Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing

Abstract

We present the first comprehensive set of firm-level total factor productivity estimates for China's manufacturing sector that spans her entry into WTO. We find that productivity growth is among the highest compared to other countries. For our preferred estimate, the weighted average annual productivity growth for incumbents is 2.7% for a gross output production function and 7.7% for a value added production function over the period [1998][1999][2000][2001][2002][2003][2004][2005][2006]. Of the various sensitivity checks we carry out, controlling for the increase in labor quality and labor hours, as proxied by the rising real wage, has the largest (downward) effect on the productivity estimates. We further document that new entrants are a particularly dynamic force and that firms experience large productivity declines before exiting from the sample. Overall, net entry contributes roughly half to total TFP growth. Aggregate productivity growth, however, is tempered by a much lower effect of reallocation of inputs towards higher productivity firms, compared to the U.S. benchmark.

Public infrastructure as a determinant of productive performance in China

“…Third, in recent studies, Young (2003) and Brandt et al (2009) find that conclusions about productivity growth are sensitive to whether labor inputs are adjusted for changes in human capital. We make no adjustment for human capital.…”

“…Panel E returns to the base specification of Table 5 and addresses the concern that the assumed depreciation of 4.5% is perhaps too low. It is the value that Chow (2003) uses, but, for example, Brandt et al (2009) assume a rate of 9%. Using this rate for depreciation of private capital only (Panel E1) changes the parameter estimates only slightly and reduces the rate of return r G by a only few percentage points.…”

Agglomeration and productivity in China: Firm level evidence

“…We use this above-size firm data for its availability at annual basis and its large coverage of China's industrial output. The data have been employed by numerous works to study the productivity growth of Chinese industries (such as Brandt, Biesebroeck, & Zhang, 2012) or factors behind the productivity growth (such as Yu, 2014). We use the sample spanning from 2000 to 2007 to compute the productivity of Chinese firms and the indicators of agglomeration.…”

“…Also, China has modified its industry coding system in 2002, switching from GB1994 to GB2002. Because our exercise requires consistent information on industry classification, we employ the method by Brandt et al (2012) to prevent our observations from switching their industries due to this administrative issue.…”

“…This implies that the recipients of agglomeration effects in this paper are SOEs and relative large non-SOEs. Given that firm-specific prices are not available, we follow Brandt et al (2012) to use the 2-digit industry-level price index to deflate a firm's output and intermediate input, and the fixed asset investment price to deflate its capital. Furthermore, we estimate product function separately for each 2-digit industry considering the fact that input coefficients differ substantially across industries.…”