A lack of response of the financial behaviors of biodiversity conservation nonprofits to changing economic conditions.

The effectiveness of conservation organizations is determined in part by how they adapt to changing conditions. Over the previous decade, economic conditions in the United States (US) showed marked variation including a period of rapid growth followed by a major recession. We examine how biodiversity conservation nonprofits in the US responded to these changes through their financial behaviors, focusing on a sample of 90 biodiversity conservation nonprofits and the largest individual organization (The Nature Conservancy; TNC). For the 90 sampled organizations, an analysis of financial ratios derived from tax return data revealed little response to economic conditions. Similarly, more detailed examination of conservation expenditures and land acquisition practices of TNC revealed only one significant relationship with economic conditions: TNC accepted a greater proportion of conservation easements as donated in more difficult economic conditions. Our results suggest that the financial behaviors of US biodiversity conservation nonprofits are unresponsive to economic conditions.

Introduction

The decade of the 2000s was characterized by highly variable economic conditions globally and within the United States (US), including a period of rapid growth followed by the largest recession since the Great Depression (Poole 2010; Fig.1). How these economic fluctuations have affected biodiversity conservation has been a subject of conjecture but little empirical evaluation. Some authors see opportunity to slow rates of habitat destruction and climate change during recessions and to decouple resumed economic growth from environmentally damaging production (Jackson 2009; Woodward 2009). Others caution that recessionary conditions may impair biodiversity conservation through diminished government revenues and related program cuts or by reduced charitable giving to nonprofit organizations (Bakker et al. 2010; Elliott 2011; Sayer et al. 2012).

Figure 1

Log10 United States (US) gross domestic product (GDP) as billions of 2010 dollars ($) by quarter for 2000–2009 (black) with linear regression fit (gray).

These divergent predictions regarding the impact of changing economic conditions on conservation may hinge on conservation organizations' responsiveness or ability to adapt to change. The ability of conservation organizations to adapt to change has been suggested as a key driver of their overall effectiveness (Chapin et al. 2006 ; Kenward et al. 2011), but has only recently begun to attract study (Brown et al. 2010; Jantarasami et al. 2010; Baral 2013). In contrast, responsiveness to changing conditions has long been an object of study in for-profit sectors (Carlsson 1989; Garvin 1993; Enlow and Katchova 2011). Economic theory suggests nonprofits may be less responsive than for-profits (Alchian and Demsetz 1972; Glaeser 2003), but empirical tests among nonprofit organizations remain scarce and primarily confined to sectors other than biodiversity conservation that have for-profit equivalents, such as health care (Duggan 2002; Malani et al. 2003).

Nonprofit organizations play an integral role in biodiversity conservation through activities including acquiring and restoring conservation lands and waters, providing environmental education, and seeking to influence government policies and their implementation through lobbying and litigation (Armsworth et al. 2012). Environmental nonprofits (of which biodiversity conservation nonprofits are a subset) have been one of the fastest growing segments of the overall US nonprofit sector in recent decades (Straughan and Pollak 2008). Yet biodiversity conservation nonprofits are reliant on revenue sources such as charitable donations, government grants, and foundation endowments that leave these organizations sensitive to economic fluctuations (Yen et al. 1997; Bakker et al. 2010). The biodiversity conservation nonprofit sector in the US is diverse, with organizations differing greatly in size and objectives (Armsworth et al. 2012), which may complicate characterizing responses to change. Yet, just as diverse organisms can evolve similar strategies to cope with highly variable “feasts and famines” of resource availability (McCue 2007; Armstrong and Schindler 2011), we expect that biodiversity conservation nonprofits may share some general financial behaviors for responding to economic booms and busts.

