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Social conflict and wage inequality

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Abstract

This paper studies the relationship between social conflict and skilled–unskilled wage inequality through the three-sector general equilibrium approach. In the basic model without the urban unskilled minimum wage, we find that when the government enhances the degree of controlling social conflict, the skilled–unskilled wage inequality will be narrowed down (resp. widened) if the urban skilled sector is more capital intensive (resp. labor intensive) than the urban unskilled sector. The extended models address the issue under different economic structures or different types of social conflict. In the extended model with the urban unskilled minimum wage, we find that the skilled–unskilled inequality will be widened when the degree of controlling social conflict is increased. In other extended models, we find that the above obtained results are still robust.

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Notes

  1. Here, it should be noted that social conflict in the sense of this paper does not include demonstrations, violence and racial conflicts. We thank an anonymous reviewer for reminding us of it. Similar to Bó and Bó (2011), we use social conflict in the sense of appropriation activities.

  2. In this paper, social conflict is incurred by labor who does not work in the productive sectors, so social conflict can indirectly generate the negative impact on production. For example, when there exists social conflict, some workers leave from the productive sectors to undertake the activity of social conflict. Thus, it reduces the amount of labor in the productive sectors.

  3. In order to better understand the differences in the perspectives, here we give some additional explanations. For social conflict, people engaged in such activities target factors owners and the owners’ corresponding returns (see Bó and Bó 2011). However, for corruption, it is treated like a tax on the productive sectors’ outputs levied by people involved in corruptive activities (see Mandal and Marjit 2010). For imperfect institutional quality, people engaged in unproductive activities directly misappropriates capital originally owned by the productive sectors (see Pi and Zhou 2013).

  4. The relevant studies that use the method similar to Mandal and Marjit (2010) and Pi and Zhou (2013) include Marjit and Mandal (2012) and Marjit et al. (2014), although they deal with different problems for different purposes.

  5. When it is necessary, the assumption that the capital market is closed in the basic model can be relaxed. The benefit of such an assumption is that it can enable a direct comparison to the effects found in the previous literature (e.g., Mandal and Marjit 2010; Pi and Zhou 2013). We thank an anonymous reviewer for reminding us of this point.

  6. For example, according to Becker (1968), there are two ways to increase the cost of crime to reduce the criminal rate. The first is to raise the probability to discover the criminal behavior, which needs more people employed in the security sector and more funds invested in the security technology. The second way is to raise the punishment, which requires the government to enforce a stricter or more reasonable law. Comparing to the first approach, the second will bear less cost. Besides, the crime in some sectors (e.g., the financial sector) is profitable due to the shortcomings of institutional arrangements. In this case, the government can implement a more perfect regulation to reduce the crime. We thank two anonymous reviewers for pointing it out to us.

  7. Here, the Inada condition works like that in Bó and Bó (2011) to ensure the existence of social conflict.

  8. Using Eqs. (6)–(9), we can find that \({w_s}{\overline{L} _s} + {w_U}({\overline{L} _U} - {L_A}) + r\overline{K} + \tau \overline{T} \) is equal to \(({a_{SX}}{w_S} + {a_{KX}}r)X + ({a_{UY}}{w_U} + {a_{KY}}r)Y + ({a_{UZ}}{w_U} + {a_{TZ}}\tau )Z\) . Using Eqs. (1)–(3), we can find that\(({a_{SX}}{w_S} + {a_{KX}}r)X + ({a_{UY}}{w_U} + {a_{KY}}r)Y + ({a_{UZ}}{w_U} + {a_{TZ}}\tau )Z\) is equal to \({p_X}X + {p_Y}Y + Z\). In this paper, for the sake of simplification, a zero trade balance is assumed. That is, the implicit trade sectors are assumed to balance their payments. Such an assumption is also adopted by the existing literature on skilled–unskilled wage inequality (e.g., Marjit and Kar 2005; Chaudhuri and Yabuuchi 2007; Beladi et al. 2008, 2010; Mandal and Marjit 2010; Pi and Zhou 2012, 2013).

  9. The dynamic adjustment process is similar to that we have discussed in Appendix 3, so we omit the details in order to save space.

  10. Technically speaking, this is because that these cases will not essentially change the characteristic of the system matrices. When we use the Cramer’s rule to solve these determinants, a certain row will always be removed.

  11. For example, agents who are plundered may take measures to protect themselves. This paper focuses on the governmental protection, not on the individual protection. In the future research, we can unify the governmental protection and the individual protection in an integrated framework.

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Acknowledgments

We are grateful to the Editor Giacomo Corneo and two anonymous referees for their helpful and detailed comments and suggestions. Pi acknowledges the financial support provided by the Program for New Century Excellent Talents in University, the Fundamental Research Funds for the Central Universities, and the Collaborative Innovation Center for China Economy. Any remaining errors are ours.

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Correspondence to Jiancai Pi.

