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Corruption, conflict and the management of natural resources

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Abstract

The documented link between natural resources and civil conflict is not well understood. This paper uses a political economy framework to explore the prevalence of resource-based civil conflict driven by group-level discontent. The theoretical model proposed here offers a policy-based explanation: under conditions related to the quality of governance, discontent about resource management can affect the likelihood of an insurgency. Resource policy arises endogenously as the corrupt government trades-off industry contributions and the cost induced by manifestations of resource-related discontent. The conservation effects of civil unrest are analyzed and government corruption emerges as an important determinant of conflict. The paper also presents some empirical support for the model’s predictions.

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Notes

  1. For an in-depth economic characterization of conflicts, see Glazer and Konrad (2003).

  2. E.g. Renner (1996), p. 59.

  3. Busse and Groening (2011)—in a contribution to the vast resource curse literature—show a statistical association between exports of natural resources and corruption.

  4. Skaperdas (2011) presents a useful review of the costs of violence literature.

  5. E.g. in Indonesia, the Aceh conflict against the government and ExxonMobil, in Papua New Guinea, the Bougainville conflict against the government and the mining corporation RTZ, in Nigeria, the Ogoniland/Niger Delta conflict against Royal Dutch/Shell and other Western oil giants and the government are just some of the largest ones. In May 2011, violent protests by Aymara Indians led the suspension by the Peruvian government of a mining license for Canadian mining firm Bear Creek (reported by Reuters on June 24, 2011: http://goo.gl/Hze8t). See Regan in Ballentine and Sherman (2003), pp. 133–166 and Renner (2002), pp. 40–47.

  6. As Hegre and Sambanis (2006) find in a ‘sensitivity analysis’ of these studies p. 509.

  7. A paper by Sarr and Wick (2010) also stresses the importance of policy decisions in the provision of public goods in the context of conflict, without explicitly modelling the resource management process.

  8. Reuters reports on August 5, 2012 how Ogoni people’s livelihoods have been disrupted by oil spills in the Niger Delta. In the October 31, 2009 issue of the Globe and Mail, Somalia‘s prime minister maintains Somali pirates are former fishermen driven to poverty by excessive international fishing in Somali waters. A 2009 New York Times article describes maoist rebels in the Indian state of Chattisgarh fighting what they considered a governments attempt to drive them off their resource-rich land. See http://www.nytimes.com/2009/11/01/world/asia/01maoist.html?_r=1.

  9. Breen and Gillanders (2012) show corruption to be a major determinant of sub-optimal regulation.

  10. This message brings us close to the central argument developed in a recent book by Acemoglu and Robinson (2012).

  11. In a comprehensive review, Garfinkel and Skaperdas (2006) point out that ‘only the surface of the dynamic effects of conflict has been scratched.’ (p. 54.)

  12. Although for tractability we introduce some restrictive assumptions in the next section.

  13. Renner (2002) notes that when resource wealth is the cause of conflict, it is usually associated with lootable minerals, while scarcity is often linked with renewables (p. 9.).

  14. Civil wars in Indonesia, Cambodia, Burma, Liberia and the Democratic Republic of Congo have involved timber. In the Ivory Coast, cacao and cotton, along with diamonds, are documented to have been connected to the conflict. See the report by Global Witness at http://www.globalwitness.org/pages/en/cote_divoire.htm.

  15. See Reuveny and Maxwell (2001), p. 720.

  16. The natural processes by which the environment absorbs part of the industrial impact would correspond to stock depletion. This broad view can accommodate real world examples whereby unrelated industrial or extractive activities damage the environment, like in the Niger Delta, while the government fails to let locals share in the resource revenue bounty as compensation for their lost livelihood.

  17. See e.g. Hotte et al. (2000) for an endogenous property rights enforcement model.

  18. The resource growth function has a decreasing slope, corresponding to the idea of congestion or environmental resistance in general, a common assumption in the literature, that mimics observed population dynamics for a variety of renewable resources.

