Chapter 3 Unequal Effects of Industrial Policy ·············································
3.3 Regional Misallocation and Productivities ········································
3.3.1 Measurement of Misallocation, Profitability and Technology
I follow Foster et.al (2008), and Hsieh and Klenow (2009) to measure profitability, technology and misallocation for firms and industries. Foster et.al (2008) first propose to use revenue total factor productivity (TFPR) and physical productivity (TFPQ) to measure profitability and technology, respectively. Subsequently, Hsieh and Klenow (2009) suggest that the larger of the variance of TFPR in an industry, the larger of misallocation within that industry. Following Foster et. al (2008) and Hsieh and Klenow (2009), I compute the revenue productivity and physical productivity as below:
1 ( ) s s si si si si si si si P Y TFPR P A Kα wL −α = = (3.1) 1 ( ) s s si si si si si Y TFPQ A Kα wL −α = = (3.2)
where TFPRsi refers to the profitability (revenue productivity) of firm
i
in industrys
, and TFPQsi denotes the technology (physical productivity) of firmi
in sectors
.si
P , Ysi and P Ysi si are the output price, output quantity and value-added of firm
i
inindustry
s
, respectively. Moreover, Asi measures firm’s technological level, which is also TFPQ, Ksi is the capital input of firmi
in industrys
, and wLsi measures firm’s aggregate labor input. In addition, for sectors
, αsis the marginal product tocapital, and 1−αs the marginal product to labor. Therefore, TFPR measures
productivity of unit value-added to capital and labor input, and TFPQ measures productivity of unit quantity to capital and labor input.
However, TFPQ values cannot be computed directly from the firms’ information in the data, therefore, I follow Hsieh and Klenow (2009) to compute TFPQ by raising P Ysi si
to the power σ σ/ ( −1) to get Ysi . TFPQ can be obtained by using the following expression: 1 1 ( ) ( ) s s si si si si si si P Y TFPQ A K wL σ σ α α − − = = (3.3)
where σ is the elasticity of substitution between firm value-added, and its value ranges from three to ten (Broda and Weinstein, 2006, Hendel and Nova, 2006). After the computation of TFPR and TFPQ for each firm, the mean of TFPR and TPFQ and the variance of TFPR in sector s in province p can also be obtained, which I use them to measure profitability, technology and misallocation in industry s in province p . In addition, the reason why misallocation could be measured by the variance of TFPR is given in Hsieh and Klenow (2009), as is shown in (3.4).
1 1
1
log log( ) var(log )
1 2 s M s si si i TFP Aσ σ TFPR σ − = = − −
∑
(3.4)As there are no computed labor and capital shares for Chinese manufacturing industries, I follow Hsieh and Klenow (2009) and use those in American manufacturing industries to measure labor and capital shares. Therefore, before computing TFPR and TFPQ values for Chinese firms, the Chinese Industrial Classification (CIC) code should be matched with American Standard Industrial Classification (SIC) code, so as to obtain the values of labor share (1−α ) for different Chinese industries. The classifications of industries in SIC and CIC are similar to each other, but there are also some small differences. If one industry in SIC is corresponding to several industries in CIC, all these industries in CIC will be given the value of labor share in SIC. However, if one industry in CIC is corresponding to several industries in SIC, this industry in CIC will be assign the average value of labor shares of the corresponding industries in SIC. For example, a 4-digit industry with code “1310” in CIC is corresponding to two industries of “2041” and “2044” in SIC. If the (1−α ) values for “2041” and “2044” were 0.2 and 0.4 respectively in 2001, the (1−α) value of industry 1310 in CIC will be given 0.3 (average of 0.2 and 0.4) in 2001. After obtaining the values of labor share for each industry in Chinese manufacturing, I still follow Hsieh and Klenow (2009) to compute labor share by scaling up 3/2 to (1−α), and then obtain the firm-level TFPR and TFPQ values. Therefore, regional misallocation in a specific industry can be obtained by taking variance of firm’s TFPR within the industry and region, and average profitability and technology can be obtained by computing the mean of industry’s TFPR and TFPQ in a region in any given year.
3.3.2 Quantifying Misallocation across Regions
Figure 3.3 shows misallocation across provinces. As Panel A illustrates, misallocation is measured by the average of variance of manufacturing industries in a given province in 1998. Panel A and B show misallocation before the issue of the Five Year Plan in 1998 and 2000. The three northeastern provinces and some provinces in middle China
have the most serious misallocation, and there is less misallocation in the more developed eastern provinces and less developed western provinces. Panel C shows misallocation in 2001, when the Five Year Plan was issued; there is no apparent difference in misallocation between 2001 and 2000. However, misallocation in 2005 in Panel D is more severe than before. There are more provinces with higher levels of misallocation, especially provinces in the middle and northeastern in China. Surprisingly, there are still lower levels of misallocation in more developed eastern coastal provinces.
Figure 3.4 shows the evolution of misallocation in each province over the years. Consistent with the above description, more developed provinces such as Shanghai, Jiangsu and Zhejiang, experienced lower levels of misallocation both before and after the Five Year Plan. Some provinces such as Tianjin and Henan, had lower levels of misallocation, but increasing in variance of TFPR after 2001. Provinces like Sichuan and Xinjiang, have a descending trend in misallocation before the Five Year Plan, but upward trend after the Five Year Plan. And some other provinces such as Qinghai and Ningxia, experienced more fluctuations in misallocations during the period. Overall, there are increases in misallocation in most provinces after the Five Year Plan.
3.3.3 Endogeneity of the Five Year Plan
There are two concerns about the endogeneity of the issue of the 10th Five Year Plan. The first one is reverse causality. If the Five Year Plan was issued because the central government expected that there was misallocation, profitability and technology problems in the supported industries, there would be reverse causality from the Five Year Plan to the outcomes of interest.
The presence of reverse causality is not likely. The official document of the 10th Five Year Plan states that the reason to support these industries is to optimize industry structure and to make them more competitive with foreign firms. For example, the central government encourages some industries like computer equipment manufacturing and biotechnology engineering by helping them to use the new advanced
technology of the world.
Moreover, Chen et al (2018) show statistical evidence that there are little concerns of endogeneity of the issue of the Five Year Plan on misallocation, revenue and physical productivity. The distributions of variance and mean values of TFPR and TFPQ before the issue of the Five Year Plan, show no evidence that central government only supports industries that are experiencing high misallocation, or lower profitability and technology There is no evidence to indicate that central government expected there would be misallocations, profitability and technology problems in these supported industries in the future.
The second concern of endogeneity is that some unobserved variables might be correlated with the issue of the Five Year Plan. The relationship between unobserved variables and the issue of the Five Year Plan is untestable. Therefore, Chen et al (2018) examine whether there are significant differences between supported and not supported industries by groups of observed variables. They find that there are industries with both most and least number of employees, with both most and least number of firms, and with both large-sized and small-sized firms in both supported and not supported industries.