the dynamics of housing deprivation

JOURNAL OF

Journal of Housing Economics 16 (2007) 72–97

HOUSINGECONOMICS

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The dynamics of housing deprivation

Luis Ayala a,*, Carolina Navarro b

a Facultad de Ciencias Jurıdicas y Sociales, Universidad Rey Juan Carlos,

Paseo Artilleros s/n, 28032 Madrid, Spainb Departamento de Economıa aplicada y Gestion Publica, Facultad de Ciencias Economicas y Empresariales,

Universidad Nacional de Educacion a Distancia, Paseo Senda del Rey 11, 28040 Madrid, Spain

Received 12 February 2005Available online 13 April 2007

Abstract

This paper aims to present an assessment of the dynamics of housing deprivation through latent var-iable models taking Spain as reference. The first five waves of the European Community HouseholdPanel (ECHP) are used to analyze the nature and extent of the persistence of housing deprivationand the determinants of the flows into and out of this state. Discrete time duration models are estimatedto identify which households have a higher risk of suffering housing deprivation on a persistent basis.Our results show that almost half the households have gone through some kind of housing deprivationduring the period under study while in cross-sectional studies only a 20% of population appears to havedone so. The results suggest not only that there are groups running a greater housing deprivation riskbut also that some face a greater probability of being in this state on a persistent basis.� 2007 Elsevier Inc. All rights reserved.

JEL Classifications: I31; R21; C41

Keywords: Housing deprivation; ECHP; Latent class model; Discrete time hazard proportional model

1. Introduction1

The study of living standards has received increasing attention over the last few years. Oneof the distinctive traits of this line of research has been the predominance of static approaches

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doi:10.1016/j.jhe.2007.03.001

* Corresponding author. Fax: +34 91532796.E-mail addresses: [email protected] (L. Ayala), [email protected] (C. Navarro).

1 The authors acknowledgefinancial support fromthe Inter-ministerial Commission on ScienceandTechnology (GrantSEJ2004-07373-c03-03) and the Instituto de Estudios Fiscales. We are also grateful to John Ermisch, Stephen Jenkins,Chetti Nicoletti, Mark Taylor and two anonymous referees, for their valuable comments and help on specific issues.

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mailto:[email protected]

L. Ayala, C. Navarro / Journal of Housing Economics 16 (2007) 72–97 73

to examine the determinants of multiple deprivation. A rapidly expanding literature, however,has focused on the political and social implications arising when a dynamic standpoint is incor-porated into the study of social deprivation. As stressed by Jarvis and Jenkins (1998), while thelengthening of these deprivation spells requires the development of structural policies, shorter-term measures may actually turn out to be sufficient to deal with temporary deprivation.

One of the determining conditions for a household’s material well-being is the availabil-ity of a stock of commodities enough to satisfy a set of basic needs. Although there arevarious dimensions that could be included in a possible inventory of basic commodities,housing is undoubtedly one of the most relevant necessities. However, the study of hous-ing conditions has paid relatively little attention to the multidimensional analysis of hous-ing deprivation. Data on how households are equipped are normally insufficient, andremarkable difficulties are encountered in developing indicators that can accurately reflectthe different dimensions of what could be considered to be housing deprivation.

The definition of a housing deprivation index involves both selecting an aggregationmethod and setting a deprivation threshold. Aggregation methods vary from the simple sum-ming up of lacking commodities to more complex multivariate analysis techniques. Variousdifficulties are encountered when trying to obtain objective indices. Nevertheless, the mainconstraint lies mainly in the recurrent arbitrariness in the setting of deprivation thresholds.In this paper, we use latent variable models to address both issues. These models synthesize agroup of indicators under a single index and provide the possibility of assigning individualsto different classes depending on the number and types of housing deficiencies they suffer.

A central issue for the analysis of housing deprivation is the extent to which this situ-ation changes with time. The number of studies dedicated to the dynamics of housingdeprivation is still small,2 and most of the studies available focus on prices and tenurerather than housing conditions.3 Certain evidence also exists on the effect and persistenceover time of receiving housing assistance benefits (Hungerford, 1996), while other studieslink housing deprivation to the dynamics of certain urban areas.4 There is a need forresearch that provides a more complete picture of the factors specifically related to tran-sitions between the different states of housing deprivation.5

This paper aims at making a preliminary approach to the study of the dynamics of housingdeprivation through latent variable models by studying the case of Spain. The first five wavesof the European Community Household Panel (ECHP) are used to analyze the nature andextent of the persistence of housing deprivation and the determinants of the flows into and

2 Some papers analysing the dynamics of poverty and deprivation by using combined income and housingindicators have included certain housing conditions such as the lack of basic facilities and the presence ofstructural problems (Betti and Cheli, 2001; Betti et al., 2000; Whelan et al., 2001; Muffels and Fouarge, 2001; andApopospori and Millar, 2002).

3 Di Salvo and Ermisch (1997) analyzed the dynamics of housing tenure finding that earnings, family background,unemployment and housing prices exert a significant influence on tenure. Kan (2000) estimated a random effectsdynamic model on the choice of housing tenure finding a significant correlation with residential mobility.

4 Murie (2002) underlined that the concentrations of low-income households in certain areas or neighbour-hoods characterized by bad housing conditions were the result of entry barriers to the labor market, insufficientincome and the lack of choice in the housing market. Aaronson (2001) also looked into the dynamics of urbanareas finding ethnicity, income, prices and the quality of housing as the main determining factors. Quillian (2000)analysed residential duration in poor neighbourhoods and found that the probability of leaving a poor urban areadiminishes as residential duration in the area increases.

5 Previous results from Dale et al. (1996) showed that household composition, housing tenure, social class andbelonging to an ethnic minority are all determining factors of long-term housing deprivation.

74 L. Ayala, C. Navarro / Journal of Housing Economics 16 (2007) 72–97

out of this state. Discrete time duration models are also estimated to identify which house-holds have a higher risk of suffering housing deprivation on a persistent basis.

Spain is an interesting case among OECD countries regarding housing markets andpolicies. First, the growth of housing prices has been even higher than in other Europeancountries. Second, the type of housing policies adopted has some singularities. And third,tenure choice patterns also differ from other countries. In recent years there has been muchpolicy discussion of the impact of public action on low-income housing-markets. In Spain,government housing subsidies are neither a major source of financial support for low-income families nor is there a substantial programme of publicly subsidized rented hous-ing. Only a low proportion of households receive some sort of public income assistancerelated to housing, and despite the fact that there are public-owned homes for low-incomeresidents, the proportion of families living in them is very low. In practice, home mortgagedeductions in income taxes have become the core of public action in this area. This modelhas raised many controversies as low-income households do not usually benefit from thesedeductions. Spain also has one the highest proportions of home owners in the EuropeanUnion. As in many other European countries, there has been a clear increasing trend ofhome ownership rates.6 The fall of mortgage interest rates and substantial fiscal advanta-ges for owners compared to renters have strengthened this trend. An outstanding featureof the Spanish housing market is that the total number of homes being built has dramat-ically grown. However, new house building has not been enough to meet demand. Addi-tionally, low interest rates in Spain have helped to maintain the rise in housing demand,boosted by a steady increase in mortgage loans. Fuelled by low interest rates, the valueof housing loans taken out by Spanish residents has risen at double-digit rates. As a result,housing prices in Spain have grown by double figures since mid 90s and house price toincome ratios has significantly worsened over the past 20 years.

