Economic Hardship, Housing Cost Burden and Tenure Status: Evidence from EU-SILC

Abstract

The primary goal of this study is to contribute on the literature on poverty by looking at household economic hardship in relation to the housing cost burden. Being one of the most significant outlays in a household balance, housing costs may indeed cause households to reduce non-housing expenditure such as health care, education, food, and clothing, thus creating serious household economic hardship. Using microdata from the European Union Statistics on Income and Living Conditions dataset (EU-SILC) regarding five European countries (Italy, Germany, UK, Spain, and France) we have examined the predictive power of housing costs in explaining family economic hardship. Furthermore, we have jointly estimated the effect of the housing cost burden upon economic hardship for renters versus home-owners paying mortgages. Results showed that housing costs represent a non negligible burden in all the five European countries. Moreover, home ownership was found to significantly reduce household hardship status.

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Notes

  1. 1.

    Mammen et al. (2014) highlighted the extent to which poverty status as well as its trajectory is determined by more than just income or employability. Albeit focusing on poverty and well being in rural areas, the authors emphasized that not only poverty, but also the process to exit or enter into poverty has a multidimensional nature.

  2. 2.

    See, among others, Alessie et al. (2002) for the Netherlands, Banks et al. (2003) for the United Kingdom, Kessler and Wolff (1991) for France and the United States, and Wolff (1994) for the United States.

  3. 3.

    In this regard, expectations for a high capital gain represent an incentive to become a home-owner (Goodman 1990).

  4. 4.

    According to the Eurofund Seminar Report on Working Poverty “workers living in a household where at least one member works and where the overall income of the household (including social transfers and after taxation) remain below the poverty line (60 % of median equivalized income) are defined as working poor”.

  5. 5.

    On this regard, this study allows to consider jointly institutional country-level factors and micro-level mechanisms.

  6. 6.

    Boarini and d'Ercole (2006) found that the probability of experiencing material deprivation is twice as large among those in the lower quartile of the income distribution than for those in the middle quartile, although these differences vary greatly across countries.

  7. 7.

    Bárcena-Martín et al. (2013) consider deprivation as the inability to afford at least four out of nine items: to pay utility bills; to keep their home adequately warm; to pay unexpected expenses; to eat meat, fish, or a protein equivalent every second day; to enjoy a week’s holiday away from home; to have a car; to have a washing machine; to have a colour TV; and to have a telephone.

  8. 8.

    Information as social exclusion and housing-condition is collected at household level, while labour, education and health information come at personal level.

  9. 9.

    EU-SILC documentation states that “the person responsible for the accommodation is the one owning or renting the accommodation. If the accommodation is provided at no cost, the person to whom the accommodation is provided is the responsible person. If two persons share responsibility for the accommodation, the oldest person is considered to be responsible”.

  10. 10.

    Indeed, the work intensity indicator that is used in order to calculate one of the indicators of hardship only refers to the population in the age range 18–59.

  11. 11.

    See appendix for a detailed definition of material deprivation, low work intensity and risk of poverty according to EUROSTAT.

  12. 12.

    A similar measure of poverty has been used by Watson and Webb (2009)

  13. 13.

    Peer group effects have been studied with reference to consumption (Charles et al. 2007; Childers and Rao 1992) and stock market participation (Hong et al. 2004).

  14. 14.

    The usage of such a self-reported measure of hardship may capture households who experience a lower level of welfare than their peers, but who may not face hardship in absolute sense. From this perspective, the usage of alternative hardship measures allows to check for the robustness of results to different measurement of households’ hardship status.

  15. 15.

    For example, the US Department of Housing and Urban Development, Office of Policy Development and Research (2007) considers households paying more than 30 % of gross income for housing as cost burdened, while those paying 50 % or more are considered severely cost burdened.

  16. 16.

    Brandolini et al. (2013), analysing the determinants of perceived housing cost burden, found indeed this measure to be strongly correlated to the effective housing cost sustained by households.

  17. 17.