In this paper, we evaluate how economic conditions during the previous decade affected the financial behaviors of organizations in the biodiversity conservation nonprofit sector in the US. Our focus on financial behavior offers a tractable insight into organizational responses to economic events, although we recognize that bridging financial behavior to conservation effectiveness requires further study (see discussion). We use financial data from the US Internal Revenue Service (IRS) tax returns for a random sample of biodiversity conservation nonprofits to calculate “financial ratios” indicative of organization behavior. Originating from for-profit applications like predicting bankruptcy risk (e.g., Ohlson 1980), financial ratios have been applied to other nonprofit sectors to characterize organization behavior (Tuckman and Chang 1991; Trussel and Greenlee 2004; Keating et al. 2005; Zietlow 2010). Chabotar (1989) argued that “[financial] ratios are a much truer indicator of institutional priorities than any strategic plan.” Financial ratios have been widely used to demonstrate sensitivity of and responsiveness to economic conditions for a variety of for-profit firms and sectors (e.g., Youn and Gu 2010; Giordani et al. 2013). Accordingly, we anticipate that financial ratios may offer insights into common trends in the behaviors and management decisions made by biodiversity conservation nonprofits in response to changing economic conditions.

We also evaluate how economic conditions impacted the conservation tactics pursued by a single organization in more detail to complement our coarser analysis of cross-sectoral trends. We chose the largest biodiversity conservation nonprofit, The Nature Conservancy (TNC; Armsworth et al. 2012), focusing on TNC's conservation expenditures and land acquisition practices over the same decade. This case study analysis of the largest biodiversity conservation nonprofit allowed us to evaluate if findings of high or low responsiveness to changing economic conditions by financial ratios were consistent with more resolved tactical behaviors within an individual organization. Our emphasis on the financial behaviors of conservation organizations in response to changing economic conditions complements studies that instead seek to relate overall conservation activity to economic growth (Pergams et al. 2004; Fuentes 2011).

Methods

Cross-sectoral data

Our cross-sectoral analyses use a stratified random sample of 90 biodiversity conservation nonprofits drawn from an existing dataset of over 1700 such organizations (Armsworth et al. 2012). Sizes of biodiversity conservation nonprofits span six to seven orders of magnitude in the full dataset and are right skewed, with more small than large organizations. To ensure representation across this size gradient, we stratified the sample to contain 30 of the 200 smallest organizations, 30 from 200 around the median size, and 30 of the 200 largest organizations (Fig.2). For each nonprofit in the sample, we collected itemized data for reported revenues, expenditures, assets, and liabilities from their US tax returns for 2000–2009. Specifically, we used IRS 990 forms, which we accessed from the GuideStar website (http://www.GuideStar.org). We standardized all monetary amounts to 2010 US dollars ($) using the Consumer Price Index (http://www.bls.gov/CPI/). A minority of our nonprofits (35 of 90) reported in fiscal years different from the calendar year; for these organizations, we standardized fiscal years by calculating averaged monthly values and summing these into corrected calendar years.

Figure 2

Inflation-corrected annual revenues and total assets for a random sample of 90 small, medium, and large biodiversity conservation nonprofits in 2000 and 2009 with a 1:1 line representing the boundary between negative and positive growth.

The 90 organizations included are registered in 35 US states. They range across a diversity of conservation objectives and business models, from land trusts, to zoos, to advocacy groups for specific taxa, to institutes dedicated to basic and applied conservation research. In general, growth of these 90 organizations between 2000 and 2009 tracked that of US gross domestic product (GDP; Appendix 2). We included only organizations that filed IRS 990 forms every year between 2000 and 2009, excluding apparent exits (failures) for two reasons. Prior to 2008, small nonprofits with gross revenues below $25,000 were not required to file IRS 990 forms, and consequently true exits were difficult to parse from the more common incidence of organizations failing to file taxes for several consecutive years (Harrison and Laincz 2008). Further, environmental nonprofits have been reported to have exceptionally low exit rates relative to for-profit businesses (Harrison and Laincz 2008; Appendix 1).

