in Britain is low (Greenword, 1997;

McCormick, 1997). However, levels of regional migration are not out of line with those in

other Northern European countries (Hughes and McCormick, 1987). An early study by

Cullingworth (1965) reports an annual rate of migration of 7%, while according to the

National Dwelling and Housing Survey of 1979, 11% of households have been at their

current address for less than one year (Minford et al, 1987). More recent work by Jackman

and Savouri (1992) finds large gross flows between regions (about 1.6% of individuals

move regions each year), but considerably smaller net migration.

Previous studies of labour migration in Britain have highlighted the relative immobility of

local authority tenants and manual workers. Hughes and McCormick (1981, 1985, 1987)

argue that the rent setting and allocation mechanisms in local authority housing impede the

residential mobility of manual workers, and this results in unemployment in areas of low

demand, and high wages in areas of high demand (Minford et al, 1987). Local authority

tenants in general are more likely to be unemployed (Hughes and McCormick, 1990;

Wadsworth, 1998), are less likely to move for job reasons and, if they do move, are more

likely to move shorter distances (Coleman and Salt, 1992).

2 The importance of panel data in studying housing market dynamics at the micro-level is highlighted inErmisch and Jenkins (1999), who find that residential mobility in later life is highly correlated with changes inthe individual or household situation such as retirement, the loss of a spouse, or changes in financialcircumstances.

5

Migrants are typically younger rather than older, and consist of young families, couples

without children or unmarried adults, have higher levels of education and are employed in

non-manual occupations (Coleman and Salt, 1992; Ermisch, 1996; Cameron and

Muellbauer, 1998). Manual workers tend to have low levels of gross migration, only a small

proportion of which is for job related reasons, and variations in regional unemployment are

largely attributed to the manual labour market. In contrast, the non-manual labour market is

more flexible with similar regional unemployment rates, relatively high rates of regional

mobility, and net migration towards regions with high employment growth (Evans and

McCormick, 1994).

In this research we address the decision to move house, conditional on actual job status and

on preferences to move. Using the first seven waves of the BHPS covering 1991 to 1997,

this paper examines individuals’ desires to move and their reasons for wanting to move at

any wave t. From the panel nature of the data it is possible to find out whether these

individuals subsequently moved and, if so, why. Relating our findings to labour market

status changes enables unique analysis of the relationship between residential and job

mobility. Panel data provide a wide range of accurate information both before and after any

move, an important consideration when analysing causation. The paper is essentially

empirical; we estimate reduced form equations rather than present structured modelling.

Our findings suggest that the unemployed are more likely to move than employees,

particularly between regions. This supports the classical economic hypothesis that

individuals move to escape unemployment, and suggests that the unemployed are not

immobile. Further, a desire to move motivated by employment reasons has the single largest

effect on the probability of moving between regions. Mortgage holders are found to have

low levels of labour market and residential mobility relative to those in other housing

tenures. This finding possibly reflects the general situation of the housing market in the

1990s, with low mobility in general and a large number of low income house purchasers

who became caught in the negative equity trap. Private renters are found to have the most

residential mobility.

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Background

For many years policy makers in Britain intervened in the housing and labour markets

without necessarily considering the secondary effects on the other. Housing market policies

have recently concentrated on increasing home ownership through, for example, generous

tax relief on loans to fund house purchases and the right-to-buy council house scheme,

monitoring the private rented sector through rent controls (e.g. the 1965 and 1974 Rent

Acts), and maintaining a considerable and subsidised local authority housing sector (Hughes

and McCormick, 1987). These policies have dramatically reduced the availability of private

rented accommodation, typically the source of short-term housing for migrants. To fully

understand the current nature of the housing and labour markets, it is necessary to briefly

discuss past policies and practices from which they originated.

The British housing market has experienced substantial change in the last fifty years, from

one dominated by private landlords to one where home-ownership is the norm. The

immediate post war period saw a huge investment in local authority accommodation, with

over 80% of new dwellings built between 1945 and 1951 in the public sector. As recently as

1951, the private rented sector accounted for 50% of the housing stock, owner-occupied

housing accounted for one-third, and local authority housing the rest (Malpass and Murie,

1994). However, the post war growth of the 1950s and 1960s saw a major increase in home

building for owner-occupation, and in 1958 private completions exceeded public

completions (English, 1992). Building for owner-occupation continued on such a scale that

by 1971, only 19% of the housing stock was accounted for by private renters, 30% by local

authority housing, and 51% by home owners (Malpass and Murie, 1994).

The 1980s saw the selling of local authority properties to tenants under the right-to-buy

scheme initiated by the 1980 Housing Act, although originally introduced in the 1977 Green

Paper. Sitting tenants were given a discount linked to the length of their tenancy, and applied

to a market valuation. Over 1.5 million homes were sold under this scheme which, together

with a low level of new building resulted in a contraction in the local authority sector

(English, 1992). In the early 1980s, local authority housing sales contributed more to the

growing home-owning sector than new building (Forrest and Murie, 1992). Purchasers

7

under this scheme tended to be more affluent middle aged households, with at least one

household member in full-time work in a skilled manual or white-collar occupation.

Households who did not buy tended to be either young or elderly households, in

unemployment or unskilled work, female headed or lone parent households (Forrest and

Murie, 1992). A result of this policy is a high concentration of benefit dependent,

unemployed and low income households in local authority housing.3

In the late 1980s, a tightening of monetary policy saw a sharp increase in interest rates,

unemployment and the onset of recession resulting in the longest sustained period of

depressed housing market activity in recent times (Malpass and Murie, 1994). Added to this

was the gradual elimination of the Mortgage Interest At Source system (MIRAS) (Henley,

1998b). Many home owners found themselves unable to meet their mortgage commitments,

and there was a dramatic increase in the number of repossessions from 16,000 in 1989 to

75,500 in 1991 (Malpass and Murie, 1994). The recession of the early 1990s had a dramatic

impact on the housing market plunging many homeowners into negative equity severely

reducing their ability to migrate (Henley, 1998a), and increasing the number of immobile

households. Gentle et al (1994), for example, estimate that 21% of mortgage holders had

negative equity by 1992. Oswald (1996, 1998) conjectures that home-ownership in today’s

climate reduces workers’ residential mobility, contributing to higher levels of

unemployment. Microeconometric evidence from the 1990s, however, suggests that home

owners have higher exit rates from unemployment than renters (Arulampalam et al, 2000).

Regional house price differentials are a significant dimension of immobility among home-

owners. Growing regional differences in the house-prices to earnings ratio reduce the

prospective gains from moves to areas of high labour demand (Gordon, 1990; Cameron and

Muellbauer, 1998), and may result in higher unemployment (Bover et al, 1989). Similarly,

high house prices discourage outward migration from an area as they imply a loss of

prospective investment gains (Gordon, 1990). The wide gap in house prices between the

3 Pissarides and Wadsworth (1992) note that the unemployment rate among local authority tenants increased bya factor of four between 1979 and 1986, and attributes this increase to the right-to-buy scheme.

8

North and South of Britain that appeared and expanded over the 1980s increased the cost of

moving into the South, where job creation was relatively high.

Demographic changes also have an impact on the housing market. The baby boom period of

the late 1950s and 1960s saw an escalation in the number of young households in the late

1970s and 1980s (Coleman and Salt, 1992; Ermisch, 1996). Owner-occupation is generally

less feasible for younger workers who have accumulated less wealth and resources, and one

might expect their mobility to be constrained by a shrinking stock of available private rental

accommodation. The increase in divorce rates and the number of people living

independently further into old age have also increased the number of households. These

developments have contributed to a fall in average household size from 4.6 in 1901 to 2.5 in

1989, while in 1911 5% of households were single person households, compared with 25%

in 1989 (Coleman and Salt, 1992).

