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The Effect of Seed Money and Matching Gifts in Fundraising: A Lab Experiment
Last registered on May 16, 2019


Trial Information
General Information
The Effect of Seed Money and Matching Gifts in Fundraising: A Lab Experiment
Initial registration date
April 11, 2019
Last updated
May 16, 2019 5:54 PM EDT
Primary Investigator
Texas A&M University
Other Primary Investigator(s)
PI Affiliation
Texas A&M University
PI Affiliation
Texas A&M University
Additional Trial Information
On going
Start date
End date
Secondary IDs
The nonprofit sector is known for significant presence of highly inefficient organizations. Apart from the well-publicized fraudulent nonprofits, such as the Cancer Fund of America, there are many organizations that may not blatantly scam donors, but are nevertheless doing a poor job of providing public benefits. Donors' lack of information is likely a contributing factor. While the presence of rating agencies can help donors, the staggering number of charitable organizations makes the available information imperfect and costly to obtain. Consequently, a big challenge for well-run nonprofits is finding ways to credibly inform donors of their quality and distinguish themselves from their poorly performing counterparts.

We aim to investigate experimentally the role that leadership giving plays in conveying information to donors. Leadership gifts can be in the form of an unconditional lump sum called “seed money” or a promise of matching small donations by a fixed ratio called “matching gift”. Our goal is to understand how the type and size of the leadership gift impact donors' beliefs about the quality of the public good and their willingness to donate.

This is based on our novel theoretical model of fundraising, which reveals that the fundraiser's choice of leadership scheme depends crucially on the lead donor's information. If the lead donor is fully informed about the quality, either exogenously or endogenously via costly information acquisition, the charity relies on the lead donor to reveal the quality to subsequent donors through the size of her donation. This eliminates the signaling concern of the high quality charity when choosing her optimal fundraising scheme. Consequently, consistent with the existing theoretical literature, we find that in absence of signaling considerations, the fundraisers should optimally choose the matching scheme, as it alleviates the free rider problem present in public goods provision. However, if the leader is not fully informed about the quality, the lead donor's gift becomes a less reliable source of information and the fundraiser may have to employ the fundraising scheme itself to inform donors. In this case, the model gives rise to a strong prediction regarding the use of the two schemes to signal quality information to donors. While, as any signaling game, multiplicity of equilibria may arise, we find that in any equilibrium with partial information acquisition by the lead donor, the use of seed money in equilibrium is associated with higher expected quality compared to matching gift. Intuitively, since seed money is less effective in alleviating the free-rider problem compared to matching, it becomes a costly signaling tool for the high quality fundraiser that aims to differentiate itself from the low quality fundraiser. As a result, in equilibrium the high quality fundraiser solicits for seed money more often and for matching gift less often than the low quality fundraiser. This, in turn, gives rise to an equilibrium belief among donors that seed money is indicative of higher quality.

We investigate the theoretical predictions regarding the use of seed money as a signal of high quality in controlled lab experiments. The lab is an ideal environment for this as it allows us to control the amount of information available to donors, which is hard to achieve in the field. Moreover, we are the first to endogenize the choice of leadership scheme in the lab to understand the information that each scheme carries, how donors form beliefs, and how they respond to different schemes. In particular, our experimental design consists of a piece-wise linear public good game, in which the marginal per-capita return (MPCR) varies with the total amount raised and the quality of the public good. This ensures sufficient variation in the equilibrium contribution amounts to make both channels of signaling, namely the scheme choice and the lead donor's gift, possible. Since the ability of matching to alleviate the free-rider problem, and thus raise more contributions relative to seed money, is a key driving factor for seed money to emerge as a costly signal of quality in the incomplete information setting, our investigation consists of two consecutive experiments- one focusing on a complete information environment, and another-on an incomplete information environment. The current experiment that will be described in the “Experimental Details” section, compares the two schemes and studies the endogenous choice of scheme when the quality is known to all players. It also provides a control for the second experiment. The second experiment, will extend the framework to incomplete information and varies the probability of verification by the lead donor in order to establish how the choice of scheme and the lead donor's contribution choice interact to convey information to the downstream donor. This will allow us to measure the signaling value of each scheme.
External Link(s)
Registration Citation
Krasteva, Silvana, Marco Palma and Piruz Saboury. 2019. "The Effect of Seed Money and Matching Gifts in Fundraising: A Lab Experiment." AEA RCT Registry. May 16. https://doi.org/10.1257/rct.4101-1.0.
Former Citation
Krasteva, Silvana et al. 2019. "The Effect of Seed Money and Matching Gifts in Fundraising: A Lab Experiment." AEA RCT Registry. May 16. http://www.socialscienceregistry.org/trials/4101/history/46623.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
(1) Scheme choice (lump sum or matching) by Player 1
(2) Contributions decisions by Player 2 and Player 3
Primary Outcomes (explanation)
Please refer to the experiment design
Secondary Outcomes
Secondary Outcomes (end points)
(1) Beliefs by PLAYER 1 and PLAYER 2 regarding the decisions made by downstream players
(2) Eye tracking and facial expression data during the experiment
(3) 4 page survey about demographics, math skills, family background, and charity activity
Secondary Outcomes (explanation)
Please refer to the experiment design
Experimental Design
Experimental Design
In this experiment, we recruit subjects to participate in a lab experiment conducted in Human Behavior Lab at Texas A&M University. We implement a 3x1 between subjects design, in which subjects are randomly assigned to one of three possible treatments: Exogenous Seed, Exogenous Match, Endogenous Scheme. The first two treatments serve as control and correspond to the cases, in which the scheme is exogenously set by the experimenter to lump sum (seed money) or matching, respectively. The goal is to compare the amount of money raised by each scheme and determine whether matching is indeed the more effective scheme. The Endogenous Scheme is the main treatment of interest and corresponds to the case, in which the fundraiser (one of the subjects randomly assigned to this role) is given the choice between lump sum and matching in order to establish the fundraisers' preference between the two fundraising schemes. In particular, using this experimental design we test the following two hypotheses:

