To ensure the accuracy of the findings, the following robustness tests were conducted separately in this study. The results of the reanalysis are shown in M(1) M(5) in Table 4.

1.
Replacing the measures of explanatory variables. Employment efficiency is calculated using the DEAMalmquist index. Human capital stock, per capita disposable income of residents, per capita GDP, capital intensity, government expenditure intensity, and R&D intensity are used as input variables and urban registered unemployment rate, the average wage of urban employed persons, employment speed, and vocational training attendance are used as output variables.

2.
Replacing the measures of core explanatory variables. In the Critic evaluation method, the degree of conflict between indicators with strong positive correlations is lower, which helps to evaluate the variables comprehensively. The Critic evaluation method was used to recalculate the level of regional digital transformation in each region.

3.
Although several variables have been controlled, factors that are difficult to measure may interfere with the interpretation of intervariable relationships. Therefore, this study incorporates the lags of explanatory variables into the model and reruns the regression analysis to minimize problems such as endogeneity due to reverse causality.

4.
During the period of the new crown epidemic within mainland China, the input of regional digital transformation may be volatile; considering that the relevant fluctuations may have an impact on the research results, the data for 2020–2022 are excluded and reestimated.

5.
Replace the panel Tobit model. Since the explanatory variables are all greater than zero, considering the emergence of extreme values, data set limits, truncated tail censoring, and other problems, using the panel data Tobit model can alleviate these problems.
The coefficient of the squared term of regional digital transformation is positive and significant, and the coefficient of regional digital transformation is negative and significant and passes the Utest. Most of the variables were able to be significant at the 10% level of significance. Although the model coefficients differ from the benchmark regression, the “Ushaped” impact trend remains unchanged.
The study found that there is a “Ushaped” relationship between regional digital transformation and employment quality. In the early stage of regional digital transformation, employment quality tends to decline in the short term, and after a certain threshold, regional digital transformation can significantly improve employment quality. The above relationship is valid after replacing the core variables, changing the model estimation method, and other robustness tests.
Analysis of the mechanism of the impact of regional digital transformation on employment quality
Nonlinear mediation model of labor force structure
Although it has been confirmed that there is a “Ushaped” relationship between regional digital transformation and employment quality, the specific mechanism between the two has not yet been identified and tested. Therefore, this study introduces the labor force structure as the mediating variable and divides it into industry level, sector level, and skill level^{18}, respectively, to test whether there is a mediation effect of different levels of labor force structure between regional digital transformation and employment quality. Among them, the ratio of employment in the tertiary industry to that in the secondary industry is used to evaluate the industrylevel labor force structure, sectorlevel labor force structure is assessed through the share of employment in highend manufacturing and highend services in overall employment, and the ratio of employment with tertiary education and above to that with tertiary education and below is used to evaluate the skill level labor force structure.
Due to the possible time lag in the transmission of the mediating variables, the impact of regional digital transformation on employment quality needs to be reflected over a period of time, such as the absorption of technology, the cultivation of talents, and the adjustment of the market, so it is treated as a twoperiod interval. The Qualy is frontloaded by one period, while the Dig is lagged by one period, and the rest of the variables are kept in the current period to overcome the possible reverse causality between variables to a certain extent. Equation (4)(5) are constructed to express the relationship of the variables separately. The existence of the indirect effect can be confirmed by testing the new parameter IND in Eq. (6), which is generated by the product of the coefficient of the squared term of regional digital transformation in Eq. (4) and the coefficient of the labor force structure in Eq. (5), and represents the strength of the curvilinear indirect effect of regional digital transformation on employment quality through the labor force structure. Therefore, the significance of IND can be an essential basis for testing the mediation effect. The nonlinear mediation model is set up as follows:
$$LaborStruc_i,t=\theta +\theta _1Dig_i,t – 1+\theta _\text2Dig_i,t – \text1^\text2\text+\sum \theta _\textjCV\texts+\sum Year+\sum Ind +\tau$$
(4)
$$Qualy_i,t+1=\varphi _\text0\text+\varphi _\text1Dig_i,t – 1\text+\varphi _\text2Dig_{i,t – \text1}^\text2\text+\varphi _\text3LaborStruc_i,t\kern 1pt \kern 1pt \kern 1pt \text+\sum \varphi _\textjCV\texts+\sum Year+\sum Ind +\varepsilon$$
(5)
$$IND=\theta _2 \cdot \varphi _3$$
(6)
The reasons for choosing labor force structure as a mediating variable are: first, the labor force structure at the industry level reflects the distribution of the number of laborers in the three major industries, and with the upgrading of the industrial structure, there is a clear trend of labor migration, which is especially obvious in the flow of laborers from the secondary industry to the tertiary industry. Regional digital transformation can accelerate market mobility and optimize the industrylevel labor force structure, thus indirectly affecting employment quality; secondly, it reflects the trend of labor migration from lowskill to highskill sectors, which is particularly obvious in laborintensive sectors, and digital transformation creates more employment opportunities and improves the efficiency of factor matching, which has a significant impact on the quality of employment; thirdly, The skill level labor force structure is reflected in a substantial increase in the share of employment of highly skilled people. With the continuous promotion of technological innovation, the demand for highskilled labor is also rising, expanding the employment gap, which in turn triggers changes in the labor force structure. The mediation path test for different labor force structure levels is shown in Table 5.
