Data Sources and Key Variables

This paper uses the firm-level annual survey data for PRC’s large and medium-sized industrial enterprises during 1995-2002, which are collected and maintained at the National Bureau of Statistics (NBS) in Beijing. The firm data set allows us to compare enterprise performance across region, ownership, industry, and time.

The NBS survey covers more than 20,000 large and medium-sized industrial enterprises in PRC. There are some unusable observations due to incomplete data reporting or small enterprises, which were classified as large and medium-sized historically based on their design production capacity. The classification standard for the size of industrial enterprises was first issued in April 1988 by a number of government agencies including the State Planning Commission, National Bureau of Statistics, Ministry of Finance, Ministry of Labor, and State Economic Commission. It includes detailed specifications based on the measurement of the output quantity or capacity in technical quantity terms, instead of in value terms. The standard is clearly a legacy of the centrally planned economy and is being phased out. It now only applies to state-owned industrial enterprises. For private enterprises, the National Bureau of Statistics is using sales as the unique variable in determining size of the enterprises.

In this study, observations satisfying one of the following screening conditions are regarded as unusable and deleted from the sample.

• Net value of fixed assets < RMB100,000;
• Intermediate inputs < RMB100,000;
• Number of employees < 30;
• Gross value of industrial inputs at current price < RMB100,000;
• Sales < RMB100,000;
• Total assets < RMB100,000;
• Total assets – liquid assets < 0;
• Total assets – gross fixed assets < 0;
• Total assets - net value of fixed assets < 0;
• Accumulated depreciation – current depreciation < 0;
• MISSING data for total assets, number of employees, gross value of industrial output at current prices, net value of fixed assets, or sales.

After deleting the unusable observations, only about 5% or less of the sample enterprises have sales values less than RMB 5 million. The unusable observations are evenly distributed across ownership, industry, and region. Hence, excluding them from the usable sample should not create much bias in our analysis. However the sample does not have the same population over time. It covers the entire large and medium-sized industrial enterprises sector in PRC, as defined above, so enterprises that become smaller and no longer qualify for the group exit from the sample every year.

Table 1.1 [ PDF 373.2KB | 73 pages ] shows the definition and summary statistics for key variables used in the regression analysis. Most variables are standard accounting variables, which do not need explanation. The variable IP, or Imputed Profit, is defined in the next section. A few other variables measuring the market environment are also explained below.

Table 1.2 [ PDF 373.2KB | 73 pages ] shows the number of firms by industry in 2002 in the cleaned sample as well as the share of the north-east region in the sample. The north-east has a high share in timber logging (79.5%), gas production (20.2%), Timber products (18.4%), petroleum processing (17.9%), furniture (16.3%), pressing ferrous metal (12.9%), and petroleum extraction (12.5%).

Table 1.3 [ PDF 373.2KB | 73 pages ] shows the weight of the sample in the context of the Chinese economy. In 2002, the value added of the sample enterprises is as high as 43.3% of PRC’s total industrial value added and 19.2% of PRC’s GDP. But the employment of the sample enterprises is only 16.7% of PRC’s total industrial employment. The total liabilities of the sample enterprises are as much as 43.6% of PRC’s total bank loans. Clearly the sample represents an important part of the Chinese economy and this makes statistical analysis of the sample useful for policy purposes. In the next section, we explore the position of north-eastern enterprises in the sample, focusing in particular on changes in ownership structure, capital allocation, and profitability.

The variable Ind3Concentration is the Herfindal index for measuring industrial concentration at 3-digit industry level. Table 1.4 [ PDF 373.2KB | 73 pages ] and Table 1.5 [ PDF 373.2KB | 73 pages ] shows both the formula and calculated value of industrial concentration over the period 1995-2002 for the sample at both the 2 and 3-digit industry level. The concentration levels in the two tables are ranked and we can see that at the 2 digit level, the most concentrated industry during the period is petroleum extraction (Ind2Concentration = 13.37%), followed by gas production (2.95%), chemical fibers (2.66%), tobacco (2.61%), and petroleum processing (2.45%).

The variable FIE_ind2MKT_Share is the market share of foreign invested enterprisers in the sample at the 2-digit industry level, as shown in Table 1.6 [ PDF 373.2KB | 73 pages ]. As can be seen in this table, foreign invested enterprises have penetrated to most industries except the highly monopolized ones such as tap water production, tobacco, coal mining, ferrous mining, nonferrous mining, and timber logging. The industries with highest concentration of foreign invested enterprises included electronic and telecom equipment (24.38%), cultural and sports products (23.85%), leather products (16.66%), furniture (13.44%), plastic products (12.66%), food production (11.37%), metal products (11.03%), garments (10.59%), and instruments (10.10%).

Table 1.7 [ PDF 373.2KB | 73 pages ] shows the price index for gross output and value added. The index is calculated for each industry with 1990 price as 1, based on the available constant and current prices for each firm in the sample.

Table 1.8 [ PDF 373.2KB | 73 pages ] shows the price index for intermediate inputs. The index is also calculated for each industry with 1990 price as 1. The calculation of this index is more complicated as we have incorporated the information for the constant and current prices for capital goods.¹

Table 1.9 [ PDF 373.2KB | 73 pages ] shows an alternative aggregate price index for output, fixed capital, and intermediate inputs. This table is based on Table 1.7 [ PDF 373.2KB | 73 pages ] and Table 1.8 [ PDF 373.2KB | 73 pages ] as well as the fixed capital price index produced by the NBS. Clearly on the whole, the aggregate price levels in PRC did not change much during the period of 1995-2002, although at the industry-level the price changes are more apparent. In the regressions, we use the aggregate price index for fixed capital, but apply the industry level price index for output and intermediate inputs.

In section 3, we will show statistical patterns of profitability and productivity for the northeastern enterprises as compared with the national average as well as the changing allocation of capital and labor over time. In section 4, we will use regression analysis to explain the gaps in enterprise performance between the north-eastern and other regions.

Download this Discussion Paper [ PDF 437.4KB| 90 pages ].

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