The labour market has experienced major changes in the 1980s and 1990s. These include

increased female labour force participation and the growth in part-time work (Hakim, 1998)

and the expansion of non-standard employment patterns (fixed term contracts, flexitime,

work sharing) and self-employment (Dex and McCulloch, 1995; Taylor, 1997). The higher

rate of female employment has resulted in more dual earner households and the decision to

move is more complex for these families. Recent years have also seen a diminishing

importance of union membership and coverage (Booth, 1995), a decline in the average

retirement age (Tanner, 1997) and economic activity (Taylor and Walker, 1996), an increase

in unemployment, and in long-term unemployment in particular. The restructuring of the

economy has brought about a fall in skilled employment in manufacturing and the growth of

the service sector. Uneven patterns of economic development have resulted in declining job

opportunities in different regions related to their industrial structure (Malpass and Murie,

1994). These factors again have an impact on the decision to migrate.

Modelling residential mobility

A classical economic approach to migration would suggest that households move away from

regions with low wages and high unemployment to those with high wages and low

9

unemployment. Similarly, migration should occur more frequently within occupations where

such regional differentials are greatest. As moving is costly, migration rates might be

expected to be lower for the unskilled than for skilled professional workers, who are more

able to meet the costs of moving and who are also more likely to receive assistance from

employers to meet these costs.

Here we develop a simple framework in which to consider the migration decision at the

household level. We assume that the decision to migrate is a choice variable determined by

expected utility flows, similar to the models of rural-urban migration developed in Todaro

(1969) and Harris and Todaro (1970). Expected utility flows from remaining at the current

address can be written:

E(Ucht) = f(Dct, Hcht, Pcht, Xht).

Expected utility from remaining at the current address, E(Ucht), is a function of the level

aggregate demand in the current locality Dct which, together with labour supply, determines

the probability of employment and expected wages of household members, the condition

and suitability of the current house of residence Hcht, the value of the house Pcht, and tastes

and preferences Xht. Similarly, the expected utility flow from moving elsewhere, E(Ueht) can

be written:

E(Ueht) = f(Det, Heht, Peht, Xht).

A household will therefore prefer to move if the expected net gains from moving are greater

than the expected gains from remaining at the current address,

E(Ueht) > E(Ucht) - E(Cht).

where Cht measures transaction costs associated with moving house. The migration decision

is continually revised as a household’s situation changes, for example through household

members finding or losing jobs, forming or dissolving partnerships, etc. The presence of

transaction costs implies that moves are typically associated with large changes in housing

consumption and, as costs are generally larger for home-owners than for renters, that private

renters will have greater residential mobility. However, some households who prefer to

10

move may be constrained by regional house price differentials (Cameron and Muellbauer,

1998). For example, households wishing to move from Scotland to central London may find

that the housing price differential is too large an obstacle to overcome.

Given this framework, the determinants of migration emerge. A household will choose to

migrate if labour demand elsewhere increases relative to that in their current locality for a

given level of supply and therefore increasing expected wages available elsewhere, or a

decline in the condition or suitability of their current house. The effect of local house prices

on the decision to migrate is ambiguous - a relative increase in local house prices may

induce individuals to cash in on their property and move elsewhere, or it may induce

households to remain at their current address with the knowledge that the value of the asset

is appreciating. While this framework is rather simplistic, it provides a basis for the later

empirical analysis.

Data

The analysis uses data collected in the British Household Panel Survey (BHPS), a nationally

representative sample of some 5,500 households recruited in 1991, containing

approximately 10,000 persons. These same individuals are interviewed each successive year.

If anyone splits from their original households to form a new household, all adult members

of the new households are also interviewed. Children in original households are interviewed

when they reach the age of 16. Thus the sample remains broadly representative of the

population of Britain as it changes through the 1990s. We examine the residential mobility

behaviour of these individuals between waves one and seven, over the period 1991 to 1997.

The core questionnaire elicits information about income, labour market status, housing

tenure and conditions, household composition and consumption, education and health at

each annual interview. Information on employment changes that have occurred within the

period between interviews is also collected. The BHPS attempts to follow all movers who

remain in Great Britain and, although attrition among migrants is substantially higher than

that among non-migrants, Buck (1997) reports that almost 75% of actual movers between

waves 1 and 2 were traced. Respondents are also asked whether they would like to move

11

house and if so for which reasons. Panel data such as these are ideally suited to the study of

migration, providing detailed information on individuals and households before and after

any move.

Excluding full-time students, who tend to live in temporary accommodation and move

frequently, and focusing on persons aged between 16 and 55 provides a sample size of

40,117 person year observations with non-missing data on job status and tenure in the first

seven years of the survey. Because our focus is on the relationship between residential and

job mobility we also exclude persons who classify themselves as retired from our sample.

We use an unbalanced panel, although individuals have to be interviewed at two consecutive

waves to be included. As a preliminary exercise and as a means of data validation, we

present several descriptive tables. Table 1 shows that the pattern of housing tenure over the

first seven years of the BHPS is relatively stable. There are modest increases in the

proportion owning their homes outright and individuals living in privately rented

accommodation over the seven waves. Note that only 8% of individuals rent their property

from other than the local authority. The proportions in each housing tenure category

correspond well with evidence from other data sets. For example, Wadsworth (1998) reports

figures from the Labour Force Survey (LFS) showing that in 1995, 73.7% of the working

age population were in owner-occupied accommodation, 17.8% were in Council housing

and 8.5% in other (private) rented. Our figures for the corresponding year (Wave 5) are

74.8%, 16.5% and 8.7%.

Table 1 also shows that self-employment as a proportion of the working age population in

the BHPS remains stable across the seven waves. The proportion in paid employment

initially declines from 68.4% in 1991 to 66.8% in 1992, but then increases to over 71% in

1997. Unemployment in the sample falls considerably from a peak of 8.4% in 1992 to under

5% in 1997. Again, these figures are consistent with the economy wide picture.4

4 For example, the average proportion of the labour force in employment, self-employment and unemploymentacross the seven waves are 79%, 12% and 8% respectively. Corresponding averages for the economy as awhole, taken from the Labour Force Survey Historical Supplement 1997, are 79%, 12% and 9%.

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In Table 2 we focus on the distribution of job status by housing tenure. The self-employed,

and employed tend to live in accommodation that they own. A similar proportion of the

unemployed and employees own their homes outright. Two fifths of the unemployed and

37% of the economically inactive live in public housing. Over 50% of the unemployed live

in rented accommodation, compared with 44% of those in inactive states, 20% of employees

and only 18% of the self-employed. These figures are comparable to those in Wadsworth

(1998), who uses LFS data for 1995 to show that 81% and 45% of those in employment and

unemployment respectively are owner occupiers, while 11% and 39% are found in local

authority housing.

Residential mobility

The BHPS collects information on individuals’ preferences to move at each wave as well as

actual moves occurring between waves. At each date of interview, respondents are asked, “If

you could choose, would you stay here in your present home or would you prefer to move

somewhere else?”, “What is the main reason why you would prefer to move?”, and “Can I

just check, have you yourself lived in this (house/flat) for more than a year, that is before

September 1st, 199{0,1,2,3,4,5,6}?”. Table 3 shows that the proportion of the sample that

moves house each year is approximately 10%.5 Therefore, one in ten individuals of working

age moves house each year. From the data we are also able to identify local moves, defined

as moves within a local authority district, moves out of a local authority district but within a

standard region, and moves that cross regional boundaries. Table 3 shows that most moves

are short distance with 66% of all moves occurring within local authority boundaries. We

find that 1.8% move regions each year, a figure almost identical to that reported in Jackman

and Savouri (1992). Regional moves account for 17.5% of all moves.