From the industrylevel analysis, the upgrading and optimization of labor force structure can significantly alleviate the problem of employment changes caused by industrial migration, as shown M(1) M(2) in Table 5, the regression coefficient of IndStrucof regional digital transformation on the labor force structure at the industry level is − 0.438, and the regression coefficient of the quadratic term of the regional digital transformation is significant as a positive value of 0.358. It passes the Utest test, which indicates that the impact of regional digital transformation on the labor force structure at the industry level presents a “Ushaped” trend. In the process of the gradual replacement of the old sector structure by the new one, the relevant employees will experience a short period of pain before adapting to the changes in the new sector structure. The penetration of the early stages of the development of regional digital transformation contributed to the creation of jobs. It relatively reduced the size of employment in the secondary sector, with a slight reduction in the labor force structure at the sector level, which is on a downward trend. However, as the degree of regional digital transformation accelerates, the match between labor supply and demand improves, and employment quality is enhanced by improving the labor force structure at the industry level, showing an upward trend. In the Bootstrap test of industrylevel labor force structure, the 95% confidence intervals of the direct and indirect effects do not contain 0, indicating a significant mediation effect. The mediation effect at the industry level is calculated according to the regression results using the “Ushaped” relationship test method of Haans et al^{27}.
$$\frac\partial IndStruc\partial L.Dig \times \frac\partial F.Qualy\partial IndStruc= – 0.\text438\left( \text1\text.289 – \text5\text.534IndStruc \right)$$
(7)
Analyzing from the sector level, the innovation of production organization and the increase of social human capital at the sector level will enhance the matching level of labor supply and demand, and the regional digital transformation can realize the transfer of labor from lowskilled sectors to highskilled sectors. From the results of M(3) and M(4) in Table 5, the regression coefficient of regional digital transformation on the labor force structure at the sector level is − 0.352, and the regression coefficient of the quadratic term of regional digital transformation is significantly positive at 0.187 and passes the Utest test. Therefore, the digital transformation shows a “Ushaped” trend in the labor force structure at the sector level. In the early stage of digitization, they are mainly affected by the pattern of “machines replacing people”. With the deepening of digitalization and creative destruction, a large number of new highend manufacturing and highend services employment demand is recreated. Regional digital transformation is usually accompanied by the application of automation and robotics, resulting in the automation of some repetitive tasks. This may lead to a reduction in work, but with the development of regional digital transformation, a part of the automated system to create jobs, manufacturing, and service industry employment ratio gradually increased, and the development of highend sectors can promote the improvement of employment quality. In the indirect effect test of sectorlevel labor force structure, the bootstrap test 95% confidence interval does not contain 0, indicating that the mediation effect is significant. The value of the mediation effect at the sector level is calculated:
$$\frac\partial \operatornameSec Struc\partial L.Dig \times \frac\partial F.Qualy\partial \operatornameSec Struc= – 0.35\text2\left( 1.\text117 – \text4\text0.094\operatornameSec Struc \right)$$
(8)
Analyzing the skill level, skill inequality increases in the predigital transformation period, with higherskilled workers better adapting to the work environment under new technologies and lowerskilled jobs increasing the risk of unemployment. As a result, employment quality declines in the short term, forming the left side of a “Ushaped” curve. Digital development first replaces groups of people in repetitive jobs. After a period of sustained regional digital transformation, the digital skills of the lowskilled workforce can be upgraded, improving the mismatch between skills and employment. With the wide application of digital technology, the demand for highskill jobs continues to rise, accelerating the prosperity of the labor market and thus improving the level of employment quality. From the results of M(5) and M(6) in Table 5, the regression coefficient of regional digital transformation on skill level labor force structure is − 0.728. The regression coefficient of the quadratic term of regional digital transformation is significantly positive at 0.021. It passes the Utest test, so the regional digital transformation on skill level labor force structure shows a “Ushape”. In the test of the mediation effect of skill level labor force structure, the bootstrap test 95% confidence interval does not contain 0, indicating that the mediation effect is significant. The mediation effect of skill level is calculated:
$$\frac\partial SkillStruc\partial L.Dig \times \frac\partial F.Qualy\partial SkillStruc= – 0.\text833\left( 0.\text021 – \text1\text0.456SkillStruc \right)$$
(9)
Instantaneous indirect effects of labor force structure
In order to further quantify the level of effect of labor force structure as a nonlinear mediator, to measure the value of the instantaneous indirect effect in the above three model formulas, which is a function of the three dimensions of the labor force structure, the nonlinear mediating effect is subject to two conditions: first, there is at least one set of nonlinear relationships between the explanatory variables and the mediating variables, and between the mediating variables and the explanatory variables; second, the mediating effect value is nonzero, the product of the rate of change of the explanatory variables and the mediating variables is zero. Stolzenberg and Land^{28} pointed out that if the independent variable has a nonlinear effect on the dependent variable through the mediating variable, the change in the dependent variable caused by the independent variable’s change in the mediator variable and thus the indirect rate of change in the dependent variable is calculated as shown in Eq. (10). Combined with the actual situation, the regional digital transformation has a “Ushaped” impact on employment quality, and the indirect rate of change in employment quality caused by changes in labor force structure due to the regional digital transformation is deduced from Eq. (11) and Eq. (12), as shown in Eq. (13).
