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To examine the average effect of the 10th Five Year Plan on misallocation for different provinces, I first consider a model without the interactions in column (1) to (4) in Table 3.1. The results in column (1) show the average effects of the Five Year Plan on misallocation across provinces. The positive and significant coefficient indicates that the 10th Five Year Plan brought more misallocation to industries that were supported by the industrial policy than those than were not supported. Even after the standard errors are clustered at province level in column (2), the coefficient of the interaction of the Five Year Plan is still positive and significant. As the 10th Five Year Plan was issued at the same time for all provinces, I cluster standard errors only at provincial level. To investigate how large is the effect of the Five Year Plan on misallocation, I use the methodology of Hsieh and Klenow (2009) to compute the effect. For example, in columns (3) and (4), the coefficients of the interaction are both 0.12, which indicates that there will be 18% larger misallocation in supported industries than not supported ones. This effect is quite large compared with those estimated in Chen et al (2018), where misallocation is measured at industry and year level. One possible explanation why larger effects of the 10th Five Year Plan on misallocation are found is that provinces with less number of firms and less aggregate value-added usually exhibit a rapid growth in variance of TFPR after the Five Year Plan.

provinces with different supporting intensities. The estimation results reported in column (5) correspond to a model that includes province-year, industry-year and province-industry fixed effects. The results reveal a positive and significant effect of the industrial policy on misallocation across provinces and industries. The coefficient on the interaction indicates that 10% increase in the ratio of intensity will improve misallocation by 3% among supported industries. After clustering the standard errors by province, the coefficient in column (6) is still positive and significant.

As the 10th Five Year Plan increases misallocation measured by variance of TFPR across provinces with different intensities, it changes dispersions of firm’s TFPR of supported industries. The next natural question to ask is whether the Five Year Plan changes the average values of TFPR and TFPQ? Table 3.2 shows the effects of the 10th Five Year Plan on revenue productivity of industries in provinces with different intensities. Columns (1) and (2) examine the effects of the industrial policy on industry’s average revenue productivity without and with clustering, respectively. The positive and significant coefficients on the interaction indicate that the Five Year Plan increases supported industry’s average revenue productivity. As revenue productivity measures profitability, the Five Year Plan increases supported industries’ average profitability.

Intensity is controlled in the models reported in columns (3) and (4). Coefficients of the Five Year Plan are still positive and significant, but the magnitude drops by half relative to those in columns (1) and (2). The coefficients on intensity are positive and significant, indicating that the Five Year Plan improves average profitability of supported industries in provinces with higher value-added shares of supported industries. Columns (5) and (6) examine the effects of the Five Year Plan and intensity on industry’s average revenue productivity, which the positive and significant coefficients show that the larger of intensity in a given province, the larger effects of the Five Year Plan on increasing average revenue productivity of industries in the province.

The effects of the 10th Five Year Plan on physical productivity are shown in Table 3.3. Columns (1) and (2) examine the effects of the industrial policy on industry’s physical productivity across provinces without and with clustering at province, respectively. The

positive and significant coefficients show that the Five Year Plan increases real productivity of supported industries at the provincial level. However, Chen et al (2018) find no significant effects of the 10th Five Year Plan on physical productivity measured by 4-digit industry level. The reason for the difference with Chen et al (2018) might be that differences in the effect of the plan on TFPQ across provinces disappear when the data is aggregated across provinces.

After adding value-added intensity into the model, as is shown in columns (3) and (4), the coefficients of intensity are positive, which indicates that the higher proportion of value-added in supported industries of all industries in a certain province, the higher of average real technology level of industries in the province. Moreover, the coefficients of the Five Year Plan are still positive and significant. Columns (5) and (6) present the results of the interactions of the Five Year Plan and supporting intensity on industry’s physical productivity, which the models include province-year, industry-year and province- industry interactions. The interaction coefficients are still positive, indicating that for provinces with higher intensities of the supported industries, the Five Year Plan increased the technology level of the supported industries more.

The estimation results reveal that the 10th Five Year Plan had different effects on misallocation, revenue productivity and physical productivity on provinces with different intensities. In provinces with higher proportion of value-added of supported industries, there increase in misallocation, average profitability, and technology levels in the province, brought by the Five Year Plan was greater.