Charitable contributions and intergenerational transfers

Auten, Gerald; Joulfaian, David

doi:10.1016/0047-2727(94)01475-2

Cited by 91 publications

(67 citation statements)

References 12 publications

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“…In Table 4, the results of the system estimation are shown, which allows for joint determination across the five different types of charitable donation, as shown in equation (5) above. Focusing upon the relationship between the head of household's age and charitable donations, relative to those heads of household aged over 60 (the omitted category) the level of the donation tends to increase with the age of the head of household, which is consistent with the findings in the existing literature, such as Lankford and Wyckoff (1991), Auten and Joulfaian (1996), and Schokkaert (2006 household and the price of making a donation, which is in accordance with the findings of Glenday et al (1986).…”

Section: Resultssupporting

confidence: 81%

Modelling charitable donations to an unexpected natural disaster: Evidence from the U.S. Panel Study of Income Dynamics

Brown

Harris

Taylor

2012

Journal of Economic Behavior & Organization

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Abstract:Using household-level data, we explore the relationship between donations to the victims of the 2004 Indian Ocean tsunami disaster and other charitable donations. The empirical evidence suggests that donations specifically for the victims of the tsunami are positively associated with the amount previously donated to other charitable causes, which accords with complementary rather than substitution effects. This relationship exists when we decompose overall charitable donations into different types of philanthropy, with charitable contributions to caring, needy and religious organizations having the largest positive association with donations to the victims of the tsunami.When we explore the impact of donations to the victims of the tsunami on future donations to charity, however, our findings suggest an inverse relationship with the largest inverse association with donations to needy and caring organizations.

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Section: Resultssupporting

confidence: 81%

Modelling charitable donations to an unexpected natural disaster: Evidence from the U.S. Panel Study of Income Dynamics

Brown

Harris

Taylor

2012

Journal of Economic Behavior & Organization

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“…a significant proportion of individuals and households do not make charitable donations. 2 The tobit approach has been adopted by a number of empirical studies of charitable donations including Kingma (1989), Auten and Joulfaian (1996) and, more recently, Brown et al (2012). 3 However, a fundamental problem with the tobit approach, relating to the treatment of the censored observations, lies in the possibility that the decision to donate and the decision regarding how much to donate, 1 It should be acknowledged that the implications of charitable behaviour have also been analysed at the country level.…”

Section: Introductionmentioning

confidence: 99%

An inverse hyperbolic sine heteroskedastic latent class panel tobit model: An application to modelling charitable donations

et al. 2015

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Abstract:We apply a latent class tobit framework to the analysis of panel data on charitable donations at the household level where the latent class aspect of the model splits households into two groups, which we subsequently interpret as "low" donators and "high" donators. The tobit part of the model explores the determinants of the amount donated by each household conditional on being in that class. We extend the standard latent class tobit panel approach to simultaneously include random effects, to allow for heteroskedasticity and to incorporate the inverse hyperbolic sine (IHS) transformation of the dependent variable. Our findings, which are based on U.S. panel data drawn from five waves of the Panel Study of Income Dynamics, suggest two distinct classes of donators. There is a clear disparity between the probabilities of zero donations across these classes, with one class dominated by the observed zero givers and associated with relatively low levels of predicted giving. We find clear evidence of both heteroskedasticity and random effects. In addition, all IHS parameters were significantly different from zero and different across classes. In combination, these findings endorse the importance of our three modelling extensions and suggest that treating the population as a single homogeneous group of donors, as is common in the existing literature, may lead to biased parameter estimates and erroneous policy inference. Although we use this model to explain charitable donations, we note that it has wide applicability.

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“…As documented by many econometric studies of charitable giving, contributions tend to rise with age, independent of income. Auten and Joulfaian (1996) , for example, present regression estimates, suggesting that giving in the 55 -64 age bracket exceeded those in the less than 45 age bracket by a ratio of 2.4:1.…”

Section: Life Cycle Effectmentioning

confidence: 99%

Data mining of university philanthropic giving: Cluster-discriminant analysis and Pareto effects

Blanc

Rucks²

2009

Int J Educ Adv

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A large sample of 33 000 university alumni records were cluster-analyzed to generate six groups relatively unique in their respective attribute values. The attributes used to cluster the former students included average gift to the university ' s foundation and to the alumni association for the same institution. Cluster detection is useful in this application because there are very likely natural groupings that may represent segments of alumni that have much in common, and for which customized marketing approaches are justifi ed. The clustering generated six groups of sizes ranging from eight to more than 25 000 records. Once the groups were established, discriminant analysis evaluated the difference(s) between the a priori determined and mutually exclusive giving groups. The fi rst four groups provided the foundation with a disproportionate share (88 percent) of its major gifts, while the last two groups afforded the alumni association with the largest part (88.3 percent) of its annual funds. The discriminant model predicted with 100 percent accuracy the membership of the extreme groups (Group 1 and Group 6), with an overall predictive accuracy of 98 percent. The same model calculated membership in Group 2 and Group 5 with accuracy of 94.9 percent and 91.3 percent, respectively. Other numbers of groups were attempted, including fi ve groups and seven groups, but no other number of clusters gave the clear distinction between groups, as did six clusters. The analysis further investigated two particular groups for their giving to the annual fund of the alumni association. These two groups comprised 98 percent of the sample, and could be characterized as one

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Charitable contributions and intergenerational transfers

Cited by 91 publications

References 12 publications

Modelling charitable donations to an unexpected natural disaster: Evidence from the U.S. Panel Study of Income Dynamics

Modelling charitable donations to an unexpected natural disaster: Evidence from the U.S. Panel Study of Income Dynamics

An inverse hyperbolic sine heteroskedastic latent class panel tobit model: An application to modelling charitable donations

Data mining of university philanthropic giving: Cluster-discriminant analysis and Pareto effects

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