Summary statistics by treatment and year

After accounting for non-deliverable addresses 4,835 and 8,611 homes received a pledge request from the Bobolink Project in 2013 and 2014, respectively. In 2015 only 322 homes received pledge requests, and these were previous donors to the project in 2013 or 2014. During the pilot year, 210 contributors participated. Over the years the number of contributors increased to 229 in 2014 and 340 in 2015. Contributions from donors ranged from $5 to $5,000. The average (non-zero) contributions for 2013, 2014 and 2015 were $145.66, $139 and $152, respectively.

While it is entirely possible that some of the non-responders never opened their packages, and therefore, never saw the pledge cards, our model assumes that all the non-responders have a zero propensity to contribute for bird-habitat. Tables 2-2, 2-3 and 2-4 summarize the average contribution by the donors by year and by different treatment variables. In both 2014 and 2015, the IPA treatments generated a higher average contribution from donors compared to the standard solicitation approach. Also, each year setting up a higher outcome target within different IPA scenarios mostly led to higher contribution on average (except IPA_60 in 2015).

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Table 2-2: Average contribution by treatment variables in 2013

Average N Std. Dev. Min. Max.

* After removing two contributions of $2250 and $2520 as outliers above $1000.

Table 2-3: Average contribution by treatment variables in 2014

Average N Std. Dev. Min. Max.

* After removing the sole $5,000 donor as an outlier, since the next highest contribution was $500.

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Table 2-4: Average contribution by treatment variables in 2015

Average N Std. Dev. Min. Max.

* After removing two contributions of $2000 and $5000 as outliers above $1000

2.8.2. Results from the pilot year 2.8.2.1. Triple hurdle model

Triple hurdle model estimation results from the pilot year are presented in table 2-5. All estimation is done using STATA 15. Column (i) shows the estimates from the first stage or, the participation stage of the model. The participation equation is important as it helps identify potential contributors and what aspects of the treatments are more likely to lead people to respond to such fundraising efforts. We model the participation behavior using the treatment variables for the pilot year (table 2-1) and the demographic variables. The treatment variables include dummy indicators for different field targets for the IPA scenarios: IPA_10 and IPA_20, while IPA_5 is used as the baseline. Other treatment variables include information on farmer availability (farmer_10 = 1 if the donors were told 10 farmers were available), contribution style (per-field =

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1 if the donors were asked to provide a per-field donation), a suggested donation amount (sug_high

= 1 if the donors were provided a high suggested donation amount) and the number of field intervals used in the solicitation (5-part = 1 if the donors were provided a 5-part solicitation). The demographic variables include a dummy variable for gender (=1 if female), age (=1 if aged below 65 years), past environmental donor (=1 if donated to environmental causes before) and marital status (=1 if married). Various income categories included dummy variables for households with annual income below $150,000 (inc_cat_1), and income between $150,000 and $199,999 (inc_cat_2), with annual income above $200,000 being the baseline category.

Table 2-5: Triple hurdle model estimates of participation and contribution in the Bobolink market in the pilot year 2013

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Standard errors in parentheses, p-values in square brackets. * p<0.10, ** p<0.05, *** p<0.01.

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Our results indicate that none of the treatment variables are significant in determining participation, except for the field target levels. Compared to the baseline target of five fields, a higher target of 10 and 20 fields significantly increases participation. Based on this observation we decided to modify our solicitation in 2014 and 2015 with a higher target. Being a previous donor to environmental causes also leads to higher participation in the Bobolink market.

Conditional on participation, in the second stage of the model, participants decide the intensity of participation in terms of how many times they add to a baseline donation, given the treatment they received. Column (ii) reports the coefficient estimates for predicting the probability of being a donor at various intensity level using an ordered probit model. In the second stage, a positive coefficient implies that as explanatory variables increase, observations are more likely to be in the higher intensity category i.e., more likely to be a low-intensity donor than a baseline donor, more likely to be a medium-intensity donor than a low-intensity donor and more likely to be a high-intensity donor than a medium-intensity donor. Results indicate that presenting a higher field target leads a potential donor to a lower intensity donor category. Also, being married leads a donor into a lower intensity category.

Columns (iii), (iv), (v) and (vi) present the coefficient estimates from stage 3, the contribution stage. In all columns of stage 3, the dependent variable is the log of contribution.

Results indicate that for a baseline donor, none of the treatment variables are significant in determining contribution, except for inc_cat_1, which is negative and significant implying that a lower income category reduces contribution compared to the baseline income of $200,000. For the low, medium and high intensity donors, a higher field target increased average contribution, which is consistent with our second hypothesis. Among the treatment variables, use of a 5-part solicitation reduced contribution for the medium and high intensity donors, which could be an

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indication of respondent fatigue. This observation led us to modify our solicitation efforts for 2014 and 2015 by reducing the number of field intervals to two and four. A higher suggested contribution led to higher donation for the low, medium and high intensity donors. This observation led us to modify our efforts in 2014 and 2015 by keeping the suggested donation at a high level for all the potential donors, and not use it as a treatment variable.

2.7.2.2. Average Partial effects

The coefficients estimated in table 2-5 are not partial effects as the likelihood function is nonlinear, so we can only analyze the direction and statistical significance of the explanatory variables. In this section, we present the partial effects of the treatment variables on the donor contribution of various intensity (table 2-6). None of the treatment variables are significant for the baseline donors. For the low intensity donors, use of an IPA solicitation that provided a maximum of 10 fields increased the contribution by 9.5 percentage points, while an IPA solicitation that provided a maximum of 20 fields increased the contribution by 7 points compared to the solicitation that provided a maximum of 5 fields. For medium and high intensity donors, the increment is higher: IPA_10 leading to an increase of 16 and 25.5 percentage points and IPA_20 to 10 and 21.8 percentage points to the donation compared to IPA_5. This observation implies that as the intensity of contribution increases, the magnitude of contribution is also increasing. For the medium intensity donors, a 5-part solicitation increased contribution 56.1 percentage points, however, for high intensity donors a 5-part solicitation eventually decreased contribution by 32.7 percentage points. For low, medium and high intensity donors, a higher suggested donation amount led to an increase in the contribution by 26.3, 39.4 and 33.3 percentage points, respectively.

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Table 2-6: Average partial effects (APE) on expected contribution (pilot year)

treatment variables that were common for both years and included a year-dummy in the regression analysis. Paired t-tests of the rest of the treatment variables (that are not common to both years) are presented in table 2-7. A paired t-test reveals that inclusion of a provision point led to a higher, but not statistically significant, donation on average. A paired t-test shows that those who received treatment with a recognition title donated a higher amount on average compared to those that did not receive a recognition title, however, the difference was not statistically significant. Among those that received treatment with a recognition title, “Bobolink Community Builder” generated a significantly higher contribution on average compared to “Bobolink Baseline Supporter”.

Providing a calculator for online donors increased average contribution, but the difference was not statistically significant.