1. The average contributions in the Exogenous Seed treatment are lower than the average contributions in the Exogenous Match treatment.
2. The probability of choosing matching by the fundraiser in the Endogenous Scheme treatment is higher than 0.5.
Experimental Design Details
Within each treatment, donors will be randomly assigned to 3 possible roles of PLAYER 1, PLAYER 2, and PLAYER 3, corresponding to the fundraiser, the lead donor, and the follower donor, respectively. Under lump sum, each donor (PLAYER 2 and PLAYER 3) is allowed to make lump sump contributions in multiples of ten up to their endowment of 40 tokens, resulting in 5 possible strategies for each player in {0,10,20,30,40}. Under matching, PLAYER 3's strategy set remains the same as lump sum, but PLAYER 2 makes a commitment to a matching percentage in {0%,25%,50%,75%,100%} of PLAYER 3's contribution. Subjects will play a sequential contribution game for 3 practice and 10 incentivized rounds, where in each round they are randomly and anonymously re-matched into groups of 3 with one of each role. A subject’s role remains the same throughout the session. One of the incentivized rounds will be randomly chosen for monetary payment based on the choices made in that round. In each round, we collect data on PLAYER 2 and PLAYER 3's contribution decisions. In the Endogenous Scheme treatment we also collect data on PLAYER 1's scheme choice. We also elicit beliefs by PLAYER 1 and PLAYER 2 regarding the decisions made by downstream players by incentivizing them to make a guess about these decisions. We will use the collected data to test Hypothesis 1 by comparing the contribution means across treatments parametrically (e.g. Student's t-test) and non-parametrically (e.g. Mann-Whitney U test). We will also estimate linear and ordered logit regressions to estimate treatment effects on individual and total contributions controlling for individual characteristics. To test Hypothesis 2, we plan to use a proportion test, such as Pearson's chi-squared test, to determine whether matching is chosen more frequently than a random realization with probability 0.5. We plan to estimate linear and probit regressions to estimate the effect of individual characteristics on the probability of choosing matching. Behavioral responses will be collected to complement the experimental economic data. Eye tracking data will be recorded using Tobii Spectrum eye-tracking devices to reveal visual attention to information presented and how the format and complexity of the experiment affect the respondent's choices. The eye tracking device collects up to 300 data points per second on specified visual ``areas of interest'' (e.g., equilibrium boxes and other player's possible contributions and earnings). The information collected includes the time to first fixation or how long it took participants to look at an area of interest for the first time; fixation duration or how long they looked at each area; and fixation count or how many times they look at an area. The eye tracking data will help us understand the process and strategic considerations used by participants to make their choices. We will also monitor participant's emotions using facial expression analysis to detect overall positive, negative, or neutral emotional responses. All the data is synchronized and recorded simultaneously to obtain a complete behavioral picture of participants.
Randomization Method
Subjects will be assigned to treatments based on session signup (they have no prior knowledge of the treatment that is pre-assigned to each session).
Role in the game and group matching is done in the lab randomly by computer program coded in z-Tree.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
There is no clustering.
Sample size: planned number of observations
(1) Average contributions in the Exogenous Match treatment: 280 rounds of game played by 84 subjects randomly re-matched into groups of 3 for each round (2) Average contributions in the Exogenous seed treatment: 280 rounds of game played by 84 subjects randomly re-matched into groups of 3 for each round (3) Scheme (lump sum or matching) chosen by Player 1 in the Endogenous Scheme treatment: 440 rounds of game played by 132 subjects randomly re-matched into groups of 3 for each round
Sample size (or number of clusters) by treatment arms
300 subjects recruited from Texas A&M University student population
This number may vary based on available funding.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Texas A&M University
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)