The structure of the paper is as follows. Section 2 introduces the notion of housingdeprivation and its dynamics. The data and methodological choices are then reviewedand a preliminary approach to the persistence of housing deprivation in Spain is made.The determinants of the probability of escaping from the state of housing deprivationare analysed in the Section 4. Special emphasis is placed on studying re-entries into thissituation. The paper ends with a brief list of conclusions.

2. The notion and measurement of housing deprivation

Housing deprivation is often assumed to be an accumulation of some deficiencies inbasic housing conditions. Such definition first entails identifying a dwelling’s basic or min-imum conditions. A second step is to aggregate these conditions into a synthetic indicator.Lastly, some kind of threshold is necessary to classify the population according to whetheror not it suffers from housing deprivation.

Regarding the first of these questions, there is an extensive literature on the possible cri-teria available in order to choose the basic dimensions of household or individual well-being. According to the principal lines of research, the choice of indicators may be madedepending on what the population considers necessary (Mack and Lansley, 1985), thecharacteristics or commodities most commonly possessed in society (Desai and Shah,

6 According to the Spanish Family Expenditure Survey the homeownership rate increased from 68% in 1980 to78% in 1990 and to 82% in the late 90s (Ahn, 2001).


1988) or other essential features of individual well-being. Following a synthetic approach,the choice of indicators can be made by taking into account three criteria: the correlationbetween income and housing conditions, the number of people lacking these conditionsand the effects on the individuals’ health. Applying these criteria to the ECHP data, thedifferent housing deprivation dimensions can be grouped together under the insufficiencyof basic facilities (hot running water, heating and overcrowding7) and structural problems(leaky roof, damp walls/floors and rot in window frames and floors).

Constructing a synthetic indicator appears to be a suitable solution in order to have anoverall interpretation of multiple deprivation. The main arguments supporting the con-struction of a deprivation index instead of employing a wide range of indicators are thegreater capacity to arouse public awareness and to transmit information, in addition tobeing more efficient when it comes to implementing public policies that attempt to mitigatesuch situations.8 Hence, the need arises to develop aggregation procedures that wouldallow the different variables chosen to be summed up by one index. The expanding liter-ature on the multidimensional analysis of living conditions has given rise to a growingavailability of different methods of establishing weighting systems.9

Multivariate analysis have become one of the most used techniques to construct syn-thetic deprivation indices. The main advantage of these methods lies in aggregating the dif-ferent living conditions (in this case housing conditions) into a single indicator, therebyminimising value judgements without completely eliminating them. Nevertheless, eventhough the problem of aggregating the different deprivation conditions can be solved,the difficulty of how to define a deprivation threshold without falling into subjective valuejudgements still persists. Latent class models offer a suitable methodological framework tocounter the twofold problem posed by the aggregation of housing conditions and the set-ting of a threshold. Such models use multivariate analysis techniques to measure unob-servable concepts based on a set of observable variables. They allow the latent conceptof multiple housing deprivation to be measured through the various basic conditions thatcan be regarded as an insufficient manifestation of the latent structure of deprivation. Inaddition to offering a technique that can empirically assess and verify whether a specific setof indicators constitutes a suitable structure to measure the same latent concept, thesemodels allow a set of partial indicators on a single phenomenon to be synthesised undera single index. This index is based on the correlation of those components and their mutualdependence on the latent variable. These techniques are specially suitable for the nature of

7 Having a number of rooms less than the number of adults (older than 16 years of age) is used as anovercrowding indicator.

8 There are numerous studies in favor and against setting up a single indicator (Harker, 2001; Hills, 2001;Micklewright and Stewart, 2001).

9 These methods vary from simple processes of adding up the commodities not possessed by an individual tomore complex methods requiring the use of multivariate analysis techniques. There are approaches consisting ofcombining different deprivation indices, which are then aggregated on an individual basis (Townsend, 1979;Schokkaert and Van Ootegem, 1990; Hutton, 1991; Erikson, 1993; Callan et al., 1999; Martınez and Ruiz-Huerta,2000; Muffels and Fouarge, 2001; Layte et al., 2001; and Tsakloglou and Papadopoulos, 2002). Other approachesconstruct indices for each deprivation dimension for all individuals and then aggregate them collectively into asingle index (Bourguignon and Chakravarty, 1998; Bourguignon and Chakravarty, 2003; Duclos et al., 2001; andAtkinson, 2003). Some studies use main components analysis (Muffels and Vriens, 1991; Hutton, 1991; andKamanou, 2000). Callan et al. (1993) and Layte et al. (2001) applied factorial analysis. A less frequently usedalternative is the use of latent variable models (Rasch, 1960; Gailly and Hausman, 1984; Perez-Mayo, 2002; andNavarro, 2006). A different option is provided by the Fuzzy Sets theory (Cerioli and Zani, 1990; Cheli and Lemi,1995; and Chiappero, 1994, 1996, 2000 and Betti et al., 2000).


housing conditions (categorical variables) and allow different weightings to be assigned tothem. By applying the criteria described previously, the hypothetical structure of housingdeprivation could be made up of an insufficiency in hot running water, heating, over-crowding, a leaky roof, damp and rot in window frames and floors.

An additional advantage is the possibility of stratifying different forms of deprivation byassigning each individual to different classes depending on the number and types of housingdeficiencies she/he suffers. In this way, the arbitrariness of setting thresholds is partially over-come if deprived and non-deprived classes are identified. Despite the advantages of thesemodels, only recently have started being used for the analysis of social policies and materialwell-being (Bago d’Uva, 2005; Whelan and Maitre, 2005; Capellari and Jenkins, 2006).

Latent trait models are very similar to factor analysis but specifically applied toobserved dichotomous variables. The probability of a randomly chosen individual suffer-ing deprivation of observed condition xi can be modelled, given her position with regard tothe vector of latent variables y, P(xi = 1|y) = pi(y). This conditional probability can beexpressed as a linear function of the latent variables:

piðyÞ ¼ ai0 þ ai1y1 þ � � � þ aiqyq þ eI ; i ¼ 1; . . . ; p ð1Þ

It is to be expected that the rate of change in the probability of a positive response (depri-vation) is not the same for the whole range of y. A nexus linking probability and the latentvariables needs to be introduced in order to take this constrain into account. In our model,the latent variable is related to each observed housing condition through a logistic regres-sion model.

The latent variable obtained, which represents housing deprivation, can be discrete orcontinuous. If the latent dimension or space is considered continuous, a latent trait modelwill be estimated. If this latent space is considered as discrete, then a latent class model willbe estimated. The latent trait model is defined as follows:

logit piðyÞ ¼ logpiðyÞ

1� piðyÞ¼ ai0 þ

Xq

j¼1

aijyj ð2Þ

where

piðyÞ ¼exp ai0 þ

Pqj¼1aijyj

� �

1þ exp ai þPq

j¼1aijyj

� � ð3Þ

The probability of a randomly chosen individual suffering deprivation in one of the ob-served housing conditions can be defined on the basis of the j latent classes withj = 1, . . .,K, with K representing the number of classes:

pij ¼ Pðxi ¼ 1jjÞ; j ¼ 1; . . . ;K ð4ÞEach household has a prior probability gj of belonging to one of the j types of deprivationdefined, given that j = 1, . . .,K and

PKj¼1gj ¼ 1.