  19. Note the implied asymmetry between the efficiency of the industry versus the migration threshold on the individual harvester’s productivity that arises from the technology gap between the two extraction techniques. While we believe this to be a realistic presumption in general, the assumed co-existence of agents that prevents the problem from becoming trivial is not particularly restrictive: it does not require even in the limit that the firm has infinite productivity, just a higher one than that of the lowest-skilled harvester.

  20. This responds in part to some critics of the economics models of conflict contending that the exclusively extrinsic, payoff-motivated players are incompatible with the many documented instances of intrinsically motivated actors. See e.g. Cramer (2002) ‘Homo Economicus Goes to War’.

  21. Note that allowing for compensation may alter the results quantitatively, by making them less stark, but not qualitatively.

  22. See also Munro (1979), p. 5.

  23. See Clark and Munro (1975), p. 95.

  24. See Lemma 2, op.cit., p. 10. A similar approach is adopted in Barbier et al. (2005).

  25. See Grossman and Helpman (1994), p. 839.

  26. This is the equivalent of condition (9), p. 839 in Grossman and Helpman (1994). By contrast, a myopic industry would prefer a quota satisfying the equality between the bribing intensity \(B_Q=\pi _Q\), as a result of the static maximization of their harvesting profits net of the bribe: \(\Pi (p,Q)-B(Q)\).

  27. If the opposite assumptions hold, namely the utility and resource growth functions are convex in S, this result does not hold. Very peculiar assumptions about the locals’ tastes and resource population dynamics, are, however required in order to generate such behaviours.

  28. ‘...the monopolist is the conservationist’s friend.’ according to Solow (1974), p. 8, among others. Note that the departure from the efficient solution here is introduced by corruption.

  29. It is evident in the optimal decision rule formula that the opportunistic government effectively attaches a weight of \(\beta >1\) to terms related to industry profits, while the peasants’ utility is assigned a unitary coefficient. Hence, the resource outcome is unambiguously less conservationist than under an honest government.

  30. This can result from peasants resolving their collective action problem. Using the language of Acemoglu and Robinson (2005), the small harvesters have de facto political power in this setting.

  31. Unlike other papers in the literature, e.g. Maxwell and Reuveny (2000), Homer-Dixon (1999), Gleditsch (1998), which assume perfectly myopic agents that deplete the resource up to a conflict-generating threshold, in this paper corruption is the underlying trigger of conflict.

  32. See, for example, the comparison of conflict and rent-seeking models in Neary (1997), or the literature review by Garfinkel and Skaperdas (2006), pp. 3–7. Skaperdas (2008) looks at civil wars as instances where there is incomplete contracting and individuals with productivities are sorted into honest producers and bandits.

  33. Thus, like in Esteban and Ray (2011), our approach is about conflict incidence rather than onset, in that the stylized model of conflict is designed primarily to provide insights on its governance and depletion drivers.

  34. UCDP defines conflict as: ‘a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths.’ See http://www.prio.no/cwp/armedconflict/current/Codebook_v4-2006b.pdf for more details.

  35. For a thorough exposition of these concepts in the context of resource conflicts see Homer-Dixon (1999), p. 136.

  36. See Renner (2002), p. 40.

  37. The effect is a metaphor for measures that—in a conflict situation—weaken the potentially hostile (rebel) party. In the case of grievance-driven popular rebellions, which are drawing on the local population for support, civilians are often direct targets, leading to mass exodus of refugees.

  38. Notice that the ideal stock level only influences the migration effect, since the discontent effect includes just the marginal likelihood of revolt \(q_{S}\), which is not a function of \(S^{*}\).

  39. This is shown in Fig. 2 in Appendix (8). The simulation is based on logistic resource growth with intrinsic growth rate r and carrying capacity K: \(F(S)=rS\left( 1-\frac{S}{K}\right) \) and the simplest industry profit function \(\pi (p,Q,S)=\left( p-\frac{c}{S}\right) Q\).

  40. See the Appendix section (8) for the derivation.

  41. See Rus (2012), pp. 1313–1314.

  42. There is a consensus in the literature that the outbreak and duration of civil conflicts have potentially different determinants. See Rus (2012) for a complementary analysis of civil conflict onset.

