Macro shocks and costly political action in non-democracies

Public Choice volume 162, pages381–404(2015)Cite this article

Abstract

This paper presents a theory of political instability in autocracies where the disenfranchised express their political preferences for a leadership replacement through costly political action. We focus on the business regulatory environment as an arena for autocratic rent-creation, as a source of information asymmetry between the autocrat and the disenfranchised, and as a potential political grievance for the disenfranchised. Within this context, we revisit the role of macro shocks as a catalyst for political instability and propose a novel informational channel through which macro shocks may trigger costly political action when the autocrat chooses to create rents through regulation that deteriorates the mean macroeconomic outcome. We then provide an empirical investigation of our theoretical hypotheses employing fixed-effects panel regressions and an instrumental variable strategy over a sample of non-democratic countries. Consistent with our theoretical hypotheses, we demonstrate that adverse economic shocks increase the probability of mass protest episodes, but that the magnitude of the effect depends on the extent to which business regulation is market-distorting.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

Notes

  1. 1.

    This seems consistent with the literature on democratization, for example. In their prominent study of the “third wave” of democratization, Haggard and Kaufman (2012) find “surprisingly limited evidence in support of the general claim that redistributive conflicts between elites and masses account for either transitions to democracy or reversions from it”. Moreover, there is evidence that leadership replacements lead to improved economic outcomes and that economic performance is closely related to leader fixed effects (Jones and Olken 2005) and that democratization is associated with economic liberalization (Haan and Sturm 2003; Rode and Gwartney 2012).

  2. 2.

    This captures the fact that the success of an initial protest is difficult to predict owing to the coordination problems inherent in a revolutionary environments which we do not model explicitly. For papers that endogenize the probability that protests lead to successful revolutions, see, among others, Bueno de Mesquita (2010), Edmond (2013), Ellis and Fender (2011), Kricheli et al. (2011), Kuran (1989), Lohmann (1994), Shadmehr and Bernhardt (2011) Even though our paper abstracts from the collective action problem, we believe that our results are of interest for the literature on the dynamics of protest. Room for improvement in this literature is to put forth an economic intuition for what determines sentiment for the dictator and, moreover, what could shift the distribution of those sentiments. In our model, macro shocks can update workers’ priors about the dictator’s type, which establishes an economic foundation for the shocks to sentiments that catalyze revolutionary entrepreneurs into action.

  3. 3.

    We demonstrate in the next subsection that our result is robust to considering the traditional opportunity cost channel with proportional cost of protest.

  4. 4.

    Even the conditions given in (3) might not be appropriate. In some economic environments it is likely that the equilibrium wage will be bounded below and above. So, if \(w \in [\underline{w},\overline{w}]\) then what we really want is \(\frac{g(\underline{w}\vert c>0)}{g(\underline{w}\vert c=0)}\) is such that this ratio defines beliefs \(b(\underline{w})\) given by (2) that satisfy (1), i.e., observing the lowest possible wage will cause workers to protest. Similarly, we want a wage of \(\overline{w}\) to result in no protest. Continuity of g implies that wages near the lower bound trigger protests and wages near the upper bound do not trigger protests. If one thinks of this section and our two hypotheses as describing what happens when extreme economic outcomes occur (due to extreme shocks) then this is not a problem.

  5. 5.

    An online appendix provides a detailed description of the data as well as a list of the countries that are included in the sample.

  6. 6.

    In our baseline sample, the unconditional probability of experiencing at least three mass protests is about 0.055. As we show later, experiencing at least three political protests increases the probability of an irregular leadership transition by about 20 % points. Experiencing at least one political protest occurs with an unconditional probability of about 0.220, but increases the probability of an irregular leadership transition by less than 5 % points. In our view, the cut-off of three mass protests concentrates on political action sequences that are sufficiently rare events and that have real political impacts.

  7. 7.