Appendices

Dynamic adjustment process of the basic model

The dynamic adjustment process is based on the idea of the excess demand function, including the Marshallian quantity adjustment process and the Walrasian price adjustment process. Our method is in line with Beladi et al. (2008). According to the excess demand functions of the basic model, the differential equations can be constructed as:

$$\begin{aligned} \dot{{X}}= & {} d_{1} \left( {p_{X} -a_{SX} w_{S} +a_{KX} r} \right) ,\end{aligned}$$
(A1)
$$\begin{aligned} \dot{{Y}}= & {} d_{2} \left( {p_{Y} -a_{UY} w_{U} +a_{KY} r} \right) ,\end{aligned}$$
(A2)
$$\begin{aligned} \dot{{Z}}= & {} d_{3} \left( {1-a_{UZ} w_{U} +a_{TZ} \tau } \right) ,\end{aligned}$$
(A3)
$$\begin{aligned} \dot{{L}}_{A}= & {} d_{4} \left( {\frac{A\left( {\alpha ,L_{A} } \right) }{L_{A} }\left( {w_{S} \bar{{L}}_{S} +w_{U} \left( {\bar{{L}}_{U} -L_{A} } \right) +r\bar{{K}}+\tau \bar{{T}}} \right) -\left( {1-A\left( {\alpha ,L_{A} } \right) } \right) w_{U} } \right) , \nonumber \\ \end{aligned}$$
(A4)
$$\begin{aligned} \dot{{r}}= & {} d_{5} \left( {a_{KX} X+a_{KY} Y-\bar{{K}}} \right) ,\end{aligned}$$
(A5)
$$\begin{aligned} \dot{{\tau }}= & {} d_{6} \left( {a_{TZ} Z-\bar{{T}}} \right) , \end{aligned}$$
(A6)
$$\begin{aligned} \dot{{w}}_{S}= & {} d_{7} \left( {a_{SX} X-\bar{{L}}_{S} }\right) , \end{aligned}$$
(A7)
$$\begin{aligned} \dot{{w}}_{U}= & {} d_{8} \left( {a_{UY} Y+a_{UZ} Z-\left( {\bar{{L}}_{U} -L_{A} } \right) } \right) , \end{aligned}$$
(A8)

where \(d_{i} >0 \quad \left( {i=1,2,...,8} \right) \) represents the speed of adjustment. The notation “.” denotes the differentiation with respect to time (e.g., \(\dot{{X}}=\frac{dX}{dt})\). The determinant of the Jacobian matrix of Eqs. (A1)–(A8) (denoted as \(\Phi _{1} )\) can be written as:

$$\begin{aligned} \Phi _{1} =H\left| \begin{array}{l@{\quad }l@{\quad }l@{\quad }l@{\quad }l@{\quad }l@{\quad }l@{\quad }l} 0&{} 0&{} 0&{} 0&{} {-\theta _{KX} }&{} 0&{} 0&{} {-\theta _{SX} } \\ 0&{} 0&{} 0&{} {-\theta _{UY} }&{} {-\theta _{KY} }&{} 0&{} 0&{} 0 \\ 0&{} 0&{} 0&{} {-\theta _{UZ} }&{} 0&{} {-\theta _{TZ} }&{} 0&{} 0 \\ {\theta _{X} }&{} {\theta _{Y} }&{} {\theta _{Z} }&{} {-1}&{} 0&{} 0&{} C&{} 0 \\ {\lambda _{KX} }&{} {\lambda _{KY} }&{} 0&{} {\lambda _{KY} \theta _{UY} \sigma _{Y} }&{} E&{} 0&{} 0&{} {\lambda _{KX} \theta _{SX} \sigma _{X} } \\ 0&{} 0&{} 1&{} {\theta _{UZ} \sigma _{Z} }&{} 0&{} {-\theta _{UZ} \sigma _{Z} }&{} 0&{} 0 \\ 1&{} 0&{} 0&{} 0&{} {\theta _{KX} \sigma _{X} }&{} 0&{} 0&{} {-\theta _{KX} \sigma _{X} } \\ 0&{} {\lambda _{UY} }&{} {\lambda _{UZ} }&{} F&{} {\lambda _{UY} \theta _{KY} \sigma _{Y} }&{} {\lambda _{UZ} \theta _{TZ} \sigma _{Z} }&{} {\lambda _{UA} }&{} 0 \\ \end{array} \right| =H\Delta _{1}, \end{aligned}$$

where \(H=\frac{\prod \nolimits _{i=1}^8 {d_{i} \left( {1-A\left( {\alpha ,L_{A} } \right) } \right) p_{X} p_{Y} \left( {\bar{{L}}_{U} -L_{A} } \right) \bar{{L}}_{S} \bar{{K}}\bar{{T}}} }{w_{S} r\tau XYZL_{A} }>0\).

