Human capital is identified as one of the main determinants of firm performance. Nevertheless, existing studies have to some extent ignored the effect of human capital structure on enterprise performance. This paper is based on Chinese A-Share Listed Companies from 2010 to 2022, not only the impact of sustainable development of human capital structure on firm performance are discussed, but also the different intermediate paths of the change of human capital structure on enterprise performance are explored. By constructing a sustainable index of human capital structure and a relative peer human capital structure index, we found that sustainable development of human capital structure is an important driving factor in improving firm performance. We also found that there is no positive interaction mechanism between human capital and innovation, and the interaction mechanism is affected by the level of human capital structure and innovation; the internal pay gap has a negative regulatory effect on the sustainable development of human capital structure on enterprise performance; labor productivity plays a part of intermediary effect in the impact of sustainable development of human capital structure on enterprise performance. Furthermore, we proved the importance of sustainable development of a human capital structure for enterprise performance based on the regional market. This paper has important reference value for human resource management. Specifically, managers should not only pay attention to improving the level of human capital structure, but also explore and establish interaction mechanism between human capital and innovation, and pay attention to the moderating role of internal pay gap.
Human resources management refers to a series of management activities of enterprises, for example, staff recruitment and selection, training and development, etc., the human capital structure of enterprises has changed in this process. However, existing studies have to some extent neglected the importance of human capital structure for firm performance.
Modern firm development relies on human capital. Since Schultz (1960) first proposed the concept of human capital, a lot of research has proved that human capital is the most important resource for firm development (Scherer, 1965; Hurwitz et al., 2002) . The measurement of human capital includes two aspects: human capital stock and human capital structure (Schultz, 1960, 1961) . Compared with human capital stock, human capital structure is more important for the development of enterprises. There are two reasons why human capital structure is important in this context. First, over the last few decades, several studies have shown that the higher education level of staff, the stronger ability to information collection and the recognition of employees, the better absorption of new ideas, and the faster adoption of new production technologies (Doms & Lewis, 2006) . Second, high-quality human capital can engage in low-level human capital work, and the converse is not true.
Nevertheless, a lot of research is almost based on human capital stock, and mainly explores the impact of human capital stock on firm development (Youndt, 1996, 2004; Weisberg, 1996; Bontis & Fitz-enz, 2002; Giannetti et al., 2015) . Especially in China, numerous scholars have focused on the management of human capital in enterprises. Alchian and Deynsetz (1972) pointed out that the enterprise is essentially a human capital team, therefore, the objective of this paper is to study the direct effects of sustainable development of human capital structure on enterprise performance, as well as indirect effects embodied in the interaction between human capital structure and innovation, while controlling for other determinants reported in the literature.
The evolution of human capital from primary to advanced level is one of the typical characteristics of a strategy of “reinvigorating China through human resource development”. This change in human capital structure has been proven to be an important source of economic growth from the macro level (Liu et al., 2018) . To examine the effect of human capital structure on firm performance, we define sustainable development of human capital structure as the process of a company or region improving its human capital structure and moving from human capital with low-level education to higher-level education. We construct a measure of a sustainable index of human capital structure and a relative peer human capital structure index. We find that sustainable development of human capital structure is conducive to improving enterprise performance, however, there are no mutually reinforcing mechanisms between the process of sustainable development of human capital structure and innovation for firm development. Labor productivity plays a part of intermediary effect in the impact of sustainable development of human capital structure on enterprise performance, while the internal pay gap plays a negative moderating role in the impact of the sustainable development of human capital structure on corporate performance.
Our paper makes two primary contributions to the literature. First, instead of simply measuring human capital structure by employee proportion at different educational levels, we construct a sustainable index of human capital structure and a relative peer human capital structure index, explore the overall impact of the sustainable development of human capital structure on enterprise performance, which is influenced by the nature of enterprise property rights. Second, we first explore the interaction between human capital structure and innovation, the intermediary effect of labor productivity and the moderating effect of the internal pay gap, which helps to uncover the black box between human capital and enterprise performance.
The remainder of the paper is organized as follows. Section 2 develops hypotheses for the impact of sustainable development of human capital structure on firm performance. Section 3 describes the data and discusses the construction of our key variables. Section 4 presents empirical tests of the impact of sustainable development of human capital structure on firm performance, at the same time, presents the tests of influence mechanism. Section 5 presents our additional analysis based on regional human capital structure. Section 6 conducts robustness tests. Section 7 concludes.
