Efforts or Gaming: How Local Governments Respond to Incentives and Monitoring

• In Event: Accountability and Responsiveness in Public Policy

Abstract

Gaming behaviors in performance measurement are prevalent in the public sector causing information distortion and impeding policy implementation. However, it remains unclear how principals respond to agents’ gaming behaviors and whether their responses are effective (Moynihan et al., 2011). Bypassing monitoring such as randomly selected reviews and spot checks has been advocated, but the central inquiry is how incentives and monitoring jointly shape agents’ decisions between applying more efforts or gaming in chasing higher performance. Intriguingly, while monitoring holds agents back from gaming, performance incentives push them to do so (Heinrich, 2007). Will the burden of being caught offset the tempting gains of gaming? Will applying more real efforts become a rational choice?
To unravel the intertwining effects of incentives and monitoring, we develop a theoretical model of how agents’ gaming behaviors are elicited and relieved by incentives and monitoring based on classic principal-agent model (Goldman & Slezak, 2006). We identify three paths of effects that determine agents’ choices between efforts or gaming, including the incentive effect that prompts agents to chase higher numbers by either efforts or gaming, the monitoring effect that forces agents to quit manipulation, and the parallel effect that discourages agents from making real efforts due to a higher cost of effort when short-term bypassing monitoring parallels performance evaluation and demands quick improvement. The results of our model show that whether agents will apply more efforts or gaming depends on the push-and-pull between the three effects.
We further apply the model to the context of China’s national inspection on environmental protection (CEPI) to demonstrate both the individual and joint impacts of the three effects. We employ satellite PM2.5 data to build the proxy for real efforts in air pollution control and measure the extent of gaming through the discrepancy between satellite data and data reported by local governments. Empirical findings support our predictions that each of the three effects has a unique impact on the equilibrium relations. Cities with better pre-inspection performance have less of an incentive effect during CEPI, and those with less pre-inspection manipulation have less of a monitoring effect. Cities who are more reliant on high-polluting industries suffer a greater parallel effect due to a larger increase in the cost of effort. As for the joint function, due to the regime’s longstanding tradition of performance legitimacy that prompts local governments to improve their performance after the central government has paid its attention, the incentive effect dominates both the monitoring effect and the parallel effect, generating a situation in which efforts and gaming both increase, as indicated by a decrease in the actual level and a wider gap between reported and actual levels.
Our research provides theoretical explanations and empirical evidence for a fine-tuned balance between incentives and monitoring for performance management in public organizations, which has been shown in earlier research to act as a performance-enhancing drug with observable side effects in inducing gaming behaviors (Hood, 2012). We find that excessive pressure on performance improvement will create a dominant incentive effect that encourages gaming more than monitoring alleviates, and highlight that greater focus should be placed on raising the burden of manipulation and reducing the heavy organizational costs of bypassing monitoring. Also, our research indicates a fruitful way forward to involve theoretical models in public policy research, which reveals the connection between policy implementation and its political contexts (Lavertu & Moynihan, 2013). Future research can expand our model and probe into the internal mechanisms of performance systems and monitoring actions so that the effects can be compared across different contexts.
References:
Goldman, E., & Slezak, S. L. (2006). An equilibrium model of incentive contracts in the presence of information manipulation. Journal of Financial Economics, 80(3), 603–626.
Heinrich, C. J. (2007). False or fitting recognition? The use of high performance bonuses in motivating organizational achievements. Journal of Policy Analysis and Management, 26(2), 281–304.
Hood, C. (2012). Public Management by Numbers as a Performance-Enhancing Drug: Two Hypotheses. Public Administration Review, 72(s1), S85–S92.
Lavertu, S., & Moynihan, D. P. (2013). The Empirical Implications of Theoretical Models: A Description of the Method and an Application to the Study of Performance Management Implementation. Journal of Public Administration Research and Theory, 23(2), 333–360.
Moynihan, D. P., Fernandez, S., Kim, S., LeRoux, K. M., Piotrowski, S. J., Wright, B. E., & Yang, K. (2011). Performance Regimes Amidst Governance Complexity. Journal of Public Administration Research and Theory, 21(suppl_1), i141–i155.

Authors

• Zhe Deng, Tsinghua University, School of Public Policy and Management

• Xiao Tang

• Mengke Guo, Tsinghua University, School of Public Policy and Management