We chose four financial ratios summarizing complementary aspects of nonprofit behavior: (1) liquid funds interval; (2) revenue concentration; (3) ratio of personnel costs to total expenditures; and (4) ratio of total liabilities to total assets (Table1). We used the liquid funds interval as an index of how many months a nonprofit could operate based on existing liquid assets (i.e., excluding land and buildings) if all incoming revenue ceased. We calculated the liquid funds interval as the ratio of total cash, savings, and investments relative to mean monthly expenditures. We anticipated that biodiversity conservation nonprofits should grow liquid funds under favorable economic conditions and deplete liquid funds during unfavorable economic conditions. For revenue concentration, we predicted that biodiversity conservation nonprofits might exploit more revenue sources under favorable economic conditions (something that requires active marketing and campaigns) and contract revenue sources under unfavorable economic conditions. Our ratio of revenue concentration scales from 1 (single revenue source) towards 0 (many revenue sources) calculated as the squared percentage share of each revenue source, of eight possible categories on IRS form 990, relative to total revenues (Tuckman and Chang 1991). We also included the ratio of expenditures specific to personnel (salaries, compensation, benefits) relative to total expenditures. Tuckman and Chang (1991) suggested that personnel or administrative costs offer a likely area for cuts during poor economic conditions. Finally, we included the ratio of total liabilities to total assets (following Trussel and Greenlee 2004) because we suspected that the willingness or ability to assume debts by biodiversity conservation nonprofits might vary under differing economic conditions in response to need (weathering a poor economy) or access (expanded or restricted lending pre- and post- 2007–2009 recession). Instances where liabilities greatly exceeded assets for a very small minority of organizations (seven) were reduced to values of one (liabilities equaling assets) to control the influence of extreme outliers. We log10 transformed liquid funds interval and arcsine square root transformed the remaining three ratios for all analyses.

Table 1

Financial ratios considered in cross-sectoral analyses with formulas for calculation and expected signs in response to increasing organizational size (log10 assets in 2000) and more favorable economic conditions (gross domestic product, GDP)

Financial ratio	Description	Formula	Organization size (assets)	Economic conditions (GDP)
Liquid funds interval	How many months could an organization operate if all incoming revenue ceased?		+	+
Revenue concentration	Is an organization reliant on one, few, or many revenue sources?		−	−
Personnel to total expenditures	What proportion of total expenditures does an organization spend on personnel?		+	+
Liabilities to assets	What is the load of debts or liabilities an organization carries relative to all assets?		+	±

We emphasize that the coarse financial ratios outlined above do not represent all of the ways that an individual organization might respond to changing economic conditions. As one example, our measure of revenue concentration can express expansion of new or contraction of previous revenue sources through time. However, it does not differentiate between the identities of these revenue sources. An organizational transition from majority reliance on one revenue source (e.g., foundation giving) to another revenue source (e.g., government grants) of equal magnitude would go undetected (see Appendix 4). For this reason, we also sought to complement our use of coarse financial ratios with a more resolved analysis focusing on land acquisition practices of one major organization. However, we recognize that focused interviews of organizational leaders might be better suited for some aspects of more fine-grained responses (e.g., Mosley et al. 2012). As such, we also include some quotes from leaders of biodiversity conservation nonprofits on the sensitivity and responsiveness of their organizations to changing economic conditions (Appendix 4).

The Nature Conservancy data

We complemented our cross-sectoral analyses with a more detailed analysis of the behavior of the largest nonprofit in US biodiversity conservation, TNC. TNC manages 16% of total revenues and 25% of total assets reported by the sample of 1700 US biodiversity conservation nonprofits examined by Armsworth et al. (2012). As TNC is primarily a land trust, we analyzed this organization's patterns of land acquisitions in the lower 48 US states between 2000 and 2009. We included lands acquired as fee simple (n  = 4333) and using conservation easements (legal agreements restricting land uses by private owners; n  = 1451). For each transaction, we considered the total area (hectares), total cost ($ US 2010 equivalent), the proportion of costs that were donated relative to fair market values estimated from independent appraisals of property values, the price per hectare of acquisitions, and finally the ratio of conservation easements to fee simple acquisitions. We aggregated these fields across deals done in each financial quarter in 2000 through 2009. All TNC responses were log10 transformed for analyses, with additional detail on sources and management of TNC data given by Fishburn et al. (2013) and Davies et al. (2010).