Over the last few decades, studies found that human capital has a direct effect on firm performance, because people with more education are more productive and innovative, which leads to the creation of new products and improving the productivity of factors (Romer, 1990; Benhabib & Spiegel, 1994; Teixeira & Fortuna, 2011; Bodman & Le, 2013) . The accumulation of human capital is mainly formed through education, so it is reasonable to believe that the higher education level of staff, the greater contribution to enterprises. The sustainable development of human capital structure is the process of increasing the proportion of high-quality human capital, thus forming a human capital structure dominated by high-quality talents, which is conducive to the digestion, absorption and application of various new technologies, as well as the promotion of enterprise innovation, and thus is conducive to improving enterprise performance. We propose that the higher level of human capital structure, the greater contribution to enterprises. Most studies show a positive and significant relationship between human capital and economic development (Bodman & Le, 2013; Hall & Jones, 1999) . At the same time, we further consider the nature of property rights of enterprises, based on which we put forward Hypothesis 1a.
Hypothesis 1a (H1a). The higher level of human capital structure, the better performance of enterprises, which is influenced by the nature of property rights of enterprises.
At the same time, because of the differences in the distribution of human capital among industries, human capital is different in different industries. Usually, there is a similar demand for talent in the same industry. Therefore, in order to further demonstrate the impact of sustainable development in human capital structure on enterprise performance, this paper proposes and constructs relative peer human capital structure index, and puts forward Hypothesis 1b on the basis of Hypothesis 1a.
Hypothesis1b (H1b). Compared with other enterprises in the same industry, the higher level of human capital structure, the smaller adverse impact on enterprises.
Over the last few decades, a large body of literature has been produced examining the role of human capital and innovation in determining the enterprises growth. However, existing studies have to some extent neglected the influence of interaction between human capital structure and innovation. Human capital and innovation essentially belong to resources and capabilities, there is interaction between them in the process of influencing the development of enterprises. For example, Chesbrough (2004) has proved that human capital and innovation have complementary effects. In the process of promoting enterprise innovation, the breakthrough of enterprise innovation will lead to the further accumulation of human capital. In other words, human capital is the source of enterprise innovation, innovation can further encourage workers and enterprises to invest in human capital.
However, the interaction effect between human capital structure and innovation is influenced by factors such as the level of human capital structure and innovation. For example, enterprises whose human capital structure level and innovation level are relatively low, there may be mutual restriction between them, that is, the low level of human capital structure will inhibit the innovation level of enterprises, and the enterprises with low innovation ability may think little of improvement their human capital. As a result, this negative chain reaction leads to the decline of enterprise performance. Specifically in China, there is a mismatch between innovation investment and human capital (China Entrepreneur Survey System, 2015) . For example, R&D investment in real estate industry is in the lower levels in all industries, but their proportion of university students is in the third place in all industries. From the above, we assume that:
Hypothesis 2a (H2a). The interaction between sustainable development of human capital structure and innovation helps to improve enterprise performance.
Hypothesis 2b (H2b). The interaction between sustainable development of human capital structure and innovation is not conducive to the development of enterprises.
The concept of human capital can be interpreted as the set of intangible resources embedded in the labor factor, which have improved its productivity. Black and Lynch (1996) and Ichniowski and Shaw (2003) have proved that human capital affects labor productivity, the higher level of human capital, the higher labor productivity and the greater contribution to enterprises. Then, the sustainable development of human capital structure can theoretically improve the labor productivity, which helps to improve the performance of enterprises. From the above it is conjectured that:
Hypothesis 3 (H3). Labor productivity plays an intermediary role in the impact of sustainable development of human capital structure on enterprise performance.
It has been proved that material incentive to talents is the fundamental way to stimulate their innovation ability. Compensation incentive is the most effective and widely accepted currently. At present, there are two opinions in compensation incentive. One is the championship theory, which advocates that a larger pay gap is conducive to improving corporate performance, and the other is the behavioral theory, which believes that a smaller pay gap is conducive to the whole team cooperation, that is conducive to improving corporate performance. So, what role does the pay gap play in the process that sustainable development of human capital structure is conducive to the development of enterprises? It has been found that Chinese pay more attention to fair distribution than other countries (Kim & Leung 2007) , Chinese have higher job satisfaction under fair distribution (Pillai et al., 2001) . Based on this, we formalize this discussion with the following testable hypotheses.
