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Crowding out or crowding in? Public and private transfers in Germany

Substituts ou compléments? Transferts publics et privés en Allemagne

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Abstract

Intergenerational support exchanges are of particular interest in the ageing populations of Europe. This paper investigates the relationship between private and public financial transfers to and from elderly people using data from Germany. First, the determinants of private transfer giving are analysed. We find a positive correlation between the amount of public transfers elderly people receive and the private transfers they give. This mechanism can be interpreted as a detour system, an inefficient backflow of pay-as-you-go financed pensions to the young generation. On the other hand, we find for the much smaller group of elderly people who receive private financial support, that these transfers are negatively correlated with the public transfers they receive. Therefore, the “crowding out” hypothesis cannot be rejected and it is possible that public transfers to older people by the German welfare state may displace private financial support which they would otherwise have received.

Résumé

Les échanges intergénérationnels de solidarité sont d’un intérêt particulier dans l’étude des populations vieillissantes de l’Europe. Cet article explore la relation entre les transferts financiers privés et publics en provenance des personnes âgées vivant en Allemagne, et à destination de ces mêmes personnes. Une corrélation positive est observée entre le volume des transferts publics que les personnes âgées reçoivent et celui des transferts privés qu’elles distribuent. Ce mécanisme peut être interprété comme un système de détournement, un retour inefficace des retraites en direction des jeunes générations qui les financent. D’autre part, il apparaît que, parmi le petit groupe de personnes âgées qui reçoivent des transferts privés, ces transferts sont négativement corrélés avec les transferts publics dont elles sont bénéficiaires. C’est pourquoi l’hypothèse selon laquelle les transferts publics se substituent aux transferts privés ne peut être rejetée, et c’est pourquoi est possible que les transferts publics aux personnes âgées par l’état providence en Allemagne puissent prendre la place des transferts privés qu’ils auraient pu recevoir en l’absence de ces transferts publics.

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Acknowledgement

I want to thank Axel Börsch-Supan, Karsten Hank, Joachim Winter, Hendrik Jürges and the participants of the EAPS/INED workshop in Paris for their valuable comments, especially John Henretta for his discussion and Emily Grundy for all her support. I also wish to thank two anonymous referees and Frans van Poppel for their remarks and Rachel Stuchbury for her efforts. I am grateful to the Gesamtverband der Deutschen Versicherungswirtschaft, the state of Baden-Württemberg and the Deutsche Forschungsgemeinschaft within the Sonderforschungsbereich 504 for financial support.

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Correspondence to Anette Reil-Held.

Appendix

Appendix

Table 3 Sample description: summary statistics of independent and explanatory variables (West)

Variables

Mean

Standard deviation

Minimum

Maximum

Age

73.9

5.5

65

85

Household size

1.4

0.5

1

2

Female

0.47

0.5

0

1

Married

0.42

0.5

0

1

Never married

0.09

0.29

0

1

Widowed

0.36

0.48

0

1

Divorced

0.10

0.3

0

1

Living separately

0.01

0.12

0

1

Disposable income

40877

26166

5156

394896

Disposable income net of transfers

40080

31065

2000

394896

Share of public transfers in disposable income

82.8

20.4

0

100

Financial Wealth

63103

129559

−146215

3363594

Homeowner

0.44

0.49

0

1

  1. These summary statistics apply to the data that was evaluated in section 4.
  2. Sampled are primary units aged at least 65 (household head if couple) in the Old States. Sample estimates are weighted to represent the West German population.

Table 4 Determinants of transfer giving (West)

 

Heckman

Regression equation (transfer amount)

Selection equation

Total derivatives (dy/dx)

Age

0.13 (1.25)

−0.14 (1.89)

−0.36 (−1.72)

Age2

−0.00 (0.99)

0.00 (1.87)

0.00 (1.78)

Female

−0.38** (4.66)

0.17** (3.31)

0.32*** (2.11)

Never married

0.31** (2.63)

−0.17* (2.29)

−0.37*** (−1.66)

Widow

0.41** (4.72)

0.17** (2.96)

0.82*** (4.89)

Divorced

0.39** (3.49)

−0.15* (2.19)

−0.27*** (−1.28)

Separated

1.06** (5.08)

0.00 (0.00)

0.67*** (1.55)

log (disposable income)

0.48** (7.48)

0.52** (12.61)

2.00 (16.53)

log(public transfers)

0.01 (0.20)

0.06** (2.72)

.21 (3.22)

Financial wealthtdm

0.00 (0.96)

−0.00 (0.37)

−0.00 (−0.11)

Homeowner

0.09 (1.77)

0.16** (4.74)

0.59*** (5.93)

Constant

−2.93 (0.72)

−0.79 (0.29)

 

Observations

6370

6370

 

Wald χ2(11)

185.68

  

Rho

−.80 (0.02)

  
  1. Notes: Absolute value of z statistics in parentheses significant at 5%; ** significant at 1%
  2. Marginal effects after heckman: (***) dy/dx is for discrete change of dummy variable from 0 to 1
  3. The Heckman two−equation model is estimated by maximum likelihood

Table 5 Determinants of transfer receiving (West)

 

Heckman

Regression equation (transfer amount)

Selection equation

Total derivatives (Dy/dx)

Age

0.27 (1.21)

−0.00 (0.04)

0.019 ( 0.15)

Age2

−0.00 (1.22)

0.00 (0.01)

−0.00 (−0.17)

Female

−0.09 (0.43)

0.25** (3.34)

0.32*** ( 3.17)

Never married

−0.58 (1.91)

−0.49** (4.62)

−0.52*** (−6.91)

Married

Widowed

−0.24 (0.98)

−0.42** (5.05)

−0.55*** (−5.55)

Divorced

0.43 (1.91)

−0.09 (0.93)

−0.07*** (−0.63)

Living separately

0.79* (2.50)

0.34* (2.19)

0.68*** ( 2.03)

Log (disposable income)

−0.13 (0.66)

−0.51** (9.61)

−0.68 (−9.80)

Log (public transfers)

−0.18** (2.77)

−0.11** (4.24)

−0.17 (−4.64)

Financial wealth (in TDM)

0.00** (4.79)

0.00** (3.78)

0.001 ( 4.85)

Homeowner

0.40** (3.59)

−0.14** (2.91)

−.14*** (−2.30)

Constant

1.00 (0.12)

5.46 (1.48)

 

Observations

6370

6370

 

Wald χ2(11)

104.96

  

Rho

0.013 (0.29)

  
  1. Notes: Absolute value of z statistics in parentheses significant at 5%; ** significant at 1%
  2. Marginal effects after Heckman: (1979) dy/dx is for discrete change of dummy variable from 0 to 1
  3. The Heckman two−equation model is estimated by maximum likelihood

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Reil-Held, A. Crowding out or crowding in? Public and private transfers in Germany. Eur J Population 22, 263–280 (2006). https://doi.org/10.1007/s10680-006-9001-x

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