Data analyses

We sought to relate our indicators of nonprofit behavior (above) to changing economic conditions. We used log10 transformed US GDP in billions of 2010 $ (Fig.1) as our predictor of economic conditions. We chose to use GDP over other measures like stock market indices (Pergams et al. 2004) because we felt that GDP would be most relevant to the breadth of biodiversity conservation nonprofits included in our cross-sectoral analyses. As sensitivity tests, we also evaluated organizational responses to changes in their own revenues from year to year and the effect of revenue growth on financial behaviors (Appendix 3). To account for the confounding of GDP with time (Fig.1), we performed regression analyses with the behavioral response indicators and GDP linearly detrended by time (i.e., residuals). We also included in our models either nonprofit size (cross-sectoral analyses) or financial quarter (TNC analyses). Quarter was included as a predictor for TNC because preliminary data investigation revealed a potential effect of quarter on land acquisition activities owing to either a preference by buyer (TNC) or sellers for fourth quarter transactions. For cross-sectoral analyses, organization size was included in models as the log10 transformed assets of each biodiversity conservation nonprofit at the beginning of our study time period (Fig.2), with organization identity incorporated as a random effect in linear mixed models (nlme library, R). We also included a set of models that incorporated an interaction term between biodiversity conservation nonprofit size and economic conditions (time-detrended GDP). Predictions of the role of organization size on financial ratio responses are given by Table1 (see also Tuckman and Chang 1991; Trussel and Greenlee 2004). Pseudo- R 2 values were calculated for cross-sectoral mixed models as the relationship of model fitted to observed response values.

Results

Three of the four financial ratios considered in cross-sectoral analyses were affected by biodiversity conservation nonprofit size (Table2). Specifically, larger organizations are characterized by having more liquid assets, more diverse revenue concentration (a lower value by our index), and higher personnel costs proportional to total expenditures (Table2). The direction of these relationships is consistent with the expectation that smaller nonprofits are more financially vulnerable than larger nonprofits (Table1). The ratio of liabilities relative to total assets was not affected by organization size.

Table 2

Results of linear regression models for financial ratios of 90 biodiversity conservation nonprofits after detrending each response and gross domestic product (GDP) by time, and including organization as a random effect. Results are given for models excluding and including a term for interaction between nonprofit size and GDP

	Size (SE)	GDP (SE)	Size × GDP (SE)	Pseudo- R 2
No Interaction
Liquid funds interval	0.181 (0.030)***	2.968 (1.211)*	–	0.206
Revenue concentration	−0.088 (0.024)**	−2.255 (0.780)**	–	0.100
Personnel to total expenditures	0.042 (0.015)**	−1.479 (0.478)**	–	0.062
Liabilities to assets	0.017 (0.017)	0.584 (0.737)	–	0.007
Interaction
Liquid funds interval	0.181 (0.030)***	8.093 (5.230)	−0.866 (0.860)	0.206
Revenue concentration	−0.088 (0.024)***	11.634 (3.334)***	−2.347 (0.548)***	0.106
Personnel to total expenditures	0.042 (0.015)**	−4.899 (2.063)*	0.578 (0.339)	0.063
Liabilities to assets	0.017 (0.017)	−1.130 (3.186)	0.290 (0.524)	0.007

Significance of coefficients is given as ≤0.001 (***), ≤0.01 (**), and ≤0.05 (*). Pseudo- R 2 is given as the relationship of model fitted to observed response values.

Specific to our focal question, two of four financial ratios (liquid funds index, revenue concentration) responded as predicted (Table1) to economic conditions (Table2). Further, the significant interaction of organization size and economic conditions reveals that large biodiversity conservation nonprofits experienced the most severe increases in revenue concentration under worsening economic conditions, as smaller organizations were characterized by concentrated revenues regardless of economic conditions (Table2). Interestingly, the ratio of personnel costs to total expenditures was significant but in the opposite direction hypothesized (Table1). Personnel costs became a larger component of total expenditures under worsening economic conditions (Table2; but see Appendix 3). The ratio of liabilities to assets was altogether unresponsive to changing economic conditions. Despite some significant coefficients for economic conditions on financial ratio responses, our low pseudo- R 2 values suggest that biodiversity conservation nonprofits are not particularly responsive to changing economic conditions (Table2).

Our analysis of TNC's land acquisition behavior provides an opportunity to test for behavioral responses to economic conditions at a much more resolved, if organization specific, level. However, again we detected little discernable response in behavior. Only one TNC land acquisition behavior was significantly affected by GDP and explained a meaningful proportion of the variance (Table3). The proportion of conservation easement costs that TNC accepted as donated relative to appraised fair market values increased under poor economic conditions and decreased under good economic conditions (Table3). As anticipated by our preliminary data explorations, quarter affected many TNC behaviors, with fourth quarter preferences for easement deal size as measured by area, the proportion of easements to fee simple acquisitions, and the proportion of deals that were donated (Table3).

