## Abstract

Nonprofit organizations (NPOs) solicit donations from individuals and in turn offer goods and services whose quality cannot be (easily) ascertained by the donors. This creates incentives for “bad” NPOs to enter the market and free ride on donor trust. This paper presents a model in which the media helps to reduce the problem of asymmetric information in the market for NPOs. This occurs through two channels. On the one hand, the media can, with some probability, uncover a “bad” organization if it has entered the market. On the other hand, the media reduces the incentives for bad types to enter the market in the first place. Overall, the media enhances the trust of the donors and increases the level of donations and the amount of public good produced.

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## Notes

- 1.
See Adena (2013) for more details on the story, other examples of misconduct by NPOs, and the role of the press in uncovering these stories.

- 2.
- 3.
This is a simplification which does not change the conclusions from the model.

- 4.
One can assume that in the case of a lie the competitors will be happy to uncover the falsehood and punish the rival media organization.

- 5.
This is a slightly modified Tullock contest success function (Tullock 1980).

- 6.
Note that the level of \(\alpha \) is different in both equilibria.

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## Appendix

### Appendix

See Tables 2, 3, 4, 5, 6 and 7.

### Robustness checks

The Least Absolute Deviations Estimation is a reasonable alternative to OLS given that in small sample OLS is very sensitive to atypical data points. The LAD estimator estimates the median regression (Greene 2008). The results are presented in Table 4. Figures 1, 2 and 3 suggest that Tanzania might be an outlier. The very high share of private philanthropy is suspicious. In fact it reflects outside funding and external dependence of the state rather than the inside NPO activity. Treating Tanzania as an outlier and repeating the estimation for the 35 countries delivers similar estimates (see Table 5). The effect of press freedom is more significant for specifications 2 and 3. The value of R square increases for all specifications.

### L-type probability of entry

To solve for \(\sigma _{L}^{*}\) note that following must hold:

Inserting

and

into (7)

reduces to

Note that in symmetric Nash equilibrium all media invest the same amount of efforts which is given by

Using (8) and

\(\sigma _{L}^{*}\) is given as the (economically reasonable) solution to the following cubic equation:

### The upper bound of costs

\(\bar{c}\) is given by the following equation:

To show is that \(\bar{c}<1/2\). Rearranging, taking both sides to the power of two (the term under the square root is strictly positive for \(k>0\), \(P>0\) and \(n>2\)) and rearranging once more leads to:

which holds for \(P>2k\) (Assumption 1).

### Proof of Lemma 1

There is no equilibrium with \(\sigma _{H}=1, \sigma _{L}=0\) because, with consistent beliefs, the L-type has the incentive to enter. There is no equilibrium with \(\sigma _{H}=0, \sigma _{L}=1\) because, with consistent beliefs in such an equilibrium the L-type makes a loss and backtracks.

### Proof of Lemma 2

Follows from positive efforts condition.

### Proof of Lemma 3

Follows from the first derivative of \(x_i\) in P being positive and the first derivative of \(x_i\) in k being negative.

### Proof of Lemma 4

Follows from the first derivative of \(x_i\) in n being negative and the first derivative of \(n*x_i\) in n being positive.

#### Proof of Lemma 5

Using Lemmatas 3 and 4 it follows that the first derivative of \(\alpha \) in *n* and *P* is positive and is negative in *k*.

### Proof of Lemma 6

Proof as in the text following the lemma.

### Proof of Lemma 7

Follows from the fact that separating equilibria do not exist (Lemma 6).

### Proof of Proposition 1

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### Cite this article

Adena, M. Nonprofit organizations, free media and donor’s trust.
*J Econ* **118, **239–263 (2016). https://doi.org/10.1007/s00712-016-0477-5

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### Keywords

- Nonprofits
- Charitable giving
- Asymmetric information
- Media

### JEL Classification

- H24
- H31
- D12