Hypothesis 4 (H4). The internal pay gap plays a negative moderating role in the impact of sustainable development of human capital structure on enterprise performance.
We construct our sample from A-share Listed Companies in China during the period from 2000 to 2022. Employee education background comes from Wind database, Choice database and manual collection of annual reports, due to the incomplete collection of this indicator in each database, the data is checked one by one through these three channels to ensure its integrity. The number of patent applications comes from the National Patent Application Network and CSMAR database. Tobin Q, total debt, total asset and net asset return rate come from Wind Database. The educational background of employee in various provinces comes from China Labor Statistics Yearbook.
We further process the initial sample data in the following procedures. First, enterprises with five consecutive years’ absence of employee education background should be deleted, and enterprises with three consecutive years’ absence of patent applications should be deleted. Second, if the number of patent applications is still missing, the number is replaced by zero. Third, all variables are winsorized at the 1st and 99th percentiles of their distributions except sustainable index of human capital structure and relative peer human capital structure index. These requirements result in an initial sample of 2367 companies with a total of 17,566 data, when Tobin Q was used to measure corporate performance. When the number of patents is used to measure the innovation level, there are 1886 enterprises with a total of 13902 data.
Sustainable development of human capital structure refers to the process in which a company or region adjusts and optimizes the structure of human capital, promotes the coordinated development of various types of human capital, gradually reduces the proportion of primary human capital and gradually increases the proportion of advanced human capital, in order to continuously meet the needs of high-quality human capital in economic and social development (Liu et al., 2018) . Schultz (1960, 1961) has proved that human capital mainly accumulates through education. We construct a measure of sustainable index of human capital structure based on employee education background. The specific construction process is as follows:
First, rank workers according to their education level. Employees in enterprises are divided into four categories according to their educational background: senior high school, junior college, undergraduate and master. Take the proportion of each type of employees as a component of the space vector in proper order, a set of four-dimensional human capital space vectors is formed as X 0 = ( x 0 , 1 , x 0 , 2 , x 0 , 3 , x 0 , 4 ) .
Second, Choose the basic unit vector group ( X 1 = ( 1 , 0 , 0 , 0 ) , X 2 = ( 0 , 1 , 0 , 0 ) , X 3 = ( 0 , 0 , 1 , 0 ) , X 4 = ( 0 , 0 , 0 , 1 ) ) as the reference vector, Calculate human capital space vector X 0 and the angle of θ j ( j = 1 , 2 , 3 , 4 ) in proper order according to Formula (1):
θ j = arccos ( ∑ i = 1 4 ( x j , i ∗ x 0 , i ) ( ∑ i = 1 4 x j , i 2 ) 1 / 2 ∗ ( ∑ i = 1 4 x 0 , i 2 ) 1 / 2 ) (1)
where x j , i represents the i-th component of the basic unit vector group X j ( j = 1 , 2 , 3 , 4 ); x 0 , i represents the i-th component of vector X 0 .
Third, define the weight of angle θ j , calculate sustainable index of human capital structure according to Formula (2):
H S = ∑ j = 1 4 ( W j ∗ θ j ) , W j is the weight of θ j (2)
W j is calculated from the coefficient of variation V j of θ j , where T V = V 1 + V 2 + V 3 + V 4 , W j = V j / T V . We have found that human capital with relatively low education level has relatively high weight. In order to facilitate the comparison between companies, set the weight of human capital about master’s degree to 1. According to the level of employees’ education from high to low, set the weights of human capital are 1, 2, 3 and 4 in order. According to the monotonous decreasing nature of anti-cosine function, if the proportion of human capital with lower education drops faster and the proportion of human capital with higher education rises more, θ j will be larger. Therefore, the bigger of HS, the higher human capital structure level.
According to the industry classification, peer firms are from the same industry. This paper further constructs relative peer human capital structure index on sustainable index of human capital structure, which can analyze the impact of different companies in the same industry on corporate performance due to their differences in human capital structure. The reason for this approach is motivated by recent research (Francis et al., 2016) . The concrete steps are as follows: according to China Securities Regulatory Commission’s Guidelines for Classification of Listed Companies (Chinese Securitise Regulatory Commission, 2012) , the enterprises in the sample are divided into 19 industries.