Table 3

Results of linear regression models for TNC land acquisition responses after detrending each response and gross domestic product (GDP) by time, given as totals and specific to either fee simple acquisitions or conservation easements

	Quarter (SE)	GDP (SE)	R2
Deal size ($)	0.043 (0.036)	3.418 (4.591)	0.050
Fee simple acquisitions	0.045 (0.038)	2.449 (4.838)	0.042
Conservation easements	0.049 (0.051)	10.319 (6.536)	0.082
Deal size (Hectares)	0.085 (0.042)	3.374 (5.328)	0.107
Fee simple acquisitions	0.060 (0.051)	−1.423 (6.429)	0.038
Conservation easements	0.154 (0.061)*	11.628 (7.723)	0.187
Easements: fee simple acquisitions	0.078 (0.035)*	1.187 (4.509)	0.116
Proportion donated	0.168 (0.035)***	−3.137 (4.459)	0.392
Fee simple acquisitions	0.147 (0.041)**	0.316 (5.279)	0.253
Conservation easements	0.106 (0.026)***	−6.671 (3.268)*	0.369
Cost ($) per hectare	−0.041 (0.030)	0.044 (3.833)	0.048
Fee simple acquisitions	−0.015 (0.030)	3.872 (3.826)	0.034
Conservation easements	−0.105 (0.050)*	−1.309 (6.343)	0.108

Significance of coefficients is given as ≤0.001 (***), ≤0.01 (**), and ≤0.05 (*).

Discussion

Scientists regularly express in popular media (e.g. , Woodward 2009) or as asides in scientific manuscripts (e.g., Bakker et al. 2010) the belief that economic events like recessions can harm or help the cause of biodiversity conservation, yet almost no studies have quantified relationships between economic conditions and conservation activity (but see Pergams et al. 2004; Elliott 2011). We propose that the effect of economic fluctuations on biodiversity conservation will be determined in part by how conservation organizations buffer themselves against and respond to change. We provide the first empirical investigation into the effects of changing economic conditions on the financial behavior of biodiversity conservation nonprofits. We found that few measures of financial behavior were meaningfully affected by economic conditions whether evaluated for a cross-sectoral sample or the largest individual organization.

There are growing calls to examine the capacity of conservation organizations to adapt to changing conditions (West et al. 2009; Barbour and Kueppers 2012), but empirical investigations of this adaptive capacity remain scarce (but see Baral 2013). Our results suggest biodiversity conservation nonprofits may have little adaptive capacity, at least with regards to changing economic conditions. Alternatively, funding processes and conservation activities may operate on too long of time lags for our methods to detect behavioral responses to changing economic conditions. For example, land trusts like TNC often negotiate transactions with private landowners and cost-sharing government partners over many years, potentially obscuring evidence of responsiveness to current economic conditions (Appendix 4). List (2011) similarly observed that charitable giving to the nonprofit sector is asymmetrical with respect to economic conditions: good economic conditions correspond with increased charitable giving to a greater extent than poor economic conditions correspond with reduced giving, likely because revenue is often tied to contracts agreed upon years in advance.

When looking across the sector, we found that organizations may grow liquid funds under favorable economic conditions and deplete them under unfavorable economic conditions. We also found that revenue concentration, particularly for larger organizations, expands and contracts inversely with economic growth. However, contradicting our predictions and those of past work on financial vulnerability in nonprofits (e.g., Tuckman and Chang 1991), we were surprised to find that biodiversity conservation nonprofits may preferentially protect personnel when economic conditions are poor, likely at the cost of program activities (but see Appendix 3). Yet the variance explained by the models remained low, and the prevailing signal was one of little discernible behavioral response to changing economic conditions. A different approach might have focused on expenditures (e.g., diversity of programmatic offerings) rather than revenues and assets. Lowry (1997) tested for such effects of economic conditions on expenditures (i.e., spending on public goods vs. fundraising incentives) by 16 environmental nonprofits in the 1990s. Consistent with our results, Lowry (1997) found no evidence that external conditions impacted behavior of these organizations.

