Regulatory Distortions and Capacity Investment: The Case of China’s Coal Power Industry (Job Market Paper)
Winner of the EARIE 2019 Young Economist Essay Award (most significant policy contribution)
China's coal electricity generation market features a unique regulatory structure: production is largely determined at the discretion of state planners, but investments in productive capacity can be made privately. Using a newly constructed dataset on the industry, this paper estimates how planners are influencing these investment incentives via a structural model: First, I build a novel discrete choice model of planners' behavior in each market which quantifies their deviations from cost-efficient electricity dispatch policies. I then develop a dynamic discrete choice model of capacity investment with a combination of traits that are uniquely important in the Chinese setting: plants forecast their expected profits in a nonstationary environment that features many large policy shocks, unobserved heterogeneity in investment costs, and market-level heterogeneity in returns to investment. I find that aggregate electricity demand could be met at a roughly 3\% lower cost per unit if plants were assigned production purely based on costs, resulting in additional significant potential gains from reduced carbon emissions. Changes in regulatory treatment result in significant responses from plants: a persistent reduction in a plant's residual allocation by one standard deviation decreases the chance of making major investments by 19-25\%. Counterfactual simulations demonstrate that this comes with significant cost consequences for an affected plant by changing its scale. Regulatory distortions come with other, more positive consequences: planning policies are aggressively flattening this market's plant size distribution, which is consistent with concerns about market concentration in the event of electricity deregulation.
Restructuring the Chinese Coal Power Market: Revenue vs. Physical Measures (draft available upon request)
This paper measures the effect of a major 2002 restructuring of the Chinese coal power market using newly available physical data on the industry. Two effects are investigated: individual firms growing more or less technically efficient on the intensive margin in response to the policy, and market allocation mechanisms allocating across firms more efficiently given efficiency distributions. To investigate the former I use a novel dataset and a difference-in-differences framework in the spirit of (Fabrizio et al., 2007) and (Gao and Van Biesebroeck, 2014). I find this measures are extremely sensitive to whether physical or revenue-based measures of efficiency are used, in some cases nullifying the positive results of (Gao and Van Biesebroeck, 2014) I find that the reforms also did little to change the optimality of input allocations across plants in the market by either measure.
Heterogeneous Technologies, Productivity and the State Sector in China (joint with Panle Jia Barwick, Shanjun Li, and Yifan Zhang)
Over the past 20 years, the manufacturing sector in China has seen both massive growth and deregulation. This has coincided with a few other well-established facts: First, this growth is due in large part both to an increase in the number of firms in the market, as well as an increase in average firm-level efficiency, as measured by total factor productivity. Second, firms controlled by the Chinese government (SOEs) generally have lower average TFP than private firms. And, third, the main distinction between SOEs and privately owned firms in the modern era is that SOEs are granted favorable access to capital markets. Analyses that incorporate these facts generally ignore that favorable access to capital markets may induce fundamentally different choices of technology across the two types of firms. This paper seeks to incorporate and estimate the differences in technology between SOEs and private firms using structural techniques, and assess the impact of incorporating this difference on standard TFP results in China. The evidence strongly favors that SOEs adopt significantly more labor-intensive technologies, and that this has implications for several key results on productivity, including (mis)allocation levels and TFP residual dispersion.