Carolina Navarro Ruiz

Luis Ayala

MULTIDIMENSIONAL HOUSING DEPRIVATION INDICES WITH APPLICATION TO SPAIN

Keywords: housing, deprivation, poverty, latent class models. JEL: I31, I32

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The main aim of this paper is to make an attempt to overcome these difficulties by defining a multidimensional housing deprivation index and identifying the main determining characteristics behind this phenomenon by using Spain as reference. In order to do so, we use a notion of housing deprivation as an insufficient basic functioning [START_REF] Sen | Commodities and Capabilities[END_REF], (1992), (2000)] A latent variable model is used as a suitable methodological option for such a concept. The main contribution is to provide the possibility of empirically assessing and contrasting whether a specific combination of conditions constitutes an appropriate structure to measure the latent notion of housing deprivation.

Constructing a latent structure model allows us to synthesise a group of indices under a single index and provides the possibility of assigning each individual to a different class depending on the level and kind of deprivation. In this way, the arbitrariness encountered when setting deprivation thresholds can be partially overcome.

The structure of the article is as follows. The main approaches used to construct housing deprivation indices are reviewed in the first section. The methodologies of the latent trait and latent class models, which will be the basis of the empirical work, are described in the second section. A model is then estimated using data from the European Union Household Panel in the following section. The socio-economic patterns of the groups affected by this kind of deprivation are analysed afterwards. The paper ends with a brief list of conclusions.

THE MEASUREMENT OF HOUSING DEPRIVATION

The construction of social deprivation indices is closely tied to the notion used to identify those suffering from this state. This paper starts off from Sen's notion of functionings [(1985), (1992), (2000)]. We could consider housing deprivation as an insufficient basic functioning of this commodity. A broad interpretation of this concept would include both the problem of individuals or households that do not have access to housing as well as those who, despite having a dwelling, suffer from insufficiencies in this commodity's basic conditions. This paper will focus on the latter. The empirical possibilities of approach have been widely discussed in Sen's own studies [(1985, 1992 and 2000)], as well as by others authors [START_REF] Nussbaum | Women and Human Development. The Capability Approach[END_REF], [START_REF] Schokkaert | Sen's Concept of the Living Standard Applied to the Belgian Unemployed[END_REF], [START_REF] Balestrino | Functioning and Capabilities: Normative and Policy Issues[END_REF], [START_REF] Brandolini | Measuring Well-Being in the Functioning Space[END_REF], [START_REF] Klasen | Measuring Poverty and Deprivation in South Africa[END_REF], [START_REF] Chiappero | Standard of Living Evaluation Based on Sen's Approach: Some Methodological Suggestions[END_REF][START_REF] Chiappero | A Multidimensional Assesment of Well-being Based on Sen's Functioning Approach[END_REF] and [START_REF] Robeyns | An Unworkable Idea or a Promising Alternative? Sen's Capability Approach Re-Examined[END_REF]]. Most of them coincide in pointing out that the main questions broached by any approach focusing on Sen's capabilities and functioning levels concern defining the appropriate space to assess well-being, the set of relevant functionings, the most appropriate criteria to measure them and the aggregation of the indicators used in order to obtain an overall assessment [START_REF] Chiappero | A Multidimensional Assesment of Well-being Based on Sen's Functioning Approach[END_REF].

Regarding the latter, constructing a synthetic deprivation indicator inevitably entails selecting the most relevant characteristics for individual well-being and developing aggregation procedures. An expanding literature on multidimensional well-being has brought forth many methods to establish weighting systems. These vary from simple processes of adding up the commodities not possessed by an individual or households to more complex methods requiring the use of multivariate analysis techniques. In some cases, the idea of unifying indicators is given greater emphasis while more stress is placed on the intersection of vectors in others. From the standpoint of statistical analyses, the main distinction can be made by differentiating the studies that synthesise information by means of arithmetic or weighted means from those that use multivariate analysis techniques.

The arithmetic addition of the commodities not possessed by an individual is the most immediate approach among the many procedures used to aggregate commodities. In a seminal contribution, [START_REF] Townsend | Poverty in the United Kingdom[END_REF] chose twelve different indicators on household's living conditions and constructed a deprivation index based on the arithmetic addition of the commodities that were not present. The condition for choosing these indicators was based on their correlation to income. This method was also used by [START_REF] Mack | Poor Britain[END_REF], who included more conditions. All commodities included were considered as a necessity by most of those being surveyed, a negative Arithmetic addition implicitly imposes a severe value judgement because it does not differentiate the weighting of each material condition or necessity. A more consistent approach is to use a weighted addition of necessary commodities. It was first proposed by [START_REF] Desai | An Econometric Approach to the Measurement of Poverty[END_REF] and consisted in analysing the times an individual enjoyed a specific commodity. The commodities enjoyed by most of society were given more weight. Various studies have more recently identified individuals suffering from deprivation as those that do not reach a minimum level in at least one of the functionings. The functioning levels reached by most of the population are given more weight [START_REF] Brandolini | Measuring Well-Being in the Functioning Space[END_REF], [START_REF] Böhnke | Poverty in a Multidimensional Perspective: Great Britain and Germany Comparison[END_REF], [START_REF] Martínez | Income, Multiple Deprivation and Poverty: An Empirical Analysis Using Spanish Data[END_REF], [START_REF] Muffels | Do European Welfare Regimes Matter in Explaining Social Exclusion? Dynamic Analyses of the Relationship Between Income Poverty and Deprivation: A Comparative Perspective[END_REF], [START_REF] Tsakloglou | Poverty, Material Deprivation and Multidimensional Disadvantage during Four Life Stages: Evidence from ECHP, in Poverty and Social Exclusion in Europe[END_REF]].

Another way to construct synthetic multidimensional deprivation indices lies on using multivariate analysis techniques. Some studies use main components analysis [START_REF] Muffels | The Elaboration of a Deprivation Scale and the Definition of a Subjective Poverty Line[END_REF], [START_REF] Hutton | Measuring Living Standards Using Existing National Data Sets[END_REF] and [START_REF] Kamanou | Poverty and Valuation: a Review of Definitional Issues and their Implications on Statistical Methodologies for Poverty Measurements[END_REF] 2 ], which enables an index to be created as a lineal combination of partial indicators. [START_REF] Callan | Resources, Deprivation and the Measurement of Poverty[END_REF] and [START_REF] Layte | Reassessing Income and Deprivation Approaches of Poverty in Ireland[END_REF] applied factorial analysis to a set of deprivation indicators. Their results showed that there are three different dimensions to deprivation: basic deprivation, secondary deprivation and residential deprivation. They also found that combining income and deprivation indices in the process of identifying disadvantaged households produces differences in the extension and composition of the estimated poor. A less frequent alternative is the use of latent variable models. [START_REF] Gailly | Desavantages relatifs a une Mesure Objetive de la Pauvrete[END_REF] used the statistical technique developed by [START_REF] Rasch | Probabilistic Model for some Intelligence and Attainment Tests[END_REF] that sums up a set of indicators in a multiple deprivation scale. Pérez- [START_REF] Mayo | Modelos dinámicos de variables latentes aplicados a la construcción de indicadores económicos y sociales[END_REF][START_REF] Mayo | Identifying deprivation profiles in Spain: a new approach[END_REF] also proposed identifying households suffering deprivation or poverty from a multidimensional perspective based on the use of latent class models. 1 The lack of commodities caused by individual preferences should not be considered as a deprivation situation.

2 [START_REF] Kamanou | Poverty and Valuation: a Review of Definitional Issues and their Implications on Statistical Methodologies for Poverty Measurements[END_REF] used an alternative version of the main components analysis technique based on a standardised uniform transformation of the set of discrete variables comprising the household wealth index. This approach allows one to take into account differences in the variance of the variables used to construct the index. These methods have been also used in Spain for analysing very different topics [START_REF] Alañon | Estimating the size of the shadow economy in Spain: a structural model with latent variables[END_REF].

A third possibility lies in the path opened up by the Fuzzy Sets theory. This theory interprets poverty and deprivation as a phenomena that appear in different degrees and levels that are difficult to separate and identify instead of as an attribute that one lacks or possesses [START_REF] Cerioli | A Fuzzy Approach to the Measurement of Poverty, in Income and Wealth Distribution, Inequality and Poverty[END_REF], [START_REF] Cheli | A Totally Fuzzy and Relative Approach to the Multidimensional Analysis of Poverty[END_REF] and [START_REF] Chiappero | A New Approach to Evaluation of Well-being and Poverty by Fuzzy Set Theory[END_REF][START_REF] Chiappero | Standard of Living Evaluation Based on Sen's Approach: Some Methodological Suggestions[END_REF]]. [START_REF] Chiappero | A Multidimensional Assesment of Well-being Based on Sen's Functioning Approach[END_REF] constructed a multidimensional index of well-being with this methodology based on the capabilities and functioning's approach proposed by Sen. The weighting structure assigned greater weight to those functioning that are reached by a wide majority of the population. Betti, D'Agostino and Neri (2000) used the same technique to construct a poverty index based on an indirect indicator (income) and a set of direct indicators (housing conditions and durable goods).

