![collective goods collective goods](https://i.pinimg.com/564x/0a/af/02/0aaf0285ea952b0e98082bea8078088c.jpg)
To date, the research has been dominated by linear models mainly based on the economic model of giving, and has reported mixed and sometimes conflictual findings about the net effect of certain individual organization-specific factors on donations. Nonprofits that compete for charitable contributions often question which are the most effective factors that lead to high levels of donations. The results of this study contribute to the debate on the effectiveness of organizational factors in attracting funds from donors willing to support environmental nonprofits. Moreover, the study reveals that providing high amounts of disclosure on the organization’s website can have a conditional effect on fundraising expenses by boosting the positive effect of these expenses on donations. By applying an extended version of the economic model of giving to a sample of 142 environmental nonprofits from the United States, the results of the regression analyses show that the following factors allow these organizations to attract more donations: devoting a high percentage of donations to programs, promoting the organization’s image through fundraising activities, having a large amount of assets that ensures a sustainable financial structure, and providing online information that demonstrates how the organization has dealt with its mission. The purpose of this study was to investigate which organizational factors can play a role in influencing the ability of these organizations to collect charitable contributions. Nonprofit organizations operating in the environmental protection and conservation sector face challenging fundraising issues in collecting from individual donors the money needed to accomplish their goals.
![collective goods collective goods](https://i.ytimg.com/vi/V5GeNnH_BiE/maxresdefault.jpg)
Taken together, the results suggest that market-wide investor sentiment impacts nonprofit organizations and the effects vary in the cross-section. Moreover, we find that these effects are stronger for organizations with large donors, who are more closely tied to the capital markets, and therefore more susceptible to investor sentiment, as well as charitable organizations, consistent with “tug-at-the-heartstrings” type appeals inducing more emotional donation response. Our inquiry separately considers cash and stock-based donations because we expect, and find, that market-related sentiment impacts these types of contributions differently. Results indicate that nonprofit organizations receive less in stock-based donations and more in cash-based donations during periods of high investor sentiment. We shed light on this issue using a large industry-diverse panel of over 115 thousand organization-years from 2008 to 2016. While donors are driven by ethical, altruistic, and other utility-maximizing motives, it is unclear whether behavioral biases stemming from sentiment would influence donors’ decisions to give. We extend this line of work by investigating whether the effects of sentiment spill over into the nonprofit sector by affecting donors’ spending to support moral causes. Prior work shows that capital market participants including investors, analysts, and managers are all impacted by the prevailing level of investor sentiment. Output price after tax adjustment Total cost after tax adjustment / project cost Weisbrod and Dominguez (1986) Project cost ratio Project cost / total cost Weisbrod and Dominguez (1986) Administrative cost ratio Administrative cost / total cost Parsons and Trussel (2009) Fundraising cost ratio Fundraising cost / total cost Weisbrod and Dominguez (1986) Net asset ratio Net asset / total revenue Parsons and Trussel (2009) Revenue concentration index Σ(revenue from each source / total revenue) 2 Parsons and Trussel (2009) Margin rate (Total revenue -total cost) / total revenue Parsons and Trussel (2009) Activated years Duration years as tax exempt organization Weisbrod and Dominguez (1986) Size Total assets Tinkelman (1998) Volunteer Time of volunteering Callen (1994) Subsidies revenue Government subsidies Posnett and Sandler (1989) Project revenue Self-earning project revenue Posnett and Sandler (1989) Other revenue Total revenue -(giving + subsidies + project revenue) Posnett and Sandler (1989) Source: Authors based on Trussel and Parsons (2008) Reputation Efficiency Stability. Table 1 shows the financial factors that affect giving as identified in previous studies.