Land acquisition activities by TNC provide a more direct measure of on-the-ground conservation behavior, but were also not particularly responsive to GDP. However, the one significant exception does provide an interesting demonstration of the potential interaction between economic conditions and behavior of biodiversity conservation nonprofits. TNC accepted a greater proportion of conservation easements as donated in less favorable economic conditions relative to good economic conditions. This result, especially when put alongside a lack of response in the overall amount of conservation activity (e.g., easements acquired whether by cash or area), suggests TNC maintains their pace of conservation activity under poor economic conditions in part by taking as donations lands they might not prefer under more favorable conditions. This behavior also likely displaces some of the cost of conservation onto state and federal governments via land owner tax deductions for easement donations at times (economic recessions) when government budgets are already stressed by decreases in revenue.

Surveys and interviews of employees or board members might be used to test our conclusion of little responsiveness by biodiversity conservation nonprofits to economic conditions, and also to further characterize how such responsiveness relates to meeting organization objectives and conservation goals (e.g., Brown et al. 2010; Jantarasami et al. 2010). Mosley et al. (2012) used such surveys to evaluate adaptive tactics of human services nonprofits to economic recessions, and found results largely consistent with our study: larger organizations had more overall capacity for responsiveness, but most nonprofits exhibited little responsiveness to changing economic conditions. To provide additional context to our analyses, we report brief quotes from a small selection of leaders (executive directors, board members, etc.) of biodiversity conservation nonprofits on how economic conditions affect their organizations and how they respond (Appendix 4). These comments reflect a breadth of ways that the economy has (or has not) affected these organizations and the diversity of their financial responses, whether strategic or opportunistic. Some biodiversity conservation nonprofits “…just got bigger and bigger…” through the recent recession while others “…proactively down-sized…”, and some organizations have “… nothing built into our by-laws to take economic conditions into account…” whereas others “…approach these issues pretty strategically…” (Appendix 4).

An alternative interpretation of our results might conclude that many biodiversity conservation nonprofits simply do not prioritize responsiveness to changing economic conditions as an organizational objective. Such an interpretation might instead argue that many organizations seek to simply balance expenditures to revenues from year to year while maintaining other financial attributes (e.g., liquid funds interval) in some kind of consistent “fiscal homeostasis.” Related, Zietlow (2010) in a study of religious nonprofits in the US under recessionary conditions categorized four financial paradigms for these organizations, ranging from those seeking to just meet or slightly exceed their budgets on one end of a gradient to those aspiring to high financial flexibility on the other. Similar to our perspective, Zietlow (2010) characterized those nonprofits not managing for financial flexibility or responsiveness as “muddling through” or only aspiring to survival at best, often because these organizations were constrained by a “current services” trap that led to underinvesting in their own financial flexibility or liquidity. We believe an argument that biodiversity conservation nonprofits should not emphasize financial responsiveness to changing economic conditions is similar: that the mission of immediate biodiversity conservation is so urgent that organizations should not manage their finances for future contingencies or flexibility. Our interviews with biodiversity conservation nonprofit leaders (Appendix 4) do reveal gradients of intended or desired financial responsiveness, and we recognize that adaptation to changing economic conditions may not be a priority for some of these organizations. Whether it should be – and what that means for biodiversity itself – is a topic that our study invites more inquiry into.

We conclude by emphasizing that efforts to characterize effectiveness of conservation activity for the sector in aggregate remain in their infancy (Gaston et al. 2006). As others have noted (e.g., Chabotar 1989), financial ratios provide one method for examining quantitatively the behaviors of very diverse nonprofit organizations in response to shared events (e.g., recessions). Given the important role of nonprofits in biodiversity conservation, we have considerable and important knowledge gaps in understanding, and perhaps enhancing, their responsiveness and adaptability to change. At a minimum, we hope our work introduces new tools (i.e., financial ratios), observations, and hypotheses to inspire and inform subsequent studies on responsiveness of biodiversity conservation nonprofits to changing conditions.

