Who Maximizes (or Satisfices) in Performance Management? An Empirical Study of the Effects of Motivation-Related Institutional Contexts on Energy Efficiency Policy in China

ABSTRACT

Research on public employee motivations in performance management has given little attention to the moderating role of motivation-related organizational and institutional contexts. Against the backdrop of China's energy intensity reduction policy, this study explores how institutional contexts pertaining to career motivation affect subnational bureaucrats' performance of central government policy goals. Empirical analysis, drawing on data for twenty-nine province-level governments from 2006 to 2010, confirms that institutional contexts related to career motivation influence policy implementation. Specifically, provinces with higher levels of bureaucratic integration with the central government had higher probability of achieving reduction targets and attaining a rating of excessive fulfillment in the national report card.

KEYWORDS:

• career motivation
• China
• energy policy
• institutional context
• performance management

Acknowledgments

An earlier version of this paper was presented at the 2013 annual research conference of the Association for Public Policy Analysis and Management, Washington, DC. I thank Editor Kaifeng Yang, four anonymous reviewers, and the conference panel participants for their helpful comments. Any remaining errors are those of the author.

Notes

This study employs the definitions of “motivation” and “incentive” proposed by Rainey (2009). Motivation refers to “a person's desire to work hard and work well—to the arousal, direction, and persistence of effort in work settings” (p. 248); and incentive refers to “an external object or condition that evokes behaviors aimed at attaining or avoiding it” (p. 252).

But as Heinrich and Marschke (2010) noted, it is difficult to come up with incentive system designs that perfectly match the agents' motivations, since the principals generally do not know the actual preference structures of the agents.

For the introduction of the organization structure of the Chinese Communist Party, see Lieberthal (1995), Sheng (2005), and Shirk (1993).

In the past decade, after serving as governor, 29.27 percent of governors are directly promoted to the post of Party secretary in the same province, 7.32 percent are transferred and promoted to Party secretary in an other province, 3.66 percent are transferred to an equivalent post as governor in another province, and 8.54 percent are promoted to other central posts (e.g., consultative placements) (based on the author's calculation).

Related to task environment, six factors (i.e., economic development condition, sectorial industry structure, energy consumption per GRP, total energy consumption, energy consumption per capita, and self-sufficiency of energy) are explicitly specified as factors for adjusting performance indicators in ways that render the targets within a province's expected capacity. Unfortunately, the State Council does not clarify the methods for generating the adjusted benchmarks (e.g., formal statistical, regression-based adjustment methods, see Barnow & Heinrich, 2010; or assignment of different weights to these variables). As discussed in the section on research design, local economic development, energy-intensive industrial sector, and coal consumption are broadly employed in extant research to account for the effects of task environment on province energy intensity. To determine the appropriate vectors of task environment variables to include in the empirical analysis, this study performs two sets of model comparison (details not reported). First, four proxies (i.e., log of lagged total energy consumption, lagged energy consumption per capita, lagged energy consumption per GRP, and percentage of industrial sector in GRP; hereinafter “Vector A”) for the major factors used for benchmark adjustments replace the investments in the fixed assets of energy-intensive industries and coal consumption (hereinafter “Vector B”). Across the models for both binary and ordinal outcomes, the goodness of fit of the models with Vector B is consistently superior to that with Vector A (i.e., for these non-nested models, the AIC and BIC statistics of the former are smaller, see Long, 1997). In addition, with the results of other variables largely unchanged, only one variable in Vector A (i.e., lagged energy consumption per GRP) is consistently, statistically significant. In contrast, as Tables 2 and 3 indicate, all two variables of Vector B are statistically significant and have more explanatory strength in predicting two outcome variables. The second comparison involves nested models. Similarly, across the models with two types of outcome variables, the restricted models (Vector B) are preferred to full models (Vector A +Vector B), as the p-values associated with the χ² for the LR tests are consistently larger than 0.10. As a result, this study uses disposable income of urban households per capita, fixed-asset investments in energy-intensive industries, and coal consumption to control for the effects of task environment.

Information on energy consumption per unit of GRP, and the target and actual percentage reduction in energy intensity is unavailable for Tibet from 2006 to 2009. In the report examining the cumulative effects of the plan, the central government did not release the results for Xinjiang but evaluated it independently, as its performance consistently lagged behind.