More than two-fifths of individuals express preferences for moving given a choice.6 Table 4

shows that the propensity to move is three times greater for those who expressed a

5 Tracing the same individuals over time allows identification of those who move more than once over theperiod. Individuals moving in the previous wave are some three times as likely to move than those not movingin the previous wave. This may reflect households using temporary (rented) accommodation while looking fora suitable home to buy. Whether there is a causal relationship between past and future moves, or whether someindividuals are more prone to move than others is a question we leave for future research.6 There is some evidence of a downward decline in this proportion over the survey period.

13

preference for moving at the previous wave than for those who did not. Almost 70% of

movers expressed a preference for moving at the previous date of interview.

Table 5 displays the main reasons individuals give for wanting to move house.7 About 43%

of people would like to move either because they dislike their current area of residence, or

because they would prefer to live in another area. The other dominant reason for wanting to

move concerns the current house or accommodation. Area and housing reasons account for

over 80% of those expressing a preference for moving. Less than 3% of those wanting to

move wish to do so for job related reasons. However, as the second column of Table 5

shows, of those who wish to move for job related reasons, 27% actually do move, compared

with 19% of those who wish to move for housing reasons, and 12% for area reasons.

Therefore those wishing to move for job related reasons are more likely to subsequently

move than those wishing to move for any other reason. The final column in Table 5 shows

that of those expressing a preference for moving and who subsequently moved, almost half

cited housing reasons for wanting to move, and a third area reasons. This table suggests that,

although job related reasons are a catalyst for a small proportion of moves, they are

important in motivating individuals to actually move.

Table 6 investigates moving preferences and propensities by housing tenure and job status

prior to any move. This shows that those in rented accommodation, especially privately

rented, are most likely to want to move, while individuals owning their property outright are

satisfied with their current accommodation. Over 30% of those in private rented

accommodation actually move each year, compared with 12% in public housing, and 7% of

those with a mortgage and owning their property outright. As expected, and predicted in the

modelling framework, individuals are most mobile from private rented accommodation.

Mortgage holders, however, account for almost 50% of all moves.

7 These categories are condensed. Area reasons include: feels isolated, wish to move to rural environment, wishto move away from urban environment, traffic, area unsafe, noise, unfriendly area, wish to move to specificarea, dislikes current area. Housing reasons include: want larger/better accommodation, want smaller/cheaperaccommodation, own accommodation, want to buy somewhere, want another type of accommodation, dislikescurrent accommodation, wants better accommodation, other aspects of accommodation. Family reasonsinclude: family reasons, more privacy, wants a change, for child’s education. Job reasons include: occupationreasons, reduce travelling, retirement. Health reasons include: health reasons, wants accommodation withoutstairs.

14

The unemployed are most likely to want to move.8 One half of those unemployed would

prefer to move, compared with 43% of the self-employed and 44% of employees. Given that

employees often benefit from relocation packages, it is perhaps surprising that the

unemployed are also more likely than those in other labour market states to actually move.9

Almost 15% of the unemployed move, compared with 10% of employees and 9% of the

self-employed. Two thirds of movers however, are employees.

Table 7 presents two step employment transition patterns for those that move between dates

of interview and those that remain at the same address. The table shows that employed and

self-employed movers are less likely to remain in work than those who do not move. Of the

self-employed who move, 73% remain self-employed, 93% remain in some form of

employment and 6% enter unemployment. Of those that remain in the same residence, 84%

remain self-employed, 94% remain in some form of employment, and only 2% enter

unemployment. Similarly for employees that move, 91% remain in some form of

employment and 5% enter unemployment. Of employees who do not move, 95% remain in

some form of employment while 2% enter unemployment. Interestingly, the proportion of

the unemployed who remain out of work is no different for movers and non-movers. A

similar proportion of the unemployed enter employment among movers and non-movers.

Housing tenure transitions are rather more volatile than employment transitions for movers

(Table 8). Of those who own their property outright and who move, only 24% remain

outright owners, 48% take on a mortgage, 22% move into other (private) rented

accommodation, while 7.2% move into public housing.10 Those with a mortgage are most

8 The finding that the unemployed and those in public housing are least satisfied with their housingarrangements is no surprise given that such individuals are likely to live in areas with relatively poor amenities.Further examination shows that the unemployed are more likely to want to move due to area reasons (and alsoemployment reasons) than those in other labour market states. Similarly, public housing tenants are most likelyto want to move for area reasons.9 The UK tax code does not permit credits for migration expenses, but allows relief against corporation tax(McCormick, 1997). This therefore favours employer-driven migration. One study of major UK companies hasfound that 97% compensate for removal expenses, 96% for temporary living and travel costs, and 93% forexpenses on the sale and purchase of a home (Merrill Lynch, 1984).10 Some of these are young adults leaving their parental home. Approximately 1 in 10 of our sample co-resideswith at least one parent, excluding these does not change our substantive results.

15

likely to remain mortgage holders after a move (69%), 22% move into private rented

accommodation, 5% buy a property outright and 5% move into public housing. 70% of local

authority tenants who move house remain in public housing, 14% purchase a property with a

mortgage and 15% rent accommodation privately. It appears that local authority tenants have

difficulty in escaping public housing. Some 47% of individuals in private rented

accommodation who move remain private tenants and 39% buy a property with a mortgage.

Private renters are most at risk of moving into public housing with almost 12% becoming

local authority tenants.

As well as collecting information on moving preferences and actual moves, the BHPS asks

recent movers why they moved. Table 9 shows that across the seven waves an average of

13% of individuals move for employment related reasons. The most common job related

reason for moving is starting a new job with a new employer, identified by 33% of those

moving for job related reasons. Moving to be closer to the place of work is another

commonly reported reason for moving, as is to start a different job with the same employer.

About 10% of respondents moving for job related reasons report that they moved in order to

look for work. The most common non-employment related reason for moving concerns the

actual accommodation, reported by almost two-fifths of movers. A further 15% stated

partnership reasons and 10% area specific reasons.11 Eviction, which could be due to non-

payment of rent or mortgage arrears, accounted for 10% of moves at Wave 2 of the survey,

although this proportion has fallen since.12

11 These categories have been collapsed. Accommodation reasons include to move to smaller or largeraccommodation, to move to own accommodation or to buy accommodation, to move to another type ofaccommodation, disliked the previous accommodation, to move to better accommodation, or because of anyother aspects of the accommodation. Partnership reasons include moving in with or splitting from partner.Family/friends includes to move in with or to move closer to family or friends. Area reasons include dislikingisolation, moving to a rural environment, moving from a rural environment, traffic, area unsafe, noise, areaunfriendly, moving to a specific place, and disliked area. Employment reasons here refer to own or othersemployment.12 These figures bear comparison with a Nationwide Building Society survey of owner-occupiers, which reportsthat 29% of mortgage holders move for housing reasons, 20% for marital reasons, and 17% for work or incomereasons (Nationwide Building Society, 1982). Ford and Burrows (1999) report that the rate of repossesions dueto mortgage arrears peaked in 1991 with some 0.8% of all mortgaged properties.

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Estimation Framework

We model the decision to move within three frameworks. The first uses a random-effects

panel probit model, modelling the decision to move as a function of employment status. The

second uses the bivariate probit approach where the decision to move and change

employment status are determined simultaneously. The third approach utilises a multinomial

logit estimation to identify differences between short- and long-distance movers. These

procedures are briefly summarised.

