Information, belief elicitation and threshold effects in the 5X1000 tax scheme: a framed field experiment

Abstract

In this paper, we study by means of a framed field experiment on a representative sample of the population the effect on people’s charitable giving of three, substantial and procedural, elements: information provision, belief elicitation and threshold on distribution. We frame this investigation within the 5X1000 tax scheme, a mechanism through which Italian taxpayers may choose to give a small proportion (0.5%) of their income tax to a voluntary organization to fund its activities. We find two main results: (i) providing information or eliciting beliefs about previous donations increases the likelihood of a donation, while thresholds have no effect; (ii) information about previous funding increases donations to organizations that received fewer donations in the past, while belief elicitation also increases donations to organizations that received most donations in the past, since individuals are more likely to donate to the organizations they rank first.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

Notes

  1. 1.

    See Andreoni (2006) and Vesterlund (2006) for economics-oriented reviews and Cialdini and Goldstein (2004); Penner et al. (2005); Weber et al. (2004) for reviews about contributions from psychology.

  2. 2.

    More specifically are eligible to receive donations all the voluntary organizations and other foundations and organizations (public or private) operating in the fields of scientific research, higher education, public health (including non-professional sport associations), cultural promotion and environmental protection that ask to be included in an official list maintained by the tax authority.

  3. 3.

    The list of organizations eligible for the 5X1000 contribution includes about 50 thousand entries (year 2016). We considered a selection of the most well-known among those that operate nationwide. We tried to diversify both in terms of total amount received and mission of the organization. We included, in alphabetical order: ADMO—Bone Marrow Donors Association, Amnesty International (Italian Section), Caritas, Emergency, Banco Alimentare Onlus, Greenpeace, L.A.V. Lega Anti-Vivisezione, UNICEF (Italian Section) and WWF - World Wildlife Foundation Italy. For a detailed description of the organizations, see Online Supplementary Material.

  4. 4.

    www.smartlabkaralis.it. The research was funded by CSV-Sardegna Solidale (www.sardegnasolidale.it).

  5. 5.

    The instructions and are provided in Appendix A, and the questionnaire is available in Online Supplementary Materials.

  6. 6.

    It is plausible to assume that non-compliance (in this case formal incompleteness of the ranking provided by the subjects) is a signal for actual non-exposition to the treatments (BeliefT and Belief&ThresholdT). This could be due to inattention in answering the survey (skipping the page) or to an inadequate level of effort exerted in order to properly understand and process the requested ranking task. These considerations lead us to prudentially ground our analysis on information generated only by compliers.

  7. 7.

    We call for simplicity non-donors those who choose not to donate to any of the listed organizations.

  8. 8.

    Model (2) in Table 5 replicates the very same findings of Model (1), controlling for further demographics such as age, gender and education level.

  9. 9.

    Computation: 0.0101 p.p. (Belief&ThresholdT)—versus—0.0609 p.p.*** (BeliefT) = −0.0508 p.p.** (Threshold); Wald test \(p\hbox { value}=0.027\).

  10. 10.

    All statistically significant (\(p\hbox { value}<0.01\)) nonparametric tests are jointly significant at the 5% level based on Bonferroni–Holm stepwise multiple-hypotheses-testing procedure (Holm 1979). See Table 9 for the criterion cut-off \(p\hbox { values}\).

  11. 11.

    Results are even more self-evident if the different charities are clustered according to the volume of funding actually given during the previous 5x1000 wave (Table 8). Cluster A groups together the two major charities/outliers (Emergency and Unicef) that are able to attract a great volume of donations (>€ 5 million each). Cluster B hosts organizations able to raise about € 1 million each (LAV, WWF Greenpeace, Amnesty International). Cluster C is represented by three less endowed organizations (Caritas, Banco Alimentare, ADMO) collecting less than € 200.000 each. Clustered analysis is presented in Appendix B.

  12. 12.

    “For unto every one that [we believe] hath shall be given, and he shall have abundance: but from him that [we believe] hath not shall be taken even that which he hath” (Matthew 25:29, King James Version).

References

  1. Andreoni, J. (1988). Privately provided public goods in a large economy: The limits of altruism. Journal of Public Economics, 35(1), 57–73.

    Article  Google Scholar 

  2. Andreoni, J. (1990). Impure altruism and donations to public goods: A theory of warm glow giving. Economic Journal, 100(401), 464–477.

    Article  Google Scholar 

  3. Andreoni, J. (2006). Philanthropy. In S.-C. Kolm & J. M. Ythier (Eds.), Handbook of giving, reciprocity and altruism (pp. 1201–1269). Amsterdam: North Holland.