The central government apparently did not determine the reduction target for each province in 2006. Therefore, the national average target (i.e., 4%) serves as a proxy indicator at the province level. Similarly, although the NDRC released the final results of the target ­accomplishment in a way that compared the 2010 performance to the 2005 benchmark, it did not specify the annual target for 2010 (National Development and Reform Commission, 2011). As a result, this study calculates the 2010 reduction target as follows: If the energy consumption per unit of GRP of a given province in 2009 had already reached the 2010 target stipulated by the plan, its target reduction for 2010 is considered as 0. If, instead, the province's actual energy intensity in 2009 did not meet the centrally mandated target in the plan, it still had to invest in improvement in the upcoming year and the corresponding target reduction is imputed with the official formula:

• ${\displaystyle Y_{2010}=(\frac{X_{2010}}{X_{2009}}-1)\times 100\%}$

where Y₂₀₁₀ is the target percentage reduction in 2010; X2010 is the target of energy consumption per unit of GRP in 2010 set by the plan; and X2009 is the energy consumption per unit of GRP in 2009. The actual percentage reduction in 2010 is calculated in an analogous manner with the energy consumption per unit of GRP of 2009 and 2010 (National Bureau of Statistics, various years). To facilitate the interpretation, this study reverses the signs of the reduction targets.

The official scores were issued from 2007 to 2009. In the pilot year of 2006, the NDRC did not produce ratings, since all but Beijing failed the benchmarks (coded as 1 for the failure). For the same reason as for the target percentage reduction in 2010, provincial performance in 2010 is not rated. Hence, this study employs linear extrapolations for the rating of Beijing in 2006 and those of all provinces in 2010 using the difference between the actual and target percentage reductions. The correlation between two measures of the dependent variable is high (r = 0.932). Furthermore, in the official evaluation over five years, there are two observations (two provinces in one year each) and sixty-one observations in the categories of basic fulfillment and fulfillment, respectively. The models thus combine these two categories and employ a three-level scale (i.e., failure, fulfillment, and excessive fulfillment).

The 14th National Congress was a landmark event in contemporary Chinese politics. It was the first national congress since the Tiananmen Square protests and crackdown in 1989. It constitutionally defined the third-generation leadership headed by Jiang Zemin. In addition, the Central Advisory Commission, whose members were the senior leading cadres during the leadership of Mao Zedong and Deng Xiaoping, was abolished.

Multicollinearity is detected if tax revenues and electricity consumption (in logarithmic form) are simultaneously controlled for, so this study does not include these two variables.

This study does not estimate models with province fixed effects. On one hand, region fixed effects are of substantive interest. On the other hand, province fixed effects substantially ­reduce the degrees of freedom, given the sample size. The western region includes Inner ­Mongolia, Ningxia, Guangxi, Gansu, Qinghai, Sichuan, Shaanxi, Yunnan, Guizhou, and Chongqing. The central region includes Shanxi, Henan, Anhui, Hubei, Hunan, Jiangxi, Jilin, and Heilongjiang. The eastern region includes Beijing, Tianjin, Hebei, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Hainan, and Liaoning.

For the binary outcome variable, random-intercept regression models and generalized estimating equation models with autoregressive correlation structure are estimated.

The author is grateful to one anonymous referee for providing insights into the positive relations between investment in the fixed assets on pollution- and energy-intensive industries and target performance.

The author is grateful to two anonymous referees for pointing out the potential intertwinement of the incentive and competence effects when assessing the mandatory retirement age (fixed-tenure) system.

Additional information

Notes on contributors

Jiaqi Liang

Jiaqi Liang is a visiting assistant professor in the Department of Government at New Mexico State University. Her research areas encompass public management, bureaucratic politics, public policy process and analysis, program evaluation, environmental and energy policies, and comparative public administration and policy. Her article on the relationship between policy stability in public resource allocation and policy outcomes in renewable energy technologies appeared in Policy Studies Journal. Liang received her Ph.D. in Public Administration with a second major in Comparative Politics from the School of Public Affairs at American University in 2014.