A possible latent class model for housing deprivation should have three components:

1. Prior probabilities gj, j = 1, . . .,K

2. The conditional probabilities of obtaining a positive response for an observed item xi,given latent class j, pij, where i = 1, . . .,p


3. And the joint distribution of all the observed items:

f ðx1; x2; . . . ; xpÞ ¼XK

j¼1

gjgðx1; x2; . . . ; xpjjÞ ¼XK

j¼1

gj

Yp

i¼1

pxiijð1� pijÞ1�xi ð5Þ

Estimating the parameters can be done through an EM algorithm in order to calculate themodel with unobserved variables.10 The assumption of conditional independence impliesthat the vector of latent variables is sufficient to explain all the associations among the hous-ing insufficiencies in each household. Individuals can be assigned into the identified classesbased on what they have responded. All the information concerning the assignment of indi-viduals to each latent class can be found in the posterior distribution of the latent classesaccording to the existence or absence of insufficiencies in the housing variables chosen:

P ðjjx1; . . . ; xpÞ; j ¼ 1; . . . ;K ð6Þ

The goodness of fit measures show how well the model fits the whole response pattern forthe individuals in the sample and what suitable number of classes there are. If we acceptthe results of the latent class model as a good fit to our data, individuals may be allocatedinto the identified classes. It must be noted that these models are not exempt from some ofthe drawbacks of multivariate analysis in order to provide an exact picture of multipledeprivation. First, using an index could throw away potentially useful information aboutthe severity of the items used. Second, there is not theoretical support to identify the num-ber of categories with different deprivation classes.

Despite these limitations, latent class models add novelties to the most generalizeddeprivation scales. These models allow us to respond to the two-fold problem of aggregat-ing housing dimensions and setting a deprivation threshold. Although there is not theoret-ical support to automatically identify the number of categories with different deprivationclasses, results can be interpreted in such a way that individuals with a certain probabilityof response for the different items can be concentrated in a specific class.11 Lastly, anaggregate index is a very helpful tool to identify groups at risk. These measures allowfor a better targeting of public policies on particular groups or areas.

3. The dynamics of housing deprivation in Spain

3.1. Data, methodological decisions and overall results

The development of the European Community Household Panel (ECHP) since 1993has made longitudinal information on household income and living conditions in differentEuropean countries available. The advantages offered by the longitudinal design of thissurvey are exceptional in order to analyse both the relationships among the differentdimensions of well-being, as well as the transitions between different social states. How-ever, the limitations that often affect this kind of longitudinal information cannot be
10 We use in this paper the EM algorithm proposed by Bartholomew and Knott (1999).11 For instance, the number of these classes could be defined in such a way that a specific class could include
individuals who have zero probability of positive response for all observed items. These situations could beinterpreted as not deprivation at all. On the other hand, a different class could include those individuals with ahigh probability of deficiencies in most of housing conditions. These situations could be interpreted as multipledeprivation.


disregarded. The extent of attrition in the ECHP has been confirmed in various empiricalstudies (Watson and Healy, 1999; Buck and McCulloch, 2001; Neukirch, 2002; Behr et al.,2002; Peracchi, 2002; Nicoletti and Peracchi, 2002; Rendtel, 2002).

In order to define a sample loss profile throughout the period under study in this paper,a binary logistic model was applied to estimate the likelihood of exiting the sample basedon the characteristics of the households and individuals interviewed in the first wave.Along the lines of the results obtained by other studies (Nicoletti and Peracchi, 2002; Per-acchi, 2002; Behr et al., 2002), our preliminary findings show that some population groupshave a greater probability of exiting the sample. Nevertheless, the low incidence of samplelosses affecting households lacking basic facilities or suffering any other kind of housingproblems stands out. The previously mentioned studies point out that attrition effectson the sample’s structure are relatively minor. Evidence also exists affirming that theuse of longitudinal weightings, which are also employed in this paper, contribute to cor-recting any possible biases.12

The data come from the ECHP waves for 1994, 1995, 1996, 1997 and 1998. Applyingthe latent variable model proposed in the previous section to the ECHP allows us to esti-mate the extent of housing deprivation throughout this period.13 More specifically, thevector of observed variables (x = (x1, . . .,xp)] is made up of those variables consideredas limiting factors for the basic functioning of a dwelling in accordance with the criteriamentioned above. These represent the lack of hot running water and heating, in additionto the existence of problems such as overcrowding, a leaky roof, damp walls or floor, androt in floor or window frames. After conducting a detailed study on the relationshipbetween a lack of central heating and weather conditions for different regions we considerthis fact does not necessarily mean being in a state of deprivation in Southern regions.14

Households that do not have central heating in these regions were considered as non-deprived. The results obtained from estimating the latent class model are shown in Table1. The estimated Pearson’s v2 statistic for combinations of two or three responses confirmsthe model’s goodness of fit.

These findings allow sorting the population into four classes depending on their levelsof housing deprivation.15 The estimated matrix p(pi1, pi2, pi3, y pi4) shows the probabilityof a randomly chosen household suffering deprivation in each one of the six housing indi-cators on the basis of its situation in the different latent classes.16 It can be seen that Class1 includes the households having the lowest probability of suffering housing deprivationgiven the small number of conditions for which they lack a favourable situation. On theother hand, households with the greatest probability of suffering multiple deprivationbelong to Class 4. It is also interesting to highlight the difference between the householdsincluded in Classes 2 and 3. The former includes households having a greater probability

12 Ayala et al. (2006) test different possibilities of correcting the attrition effects on longitudinal analyses with theECHP by means of alternative longitudinal weighting schemes. Their results show that different ways ofcorrecting the problem of selective attrition (unitary weights, Eurostat’s weights and probabilistic weights) yieldsimilar results.13 The number of the sample’s observations used when applying the latent variable model amounted to 31,190

households.14 Navarro and Ayala (forthcoming) test the sensitivity of the results to different specifications of central heating

as a housing problem.15 Some of the estimated parameters are close to zero and one. As De Menezes (1999) has shown, this does not

imply that the solutions obtained from the four-latent-class model cannot be used.16 A positive response for each of the indicators implies the existence of deprivation.

Table 1Classes of housing deprivation: conditional probabilities and prior probabilities

Items pi1 ¼ Pðx1 ¼ 1j1Þ pi2 ¼ P ðx2 ¼ 1j2Þ pi3 ¼ Pðx3 ¼ 1j3Þ pi4 ¼ P ðx41 ¼ 1j4ÞHot running water 0.005 0.142 0.023 0.294Heating 0.376 0.710 0.538 0.800Leaky roof 0.020 0.145 0.272 0.717Damp 0.000 0.001 0.892 0.948Rot in window frames/floors 0.010 0.107 0.167 0.686Overcrowding 0.050 0.088 0.060 0.111

g1 g2 g3 g4

0.701 0.067 0.180 0.052v2(34) = 77.9G2(34) = 79.1


of suffering a lack of basic housing facilities (such as hot running water, heating or space)than having structural problems. Households included in Class 3 have a very high prob-ability of suffering structural problems and a very small, almost negligible, probability oflacking basic facilities. From the estimated model it can be deduced that 70% of the house-holds included in the sample belong to Class 1 (g1), 7% to Class 2 (g2), 18% to Class 3 (g3)and 5% to Class 4 (g4).