    Following Burke and Leigh (2010), it takes the value 0 when a country’s GDP per capita in \(t-1\) is within 30 log points of its sample average, takes the value 1 (\(-\)1) when GDP per capita is between 30 and 60 log points above (below) its sample average, and is 2 (\(-\)2) when GDP per capita is more than 60 log points above (below) its sample average.

  8. 8.

    Raw data for all of our instruments come from Bazzi and Blattman (2014). For our weather instruments, we follow the intuition of Ciccone (2011) in considering standardized deviations from the mean. As Ciccone (2011) notes, rainfall is strongly mean reverting and using rainfall growth is problematic since a negative trend may correspond either to a decrease from very high level or to a decrease from normal level, which corresponds to the shock we want to identify.

  9. 9.

    First stage results are presented in an online appendix table.

  10. 10.

    The sample’s 25th percentile value for \(regulation\) is 2.574, while its 75th percentile value is 5.676.

  11. 11.

    There is, however, evidence that this is the case concerning regulations on entry, for example (Djankov et al. 2002). Moreover, the assumption that regulation in autocracies is a tool for rent-creation that depresses economic potential is a feature of some well-known theoretical studies, such as Acemoglu (2006, 2010) or Parente and Prescott (2000).

  12. 12.

    We get nearly identical results using a fixed-effects negative binomial model.

  13. 13.

    The regression results presented in table 4 do not include regulatory quality as an explanatory variable, and thus include more countries and years (hence observations) than the previous regressions since regulatory quality data were not available for all of the countries in the panel.

  14. 14.

    This result is also robust to the use of our two alternative constructions of the political action variable. These robustness results are available upon request. A related result is provided by Aidt and Leon (2014), who demonstrate that (instrumented) urban riots in Sub-Saharan Africa are important drivers of democratic concessions by autocratic regimes. Note that our results are of a different nature than Aidt and Leon (2014), which focus on political transition. Our paper is concerned with political turnover within an autocratic political institution. Nevertheless, we view our result as complementary to theirs, as, though we don’t discuss it, democratization may be associated with the forced removal of autocrats that we consider in our paper.

References

  1. Acemoglu, D. (2006). A simple model of inefficient institutions. Scandinavian Journal of Economics, 108, 515–546.

    Article  Google Scholar 

  2. Acemoglu, D. (2010). Institutions, factor prices and taxation: Virtues of strong states? American Economic Review, 100(2), 115–119.

    Article  Google Scholar 

  3. Acemoglu, D., & Robinson, J. (2001). A theory of political transitions. American Economic Review, 91(4), 938–963.

    Article  Google Scholar 

  4. Acemoglu, D., & Robinson, J. (2006). The economic origins of dictatorship and democracy. New York: Cambridge University Press.

    Google Scholar 

  5. Aidt, T., & Leon, G. (2014). The democratic window of opportunity: Evidence from riots in Sub-Saharan Africa. Journal of Conflict Resolution, forthcoming.

  6. Banks, A. S. (2011). Cross-national time-series data archive. Jerusalem: Databanks International.

    Google Scholar 

  7. Barro, R. (1973). The control of politicians: An economic model. Public Choice, 14(1), 19–42.

    Article  Google Scholar 

  8. Bazzi, S., & Blattman, C. (2014). Economic shocks and conflict: Evidence from commodity prices. American Economic Journal: Macroeconomics, 6(4), 1–38.

    Google Scholar 

  9. Berman, N., & Couttenier, M. (2014). External shocks, internal shots: The geography of civil conflicts. CEPR Discussion Paper No. DP9895.

  10. Brückner, M., & Ciccone, A. (2011). Rain and the democratic window of opportunity. Econometrica, 79(3), 923–947.

    Article  Google Scholar 

  11. Bueno de Mesquita, E. (2010). Regime change and revolutionary entrepreneurs. American Political Science Review, 104(3), 446–466.

    Article  Google Scholar 

  12. Burke, P., & Leigh, A. (2010). Do output contractions trigger democratic change? American Economic Journal: Macroeconomics, 2, 124–157.