We rank both firm i and its peer companies by their HS of the previous year and assign a value of zero to the firm that has the lowest human capital structure score and the value N − 1 to the firm that has the highest human capital structure score, assuming a firm i has N peers. The relative peer structure index (RPS) of firm i at the beginning of fiscal year t can be formally defined as Formula (3):
R P S i , t = 1 − r a n k i , t N i , t (3)
where r a n k i , t represents the rank value of firm i among its peers in ascending order, N i , t represents the total number of companies in the same industry in year t for firm i. The higher of RPS, the lower level of human capital structure. For example, firm i have 10 peers in the same industry. If firm i ranks second in that year according to H S t − 1 in ascending order, the RPS score of firm i should be 0.9, if firm i ranks eighth in that year based on H S t − 1 in ascending order, its RPS score should be 0.3. RPS reports the fraction of peers that have higher human capital structure scores than firm i. By construction, it is always between zero and one. The higher of RPS, the higher level of human capital structure peer firm i faces.
Griliches et al. (1991) pointed out that the number of patent applications of enterprises can more truly reflect the level of innovation of enterprises. Compared with R&D investment, this index can reflect the contribution of all human capital better. So in this paper, the number of patents applied by enterprises is used to measure the innovation ability of enterprises. We use Tobin Q to measure corporate performance, because existing research has found that Tobin Q can combine corporate market data with corporate financial data, and it is not easy to operated by people, and can reflect the long-term performance of the company. The control variables include company size, debt asset ratio, asset growth rate and net asset return rate. The control variables at the regional level involve the average wage and GDP per worker. All variables are detailed in
Based on the existing literature, Model (4) and Model (5) are established to test the impact of sustainable development of human capital structure on corporate performance, Model (6) is established to test the interaction between human capital structure and innovation, Model (7) and Model (8) are established in order to test the intermediary effect of labor productivity, Model (9) is established to test the moderating effect of internal pay gap.
Q i t = α 0 + α 1 H S i t − 1 + α c o n t r o l i t − 1 + μ i + ν i + ε i t (4)
| Variable | Description |
|---|---|
| Q | Tobin Q |
| HS | Sustainable index of human capital structure, calculated by Formula (2) |
| RPS | Relative peer human capital structure index, calculated by Formula (3) |
| Leverage | Debt asset ratio |
| Size | Company size, is computed as the log of market value |
| Growth | Asset growth rate, ( a s s e t t − a s s e t t − 1 ) / a s s e t t − 1 |
| Equity | Return on net assets, net profit divided by the average value of shareholder’s equity at the beginning of year plus equity at the end of year |
| Wage | Average wage increase rate of employees in the province where the enterprise are headquartered |
| Per GDP | Growth rate of per capita gross domestic product in the province where the enterprise are headquartered |
| Apply | Measuring innovation level by the number of patent applications annually in company |
| Lz | Labor productivity growth rate, labor productivity in t-year minus labor productivity in t − 1 year, divided by labor productivity in t − 1 year, labor productivity is the main business income divided by the number of employees |
| Gap | Internal pay gap, per capita compensation of management divided by per capita compensation of ordinary employees. Management includes board of directors, board of supervisors and executives |
Q i t = α 0 + α 1 R P S i t − 1 + α c o n t r o l i t − 1 + μ i + ν i + ε i t (5)
Q i t = α 0 + α 1 H S i t − 1 + α 2 H S i t − 1 ∗ a p p l y i t − 1 + α 3 a p p l y i , t − 1 + α c o n t r o l i t − 1 + μ i + ν i + ε i t (6)
l z i t = α 0 + α 1 H S i t − 1 + α c o n t r o l i t − 1 + μ i + ν i + ε i t (7)
Q i t = α 0 + α 1 H S i t − 1 + α 2 l z i t − 1 + α c o n t r o l i t − 1 + μ i + ν i + ε i t (8)
Q i t = α 0 + α 1 H S i t − 1 + α 2 H S i t − 1 ∗ g a p i t − 1 + α 3 g a p i t − 1 + α c o n t r o l i t − 1 + μ i + ν i + ε i t (9)
From Model (4) to Model (9), i represents the firm, t represents the year, u i represents the firm fixed effect, ν i represents year fixed effect, control represents control variables.