Therefore there are various options to construct synthetic deprivation indices. Despite its greater complexity, the main advantage of multivariate analysis to aggregate the different functionings -in this case housing conditions-into a single indicator is to minimise value judgements without completely eliminating them. Additionally, since one of the ideas underlying Sen's approach is that functionings are a non-observed concept, some of these models, such as the latent variable model, facilitate an approximation to this notion trough different combination of observable housing conditions. Some of the aforementioned studies include specific housing indicators among the dimensions of deprivation. In addition to including not having a toilet and bath as components of deprivation, [START_REF] Townsend | Poverty in the United Kingdom[END_REF] analysed the problem of housing in a particular way. He added a wider range of indicators, which included structural problems, the lack of basic facilities (bath, toilet, gas or electric cooker, heating), overcrowding and satisfaction regarding housing conditions. Several of these indicators were also used by subsequent studies following a similar approach [START_REF] Mack | Poor Britain[END_REF], [START_REF] Hausman | National Report for Luxembourg on the EUROPASS project[END_REF] and [START_REF] Nolan | Measuring Poverty using Income and Deprivation Indicators: Alternative Approaches[END_REF]]. [START_REF] Brandolini | Measuring Well-Being in the Functioning Space[END_REF] F o r P e e r R e v i e w 6 specifically defined housing functionings based on indicators such as the lack of heating, overcrowding and subjective quality and location indices to find that the set of housing functionings had the highest correlation with income. [START_REF] Martínez | Income, Multiple Deprivation and Poverty: An Empirical Analysis Using Spanish Data[END_REF], [START_REF] Muffels | Do European Welfare Regimes Matter in Explaining Social Exclusion? Dynamic Analyses of the Relationship Between Income Poverty and Deprivation: A Comparative Perspective[END_REF],and [START_REF] Tsakloglou | Poverty, Material Deprivation and Multidimensional Disadvantage during Four Life Stages: Evidence from ECHP, in Poverty and Social Exclusion in Europe[END_REF] also proposed a specific dimension of housing deprivation which included the lack of a bath, overcrowding, a leaky roof, damp and rot in window frames and floors.

Among the studies using multivariate analysis techniques, [START_REF] Kamanou | Poverty and Valuation: a Review of Definitional Issues and their Implications on Statistical Methodologies for Poverty Measurements[END_REF] used the main components analysis technique to construct a wealth index based on a set of housing characteristics. [START_REF] Arévalo | Construcción de un índice de calidad de la vivienda[END_REF] used also this technique to construct a housing quality index. Using fuzzy set theory, [START_REF] Chiappero | A Multidimensional Assesment of Well-being Based on Sen's Functioning Approach[END_REF] tried to define a housing functionings based on two indicators: overcrowding and a lack of facilities such as hot running water, heating and a telephone.

To sum up, most of the studies that look into different forms of deprivation and focus on housing include basic facilities (hot running water, heating and bath), structural problems (leaky roof, damp and rot in floors and window frames) and overcrowding as important features of housing deprivation.

The sensitivity of the results to the aggregation procedures are analysed more deeply by studies that primarily focus on housing deprivation. For instance, [START_REF] Whitehead | The Benefits of Better Homes: The Case for Good Quality Affordable Housing[END_REF] studied the minimum conditions a dwelling should meet to be considered as adequate. [START_REF] Dale | Housing Deprivation and Social Change : A report based on the analysis of individual level census data for 1971, 1981 and 1991 drawn from the Longitudinal study and the Samples of anonymised records[END_REF] conducted an analysis on the changes in housing deprivation over two decades. One of the criteria used was the association of some housing deprivation components with health. [START_REF] Marsh | Home Sweet Home? The Impact of Poor Housing on Health[END_REF]

LATENT VARIABLE MODEL FOR HOUSING DEPRIVATION

Previous section's review allows us to have a wide range of housing characteristics to construct a specific deprivation index. Once these characteristics are chosen there are three questions to be resolved: what method should be used to test if a set of indicators is suitable to define the latent notion of housing deprivation, how are the different insufficiencies to be aggregated, and where the deprivation threshold should be set.

The review above offers different answers concerning the aggregation of indicators. Like income poverty, no clear consensus has been reached regarding how to set a deprivation threshold. Some authors set the threshold based on the insufficiency of a specific number of commodities, others suggest that simply lacking a commodity implies deprivation and yet another group of studies set relative deprivation thresholds that are similar to income thresholds.

Latent trait and class models offer a suitable methodological framework to provide a response to the two-fold problem posed by the aggregation of housing conditions and the setting of a threshold. The notion of housing deprivation fits in well with the idea of an insufficient functioning. Latent variable models use multivariate analysis techniques to measure non-observable This technique can empirically assess and test whether a specific set of indicators constitutes a suitable structure to measure the same latent concept 3 . Furthermore, these models allow one to synthesise a set of partial indicators on a single phenomenon under a single index based on the correlation of its components and their mutual dependence on the latent variable. These techniques are appropriate for the nature of the set of observed variables and allow different weightings to be assigned to them. More specific latent class models have the advantage of assigning each individual to a different class depending on the level and kind of deprivation suffered. In this way, the arbitrariness of setting thresholds is partially overcome.

The key lies on determining whether the correlations between the observed housing conditions can be explained by a small number of latent variables and to test whether this set of indicators reveals a previously supposed hypothetical structure. Following the lines of the studies reviewed above, the hypothetical structure of housing deprivation could be made up of an insufficiency in hot running water, heating, space, a leaky roof, damp and rot in window frames and floors.

A Latent Trait Model for Housing Deprivation

Latent trait models are very similar to factorial analysis. However, they can be specifically applied to observed dichotomous variables. When focusing our attention on housing conditions, one can model the probability of a randomly chosen individual suffering deprivation of observed condition xi, given his/her position with regard to the vector of latent variables y, P(xi=1|y)= i(y). This conditional probability can be expressed as a linear function of the latent variables:

3 An alternative would consist of assessing the consistency of the deprivation indicators by estimating the Cronbach Alpha coefficient. Nevertheless, the use of such methods suffers from some important constraints [START_REF] Moisio | Latent Class Application to the Measurement of Poverty[END_REF].

F o r P e e r R e v i e w i (y) = i0 + i1 y1 + … + iq yq + i i = 1, .., p (1)
The hypothesis of linearity is subject to two important constraints. In (1), i(y) is a probability that takes on values between zero and one, while no constraints have been imposed on the right-hand side of the equation, which is why it can take on any real value. In addition, it is to be expected that the rate of change in the probability of a positive response (deprivation) is not the same for the whole range of y. In this case, a curvilinear relationship could be more suitable. A nexus linking probability and the latent variables needs to be introduced in order take these constraints into account. This nexus should project the range [0,1] in the range (-,+ ) and should be s-shaped.

The two commonly used nexus are the logit and probit functions. The latent variable is related to each observed housing condition through a logistic regression model in the model.

The latent variable obtained, which represents housing deprivation, can be discrete or continuous.

If the latent dimension or space is considered continuous in the application, the latent trait model will be estimated. If this latent space is considered as discrete, then the latent class model will be estimated. The latent trait model is defined as follows:

logit i (y)= log ( ) ( ) y y i i 1 = i0 + = q j j ij y 1 (2)
where i (y)=

+ + + = = q j j ij i q j j ij i y y 1 1 0 exp 1 exp (3)
where in the unidimensional case we use instead of the sum the expresion ( ) is for an observed condition, the greater will be the difference in the probability of obtaining a positive response from two individuals situated at a certain distance from the latent dimension. The higher the parameter is, the easier will it be to discriminate between two individuals depending on their deprivation concerning each observed condition.

y i i + 0 exp .

F

A special case of the one-dimensional latent trait model is the model developed by Rasch when all the discriminating parameters are equal ( 1= 2= ...= p):

( ) ( ) ( ) j i j i i y + + + = 0 0 exp 1 exp (4)
The latent variable y is substituted by j, with j=1,...,n and, as in the general case, i represents the probability that the average individual will suffer deprivation of the observed variable. This model meets the requirement that the values obtained for the latent variable based on The assumptions adopted by the latent trait model are conditional independence, the independent nature of the latent variables with standard normal distributions so that yj N (0,1), j = 1,...,q, and that the link function can be either a logit or a probit function. (5) where we assume the conditions of conditional independence, a Bernoulli distribution for each xi and independent latent variables:

( ) ( ) ( ) ( ) = = = p i i p p y x g y x g y x g y x x g 1 1 1 | | ... | | ,..., , ( ) ( ) { } ( ) { } ( ) i i x i x i i y y y x g = 1 1 | , ( ) ( ) ( ) q y h y h y h × × = ... 1 . ( 6
)
The parameters i0 and i1,..., iq, included in i (y) can be estimated by maximum likelihood. An EM algorithm [START_REF] Bock | Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm[END_REF][START_REF] Bartholomew | The Goodness-of-Fit of Latent Trait Models in Attitude Measurement[END_REF] is employed to estimate the model using the TWOMISS program (Albanese and Knott, 1990). Using the logit link function yields that the posterior distribution depends on the observed variables through q components. These components, called 'sufficient statistics', are given by:

Xj = = q i i ij x 1 , j = 1,...,q, with q<p (7)
The components, which are a weighted sum of the observed responses using as weights the discrimination coefficients ( ij), are used to score the individuals on the latent dimensions. The mean of that distribution, E (yj | x1,..., xp), j=1,...,q, can also be used to scale individuals. In order to validate the model, there are various goodness-of-fit measures [START_REF] Bartholomew | The Goodness-of-Fit of Latent Trait Models in Attitude Measurement[END_REF]]. The most common involve computing a Pearson 2 or the LR. Both statistics compare the observed frequency of each response pattern with the expected frequency:

2 = ( ) ( ) ( ) ( ) p r r E r E r O 2 2 (8) LR = 2 ( ) ( ) ( ) = p r r E r O r O 2 1 ln (9)
where r represents a response pattern, and O(r) and E(r) represent, respectively, the observed and expected frequencies. Both statistics are distributed under the null as a 2 , with degrees of freedom equal to the number of different response patterns minus one minus the number of independent parameters.

Another option could be to use the relative change in the likelihood ratio statistic when we move from the independence model to the latent variable model in which the discriminating parameters are equal to zero ( i1 =...= iq = 0). This comparison offers information on the amount of association among the x variables explained by the latent variables, T =

2 0 2 1 2 0 G G G
, where G 2 0 is the likelihood ratio test of the independence model. A final alternative, which we will also use in our subsequent estimation, is to compute the Pearson's 2 statistic for pairs and triplets of responses. These values are equivalent to the residuals and offer us information on how well the model predicts the two and three way margins. The probability of a randomly chosen individual suffering deprivation in one of the observed housing conditions is now defined conditional on the latent class j (j = 1,..., K, where K denotes the number of latent classes):

Types of Housing Deprivation

ij = P(xi = 1| j), j = 1,...,K (10)
Each household has a prior probability j of belonging to one of the j types of deprivation defined, given that j = 1,...,K and

1 1 = = K j j .
A possible latent class model for housing deprivation should have three components:

1) Prior probabilities j, j = 1,.