We can therefore differentiate Class 1 households, which includes households not suf-fering from any kind of housing deprivation or suffering a minimal amount, from Class 2and 3 households with different forms of deprivation and class 4 households, which areaffected by multiple housing deprivation. Class 1 can be defined as the group of house-holds not affected by housing deprivation problems and those belonging to the other clas-ses as the ones that do indeed suffer it. We create a dummy variable that providesinformation on the presence or absence of housing deprivation in each household: house-holds belonging to classes 2, 3 and 4 are considered as suffering deprivation. In order toverify the sensitivity of the results, we also took into account another threshold thatrestricts the problem to the households belonging to classes 3 and 4.

The average level of housing deprivation throughout the five waves amounted toaround 24% of all households (Table 2). The level of housing deprivation fell over the per-iod under analysis. It is worth noting the increase in the number of Class 1 householdsthroughout the time period considered. The reduction in the number of households suffer-ing some kind of deprivation can be mainly attributed to the fall in the percentages ofClass 2 (a fall of 40%) and Class 4 households (40.5%). The reduction in Class 3 house-holds is also of importance, though to a lesser degree than that of the other classes(27.2%).

A relevant issue for possible political inferences is whether or not the extent and persis-tence of housing deprivation differs between populations groups. All the more so as hous-ing policies are usually targeted to specific types of households. Regarding the percentageof deprived households, an interesting question is whether or not households with childrenshow better results than households without children. Despite the fact that breaking downthe general model into the two types of households does not dramatically change the gen-eral picture of a decreasing tendency, the incidence of housing deprivation is considerablylower among households with children. This result could be explained by the fact thathousing subsidies are mainly targeted to couples with children. However, the low level

Table 2Percentage of deprived households and classes of housing deprivation

Year Deprived households (%) Classes

Threshold 1 Threshold 2 Class 1 Class 2 Class 3 Class 4 Total

Total1994 29.48 25.10 70.52 4.38 20.37 4.72 100.001995 23.63 19.46 76.37 4.17 15.92 3.54 100.001996 23.33 20.41 76.67 2.92 16.82 3.59 100.001997 23.81 20.86 76.19 2.94 17.44 3.42 100.001998 20.28 17.65 79.72 2.63 14.83 2.81 100.00

Households with children1994 27.07 23.41 72.93 3.66 19.82 3.59 100.001995 21.75 18.53 78.25 3.21 15.70 2.84 100.001996 21.54 19.37 78.46 2.17 16.27 3.09 100.001997 22.96 21.02 77.04 1.94 18.30 2.72 100.001998 18.65 16.74 81.35 1.91 14.45 2.29 100.00

Households without children1994 32.74 27.34 67.26 5.40 21.06 6.29 100.001995 26.79 20.99 73.21 5.79 16.26 4.73 100.001996 26.53 22.27 73.47 4.26 17.79 4.48 100.001997 25.26 20.59 74.74 4.67 15.96 4.63 100.001998 22.94 19.12 77.06 3.82 15.45 3.67 100.00

Note. Weighted data using cross-sectional weightings.


of benefits makes it hard to interpret them as a key resource for avoiding housing depri-vation. The assumption that these households value housing conditions more than house-holds without children could also be reasonable.

3.2. The persistence of housing deprivation

As aforementioned, a key issue in the analysis of housing deprivation is the extent towhich this situation changes with time. The dynamic analysis of housing deprivationrequires building a balanced panel based on the available five ECHP waves. The totalnumber of households interviewed in all the waves amounts to 4548. The samples usedin the econometric model include the households present in at least the first four waves.The data used in the event history analysis were rearranged so that each unit of observa-tion corresponds to the period in which each household is exposed to the risk of exitingfrom a situation of housing deprivation. Those cases in which deprivation could alreadybe observed in the first ECHP wave were excluded from the model.

A recurrent problem suffered by this kind of analysis lies in determining whether theevent or transition is ‘‘genuine’’.17 Given the fact that some observed transitions may sim-ply be reflecting measurement errors or sporadic changes, it turns out to be difficult todefine if a specific transition is sufficiently relevant. In the case of income poverty, Jenkins(2000) defines any movement involving a change of at least 10% above or below the pov-erty line as an effective transition. In the case of housing deprivation there may be changes
17 Most of the papers analyzing income poverty dynamics suggest that there is a great deal of mobility regarding
entries into and exits from such states (Jarvis and Jenkins, 1997; Devicienti, 2001; Stevens, 1999).


resulting from small temporary reforms that attempt to provide a response to a dwelling’sstructural problems. The insufficiency of such actions means that the initial state will onceagain be observed in the period following the transition. The criterion followed for theeconometric estimation of the hazard function was to consider any exit spells with a sub-sequent re-entry within a year as also being in a state of deprivation.

Persistent housing deprivation is defined as undergoing such a situation for four ormore waves. Thus, different categories can be established, including: households that donot suffer any kind of deprivation at all in any of the years considered; households thatsuffer deprivation between 1 and 3 years, which can be interpreted as temporary depriva-tion; and households that undergo a situation of deprivation throughout the period, whichcan be considered as persistent deprivation.

Table 3 reflects the results of an initial descriptive approach to the dynamics of housingdeprivation. First, it is striking to note that almost half the households have undergone asituation of housing deprivation at some time (almost 49% and 44%, respectively, depend-ing on whether we are dealing with the first or the second threshold). Such figures contrastwith the findings of the static analysis in which the average level of housing deprivationamounted to 24% and 20% of the households, respectively.

The second outstanding trait is the presence of a significant percentage of householdsthat suffer from housing deprivation four or more years (13.9% and 10.7%, respectively).The remaining third of households is affected by processes of temporary deprivation. Onthe positive side, the data referring to whether or not the exits are definitive reveal thatmost of the households that exit from a situation of deprivation do not re-enter it. How-ever, it must be remembered that the time period under study is insufficient to accuratelyassess the definitive nature of these exits.

As abovementioned, the possibility of remarkable differences among different types ofhouseholds in the length of deprivation spells raises numerous interesting questions andcan without doubt be a major focus of policy analysis. Housing policies should not onlyalleviate the problems of reduced housing quality but also their duration in families withchildren. Recurrent and persistent deprivation in childhood could give rise to structuraldifficulties in the future. Table 3 shows that the shift in focus towards the dynamic aspectsof deprivation confirms that households with children enjoy better housing conditions

Table 3Persistence of housing deprivation according to different thresholds

Number of years inhousingdeprivation

Threshold 1 Threshold 2

Total Householdswith children

Householdswithoutchildren

Total Householdswith children

Householdswithoutchildren

0 51.12 53.62 46.57 56.07 57.64 53.201 15.53 15.56 15.49 15.14 14.91 15.562 10.88 9.97 12.51 9.79 9.32 10.613 8.50 8.63 8.23 8.33 8.07 8.754 7.48 6.75 8.82 6.18 5.99 6.545 6.47 5.48 8.38 4.50 4.08 5.35

Total 100.00 100.00 100.00 100.00 100.00 100.00

Note. Weighted data using longitudinal weightings for the last wave of the ECHP (balanced panel).


than households without children. The proportion of households that did not enter into astate of housing deprivation during the time period under study is higher in the case offamilies with children (54% and 58%, respectively), while an opposite result is found fordeprivation spells of four or more years.