| Variable | Number | Mean | Max | Min | Std. dev. | Median | Skewness | Kurtosis |
|---|---|---|---|---|---|---|---|---|
| HS1 | 17566 | 10.1642 | 12.8142 | 9.4579 | 0.7159 | 9.8732 | 1.3793 | 1.2271 |
| RPS1 | 17566 | 0.4044 | 0.9935 | 0.0013 | 0.2837 | 0.3651 | 0.3753 | −1.0228 |
| HS2 | 17566 | 26.0806 | 28.1787 | 23.3849 | 0.9765 | 25.7866 | 1.0342 | −0.1334 |
| RPS2 | 17566 | 0.3601 | 1.0000 | 0.0323 | 0.2745 | 0.2903 | 0.3894 | −1.1864 |
| Leverage | 17566 | 0.4447 | 0.9787 | 0.0334 | 0.2098 | 0.4316 | 0.2816 | −0.7205 |
| Size | 17566 | 0.1633 | 9.1706 | −0.5606 | 0.5301 | 0.0875 | 161.7979 | 161.7979 |
| Growth | 17566 | 0.2252 | 15.6193 | −0.3803 | 0.7537 | 0.1166 | 16.8157 | 335.2482 |
| Equity | 17566 | 0.0616 | 0.4017 | −1.8215 | 0.1561 | 0.0709 | −5.8226 | 57.1527 |
| Wage | 17566 | 0.1087 | 0.1983 | 0.0258 | 0.0285 | 0.1115 | −0.1125 | 0.6137 |
| Per gap | 17566 | 0.0947 | 0.2730 | −0.0369 | 0.0434 | 0.0863 | 1.4601 | 3.2393 |
Note: HS1 represents sustainable index of human capital structure for enterprises; RPS1 represents relative peer human capital structure index based on HS1; HS2 represents sustainable index of human capital structure based on provinces; RPS2 represents relative peer human capital structure index based on HS2. From: Calculated by the author.
among provinces. The remainder of
The model specified previously (see Model (4)) serves to test whether the sustainable development of human capital structure is increasing firm performance (Hypothesis 1) with our sample. The estimation also involves a set of control variables, which the empirical literature identifies as relevant factors (see
We continue to report regressions with RPS1 in Column (4) in
| Variable | (1) | (2) | (3) | Variable | (4) |
|---|---|---|---|---|---|
| HS1 | 0.2905*** (5.76) | 0.0927* (1.75) | 0.2911** (2.88) | RPS1 | −0.4122*** (−3.65) |
| Leverage | 1.3201*** (12.11) | −1.6037*** (−10.93) | 3.1206*** (17.72) | leverage | 1.4485*** (12.31) |
| Size | −0.0002*** (−22.27) | −0.0002 (−0.65) | −0.0002*** (−15.28) | size | −0.0002*** (−21.06) |
| Growth | −0.0028 (−1.31) | −0.0079*** (−3.00) | 0.0034 (1.03) | growth | −0.0025 (−1.12) |
| Equity | 0.0022*** (3.41) | 0.0105*** (10.79) | 0.0021** (2.12) | equity | 0.0024*** (3.34) |
| Adjusted R2 | 0.5099 | 0.5996 | 0.4328 | Adjusted R2 | 0.5065 |
| Year FE | Y | Y | Y | Year FE | Y |
| Firm FE | Y | Y | Y | Firm FE | Y |
Note: The table reports the results of OLS regressions. All variables are defined in
We argue in Section 2 that human capital and innovation are two different strategic resources for company, so we test whether the interaction between sustainable changes in human capital structure and innovation promotes enterprise development. The regressions in
At the beginning of each year, companies are sorted to three groups according to sustainability index of human capital structure in the previous year, the groups last for one year, and it is adjusted in next year. The sample in Column (2) is the highest group according to HS1, the sample in Column (3) is the lowest group according to HS1. The coefficient on H S 1 ∗ a p p l y is positive in Column (2), although not significant, the coefficient on H S 1 ∗ a p p l y is negative but not significant in Column (3). We sort companies according to the number of patent
| Variable | (1) | Sort by HS1 | Sort by apply | Double sort by HS1 and apply | Sort by the nature of enterprise property rights | ||||
|---|---|---|---|---|---|---|---|---|---|
| High (2) | Low (3) | High (4) | Low (5) | High (6) | Low (7) | State-owned enterprises (8) | Non-state- owned enterprises (9) | ||