Table A1

Results of linear regression models for financial ratios of 90 biodiversity conservation nonprofits after detrending each response and GDP by time, and including organization as a random effect. Results are given for models excluding and including a term for interaction between nonprofit size and GDP. Sample selection bias has been simulated by excluding three small and two medium organizations with the largest loss in assets between 2000 and 2009 (Fig.2). The percent change in parameter estimates relative to the full model (Table2) is also given

	Size (SE); %	GDP (SE); %	Size × GDP (SE); %
No interaction
Liquid funds interval	0.180 (0.030); −0.21	2.827 (1.218); −4.99	–
Revenue concentration	−0.088 (0.024); −0.15	−2.282 (0.787); 1.20	–
Personnel:Expenditures	0.042 (0.015); −0.01	−1.432 (0.480); −3.27	–
Liabilities:Assets	0.017 (0.017); −1.54	0.480 (0.741); −21.65	–
Interaction
Liquid funds interval	0.180 (0.03); −0.21	7.495 (5.256); −7.99	−0.787 (0.863); −9.98
Revenue concentration	−0.88 (0.024); −0.12	11.581 (3.362); −0.45	−2.339 (0.552); −0.35
Personnel:Expenditures	0.042 (0.015); 0.01	−4.825 (2.069); −1.54	0.572 (0.034); −0.98
Liabilities:Assets	0.017 (0.017); −1.52	−1.597 (3.200); 29.29	0.351 (0.525); 17.38

Table A2

Results of linear regression models for financial ratios of 90 biodiversity conservation nonprofits after detrending each response and annual revenues by time, and including organization as a random effect. Results are given for models excluding and including a term for interaction between nonprofit size and annual revenue

	Size (SE)	Revenue (SE)	Size × Rev (SE)	Pseudo- R 2
No interaction
Liquid funds interval	0.181 (0.030)***	0.031 (0.032)	–	0.204
Revenue concentration	−0.088 (0.024)**	−0.028 (0.020)	–	0.098
Personnel to total expend	0.042 (0.015)**	0.017 (0.012)	–	0.060
Liabilities to assets	0.017 (0.017)	−0.011 (0.019)	–	0.007
Interaction
Liquid funds interval	0.181 (0.184)***	0.133 (0.093)	−0.018 (0.015)	0.204
Revenue concentration	−0.088 (0.024)**	0.115 (0.059)	−0.025 (0.010)*	0.100
Personnel to total expend	0.042 (0.015)**	0.123 (0.036)**	−0.019 (0.006)*	0.063
Liabilities to assets	0.017 (0.017)	−0.019 (0.056)	0.001 (0.009)	0.007

Significance of coefficients is given as ≤0.001 (***), ≤0.01 (**), and ≤0.05 (*). Pseudo- R 2 is given as the relationship of model fitted to observed response values.

Table A3

Results of linear regression models for financial ratios of 90 biodiversity conservation nonprofits after detrending each response and GDP by time, and including organization as a random effect. These models also evaluate whether geometric growth in revenue over the time period (2000–2009) for each biodiversity conservation nonprofit affects their financial behaviors as manifested by financial ratios. As in the main text, results are given for models excluding and including a term for interaction between nonprofit size and GDP

	Size (SE)	GDP (SE)	Size × GDP (SE)	Revenue (SE)	Pseudo- R 2
No interaction
Liquid funds interval	0.181 (0.030)***	2.968 (1.211)*	–	0.008 (0.213)	0.206
Revenue concentration	−0.089 (0.024)**	−2.255 (0.780)**	–	−0.175 (0.171)	0.108
Personnel to total expend	0.042 (0.015)**	−1.479 (0.478)**	–	0.030 (0.109)	0.063
Liabilities to assets	0.017 (0.017)	0.0584 (0.737)	–	0.044 (0.119)	0.008
Interaction
Liquid funds interval	0.181 (0.030)***	8.093 (5.230)	−0.866 (0.860)	0.008 (0.213)	0.206
Revenue concentration	−0.088 (0.024)***	11.634 (3.334)***	−2.347 (0.548)***	−0.175 (0.171)	0.113
Personnel to total expend	0.042 (0.016)**	−4.899 (2.063)*	0.578 (0.339)	0.030 (0.109)	0.063
Liabilities to assets	0.017 (0.017)	−1.130 (3.186)	0.290 (0.524)	0.044 (0.119)	0.008

Significance of coefficients is given as ≤0.001 (***), ≤0.01 (**), and ≤0.05 (*). Pseudo- R 2 is given as the relationship of model fitted to observed response values.