Home ownership has potentially significant consequences for welfare state policy. High owner-occupancy rates may function as private insurance where social spending is low (a substitution effect). Alternatively, state income redistribution policies could raise the number of home owners (an income effect). Cross-national time-series data show that social spending is negatively related to home ownership, and mediates the positive relationship between income inequality and owner-occupancy rates. This suggests that owner-occupancy acts as a form of social insurance over the life course. Future welfare state researchers should consider the issue of home ownership in analyses of inequality and the social safety net.
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Notes
Home ownership, of course, does not cover all types of asset or equity accumulation; however, it may be the most important one, since home equity represents the modal form of household wealth (Wolff, 1996). While modeling total net worth itself might be more comprehensive, cross-national data on net worth are thin (Wolff, 1996). By contrast, home ownership is a categorical variable that is readily comparable across time and place (with some minor comparability issues) and is collected periodically on many national surveys.
Esping-Andersen (1985:179) writes about a twofold role of housing within the welfare state: To provide homes and to provide jobs through construction. This does not distinguish housing policy from any other social spending investment that may have the added, indirect benefit of providing jobs in addition to satisfying a specific need.
This is certainly true in the United States; see Joint Center for Housing Studies (1997).
One notable exception is the work of Chiuri and Jappelli (2000a) that examines the impact of down payment ratios on the age-profile of housing tenure using many of the same countries we use here; however, they do not explore welfare state variables in their analysis (with the exception of judicial efficiency).
Moreover, the increase in the number of stakeholders occurs without redistributing control over productive capital. Widely distributed home ownership is thus an institutional arrangement that potentially minimizes class tensions that may arise from the inequitable distribution of property. It thereby resolves one of the contradictions between capitalism and liberal democracy (see Daunton, 1987; Geurts and Goossens, 2004; Kurz, 2004; Pahl, 1975; Saunders, 1990).
In a similar fashion, Szelenyi (1983) found that the equitable distribution of incomes in state-socialist Hungary contributed to the toleration of inequality in the distribution of housing.
As far back as 1887, Engels argued that home ownership—which at that time was still relatively rare—and its accompanying debt were another way in which financial obligations and geographic ties to local employers diminished workers’ autonomy (Engels, 1969).
This is a particularly unfortunate omission since in other work, Wilensky has shown that property taxes (as opposed to income or consumption taxes) are the most difficult to sustain—an issue obviously related to housing provision as well (Wilensky, 1976:14–23).
Schmidt (1989) finds a bivariate correlation of −.90 between total public expenditure and owner occupancy for a group of 17 countries; the correlation is −.83 when he substitutes public social security expenditure for total expense.
However, individual-level research conducted in Britain failed to show a link between an individual’s political views on state welfare programs and housing tenure (Saunders, 1992).
For a fuller discussion of the LIS design, please see de Tombeur (1997). For LIS data, visit http://www.lisproject.org.
Data for Luxembourg in 1991 and 1994 were eliminated since 80 and 93%, respectively, of the respondents were “not applicable” for the housing tenure variable for those years.
Most comparative analyses of the welfare state focus primarily on a small set of advanced capitalist democracies in the post-World War II period. However, for the purposes of examining the housing impact of income inequality—without any welfare state considerations—there is no a priori reason to exclude post-Communist or non-Western countries (for example, see Szelenyi, 1983 on the distribution of housing in 1970s Hungary).
To assess the accuracy of our calculations, we cross-checked these data against available from official statistics and secondary calculations for eight countries: Denmark, France, Germany, Great Britain, Italy, The Netherlands, Norway, and the United States. Only for two country-years did our calculations vary substantially from reasonably well-known patterns of owner occupancy. Our calculated UK rate for 1979 was only 50% of the expected rate based on the trend at that point in 1979; Italy’s rate for 1991 was similarly low. Rather than lose the case-year we were able, for Italy, to substitute that year’s data from official sources (Instituto Nazionale di Statistica, as cited in Bernardi and Poggio, 2004). No official rate was available for the United Kingdom, and so 1979 is dropped from the analysis.
A related variable that was tested but is not presented in the tables is the decommodification score calculated by Esping-Andersen (1990) as a measure of welfare state effectiveness. It is calculated for one point in time (1980); thus, it cannot vary within countries and drops out of a fixed-effects framework, as it is de facto controlled. This variable was highly collinear with the welfare-spending measure and had the same effects. Thus, cross-national results were not sensitive to the substitution of the welfare-spending variable for the decommodification score.
Huber et al. (1997) discuss several comparability issues for these data.
See Kennedy and Anderson (1994), Annex I, for a full description of the data coverage. While the authors provide data for only a single time period—and therefore omit within-country variation—Kennedy and Anderson show that across the 15 countries they examine, the range of nominal growth in housing prices averaged 8.5% between 1970 and 1992, while real growth rates (adjusted for consumer-price inflation) averaged only 1.1%.
Belgium, Denmark, Finland, and Sweden allowed deductions from capital gains and real estate income gains (with some credits for losses, comparable to credits for any other business losses). Only France taxed all capital gains, while the United States only taxes gains above a very high level. This dimension is therefore excluded from the typology.
In light of the small number of cases, our results may not reflect the parameters of a hypothetical population of country-years up to 10% of the time.
For all models, this leaves the ratio of cases to regressors above the minimum level of 5 recommended by Kleinbaum et al. (1988:318).
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APPENDIX: SENSITIVITY ANALYSES
APPENDIX: SENSITIVITY ANALYSES
We performed two types of sensitivity analyses to determine whether the results are driven by the nature of the unbalanced panels or the misspecification of the models: Jackknife and extreme-bounds analysis. In the Jackknife analysis, we exclude each country in turn and estimate the model using the remaining subset of cases. This indicates whether influential cases drive the results. The extreme-bounds analysis tests the coefficients’ sensitivity to the models’ specification by excluding each variable in turn and estimating the reduced model.
Table AI reports the lower and upperbounds of the random-effects coefficient estimates for the model as specified in Table V, Model 3. The results indicate that the main effect of social spending is robust to the exclusion of any other predictor or the exclusion of each country. In no instance does the direction of the coefficient change. At most, the magnitude of the social-spending coefficient is reduced by 45% of the lower bound estimate in Table V (this occurred when the year variable was excluded). Excluding Finland produces the largest drop in the magnitude of the social-spending effect, reducing it by 13% of the observed effect in Model 3. The variable is still significant, however. When we exclude the post-Communist nations of Poland and Hungary, the social-spending coefficient for Table V, Model 3 is −.974 (p=.001). The coefficient for Gini is sensitive to the specification of the model and the composition of the countries analyzed. Without a measure of GDP per capita, the coefficient for Gini becomes negative. The strongest negative effect occurs when Sweden is excluded; Gini is most positive when Finland is excluded.
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Conley, D., Gifford, B. Home Ownership, Social Insurance, and the Welfare State. Sociol Forum 21, 55–82 (2006). https://doi.org/10.1007/s11206-006-9003-9
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DOI: https://doi.org/10.1007/s11206-006-9003-9