| HS1 | 0.4916*** (5.23) | −1.0778 (−1.04) | 0.1550 (0.92) | −0.1618 (−1.29) | 0.5495** (2.41) | −0.0262 (−0.09) | −2.7502 (0.93) | 0.0680 (1.15) | 0.6194*** (5.94) |
| H S 1 ∗ g a p | −0.00003*** (−5.54) | 0.0048 (0.58) | −0.00000 (−0.02) | 3.46e−06 (0.04) | 0.0042 (0.19) | 0.00004 (0.27) | −0.1263 (−1.27) | −0.0003** (−2.56) | −0.0006** (−2.46) |
| Apply | 0.0012 (0.96) | −0.0475 (−0.59) | −0.0002 (−0.13) | −0.0001 (−0.22) | −0.0445 (−0.20) | −0.0005 (−0.36) | 1.2003 (0.27) | 0.0024** (2.35) | 0.0058** (2.10) |
| Leverage | −1.2831*** (−9.23) | −2.6845*** (−9.95) | −0.0498 (−0.10) | −1.8533 (−5.96) | −1.3343*** (−3.19) | −0.5258 (−0.70) | −3.6274*** (−6.53) | −1.2613*** (−8.98) | −1.2927*** (−5.79) |
| Size | 1.2984*** (44.74) | 1.0306*** (19.35) | 1.8242*** (18.56) | 0.8096*** (15.36) | 1.8292*** (16.37) | 0.9899*** (8.03) | 1.3657*** (10.89) | 0.9583** (−2.56) | 1.5149*** (26.50) |
| Growth | 0.0035 (1.51) | −0.0514** (−2.85) | 0.0036 (0.09) | −0.0029 (−0.88) | −0.1863** (−2.85) | 0.0868 (0.72) | −0.6111*** (−3.91) | −0.0467*** (−4.41) | 0.0051 (1.53) |
| Equity | 0.0007 (1.29) | 0.0215*** (3.10) | 0.0004 (0.26) | 0.1474*** (3.55) | 0.0088 (2.96) | 0.2908 (1.48) | 0.1254 (0.70) | 0.0024*** (4.49) | 0.0020 (1.06) |
| Adjusted R2 | 0.4612 | 0.4978 | 0.4666 | 0.5180 | 0.4254 | 0.5086 | 0.4957 | 0.5316 | 0.4670 |
| Year FE | Y | Y | Y | Y | Y | Y | Y | Y | Y |
| Firm FE | Y | Y | Y | Y | Y | Y | Y | Y | Y |
Note: ***, **, and * denote statistical significance at the 1%, 5% and 10% levels, respectively. From: Calculated by the author.
applications in the same way. The sample in Column (4) is the highest group according to apply, the sample in Column (5) is the lowest group according to apply. The coefficient on H S 1 ∗ a p p l y is positive in Column (4) and Column (5), although not significant. Furthermore, we sort companies into three groups according to HS1 from low to high, within each group, we sort companies into three groups according to apply from low to high, the groups last for one year, and it is adjusted in next year. The sample in Column (6) is the highest group when HS1 and apply are highest simultaneously, the sample in Column (7) is the lowest group when HS1 and apply are lowest simultaneously. Similarly, we found that the coefficient on H S 1 ∗ a p p l y is positive in Column (6), although not significant, the coefficient on H S 1 ∗ a p p l y is negative but not significance in Column (7).
Overall, the results from Columns (2) to (7) proved that whether the interaction between human capital and innovation can play the role of improving enterprise performance is greatly influenced by the sustainable level of human capital structure.
In China, there are state-owned enterprises and non-state-owned enterprises, there are differences between the two types in innovation input and talent input. We further test whether the interaction between the sustainability of human capital structure and innovation is affected by the nature of enterprise property rights. Companies are allocated to two groups according to the nature of enterprise property rights. The sample in Column (8) is state-owned enterprises, the sample in Column (9) is non-state-owned enterprises, the coefficients on H S 1 ∗ a p p l y are significance negative in Column (8) and Column (9). This suggests that there is no mutual aid mechanism to promote the development of enterprises in human capital and innovation in China.
Overall consistence with Hypothesis 2b, the interaction between sustainability development of human capital structure and innovation has not played a role in improving enterprise performance.