    Google Scholar 

  4. Andreoni, J., & Bernheim, D. (2009). Social image and the 50–50 norm: A theoretical and experimental analysis of audience effects. Econometrica, 77(5), 1607–1636.

    Article  Google Scholar 

  5. Becker, G. S. (1974). A theory of social interactions. Journal of Political Economy, 82, 1064–93.

    Article  Google Scholar 

  6. Benabou, R., & Tirole, J. (2006). Incentives and prosocial behavior. American Economic Review, 96(5), 1652–1678.

    Article  Google Scholar 

  7. Bernheim, D. (1994). A theory of conformity. Journal of Political Economy, 102(5), 841–877.

    Article  Google Scholar 

  8. Cialdini, R. (1984). Influence, the psychology of persuasion. New York: Harper Collins.

    Google Scholar 

  9. Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55, 591–622.

    Article  Google Scholar 

  10. Ellingsen, T., & Johannesson, M. (2007). Paying respect. Journal of Economic Perspectives, 21(4), 135–49.

    Article  Google Scholar 

  11. Ellingsen, T., & Johannesson, M. (2008). Pride and prejudice: The human side of incentive theory. American Economic Review, 98(3), 990–1008.

    Article  Google Scholar 

  12. Falk, A., & Zimmermann, F. (2013). A taste for consistency and survey response behavior. CESifo Economic Studies, 59(1), 181–193.

    Article  Google Scholar 

  13. Fong, C., & Luttmer, E. (2011). Do race and fairness matter in generosity? Evidence from a nationally representative charity experiment. Journal of Public Economics, 95(5–6), 372–394.

    Article  Google Scholar 

  14. Frey, B. S., & Meier, S. (2004). Pro-social behavior in a natural setting. Journal of Economic Behavior and Organization, 54, 65–88.

    Article  Google Scholar 

  15. Güth, W. (2010). The generosity game and calibration of inequity aversion. Journal of Socio-Economics, 39, 155–157.

    Article  Google Scholar 

  16. Güth, W., Levati, M. V., & Ploner, M. (2012). An experimental study of the generosity game. Theory and Decision, 72(1), 51–63.

    Article  Google Scholar 

  17. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.

    Google Scholar 

  18. Kolm, S. C., & Ythier, J. M. (2006). Handbook of giving and reciprocity and altruism. Amsterdam: North Holland.

    Google Scholar 

  19. Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 56–63.

    Article  Google Scholar 

  20. Johansson-Stenman, O., & Svedsater, H. (2008). Measuring hypothetical bias in choice experiments: The importance of cognitive consistency. The B.E. Journal of Economic Analysis and Policy, 8(1), Article 41.

  21. Pelligra, V., & Stanca, L. (2013). To give or not to give? Equity, efficiency and altruistic behavior in an artefactual field experiment. Journal of Socio-Economics, 46, 1–9.

    Article  Google Scholar 

  22. Penner, L. A., Dovidio, J. F., Piliavin, J. A., & Schroeder, D. A. (2005). Prosocial behaviour: Multilevel perspectives. Annual Review of Psychology, 56, 365–92.

    Article  Google Scholar 

  23. Powell, W., & Steinberg, R. (2006). The nonprofit sector: A research handbook. New Haven, CT: Yale University Press.

    Google Scholar 

  24. Roberts, R. (1984). A positive model of private charity and public transfers. Journal of Political Economy, 92(1), 136–148.

    Article  Google Scholar 

  25. Salganik, M., Dodds, P., & Watts, D. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311, 854–856.

    Article  Google Scholar 

  26. Scharf, K. (2014). Private provision of public goods and information diffusion in social groups. International Economic Review, 55, 1019–1042.

    Article  Google Scholar 

  27. Shang, J., & Croson, R. (2009). A field experiment in charitable contribution: The impact of social information on the voluntary provision of public goods. Economic Journal, 119(540), 1422–1439.

    Article  Google Scholar 

  28. Sugden, R. (1984). Reciprocity: The supply of public goods through voluntary contributions. Economic Journal, 94, 772–87.

    Article  Google Scholar 

  29. Vesterlund, L. (2003). The informational value of sequential fundraising. Journal of Public Economics, 87(3), 627–657.

    Article  Google Scholar 

  30. Vesterlund, L. (2006). Why do people give? In R. Steinberg & W. Powell (Eds.), The nonprofit sector (2nd ed.). New Heaven: Yale Press.