    According to Boeri and Brandolini (2005), subjective factors such as disappointed expectations, high income mobility and high income inequality are good candidates to explain Italian households poverty perception.

  18. 18.

    In this regard, MacLennan et al. (1998) noticed how different levels of financial market regulation affect differently housing market in different countries.

  19. 19.

    When looking at results by country (Tables 14, 15 in the Appendix) results are confirmed.

  20. 20.

    The definition of social renting differs in the five countries taken into account (Pittini and Laino 2011).

  21. 21.

    The fact that home ownership rate in Italy is the lowest with respect to other countries is not surprising. Indeed, descriptive statistics only refer to households with outstanding mortgage, while in Italy the majority of households count on parental help. This is in line with statistics provided by Georgarakos et al. (2010) using HCHP.

  22. 22.

    We are only considering houses which are mortgage-burdened, and in Italy only a minority of households have mortgages (Georgarakos et al. 2010). This may explain a level of home ownership that is not as high as expected.

  23. 23.

    Including social renters into the analysis would allow for a substantial degree of heterogeneity across countries. Social renting in EU countries differs indeed in terms of tenures, providers, beneficiaries and funding arrangements (Housing Europe Review 2012).

  24. 24.

    This is in line with Brandolini et al. (2013), who eventually found that home-ownership, as well as living in a rent-free accommodation, affected negatively the subjective measure of housing cost burden. Furthermore, households with mortgages in Italy and Spain were more likely to declare heavy housing cost burdens.

  25. 25.

    Households who are not homeowners would probably allocate money in private pension plans, saving accounts, or private insurance, thus limiting their spending capacity.

  26. 26.

    Maddala (1996) called it switching regression model with endogenous switching. Amemiya (1978) suggested bivariate probit models to correct endogeneity in the case of binary models.

  27. 27.

    Two stage procedures such as Heckman (1979) are approximate, since they do not allow making distributional assumptions regarding estimators.

  28. 28.

    Miranda and Rabe-Hesketh (2006) noticed that their method differed from bivariate probit for the parametrization of the variance-covariance matrix, where the variances of the errors were set to be 1.

  29. 29.

    Particularly, it is an indicator regarding price changes of residential properties purchased by households (flats, detached houses, terraced houses, etc.), both newly-built and existing ones, independently of their final use and independently of their previous owners. Data come from ECB statistical warehouse.

  30. 30.

    Furthermore, there is evidence that ignoring endogeneity of tenure status leads to biased coefficients. In the specification with H2 and HBU, when endogeneity is not considered being a home owner reduces the probability to face material hardship (H2) of 18 %. This probability is almost 60 % when endogeneity is taken into account. Similar results hold when other specifications are considered.

  31. 31.

    In this regard, expanding households’ access to financial products and enhancing their functioning plays a non negligible role in helping households to reduce hardship. See, for example, Huang et al (2014) who examined the correlation between the probability of experiencing hardship (defined as self-reported inability to meet basic needs) correlated with financial capability in a sample of older Asian immigrants. A related study (Leonard and Di 2014) as well highlighted the role played by financial behaviour. Focused on asset poverty (defined as having a net worth to sustain income for 3 months above the federal income poverty level, or net worth equal to 25 % of the annual income poverty level), they stated that financial behaviour directed towards debt minimization and inclusion of productive assets may reduce the likelihood of asset poverty re-entry.

  32. 32.

    On this regard, we can assume that the ignorability hypothesis holds for our data. First of all, ignorability implies distinctness of parameters of the data model and the missing data mechanism. Second, the missing at random (MAR) hypothesis is also assumed, implying that the probability that an observation is missing may depend on observed values but not on missing ones. On this regard, we are including a set of controls (i.e. income, household size and composition) that can predict the probability of not reporting missing information. Further, it is often difficult to establish a clear border line between the MAR and Missing not at random (MNAR) assumption. However, as pointed out by Schafer and Graham (2002), multiple imputation can still be unbiased with NMAR data even if we assume data is MAR. Moreover, multiple imputation does not require that nonresponse is ignorable (Schafer 1999), so that inferences created under any kind of assumptions for the missing-data mechanism will be valid.