3.3. Housing deprivation and long-term poverty

Focusing on measures of multiple deprivation, one relevant issue concerns the likeli-hood of a strong relationship between housing difficulties measures and income povertyindicators. From a policy viewpoint, it would be helpful to compare housing deprivationresults to poverty rate spells. In the United States, following up on previous contributionstesting the correlation between the official poverty line and measures of material hardship(Mayer and Jencks, 1989; Rector et al., 1999), some studies have considered whether var-ious measures of economic well-being are identical, including income poverty and materialhardship. The evidence is mixed (Edin and Lein, 1997; Danziger et al., 2002; Iceland andBauman, 2004; Meyer and Sullivan, 2006). For the European Union, a relative large liter-ature has traditionally shown a very limited correlation between income poverty and mea-sures of multiple deprivation. Some authors have tested the equivalence between the twomeasures in a large set of European countries using the ECHP. The general result is thelack of correlation between income measures and material hardship (Callan et al., 1993;Nolan and Whelan, 1996; Layte et al., 2001; Tsakloglou, 2006; Watson and Maitre,2006). The evidence for Spain is relatively scarce. Most of empirical research also findsonly limited evidence of a significant relationship between the two measures (Martınezand Ruiz-Huerta, 2000; Perez-Mayo, 2005).

There is no evidence about this relationship when we focus on housing conditionsadopting a longitudinal perspective. Table 4 provides a thumbnail sketch of the evolutionof income poverty measured with two different thresholds (60% and 25% of the medianincome, respectively). The magnitude and sign of the change is clearly different when com-pared to housing deprivation. Unlike the observed decreasing trend in housing depriva-tion, poverty rates do not seem to change at all during the time period considered.

The dynamics of poverty and housing deprivation also differ (Table 5). A minority ofhouseholds (4.3%) suffered from income poverty during the five waves of the ECHP. Thispercentage is relatively similar to the ones found for housing deprivation (6.5% and 4.5%for the two thresholds). However, the probability of not falling into poverty is somewhathigher (63% vs. 51.1% and 56.1%, respectively). Different trends of change in income pov-erty and housing deprivation, and diverging patterns of persistence therefore seem to con-firm the existence of specific determining factors for each problem.

Actually, the characteristics of the fraction of households in long-term housing depri-vation substantially differ from those of the chronic poor (Table 6). There is a higher fre-quency of housing deficiencies—summarized in the latent variable index—among peopleover 65 living alone and widows. These characteristics do not appear to be associated withpersistent poverty. The risk of chronic income poverty is higher in families with children.One of the reasons behind these differences is the limited adequacy of pensions in Spain.While these benefits help the elderly to maintain their income slightly above the povertythreshold they are clearly insufficient for achieving an adequate level of living standards.

Among the relevant questions regarding poverty and housing deprivation, an impor-tant one relates to the probability of leaving the latter when incomes are below the poverty

Table 4Poverty rates

Year Poor households (%)

Threshold 1 (60%) Threshold 2 (25%)

1994 17.90 3.051995 17.56 2.851996 17.51 3.621997 17.97 3.561998 17.82 3.65


Table 5Persistence of income poverty

Number of years in poverty Threshold 1 (60%) Threshold 2 (25%)

0 62.70 89.571 13.64 6.042 7.91 2.943 6.28 1.044 5.21 0.205 4.26 0.21

Total 100.00 100.00



line. The persistence of poverty could result in chronic difficulties in achieving satisfactoryhousing standards. Table 7 presents the proportion of leavers and stayers among deprivedhouseholds according to the number of years in poverty. Results give general support tothe notion that exits from housing deprivation are associated with the number of years inpoverty. More than half of poor households who suffer form permanent housing depriva-tion were in poverty four or more years. This leads to the expectation that exiting incomepoverty should mean reducing housing hardship. It also suggests that long periods inincome poverty need to be of major concern for policies aimed at reducing housingdeprivation.

4. Determinants of the duration of housing deprivation

4.1. Econometric specification

In order to correctly identify the determinants of housing deprivation, it is essential toestimate a conditional probability model for exiting this state given duration dependenceand a broad range of household and individual characteristics. A discrete time propor-tional hazard model is estimated, with housing deprivation as the binary dependent vari-able. The hazard rate in discrete time of exiting this situation for household i in period jcan be specified as:

hjðX ijÞ ¼ 1� exp � expðbX ij þ hðtÞÞ� �

ð7Þ

Table 6Characteristics of persistent deprived and poor households at the beginning of the period

Variables Housing deprived Poor All

Household sizeOne member 18.88 5.71 9.39Two members 24.99 30.06 23.33Three, four or five members 33.59 42.75 55.81Six or more members 22.54 21.48 11.48

Household compositionOne person aged 65+ 13.64 3.30 6.02One person aged 30–64 6.09 2.59 3.69One person aged <30 0.00 0.00 0.12Single parent, kids <16 0.76 3.27 0.76Single parent, at least 1 kid 16+ 12.85 4.92 7.06Couple, no kids, at least 1 aged 65+ 10.58 20.13 10.16Couple, no kids, both <65 6.31 8.97 8.80Couple, 1 kid <16 3.16 1.43 11.16Couple, 2 kids <16 4.00 14.64 12.12Couple, 3+ kids <16 0.63 4.64 1.54Couple with 1+ kids aged 16+ 25.16 27.42 28.60Other households 16.84 8.68 9.97

Tenure statusOwner 65.30 73.20 80.14Rent 18.97 15.75 12.91Accommodation provided free 15.73 11.05 6.95

Age<30 years 8.47 3.83 9.3330–50 years 36.78 41.71 44.4950–65 years 22.70 27.76 25.72>65 years 32.06 26.71 20.47

GenderMale 63.03 77.22 74.04Female 36.97 22.78 25.96

Marital statusMarried 55.98 81.22 73.46Separated 1.74 3.66 1.37Divorced 1.66 1.40 0.96Widowed 20.30 8.94 10.65Never married 20.33 4.78 13.55

Highest level of education completed3rd level (isced 5–7) 6.88 1.65 17.412nd stage (isced 3) 4.34 3.80 12.67<2nd stage (isced 0–2) 88.79 94.55 69.91

Total 100.0 100.0 100.0



Here Xij represents the set of explanatory variables (fixed or variable over time), b repre-sents the coefficients to be estimated and h(t) is the functional form of duration depen-dence, which reflects the influence of the length of deprivation on the likelihood of

Table 7Years in poverty and exits from housing deprivation

Number of years in poverty Housing deprivation

Stayers Leavers

1 20.83 15.382 15.83 12.823 19.17 18.594 25.00 25.645 19.17 27.56


exiting this state. The dependent variable in this model is a logarithmic transformation(the complementary log–log) of the hazard rate:

log ðhijÞ ¼ hðtÞ þ bX ij ð8Þ

Due to the fact that we do not wish to impose any constraint on the form the durationdependence function should take, a non-parametric specification is used in which theh(t) function is specified on the basis of a set of dummies corresponding to each periodof the deprivation interval (when t = 1, . . .,T, it takes the value of 1 for the deprivationinterval’s t year and the value of 0 otherwise). Following Jenkins (2002), if an indicatorvariable is defined yit = 1 if household i exits the state of housing deprivation duringthe interval (t � 1, t), the likelihood function can be expressed in a sequential binary re-sponse form:

log L ¼Xn

i¼1

Xti

j¼1

yijl log hjðX ijÞ þ ð1� yijÞ log 1� hjðX ijÞ� ��

ð9Þ

The first type of explanatory variables included in the regression model provides informa-tion on the probability of exiting the situation of housing deprivation as it lengthens overtime. The second kind of variables gathers information on the differences in the individualand households’ characteristics, which can be either variable or fixed over time. They re-flect income,18 housing tenure (differentiating whether or not there are any outstandingpayments for the purchase of the dwelling), household composition and size, number ofchildren, in addition to the household head’s situation in the labor market, educationalattainment, age, gender and social relationships. We also include a dummy representingwhether or not households receive housing subsidies. In practice, only a small percentageof Spanish households receive housing subsidies. As a result, the proportion of householdsin the sample reporting the receipt of these benefits is rather low (0.6%).