  43. The net forest depletion calculation is based on estimated depletion rents. See Bolt et al. (2002).

  44. See Gleditsch et al. (2002).

  45. See UCDP/PRIO Armed Conflict Dataset Codebook, p. 4.

  46. International Country Risk Guide (Table 3B), C The PRS Group, Inc., 1984-Present. For more details see http://www.prsgroup.com.

  47. Marshall and Jaggers, Polity IV Project: Data User’s Manual at www.cidcm.umd.edu/inscr/polity.

  48. For a theoretical model linking democracy and conflict, see Aslaksen and Torvik (2006).

  49. See Cederman et al. (2009).

  50. The main result is robust to including measures of fractionalization taken from Alesina et al. (2003), referring to ethnic, language and religious dimensions, which are time-invariant and thus including them is akin to using fixed effects, as well as to controlling for ethnic and religious polarization measures instead. See Esteban and Ray (1994), Duclos et al. (2004), Montalvo and Raynal-Querol (2005).

  51. When employing a fixed effects panel logit instead, many groups drop out (the sample is significantly reduced, due to the existence of all positive or negative outcomes within groups. The panel fixed effects logit estimator is based on the probability of observing a positive outcome in the panel. Consequently, groups with all-positive outcomes have a conditional probability of being observed equal to one, and so are uninformative. See Gould, W. 1999. Within-group Collinearity in Conditional Logistic Regression. In Stata FAQs. College Station, TX: Stata Corporation.

  52. For concreteness, the basic probit specification (right-hand side variables lags are omitted for simplicity) can be written as follows: \(\Phi ^{-1}(Conflict_{it})=\beta _{0}+\beta _{1}Income_{it}+\beta _{2}Population_{it}+\beta _{3}Gov.Quality_{it}+\beta _{4}Depletion_{it}+\beta _{5}(Gov.Quality*Depletion)_{it}+\beta _{6}Z_{it}+\nu _{i}+\epsilon _{it}\), where Z is the vector of other controls.

  53. Note that higher corruption control scores equal lower corruption.

  54. The individual coefficients of the depletion and governance terms do not accurately reflect the marginal impacts on conflict incidence, due to the fact that the two variables enter both individually and in interaction with one another. Moreover, the models are non linear, so the marginal effects should be determined as partial derivatives, rather than the algebraic sum of coefficients. When we calculate the marginal effects this way they have the expected signs: the marginal effect of forest depletion on the incidence of civil conflicts is positive and the marginal effect of corruption control on the incidence of civil conflicts is negative. As for the magnitudes, these marginal effects are calculated using the sample average of the interacting variable (Stata code available upon request) and their statistical significance is not robust across all specifications. Due to space constraints, we leave for further study a more detailed analysis of these magnitudes.

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Appendix

Appendix

  1. 1.

    The first order conditions for the honest government’s optimal control problem are the following:

    $$\begin{aligned} \frac{d\mathcal {H}}{dQ} =\pi _{Q}-\mu . \end{aligned}$$

    Notice that for specific profit functions that are multiplicatively separable in Q and take the form \(\Pi (p,S)Q\) this condition does not depend on the control variable, and so the solution is reached by taking the most rapid approach path (MRAP) as follows: if \(\frac{d\mathcal {H}}{dQ}>0\) by setting harvesting at the maximum level \(Q=Q_{max}\), where \(Q_{max}\) is the exploitation resulting from employing the maximum effort available (bounded perhaps by the available industry labour force and/or technological parameters), while if \(\frac{d\mathcal {H}}{dQ}<0\) by setting harvesting at the minimum level \(Q=Q_{min}\) (which can be zero or the break-even point for the firms). This is allowed until the shadow price of the resource \(\mu \) respectively increases or decreases to equal the unit profit. The other optimality relations are derived from:

    $$\begin{aligned} \frac{d\mathcal {H}}{dS}&= \delta \mu -\dot{\mu }=\pi _{S}+U_{S}+\mu (F_{S}-\bar{H}_S)\\ \frac{d\mathcal {H}}{d\mu }&= \dot{S}=F(S)-H-\bar{H}(S). \end{aligned}$$

    while the transversality condition has to hold:

    $$\begin{aligned} \mathop {lim}\limits _{t\rightarrow \infty }\quad e^{-\delta t}\mu (t)S(t)=0. \end{aligned}$$