    Google Scholar 

  13. Campante, F., & Chor, D. (2012). Why was the Arab world poised for revolution? Schooling, economic opportunities, and the Arab Spring. Journal of Economic Perspectives, 26(2), 167–188.

    Article  Google Scholar 

  14. Caselli, F., & Tesei, A. (2011). Resource windfalls, political regimes, and political stability. Cambridge: Tech. rep., National Bureau of Economic Research.

    Google Scholar 

  15. Chassang, S., & Padro-i-Miquel, G. (2009). Economic shocks and civil war. Quarterly Journal of Political Science, 4(3), 211–228.

    Article  Google Scholar 

  16. Ciccone, A. (2011). Economic shocks and civil conflict: A comment. American Economic Journal: Applied Economics, 3(4), 215–227.

    Google Scholar 

  17. Coate, S., & Morris, S. (1995). On the form of transfers to special interests. Journal of Political Economy, 103, 1210–1235.

    Article  Google Scholar 

  18. Collier, P., & Hoeffler, A. (1998). On economic causes of civil war. Oxford Economic Papers, 50(4), 563–573.

    Article  Google Scholar 

  19. Collier, P., & Hoeffler, A. (2004). Greed and grievance in civil war. Oxford Economic Papers, 56(4), 563–595.

    Article  Google Scholar 

  20. De Haan, J., & Sturm, J.-E. (2003). Does more democracy lead to greater economic freedom? New evidence for developing countries. European Journal of Political Economy, 19(3), 547–563.

    Article  Google Scholar 

  21. Djankov, S., La Porta, R., Lopez-De-Silanes, F., & Shleifer, A. (2002). The regulation of entry. Quarterly Journal of Economics, 117(1), 1–37.

    Article  Google Scholar 

  22. Dorsch, M. T., & Maarek, P. (2015). Inefficient predation and political transitions. European Journal of Political Economy, 37, 37–48.

    Article  Google Scholar 

  23. Dube, O., & Vargas, J. F. (2013). Commodity price shocks and civil conflict: Evidence from Colombia. The Review of Economic Studies, 80(4), 1384–1421.

    Article  Google Scholar 

  24. Edmond, C. (2013). Information manipulation, coordination, and regime change. The Review of Economic Studies, 80(4), 1422–1458.

    Article  Google Scholar 

  25. Ellis, C., & Fender, J. (2011). Information cascades and revolutionary regime transitions. The Economic Journal, 121, 763–792.

    Article  Google Scholar 

  26. Ferejohn, J. (1986). Incumbent performance and electoral control. Public Choice, 50(1–3), 5–25.

    Article  Google Scholar 

  27. Goemans, H., Gleditch, K., & Chiozza, G. (2009). Introducing Archigos: A dataset of political leaders. Journal of Peace Research, 46(2), 269–283.

    Article  Google Scholar 

  28. Gwartney, J., Lawson, R., & Hall, J. (2013). Economic Freedom Dataset. Vancouver: Fraser Institute.

    Google Scholar 

  29. Haggard, S., & Kaufman, R. R. (2012). Inequality and regime change: Democratic transitions and the stability of democratic rule. American Political Science Review, 106(03), 495–516.

    Article  Google Scholar 

  30. Jones, B. F., & Olken, B. A. (2005). Do leaders matter? National leadership and growth since World War II. The Quarterly Journal of Economics, 120(3), 835–864.

    Google Scholar 

  31. Kleibergen, F., & Paap, R. (2007). Generalized reduced rank tests using singular value decomposition. Journal of Econometrics, 127(1), 97–126.

    Google Scholar 

  32. Kricheli, R., Livne, Y., & Magaloni, B. (2011). Taking to the streets: Theory and evidence on protests under authoritarianism. In: APSA 2010 Annual Meeting Paper.