In order to address the potential effects of the business cycle we also include regionalunemployment rates as covariates. During the period under study there was a substantialfall of unemployment. However, the reduction was not homogenous across regions. Wemake an attempt to parse out the cyclical effects by looking regionally. Regional unem-ployment rates of the Labor Force Survey (EPA) were used to this end.

Any possible correlations between the observations of the data arranged for the eventhistory analysis are controlled by using robust estimates of the model parameters’
18 The income variable represents the household’s total net income corrected by the OECD’s equivalence scale.
The variable takes the value of 1 when the household is situated in the upper part of the income distribution (lastfour deciles) and the value of 0 otherwise.


standard errors. Likewise, the possibility of unobserved heterogeneity existing is also takeninto account. This heterogeneity may be the result of the model being incorrectly specified,such as omitting relevant information needed to explain housing deprivation. This could,for instance, consist of situations involving a lack of personal independence making theprogress of housing refurbishment difficult or alternatively of different levels of entitlementto public assistance programs. Factors related to both individual motivations as well aspossible problems of asymmetric information on the provision of public goods have abearing on this matter.

4.2. Results

Table 8 shows the results obtained from estimating the discrete time proportional haz-ard model. The results correspond to the two deprivation thresholds defined above. Thereare no great changes in the size and direction of the coefficients depending on whether oneor other criterion is used, except for some kinds of households and housing tenure. Inorder to shorten the discussion of the results, the comments below solely focus on the coef-ficients obtained with the first threshold.

The coefficients of the variables summarizing duration dependence show that house-holds remaining in a state of deprivation during two consecutive periods have a probabil-ity of exiting that state that is 56% lower than households that underwent such a situationduring only one period. However, the dummy variable, which reflects the effect of extend-ing the state of deprivation during three years as opposed to remaining in it for only oneperiod does not turn out to be significant. This result may be due to the fact that some ofthe possible effects of the duration dependence indicator are absorbed by other character-istics included in the regression model.

Household income is one of the significant factors for exiting the situation of depriva-tion, thus confirming the importance the monetary dimension has on other aspects of indi-vidual well-being. The households situated in the higher income deciles have a probabilityof leaving a situation of housing deprivation that is 22% higher than households belongingto the lower part of the income distribution. The dwelling’s tenure regime is another deter-mining factor of transitions towards a situation of not suffering deprivation. Householdsthat own their homes have a higher probability of exiting a situation of deprivation thanhouseholds living in rented or freely provided accommodation.

Households with extreme sizes run a greater risk of remaining in a state of deprivationfor longer periods of time. This evidence is consistent with the inclusion of overcrowdingamong the indicators used for estimating the latent variable. Regarding the type of house-hold, single-individual and single-parent households have a greater likelihood of goingthrough prolonged situations of deprivation. This result is again consistent with the factthat the number of children in the household does not seem to be significant. It can alsobe observed that the likelihood of exiting housing deprivation seems to increase with age.However, this result should not hide the high level of heterogeneity in elderly households.People over 65 living alone have a higher probability of suffering housing problems in thelong-term.

Labor market participation also constitutes a determining factor for leaving housingdeprivation. As expected, situations of unemployment of the household’s head have a sig-nificant and negative effect on the probability of exiting from such a situation. Likewise,persistence in a state of deprivation is more visible for households whose head is retired

Table 8Results of the discrete time proportional hazard model (genuine transitions)


Coefficients z exp (b) Coefficients z exp (b)

Duration2 periods (waves or years) �0.817 �6.460 0.442 �1.016 �7.420 0.3623 periods (waves or years) �0.071 �0.500 0.932 �0.357 �2.310 0.700

Equivalent incomeDeciles 7–10 0.201 1.880 1.223 0.217 2.000 1.242

Household size square �0.019 �3.330 0.982 �0.015 �2.850 0.985Household sociological typology

1 adult �0.619 �3.290 0.539 �0.565 �3.030 0.568Single parent �0.586 �2.660 0.557 �0.730 �3.130 0.482Couple kids 0.160 1.280 1.174 0.146 1.130 1.158

Number of children < 16 0.034 0.470 1.034 0.040 0.530 1.040Self-defined main status

Self-employment �0.163 �1.140 0.849 �0.056 �0.380 0.946Unemployed �0.439 �2.010 0.645 �0.342 �1.640 0.710Retired �0.575 �3.110 0.563 �0.609 �3.270 0.544Housework/caring �0.150 �0.540 0.861 �0.150 �0.500 0.860Other inactive �0.470 �2.330 0.625 �0.460 �2.220 0.631

Social relationshipOnce/twice a week �0.383 �3.270 0.682 �0.387 �3.240 0.679Once/twice a month �0.153 �0.790 0.858 �0.364 �1.680 0.695Less often 0.042 0.130 1.043 �0.050 �0.150 0.951

Tenure statusOwner (any outstanding mortgage) 0.587 3.520 1.798 0.446 2.590 1.563Owner (not outstanding mortgage) 0.256 1.920 1.292 0.115 0.840 1.122

Age 0.019 4.420 1.019 0.020 4.690 1.020Gender

Female �0.136 �0.990 0.872 �0.139 �0.970 0.871Education

2nd stage (isced 3) �0.077 �0.400 0.926 �0.111 �0.590 0.895<2nd stage (isced 0–2) �0.204 �1.420 0.815 �0.138 �0.950 0.871Regional unemployment rate �3.917 �4.360 0.020 �3.808 �4.220 0.022Housing allowance 1.102 2.920 3.010 1.066 2.570 2.904

Lnr2u �14 �14

ru 0.00091 0.00091q 5.06E�07 5.06E�07

Number of observations 1126 1056LogL �699.861 �648.853

Note. Categories of reference: only one period of duration, average-low income (first six income deciles), male,salaried employment, sees friends and family daily, rented or free accommodation, university or third-stageeducation and non housing allowance-recipient.


or inactive. The level of individuals’ social integration, measured by the frequency of theirsocial relationships, also constitutes a risk factor. Households that only maintain relation-ships with their friends or family once a week have a 32% lower chance of exiting a situ-ation of deprivation when compared to those that maintain social relationships on a morefrequent basis.

Regional unemployment rates seem to exert a negative effect on the probability of leav-ing housing deprivation situations. The empirical evidence we have found reveals that


exogenous factors play a part in the dynamics of this problem. A plausible case can bemade that macroeconomic conditions can reduce the scope of public initiatives. However,even in recessive economic contexts there is still margin for implementing more efficientpublic initiatives. The strong and significant coefficient of housing subsidies gives generalsupport to the notion that adequately targeted policies aimed at alleviating housing prob-lems could have substantial positive effects.

Lastly, the corresponding test of the possible existence of some kind of unobserved het-erogeneity among the households do not show any evidence of the presence of suchdifferences.19

These results lead to some suggestions that could be interesting for policy makers andanalysts.20 By identifying the specific effects of the different categories on the probability ofexiting from a state of housing deprivation our estimates enable a better targeting of qual-ity-oriented housing policies. Our estimates may also aid in the design of new housing pol-icies which take into account possible links with different social policies (e.g., pensions’adequacy or sufficiency of family benefits).