    Imposing the conditions of zero-growth of the state and co-state variables in a steady-state equilibrium and assuming the transversality condition holds, we obtain:

    $$\begin{aligned} (F_{S}-\bar{H}_S)+\frac{\pi _{S}+U_{S}}{\pi _{Q}}=\delta . \end{aligned}$$

    To determine the transitional path dynamics, take time-derivative of the first condition to obtain:

    $$\begin{aligned} \dot{\mu }=\pi _{QQ}\dot{Q}+\pi _{QS}\dot{S} \end{aligned}$$

    which can be substituted into the second condition as follows:

    $$\begin{aligned} (\delta -F_{S}-\bar{H}_S)\pi _{Q}-\pi _{QQ}\dot{Q}-\pi _{QS}[F(S)-Q-\bar{H}(S)]=\pi _{S}+U_{S}. \end{aligned}$$

    The \(\dot{H}=0\) locus can then be determined as:

    $$\begin{aligned} H=\underbrace{\frac{\pi _{S}+U_{S}-(\delta -F_{S}-\bar{H}_S)\pi _{Q}}{\pi _{QS}}}+\left( F(S)-\bar{H}(S)\right) , \end{aligned}$$

    where the sign of its slope \(\left( \frac{dH}{dS}|_{\dot{H}=0}\right) \) is ambiguous. Notice that setting the numerator of the fraction to zero, which is our golden rule above, is equivalent to imposing the steady-state harvesting condition. Transitional harvesting is decreasing towards the steady-state value when the fraction (its numerator) is positive, and increasing when it is negative.

  2. 2.

    According to the maximum principle, the first order conditions for an interior solution to this corrupt government optimal control problem are:

    $$\begin{aligned} \frac{d\mathcal {H}}{dQ}&= 0=\pi _{Q}+(\beta -1)B_{Q}-\mu \\ \frac{d\mathcal {H}}{dS}&= \delta \mu -\dot{\mu }=\pi _{S}+U_{S}+\mu (F_{S}-\bar{H}_S)\\ \frac{d\mathcal {H}}{d\mu }&= \dot{S}=F(S)-Q-\bar{H}(S), \end{aligned}$$

    and we assume the transversality condition holds. Taking the time derivative in the first condition above yields: \(\dot{\mu }=\pi _{QQ}\dot{Q}+(\beta -1)B_{QQ}\dot{Q}+\pi _{QS}\dot{S} \) and plugging it into the second condition: \((\delta \!-\!F_{S}\!+\!\bar{H}_S)[\pi _{Q}\!+\!(\beta \!-\!1)B_{Q}]\!-\!\pi _{QQ}\dot{Q}\!-\!(\beta -1)B_{QQ}\dot{Q}\!-\!\pi _{QS}\dot{S}\!=\!\pi _{S}\!+\!U_{S}. \)

  3. 3.

    The problem of maximizing the joint industry-government payoffs is solved in a very similar fashion: Use current value Hamiltonian

    $$\begin{aligned} \mathcal {H}=2\pi (p,S,Q)+(\beta -2)B(Q)+U(S)+\lambda [F(S)-H-\bar{H}(S)] \end{aligned}$$

    to get first order conditions:

    $$\begin{aligned} \frac{d\mathcal {H}}{dQ}&= 0=2\pi _{Q}+(\beta -2)B_{Q}-\lambda \\ \frac{d\mathcal {H}}{dS}&= \delta \lambda -\dot{\lambda }=\pi _{S}+U_{S}+\lambda (F_{S}-\bar{H}_S)\\ \frac{d\mathcal {H}}{d\lambda }&= \dot{S}=F(S)-Q-\bar{H}(S). \end{aligned}$$

    Together these conditions imply:

    $$\begin{aligned} (\delta \!-\!F_{S}\!+\!\bar{H}_S)[2\pi _{Q}\!+\!(\beta \!-\!2)B_{Q}]\!-\!2\pi _{QQ}\dot{Q}\!-\!(\beta \!-\!2)B_{QQ}\dot{Q}\!-\!2\pi _{QS}\dot{S}\!=\!2\pi _{S}\!+\!U_{S}. \end{aligned}$$
  4. 4.