2) The conditional probabilities of obtaining a positive response for an observed variable xi,

given latent class j, ij, where i = 1,..., p 3) And the common distribution of all the observed variables:

f(x1, x2, ..., xp) = ( ) ( ) = = = = p i x ij x ij K j j K j p j i i j x x x g 1 1 1 1 2 1 1 | ,..., , (11)
The parameters are estimated through an EM algorithm in order to calculate the model with unobserved variables 4 . The model rests on the assumption of conditional independence. This implies that the vector of latent variables is sufficient to explain all the associations among the housing insufficiencies in each household regarding the different housing characteristics. All the information concerning the assignment of individuals to each latent class can be found in the ensuing distribution of latent classes according to the existence or absence of insufficiencies in the housing variables.

P(j|x1,...,xp), j= 1,…,K (12)
The latent trait and class models meet the requirements set out to construct a housing deprivation index. On the one hand, they allow a set of housing conditions to be synthesised into a single index based on the correlation of these characteristics and their mutual dependence on the latent variable.

On the other, these techniques are suitable for the nature of observed conditions and allow us to assign different weightings to them. Lastly, the latent class model has the advantage of assigning each individual to a different class of housing deprivation.

AN ESTIMATE INDEX OF HOUSING DEPRIVATION FOR SPAIN

Choosing Indicators

4 We use the EM algorithm proposed by [START_REF] Bartholomew | The Goodness-of-Fit of Latent Trait Models in Attitude Measurement[END_REF]. The unit of analysis used is the household. However, in some cases, the need to analyse personal Applying the aforementioned criteria allows us to obtain a set of conditions representing a dwelling's functionings. These can be grouped together under the insufficiency of basic facilities (hot running water, heating and overcrowding) and under structural problems (leaky roof, damp walls/floors and rot in window frames and floors). As Table 1 shows, these indicators have a significant relationship with households' equivalent income6 . The Pearson correlation coefficient is significant and the association coefficient (Cramer's V) is greater than 0.1. The set of indicators chosen also groups together the housing conditions enjoyed by most of society. Apart from heating, between 85% and 90% of Spanish households do not suffer from insufficiencies in the characteristics chosen. Concerning the third criterion, it can also be observed that the households lacking hot running water or heating, or those suffering from structural problems such as damp or rot in window frames or floors also concentrate the greatest health disadvantages7 .

[

TABLE 1]

Among the set of housing conditions, the most controversial are the lack of heating and overcrowding. There are doubts about whether the lack of heating in some households really constitutes a problem of deprivation due the benign climate in some regions of Spain. For this reason a specific analysis of households stating that they lacked this commodity was conducted.

Results showed that most households lacking heating (around 70%) could not afford it.

Nevertheless, an analysis of the relationship between the lack of heating and the geographical location of households revealed that most households located in regions with high temperatures did not have heating. This fact meant that the indicator had to be redefined. We chose to consider that the lack of heating in these regions did not imply a state of deprivation.

Secondly, establishing the space a person needs to live is necessarily a subjective question. An usual practice is defining overcrowding as having less than one room per person or more than one person per room [START_REF] Dale | Housing Deprivation and Social Change : A report based on the analysis of individual level census data for 1971, 1981 and 1991 drawn from the Longitudinal study and the Samples of anonymised records[END_REF]. A smaller space can lead to health or psychological problems, such as the lack of privacy. In this paper, having a number of rooms less than the number of adults (older than 16 years of age) making up a household is used as a general indicator of overcrowding 8 .

However, the contrast between the self-assessment of overcrowding made by households and the indicator above is striking. Only 25% of households that stated they lacked space in their dwellings actually suffered from overcrowding as defined herein. This makes it necessary to interpret the results of this variable with caution. As will be seen further below, it also suggests the need of estimating the sensitivity of alternative measures that take into account the composition and size of the household.

Results of the Latent Variable Model

The possible combinations of the housing conditions chosen give rise to different levels of deprivation. Applying the latent variable model to ECHP data makes it possible to have a housing deprivation index. The partial indicators chosen make up the supposed a priori structure that will be tested.

[TABLE 2]

Estimating the latent trait model shows that the vector of observed variables (having hot running water, heating, a leaky roof, damp walls or floor, rot in window frames and floors, and overcrowding) and the correlations between such variables can be summed up by a single latent variable (Table 2). The goodness-of-fit measurements show an acceptable fit and confirm the assumed a priori structure. The last column of the table shows the probabilities of a median individual suffering deprivation of the six housing indicators. The estimates of the discrimination parameters ij are shown in the fourth column. These represent the weight given to each one of the observed variables. The values of these parameters show that the heating and overcrowding 8 Another possible way of defining overcrowding could be to consider the number of rooms available corrected by an equivalence scale [START_REF] Chiappero | A Multidimensional Assesment of Well-being Based on Sen's Functioning Approach[END_REF]. Once the housing deprivation index is obtained, two especially relevant questions arise. Firstly, an attempt is made to analyse whether there are different types of deprivation, such as an insufficiency in basic facilities or the presence of structural problems. Secondly, we could also test whether or not there are differences in the households' personal or socio-economic characteristics. In order to provide a response to the first question, a latent class model can be estimated. This allows us to differentiate the results into four different kinds of housing deprivation (Table 3).

[TABLE 3]

The estimated matrix ( i1, i2, i3 y i4) shows the probability of a randomly chosen household suffering deprivation of each one of the six housing indicators given its situation in the different latent classes. It can be seen that Class 1 includes those households having the lowest probability of suffering housing deprivation. On the other hand, households with the highest probability of suffering multiple deprivation belong to Class 4. It is also very interesting to highlight the difference between households included in Classes 2 and 3. The former includes households having a greater probability of suffering a lack of basic housing facilities (such as hot running water, heating or space) than having structural problems. On the other hand, households included in Class 3 have a very high probability of suffering structural problems and a very small, almost negligible, probability of lacking basic facilities. From the estimated model it can be deduced that 69% of the households included in the sample belong to Class 1 ( 1), 16% belong to Class 2 ( 2), 12% to Class 3 ( 3) and 3% to Class 4 ( 4). Various questions arise from the previous empirical exercises. Is housing deprivation homogenously distributed among the population? Which demographic groups run a greater risk?

DETERMINANTS OF HOUSING DEPRIVATION

How does this risk increase in the face of specific changes in socio-economic characteristics? Table 4 gathers information on the statistical associations among the different housing deprivation indicators and different socio-economic factors. Results show that most of the partial housing deprivation indicators have a significant relationship with the various socio-economic factors included. As was mentioned previously, the housing deprivation indicators have a significant relationship with households' income. Likewise, the main source of household income, the ability to make ends meet, whether the dwelling is rented or owned, its geographical location and the household's composition and size, as well as the household head's educational attainment, state of health, age and civil status all have a significant relationship with the housing deprivation indicators.

The relationship with the household head's social relationships, however, is limited.

[TABLE 4]
Generally speaking, the overcrowding indicator yields different results from the rest of the housing conditions. As was mentioned above, establishing the amount of space a person needs to live is an issue permeated by value judgements. It is for this reason that we estimated alternative indicators in order to assess the consistency of the definition initially adopted. Most of the studies focusing on housing deprivation use a value of less than one room per person as a criterion to measure overcrowding. Other studies, however, criticise this definition and propose others that are more sensitive to a household's composition [START_REF] Murie | Housing Inequality and Deprivation[END_REF], [START_REF] Chiappero | A Multidimensional Assesment of Well-being Based on Sen's Functioning Approach[END_REF]]. We defined two new overcrowding indicators:

( ) where S represents the size of a household and Z indicates whether it is composed of a couple or an adult. The first indicator would reflect problems of overcrowding when there are two or more people per room. One of the rooms would belong to the couple making up the household or the adult considered as household head. However, this indicator only takes into account the number of bedrooms without designating any room as a "common living area". The second indicator is constructed in the same way as the first. In this case, however, a room is designated as the family's common living area. To a certain extent, these indicators define overcrowding problems more strictly, as the threshold below which a household is considered as overcrowded is having two or more people per room. Estimates carried out with these alternative indicators generally confirm that the singularities of overcrowding, when compared to other housing deprivation indicators, are repeated with these alternative definitions 9 . Due to this, the initial definition will therefore be adopted here with the necessary caution.

2 1 Z S H d + = and ( ) 2 2 Z S H v + = (13)
Once the intensity of the possible relationships between housing deprivation and the household's characteristics, as well as those of the household head, have been analysed, the most immediate question arising is which categories of the population run a greater risk of suffering this problem. In order to identify the specific effect of each of these variables on housing deprivation, it is necessary to estimate an empirical model that integrates the different dimensions set out previously.

The different classes of deprivation identified by the latent class model were chosen as dependent variable. This variable requires the estimation of a multinomial logistic regression model with four alternatives. Alternative 1 represents the first latent class (very low or negligible levels of housing deprivation). Alternative 2 represents the second latent class (notable insufficiency in basic housing facilities). Alternative 3 represents the third latent class (large presence of structural problems).

Alternative 4 represents the fourth latent class (multiple housing deprivation). The probability of a household belonging to a specific class is compared to the probability of belonging to another class, while the former serves as a reference. More precisely:

9 Other studies have also found that the results of the overcrowding variable are different from the other housing indicators [START_REF] Marsh | Home Sweet Home? The Impact of Poor Housing on Health[END_REF]. As in the previous analysis, the explanatory variables included in this model offer information on households' economic, work, social and, in general terms, living conditions. More precisely, the variables included are income deciles10 , the main source of income, whether the dwelling is rented, provided free or owned, the region of residence, the household's size and composition, the frequency with which family and friends are visited and the household head's educational attainment, age, sex and civil status.

