Integración Vertical

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.

34 Table 2-7: paired t-test of average donation

Year 2015

Bobolink Baseline Supporter 105.11 (46)

Bobolink Community Builder 167.5 (40)

t-stat 2.73

Triple hurdle model estimation results from 2014-2015 are presented in table 2-8. Column (i) reports the coefficient estimates from the participation stage of the model. We model the participation behavior using the treatment variables and demographic variables. The treatment variables include dummy indicators for different target numbers of fields for the IPA scenarios:

IPA_20, IPA_40, IPA_60, and IPA_100. These IPA scenarios are compared against the baseline of a standard solicitation treatment (flat). The demographic variables included dummy variables for gender (=1 if female), marital status (=1 if married), responsiveness to mail orders in last six months (prev_mail = 1 if yes), past environmental donors (env_donor=1 if yes). The demographics also included various age categories: between 40 and 65 years of age (age_40_65) and above 65

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years of age (age_above_65), with below 40 years being the baseline category. Various income categories included dummy variables for household with annual income below $100,000 (inc_cat_I), income between $100,000 and $149,999 (inc_cat_II), and income between $150,000 and $199,999 (inc_cat_III), with annual income above $200,000 being the baseline category.

Our results indicate that the use of a standard solicitation (flat) increased participation.

Among the IPA treatments, the use of IPA_20 decreased participation and use of IPA_60 increased participation, while IPA_40 had no significant impact on participation compared to the baseline of IPA_100. Results also show that married people are less likely to respond. Participants with previous experience with responding to mail orders (within last six months) and donating to environmental causes (within last six months) are more likely to respond to our marketing campaign. Female donors are more likely to respond.

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) presents coefficient estimates for predicting the probability of being donors with varying intensities, conditional on being a participant using an ordered probit regression. In the second stage, positive coefficient estimates imply that as explanatory variables increase, observations are more likely to be in the higher intensity category. Presenting a higher target number of fields increases the chances that move a potential donor into a higher intensity category compared to IPA_20. The coefficient for married is negative and significant, implying that being married leads a donor into a lower intensity category. Previous experience with being an environmental donor and responding to mail orders leads a donor into a higher intensity category.

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In table 2-8, columns (iii), (iv), (v) and (vi) report the coefficient estimates for predicting the contributions of donors of varying intensity of participation (stage 3). In all columns of stage 3, the dependent variable is the log of contribution. The robust standard errors are in parentheses below each estimate. Results indicate that, for a baseline donor, use of a standard solicitation format significantly reduces contribution relative to the treatment IPA_100. Demographically, older people tend to donate more when they are baseline and low intensity donors, however, younger people donate more when they are high-intensity donors. Participants who donated to environmental causes previously tend to donate more when they are medium intensity donors, however, they donate less when they decided to be baseline donors.

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Table 2-8: Triple hurdle model estimates of participation and contribution in the Bobolink market

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* p<0.10, ** p<0.05, ***p<0.001. Standard errors in parentheses.

2.8.3.2. Average partial effects

In this section, we present the average partial effects of the treatment variables from 2014 and 2015 on the donor contribution at different levels of contribution intensity (table 2-9). For the baseline donors, use of a standard solicitation reduced average contribution by 15.5 percentage points and use of an IPA_40 solicitation reduced average contribution by 3.8 percentage points.

For the low intensity donors, the use of IPA_20 reduced contribution by 11.6 percentage points.

For both medium and high intensity donors, IPA_60 reduced contribution, by 2.5 and 0.8 percentage points respectively. For high intensity donors, IPA_20 led to a higher contribution.

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Table 2-9: Average partial effects (APE) for 2014 – 2015

Baseline

Our goal in this paper is to analyze the donor behavior in an experimental market for Bobolink habitat. We implemented an IPA-inspired solicitation approach to generate revenue. This IPA approach calls for a multiple level solicitation strategy that links successive donations (or pledges) to the ability of a fundraiser to support higher levels of outcome for the quantity of a local public good. This solicitation strategy based on outcome success is different from a standard approach where an individual donor makes a flat donation irrespective of the outcome. Our multi-tiered approach in the preceding sections incorporates donation intensity as a separate hurdle and the approach models donors with varying donation-intensity separately. In this section, we model the donor behavior using a simpler, double hurdle model, and we assume that the donors have the same intensity of donation. In this model, the first hurdle is the same as the previous model; the decision to participate or not. When a potential donor crosses this hurdle, she chooses a level of donation in the second hurdle. The hurdles are modeled jointly (by FIML) using a probit regression and a log-linear regression respectively.