    (i) A higher discount rate \(\delta \) means that in order for Eq. (4) to hold, the equilibrium stock of the resource will adjust to a lower level, corresponding to a higher net growth rate \(F_{S}-\bar{H}_s\) and a lower marginal stock effect (MSE), both evaluated at the optimal stock level. A more impatient corrupt government will regulate harvesting so that the resource stock settles at a lower long-run level. (ii) To show Lemma 3, assume we have two functions of the stock of the same resource \(MSE_{1}(S)\) and \(MSE_{2}(S)\), where \(MSE_{2}(S)>MSE_{1}(S),\) \(\forall S\in [0,K]\). This is then true, in particular, for \(S=S_{2}^{*}:\) \(MSE_{2}(S_{2})>MSE_{1}(S_{2}) (*)\). We also know that the following two relations governing optimal exploitation are verified at the optimum levels of stock \(S_{1}^{*}\) and \(S_{2}^{*}\):

    $$\begin{aligned} F_{S}(S_{1}^{*})+MSE_{1}(S_{1}^{*})=\delta \hbox {and}\\ F_{S}(S_{2}^{*})+MSE_{2}(S_{2}^{*})=\delta , \end{aligned}$$

    where the resource dynamics and the discount rate are common across the two problems. Assume, by contradiction, that \(S_{2}^{*}<S_{1}^{*}\). It follows that \(F_{S}(S_{1}^{*}<F_{S}(S_{2}^{*})\) and then for the above equations to hold concomitantly, we need \(MSE_{1}(S_{1}^{*})>MSE_{2}(S_{2}^{*}) (**)\). Put together, the two starred relations imply that \(MSE_{1}(S_{1}^{*})>MSE_{1}(S_{2}^{*})\), which further implies—since the functions representing the marginal stock effects under the different policy regimes are decreasing in the level of stock—that \(S_{2}^{*}>S_{1}^{*}\), which is a contradiction.

  5. 5.

    It is easy to see that \(MSE^{c}_{n/c}\) \(<\) \(MSE^{h}_{n/c}\) (where the subscripts refer to ‘no-conflict’ and the superscripts to government corruption) iff \(\frac{\beta \pi _{S}+U_{S}}{\beta \pi _{H}}< \frac{\pi _{S}+U_{S}}{\pi _{H}} \). This is true in all cases, given the assumption that \(\beta >1\). Alternatively, differentiate (4) with respect to the corruption coefficient to obtain:

    $$\begin{aligned}&\beta ^{2}\pi _{Q}^{2}(F_{SS}-\bar{H}_{SS})S_{\beta }+S_{\beta }[(\beta \pi _{SS}+U_{SS})\beta \pi _{Q}-(\beta \pi _S+u_S)\beta \pi _{QS}]\\&+\beta \pi _{Q} \pi _{S}-(\beta \pi _{S}+U_{S})\pi _{Q}=0, \end{aligned}$$

    which implies:

    $$\begin{aligned} S_{\beta }=\frac{U_{S}\pi _{Q}}{\beta ^{2}\pi _{Q}^{2}(F_{SS}-\bar{H}_{SS})+(\beta \pi _{SS}+U_{SS})\beta \pi _{Q}-(\beta \pi _S+u_S)\beta \pi _{QS}}<0, \end{aligned}$$

    where the inequality derives from the concavity of functions F, \(\pi \) and u.

  6. 6.

    The current value Hamiltonian for the government utility maximization problem when conflict is possible is:

    $$\begin{aligned} \mathcal {H}\!=\!\pi (p,S,Q)\!+\!(\beta \!-\!1)B(Q)\!+\!U(S)\!-\!\rho (S)C(L(S,w))\!+\!\mu [F(S)\!-\!H\!-\!\bar{H}(S)] \end{aligned}$$

    and the first order conditions are:

    $$\begin{aligned} \frac{d\mathcal {H}}{dQ}&= 0 \rightarrow \pi _{Q}+(\beta -1)B_Q=\mu \\ \frac{d\mathcal {H}}{dS}&= \delta \mu -\dot{\mu }=\pi _{S} +U_{S}-\rho _{S}C-\rho C_LL_S+\mu (F_{S}-\bar{H}_S)\\ \frac{d\mathcal {H}}{d\mu }&= \dot{S}=F(S)-H-\overline{h}. \end{aligned}$$