[TABLE 5]

Table 5 shows the estimated model's variables and significant categories. Similarly to the previous descriptive analysis, it can be seen that household income is one of the variables exerting the greatest influence on the risk of suffering housing deprivation. Belonging to different income deciles causes different effects on the probability of belonging to one or another of the housing deprivation classes11 . Concerning insufficiencies in basic facilities, the source of income constitutes a significant factor. Households whose primary source of income is made up of welfare benefits have a greater probability of belonging to the aforementioned class when compared to households with earnings as their main source of income.

The factor having the greatest influence in quantitative terms is whether the dwelling is rented, provided free or owned. The relative risk of lacking basic facilities, suffering from structural problems or suffering from multiple housing deprivation is greater for households living in rented or provided free dwellings than those living in owned properties. There are also significant differences in the possibility of belonging to one or other category depending on the household's composition. The relative risk run by couples with children of suffering an insufficiency in basic facilities or multiple deprivation as opposed to not suffering any kind of deprivation at all is lower than the risk run by people over 65 years of age living alone.

Generally speaking, single people run a greater relative risk of suffering some kind of housing deprivation. The same happens with separated people 12 . Age also constitutes an influencing factor in the probability of suffering structural housing problems. The household head's educational attainment and level of social integration only appear to be significant for the relative risk of lacking basic housing facilities.

Lastly, the regions running the greatest relative risk of suffering housing deprivation differ depending on the kind of housing deprivation considered. Most regions have a lower probability of suffering some kind of housing deprivation than the Northeastern region (Galicia, Asturias and Cantabria), except for the central area (Castilla y León, Castilla-La Mancha and Extremadura), which has a greater probability of suffering structural housing problems.

CONCLUSION

Housing constitutes one of the basic commodities that determine the individual's well-being.

However, defining what an adequate dwelling is raises numerous questions. Among others, these include what conditions must housing meet, what dimensions are relevant, how should they be measured and what combinations of conditions allow to reach a minimum level of well-being. This paper offers a methodology, the latent variable models, which have been rarely used until now to offer a response to these questions. These models allow responding to the two-fold problem of aggregation and setting a threshold. Assigning each individual to a different class depending on the level and kind of deprivation enables the habitual arbitrariness of establishing thresholds to be partially overcome.

12 Several studies point out the fact that the lack of the spouse influence on the stability in their income level (Canto, 2002). Other important implications are the answer provided to the question whether housing deprivation is homogenously distributed among the population or whether there are specific characteristics of deprived households. It appears that the incidence of housing deprivation is strongly tied to a household's level of income. However, the impact of this factor is different according to the class of housing deprivation. The source of income also contributes to explain the risk of suffering housing deprivation, but specifically deprivation of basic facilities. Other important result affecting public policies is that the incidence of the different kinds of deprivation is greater among households that rent dwellings. The household composition is other of the determinant factors of the risk of suffering housing deprivation. Being single person household has more probabilities of being deprived than couples.

These results could lead to a deeper discussion on the design of some policies aimed at aiding disadvantaged households. The heterogeneity found concerning both the kind of deprivation suffered, as well as the different kinds of households affected by each problem puts into question

  between lacking a commodity and income, and information should exist about the reasons behind the lack of commodities 1 .

  also looked into the effects of housing deprivation on individuals' health, but used longitudinal data. The criterion they used to select housing indicators was the association with health status and the existing correlations among the indicators.

  regarding the basic dimensions of housing deprivation can be gleaned from this review. These dimensions include: the lack of basic facilities, structural problems and overcrowding. Nevertheless, the set of indicators defined varies among the different studies depending on the criteria used to choose them. Generally speaking, the indicators are usually chosen according to the existing correlation with households' income. In other cases, however, the decision depends on what the individuals state they perceive as basic needs, which generally coincide with the commodities or conditions enjoyed by the majority of society. In other studies the definition of housing deprivation is determined by the housing characteristics more related to individual's health.

  a set of observable variables. They allow the latent concept of multiple housing deprivation to be measured through various basic conditions.

  model, each observed housing condition would correspond to q+1 parameters ( i0 and the discriminating parameters i1,..., iq) to be estimated. If y1=...=yq=0, i(0)=[exp( i0)/(1 + exp ( i0))]. Parameter i0 represents the probability of the average individual suffering deprivation of the observed condition, while ij with j=1are discriminating parameters. The greater the value of ij

  j and that the total number of positive responses for the observed condition xi,

  Given that only the observed variables x1,...,xp can be known, the estimation of the unknown parameters is based on their joint distribution function:

  Estimating the parameters allows us to assign the latent variable values to each individual or household as a function of the presence or lack of the observed conditions. All the information about latent variables is contained in the posterior distribution of such variables given a set of observed responses (h(y | x1,..., xp)), which we will call the response pattern [x = (x1,..., xp)].

  can be considered as a special case of the latent trait model. The main differences between the latent trait and the latent class model consist of, as pointed above, the latent trait model assumes that the latent variables are continuous where the latent class model assumes that the latent space consist of a number of mutually exclusive classes. Additionally, the latent trait model imposes an explicit relationship between the probability of a positive response and the latent variables through the logit model where the latent class model does not impose any functional form on the probability. Despite the differences outlined above the latent class model can be considered as a special case of the latent trait model if it is assumed that the distribution of the continuous latent variables is discretised into a number of points.One of the advantages of the latent class model is the possibility of stratifying different forms of deprivation. The specific objective of this model is to reduce the dimensions of the observed variables by using a number of mutually exclusive classes. Therefore, we can assign each individual to the relevant class depending on the deprivation suffered in each observed housing condition.

  The two fold goals of estimating the extent of housing deprivation in Spain and identifying the socio-economic patterns of this problem make it necessary to search for an adequate data source. It should contain enough information on housing conditions and household characteristics. The European Community Household Panel (ECHP) contains valuable information on the facilities and specific problems of each individual's dwelling, in addition to offering a wide range of household socio-economic characteristics. It includes information on basic facilities (having a separate bath or shower, indoor flushing toilet, separate kitchen, hot running water, heating, terrace, courtyard or garden, number of rooms), the presence of specific structural problems (having noise problems, being too dark, a leaky roof, damp walls or floors and rot in window frames or floors), as well as the existence of problems in the surrounding areas such as environmental problems or crime and vandalism.

  and socio-economic characteristics associated with running a greater risk of suffering housing deprivation has made it necessary to consider representative individuals of each household, such as the household head. The person providing the greatest amount of resources to the household is defined as such. The data used in this paper are from 1998 and comprise a sample made up of 5476 households. The reason for choosing this year is twofold. On one side, preliminary work with the ECHP showed a high number of transitions between the different states of housing deprivation.Choosing different waves of the ECHP could therefore yield different results. The main reason for choosing an intermediate year (1998) is the fall in the number of observations as new waves of the ECHP were available. On the other side, 1998 results are comparable to those previously obtained in the Spanish literature on housing deprivation 5 . The choice of indicators was made by taking into account three criteria, namely: the correlation between income and housing conditions, the choice of conditions enjoyed by most of society and conditions that harm individual's health.

  weight than the others. Simultaneously, a very small percentage of the population lacks heating or suffers overcrowding conditions. Hence, to a certain extent, we could say that the estimated deprivation index contains an important relative component.

  yi is a latent variable indicating the probability of belonging to each class, is the vector of parameters corresponding to the x explanatory variables and is the random error, which is assumed to follow a logistic distribution.This model provides a set of probabilities for the J+1 possible alternatives for each household having xi characteristics. If the J perturbations are independent and distributed identically with a log-Weibull function, ( )( ) ij e F ij = exp, and the 1=0 normalization rule is applied, then: model's coefficients cannot be interpreted in the same way as the ones derived from the probability of suffering a specific kind of deprivation in the face of a change in one of the explanatory variables. They show the effect of the variables on the probability of a specific alternative when compared to the reference. The sign of these coefficients does not necessarily have to coincide with the sign of the marginal effects. The individual characteristics' marginal effects on the probabilities can be estimated as follows:

  Applying these models to the ECHP data has enabled us to estimate and characterise housing deprivation in Spain. Results of the latent trait model show that a single latent variable allows us to identify the variable underlying housing deprivation, and confirm the assumed a priori structure (having hot running water, heating, a leaky roof, damp, rot in window frames or floors and overcrowding). They also corroborate that the set of housing conditions are satisfactory indicators of insufficiencies in the basic functioning of a dwelling. The estimated parameters shows, on one hand, that the lack of heating and, to a lesser extent, overcrowding are generalised problems throughout society. On the other hand, the weighting structure of the model assigns these indicators less importance than bad housing conditions suffered by a small part of population.Results of the latent class model also show that we can differentiate among different kinds of housing deprivation.

  . Results suggest the need for designing policies that are differentiated according to the different classes of problems and social groups affected.