According to the Routh–Hurwitz theorem, the local stability can be achieved if \(\Phi _{1} >0\), which implies that \(\Delta _{1} >0\). In order to make our economic system locally stable, we need \(\Delta _{1} >0\).

Dynamic adjustment process of the model in Sect. 3.1

Replace (A2) and (A8) with the following differential equations, respectively:

$$\begin{aligned} \dot{{Y}}= & {} d_{2} \left( {p_{Y} -a_{UY} \bar{{w}}_{U} +a_{KY} r} \right) ,\\ \dot{{w}}_{U}= & {} d_{8} \left( {a_{UY} Y\left( {1+\lambda } \right) +a_{UZ} Z-\left( {\bar{{L}}_{U} -L_{A} } \right) } \right) . \end{aligned}$$

Under similar demonstrations in Appendix 1, we need \(\Delta _{2} >0\) in order to make our economic system in Sect. 3.1 locally stable.

Dynamic adjustment process of the model with full employment in Sect. 3.2

Replace (A4), (A7), and (A8) with the following differential equations, respectively:

$$\begin{aligned}&\dot{{L}}_{A} =d_{4} \left( {\frac{A\left( {\alpha ,L_{A} } \right) }{L_{A} }\left( {w_{S} \left( {\bar{{L}}_{S} \!-\!L_{A} } \right) \!+\!w_{U} \bar{{L}}_{U} \!+\!r\bar{{K}}\!+\!\tau \bar{{T}}} \right) \!-\!\left( {1-A\left( {\alpha ,L_{A} } \right) } \right) w_{S} } \right) ,\nonumber \\ \end{aligned}$$
(A9)
$$\begin{aligned}&\dot{{w}}_{S} =d_{7} \left( {a_{SX} X-\left( {\bar{{L}}_{S} -L_{A} } \right) } \right) , \end{aligned}$$
(A10)
$$\begin{aligned}&\dot{{w}}_{U} =d_{8} \left( {a_{UY} Y+a_{UZ} Z-\bar{{L}}_{U} } \right) . \end{aligned}$$
(A11)

The determinant of the Jacobian matrix of Eqs. (A1)–(A3), (A5), (A6), and (A9)–(A11) (denoted as \(\Phi _{2} )\) can be written as:

$$\begin{aligned} \Phi _{2} =I\left| \begin{array}{l@{\quad }l@{\quad }l@{\quad }l@{\quad }l@{\quad }l@{\quad }l@{\quad }l} 0&{} 0&{} 0&{} {-\theta _{SX} }&{} {-\theta _{KX} }&{} 0&{} 0&{} 0 \\ 0&{} 0&{} 0&{} 0&{} {-\theta _{KY} }&{} 0&{} 0&{} {-\theta _{UY} } \\ 0&{} 0&{} 0&{} 0&{} 0&{} {-\theta _{TZ} }&{} 0&{} {-\theta _{UZ} } \\ {\theta _{X} }&{} {\theta _{Y} }&{} {\theta _{Z} }&{} {-1}&{} 0&{} 0&{} C&{} 0 \\ {\lambda _{KX} }&{} {\lambda _{KY} }&{} 0&{} {\lambda _{KX} \theta _{SX} \sigma _{X} }&{} E&{} 0&{} 0&{} {\lambda _{KY} \theta _{UY} \sigma _{Y} } \\ 0&{} 0&{} 1&{} 0&{} 0&{} {-\theta _{UZ} \sigma _{Z} }&{} 0&{} {\theta _{UZ} \sigma _{Z} } \\ 0&{} {\lambda _{UY} }&{} {\lambda _{UZ} }&{} 0&{} {\lambda _{UY} \theta _{KY} \sigma _{Y} }&{} {\lambda _{UZ} \theta _{TZ} \sigma _{Z} }&{} 0&{} F \\ {\lambda _{SX} }&{} 0&{} 0&{} {-\lambda _{SX} \theta _{KX} \sigma _{X} }&{} {\lambda _{SX} \theta _{KX} \sigma _{X} }&{} 0&{} {\lambda _{SA} }&{} 0 \\ \end{array} \right| =I\Delta _{3} , \end{aligned}$$

where \(I=\frac{\prod \limits _{i=1}^8 {d_{i} \left( {1-A\left( {\alpha ,L_{A} } \right) } \right) p_{X} p_{Y} \bar{{L}}_{U} \left( {\bar{{L}}_{S} -L_{A} } \right) \bar{{K}}\bar{{T}}} }{w_{U} r\tau XYZL_{A} }>0\).

According to the Routh–Hurwitz theorem, the local stability can be achieved if \(\Phi _{2} >0\), which implies that \(\Delta _{3} >0\). Thus in order to make our economic system locally stable, we need \(\Delta _{3} >0\).

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Pi, J., Zhang, P. Social conflict and wage inequality. J Econ 121, 29–49 (2017). https://doi.org/10.1007/s00712-016-0515-3

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