Page 88 - IJPS-10-2

International Journal of
Population Studies Employment-driving effect

section. In conjunction with China’s National Standard 2.2.3. Control variables
GB/T 4754 – 2017, the producer services are subdivided The control variables in this study are as follows:
into seven industries: electricity, heat, gas, and water (i) Enterprise equity nature: All listed companies are
production and supply; construction; transportation, divided into two types of enterprises: Atate-owned
storage, and postal services; information transmission, enterprises and non-state-owned enterprises,
software, and information technology services; wholesale represented by values 1 and 0, respectively.
and retail trade; finance; and leasing and business services. (ii) Operation duration: The duration of operations
In the actual process of data processing, non-manufacturing correlates with the level of expertise within the
enterprises and non-producer services enterprises were business, the utilization of fixed resources, and other
initially excluded. Subsequently, the samples were refined factors. As the scale expands, there is a subsequent
by further eliminating missing values of key variables such increase in the number of employees.
as operating time, nature of enterprise equity, enterprise (iii) Employee wage level: Higher salaries generally
assets, and the number of employees. The manufacturing correspond to increased motivation, making good
enterprises and producer services enterprises were, then, salary treatment highly attractive to employees.
matched using the propensity score matching method, (iv) Employee welfare: Companies with excellent
adopting a one-to-one nearest neighbor matching of retirement and other benefits are assigned a value of
balanced samples. The final sample comprises 562 pairs. 1; otherwise, they are assigned a value of 0.
Macrodata was sourced from the China Statistical (v) Staff safety production training: This variable
Yearbook.
indicates whether the company provides production
2.2. Measurements safety training to its employees and is measured as a
binary variable (0/1).
2.2.1. Dependent variables
(vi) Research and development (R&D) innovation:
The previous studies have demonstrated two common Measured by enterprise R&D expenditure.
approaches to measuring the employment-driving effect (vii) Enterprise assets: Measured by the value of the
at the enterprise level: one involves the employment scale enterprise’s existing assets.
index, measured by the annual number of employees (John (viii) Unemployment rate: Measured by the surveyed
et al., 2013). The alternative is the employment growth urban unemployment rate at the provincial level.
index, gauged by the average growth rate of employees over (ix) Gross domestic product (GDP) per capita: Matching
a specific period (Song & Li, 2018). Due to data limitations, is done according to the data, specifically measured
the second method, measuring the employment-driving by GDP per capita at the city level.
effect through the growth index, is impractical. Thus, we
opted for the first measure. The dependent variables in this 2.3. Analytical analyses
paper are as follows: 2.3.1. Descriptive statistics
(i) Number of employees in producer services: When
studying the driving effect of manufacturing, the According to the research framework of this paper, statistical
core explanatory variable is the number of employees analysis was conducted separately for manufacturing and
in producer services, expressed by the number of producer services. Two conclusions can be drawn from the
employees in producer services enterprises. descriptive statistics, as shown in Table 1.
(ii) Number of employees in manufacturing: When First, concerning the mean level, producer services
studying the employment-driving effect of producer exhibit a stronger ability to absorb employment among
services, the core dependent variable is the number A-share listed companies. Data in Table 1 reveal that the
of employees in manufacturing, expressed by the number of employees in producer services companies is
number of employees in manufacturing enterprises. approximately 1.1 times higher than that in manufacturing
companies. Notably, producer services companies appear
2.2.2. Independent variables to invest more in “soft power,” such as employee safety
As this paper examines the employment-driving effect training, making them more attractive to employees.
of manufacturing and producer services, naturally, the A “high incentive” corporate culture might explain why
core independent variables corresponding to the above producer services in A-share listed companies have a more
dependent variables are the number of manufacturing robust employment-absorbing ability than manufacturing
employees and the number of producer services employees, companies. Second, the R&D innovation ability of producer
respectively. The specific measurement method and services companies in A-share listed companies is weaker
meaning are consistent with those detailed above. than that of manufacturing companies. Manufacturing

Volume 10 Issue 2 (2024) 82 https://doi.org/10.36922/ijps.0316