Baron Diermeier (2006) - Strategic Activism And Nonmarket Strategy

• This page is referenced in BPP Field Exam Papers

Reference(s)

Baron, D. and D. Diermeier (2007), Strategic Activism and Nonmarket Strategy, Journal of Economics and Management Strategy 16, 599-634. pdf (2006 Draft Paper pdf)

Abstract

Activist NGOs have increasingly foregone public politics and turned to private politics to change the practices of firms and industries. This paper focuses on private politics, activist strategies, and nonmarket strategies of targets. A formal theory of an encounter between an activist organization and a target is presented to examine strategies for lessening the chance of being a target and for addressing an activist challenge once it has occurred. The encounter between the activist and the target is viewed as competition. At the heart of that competition is an activist campaign, which is represented by a demand, a promised reward if the target meets the demand, and a threat of harm if the target rejects the demand. The model incorporates target selection by the activist, proactive measures and reputation building by a potential target to reduce the likelihood of being selected as a target, fighting a campaign, and credible commitment.

The Model

There are two (types of) players:

• A firm with a concave profit function [math]\pi(\cdot)\,[/math], that has a default activity [math]x_0\,[/math]
  • Firms may be Strategic (and responsive to demands) with probability [math]p\,[/math], or Recalcitrant with probability [math]1-p\,[/math]
• An activist with a strictly increasing utility function [math]v(\cdot) There is a campaign consisting of: *\lt math\gt x_D\,[/math] a demand
• [math]r\,[/math] a reward
• [math]h\,[/math] a harm

The simplest model

An activist gives a firm a demand, with a promise of reward if it accepts and of harm if it rejects. The offer is TIOLI.

The firm concedes iff:

[math]\pi(x_D) + r \ge \pi(x_0) -h\,[/math]

The activist has utility:

[math]u(x_D,r,h) = p(v(x_D) - c(r)) + (1-p)(v(x_0) - g(h))\,[/math]

where [math]c(\cdot)\,[/math] and [math]g(\cdot)\,[/math] are both cost functions that satisfy Inada conditions.

Rearranging the firms concession function (when it binds with equality) gives:

[math]r = \pi(x_0) - \pi(x_D) -h\,[/math]

Substituting this into the activitist's utility function and maximizing with respect to [math]x_D\,[/math] and [math]h\,[/math] give two first order conditions that the optimal campaign must satisfy, as well as [math]r^* = \pi(x_0) - \pi(x_D^*) -h^*\,[/math], providing the gain to the campaign is positive:

[math]G = u(x_D^*,r^*,h^*) - v(x_0)\,[/math]

which gives:

[math]G = p(v(x_D^*) - c(r^*) - v(x_0)) - (1-p)(g(h^*)) \ge 0\,[/math]

If this condition is not satisfied then the activist doesn't conduct the campaign.

Comparative statics

The campaign ([math]x_D^*,r^*,h^*\,[/math]) has the following comparative statics:

• The higher [math]p\,[/math] (i.e. more responsive the target) the more aggresive the demands [math]x_D^*\,[/math]
• The higher [math]p\,[/math] (i.e. more responsive the target) the more aggresive the harm [math]h^*\,[/math]
• The effect on rewards is ambiguous
  • The reward increases when the demand goes up
  • The reward decreases when the threat of harm goes up
• The demand is strictly increasing in [math]x_0\,[/math], so better current practices result in higher demands
• The optimal harm may be increasing or decreasing in [math]x_0\,[/math] depending on the functional forms of [math]\pi\,[/math] and [math]v\,[/math]

A Specific Functional Form

From here on we will assume that:

• [math]v(x)=\gamma x\,[/math] : [math]\gamma\,[/math] is the marginal valuation of the target's practices
• [math]\pi(x) = \overline(\pi) - \eta x\,[/math] : [math]\eta\,[/math] acts as a marginal cost of conceding
• [math]c(r) = \alpha r^2\,[/math] : When rewards are difficult to provide [math]\alpha\,[/math] is high
• [math]g(h) = \beta h^2\,[/math] : When harm is expensive [math]\beta\,[/math] is high

This form gives a demand that is linear and increasing in harm, and the optimal campaign is given by:

[math]x_D^* = x_0 + \frac{\gamma(p\alpha + (1-p)\beta)}{2\eta^2 \alpha \beta (1-p)}\,[/math]

[math]r^* = \frac{\gamma}{2 \eta \alpha}\,[/math]

[math]h^* = \frac{p \gamma}{2 \eta \beta (1-p)}\,[/math]

Which has the following comparative statics:

• The demand [math]x_D\,[/math] are strictly increasing in [math]\gamma ,p, x_0\,[/math]
• The demand [math]x_D\,[/math] are strictly decreasing in [math]\alpha, \beta, \eta\,[/math]
• Higher costs of a campaign lead to lower demands
• The more responsive a target the higher the demand

You can derive a rewards to cost ratio, which suggest that more responsive firms will be threatened with harm over rewards, and that harm will be preferred with rewards are costly. If the internet reduces [math]\beta\,[/math], then we would expect to see campaigns with higher demands that emphasize more harm after its adoption.