  .., K

	F o r
	P
	e
	e r
	R e v
	i e
	w

Table 1 Housing Conditions Association with Income and Health Status

 1 Weighted data based on the variable representing cross-sectional weighting for the last wave or period of the ECHP.

	Housing Conditions	Frequencies (%) Deprivation No Deprivation	Cramer's V ( 2 ) Equivalent Income (deciles)	Health Status
	Kitchen	1,4	98,6	0,041 (0,411)	0,028 (0,493)
	Bath	1,1	98,9	0,080 (0,000)	0,058 (0,000)
	Toilet Hot Running	0,6 2,1	99,4 97,9	0,056 (0,053) 0,109 (0,000)	0,038 (0,156) 0,109 (0,000)
	Water
	Heating	42,3	57,7	0,213 (0,000)	0,083 (0,000)
	Garden Noise	24,8 30,7	75,2 69,3	0,080 (0,000) 0,041 (0,414)	0,056 (0,004) 0,030 (0,461)
	Light	14,5	85,5	0,065 (0,000)	0,040 (0,063)
	Leaky Roof	8,9	91,1	0,151 (0,000)	0,096 (0,000)
	Damp Rot in	17,6 5,3	82,4 94,7	0,175 (0,000) 0,122 (0,000)	0,098 (0,000) 0,084 (0,000)
	Window
	Frames or
	Floor Overcrowding	7,5	92,5	0,130 (0,000)	0,040 (0,115)
	Pollution	13,5	86,5	0,064 (0,001)	0,028 (0,582)
	Crime and	17,7	82,3	0,048 (0,122)	0,061 (0,000)
	Vandalism

Note:

Table 2 Results of the Latent Trait Model for Housing Deprivation (1998)

 2

	Indicators	0i	Standard Error	1i	Standard Error	Standardised 1i P(X=1/Z=0)
	Hot Running Water	-4.997	0.209	1.612	0.142	0.850	0.007
	Heating	-0.395	0.029	0.406	0.044	0.376	0.402
	Leaky Roof	-4.521	0.284	2.859	0.246	0.944	0.011
	Damp	-3.530	0.352	3.431	0.415	0.960	0.028
	Rot in Window	-4.365	0.181	2.037	0.139	0.898	0.013
	Frames or
	Floor
	Overcrowding	-2.639	0.056	0.205	0.076	0.201	0.067
	Tests
	% G 2 explained: 89.5
	Likelihood Test 90.128
	2 (22) of observed responses: 52.904
	2 (22) of all responses: 71.736
	Total expected frequencies: 5457

Table 3 Classes of Housing Deprivation

 3

	Indicators	( ˆ1 1 = x P i	=	1	|	) 1	( ˆ2 2 = x P i	=	1	|	2	)	( ˆ3 3 = x P i	=	1	|	) 3	( ˆ41 4 = x P i	=	1	|	4	)
	Hot Running	0.0000				0.0500					0.0307				0.3366
	Water
	Heating	0.2963				0.6276					0.5620				0.7166
	Leaky Roof Damp	0.0155 0.0000				0.0764 0.0587					0.3657 0.9679				0.8710 0.9998
	Rot in	0.0064				0.0698					0.1799				0.6339
	Window
	Frames / Floors
	Overcrowding	0.0602				0.0926					0.0641				0.1054
		1 ˆ					ˆ	2						ˆ	3					ˆ	4
		0.6901				0.1660					0.1172				0.0266
	Test
	2 (21)=33.40 G 2 (21)=46.14

Table 4 Housing Conditions and Household Characteristics (Association Coefficients)

 4

				Cramer's V ( 2 )
	Characteristics	Hot Running Water	Heating	Housing Conditions Leaky Roof Damp	Overcrowding	Rot in Window Frames or Floors
	Region	0.085 (0.000) 0.567 (0.000) 0.139 (0.000) 0.242 (0.000)	0.105 (0.000)	0.127 (0.000)
	Normalized Equivalent income	0.046 (0.004) 0.153 (0.000) 0.083 (0.000) 0.116 (0.000)	0.103 (0.000)	0.061 (0.000)
	Equivalent income (deciles)	0.109 (0.000) 0.213 (0.000) 0.151 (0.000) 0.175 (0.000)	0.130 (0.000)	0.122 (0.000)
	Main source of income	0.119 (0.000) 0.135 (0.000) 0.124 (0.000) 0.108 (0.000)	0.103 (0.000)	0.094 (0.000)
	Ability to make ends meet	0.141 (0.000) 0.114 (0.000) 0.163 (0.000) 0.171 (0.000)	0.105 (0.000)	0.141 (0.000)
	Rental or Owned	0.081 (0.000) 0.103 (0.000) 0.047 (0.000) 0.099 (0.000)	0.022 (0.275)	0.115 (0.000)
	Educational attainment	0.091 (0.000) 0.200 (0.000) 0.095 (0.000) 0.117 (0.000)	0.027 (0.264)	0.066 (0.000)
	Health	0.109 (0.000) 0.083 (0.000) 0.096 (0.000) 0.098 (0.000)	0.040 (0.115)	0.084 (0.000)
	Chronic Illness	0.077 (0.000) 0.065 (0.000) 0.098 (0.000) 0.071 (0.000)	0.042 (0.008)	0.062 (0.000)
	Social relationships 0.034 (0.286) 0.035 (0.232) 0.039 (0.133) 0.058 (0.002)	0.046 (0.041)	0.034 (0.291)
	Sex	0.058 (0.000) 0.028 (0.039) 0.055 (0.000) 0.061 (0.000)	0.005 (0.714)	0.050 (0.000)
	Age	0.094 (0.000) 0.125 (0.000) 0.077 (0.000) 0.052 (0.002)	0.165 (0.000)	0.049 (0.005)
	Satisfaction with dwelling	0.170 (0.000) 0.142 (0.000) 0.212 (0.000) 0.268 (0.000)	0.128 (0.000)	0.276 (0.000)
	Household size	0.152(0.000)	0.164(0.000)	0.096(0.000)	0.111(0.000)	0.554(0.000)	0.099(0.000)
	Household composition	0.154 (0.000) 0.184 (0.000) 0.131 (0.000) 0.132 (0.000)	0.327 (0.000)	0.133 (0.000)
	Marital status	0.120 (0.000) 0.132 (0.000) 0.124 (0.000) 0.093 (0.000)	0.127 (0.000)	0.114 (0.000)

Table 5 (

 5 Continued) Categories of reference: single-person household, resident in northeast region, in first decile of equivalent income, with owned dwelling, with male household head, married, salary earner, sees friends or family most days, university degree, less than 30 years of age.

	Class 4 Income Income (decile 7) Income (decile 8) Income (decile 9) Income (decile 10) Household Size Household Composition Couple 2 children<16 Dwelling Rented or Owned Rented Provided free Regions Northeast Region Madrid Region Central Region Eastern Region Southern Region Civil Status Divorced Single Note: Page .410 .200 -1.820 0.069 .192 .127 -2.490 0.013 .186 .124 -2.510 0.012 .268 .171 -2.060 0.039 1.210 .124 1.860 0.063 .058 .066 -2.480 0.013 .288 .158 -2.260 0.024 3.020 .774 4.310 0.000 2.151 .774 2.130 0.033 .489 .160 -2.180 0.029 .399 .175 -2.080 0.037 .532 .164 -2.040 0.041 .208 .076 -4.270 0.000 .350 .113 -3.250 0.001 3.951 2.746 1.980 0.048 2.976 1.124 2.890 0.004

36 of 36 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

[START_REF] Ayala | Housing deprivation and health status: Evidence from Spain[END_REF] estimated to what extent living in poor housing conditions could determine individuals' health status using the 1998 ECHP data.

The modified OECD scale is applied (taking a single-person household as a reference and giving a weighting of 0.5 to the rest of the adults and 0.3 to children under 14 years old).

Health status is defined based on a self-assessment made by the individuals themselves: very bad, bad, regular, good and very good.

We consider income as one of the covariates explaining housing deprivation despite this variable was one of the criteria used to select housing indicators. This procedure allows us to identify income's influence on housing deprivation once we have controlled for other factors. Furthermore, it is also interesting to test whether or not belonging to different income deciles cause different effects on the probability of belonging to a specific class of housing deprivation.

Households with the highest income levels (ninth decile) can serve as an example. They suffer a relative risk of belonging to the second class that is 57% lower than that of the households in the first decile. This percentage falls to 49% for the third class and rises to 81% for multiple deprivation.


Alan Collins
email: alan.collins@port.ac.uk

Victor Fernández Blanco

Juan Prieto-Rodriguez

Víctor Fernández-Blanco

Juan Prieto-Rodríguez

Víctor Fernández Blanco

CHARACTERISTICS OF BUYERS AND RENTERS OF CULTURAL GOODS: THE CASE OF MOVIESª

Keywords: video, consumers, demand, rent, multinomial probit video, rent, demand, domestic leisure, multinomial probit models

published or not. The documents may come   L'archive ouverte pluridisciplinaire

Introduction

Empirical academic research on the movie video market, especially on the demand side, has been relatively sparse 1 . To some extent, this deficiency has been addressed by the availability of data from the Spanish Cultural Consumption Habits Survey (Encuesta sobre Hábitos de Consumo Cultural -EHCC) established by the Office of the Spanish Rights of Authors Collector (SGAE).

The survey was conducted in Spain during 1998. Our aim was to use this database to analyse some demand aspects of the Spanish video industry. In particular, the key objective was to inform decision-making by identifying the key characteristics of consumers of videotape movies. on videotape. To this end, models are estimated of the frequency of video buying and renting.

Multinomial probit models are developed to investigate whether there are two different video markets (renting and buying) and to identify the respective socio-economic characteristics of the consumers in these markets. The influences of various film genres, on the renting of videotapes 2 are also considered.

Watching movies on home videos is clearly a different experience from a movie theatre trip and the medium of television has also evolved to become a powerful force and rival to the film industry. That said, while movies lost a significant part of its audience at the movie theatres, movies themselves had become one of the principal elements of television channel programming. Hence, it might be argued that people were 'reminded' about consuming movies even more than before the development of the television industry 3 .

The development of videotapes during the 1980s and 1990s created new opportunities to watch movies, since they increased consumers' autonomy in their leisure decisions and opened an important new audiovisual market. Thus, in 1996, in the most developed countries, over two-thirds of households had a VCR and, in the USA, film distributors obtained more revenues from home video than from movie theatres (Vogel, 2004) 4 . This situation currently remains since "...in 2003, Americans spent $22.5 billion on DVDs and videocassettes compared with $9.2 billion at the box office." [p.57] (The Economist, 2004). These figures reflect how important revenues from rentals The Spanish experience mirrors the US case albeit with some time delay. By the 1990s consumption at-home was the most important way to watch films. Television, videos and, in very recent years, DVDs, are the principal windows that allows this domestic consumption. There is a big difference between TV and video (or DVD). In the last two cases, the consumer has greater autonomy in deciding what movie s/he is going to watch and to determine his (her) own viewing schedule. However, on TV, the programmers decide movie show times and the nature of the movies supply. A similar restriction applies to movie theatre attendance since consumers cannot modify the movie theatre's line up of movie presentations. Videotapes (and now DVDs) have allowed consumers to adjust their film consumption to their own timetable or time preferences and this characteristic is not shared with any other form of film consumption. One may consider TV film watching (including pay-per-view broadcasting) as the closest competitor for videotape film consumption since it is a feasible alternative for at-home film watching; however, consumers' autonomy is still lower, even though cable and satellite TV have increased the range of movies supplied and hence, consumers' flexibility in viewing. Given all these relationships, an important goal of this paper is to analyse the links between the movie videotape market and its alternatives for movie watching.