Random Effects Probit

We observe each individual i=1,2, ... N at times t=1, ..., T and observe whether a move has

happened since the last observation. An individual’s propensity to move can be written:

yit*=Xitb + ni + eit [1]

where

yit = 1 if yit* > 0

0 otherwise,

and ni~IN(0, sn2), captures the individual-specific unobservable effect and eit~IN(0,se

2) is

random error. Further, ni and eit are independent of each other and of Xit, the set of

explanatory variables.13 To ensure identification se is set to one and the likelihood function

is parameterised in terms of the within-subject correlation rho,

rho = sn2/(s2

n+s2e). [2]

Bivariate Probit

In many cases the decision to move house and to find or change a job will be taken at the

same time, one decision will influence the other. In order to address this correlation between

the two decisions in our econometric model we use a bivariate probit estimation. The basic

formulation of such a model is given by

13 Greene (1997) and Baltagi (1995) provide more details on the random effects probit approach.

17

yi1 = Xi1b + ei1 [3]

yi2 = Xi2b + ei2 [4]

where the error terms, ei1 and ei2, are bivariate normal distributed, ~BiN(0, 0, σ2I, ρI). Note

that the vectors of covariates, Xi1 and Xi2, need not contain the same variables. Under this

formulation, an individual will not move nor change employment status if yi1=0 and yi2=0,

will not move but change employment status if yi1=0 and yi2=1, will move but not change

employment status if yi1=1 and yi2=0, and will both move and change employment status if

yi1=1 and yi2=1. This approach allows the simultaneous formation of residential and job

mobility decisions, with rho the correlation coefficient between the disturbances of the two

equations.14 A positive (negative) correlation between the error terms ei1 and ei2 indicates

that the unobservable characteristics that make a person change jobs also would make them

more (less) likely to move house. This, for example, could be the case if an individual

becomes unemployed — on having exhausted the employment possibilities in a particular

area the individual would then move house. An inherently more mobile person would

change jobs more often, and would therefore be more likely to change address.

Multinomial Logit

To estimate whether people are different with respect to the distance they move house, we

employ a multinomial logit model. This model estimates the determinants of the probability

of moving within the Local Authority boundaries, across these boundaries but within a

standard region (“intraregional moves”), or moving across regional boundaries, relative to

the reference state, not moving house. The model can be written

0)bexp(X+1

1]0Prob[y

3,2,1)bexp(X+1

)bexp(X =j]=Prob[y

J

1=kki

i

J

1=kki

jii

===

=

∑

∑

j

j

[5]

14 Tunali (1986) applies a similar bivariate probit approach to study individuals moving more than once.

18

where state 1 are local moves, state 2 are intraregional moves, state 3 are regional moves,

and state 0, the reference state, are no moves. The results are reported as relative risk ratios

(RRR), ie exp(b), rather than b itself. For an interpretation consider the following example:

the value 1.416, the RRR associated with being single in the equation for moving locally,

Table 12, states that the relative chances for moving locally rather than not moving house at

all is about two fifths higher for single persons relative to couples with children, all other

things equal.

Estimation Results

We limit the discussion to a set of core variables, which we set out to investigate in detail.

Each estimation used various other covariates as controls, the results on which correspond to

well documented findings in the literature. They are tabulated in the Appendix and not

discussed in the text.

All Moves

Table 10 reports the estimation results from a pooled probit and from the random effects

probit, and the marginal effects from the former calculated at the sample means.15 It should

be noted that here the definition of mover considers all moves, including those within the

same local authority district. Although this is a generous definition of residential mobility,

Table 12 compares the determinants of regional and local moves and is discussed later. The

pooled probit does not control for unobserved individual heterogeneity, although the

standard errors are corrected for repeated observations on the same persons. Both

estimations yield similar results which correspond well with previous findings, and suggests

that unobserved heterogeneity is not a serious issue. Despite this note that the estimate of

rho, although small at 0.103, is well determined. Therefore 10.3% of the variation is

attributable to the unobservable individual specific term.

Family composition influences on migration are apparent. In particular, school aged children

appear to reduce residential mobility. Single persons (with or without children) have higher

15 The values of the covariates are determined prior to any move, identified at time t with any move takingplace between t and t+1.

19

migration probabilities relative to couples with children (by 2% in the pooled model).

However, the coefficients on the number and age of children suggest that it is children aged

10 to 16 that reduce residential mobility. Up-rooting children from their school, friends and

educational environment is something that parents are not willing to do.16 The household

nature of the decision to move is reflected by the negative coefficient on the spouse in work

variable (see Appendix tables for details). This suggests that individuals with a spouse in

work have a lower probability of moving house. Recent partnership dissolution encourages

residential mobility, increasing the probability of moving house by 2.3%.

The next group of variables is of most interest to the current paper and concerns the effect of

labour market status on the probability of moving home. The self-employed have a

marginally higher probability of moving than employees, although this is not statistically

different from zero. A priori we expect the self-employed to have lower levels of residential

mobility than employees as they accumulate physical and location specific capital such as

suppliers and customers which reduce inducements to move. Also, employees are often

entitled to subsidies to reduce the costs of moving for job related reasons, which the self-

employed typically do not enjoy. The unemployed have a higher probability of moving than

employees (by 1.6% in the pooled model). This result is intuitively appealing and supports

both the predictions of our model and the classical economic hypothesis that individuals

move to escape unemployment. The unemployed do not, therefore, appear to be immobile.17

Hughes and McCormick (1989) report a similar result, finding that personal unemployment

increases an individual’s propensity to migrate. However, the probability of moving falls

with unemployment duration (Jackman and Savouri, 1992, report similar results). Therefore

long spells of unemployment hinder mobility. Manual workers are significantly less likely to

move (by 1.8% in the pooled probit), a common finding in the literature (see, for example,

16 The effect of family relations was confirmed in experiments with the data, adults living with their parentshave a reduced likelihood of moving.17 To further investigate the relationship between housing tenure, unemployment and residential mobility,interactions between employment status and housing tenure were included in all models. The coefficient on theunemployed and having a mortgage term were negative, suggesting that unemployed mortgage holders haveless residential mobility than the unemployed in general, but were small and poorly determined. The magnitudeand significance of the other coefficients were not changed by the inclusion of these additional variables. Wetherefore report the specifications excluding these interactions.

20

Evans and McCormick, 1994).18 Residential mobility is increasing in household income,

wealthier households are better able to meet the costs of moving.

Individuals in all housing tenures are more likely to move relative to the omitted category of

mortgage holders.19 Individuals who own their property outright or who live in local

authority rented housing are significantly more likely than mortgage holders to move (in the

pooled specification, it increases the probability by 2% and 3.5% respectively). The largest

coefficient is found on the private rented variable, increasing the probability to move by

over 20%. As predicted by our theoretical framework, private renters are the most mobile

group. This is a common finding in the literature. The finding that home owners with a

mortgage are the least mobile group is a surprising finding, and one that questions

government policies of the 1980s of promoting home-ownership.20 Oswald (1996, 1998)

points towards the lower mobility of owner-occupiers using data from developed countries,

and suggests that the increase in home-ownership has contributed to higher and persistent

unemployment. Buck (1997) reports similar findings, and suggests that this reflects the

situation of the British housing market in the early to mid 1990s, a period of low overall

mobility and a large number of lower income house purchasers who became caught in the

negative equity trap.

