Industrial Agglomeration and Firm Size: Evidence from China

Industrial Agglomeration and Employer Compliance With Social Security Contribution: Evidence From China

“…Two IVs are used to instrument China's industrial agglomeration from 2001 to 2007: the cross-city population of China in 1986 ( Population) and the number of manufacturing firms that were established before 1978 in city r and four-digit industry i ( Firm number). 15 While similar IV strategy is adopted by Li and Lu (2009) and Li et al (2012), it is still worthwhile to clarify the following premises that validate the use of these IVs in our setting: (1) the demand of a larger population attracts more manufacturers in each industry (Krugman, 1980;Davis and Weinstein, 2003;Hansen, 2005), and given the long-term impacts of population, the distribution of population in 1986 may affect the pattern of industrial agglomeration after 2000. (2) According to Krugman (1991), the historical condition has impacts on the current situation; in particular, though the economic environment prior to 1978 was very different from that in the 2000s, a larger number of firms established before 1978 laid a better foundation for developing manufacturing industries in the particular region during the economic reform.…”

“…However, because Population and Firm number are time-invariant, they should enter the panel IV estimation interacted with year dummies. Similar IV strategy is employed by Li et al (2012). The estimated results are reported in columns (3) and (4) of Table 5.…”

“…In each year, about 190,000 firms are surveyed, covering both SOEs and non-SOEs with annual sales of 5 million yuan or above. The same data set was also used by Cai andLiu (2009), Li, Lu, andWu (2012), Song, Storesletten, and Zilibotti (2011), Hsieh and Klenow (2009), etc.…”

Dots to dots: a general methodology to build local indicators using spatial micro-data

“…Li et al (2012) propose a simple extension of the location quotient based on the proposition of Holmes and Stevens (2002) by subtracting, for each point, its own employment level. By doing so, each point has a unique value of the location quotient reflecting the regional specialization excluding the given point.…”

Spatial Economics Model Predicting Transport Volume

“…The historical data for researching prediction have to be substituted by the related cargo quantity, which affect the empirical study of the prediction methods [12]. Especially in china, most of the literatures [6,13] are only an overview of the method, and almost no actual regional logistics data are used for predictive analysis [15], which makes many studies lack of practical significance. Fourth, the selection method of indicators of regional logistics mainly which support the prediction [14,16], relies on the experiences of the industry researchers and practitioners, the principal component analysis, factor analysis and other traditional methods, hence they are lack of new research method.…”