    Take derivative with respect to time in the first condition above and plug into the second to obtain:

    $$\begin{aligned} (\delta \!-\!F_{S}\!+\!\bar{H}_S)[\pi _{Q}\!+\!(\beta \!\!-\!\!1)B_{Q}]\!\!-\![\pi _{QQ}\!+\!(\beta \!-\!1)B_{QQ}]\dot{Q}\!\!=\!\pi _{S}\!+\!U_{S}\!-\!\rho _{S}C\!-\!\rho C_LL_S \end{aligned}$$

    In the steady state:

    $$\begin{aligned} (\delta -F_{S}+\bar{H}_S)[\pi _{Q}+(\beta -1)B_{Q}]=\pi _{S}+U_{S}-\rho _{S}C-\rho C_LL_S \end{aligned}$$

    and this implies:

    $$\begin{aligned} B_{Q}=\frac{1}{\beta -1}\left[ \frac{\pi _{S}+U_{S}-\rho _{S}C-\rho C_LL_S}{\delta -F_{S}+\bar{H}_S}-\pi _{Q}\right] . \end{aligned}$$

    Similarly, the joint industry-government surplus maximization yields in the steady-state:

    $$\begin{aligned} (\delta -F_{S}+\bar{H}_S)[2\pi _{Q}+(\beta -2)B_{Q}]=2\pi _{S}+U_{S}-\rho _{S}C-\rho C_LL_S. \end{aligned}$$

    Plug in \(B_{Q}\) from above to get the steady state equilibrium policy rule for renewable resource exploitation when conflict is possible:

    $$\begin{aligned} F_{S}-\bar{H}_S+\frac{\beta \pi _{S}+U_{S}-\rho _{S}C-\rho C_LL_S}{\beta \pi _{H}}=\delta . \end{aligned}$$
  7. 7.

    Compare this to the case without conflict and with a corrupt government in (4). Notice that the marginal stock effect with conflict is larger than the marginal stock effect without conflict if \(-\rho _{S}C-\rho C_LL_S>0\) or if \(\frac{-\rho _S}{\rho }>\frac{C_LL_S}{C}\). Given the expressions for \(\rho \), C and their derivatives with respect to stock provided in the text, this becomes equivalent to

    $$\begin{aligned} \ln {\frac{\bar{q}S}{w}}\left( \bar{q}-\frac{w}{S}\right) >\frac{w}{S}-\frac{w}{S^*}+\frac{w\ln {\frac{\bar{q}S}{w}}}{S}-\frac{w\ln {\frac{\bar{q}S^*}{w}}}{S^*}, \end{aligned}$$

    which is further equivalent to

    $$\begin{aligned} \frac{(\frac{\bar{q}S}{w}-2)\ln {\frac{\bar{q}S}{w}}-1}{S}>-\frac{\ln {\frac{\bar{q}S^*}{w}}+1}{S^*}=-\varepsilon \Leftrightarrow \left( \frac{\bar{q}}{w}\ln {\frac{\bar{q}}{w}}+\varepsilon \right) S\\ +\left( \frac{\bar{q}S}{w}-2\right) \ln S-2\ln {\frac{\bar{q}}{w}}-1>0. \end{aligned}$$

    Denoting by \(\theta \) the coefficient of S and with \(\phi \) the constant, the previous inequality can be written: \(G(S)=\theta S+(\frac{\bar{q}S}{w}-2)ln S-\phi >0\). Function \(M(S)\) is defined in the text based on Eq. (4) as being equal to \((\delta -F_S+\bar{H}_S)\beta \pi _Q-\beta \pi _S+U_S\). Substituting the given functional forms, it becomes:

    $$\begin{aligned} M(S)=\frac{4pr}{K}S^4+\left( \delta p-2pr-\frac{5cr}{K}\right) S^3+c(3r-\delta )S^2\\ +\bar{h}\left( 2pw-c\bar{q}-\frac{1}{\beta }\ln {\frac{\bar{q}}{w}}\right) S-\frac{\bar{h}}{\beta }S\ln {S}-c\bar{h}w. \end{aligned}$$