$$\theta \text=\frac\partial LaborStruc_i,t\partial Dig_i,t – 1 \times \frac\partial Qualy_i,t+1\partial LaborStruc_i,t$$
(10)
$$LaborStruc_i,t=i_\text1\text+aDig_i,t – 1$$
(11)
$$Qualy_i,t+1=i_2+c_1^\prime Dig_i,t – 1+c_2^\prime Dig_i,t – 1^2+b_1LaborStruc_i,t+b_\text2LaborStruc_i,t^2c_1^\prime $$
(12)
$$\theta =\frac\partial LaborStruc_i,t{\partial Dig_i,t – 1} \times \frac{\partial Qualy_i,t+1}{{\partial LaborStruc_i,t}}=a\left( b_1+2b_2LaborStruc_i,t \right)$$
(13)
Next, this study uses the method proposed by Preacher^{29} by calculating the instantaneous indirect effect, assigning a specific value x to the independent variable, calculating the corresponding indirect rate of change, and employing the Bootstrap method to test the significance of the instantaneous mediation effect corresponding to different xvalues. By using the MEDCURVE program^{30}, the instantaneous mediation effect of labor force structure located at the industry, sector, and skill level between regional digital transformation and employment quality was tested separately. If zero is not included in the confidence interval, then it means that the instantaneous indirect effect of labor force structure is significant, and there is a mediation effect.
According to Table 6, it can be seen that the instantaneous mediation effect of industrylevel labor force structure is − 0.4038, − 0.3166, and − 0.2294; all confidence intervals do not contain 0, and the indirect effect is negative and gradually weakening. With the deepening of regional digital transformation, the adjustment of the labor force structure at the industry level is stabilizing, and the labor force gradually adapts to this change. The labor force structure at the industry level has a significant nonlinear mediation effect between regional digital transformation and employment quality, and hypothesis H2a holds.
The instantaneous mediation effect of labor force structure at the sector level is − 0.4066, − 0.3585, and − 0.3104, and all confidence intervals do not contain 0. The regional digital transformation pushes the adjustment of labor force structure in the sector, and the negative impact is weakening, indicating that the labor force is gradually adapting to this change, and the labor force structure at the sector level has a significant nonlinear mediation effect between regional digital transformation and employment quality. Hypothesis H2b holds.
While the skill level shows a different trend, the instantaneous indirect effect is 0.5405, 0.3872, and 0.3568; all confidence intervals do not contain 0. The indirect effect is positive and gradually weakening, indicating that the increase in demand for highskilled labor has a positive impact on the enhancement of employment quality. Still, this marginal effect might gradually decrease, and the labor force structure at the skill level has a significant nonlinear mediating effect between regional digital transformation and employment, as hypothesis H2c holds. In summary, labor force structure under different microlevels plays an important mediating role in the impact of regional digital transformation on employment quality.
Conclusions and discussion
Research conclusion
In essence, one of the significance of regional digital transformation and development is the ability to closely integrate social and employment development trends, formulate appropriate policies and plans, and play a key role in driving the growth of digitization and employment quality, and thus social optimization. This study uses a nonlinear model and an instantaneous mediation effect model to quantitatively assess the path between regional digital transformation and employment quality. A comprehensive study that considers industry level, sector level, skill level, and other dimensions of refinement demonstrates the role of labor force structure as a bridge between the two. This multidimensional approach deepens the understanding of the impact of the doubleedged sword of digitization and provides valuable insights into the perspective of improving employment quality.
From the perspective of labor force structure, this study analyzes the nonlinear influence mechanism of regional digital transformation and employment quality, scientifically understands the trends and causes, stabilizes employment quality in the early stages of regional digital transformation, raising employment levels in the middle and late stages, and constructing a labor resource system with high quality, stable allocation, and even distribution. Firstly, the direct path of the impact of regional digital transformation on employment quality and the indirect path of the effect through labor force structure is proposed, and the relevant hypotheses are put forward on this basis. Secondly, this study constructs an evaluation index system of regional digital transformation and employment quality and comprehensively evaluates the level of regional digital transformation and employment quality of 31 provinces in mainland China from 2013 to 2022. Finally, this study establishes a relevant empirical model and analyzes the impact mechanism of regional digital transformation on employment quality and so on. The conclusion shows that:
Regional digital transformation has a direct impact on employment quality and with the gradual development of regional digital transformation, employment quality roughly shows a “U” curve, with a shortterm decline in employment quality at the initial stage due to the difficulty of some workers in adapting quickly, and an upward trend in the later stage as they adjust to regional digital transformation. At the same time, a nonlinear mediation effect model is established to verify the indirect path, and regional digital transformation affects the change of employment quality through the labor force structure at the industry, sector, and skill level, respectively, and shows a “U”shaped change trend.
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