Our discussion in Section 2 suggests that with the improvement of human capital structure level, the labor productivity will also increase in the company, which is conducive to promoting the performance of enterprises. This idea is proved in this section. First, in OLS regression, the growth rate of labor productivity is dependent variable, the sustainable index of human capital structure is the main independent variables. Using the regression reported in Column (1) in
| Variable | The mediating effect of the growth rate of labor productivity | Variable | Moderating effect of internal pay gap | |
|---|---|---|---|---|
| (1) | (2) | (3) | ||
| HS1 | 2.4178*** (6.50) | 0.4951*** (12.68) | H S 1 ∗ g a p | −0.0781** (−2.69) |
| Lz | 0.0117 (1.57) | HS1 | −0.0554 (−0.38) | |
| Leverage | 34.3082*** (41.57) | −2.0733*** (−17.17) | Leverage | −0.7290** (−2.92) |
| Size | −0.6010** (−2.85) | −0.3849*** (−13.17) | Size | 0.1878** (2.90) |
| Growth | 0.0103 (0.57) | −0.0027 (−0.62) | Growth | 0.0009 (0.21) |
| Equity | −0.0021*** (−33.37) | −0.0001 (−1.44) | Equity | −0.0001 (−0.93) |
| Gap | 0.7448** (2.59) | |||
| Adjusted R2 | 0.5112 | 0.3087 | Adjusted R2 | 0.5462 |
| Year FE | Y | Y | Year FE | Y |
| Firm FE | Y | Y | Firm FE | Y |
Note: ***, **, and * denote statistical significance at the 1%, 5% and 10% levels, respectively. From: Calculated by the author.
With the improvement of the sustainable level of human capital structure, the pursuit of fairness and justice is more urgent for people. What role does internal pay gap play in the impact of human capital structure on enterprise performance? Column (3) in
So far, we have proved the Hypothesis 3 and Hypothesis 4, labor productivity plays a part of intermediary effect in the effect of human capital on enterprise performance, internal pay gap plays a negative regulatory role in the effect of human capital on enterprise performance.
The distribution of human capital differs across cities (Berry & Glaeser, 2005) . China has a vast area, there are difference in human capital structure level in different provinces, for example, the eastern coastal areas, relying on geographical advantages, economic strength and resource endowment, are more likely to absorb high-quality talents, and their overall level of human capital structure is relatively high. Therefore, we further examine the impact of human capital structure on corporate performance from the regional level. China has 31 provincial regions, excluding Hong Kong SAR, Macao SAR and Taiwan region. Every year, we classify employees in each province into six categories according to their educational level: illiteracy, primary, junior, senior, tertiary and undergraduate, set the corresponding weights to 6, 5, 4, 3, 2, and 1 respectively. Then, we construct the sustainable index of human capital structure at the regional level according to Formulas (1) and (2). Every year, all companies in the same industry are ranked in ascending order according to HS2. According to Formula (3), we calculate relative peer human capital structure index based on regional, in there, r a n k i , t indicates the ranking of company i in the same industry in the year t, N i , t indicates the total number of company headquarters in the same industry in the year t.
Specifically, if headquarters of company A and B are located in the same province, then they have the same HS2. If headquarters of company A and B are located in different provinces, they have different HS2. If company A and B are headquartered in the same province and belong to the same industry, they have the same RPS2. If company A and B are headquartered in the same province but not belong to the same industry, they have different RPS2. If company A and B are headquartered in different provinces and belong to the same industry, they have different RPS2. If company A and B are headquartered in different provinces and not belong to the same industry, they have different RPS2.
The model specified previously (see Models (4) and (5)) serves to test the effect of sustainable development of human capital structure on firm performance based on regional.
In this paper, the robustness test is carried out from three aspects. First, the sample is divided into two sub-samples according to time, one sample period is 2010-2016, and the other sample period is 2017-2022. The regression results are shown in Columns (1) and (2) in
| Variable | (1) | Variable | (2) |
|---|---|---|---|
| HS2 | 0.1978* (1.70) | RPS2 | −2.0092*** (−5.63) |
| Leverage | −1.5924*** (−7.32) | Leverage | 0.2949*** (6.86) |
| Size | 0.0609 (5.32) | Size | 0.0073*** (6.20) |
| Growth | −0.0510*** (−3.74) | Growth | −0.0821*** (−17.92) |
| Equity | 0.0091*** (10.66) | Equity | 0.0073*** (6.20) |
| Wage | 0.4123 (0.69) | Wage | −5.7955*** (−7.33) |
| Per GDP | 3.9275** (5.45) | Per GDP | −5.5304*** (−10.91) |
| Adjusted R2 | 0.5497 | Adjusted R2 | 0.4484 |
| Year FE | Y | Year FE | Y |
| Firm FE | Y | Firm FE | Y |
Note: ***, **, and * denote statistical significance at the 1%, 5% and 10% levels, respectively. From: Calculated by the author.