  33. 33.

    As shown in the tables that have been inserted in the appendix, the coefficient associated to housing cost is slightly higher when missing values are imputed using both methods of imputation. Therefore, when the observations with missing values in the key variables are deleted from the sample, a downward bias in the coefficient associated to housing cost is present.

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Appendix

Appendix

Variables Definition

Housing Cost (HC)

Monthly housing costs sustained by owners include the following components: mortgage principal repayment, mortgage interest payments (net of any tax relief), gross of housing benefits, (i.e., housing benefits should not be deducted from the total housing cost), structural insurance, mandatory services and charges (sewage removal, refuse removal, etc.), regular maintenance and repairs, taxes, and the cost of utilities (water, electricity, gas and heating).

Monthly housing costs sustained by renters include the following components: rent payments, gross of housing benefits (i.e., housing benefits should not be deducted from the total housing cost), structural insurance (if paid by the tenants), services and charges (sewage removal, refuse removal, etc.) (if paid by the tenants), taxes on dwelling (if applicable), regular maintenance and repairs and the cost of utilities (water, electricity, gas and heating).

Housing Cost Financial Burden (HBU)

Households were asked the following question: “Please think of your total housing costs including mortgage repayment (instalment and interest) or rent, insurance and service charges (sewage removal, refuse removal, regular maintenance, repairs and other charges). To what extent are these costs a financial burden to you?”. Households are considered to perceive high financial burden if they declare housing costs to be a heavy burden.

Material Deprivation (H2)

Material deprivation refers to households’ inability to afford at least three of the following items:

  • to pay rent, mortgage or utility bills;

  • to keep the home adequately warm;

  • to face unexpected expenses;

  • to eat meat or proteins regularly;

  • to go on holiday;

  • to own a television set;

  • to own a washing machine;

  • to own a car;

  • to own a telephone.

When the household cannot afford at least four of the above items it comes to be severe material deprivation. Material deprivation does not refer to the case when the household does not own the item for reason different from their affordability (i.e., the household does not need the good).

Work Intensity

Eurostat defines work intensity as the ratio of the total number of months that all working-age household members have worked during the income reference year and the total number of months the same household members theoretically could have worked in the same period. A working-age person is a person aged 18–59 years, with the exclusion of students in the age group between 18 and 24 years.

Risk of Poverty

A householder is at risk of poverty if her income is relatively low compared with other residents in the country where she lives. In particular, risk of poverty refers to having an equivalised disposable income below the risk of poverty threshold, set at 60 % of the national median equivalised disposable income after social transfers.

Probit Regression, by Country

See Tables 14 and 15

Table 14 HBU used as main explanatory variable
Table 15 HC used as main explanatory variable

Missing Values

See Tables 16, 17, 18, 19, 20.

Table 16 Descriptive statistics
Table 17 Determinants of household economic hardship, probit regression using H1 as dependent variable and HBU as main explanatory variable: comparison between original sample, mean substitution and multiple imputation
Table 18 Determinants of household economic hardship, probit regression using TARG as dependent variable and HBU as main explanatory variable: comparison between original sample, mean substitution and multiple imputation
Table 19 Determinants of household economic hardship, probit regression using H1 as dependent variable and HC as main explanatory variable: comparison between original sample, mean substitution and multiple imputation
Table 20 Determinants of household economic hardship, probit regression using TARG as dependent variable and HC as main explanatory variable: comparison between original sample, mean substitution and multiple imputation

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Deidda, M. Economic Hardship, Housing Cost Burden and Tenure Status: Evidence from EU-SILC. J Fam Econ Iss 36, 531–556 (2015). https://doi.org/10.1007/s10834-014-9431-2

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Keywords

  • Financial distress
  • Household finance
  • Housing cost burden
  • Tenure status

JEL Classification

  • D12
  • D14
  • C24