The paper is structured in the following manner. In the next section some connected literature is explored and then in the following section the main characteristics of the data set are briefly described. In section 4, the basic modelling framework is presented along with commentary regarding the basic a priori expectations concerning the buying and renting of videotapes. Section 5 then sets out a specific empirical investigation of the determinants of buying movies, including child-orientated movies. In section 6, the renting of videotapes is specifically analysed, taking into explicit account the effect of different movie genres. Concluding remarks are then offered in the final section.

Earlier Work

Although there are some works on video economics, specially on release strategies (Frank, 1994; Waterman and Lee, 2003), there are no previous specific studies of the demand for movies on videotape, though the impact of home video cassette recorders (VCRs) has been considered in the context of time series models of movie theatre demand (Cameron 1988; and, specifically Spanish case, Fernández-Blanco et al 2002). There has also been some exploration of revenue sharing in the video rental industry (Dana and Spier 2001). Additionally, there has also been some work in the international trade of VCRs with respect to the role of voluntary export restraints (Ohashi 2002).

Arguably, of more direct relevance to this study is work based on purchase or rent decisions.

Yet inevitably much of this work is based on corporate plant capacity decision-making and on the consumer side, most of the work is based on housing tenure choice issues, or the market for cars (e.g. Mannering et al 2002, Johnson and Waldman 2003). Yet arguably these studies do not sufficiently relate to consumer decisions in the market for small low-value durable informationbased goods, such as movies on videotape. Interestingly, on the consumer side there has been considerable work on the hybrid form of 'rent-to-own' contracts on durable household consumer goods as distinct from direct purchasing (Anderson and Jackson 2001, McKernan et al 2003). Such hybrid contractual arrangements, however, do not seem to feature in the market for movies on videotape since renting a movie is undertaken for its direct use value while buying a video principally offers the option value of having the film to view at any time. This difference between direct use value and option value may very partially explain some of the differences in the profiles of renters' and buyers' found in this study.

Of some further related interest is the work of Varian (2000) who investigated information goods such as books, journals, computer software, music and videos. He noted that these can be copied, shared, resold, or rented. He found that when such opportunities for sharing are present, the content producer will generally sell a smaller amount at a higher price which may increase or decrease profits. Furthermore, he identified three circumstances where profits increase: (1) when the transactions cost of sharing is less than the marginal cost of production; (2) when content is viewed only a few times and the transactions costs of sharing are low; and (3) when a sharing market provides a way to segment high-value and low-value among consumers. While not focusing on an analysis of content producer strategy, this empirical study of buying and renting consumer (demand) characteristics does provide some limited sidelight into the nature of sharing behaviour, but only within the specific context of the household.

Key Features of the Data

The EHCC survey from which the dataset is assembled was conducted in Spain during 1998. interviewed. The total sample size was thus 12072 people. 6 This survey can be defined as an opinion survey dedicated to analyzing the nature of cultural behaviour in Spain. It covers the most important fields of cultural consumption, including: performing arts, cultural industries (music, publishing and the audiovisual industry -including movie theatre attendance, video and TV viewing) and other group of leisure activities such as cultural formation, attendance at museums, natural parks, etc. It combines all this information with a set of socio-economic characteristics for the individuals interviewed, including: age, level of educational attainment, marital status, familiar responsibilities, and employment activity and family income. The key variables used, their variable format and their basic descriptives are presented in the Appendix.

By way of a brief snapshot from this dataset it can be seen that in 1998 home video consumption was more important than movie theatre attendance in Spain. In Figure 1, only 5.9% of Spanish people went to movie theatres once a week, meanwhile 13.8% rented one or more videotapes every week7 . These figures do not include movie watching on TV or self-recorded videotape watching. Including them we can see that, film consumption at home is at least three times higher than that associated with movie theatre attendance.

Modelling Framework and Key Expectations

In the EHCC survey there are two questions about how frequent the interviewee buys or watches rented videotapes with a wide range of possible ordered answers8 . Hence, we define two dependent variables (BUYVIDEO and RENTVIDEO) that allow us to discover buyers' and renters' main characteristics applying two ordered probit models.

Since renting videotapes is a more frequent activity than buying, the EHCC has included two different scales of frequencies for these two activities. Thus, the relationships between the unobserved preferences and the structure of answers are different in both cases and can be defined, following Greene (2003) as follows: a) Buying videotapes

y i = never = 0 if y i * V 0 y i = less than one a year = 1 if 0 < y i * V µ 1 y i = once a year = 2 if µ 1 < y i * V µ 2
y i = two or three times a year = 3 if µ 2 < y i * V µ 3 y i = four or five times a year = 4 if µ 3 < y i * V µ 4 y i = six to nine times a year = 5 if µ 4 < y i * V µ 5 y i = ten or more times a year = 6 if µ 5 < y i * b) Watching rented videotapes y i = never = 0 if y i * V 0 y i = less than five times a year = 1 if 0 < y i * V µ 1 y i = five or six times a year = 2 if µ 1 < y i * V µ 2 y i = once a month = 3 if µ 2 < y i * V µ 3 y i = two or three times a month = 4 if µ 3 < y i * V µ 4 y i = once a week = 4 if µ 4 < y i * V µ 5 y i = more than once a week = 5 if µ 5 < y i *

The set of independent variables includes the interviewee's main socio-economic characteristics, the audiovisual home equipment and some variables that try to represent the relationships between video, movie theatre usage and TV.

We consider the characteristics of people who buy or watch a rented videotape via estimation of two ordered probit models. We can discern some characteristics in the models that simultaneously feature the renters and buyers general profile and confirm the basic a priori expected relationships (see Table 1). As expected, age has a negative effect, in that, the older a person is, the higher is the likelihood of them never buying or watching a rented videotape.

Moreover, we can detect evidence for a positive income effect, which is a common hypothesis in cultural consumption research and has been a feature of the results of many previous empirical studies. For instance, Frey and Pommerehne (1989, page 9) found that 'an increase in per capita income of 1 per cent raises the number of visits to performing arts institutions per adult by roughly 3 per cent' and Withers (1980) Living in a city is also shown to be positively related to consuming rented videotapes given the likely higher density and hence ease of exercising their renting opportunities (i.e. video stores).

The effect on buying is weaker since this is likely to be a much more infrequent activity. Since a significant percentage of the buyers are also collectors -people who want to build their own film library, they are likely to be more insensitive (or inelastic) to searching and other transactional costs.

We have found a complementary relationship between movie theatre attendance and video consumption: i.e. the greater the interest in movies declared by the interviewee, the higher the probability of buying and watching rented videotapes. This phenomenon is confirmed in other studies in other markets (see, for example, Collins and Hand 2005). Furthermore, these probabilities also increase with increased attendance at movie theatres. People who go to movie theatres monthly have the highest probability of watching rented videotapes, largely because they are typically people who have assumed household responsibilities and they may desire the video option to continue to satisfy their more intensive movie watching preferences. In a similar vein, we may posit a positive relationship between the consumption of television and video. They can be considered as complementary domestic (at-home) goods.

Determinants of Movie Videotape Purchases

Having examined the general characteristics of video buyers in the previous section we proceed to explore whether there are significant differences if we restrict our focus purely to movie buyers of videos.

As can be seen in Figure 2, combining general films and children's movies covers 90 percent of videotapes bought in Spain in 1998. This market share, capture almost the whole market.

Using two probit models, it is possible to analyse the features that define, both, films and childrenoriented movie buyers. The dependent variables (PVIDEOFILM and PCHILDRENV) take the value one when the last acquired videotape was a film (or a children movie, in the other case) and zero otherwise. Table 2 presents the results of these two model estimates. consumers for the big screen, are also more likely to use these means to watch movies at home. This is not to say, however, that it is the children themselves who necessarily want them to watch movies at home, since it may actually reflect their guardians' preferences. This probability of purchasing also increases with household size, especially with the number of household members above 14 years old, and this fact can be a sign of the presence of economies of scale in domestic movie consumption. While the direct household income variable is not statistically significant, it is contended that there is a positive income effect linked to the positive influence of social-economic grouping (the probability of buying a film is 3 percent higher when the interviewee belongs to higher socio-economic groups) and the negative effect of the variable STUDENT, usually related to people with scarce monetary resources, in terms of marginal effects being a student decreases by 3% the probability of buying a film. This argument is reinforced by the observation that heads of households buy more film videotapes that any other household member category.

Figure 2 Types of videotapes bought

Those who are greater consumers of audiovisual media generally also have a higher probability of buying a videotape, especially among those who go to a movie theatre or rent a video at least once a week. Indeed, their probabilities of buying increase 5 and 6 percent, respectively. So film lovers, who consume movies in any window, are also good buyers of films in videotapes 10 .

Since interest in theatre programmes on TV has a positive and significant impact on buying (interest in films on TV has also a positive but lesser effect) we can infer that culture-oriented people are more interested in buying films, perhaps because they want to construct their own film library or to own their favourite films.

Finally, the positive and significant coefficient of the HABITAT variable can be interpreted in terms of the higher the size of the population, the higher the presence of delivery opportunities.