    Google Scholar 

  31. Warr, P. (1982). Pareto optimal redistribution and private charity. Journal of Public Economics, 19(1), 131–138.

    Article  Google Scholar 

  32. Weber, J. M., Kopelman, S., & Messick, D. M. (2004). A conceptual review of decision making in social dilemmas: Applying a logic of appropriateness. Personality and Social Psychology Review, 8(3), 281–307.

    Article  Google Scholar 

  33. Yariv, L. (2005). I’ll see it when I believe it: A simple model of cognitive consistency. Working paper, Department of Economics, UCLA. http://people.hss.caltech.edu/~lyariv/papers/Believe.pdf.

Download references

Acknowledgements

We thank Maria Bigoni, Federico Revelli, Rainer Michael Rilke, Matteo Rizzolli, Robert Sudgen, Daniel Zizzo and an anonymous referee for helpful comments on a previous version of the paper, as well as the participants to the Behavioral Science and Policy—Network for the Integrated Behavioral Science Annual Conference, Nottingham, 21-23/04/2015. Financial support from CSV-Sardegna Solidale is gratefully acknowledged.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Vittorio Pelligra.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (docx 23 KB)

Appendices

Appendix A: Instructions

General instructions

Sardegna Solidale and the University of Cagliari are grateful for your decision to participate to this research. We ask you to fill the following questionnaire in all its parts. Try to answer autonomously to all questions and, in case of necessity, ask for the support of our researcher. Answers will be evaluated by us in anonymous form and elaborated in aggregate. Researchers will not be able in any case to retrieve the respondent’s identity.

The choice questionnaire task

By filling this questionnaire, you will take part in a lottery organized by the Department of Economics and Business of the University of Cagliari. The drawn will take place on the 31st of October 2013, one all the questionnaires will be collected. The winner’s identity will be kept anonymous to the researchers and she/he will be contacted by the Sardegna Solidale volunteers by means of the numeric code you received.

The first and unique prize is equal to €1000. However, this money will not go to the winner. She/he can decide whether to give it or not to one charitable organization among those from the list below. If the prize is not given the winner will not receive anything anyway.

Now we ask you to imagine that you have already won the prize. You now have € 1000 that you could give or not to give to one among the not-for-profit organization listed below.

If after the drawn you will result as the lottery winner, the decision you are about to make will be implemented for real. That means that if you decided to give to some organization the prize, such an organization will receive the money for real, otherwise, if you decided not to give, the prize will not be distributed.

(In the no-info treatment)

Tick the box corresponding to your choice:

Tick your preferred option Organizations
\(\square \) NO DONATION
\(\square \) EMERGENCY
\(\square \) UNICEF – ITALIA
\(\square \) L.A.V. _ LEGA ANTIVIVISEZIONE
\(\square \) WWF _ WORLD WIDE FOUNDATION ITALIA
\(\square \) GREENPEACE
\(\square \) AMNESTY INTERNATIONAL - SEZIONE ITALIANA
\(\square \) CARITAS ITALIANA
\(\square \) BANCO ALIMENTARE ONLUS
\(\square \) ADMO _ ASSOCIAZIONE DONATORI MIDOLLO OSSEO

(In the info treatment)

Tick the box corresponding to your choice:

Tick your preferred option Organizations Funding Received in 2011 through 5X1000 (€)
\(\square \) NO DONATION
\(\square \) EMERGENCY 11,023,415.00
\(\square \) UNICEF—ITALIA 5,460,307.00
\(\square \) L.A.V._LEGA ANTIVIVISEZIONE 1,176,578.00
\(\square \) WWF_WORLD WIDE FOUNDATION ITALIA 1,021,070.00
\(\square \) GREENPEACE 758,835.00
\(\square \) AMNESTY INTERNATIONAL—SEZIONE ITALIANA 753,674.00
\(\square \) CARITAS ITALIANA 193,890.00
\(\square \) BANCO ALIMENTARE ONLUS 170,351.00
\(\square \) ADMO_ASSOCIAZIONE DONATORI MIDOLLO OSSEO 68,828.00

(In the no-info+beliefs elicitation treatment)

Before making your choice we ask you to order (by assigning a specific rank) each organization in terms of how much funding you think they received the previous year through the 5X1000 mechanism (denote with 1 the organization that raised more money and with 9 the one that raised less and with all the other numbers 2-8 those in the intermediate positions)

Tick the box corresponding to your choice:

Tick your preferred option Organizations Ranking Indicate the position of each organization in term of funding received latest year (1 = first... 9 = last)
\(\square \) NO DONATION
\(\square \) EMERGENCY  
\(\square \) UNICEF—ITALIA  
\(\square \) L.A.V. _ LEGA ANTIVIVISEZIONE  
\(\square \) WWF _ WORLD WIDE FOUNDATION ITALIA  
\(\square \) GREENPEACE  
\(\square \) AMNESTY INTERNATIONAL - SEZIONE ITALIANA  
\(\square \) CARITAS ITALIANA  
\(\square \) BANCO ALIMENTARE ONLUS  
\(\square \) ADMO _ ASSOCIAZIONE DONATORI MIDOLLO OSSEO  

(In the no-info+beliefs elicitation + threshold treatment)

Before making your choice we ask you to order (by assigning a specific rank) each organization in terms of how much funding you think they received the previous year through the 5X1000 mechanism (denote with 1 the organization that raised more money and with 9 the one that raised less and with all the other numbers 2–8 those in the intermediate positions)

Note that if the amount of the aggregate donations is greater than a given threshold, only a fraction of the € 1000 will be actually distributed to the organization.

Tick the box corresponding to your choice:

Tick your preferred option Organizations Ranking Indicate the position of each organization in term of funding received latest year (1 = first... 9 = last)
\(\square \) NO DONATION
\(\square \) EMERGENCY  
\(\square \) UNICEF–ITALIA  
\(\square \) L.A.V. _ LEGA ANTIVIVISEZIONE  
\(\square \) WWF _ WORLD WIDE FOUNDATION ITALIA  
\(\square \) GREENPEACE  
\(\square \) AMNESTY INTERNATIONAL - SEZIONE ITALIANA  
\(\square \) CARITAS ITALIANA  
\(\square \) BANCO ALIMENTARE ONLUS  
\(\square \) ADMO _ ASSOCIAZIONE DONATORI MIDOLLO OSSEO  

Appendix B: Clustered analysis

B.1 Information effect (NoInfot vs InfoT)—Sect. 3.2

Information provision leads donors to act systematically more in favor (with respect to the no information treatment) of the less endowed charities of cluster C.

The main result is confirmed also in this alternative clustered setting (see Table 8; Fig. 3). The categorical distribution of recipients under InfoT is contrasted against the NoInfoT distribution. Donations density moves from cluster A (−5 percentage points) and B (−11 percentage points) in favor of cluster C’s charities (+16 percentage points). This qualitative polarization of the donation behavior results to be highly statistically significant according to Pearson \(X^{2}\) test (\(p\hbox { value}<0.001 {\vert }\hbox { tag}: \hbox {b}\)).

Table 8 Donations received by each of the nine organizations in the previous fiscal year through the 5X1000 mechanism
Fig. 3
figure3

Distribution of donations (by treatment)—clustered

B.2 The beliefs effect (NoInfoT vs BeliefT)–Sect. 3.3

Also in this case, the analysis at cluster level helps to better highlight the underling dynamic (Table 8; Fig. 3). Visual inspection of Fig. 3. suggests how the U-shaping effect is mainly driven by an increase of donations in favor of better endowed charities belonging to cluster A (+10 percentage points) and a correspondent decrease of donations to the less endowed ones of cluster B (−8 percentage points) and C (−2 percentage points). As for the previous test, also in this further clustered configuration the Pearson \(X^{2}\) test rejects \((p\hbox { value}=0.008\, {\vert }\, \hbox {tag}: \hbox {d})\) the null hypothesis of independence between experimental conditions and donation allocations to the clustered recipients.

B.3 Threshold effect (BeliefT vs Belief&ThresholdT)—Sect. 3.4

The same result holds true when the clustered categorization is considered. Visual inspection of Fig. 3 (see also Table 8) confirms how the two clustered categorical distributions display analogous patterns of donations by treatments (cluster \(\hbox {A}\approx 35\%\); cluster \(\hbox {B}\approx 16.5\%\); cluster \(\hbox {C}\approx 48.5\%\)). The Pearson \(X^{2}\) test fails to reject \((p\hbox { value}>0.7 {\vert } \hbox {tag: f})\) the null hypothesis of independence between experimental conditions and donation allocations to the clustered recipients (Tables 9, 10).

Table 9 Bonferroni–Holm stepwise multiple-hypotheses-testing procedure (Holm 1979)
Table 10 Distribution of donations (by information, belief elicitation and threshold)—clustered

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Becchetti, L., Pelligra, V. & Reggiani, T. Information, belief elicitation and threshold effects in the 5X1000 tax scheme: a framed field experiment. Int Tax Public Finance 24, 1026–1049 (2017). https://doi.org/10.1007/s10797-017-9474-z

Download citation

Keywords

  • Charitable giving
  • Framed field experiment
  • Social information effect
  • Inequity aversion

JEL Classification

  • C91
  • D64
  • H00