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Personalized fundraising: A field experiment on threshold matching of donations
Last registered on October 23, 2019


Trial Information
General Information
Personalized fundraising: A field experiment on threshold matching of donations
Initial registration date
October 23, 2019
Last updated
October 23, 2019 10:43 AM EDT
Primary Investigator
WZB Berlin
Other Primary Investigator(s)
PI Affiliation
WZB Berlin
Additional Trial Information
Start date
End date
Secondary IDs
We propose a form of threshold matching where donations above a certain threshold are topped up with a fixed amount. We show theoretically that threshold matching can induce crowding in if appropriately personalized. In a field experiment, we explore how thresholds should be chosen depending on past donations. Additionally, we explore how thresholds should be set for new donors as a function of their personal characteristics and compare personalization to setting general thresholds applying to all recipients of a fundraising call.
External Link(s)
Registration Citation
Adena, Maja and Steffen Huck. 2019. "Personalized fundraising: A field experiment on threshold matching of donations ." AEA RCT Registry. October 23. https://doi.org/10.1257/rct.4848-1.0.
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Experimental Details
In order to address the shortcomings of linear matching, we propose and test an alternative matching scheme where donations above a personalized threshold are matched with a fixed amount and show how they can crowd in donations.
In a brief theory section, we explore the effects of varying thresholds around the donation that would be chosen in the absence of matching. While the details depend on the precise local shape of individuals’ indifference curves, we show that an appropriately set threshold can always generate crowding in.
In a field experiment, we vary threshold levels relative to past donations for recipients who responded to previous calls and relative to predicted donations for recipients who have not donated in the past but for whom we observe some characteristics that correlate with giving behavior among donors.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
donation values
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We partnered with an opera house that provides a social youth program for children from disadvantaged rural areas offering access to culture and music. The project is financed through donations and the recipients of the donation ask were individuals from the database of opera customers. The opera started engaging in this type of fundraising just two years earlier and had run a total of two fundraising drives prior to this one. Thus, we have a (small) set of past donors we can draw on and previous non-donors who can be partitioned into a set of regular customers and a set of new customers. For the regular customers we know a number of individual characteristics including the number and value of tickets purchased that serve as proxies for income and affinity with the opera house, as well as (self-indicated) gender, family, academic status, and the place of living. For the set of new customers the personal information was not available ex ante.

Unlike the personalization studied in Edwards and List (2014), we did not want to make the connection between the personal characteristics and the threshold obvious. Therefore, we offered a fixed matching of €10 for donations exceeding a specific threshold that was referred to as “large donation” and was not flagged as personalized. In total, we sent 10,004 letters to the subset of opera goers who purchased at least one opera ticket in the last season and, based on their past purchasing behavior, were expected to donate the largest amounts, including all past donors. The recipients consisted of three groups: those who had donated at least once in the two last fundraising campaigns (769 past donors), customers who had attended the opera house in the last three seasons and who had received a fundraising call in the last two calls but did not donate (3,859 regular customers), and new customers (5,376) for whom it was the first fundraising call from the opera house.
The letter informed the recipients that a generous lead donor had been found who would top up an individual donation with €10 if this donation met a minimum threshold (called “large donation”) or exceeded it.

The literature has documented sizable persistence in donative choices. Charitable giving in one year is the best predictor for giving next year (Meier 2007; Landry et al. 2010) and the amounts chosen are usually very close to previous amounts (Adena and Huck 2019). The data from previous campaigns of the opera house reveals that a subset of past donors gave twice in the previous years (a retention rate of 36.5% in the second call). There is a high correlation between the gift levels of repeat donors (0.778) with a paired t-test p-value of 0.482. Consequently, we assume that past behavior is a good proxy for the optimal donation in the absence of a match and we use the (maximum) past donation for the 769 past donors in our sample as such proxy.

For established customers (past non-donors) we predict optimal donations by extrapolating from the estimated giving equation of past donors. More specifically, guided by a lasso selection procedure, we use information on ticket purchasing behavior (from 2016: ticket revenue, ticket revenue (log), average price, dummy equal to one if any tickets bought in a particular year; from 2015: number of tickets, ticket revenue, ticket revenue (log), average price) and individual characteristics (dummies for living in Dresden, living in Germany, for subscription holders, female, couple, an academic and a professorial title). The raw predicted donation is, of course, almost never a round number, and on average, somewhat smaller than the average donation of past donors. In order to address this issue, we ordered individuals according to their predicted donation and then assigned them to the same rank of the actual distribution of past donations. We shall simply refer to the resulting amount as the predicted donation.

We assigned the following thresholds: For one third of past donors and regular customers the threshold was set equal to either the maximum past donation (for donors) or to the predicted donation (for non-donors). For another third of these recipients the above thresholds were lifted up to the next “category” of previously observed donations. With few exceptions this resulted for past donations below €40 in threshold increases of €5, for donations up to €95 in increases of €10, for donations up to €120 in increases of €20, and for higher donations in increases of €50. For the remaining past donors, established customers and all new customers, the threshold values were drawn at random from the distribution of past donations (for the first two groups excluding own past or predicted donations).
Experimental Design Details
Randomization Method
randomization done in office by a computer
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
no clusters
Sample size: planned number of observations
769 past donors, 3,859 regular customers, 5,376 new customers
Sample size (or number of clusters) by treatment arms
1) threshold equal to past or predicted donation: 1/3 of each past donors and regular customers
2) threshold higher than past or predicted donation (next donation category): 1/3 of each past donors and regular customers
3) threshold at random excluding to past or predicted donation: 1/3 of each past donors and regular customers, all new customers (no predicted donation)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We study the percentage increase in out-of-pocket donations. As a proxy for the standard deviation, we use the standard deviation of percent changes in donations from year 2015 to 2016 (two years of fundraising campaing before the field experiment). The standard deviation is 0.8953779. Assuming a 1/3 response rate among past donors, one sample, one-sided test (increase), and alpha 0.05, we arive at 0.9257 power to detect a 30% increase in donations (in treatment 1 or 2).
IRB Name
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)