  33. Krueger, A. (1973). The political economy of the rent-seeking society. American Economic Review, 64, 291–303.

    Google Scholar 

  34. Kuran, T. (1989). Sparks and prairie fires: A theory of unanticipated political revolution. Public Choice, 61, 41–74.

    Article  Google Scholar 

  35. Lohmann, S. (1994). Information aggregation through costly political action. American Economic Review, 84(3), 518–530.

    Google Scholar 

  36. Marshall, M., Jaggers, K., & Gurr, T. (2010). Polity IV project: Dataset users’ anual. Arlington: Center for Systemic Peace.

    Google Scholar 

  37. Miguel, E., Satyanath, S., & Sergenti, E. (2004). Economic shocks and civil conflict: An instrumental variables approach. Journal of Political Economy, 112(4), 725–753.

    Article  Google Scholar 

  38. Milgrom, P. (1981). Good news and bad news: Representation theorems and applications. The Bell Journal of Economics, 12(2), 380–391.

    Article  Google Scholar 

  39. Olson, M. (1965). The logic of collective action: Public goods and the theory of groups. Cambridge: Harvard University Press.

    Google Scholar 

  40. Parente, S., & Prescott, E. (2000). Barriers to riches. Cambridge: MIT Press.

    Google Scholar 

  41. Rode, M., & Gwartney, J. (2012). Does democratization facilitate economic liberalization. European Journal of Political Economy, 28, 607–619.

    Article  Google Scholar 

  42. Shadmehr, M., & Bernhardt, D. (2011). Collective action with uncertain payoffs: Coordination, public signals, and punishment dilemmas. American Political Science Review, 105(4), 829–851.

    Article  Google Scholar 

  43. Stigler, G. (1971). The theory of economic regulation. Bell Journal of Economics and Management Science, 2, 3–21.

    Article  Google Scholar 

  44. Tullock, G. (1971). The paradox of revolution. Public Choice, 11(1), 89–99.

    Article  Google Scholar 

  45. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge: MIT press.

    Google Scholar 

  46. World Bank. (2013). World development indicators. Washington DC: The World Bank Group.

Download references

Acknowledgments

An appendix of additional empirical tables is available from the corresponding author’s website (http://dorschmichael.wix.com/academic). This paper has benefited from the insightful comments of seminar participants at the Centre d’Économie Industrielle of MINES ParisTech, Humboldt University Berlin, CSAE at University of Oxford, the Public Choice Society at New Orleans, Central European University Budapest, the European Public Choice Society at ETH Zurich, the European Political Science Association in Barcelona, the DIAL Development Conference at Université Paris-Dauphine, the Silvaplana Political Economy workshop, and Koç University Istanbul. We are particularly thankful for the thoughtful comments of Toke Aidt, Sumru Altug, Thilo Bodenstein, Benjamin Ho, Randall Holcombe, Richard Jong-A-Pin, an anonymous referee, and the editors in charge of our submission, Pete Leeson and William Shughart II. All errors, of course, remain our own.

Author information

Affiliations

  1. School of Public Policy, Central European University, Nádor u. 9, Budapest, 1051, Hungary

    Michael T. Dorsch

  2. New Orleans, LA, USA

    Karl Dunz

  3. Department of Economics, Université de Cergy-Pontoise, 33 boulevard du Port, 95011, Cergy-Pontoise Cedex, France

    Paul Maarek

Authors
  1. Michael T. Dorsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karl Dunz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul Maarek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael T. Dorsch.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dorsch, M.T., Dunz, K. & Maarek, P. Macro shocks and costly political action in non-democracies. Public Choice 162, 381–404 (2015). https://doi.org/10.1007/s11127-015-0239-x

Download citation

Keywords

  • Asymmetric information
  • Macro shocks
  • Mass protest
  • Political instability
  • Political turnover
  • Regulation
  • Rent-seeking

JEL codes:

  • D72
  • D74
  • D82
  • E32
  • O43
  • P48