We also find that increased density in a household encourages individuals to move (see also

Henley, 1998a), and that the probability of moving declines with the length of time spent at

the address. Each year spent at an address reduces the probability by 0.3%. This too is a

common finding (Buck, 1997), and agrees with our a priori expectations. The shorter the

18 To investigate the relationship between employment status and occupation, we entered an interactionbetween unemployed and manual worker. The coefficient on this variable was quantitatively small andstatistically insignificant, suggesting that the residential mobility patterns of unemployed manual workers areno different to those of unemployed workers in general.19 There are clearly some selection effects in that individuals who expect to move house are unlikely to becomehome owners because of the relatively high transaction costs involved in moving out of owner-occupiedhousing. However, we are not estimating a parsimonious specification and instead control for many factorsinfluencing the housing tenure decision.20 It can be argued that, because mortgage holders typically face higher transaction costs when moving house,they would be more willing to commute longer distances. We include a time to work variable in ourspecification which remains statistically insignificant throughout (see Appendix tables for details).

21

duration in a locality the less an individual has invested in and becomes attached to that

locality.

Henley (1998a) finds that regional unemployment reduces mobility, and argues that this

illustrates the effects of unemployment on the local housing market and the ability to sell the

home and migrate. Our results do not support this, the effect of the local unemployment rate

is large and positive (10% marginal effect) suggesting again that individuals move from

areas of low demand.21 High regional house prices encourage moving, which may indicate

individuals ‘trade-up’ their property. Again this supports evidence found elsewhere

(Jackman and Savouri, 1992).

The final set of covariates considers the reason individuals give for wanting to move at the

previous date of interview. Unsurprisingly, we find that all reasons increase the probability

of moving relative to the omitted category of not wanting to move.22 Given this, it is the size

of the effects that are of interest. The smallest increases in the probability of moving are

associated with area, family and housing reasons, which increase the probability of moving

by between 7% and 12% relative to those not wanting to move. Health and other reasons

have a similar quantitative impact, increasing the probability of moving by 15%. The largest

impact however emerges from wanting to move for job reasons which has a particularly

large and well determined effect, increasing the probability of moving by 18%. A desire to

move motivated by employment reasons has thus a quantitatively large and positive effect

on the probability of moving house.

In Table 11 we report the results from a bivariate probit allowing an employment status

change and migration decision to be modelled simultaneously.23 Again the standard errors

21 Henley (1998a) uses data covering the early 1990s, when the housing market was still emerging fromdepression. This may account for the difference in results.22 It is possible that these moving preferences variables are endogenous, given that moving house can takesome time to plan and organise. Individuals may already have a move planned at the date of interview prior tothe move. For this reason we have run all models excluding these moving preferences. This has little effect onthe size, sign, or significance of the other coefficients.23 The definition of a job change here is a change in an individual’s main status (i.e. a move between self-employment, employee, unemployment and economic inactivity), or changing employers, or changing jobs

22

are corrected for multiple observations on the same individuals. The estimated covariance

between the error terms of the migration and labour market mobility equations is positive

and well determined (0.114, with a t-statistic of 8.3). This implies that unobservable

characteristics increasing the probability of individuals moving home also increase their

labour market mobility (or vice versa), our a priori expectation.24

The coefficients from the probit for moving house are almost identical to those reported in

Table 10, verifying the robustness of our results. The variables in the labour market mobility

equation that are of primary interest are housing tenure, regional characteristics and

employment status. The coefficients suggest that mortgage holders are as likely to change

job status as local authority tenants, more likely to than outright-owners, and less likely to

than individuals renting from housing associations or privately. It therefore appears that

mortgage holders have least residential mobility in 1990s Britain, and more labour market

mobility than outright owners only (statistically significant at the 15% level only). This

evidence would suggest that policies promoting home ownership are not the best mechanism

for encouraging residential or job mobility.

As expected, the unemployed are most likely to experience a labour market status change

(again this declines with duration). Although personal unemployment increases labour

market mobility, the regional unemployment rate has no impact on changing labour market

status.

Local vs Regional Moves

The definition of mover used previously considers all moves, including those within the

same local authority district. Table 12 presents a multinomial logit model for local (within a

local authority district), intraregional moves (between local authority districts but within a

within the same employer (a promotion, for example). The main results remain unchanged by focusing solelyon labour market participants.24 To further investigate the relationship between the error terms in the migration and labour market mobility,we have run the bivariate probit separately for regional, intraregional and local moves. The value for rho in allcases remains well determined, but its size falls with distance moved from 0.266 for regional moves to 0.033for local moves. From this we conclude that the relationship between job and house moves is stronger forregional moves.

23

region) and regional (between the standard regions), highlighting the differences in the

determinants of local and regional moves. The figures reported are the relative risk ratios

(RRRs) rather than the coefficients. Thus figures greater than (less than) one indicate a

higher (lower) risk of moving relative to not moving.

The unemployed are more likely to move, particularly between regions, again refuting the

argument that the unemployed are immobile. Regional moves can be seen as a response to

personal unemployment. Jackman and Savouri (1992) find that long-term unemployment

reduces out-migration from a region, illustrating the effect of a depressed local economy on

mobility. Our results are consistent with this. Each month of unemployment reduces the

probability of moving from a region by 2.8%. Manual workers have lower probabilities of

moving, especially long distance (by 40%). Mobility of all types is increasing in household

income — those with greater wealth are able to overcome the higher costs of (long) distance

moves.

Living in a council house has a positive effect on local and, to a lesser extent, intra-regional

moves relative to mortgagees, but has no significant effect on inter-regional moves. Hughes

and McCormick (1991) find a negative relationship between local authority tenancy and

regional moves, and blame the council house waiting list system for low regional migration

rates. Private renters have greater local mobility than mortgage holders (by a factor of 5).

They also have significantly higher rates of within and between region migration (by factors

of 5 and 4 respectively). Mortgage holders therefore have the least local and within region

mobility, and are less likely to migrate between regions than private renters.

The number of persons per room has a positive and significant effect on the probability of

moving. Perhaps unsurprisingly, households that out-grow their accommodation move

locally, while the probability of any move falls with the duration at an address. In particular,

each year at an address reduces the probability of moving regions by almost 5%. Family

characteristics have most impact on local moves. Unmarried adults (with or without

children) are more likely to move locally than couples with children (by 45%). Larger

24

families have the least mobility, although again it appears to be older children (aged 10 and

above) that restrict residential moves, particularly locally and within regions.

Reasons for moving are positively associated with all moves (although wanting to move for

health reasons has no significant impact on intra-regional moves). Wanting to move for job

reasons has the largest quantitative impact on the probability of moving between and within

regions, increasing the probability by factors of 8 and 6 respectively. A desire to move

motivated by employment reasons has the single largest effect on the probability of moving

between regions.

Conclusions

This paper has focused on the relationship between individuals residential mobility

preferences and behaviour, their choice of housing tenure and labour market status in Britain

in the 1990s. Panel data from the BHPS provide accurate information on the timing of house

moves, preferences regarding moving, their labour market status and on a wide range of

demographics both before and after any change of address. Our analyses of these data show

that the BHPS provides representative data for Britain on housing tenure and labour market

status in Britain in the 1990s.

Approximately 10% of individuals aged between 16 and 55 (excluding students and the

retired) move house every year. This proportion has remained stable across the decade. Two

thirds of moves occur within local authority boundaries, 18% across regional boundaries.

The paper has also revealed a substantial proportion of individuals who would prefer to

move away from their current residence. These individuals are three times more likely to

subsequently move than those expressing no desire to move.

Mortgage holders do not have high levels of labour market or residential mobility relative to

other tenures. This finding may reflect the general situation of the housing market in the

early 1990s, with low mobility in general and a large number of low income house

purchasers who became caught in the negative equity trap. Policies promoting home

25

ownership might not be the most efficient or effective mechanism for promoting residential

and labour market mobility in Britain in the 1990s.

Clear relationships between employment status and residential mobility emerge. Changing

job or employment status and moving house are positively correlated. A desire to move

house motivated by employment related reasons has the single largest impact on the

probability of moving between regions. Manual workers are less likely to move, even when

controlling for a wide range of other demographic and local labour market characteristics.

