Control variables

We add variables to our model to control for foundation-specific differences in governance choice. Child and Grønbjerg (2007) argument for the use of foundation characteristics such as field of activity, size and age as explanatory variables for foundation behavior. Core, Guay, and Verdi (2006) observe that there are differences among mission industry categories (ICNPO) in endowments, program expenses and compensation and that controlling for industry variation is important. Desai and Yetman (2015) control for industry effects, size and revenues. In our sample we observe similar differences in age, wealth, revenues and competition (see Table 8 below) and also deduce from anecdotal evidence that, e.g., housing

101 A direct consequence of this definition is that industry and competition variables will correlate perfectly. Thus they cannot be used in the same regressions. However, industry is a dummy variable whereas competition is a scale measure, indicating the availability of alternative similar non-profit causes.

102 See discussion on rationale for third sector in Introduction, section 2. Reputation and non-profit status mitigate information asymmetry between customers and producers.

See also seminal work on reputation and quality by Glaeser and Shleifer (2001) and on growing importance of reputation in Ben-Ner (2002).

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foundations have a fully different character from business advocacy foundations. We believe these differences are likely to present foundation-specific fixed effects on our independent variable, the quality of governance.

We use foundation age, industry¹⁰³ and revenues¹⁰⁴ as our control variables.

These are expected to alter the dependent or independent variables, but are not of substantive interest to our hypotheses under examination. If excluded from the model and if they have a non-zero covariance with one or more of the independent variables of interest, their omission could bias our ordinary least square regressions’ results for the effect of our independent variables of interest.

However, if our control variables are correlated with our independent variable they may also be a consequence of it. The inclusion of control variables that may be influenced by our independent variable (the quality of governance) may lead to endogeneity problems where the regression coefficient in an ordinary least squares regression is biased. However, if the correlation is not contemporaneous, then it may still be consistent. We maintain that good governance may lead to higher age and revenues, but they are not codetermined; governance model is formed over time (Δ age), and the growth of a foundation (Δ revenues) may slowly start to increase pressure to alter governance model (see our discussion on causality in Section 3.1).

Our analysis is not a time series analysis so we present no structural model.

We posit that at most, age and revenues may be endogenous over a long period of time, but exogenous in our one period model. Foundation industry variable is in our view fully exogenous: it is decided at the establishment of the foundation and almost never changed¹⁰⁵.

Foundation age is calculated from the year of registration to the foundation register. The register was established in 1930, and many existing foundations registered then. However, we have corrected all known older cases to their correct, older establishment date. This information was collected from the foundations’ internet pages.

In Section 3 we noted that either grantees or financiers can be seen as a foundation’s owners. Along this analogy, the mission industry can act as a proxy for the nature of the grantee-owner: the grantees’ characteristics are similar in one mission industry group. For instance, grantees (or beneficiaries) of Social services can be considered less vocal in a foundation’s governance than beneficiaries of Business foundations. Typically, research

103 In “Industry” we adopt a term familiar from corporate world. It applies well to the third sector because it distinguishes between the final “customers” of the operations, not method of financing or other characteristics. As in Child and Gronberg we mean “…the field of activity the nonprofit primarily operates …”. We may refer to it also as “mission industry”.

104 We use foundation revenues as a size indicator because balance sheet items correlate strongly with the Source of financing –variable.

105 As there are no mergers or restructurings of foundations, they do not present avenues for changing the mission (e.g., industry clause) of a foundation. An administrative change of foundation rules, especially the mission clause, are typically cumbersome processes.

What determines a non-profit governance model - and does it matter?

grantees are represented in foundation boards, whereas international help recipients are not (groups 2 vs. 9). This thinking – “recipients are the true owners of a foundation” – would allow us to use the mission industry as an ownership proxy and discuss the implications of both competition and

“ownership” to the governance of foundations.

To summarize descriptive characteristics of our data we present Table 8 with financial, competition, governance and age characteristics of the 891 foundations, grouped by the foundations’ mission industry.

Table 8. The descriptive statistics of the sample foundations. N=891. Age is measured at the end of 2012. Revenues and Market Value of Balance sheet are category averages of each foundation’s average for 2010-2012. The source of finance is determined case-by-case, based on the share of total revenues or of the net profit of each type of finance. These relative shares are calculated with the average values for 2010-2012 of each item. See Appendices 1 and 2 for classification and detailed foundation financial reporting. Competition is a scale factor obtained from the number of sample foundations in each ICNPO-category.

FGI is Foundation Governance Index, a measure of the concentration of decision powers to the foundation board, as explained in Section 4.1.1. A higher FGI indicates governance that is concentrated to the board, instead of a more open control structure. FGI takes values 0 to 5.

DESCRIPTIVE STATISTICS

Foun-dation

age Revenues Market value of

Balance sheet Source of finance petition FGI Com-Foundation's

mission industry

Mean years

Mean Mean Median Endowed Donative Operative Public

sector Number Mean

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Table 8 shows little variation in the average age of foundations. Business foundations have on average been founded much earlier (average age 47 years) than environmental or international foundations (19 and 22 years).

It seems that all types of foundations are being currently established as their average age does not vary much from category to another.

The large difference between the average and median values of financials highlights the concentration of wealth in the third sector. At the end of 2012, the largest 100 foundations possessed some 87 percent of the sample foundations’ wealth. At the other end of the spectrum there are numerous small foundations whose economic activities are almost insignificant. We use log-alterations for our financial variables to smooth the skewness of data.