4.3. Other specifications and robustness

The estimates contained in the previous section correspond to a discrete time propor-tional hazard model of exiting housing deprivation using genuine transitions from thisstate. It seems reasonable to estimate also the model without imputations and observewhether there are any appreciable differences in the results. First, in order to do so, thesame model of the conditional probability of exiting a situation of housing deprivationis estimated using the original data. Table 9 shows the results of this estimation, accordingto the two housing deprivation thresholds defined above. As occurred in the previous esti-mation, the use of one or other threshold does not change either the size or sign of thecoefficients.

The main difference when compared to the previous results lies in the variables that sum upduration dependence. The conclusion of a negative duration dependence of housing depriva-tion seems to be more robust. Regarding the second set of explanatory variables, it can beseen that the results do not change substantially either. Income, housing tenure, demo-graphic characteristics, labor status, level of social integration, regional macroeconomic con-ditions and housing subsidies all appear to be significant determinants for remaining in astate of housing deprivation. It can also be seen that most of the variables’ coefficients aresomewhat lower although their statistical significance does not generally change.

One of the implications arising from counting transitions characterised by temporarydiscontinuities in the situation of housing deprivation as effective transitions is the possi-bility of also developing a conditional probability model of re-entry into housing depriva-tion, given duration dependence and a set of household characteristics. To achieve this, itis necessary to rearrange the data so that the re-entry model’s sample includes all the
19 Due to the fact that the size of the variance is somewhat reduced, it is necessary to interpret the results of such
tests with caution.20 Results of the latent variable models can be extended to the case of considering the different univariate

indicators. Ayala et al. (2005) use the same indicators and a latent trait model in order to assess the impact ofdifferent housing characteristics on health. They show that most of the results found for each of the indicatorsmaking up the housing deprivation index as well as the latent variable serve as proxies for the health status.

Table 9Results of the discrete time proportional hazard model (original data)




Equivalent incomeDeciles 7–10 0.231 2.310 1.260 0.287 2.830 1.332

Household size square �0.012 �2.450 0.988 �0.010 �2.220 0.990Household sociological typology



Self-employment 0.019 0.130 1.019 0.032 0.220 1.032Unemployed �0.431 �2.100 0.650 �0.179 �0.900 0.836Retired �0.347 �1.910 0.707 �0.489 �2.680 0.613Housework/caring 0.008 0.030 1.008 �0.286 �1.020 0.751Other inactive �0.264 �1.360 0.768 �0.436 �2.220 0.647

Social relationshipOnce/twice a week �0.367 �3.300 0.693 �0.393 �3.410 0.675Once/twice a month �0.196 �0.990 0.822 �0.321 �1.490 0.725Less often 0.154 0.480 1.167 0.138 0.400 1.148


Age 0.013 3.150 1.013 0.020 4.920 1.020Gender


2nd stage (isced 3) �0.118 �0.640 0.889 �0.065 �0.350 0.937<2nd stage (isced 0–2) �0.176 �1.240 0.838 �0.160 �1.110 0.852

Regional unemployment rate �2.701 �3.230 0.067 �3.006 �3.580 0.049Housing allowance 1.362 3.870 3.902 1.782 5.110 5.940

Lnr2u �14 �14

ru 0.00091 0.00091q 5.06E�07 5.06E�07


Note. Categories of reference: only one period of duration, average-low income (first six income deciles), male,salaried employment, sees friends and family daily, rented or free accommodation, university or third-stageeducation and non-housing allowance-recipient.


households that have just exited a period of housing deprivation and are running a risk ofre-entering it.

Table 10 shows the re-entry model’s results, using the two housing deprivation thresh-olds. As in the two cases above, we can observe that the estimates with both thresholds donot differ significantly with regard to either the coefficients’ size or sign. The variables sum-ming up duration dependence show a negative pattern. The probability of falling below

Table 10Results of the re-entry model




Equivalent incomeDeciles 7–10 �0.341 �3.700 0.711 �0.354 �3.590 0.702

Household size square 0.004 0.920 1.004 0.004 0.920 1.004Household sociological typology

1 adult 0.211 1.500 1.235 0.049 0.340 1.051Single parent 0.005 0.030 1.005 0.004 0.020 1.004Couple kids �0.285 �2.920 0.752 �0.239 �2.360 0.787

Number of children < 16 �0.025 �0.410 0.975 �0.073 �1.140 0.930Self-defined main status

Self-employment 0.033 0.240 1.033 0.023 0.160 1.023Unpaid family worker 1.341 1.760 3.823 1.270 1.580 3.562Unemployed 0.447 3.190 1.564 0.407 2.800 1.502Retired 0.263 1.750 1.301 0.232 1.460 1.262Housework/caring 0.153 0.620 1.166 0.307 1.260 1.359Other inactive 0.027 0.150 1.027 0.056 0.300 1.058

Social relationshipOnce/twice a week �0.100 �1.080 0.905 �0.069 �0.710 0.933Once/twice a month �0.010 �0.060 0.990 0.055 0.320 1.057Less often �0.288 �0.920 0.749 �0.054 �0.180 0.947Never 0.414 1.010 1.512 0.749 1.510 2.114

Tenure statusOwner (any outstanding mortgage) �0.569 �4.150 0.566 �0.499 �3.580 0.607Owner (not outstanding mortgage) �0.157 �1.650 0.855 �0.127 �1.290 0.881

Age �0.015 �4.410 0.985 �0.014 �3.890 0.986Gender

Female 0.053 0.480 1.055 0.020 0.180 1.020Education


Regional unemployment rate 1.328 1.800 3.773 1.484 1.950 4.409Housing allowance �0.998 �0.980 0.369 �0.365 �0.510 0.694

Lnr2u �14 �14

ru 0.00091 0.00091q 5.06E�07 5.06E�07


Note. Categories of reference: only one period of duration, average-low income (first six income deciles), male,salaried employment, sees friends and family daily, rented or free accommodation, university or third-stageeducation and non-housing allowance-recipient.


the housing deprivation threshold is greater as the time elapsed since the last period ofhousing deprivation is shortened. Concerning the second set of variables, those exertinga positive influence on the probability of exiting deprivation have negative effects on thehazard rate of re-entering such a situation. For instance, households with lower income,living in rented or freely provided accommodation or whose head is unemployed have a


greater probability of re-entering a situation of deprivation. On the other hand, householdsize, the level of social integration and housing subsidies has no influence on the hazard ofre-entering such a situation.

Table 11Results of the discrete time proportional hazard model including the number of years in poverty (genuinetransitions)




Household size square �0.018 �3.220 0.982 �0.014 �2.650 0.986Household Sociological typology



Self-employment �0.137 �0.910 0.872 �0.029 �0.190 0.971Unemployed �0.442 �2.020 0.643 �0.295 �1.390 0.744Retired �0.604 �3.230 0.547 �0.595 �3.190 0.551Housework/caring �0.178 �0.650 0.837 �0.114 �0.380 0.892Other inactive �0.508 �2.490 0.602 �0.464 �2.200 0.629

Social relationshipOnce/twice a week �0.381 �3.260 0.683 �0.390 �3.250 0.677Once/twice a month �0.144 �0.730 0.866 �0.355 �1.650 0.701Less often 0.052 0.170 1.053 0.018 0.050 1.019


Age 0.021 4.780 1.021 0.022 5.080 1.022Gender



Regional unemployment rate �3.745 �4.110 0.024 �3.513 �3.850 0.030Number of years in poverty

1 �0.232 �1.730 0.793 �0.168 �1.150 0.8452 �0.199 �1.170 0.820 �0.144 �0.830 0.8663 0.093 0.490 1.098 �0.132 �0.720 0.8764 �0.111 �0.560 0.894 �0.219 �0.950 0.8035 �0.342 �1.560 0.711 �0.768 �2.980 0.464

Housing allowance 1.143 3.070 3.135 1.094 2.790 2.985

Lnr2u �14 �14

ru 0.00091 0.00091q 5.06E�07 5.06E�07


Note. Categories of reference: only one period of duration, male, salaried employment, sees friends and familydaily, rented or free accommodation, university or third-stage education and non-poor during the time periodunder study and non-housing allowance-recipient.