| Variable | (1) 2010-2016 | (2) 2017-2022 | (3) | (4) |
|---|---|---|---|---|
| HS1 | 0.5535*** (8.26) | 0.1567* (1.94) | 0.5932* (1.64) | 11.3861*** (7.02) |
| Leverage | 7.6845*** (6.25) | 2.6154* (1.96) | −4.2848*** (−6.16) | 23.7121*** (39.20) |
| Size | −0.1842 (−1.27) | −0.2085 (−1.47) | −0.3155* (−1.71) | −33.4165** (−2.56) |
| Growth | −0.0468*** (31.16) | −0.0093*** (−3.37) | −.1932 (−1.00) | 0.5053 (0.45) |
| Equity | −11.0172* (−1.76) | −0.1031* (−1.65) | 1.7216*** (6.14) | −0.1208*** (−31.41) |
| Adjusted R2 | 0.5710 | 0.4393 | 0.1569 | 0.4797 |
| Year FE | Y | Y | Y | Y |
| Firm FE | Y | Y | Y | Y |
Note: ***, **, and * denote statistical significance at the 1%, 5% and 10% levels, respectively. From: Calculated by the author.
Last, we use ROA (return on asset) to measure enterprise performance, coefficient on HS1 is significantly positive in Column (4) in
Our paper differs from existing literature in that our focus is on the sustainable development of the human capital structure. Developing a measure of sustainable index of human capital structure and relative peer human capital structure index, we test whether the development of company benefits from the sustainable development of human capital structure. Consistent with our hypotheses, we find strong evidence that the sustainable improvement of human capital structure is beneficial to enterprise performance. We also reveal that these effects are affected by innovation, labor productivity and internal pay gap.
More concretely, we find that human capital and innovation are two important strategic resources for enterprise development, but there is no positive interaction mechanism between them, which is related to the level of human capital structure, but has nothing to do with the nature of enterprise property rights. With the improvement of the level of human capital structure, the labor productivity of the company will be further improved. Labor productivity plays a part in the intermediary effect in the impact of human capital structure on enterprise performance. However, the internal pay gap is not conducive to the positive effect of human capital on promoting enterprise performance, which means that with the improvement of human capital structure, managers should pay attention to employees’ sensitivity to the pay gap. Further, we prove that sustainable development of human capital structure is conducive to improving enterprise performance based on the region.
This research has certain practical significance and application value. Suggestions for the enterprise: First, in human resources management, compared with the stock of the human capital, managers should pay more attention to the sustainable development of human capital structure. Second, managers should establish a multi-channel mechanism of good coordination and mutual promotion between human capital and innovation, so that the two resources can promote enterprise development and improve enterprise performance. For example, implementing of an innovation strategy should take full account of the company’s existing human capital level. If the innovation strategy does not match the existing level of human capital structure, it will affect the realization of the innovation strategy. Third, managers should pay attention to the negative impact of the internal pay gap, that is, with the improvement of the human capital structure level, the pursuit of fairness and justice will become more urgent for people, and therefore, managers should implement a differentiated salary strategy.
Suggestions for the Chinese Government: China should further implement the strategy of promoting economic development through talent, and at the same time, we should adjust the imbalance of talent in different regions. For example, for different provinces, differentiated talent strategy is promulgated from the national level.
This paper attempts to explore the impact of sustainable development in human capital structure on corporate performance, but there are still some shortcomings due to data and methods. Firstly, there are many intermediate paths and influencing factors for the change of human capital structure to affect enterprise performance. This study can not fully cover other influencing factors or influencing channels. Secondly, this paper finds that there is no interactive mechanism between the change of human capital structure and innovation to promote the development of enterprises, but we still don’t know how to establish an interactive mechanism between them to promote the development of enterprises. Finally, the sample used in this study is listed companies, these enterprises are relatively successful enterprises in China, and there are many non-listed companies in reality, so what is the impact of the human capital structure of these non-listed companies on corporate performance? These nevertheless remain to be proved and demand further research.
Supported by a postdoctoral funding project from Shanxi Province titled “Mechanism and Empirical Research on the Mutual Integration and Promotion of Economic Development and Social Stability” (project number: 104521005).
The authors declare no conflicts of interest regarding the publication of this paper.
Zhang, Y., & Lian, R. J. (2023). Sustainable Development of Human Capital Structure and Firm Performance. Theoretical Economics Letters, 13, 549-567. https://doi.org/10.4236/tel.2023.133035