    The following derives the shape of the curves in Fig. 1. To obtain the shape of the discontent effect \(D=-\rho _{S}C\), calculate its first derivative with respect to \(S\) as

    $$\begin{aligned} D_{S}=\frac{\bar{h} \gamma }{U^{*}S^{3}}\left[ \left( \bar{q}-\frac{w}{S}\right) -ln{\frac{\bar{q} S}{w}}\left( 2\bar{q}-3\frac{w}{S}\right) \right] . \end{aligned}$$

    Then

    $$\begin{aligned} D_{S}>0 \iff \frac{\bar{q}-\frac{w}{S}}{2\bar{q}-3\frac{w}{S}}>ln{\frac{\bar{q} S}{w}}. \end{aligned}$$

    Given that the left-hand side of this inequality is between \(\left( \frac{1}{3},\frac{1}{2}\right) \), a sufficient condition for the ‘discontent’ function \(D\) to slope downwards is that

    $$\begin{aligned} ln{\bar{q}}+ln{\frac{w}{S}}>\frac{1}{2} \iff S>\frac{\sqrt{e}w}{\bar{q}}. \end{aligned}$$

    The ‘(e)migration effect’ curve

    $$\begin{aligned} E=\frac{\bar{h}}{U^{*}}\left[ \left( \frac{1}{S}-\frac{1}{S^{*}}+\right) +\frac{ln{\frac{\bar{q} S}{w}}}{S}-\frac{ln{\frac{\bar{q} S^{*}}{w}}}{S*}\right] \gamma \frac{w}{S^{2}} \end{aligned}$$

    monotonically decreases in \(S\). The curves illustrating the two effects are reproduced in Fig. 1a in the text. Moreover, one can show a single crossing property for the two curves in Fig. 1a, or that \(E(S)-D(S)=0\) has a unique solution:

    $$\begin{aligned}&D(S)-E(S)=0 \Leftrightarrow ln{\frac{\bar{q}S}{w}}\left( \bar{q}-\frac{2w}{S}\right) -\frac{w}{S}+\frac{w}{S^{*}}\left( 1+ln{\frac{\bar{q}S^{*}}{w}}\right) \\&=0 \Leftrightarrow y(1+2ln{\bar{q}})+ln{y}(\bar{q}+2y)-k=0. \end{aligned}$$

    This last equation is monotonically increasing in \(y\) and has a unique solution, where we denoted variable \(y\equiv \frac{w}{S}\) and constant

    $$\begin{aligned} k\equiv \bar{q}ln{\bar{q}}+\frac{w}{S^{*}}\left( 1+ln{\frac{\bar{q}S^{*}}{w}}\right) . \end{aligned}$$
Fig. 2
figure 2

Equilibrium stock levels

  1. 8.

    We have \(P(S)=\rho (S)C(L(S,w))\) and it was shown above that \(P_{S}=0\) has a unique solution, \(P_S=\rho _SC+\rho C_LL_S<0\) if and only if \(S>S'\) and \(P_{S}>0\) for \(S>S'\). The following determines the curvature of \(P(S)\). Given the derivation above, the slopes of the two curves representing the discontent and the migration effects are:

    $$\begin{aligned} D_{S}=\frac{\bar{h}\gamma }{U^{*}S^{3}}\left[ \bar{q}-\frac{w}{S}-ln{\frac{\bar{q}S}{w}}\left( 2\bar{q}-3\frac{w}{S}\right) \right] \end{aligned}$$

    and

    $$\begin{aligned} E_{S}=-\frac{\gamma w\bar{h}}{U^{*}S^{3}}\left[ \frac{1}{S}\left( 1+2ln{\frac{\bar{q}S}{w}}\right) -\frac{1}{S^{*}}\left( 1+ln{\frac{\bar{q}S^{*}}{w}}\right) \right] , \end{aligned}$$