The main difference is found in couples with children. This kind of households has thelowest probability of falling below the deprivation threshold once again. This seems tohighlight not only the relevance of domestic stability but also the importance a secondsource of income may have to avoid re-entering housing deprivation. Concerning laborstatus, it can be observed that households with an unemployed head have a greater prob-ability of re-entering deprivation than households with a salaried head. The explanatorycapacity of this variable is even greater for the probability of re-entering than for exitinga situation of housing deprivation. On the other hand, inactive situations do not constitutea risk factor for recurrent deprivation. Once this group manages to escape from such a sit-uation, it does not run a high risk of re-entering it.

Finally, as a last test of our original model, we estimated it including a set of dummiesrepresenting the number of years in poverty. As stated before, prolonged poverty spellscould be associated with chronic housing problems. Our income deciles variable showeda significant effect on the probability of leaving the state of housing deprivation for richerhouseholds. However, the effect of permanent income on persistent housing deprivationshould be stronger than the one exerted by current disposable income. The permanentincome hypothesis suggests that some families may avoid hardships by borrowing or dis-saving when income is temporarily low. This hypothesis has been recently tested for theU.S. by Sullivan et al. (2006), finding that those individuals facing less liquidity constraintsare significantly less likely to experience hardship.

We test the effects of persistent low-income on the dynamics of housing deprivation byincluding as covariates a set of dummies representing the number of years in poverty(Table 11). The inclusion of the poverty variables produces modest changes in the resultsof the model. Coefficients have the expected negative sign although significance is low. Thecoefficient is especially high in the case of having spent five years in poverty. Among all thepoverty dummies, the most important turns out to be the one for the longest poverty spell.This result would confirm that prolonged situations of income poverty are associated withincreasing difficulties for reducing structural housing problems. In this sense, generalincome maintenance programs should also play a role in shaping overall policies aimedat reducing housing deprivation.

5. Conclusions

Housing deprivation occupies a relatively minor place in the dynamic analysis of livingstandards. This bias is a result of both limited data availability on the presence or absenceof housing deficiencies, as well as a consequence of the existing difficulties encountered inthe development of housing deprivation indicators. The availability of new longitudinal infor-mation and the development of alternative methods of analysis, namely the latent class model,have allowed us to partially overcome both constraints. The latent variable models offer a suit-able methodological framework to define housing deprivation based on a set of indicators onthe insufficiencies of basic facilities (hot running water, heating and space) as well as on thepresence of structural problems (leaky roof, damp wall or floor, and rot in floor or windowframes). A considerable advantage is to partially alleviate the customary constraints affectingthe building of deprivation indices, like aggregating and weighting the different items andassigning each household to a different class based on the level and kind of deprivation.

Our results show that almost half the households have gone through some kind of depri-vation during the period under study while in cross-sectional studies only a percentage close to


l

one-fourth of population appears to have done so. Another important finding is the presenceof a significant percentage of households for which this a persistent phenomenon. It is alsoimportant to highlight that most of the households that manage to exit a situation of housingdeprivation do not re-enter it. We have also found that both the incidence as well as the dura-tion of housing deprivation is considerably lower among households with children.

This information may make a relevant contribution to improving the design of publicpolicies if taken with due caution. Some types of public intervention could turn out to bemore suitable than others depending on the nature of housing deprivation dynamics. Inthe case of households suffering this situation on a persistent basis, it seems logical to thinkthat structural measures would be necessary. On the other hand, short-term initiatives maybe sufficient for households suffering from transitory situations of deprivation. It thereforeseems relevant to accurately identify the main determinants behind the probability of exit-ing a situation of deprivation in order to optimize the design of housing assistance pro-grammes. The discrete time proportional hazard model has served to show not onlythat there are groups running a greater housing deprivation risk but also that some facea greater probability of suffering such a situation on a persistent basis. Our estimationsalso provide certain evidence for negative housing deprivation duration dependence onceboth observed and unobserved household characteristics have been controlled.

Measuring the sensitivity of these results to the imputation criteria chosen gives acertain degree of robustness to the description mentioned above. In addition, a re-entrymodel for housing deprivation based on duration dependence and a set of householdcharacteristics was also developed. Its results have revealed the existence of negativeduration dependence and the variables that exert a positive influence on the likelihoodof exiting a situation of housing deprivation generally exert a negative effect on theprobability of re-entering such a situation. We have also found that prolonged situa-tions of income poverty are associated to increasing difficulties for reducing structurahousing problems.

Thus, this diagnosis may, with all due caution, serve as the basis for developing moreselective housing policies. It seems necessary to reflect upon the way the different publicintervention instruments in housing are affecting the situations identified. A greater allo-cation of specific expenditure on households suffering from prolonged deprivation spellsshould contribute to reducing its incidence in a segment of society in which it appearsto be very deeply rooted. General income maintenance programs can also play a role inreducing housing difficulties.

Appendix A

Descriptives of variables at the beginning of the period

Variables/definition
Mean StandardDeviation
Household size
3.157 1.459 Household composition variables
One person household
0.133 0.340 Couple with kids 0.514 0.500 Single parent with kids 0.080 0.271
(continued on next page)


Appendix A. (continued)

Variables/definition
Mean StandardDeviation
Couple no kids
0.186 0.389 Other households 0.087 0.281
Number of kids (aged < 16)
0.586 0.873 Tenure status variables
Rent or free accommodation
0.212 0.408 Owner (outstanding mortgage) 0.167 0.373 Owner (no outstanding mortgage) 0.621 0.485
Equivalent Income (OECD modified) (euros)
6993 5104 Self-defined main-activity status variables
Paid employment
0.454 0.498 Paid apprendiceship 0.002 0.044 Self-employment 0.120 0.324 Unpaid family worker 0.003 0.055 Education/training 0.003 0.055 Unemployed 0.057 0.232 Retired 0.219 0.414 Housework/caring 0.071 0.257 Other inactive 0.070 0.256
Household head’s age
50.7 16.7 Household head’s gender
Male
0.748 0.434 Female 0.252 0.434
Housing allowance
0.003 0.054 Regional unemployment rate 0.241 0.045 Education variables
Third level of education (isced 5–7)
0.181 0.385 Second stage of secondary level of education (isced 3) 0.125 0.330 Less than second stage of secondary level of education(isced 0–2)
0.694
0.461
Social relationship variables
Meet friends or relatives most days 0.624 0.484 Meet friends or relatives once/twice a week 0.280 0.449 Meet friends or relatives once/twice a month 0.064 0.245 Meet friends or relatives less often 0.026 0.159 Meet friends or relatives: never 0.005 0.072

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