possibility is that the entry process in these sectors depended less (and not more) on economic factors. So, the firms with the highest potential did not get the opportunity to enter.<sup>13</sup> Estimates of the rate of entry of new industrial firms with annual sales above RMB 20 million indicate that, after falling slightly during the Asian financial crisis (1997), entry rates rose from less than 8 percent a year in the late 1990s to more than 12 percent in 2004. Subsequently, entry rates began to fall and by 2013 were 3.5–4 percentage points lower. This suggests that a potentially important reason for the declining role of new firms in manufacturing productivity growth was fewer new firms. In the end, most productivity growth in a sector came from incumbents.

# **2.2 The role of the state sector in manufacturing**

Over the last 40 years, market-oriented reforms significantly transformed the state sector in manufacturing. Wholly or majority state-owned firms accounted for 40 percent of real value added in manufacturing in 1998 and for less than 7 percent in 2013. In addition to reducing the size of the state sector, the reforms also resulted in an increase in manufacturing SOE productivity. We follow the approach in Dollar and Wei (2007) to estimate the differences in productivity and profitability between state-owned and private firms before and after the global financial crisis. The pooled regression specification is: In each of the five regressions, is a measure of the performance of firm _j_ at time _t_ : the log of average real revenue product of capital (calculated as the ratio of real value added to the real jj,tt capital stock), the log of average real revenue product of labor (real value added divided by YY total labor), TFP, return on capital, or leverage (i.e., the ratio of total liabilities to total assets). The equation includes a set of dummy variables: firm ownership, year, industry (at the 2-digit level), province, and interaction terms for year/ownership and year/sector. The ownership variable, _SOE_ , takes the value of 1 for majority state-owned enterprises and 0 for all other firms.<sup>14</sup> The sum of the coefficients 1 is interpreted as the relative efficiency of SOEs compared to private firms, holding other factors constant. 2,tt ββ + ββ Including additional dummy variables in the regression allows us to separate the influence of factors unrelated to ownership structure, such as regional differences (for example, if SOEs are overrepresented in regions with lower returns), industry- and year-specific shocks (say, if SOEs dominate an industry exposed to a large but temporary negative shock such as high commodity prices), and also firm size, with larger enterprises (in the number of workers) being generally less productive.

Confirming the results of Hsieh and Song (2015) for the period 1998–2007, the gaps between returns to capital and labor in SOEs and in private competitors within manufacturing have

> 13 Brandt et al. (2020) report similar results at the prefecture level over time.

> 14 SOEs are firms for which the state paid-in capital share is larger than 50 percent, and private are all other firms. Similar results are obtained if SOEs are firms with 30-percent or more state ownership and also if SOEs with foreign equity stakes are treated as non-SOEs (around 10 percent of majority state-owned companies had some foreign ownership in the later years of the sample; that share was smaller in the earlier years).

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