The children's movie buying model presents some quite different results, probably because now the buyers purchase the videotapes for the children and principally not for him/herself. Hence, personal attitude and family variables are particularly influential. Women have a 1.5 percent higher probability of buying children's movies than men. The age variable displays U-shaped effect. It is decreasing till a minimum, at 21 years. Then, the probability of buying this type of videos begins to increase, corresponding to the range of ages when people take on family childrearing and childcare responsibilities.

change in each independent continuous variable or the discrete change for dummy variables shifting from cero to one. 10 If we consider the positive and statistically significant effect of the variable INTVTHEATER, that measures the interviewee's interest on theatre programmes on TV, we can conclude that film buyers are also people with some significant measure of cultural consumption-based motivation. The presence of children is clearly the most influential factor. Each additional family member under 14 years increases by 1.5% the probability of buying children's movie. This effect is reinforced with the negative influence of the number of household member above 14 years and the SINGLE variable that reduces this probability by 4%.

The interviewee and head of household's educational levels also have a significant effect.

But, in the former case, the highest effect corresponds to only primary educational studies.

However, the head of household's educational attainment effect is positive, with those having a university education displaying the highest probability of buying children oriented videotapes.

Hence, it is possible to contend that the use of video in children's leisure is more common within families having a strong educational level.

Variables related with income are not significant, except in the case of STUDENT. And this is also the situation in the HABITAT variable. Then, the demand for children's movies seems to be less sensitive to certain economic variables like income or supply opportunities which can be interpreted as a proxy for transactional costs.

When the audiovisual variables are analysed, some interesting results are found. First, attendance at movie theatres and watching rented videotapes in particular, have a positive and statistically significant effect on the probability of buying children's videotapes. Both set of coefficients are positive and decreasing with frequency, hence the higher the regularity that a person attends movie theatres or watches a rented film the higher the probability of buying a children oriented videotape. We can suggest that there are good movie consumers who buy videotapes for (their) children too and, hence, cultivate their cinema taste. This behaviour is very important to establish the future customers of this industry. On the other hand, interest in films on TV (INTVFILM) is not statistically significant. Hence, it may be suggested that film lovers that mainly In summary, in comparing the determinants of buying films or children's videotapes, it may be found that, the presence of children and the cultural environment of the family are the principal determinants of buying children's videotape. All the buyers are regular audiovisual media consumers, but TV consumption does not have a significant influence on the probability of buying children's videotape. There is a positive income effect on buying film videotapes, but this effect is not found for buying children's film videotapes.

Renting Movie Videotapes by Genre

In section 4 the main characteristics of people who watch rented videos was presented. We can further hypothesize that there may be significant differences among consumers of different movie genres11 . We can test this hypothesis because the EHCC survey also asks the interviewee about the genre of the film s/he had rented12 .

An initial feel for the relative attractiveness of each of these genres can be found by looking at Figure 3. Action films are the most watched: about 1/3 of the interviewees who had rented a movie, chose this type of films. Other genres with a considerable share of the video renting market are mystery/thriller movies (15%), comedies (14%) and adventures films (13%). It is perhaps a little surprising to note that children's movies, which have a 36% in the videotapes buying market, have only a 6% share in the renting market. One feasible explanation may be that due to the fact that children are more able to watch a film more than once, so that buying this type of film offers scale economies. Source: EHCC 1998

F

Moving on beyond this descriptive analysis, model estimation is undertaken to help discern if there are consumer specific profiles for each of these genres. From the EHCC data it is possible to define a set of qualitative variables associated with each genre. Each dummy variable takes the value one if the last movie hired belonged to that genre and zero otherwise 13 . For each genre, a probit model was developed to examine how the probability of hiring a film is affected by a set of exogenous variable related to the use of audiovisual media and some other individual, household and socio-economic characteristics. Table 3 present the results by film genre 14 .

At first sight, it is difficult to define a typical profile linked to each genre, except for children's movies. Only a few variables, particularly gender and educational attainment level, introduce some significant differences.

At first sight, we could argue that adventure and comedy are the more eclectic genres: these kinds of movies are rented by males and females of any age or level of income -although in the case of adventure, the lower the income, the higher is the probability of renting. Further, illiterate people seemingly have the lowest probability of renting. 13 The genres (and variables) included are the following: action (VACTION), mystery (VMYSTERY), adventure (VADVENTURE), comedy (VCOMEDY), drama (VDRAMA), romantic (VROMANCE), message (VMESSAGE), children's (VCHILD). Detailed definitions of all these variables are in the Appendix. 14 The log likelihood ratios endorse the goodness of fit of these model estimates. Drama and message, the more reflective genres, are particularly consumed by well-educated people (i.e. e. the probability of renting drama movies increases between 7 and 10% when the interviewee has university level education) and are culturally-oriented (if we consider interest in theatre programmes on TV as a proxy of cultural concern). In dramas this idea is reinforced by the negative effect of TV consumption.

In general, educational attainment level has a positive and increasing effect, so people with a university education have the highest probability of renting any kind of film, except for action movies. In this case, we find the opposite effect: that being, people with the lowest educational attainment have the highest probability of renting while this probability diminishes 25% among people with university level education.

The gender variable is of some interest. Women's probability of hiring is significant higher than men's one in the cases of mystery, message, romantic and drama films and looking at the size of the coefficients and the marginal effects, it seems that these latter genres are the closest to women's preferences overall. Only for action movies, men's probability is about 15% higher than women's.

Other variables only introduce some differences in certain genres. For instance, attendance at movie theatres has a positive and statistically significant effect on message movies, and a negative impact on dramas. Meanwhile, TV consumption increases the probability of hiring action movies and decreases the probability of hiring message pictures. Adventure films seem to be a family favourite because their probability of being hired on video increases with the family size.

However, the consumption of love stories and mystery films decreases with the number of family members under fourteen. This fact could reflect that these movies supplied on video are orientated to adult audiences. Finally, it can be highlighted that TV consumption only has a positive influence on action movies

As has been pointed out previously, children's movies on video are clearly a distinct market segment. The key characteristic is that buyers and consumers are different people. Second, the presence of children in the family is the most relevant feature: each additional family member under fourteen increases 2% the probability of hiring these movies, while it decreases about a 0.75% with each additional member above fourteen. Sex and age variables are also relevant influences. Women hire more children's movies than men, perhaps as a consequence of the fact that the former are more concerned about children's care. Furthermore, age has a quadratic effect on the probability of hiring children oriented movies. This probability increases until the interviewee is 39 year old, that is during the more likely period to have children under his (her) responsibility.

Two final results can be highlighted. First, the probability of hiring children's movies increases with the educational level of the head of the household, especially when s/he has had a university education. Hence, video can be observed as a more attractive leisure, or even learning, activity in families who may be perceived to have a higher cultural status or set of cultural aspirations. Second, that probability decreases with movie theatre attendance. This fact is fairly easy to rationalise when taking into account that young people are the greatest movie theatre attenders, and, in general, they do not typically have children.

Concluding Remarks

In this paper, using the EHCC 1998 survey data, multinomial probit econometric models are applied to consider whether there are two distinct video markets (renting and buying) and to identify the respective socio-economic characteristics of their consumers. Finally the influence of certain qualitative characteristics, especially film genre, on the renting and buying videotapes is examined. The key results from this modelling exercise show that video consumption can be considered as a complementary good to the other audiovisual commodities (TV and Cinema). It is also found that video buyers may be considered to be more discerning consumers than video renters, in the sense that among the former, educational level has a positive effect. We may suggest that from their perspective, movies are not only a leisure pursuit but also a cultural commodity.

Accordingly, they may be contended to have a higher valuation of cinema in general, and certain art house or minority cinema output, such as those that more commonly feature in Europe. They also display an interest in other cultural goods such as theatre-related performances on TV.

Looking at video consumption itself, it is suggested that this activity offers household economies of scale in leisure. Further, it may be observed to be a more attractive leisure option, or even learning activity, in families categorized in terms of a higher cultural status, or by virtue of their cultural aspirations.

Looking to video consumption by genre it is found that action, mystery and comedy are the most preferred genres by video consumers in Spain. Women are more interested than men in thriller/mystery, drama, romantic and message films. Moreover, women hire more children's movies than men, perhaps as a result that the former are possibly more involved in the care of children (or at least in keeping them quiet and happy). Since children's movies can be seen more than once and can be considered as part of the formative process, their higher percentage in buying than in renting allows us to think that we have found some empirical evidence for the existence of durable good consumption in buying videos.

This idea is reinforced by indications of the presence of people seemingly interested in building their own film library. It has been observed that the probability of buying increases with movie theatre attendance, so recent blockbusters inevitably must continue to be a substantial part of the product range in video retailers; but it may also be worthwhile for them to persist in offering other kinds of films such as past blockbusters, classical movies etc. RENTVIDYEAR: Dummy variable; it takes value one when the interviewee watches less than five movies on video in a year, and zero otherwise.

F

The base case is defined as when the interviewee never watches movies on video.

CINEWEEK: Dummy variable; it takes value one when the interviewee goes to the cinema at least once a week, and zero otherwise.

CINEMONTH: Dummy variable; it takes value one when the interviewee goes to the cinema monthly, and zero otherwise.

CINEYEAR: Dummy variable; it takes value one when the interviewee goes to the cinema less than seven times in a year, and zero otherwise.