Perhaps most important for policy purposes is the finding that the unemployed are more

likely to move than employees. This supports the classical economic hypothesis that

individuals move to escape unemployment, and suggests that the unemployed are not

immobile. However, the probability of regional migration declines with unemployment

duration — the long term unemployed are less likely to migrate to find work. Therefore

policies aimed at getting the long term unemployed back into work could have a

considerable impact on the degree of residential mobility in Britain.

26

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29

Table 1: Housing tenure and Employment status by wave, BHPS Waves 1-7(Column percentages)

Housing tenure Wave Mean1 2 3 4 5 6 7

Owned outright 10.7 10.4 11.6 11.1 11.5 11.2 11.1 11.0Owned mortgage 63.0 63.6 62.6 63.3 63.3 63.2 63.6 63.2Local authority rent 18.1 17.8 17.6 17.5 16.5 17.1 16.6 17.3Other rent 8.2 8.3 8.2 8.1 8.7 8.5 8.7 8.4Employment statusSelf-employed 9.9 9.6 9.7 9.1 9.5 9.7 9.2 9.6Employment 68.4 66.8 67.2 68.1 69.3 69.4 71.4 68.7Unemployment 7.3 8.4 7.9 7.2 5.8 5.8 4.9 6.8Other inactive 14.4 15.2 15.1 15.6 15.4 15.1 14.5 15.0

Note: BHPS. Cross-sectional weights. N(housing tenure)=40,048, N(employment status)=40,117.Figures not corrected for household size.

Table 2: Employment status by tenure, BHPS waves 1-7

(Column percentages)

Housing tenure Job statusSelf-employed Employed Unemployed Other

Owned outright 14.2 10.6 10.7 11.4Owned with a mortgage 67.9 69.2 36.1 45.0Local authority 8.6 12.0 40.8 36.7Other rented 9.3 8.2 12.5 7.0Total 9.5 68.7 6.8 15.0

Note: BHPS. Cross-sectional weights, N=40,023.

Table 3: National, regional and local moves, BHPS waves 1-7

All moves BetweenRegions

Between LAdistricts but

within regions

Within LocalAuthorityDistricts

Number of movers t+1 3198 560 522 2116Per cent 10.2 1.8 1.7 6.8Per cent of moves 100 17.5 16.3 66.2N=30,814.

Note: BHPS. Movers are defined on a wave-on-wave basis. Weighted using cross-sectional weights.

Table 4: Preferences to move and actual moving, BHPS waves 1-7

All Percentage who moved Percentage of moversPrefers to stay 56.3 5.6 30.6Prefers to move 43.7 15.9 69.4

Note: BHPS. N=30,473. Cross-sectional weights

30

Table 5: Main reason for wanting to move by actual move, BHPS waves 1-7

Reason Per cent Per cent moved Per cent of moversHousing 39.0 19.3 47.1Health 1.7 12.7 1.3Area 42.8 12.2 32.8Job 2.8 26.7 4.7Family 8.4 15.5 8.2Other 5.4 17.7 5.9Total 100 15.9 100

Note: Only respondents who stated that they would like to move. Cross-sectional weights, N=13,419.Last column reports only movers with a preference to move.

Table 6: Housing tenure and employment status prior to move, BHPS waves 1-7

Housing tenure Per cent who wantto move

Per cent who move Per cent of allmovers

Owned outright 38.8 7.4 7.7Owner with mortgage 42.4 7.4 46.7Local Authority 50.0 11.6 19.6Other rented 54.8 34.0 26.0Total 44.3 10.2 100Employment statusSelf-employed 43.1 8.8 8.3Employed 44.0 9.9 66.2Unemployed 50.8 14.6 9.4Other 43.7 10.9 16.0Total 44.3 10.2 100

Note: BHPS. Cross-sectional weights. N(housing tenure)=31,140. N(employment status)=31,000.

Table 7: Change in employment status for movers and non-movers,BHPS waves 1-7

(Row percentages)

Wave t Wave t+1Movers Self-employed Employed Unemployed OtherSelf-employed 73.2 19.7 5.9 1.3Employed 2.1 88.5 4.5 4.9Unemployed 5.8 32.0 43.1 19.1Other 3.2 17.0 6.8 73.0Non-moversSelf-employed 83.5 10.9 2.4 3.1Employed 1.8 92.7 2.4 3.1Unemployed 5.8 31.8 45.9 16.5Other 1.5 15.3 4.3 78.9

Note: BHPS. Cross-sectional weights, N(movers)=3,187; N(non-movers)=28,022.

31

Table 8: Tenure transitions for movers

(Row Percentages)

Wave t Wave t+1Owned outright Owned with mortgage Local Authority Other rented

Owned outright 23.6 47.1 7.2 22.2Owned with mortgage 4.8 68.9 4.8 21.5Local Authority 0.8 14.4 69.9 14.9Other rented 2.1 38.7 11.8 47.4

Note: BHPS. Cross-sectional weights. N= 3,154 movers.

Table 9: Reasons for moving by wave, BHPS waves 1-7

Wave Total2 3 4 5 6 7

Per cent movers who moved for job-relatedreason

14.2 11.4 13.5 14.1 13.3 13.5 13.4

Job-related reason for movinga

Employer moved job to another workplace 7.3 10.3 6.7 5.2 6.3 5.4 6.7Different job with same employer 13.9 16.2 8.8 19.3 11.8 15.4 14.2New job with new employer 31.9 34.9 33.9 29.2 38.3 28.6 32.6Nearer work, same workplace 30.0 16.2 17.6 19.9 17.6 28.5 22.0Started own business 3.9 10.2 6.8 3.4 3.5 6.7 5.6Relocated own business 4.9 1.0 10.4 0.0 3.1 3.0 3.9Salary increase and moved home 0.0 4.3 9.0 5.0 1.3 7.3 4.6Moved to look for work 13.9 8.1 5.9 7.5 11.4 5.6 8.6Other job-related reason 17.9 5.2 12.5 10.6 9.1 7.1 10.8Non-job related reason for movingb

Accommodation reasons 37.4 37.2 41.7 36.6 38.0 40.4 38.6Partnership reasons 16.4 17.1 13.1 14.4 14.7 16.8 15.4Eviction 10.1 7.0 6.5 8.4 6.9 7.1 7.7Family/friends 6.6 6.8 7.2 9.9 8.1 7.4 7.6Area reasons 7.5 11.4 9.3 11.4 11.6 8.1 9.8College 1.4 0.1 0.2 0.7 0.6 0.0 0.5Employment 7.8 6.5 9.1 5.9 5.5 6.5 6.9Health reasons 3.5 3.4 3.0 1.7 3.2 3.5 3.1Other 7.2 8.5 8.9 8.7 10.0 9.1 8.7Number of movers 519 423 492 448 447 525 2854

Note: BHPS. Cross sectional weights. a Column percentages do not add to 100 because reasons are notmutually exclusive. b Column percentages do not add to 100 because respondents did not have toprovide a non-job related reason for moving.

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Table 10: Estimation results from probit models for moving residence

Moving=1 Pooled Random EffectsCoef t-stat Marg.

