No free lunch, buddy: past housing transfers and informal care later in life


Previous empirical literature on the relation between intergenerational transfer of assets and services has mostly focused on contemporary exchanges. By contrast, we provide novel evidence showing that parents who helped their adult children in the past are rewarded by higher chances of receiving informal care later in life. To this end we use Italian data containing precise retrospective information about the help with housing that couples received from their parents when they got married, such as a real estate donation or down payment. Our estimates show that this type of past help is positively associated with the current provision of informal care to the parents. This result is robust to controlling for a large set of individual and family characteristics and is only partially due to increased geographical proximity. We suggest that this finding can be explained by mixed self-interest motives, related to theories based on either bilateral exchange or the presence of a third generation (grandchildren), such as the demonstration effect model or the family constitution model.

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Fig. 1


  1. 1.

    Cigno et al. (1998) used some statistics from an earlier wave of the survey (1987–1991) to show that it is not only low-economic status parents, but also those who report having excellent financial resources, who have a high probability of receiving personal or financial assistance from relatives or friends. This suggests that altruism is not likely to be the only explanation. Unfortunately, the micro-data from this earlier wave are not currently available.

  2. 2.

    Recently, Groneck and Krehl (2017) provided evidence that those who provide more elderly care are more likely to receive a larger proportion of future bequests. There is a rich literature that discusses the role of bequests and their differences from inter vivos transfers; see, among others, Tomes (1981); McGarry (1999); Laitner and Ohlsson (2001); Nishiyama (2002).

  3. 3.

    This is particularly true in southern Europe, where liquidity constraints for the young are more severe and households traditionally acquire real estate either using personal savings or through family transfers or inheritance (Chiuri and Jappelli, 2003).

  4. 4.

    This may not be true in some extreme circumstances, in particular when the parents already own a house that could be potentially transferred to the children, but the house is too big for their needs and the increase in price is large enough to make the optimal transfer smaller than the income loss from donating the house. In this case the parent could switch from a real estate transfer to a monetary transfer and our data may not capture this.

  5. 5.

    The predictions regarding the quantity of downstream transfers have been tested by a number of studies. A non-exhaustive list includes Cox and Rank (1992); Altonji et al. (1997, 2000); Cox et al. (2004); Alessie et al. (2014).

  6. 6.

    We also know from Solon et al. (2015) that, in the case of misspecification, it is not guaranteed that the use of weights leads to a good approximation of the effect of interest. Moreover, to guarantee anonymity, the dataset is released in two versions that cannot be merged. In the first, the region of residence is provided but not the size of the town; in the other, the size is provided but only broader geographical areas are available. We prefer to use the former, as it allows us to control for aggregate differences across regions, for instance heterogeneity in mortgage accessibility.

  7. 7.

    The results are similar when excluding the past cohabitation cases.

  8. 8.

    This can be due to unemployment, eviction, insufficient household income, debt, health-related problems and financial needs to set up or run a business.

  9. 9.

    This refers to those who reported that the main help received by the family was an economic transfer from parents or in-laws.

  10. 10.

    Results for the sub-sample with greater care needs (e.g. older parents) can be found in online Appendix C.

  11. 11.

    The regression in column (3), run only on data from 1998 and 2003, gives a similar positive and significant coefficient.

  12. 12.

    Indeed, those with Help with house equal to one were much more likely, at the time of the survey, to live in a house that does not belong to them but for which they do not pay the rent (26.5% in the sample of those who received help with house vs 7.0 for those who did not). The difference in the proportion of those who live in their own house is, instead, much smaller (70.0 vs 69.4).

  13. 13.

    In order to further check whether our estimates are simply capturing a switch between different forms of assistance, we tried excluding those couples for whom it might have been less convenient to acquire a house, namely those who moved into rented accommodation or to a house already owned by one of the partners. The result is more robust and still shows a negative relation between house prices and providing help with the house.

  14. 14.

    We get similar results in terms of magnitude when rescaling housing prices in accordance with the consumer price index to account for inflation.

  15. 15.

    In the Multipurpose Survey, the Aosta Valley region is aggregated with Piedmont (in principle, there are 20 Italian regions). Given that the latter is much larger and more populated, we always use only its house price level.

  16. 16.

    We thank Claudio Labanca for sharing his code for calculating wild bootstrap p-values with us. We took inspiration from it and from Cameron and Miller (2015). We also inspected the distribution of t-tests generated and never found specific problems, such as mass points around particular values or missing values. We finally tried with a more standard pair bootstrap, but p-values tended to be smaller than those obtained using clustered standard errors, which is in line with the poor performance of this method when clusters are not frequent.

  17. 17.

    Each distance bar is the average fitted probability across the whole sample, fixing the Help with house dummy either to 0 or 1.

  18. 18.

    We could also include the distance from parents at the time of marriage. However, we statistically test the joint significance of the dummies and we conclude that we cannot reject H0 with an F-test with p-value 0.28.

  19. 19.

    We presumably slightly underestimate the fertility effect because the question (“how many children alive/adopted do you have?”) was asked only to people over 25 years old.

  20. 20.

    If we add dummies for distance from parents and in-laws, which are strongly negatively correlated with ICP, the coefficient for help received from parents shrinks to 4.6 percentage points and is still statistically significant at the 1% level.

  21. 21.

    The results are unchanged once we control for the geographical proximity to parents and in-laws.


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This paper has been presented at the European Commission seminar series, at the University of Essex (2016), at the 18th IZA European Summer School in Labor Economics, at the Royal Economic Society Conference at the University of Manchester (2015), at the 2nd CIdE workshop in Econometrics and Empirical Economics (WEEE) and at the VII Italian Workshop in Empirical Economics at the Collegio Carlo Alberto, Moncalieri (Turin, Italy). We would like to thank Matthias Parey, David Reinstein, Giovanni Mastrobuoni, Daniel Hamermesh, Stephen Machin, Marco Francesconi, Giulio Zanella, Massimiliano Bratti, Claudio Labanca, Effrosyni Adamopoulou, Vincenzo Mariani, Ludovica Giua, Paolo Sestito, Federico Signorini, Raffaello Bronzini, Matthias Kredler, Federico Vaccari, the editor and two anonymous referees for valuable comments and suggestions. Opinions expressed herein are those of the authors only. They do not necessarily reflect the views of, or involve any responsibility for, the institutions to which they are affiliated. Any errors are the fault of the authors.

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Correspondence to Emanuele Ciani.

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Ciani, E., Deiana, C. No free lunch, buddy: past housing transfers and informal care later in life. Rev Econ Household 16, 971–1001 (2018).

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  • Informal care
  • Housing
  • Intergenerational transfers
  • Geographical proximity

JEL codes

  • D10
  • J13
  • J14