Database and Indicator Descriptions

This analysis is based on a two longitudinal datasets. One is CASIF survey conducted annually by the National Bureau of Statistics of China (NBS). It includes all Chinese industrial firms that are either state-owned or above-scale13 _{non-state owned firms.}

Industrial firms here refer to the firms from mining, manufacturing, and public utilities (gas, water service, electric power) sectors14_{. The CASIF survey contains firm-level}

information such as ownership, location, data about firm production activities and financial data etc. Since year 2007, the CASIF survey data has been used extensively in studying topics like macroeconomics, international economics industrial organization (Brandt et al., 2014). The other one is CCSY which provides information about all 287 Chinese cities15_{. Variables relating to industry level are derived from both}

datasets.

We omit firms with incomplete records and get an unbalanced panel database of 857,753 observations from 39 sub-sectors (by using four-digit industry code) in 286 cities over 2005-2007. Altogether 369985 firms are included. During this period, no related firms exist in the Lhasa city (code: 5401) in Tibet. This leads to 286 cities16 _in

regional level including four largest municipals of China (Peking, Shanghai, Tianjin and Chongqing). In addition, to provide a basis for different regional development policies, the Chinese government divided the whole China into three economic areas (eastern, central and western area). Following this criterion, we obtain three sub- samples for each area.

One purpose of this essay is to discuss the determinants of firm-level innovation performance. A variety of indicators are available to measure firm’s innovation such as R&D expenditure, patents, new products announcements and outputs. However, it has not lead to a consensus on a generally accepted indicator (Hagedoorn & Cloodt, 2003)

13 According to NBS, Firms with revenues above 5 million RMB are referred to as “above-scale” firms

(The criteria changes in year 2010).

14 _{The sector classification relies on NBS’s industrial classification standard (GB/T 4754- 2002). In 2011}

Chinese NBS made adjustment for industrial classification standard (GB/T 4754- 2002) and published new standard (GB/T 4754-2011) for national economic activities. This adjustment has no impact upon our analysis because our resulted panel data is from year 2005 to year 2007.

15 According to NBS, there are now altogether 287 cities with 4-digit codes in China.

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because empirical settings are diversified in industries, counties, constructs and measurement methods etc. In consideration of our research focus, R&D expenditure is generally viewed as an input of innovation production process and thus is problematic as indicator here. Patent represents firm’s purely technological endeavor and is usually adopted in the research about science and technology firms. In terms of observed manufacturing firms, we choose new product intensity (int_npv) as indicator of firm’s innovative performance. New product intensity is the ratio of new product sales to total sales. The advantage is that it represents the demands of the market and meets the general conception of the market introduction of innovation (Knoben, 2009).

Table 3. 1 Explanatory variables ( : firm, : industry, : region )

Firm-level variables Indicator Definition

age age number of years since firm began operations

size emp number of employees inside a firm

R&D input int_rd R&D intensity=R&D input/sales

innovation

experience d_exper Dummy variable: whether firm has (1) or has not (1) new products output in previous years.

export activity d_export Dummy variable: whether firm exports (1) or not (0)

ownership d_owner dummy variable: firm is (1) or is not(0) foreign (including HMT) company

sector-level variables Indicator Definition

industry size indu_emp Number of employees of one industry in one region

R&D input indu_rd R&D input per resident of one industry in one region

industry structure indu_HI

Herfindahl index= ( )

foreign competition indu_foreign

= ( & the ownership of firm is

foreign (d_owner=0) ) Region-level

variables

Indicator Definition regional market size reg_area Region’s area

public R&D input reg_rd amount of governmental science and education input in one region

reg_uni Number of universities in one region

Regional industry

structure reg_spe

reg_div

FDI reg_fdi Foreign invest amount in one region

i j k







Explanatory variables from three levels are described (Table 3. 1). At firm-level, dummy variable (d_owner) is to indicate whether the firm is owned by foreign investors including investors from Hong Kong, Macau and Taiwan (HMT) or not. Firm’s export activity is represented also by a dummy variable (d_export) indicating whether the firm exports at that year. At sector-level, we use Herfindahl index to examine one specific industry’s structure. Foreign competition of an industry (Indu_foreign) is represented through the proportion of all foreign firm’s sales in the whole industry. As many literatures suggest, region’s area (reg_area) is used to indicate region’s market size. For the public R&D input, we use two indicators (reg_rd, reg_uni). Specialization and diversification measurement are used to describe regional industry structure. We refer to the measurement criteria from Mukim (2012). Specialization (spe) is measured as the proportion that one sector’s employment in one district accounts for in the total employment of this sector in whole country. Diversification (div) is measured as the sum of squares of one sector’s employment shares in total employment of all sectors in one district. Statistics for the whole sample are in appendix.