level firm outcomes. We hypothesize that since local government officials wanted to achieve high GDP and employment growth, they would have an incentive to utilize their new administrative remit to promote firm growth and hence we should observe better firm-level outcomes in the newly upgraded cities versus their similar counties. We exploit the ad hoc nature in which the countyupgrading takes place to identify the effect of an increase in city powers on firms’ economic outcomes. The paper adds to the existing literature in two ways – it identifies whether and to what extent firm-level outcomes improve post-upgrading, and importantly, it attempts to identify the channels through which these outcomes take place. In addition, this paper uses two proxies to measure local government capacity and documents the extent to which local government capacity matters for firm and job growth in China. The rest of the paper is organized as follows. Section 2 describes the data sets and how these were prepared. Section 3 presents the empirical strategy used to identify the effects of city-upgrading on firm outcomes. Section 4 reports the econometric results for city-level outcomes and firm-level outcomes and Section 5 concludes.

# **2 Data Sets and Data Treatment**

This paper utilizes two panel data sets, one at the level of counties and the other at the level of firms. We use variables from 1993 to 2004 from the annual series of the Public Finance Statistical Materials of Prefectures, Cities, and Counties published by the Ministry of Finance of China to construct a county-level public finance data set (henceforth public finance data set). As of 1998, when the policy of county-to-city upgrading had come to an end, there were 99 counties that were upgraded from counties to county-level cities between 1994 and 1997.

The firm-level data set spans from 1998 to 2009 and is compiled from the Annual Survey of Industrial Firms (ASIF) collected by the National Bureau of Statistics of China. This data set is often referred to as the 5 Million RMB data set, since it contains non-SOE firms with main operating revenue (i.e., sales) of more than five million RMB, and all state-owned enterprises (SOEs) regardless of sales volume. Thus, the ASIF survey data set tracks the performance of all SOEs and all big non-SOEs. The ASIF data set details all operational, financial, and managerial facts of firms in three broad categories: (1) mining, (2) manufacturing, and (3) production and distribution of electricity, gas, and water, and classifies each firm to 6-digit sub-industries. Compared with the 2004 China Economic Census, the firms covered by this data set represented 89.5% of the total main operating revenue (sales) from all enterprises in China (Nie, Jiang, & Yang, 2012).

The 1998-2009 ASIF data set covers a period when some major structural reforms happened in China, such as the SOE reform in the mid-to-late 1990s. Following the end of the county-to-city upgrading policy in 1997, China carried out a major reform of SOEs between 1998 and 2000. The aim of the reform was to privatize or close down small and unprofitable SOEs (Song and Hsieh, 2013). In 2004, an economic census covering all firms regardless of size was carried out and more detailed information on firms was collected.<sup>1</sup> To mitigate the effects of the above SOE changes, and since we are only interested in understanding the effect of county-to-city upgrading, we restrict

> 1 For example, in this year, breakdown of employment by education level, gender, technical titles was collected (Brandt, Van Biesebroeck, & Zhang, 2014).

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