EffectMean Coef t-stat

Family (couple with children omitted)Couple, no child 0.046 0.90 0.007 0.31 0.048 0.89Single 0.149 2.54 0.023 0.23 0.170 2.77Single parent 0.141 2.53 0.023 0.05 0.144 2.43Number of children -0.073 3.90 -0.011 0.81 -0.081 4.000<=age youngest child<6 0.083 1.77 0.013 0.23 0.100 2.046<=age youngest child<10 -0.033 0.59 -0.005 0.09 -0.030 0.5210<=age youngest child<16 -0.206 4.09 -0.027 0.13 -0.211 4.10Marital status changed during last 12months

0.146 3.78 0.023 0.06 0.142 3.58

Employment status (employed omitted)Self-employed 0.046 1.17 0.007 0.09 0.052 1.25Unemployed 0.102 2.08 0.016 0.06 0.092 1.81Other job status 0.003 0.08 0.000 0.15 0.005 0.14Household income (log) 0.097 4.85 0.014 7.41 0.102 5.23Duration of unemployment spell, ifunemployed (months)

-0.002 1.58 -0.000 1.44 -0.002 1.49

Manual worker -0.125 4.68 -0.018 0.31 -0.130 4.61Housing tenure (mortgage holders omitted)

Outright owner 0.132 3.56 0.021 0.10 0.136 3.28Local Authority 0.215 4.67 0.035 0.14 0.223 4.65Housing Association 0.338 5.11 0.061 0.03 0.349 5.10Renter 0.882 19.39 0.204 0.08 0.934 19.83Number of persons per room 0.312 8.39 0.046 0.73 0.350 8.69Tenure at current address (in years) -0.018 8.06 -0.003 7.72 -0.012 5.38

Moving preferencesWanted to move for

Housing reasons 0.631 24.68 0.122 0.17 0.685 24.29Health reasons 0.681 5.92 0.152 0.01 0.728 6.22Area reasons 0.415 15.12 0.073 0.18 0.458 15.63Job reasons 0.779 10.51 0.182 0.01 0.847 11.22Family reasons 0.506 9.98 0.101 0.04 0.556 10.68Other reasons 0.674 11.60 0.148 0.02 0.734 12.00

Regional characteristicsRegional unemployment rate 0.692 1.47 0.110 0.08 0.677 1.33Regional houseprice index 0.335 1.82 0.049 1.04 0.368 1.90

Within subject correlation 0.103 14.25N (Mover) 33519 (3754) 7797 personsPseudo R2 0.162Log-likelihood (χ2) -9844.8 (2990) -9813.2 (3315)Note: BHPS. All variables measured at wave before the move. Standard errors corrected for multiple observations. Householdincome is at January 1997 prices. Moving includes all house moves. Estimation included variables for gender, age, ethnicity andsocio-economic characteristics - estimation results are given in the Appendix, Table A1.

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Table 11: Estimation results from bivariate pooled probit for moving andchanging job (t-statistics in italics)

Moving house=1 Changing job=1Family (couple with children omitted)

Couple, no child 0.040 0.79 0.099 2.29Single 0.162 2.74 0.009 0.18Single parent 0.155 2.78 0.036 0.71Number of children -0.074 3.83 -0.025 1.550<=age youngest child<6 0.090 1.92 0.076 1.856<=age youngest child<10 -0.048 0.86 0.085 1.8710<=age youngest child<16 -0.216 4.27 0.072 1.80

Employment statusSelf-employed — — -1.287 26.58Unemployed 0.167 3.57 — —Employed — — -0.992 25.01Other status — — -0.954 21.23Earnings, if employed (log)* — — -0.003 2.26Household income (log) 0.117 5.83 -0.010 0.60Duration of employment spell, if employed(in months)

— — -0.004 15.50

Duration of unemployment spell, ifunemployed (in months)

-0.002 1.93 -0.002 2.48

Manual worker — — 0.074 3.36Housing tenure (mortgage holders omitted)

Outright owner 0.133 3.53 -0.047 1.44Local Authority 0.227 4.92 -0.003 0.09Housing Association 0.330 4.94 0.094 1.71Renter 0.894 19.42 0.095 2.58Number of persons per room 0.266 7.23 0.013 0.38Tenure at current address (in years) -0.018 8.03 — —

Moving preferencesWanted to move for

Housing reasons 0.633 24.65 — —Health reasons 0.650 5.57 — —Area reasons 0.412 14.94 — —Job reasons 0.795 10.75 — —Family reasons 0.496 9.73 — —Other reasons 0.670 11.53 — —

Regional characteristicsRegional unemployment rate 0.900 1.89 0.379 0.94Regional average house price (index) 0.368 1.98 -0.009 0.06

rho 0.114 8.31N (Movers, jobchangers) 32520 (3642, 9320)Log-likelihood (χ2) -27,410.3 (5211)Note: BHPS. All variables measured at wave before the move. Standard errors corrected for multiple observations.Jobchange includes promotions. Estimation included variables for gender, age, ethnicity and socio-economiccharacteristics - estimation results are given in the Appendix, Table A3. * Individuals not working are given a valueof zero.

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Table 12: Estimation results from multinomial pooled logit for regional,intraregional and local moves (t-statistics in italics)

Local Intraregional RegionalRelative

Risk Ratiot-stat RRR t-stat RRR t-stat

Family (couple with children omitted)Couple, no child 1.139 1.14 1.224 0.95 0.821 0.84Single 1.416 2.68 1.219 0.78 0.942 0.22Single parent 1.455 3.17 1.366 1.28 0.629 1.66Number of children 0.865 3.58 0.876 1.36 0.813 2.320<=age youngest child<6 1.255 2.34 1.001 0.00 1.158 0.696<=age youngest child<10 0.921 0.66 0.678 1.48 1.246 0.9110<=age youngest child<16 0.671 3.51 0.570 2.46 0.743 1.33

Employment status (employed omitted)Self-employed 1.076 0.80 1.226 1.30 0.937 0.39Unemployed 1.172 1.56 1.220 0.94 1.781 2.68Other job status 1.012 0.14 0.982 0.11 0.994 0.04Household income (log) 1.135 2.73 1.339 3.72 1.351 3.80Duration of unemployment spell, ifunemployed (in months)

0.998 0.85 0.993 0.88 0.972 1.83

Manual worker 0.884 2.08 0.669 3.34 0.592 4.18Housing tenure (mortgage omitted)

Outright owner 1.294 2.97 1.799 3.84 0.940 0.37Local Authority 1.666 5.04 1.338 1.42 1.019 0.08Housing Association 2.127 5.41 1.719 1.86 1.366 0.99Renter 5.105 16.36 4.713 8.87 3.851 7.62Number of persons per room 2.075 10.03 1.240 1.21 1.325 1.89Tenure at current address (years) 0.967 6.414 0.960 3.91 0.954 4.52

Moving preferencesWanted to move for

Housing reasons 3.437 22.00 3.764 12.13 2.142 6.76Health reasons 3.883 5.44 0.929 0.07 4.586 3.25Area reasons 2.015 11.43 2.678 8.47 2.559 8.30Job reasons 2.351 4.88 6.328 8.05 8.447 10.70Family reasons 2.026 6.05 3.876 7.35 3.757 7.41Other reasons 2.707 7.47 5.988 8.94 4.836 8.21

Regional characteristicsRegional unemployment rate*10 1.445 1.28 1.286 1.28 1.013 0.06Regional average house price(index)

1.479 0.95 3.221 1.60 2.949 1.42

N movers 2481 636 637Pseudo R2 0.146Log-likelihood (χ2) -12841.2 (32981)Note: BHPS, comparison group is “not moved” (N=29765). Total sample size = 33519. All variables measured at wave beforethe move. Standard errors corrected for multiple observations. Jobchange includes promotions. “Local moves” are moves withina local authority boundary, intraregional moves are moves outside a local authority boundary but within the same region.Regional moves are moves that involve crossing a regional boundary. Estimation included variables for gender, age, ethnicityand socio-economic characteristics - estimation results are given in the Appendix, Table A2.