The base case is defined when the interviewee never goes to the cinema. -0,1167 0,0502 -0,0062 0,0037 0,0467 0,1015 -0,0261 -0,0001 0,0420 0,0179 0,0522 -0,0408 0,0070 -0,0404 -0,1074 0,0528 0,0001 0,0488 0,0668 -0,1863 0,0204 0,1513 0,1554 -0,1112 0,1145 -0,0595 -0,0004 0,0443 0,0119 -0,0839 0,0493 0,0345 0,0112 0,0332 0,1227 0,0621 0,0163 -0,0050 0,3837 -0,2001 -0,0025 -0,0021 0,0000 0,0079 0,0074 0,0072 0,0046 -0,0022 -0,0015 -0,0055 -0,0068 0,0000 -0,0008 0,0041 -0,0013 -0,0025 0,0181 0,0138 0,0267 0,0122 0,0062 0,0125 -0,0175 0,0031 -0,0283 0,0100 -0,0014 0,0024 0,0035 0,0129 0,0229 0,0608 0,0339 0,0247 0,0471 0,0280 0,0162 0,0019 -1,9252 -0,3184 0,0173 -0,0004 0,4863 0,4435 0,3324 0,4023 0,0837 0,1548 0,3136 0,3284 0,0000 0,3134 -0,0874 -0,8139 0,0323 -0,1091 -0,3970 -0,0042 0,0817 -0,0576 0,0263 -0,1356 -0,0358 -0,2414 -0,0040 -0,0410 0,0014 -0,0005 0,0365 0,1184 0,3384 0,1302 0,0756 0,2063 0,1842 0,1200 -0,0021 -0,0885 -0,0146 0,0008 0,0000 0,0224 0,0204 0,0153 0,0185 0,0039 0,0071 0,0144 0,0151 0,0000 0,0144 -0,0040 -0,0374 0,0015 -0,0050 -0,0183 -0,0002 0,0038 -0,0026 0,0012 -0,0062 -0,0016 -0,0111 -0,0002 -0,0019 0,0001 0,0000 0,0017 0,0054 0,0156 0,0060 0,0035 0,0095 0,0085

0 0 0 0 0 0 0 12500 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 98 9604 6 6 6 6 6 40 90 1 1 6 1 1 1 1 1 1 1 1 1

  nowadays for the film industry 5 .

  Figure 1 Cinema Attendance and Renting Videos(percentage)

  probability of buying a film decreases with age (0.2 percent each additional year, taking into account the marginal effects) 9 . Hence, young people, who are the most important movie 9 Reported marginal effects account for the variation in the probability of the dependent variable due to an infinitesimal

  consume movies through TV (perhaps typically older people without an established interest in the new technologies) do not buy more children's videotapes than other people who are not interested in films. Second, TV consumption does not have any significant influence. This result could be due to the fact that the youngest and oldest interviewees are the most important TV consumers and they have a low probability of having children under their care. Furthermore, the idea of a positive relationship between cultural level and the probability of buying children's videotapes is reinforced by the positive effects of the head of household's educational attainment and interest in theatre programmes on TV (INTVTHEATER) and the inverse relationship between their educational attainment and TV consumption.

  of days that the interviewee watches TV in a week. HOURSTV: Number of hours that the interviewee watches TV in a day. INTVFILM: Dummy variable; it takes value one when the interviewee is very interested on films on TV, and zero otherwise. INTVTHEATER: Dummy variable; it takes value one when the interviewee is very interested on theatre programmes on TV, and zero otherwise. VNORMAL: Number of "normal" videotapes, delivered in shops, acquired by the interviewee last three months. VPART: Number of videotapes, linked to parts and delivered in newsstands, last three months. RENTVIDWEEK: Dummy variable; it takes value one when the interviewee watches movies on video at least once a week, and zero otherwise. RENTVIDMONTH: Dummy variable; it takes value one when the interviewee watches movies on video monthly, and zero otherwise.

  * Statistically significant at the 5% level ** Statistically significant at the 10% level

  Each quarter of that year a new random sample of 3018 people over fourteen years of age was

	F o r
	P
	e
	e r
	R e v
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	w

  also found that performing arts are luxuries. Moreover, Prieto et al., 2005, found the same result for Spain through the estimation of a complete demand system.

	A range of other household characteristics are also shown to be influential in intuitively
	clear economic terms. First, household size is likely to increase both buy and rent probabilities,
	since film video consumption offers household economies of scale. The presence of children under
	fourteen years of age is shown as likely to accentuate this effect. And both probabilities are likely to
	increase when the interviewee is the head of household or is his/her partner. Put simply, these are
	the individuals likely to have the greatest influence on household decision-making. People with no
	educational qualifications display the lowest probability of buying videotapes, reinforcing the
	previously highlighted income effect.

Figure 3 Percentage of videotapes rented by genre

	Message 2,9%	Erotic 0,5%	Children 6,2%
	Romance
	6,9%		Action
	Drama		33,5%
	7,4%
	Comedy
	13,8%
	Adventure 13,2%	Mystery 15,6%
	o r
		P
		e
		e r
			R e v
			i e
			w

Table 1 Determinants of Renting and Buying Videotapes

 1

	1 1

Table 2 Determinants of Buying Film and Children's Movies Videotapes

 2

	0,2893 0,3035 0,2265 0,0461

Table 3 Determinants of Video Renting for Different Movie Genres

 3

		ACTION	MYSTERY	ADVENTURE	COMEDY
		coefficient	marginal effect	coefficient	marginal effect	coefficient	marginal effect	coefficient	marginal effect
	CONSTANT	-0.7885
	MAN	(-2,009) 0.4223
		(6.472)
	AGE	0.0094 (0.679)
	AGE2	-0.0002 (-1,303)
	PRIMARY	-0.3667
	HIGH SCHOOL	(-2,515) -0.5149
	DIPLOM	(-3,362) -0.6795
		(-3,913)
	LICENC	-0.6958 (-3,774)
	HPRIMARY	-0.0799 (-1,038)
	HHIGH SCHOOL	-0.2147
	HDIPLOM	(-2,383) -0.2288
	HLICENC	(-1,827) -0.2678
		(-1,707)
	INCOME	-0.0001 (-1,544)
	N14	-0.0425 (-1,352)
	N14MORE	0.0126
	SINGLE	(0.59) 0.163
	MARRIED	(1.032) 0.1279
		(0.928)
	HOUSEHOLD	-0.0319 (-0,266)
	SPOUSE	0.1766 (1,325)
	HICLASS	0.128
	MIDCLASS	(0,798) 0.0717
	SELFEMPLOYEE	(0,797) 0.1376
	EMPLOYEE	(1,012) 0.0919
		(0,934)
	UNEMPLOYED	0.1625 (1,242)
	RETIRED	0.028 (0,151)
	STUDENT	0.0106
	COMPUTER	(0,086) -0.0211
	INTERNET	(-0,389) 0.1466
		(1,127)
	TVDAYS	0.0329 (1.426)
	TVHOURS	0.0711 (4.713)
	INTVFILM	0.1067
	INTVTHEATER	(2.152) -0.0101
	VNORMAL	(-0,132) 0.0068
		(0,432)
	VPART	0.015 (0.989)
	CVIDEOFILM	0.0835 (1.113)
	CINEWEEK	0.0134
	CINEMONTH	(0.
	CINEYEAR
	HÁBITAT

Table 3 Determinants of Video Renting for Different Movie Genres (cont.)

 3

		DRAMA	ROMANCE	MESSAGE	CHILDREN
		coefficient	marginal effect	coefficient	marginal effect	coefficient	marginal effect	coefficient	marginal effect
	CONSTANT	-1.7415
	MAN	(-3.003) -0.3499
		(-3.803)
	AGE	-0.0188 (-0.992)
	AGE2	0.0003 (1.175)
	PRIMARY	0.3076
	HIGH SCHOOL	(1.167) 0.2951
	DIPLOM	(1.089) 0.7946
		(2.782)
	LICENC	0.5191 (1.744)
	HPRIMARY	0.0527 (0.434)
	HHIGH SCHOOL	0.0104
	HDIPLOM	(0.075) -0.2286
	HLICENC	(-1.252) 0.3093
		(1.458)
	INCOME	0.0001 (1.769)
	N14	0.0305 (0.692)
	N14MORE	0.0272
	SINGLE	(0.901) 0.2693
	MARRIED	(1.253) -0.0878
		(-0.462)
	HOUSEHOLD	0.2354 (1.351)
	SPOUSE	0.1187 (0.652)
	HICLASS	-0.0378
	MIDCLASS	(-0.162) 0.1607
	SELFEMPLOYEE	(1.099) -0.0398
	EMPLOYEE	(-0.204) -0.1207
		(-0.896)
	UNEMPLOYED	0.0468 (0.259)
	RETIRED	0.1074 (0.43)
	STUDENT	-0.2366
	COMPUTER	(-1.367) 0.0658
	INTERNET	(0.871) -0.2011
		(-1.01)
	TVDAYS	0.0227 (0.679)
	TVHOURS	-0.022 (-0.944)
	INTVFILM	-0.0686
	INTVTHEATER	(-0.97) 0.158
	VNORMAL	(1.58) -0.0054
		(-0.212)
	VPART	0.002 (0.094)
	CVIDEOFILM	-0.0102 (-0.093)
	CINEWEEK	-0.3469
	CINEMONTH	(-2.382) -0.0532
	CINEYEAR	(-0.535) -0.1282
		(-1.334)
	HÁBITAT	0.0422 (2.

The EHCC was also conducted quarterly during 1997 but during this year, the survey was in a developmental phase and the interview questionnaire content was significantly different, such that it would not support the type of work we report in this paper. Accordingly, it was decided to use only the 1998 data.

This fact can be easily understood when we consider that, in 1998, 73.4% of Spanish homes had a video set. On the other hand, theater ticket prices were increasing, while renting and buying video prices were both decreasing, probably because of the presence of strong new competitors such as piracy and pay per view TV systems.

It is important to note that the EHCC survey does not ask if the interviewee rented videotape, only if he or she watched a rented videotape. Furthermore, when the survey asks about video buying frequency it does not specify video type: movie, documentary, music etc…-although movies represent

per cent of the whole market.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Walls (1997),[START_REF] Bagella | The Determinants of Motion Picture Box Office Performance: Evidence from Movies Produced in Italy[END_REF] Bechetti (1999), De Vany and[START_REF] De Vany | Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box Office?[END_REF] andFernández -Blanco and Prieto-Rodríguez (2003) have analysed the effect of genres on movie theatre attendance.

This question was only available in the interview questionnaire used in the first quarter of 1998. Hence, our sample size is reduced to 3,300 individuals.

ª This research has been financed by the Fundación Autor and Sociedad General de Autores y Editores (SGAE). Samuel Cameron and Chris Hand. The usual caveat applies.