    respectively. Then \(P_{SS}>0 \Leftrightarrow E_{S}>D_{S}\). After substituting in the functional forms and simplifying, this is equivalent to

    $$\begin{aligned} \frac{w}{S^{*}}\left( 1+ln{\frac{\bar{q}S^{*}}{w}}-\bar{q}\right) >ln{\frac{\bar{q}S}{w}}\left( \frac{5w}{S}-2\bar{q}\right) , \end{aligned}$$

    where the left hand side is a constant and the right hand side is monotonically decreasing in S. Thus, even though there is no closed form solution, \(\exists S''\) such that \(P_{SS}>0\) \(\forall \) \(S>S''\). This inflection point occurs for stock values above \(S'\), like in the Fig. 1b. Again, for illustration purposes, one can get more concrete by employing specific functional forms. We calculate the condition \(P_{SS}=\rho _{SS}C+2\rho _SC_LL_S+\rho C_LL_{SS}>0\) by using the functional forms provided above as follows:

    $$\begin{aligned}&\left( 2\ln {\frac{\bar{q}S}{w}}-1\right) \left( \bar{q}-\frac{w}{S}\right) -\frac{2w}{S}\ln {\frac{\bar{q}S}{w}}-2w\left( \frac{1}{S}-\frac{1}{S^*}\right) -2w\left( \frac{\ln {\frac{\bar{q}S}{w}}}{S}\right. \\&\left. -\frac{\ln {\frac{\bar{q}S^*}{w}}}{S^*}\right) >0 \Leftrightarrow 2(\frac{\bar{q}S}{w}-3)\left( \ln {\frac{\bar{q}}{w}}+\ln S\right) -1>-aS, \end{aligned}$$

    where

    $$\begin{aligned} a=\frac{2}{S^*}\left( 1+\ln {\frac{\bar{q}S^*}{w}}\right) -\frac{\bar{q}}{w} \end{aligned}$$

    which further yields

    $$\begin{aligned} \left( \frac{2\bar{q}}{w}\ln {\frac{\bar{q}}{w}}+a\right) S+2\left( \frac{\bar{q}S}{w}-3\right) \ln S-\left( 6\ln {\frac{\bar{q}}{w}}+1\right) >0. \end{aligned}$$

    Denoting with b the coefficient of S and with c the constant, this becomes: \(J(S)=bS+2(\frac{\bar{q}S}{w}-3)\ln S-c>0\). The analysis is now similar to the one in section (7) above, and the following graphical solution illustrated in Fig. 2 points to the fact that \(P_{SS}>0\) if and only if \(S>S''\), with the latter being lower for higher \(\bar{q}\) and lower \(w\) and \(S^*\). Differentiating (7) implicitly with respect to \(\beta \) yields:

    $$\begin{aligned}&(\pi _S+\beta \pi _{SS}S_\beta +U_{SS}S_\beta -\rho _{SS}CS_\beta -\rho _{S}C_LL_SS_\beta -\rho _SC_LL_SS_\beta \\&\qquad -\rho C_LL_{SS}S_\beta )\beta \pi _H-(\beta \pi _S+u_S-\rho _SC-\rho C_LL_S)(\pi _H+\beta \pi _{HS}S_\beta )\\&\quad =-F_{SS}S_\beta \beta ^2\pi _H^2. \end{aligned}$$

    This implies:

    $$\begin{aligned} S_\beta = \frac{\pi _H(u_S-\rho _SC-\rho C_LL_S)}{(\beta \pi _{SS}+U_{SS}-\rho _{SS}C-2\rho _SC_LL_S-\rho C_LL_{SS})\beta \pi _H+F_{SS}\beta ^2\pi _H^2-(\beta \pi _S+u_S-\rho _SC-\rho C_LL_S)\beta \pi _{HS}} \end{aligned}$$

    is less than zero if \(S>S'\) and \(S>S''\), since under the first condition the numerator is positive as \(P_{S}<0\), while under the second, the denominator is negative, as \(P_{SS}>0\).

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Rus, H.A. Corruption, conflict and the management of natural resources. Econ Gov 15, 355–386 (2014). https://doi.org/10.1007/s10101-014-0148-3

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