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Appendix

Table A1: Estimation results from probit models for moving residence

Moving=1 Pooled Random EffectsCoef t-stat Marg.

EffectMean Coef t-stat

Female 0.175 0.73 0.003 0.52 0.021 0.84Age (aged under 30-39 omitted)

Aged 16-22 0.179 3.66 0.029 0.06 0.158 3.24Aged 23-29 0.211 7.46 0.034 0.21 0.221 7.47Aged 40-49 -0.241 7.45 -0.033 0.28 -0.280 8.17Aged 50 or over -0.402 8.35 -0.048 0.13 -0.468 9.17

Ethnicity (white omitted)Afro-caribbean -0.153 1.38 -0.020 0.01 -0.171 1.44Indian -0.516 5.17 -0.052 0.01 -0.555 4.21Pakistani/Bangladeshi -0.282 1.68 -0.034 0.004 -0.284 1.67Other non-white -0.396 2.85 -0.044 0.01 -0.433 3.16

Education (no qualifications omitted)Higher education 0.098 2.22 0.015 0.11 0.120 2.55`A’-level 0.005 0.14 0.001 0.34 0.011 0.32`O’-level -0.026 0.71 0.004 0.24 -0.024 0.67Basic education -0.006 0.13 0.001 0.11 -0.009 0.20

Housing tenureHouse value if owner (log)* 0.005 1.56 0.001 6.95 0.005 1.39Original mortgage > current house value 0.172 1.80 0.028 0.01 0.117 1.20

Region (London omitted)Southeast 0.247 5.73 0.041 0.19 0.263 5.68Southwest 0.244 4.71 0.041 0.09 0.265 4.86Midlands 0.153 3.60 0.024 0.21 0.162 3.58North 0.155 3.62 0.024 0.26 0.164 3.60Wales 0.077 1.24 0.012 0.05 0.077 1.15Scotland 0.182 3.50 0.030 0.09 0.201 3.60

Spouse has a job -0.125 3.71 -0.019 0.58 -0.128 3.55Spouse changed job 0.050 1.01 0.008 0.04 0.056 1.10Time to work (in 15 min) 0.004 0.50 0.001 1.08 0.005 0.57Fixed-term contract 0.061 1.15 0.009 0.03 0.060 1.08Temporary contract 0.056 1.05 0.008 0.03 0.053 0.90Difficulties paying for housing (1=yes) 0.033 1.05 0.005 0.12 0.036 1.11Constant -3.024 10.87 -3.266 11.39Within subject correlation 0.103 14.25N (Mover) 33519 (3754) 7797 personsPseudo R2 0.162Log-likelihood (χ2) -9844.8 (2990) -9813.2 (3315)Note: BHPS. All variables measured at wave before the move. Standard errors corrected for multiple observations. Householdincome is at January 1997 prices. Moving includes all house moves. * Non owner-occupiers are given a value of zero.

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Table A2: Estimation results from bivariate pooled probit for moving andchanging (t-statistics in italics)

Moving house=1 Changing job=1Female 0.042 1.85 0.068 3.34Age (aged 30-39 omitted)

Aged 16-22 0.153 3.12 0.347 8.23Aged 23-29 0.212 7.49 0.141 5.61Aged 40-49 -0.245 7.49 -0.117 4.67Aged 50 or over -0.406 8.32 -0.206 6.00

Ethnicity (white omitted)Afro-caribbean — — -0.039 0.38Indian — — -0.028 0.38Pakistani/Bangladeshi — — -0.392 2.56Other non-white — — -0.022 0.19

Education (no qualifications omitted)Higher education — — 0.161 4.09`A’-level — — 0.163 5.52`O’-level — — 0.065 2.17Basic education — — 0.057 1.55

Housing tenureHouse value if owner (log)* 0.005 1.53 0.001 0.68Original mortgage > current house value 0.193 2.02 — —Difficulties paying for housing (1=yes) 0.016 0.52 0.114 4.47

Region (London omitted)Southeast 0.255 5.95 0.104 2.76Southwest 0.261 5.02 0.146 3.30Midlands 0.151 3.56 0.070 1.92North 0.164 3.86 0.020 0.54Wales 0.082 1.31 0.024 0.46Scotland 0.192 3.68 -0.010 0.22

Spouse has a job -0.121 3.54 0.050 1.76Spouse changed job 0.055 1.09 0.105 2.70Time to work (in 15 min) 0.008 1.01 0.029 4.36Fixed-term contract — — 0.507 11.58Temporary contract — — 0.826 18.49Constant -3.250 11.63 0.149 0.69rho 0.116 8.49N (Movers, jobchangers) 32520 (3642, 9320)Log-likelihood (χ2) -27,400.1 (5222)Note: BHPS. All variables measured at wave before the move. Standard errors corrected for multiple observations.Jobchange includes promotions. * Non owner-occupiers are given a value of zero.

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Table A3: Estimation results from multinomial pooled logit for regional, intraregional andlocal moves (t-statistics in italics)

Local Intraregional RegionalRelative

RiskRatio

t-stat RRR t-stat RRR t-stat

Female 1.031 0.57 1.077 0.73 1.093 0.89Age (aged 30-39 omitted)

Aged 16-22 1.512 4.14 1.145 0.65 1.185 0.79Aged 23-29 1.459 6.17 1.415 3.10 1.381 2.89Aged 40-49 0.626 5.92 0.601 3.59 0.618 3.44Aged 50 or over 0.469 6.41 0.360 4.59 0.371 3.72

Ethnicity (white omitted)Afro-caribbean 0.681 1.48 0.819 0.47 0.984 0.04Indian 0.575 2.60 0.113 2.25Pakistani/Bangladeshi 0.556 1.51 0.348 1.01 0.825 0.30Other non-white 0.469 2.05 0.560 1.15 0.343 1.90

Education (no qualifications omitted)Higher education 1.031 0.30 1.449 1.91 1.637 2.52`A’-level 0.958 0.56 1.175 0.97 1.247 1.32`O’-level 0.934 0.86 1.175 0.94 0.874 0.75Basic education 1.058 0.62 0.819 0.90 0.836 0.74

Housing tenureHouse value if owner (log)* 1.009 1.11 1.006 0.46 1.019 1.34Original mortgage>current house val. 1.246 0.98 1.279 0.66 1.692 1.52Difficulties paying for housing(1=yes)

1.025 0.36 1.156 1.18 1.059 0.42

Region (London omitted)Southeast 2.277 7.83 1.847 3.70 0.591 3.48Southwest 2.200 6.25 1.835 3.04 0.746 1.60Midlands 2.008 6.81 1.173 0.95 0.612 3.18North 2.122 7.22 0.766 1.48 0.706 2.36Wales 2.053 5.07 0.591 1.51 0.309 3.79Scotland 2.174 6.34 1.602 2.34 0.368 4.33

Spouse has a job 0.841 2.34 0.718 2.17 0.665 2.66Spouse changed job 0.964 0.30 1.157 0.75 1.481 2.15Time to work (in 15 min) 0.991 0.45 1.035 1.38 1.016 0.50Fixed-term contract 0.982 0.15 1.074 0.37 1.528 2.42Temporary contract 1.110 0.91 1.193 0.85 1.131 0.57N movers 2481 636 637Pseudo R2 0.146Log-likelihood (χ2) -12841.2 (32981)Note: BHPS, comparison group is “not moved” (N=29767). Total sample size = 33519. All variables measured at wave before the move.Standard errors corrected for multiple observations. Jobchange includes promotions. “Local moves” are moves within a local authorityboundary, intraregional moves are moves outside a local authority boundary but within the same region. Regional moves are moves thatinvolve crossing a regional boundary. * Non owner-occupiers are given a value of zero.