Nonlinear impact of minimum wage on labor employment in China

1.1 Literature review

Minimum wage policies, which are in a process of continuous adjustment in China, have persistent and vital influences on employment. Local employers are affected by these policies, and since the effects are heterogeneous among different provinces, they also impact the regional distribution of employment. The minimum wage in China varies significantly across provinces, ranging from 1,650 RMB to 2,590 RMB in 2023. Moreover, different provinces adjust the minimum requirements at varying frequencies [1]. The timing of these changes is also noteworthy. Since China became a member state of the world trade organization in 2001, foreign direct investment has become a vital component of China’s economy. Foreign investors were primarily attracted by the relatively low wages in China [2,3]. As a result, increases in the minimum wage affect foreign direct investment and Gross domestic product (GDP), which, in turn, influence employment. As of now, low labor costs are no longer a significant comparative advantage for China, especially when compared with neighboring states like Vietnam and India [4,5,6,7]. Generally, the effect of the minimum wage is significant and has become more pronounced with its increasing value and regional heterogeneity in recent years.

Numerous studies have been conducted using various methods to examine the relationship between minimum wage restrictions and employment in China. A basic model in labor economics is the search-matching model. Liu [8] uses the search-matching model as a theoretical method and linear regression as empirical evidence to analyze the labor supply (LS)–demand relationship. Zheng et al. [9] attributed mismatching as the main reason for overeducation and the wage penalty. Researching labor issues under a general equilibrium benchmark is also widely accepted. Li et al. [10] established a small open general equilibrium model to investigate the effects of unemployment and wage inequality. They also incorporated motional simulation methods with static analysis. Zhao and Sun [11] applied some new Keynesian assumptions, such as frictions and dynamic incentive contracts, into their model, finding that a higher minimum wage has two impacts: increasing the unemployment rate and causing a spillover effect on higher-income workers. Developing a general equilibrium model with heterogeneous firms in terms of capital intensity and industry level to predict the effect of minimum wages is also a recent area of focus [12].

Recently, researchers have focused more on regional, operational, and property heterogeneity in data and regression methods. Yang and Gunderson [13] conducted a causal estimation of employment, hours, and wages among Chinese immigrant laborers using difference-in-differences (DID) analysis and tested the results through robustness checks. Beladi et al. [14] used the average wage of employees at the firm level as a proxy for labor costs to determine the impact on heterogeneous companies and regions. A previous study examined the differing impacts on domestic and import companies using city-level panel data. Wang et al. [15] employed an instrumental variable strategy and DID analysis to research firms’ reactions to higher minimum wages. In a previous study, the regional variations in firms’ reactions were analyzed, focusing on input substitution and wage shares when experiencing minimum wage shocks.

It is worth noting that until recent years, as mentioned above, contemporary research has mainly focused on linear regression models. Although some very recent studies explore nonlinear relationships involving financial development and innovations with employment [16,17], the models they use are still in linear formats. However, the relationship between labor employment and minimum wage is typically not linear. The basic search-matching model can deduce a nonlinear relationship, and some important studies also support this [18,19]. Given this situation, it is significant to seek a nonlinear model to analyze the impact of minimum wages on employment rates.

There are many classical nonlinear models in physics and engineering. Although nonlinear models are not common in economics, we can extract methods from similar fields. Typically, we can develop a nonlinear model by analyzing the residuals of a linear model and incorporating nonlinear and linear components to construct the model. We can also study the equations governing the differentiation of variables to construct the model. Based on this, we combined the traditional ordinary least squares (OLS) model in econometrics with nonlinear models to study this effect. We also analyzed the regional and time distributions of wages and a series of auxiliary variables, such as GDP per capita, living standards, and others. First, we applied OLS analysis and conducted tests on significance, correlation, and stationarity. Subsequently, we extended the research by applying a nonlinear model. The results of the two types of analysis were aligned.

Our work contributes to current research mainly by combining the two types of models and applying a novel model in labor economics research. The results show that our model provides a better fit for the regression on the relationship between minimum wages and employment in the labor market.

1.2 Research method and content

2.1 Legal basis

The minimum wage system is mainly set in accordance with the Labor Law of the People’s Republic of China and the Provisions on Minimum Wages. The Labor Law provides the basic framework of the minimum wage system, while the Provisions on Minimum Wages provide detailed provisions on the setting of minimum wage standards, adjustment procedures, and other relevant matters.

2.2 Local governments are responsible

The minimum wage standard in China is not set uniformly by the central government but is set by the people’s governments of each province, autonomous region, and municipality directly under the Central Government on their own according to the local economic development level, price level, social average wage, and other factors. This approach to local governance allows regions to set minimum wages more in line with local realities.

2.3 Key considerations

When setting the minimum wage standard, local governments usually consider the following main factors: The average wage level of local employees: the minimum wage standard usually cannot be lower than a certain proportion of the average wage of local employees. Basic living expenses of local residents: This point mainly considers the cost of maintaining the basic living of workers, including food, housing, education, and other basic expenses. Local employment situation and economic development level: This includes regional economic growth rate, labor productivity, employment rate, etc., to ensure that the minimum wage standard will not have a negative impact on employment. Social insurance premium and housing accumulation fund paid by workers individually: The actual income of workers after paying social insurance and housing accumulation fund will be considered when setting.

2.4 Adjustment mechanism

According to the Provisions on Minimum Wages, the minimum wage standards shall be adjusted regularly. The people’s governments of provinces, autonomous regions, and municipalities directly under the Central Government generally adjust the minimum wage standards at least once every 2 years, but if the economic situation changes significantly, the local governments can also make timely adjustments according to the actual situation.

2.5 Procedures and processes

Relevant departments of local governments will conduct extensive market research to understand the current price level, residents’ living costs, and enterprises’ ability to pay, etc. Before the minimum wage is set or adjusted, hearings are usually held to solicit opinions from trade unions, business representatives, experts, and academics. The final minimum wage is approved by provincial or municipal governments and released to the public in the form of government documents.

3.1 Analysis of minimum wages before 2010

As can be seen from Table 1, the minimum wage in all Chinese provinces has been increasing year by year. Government reinforced minimum wage regulations since 2004, in the 4 years prior to the promulgation of the Minimum Wage Regulations, China’s minimum wage increased slowly. For example, in the first-tier city of Beijing, the minimum wage increased from 406 yuan to 465 yuan between 2000 and 2003, an increase of only 59 yuan in the 4 years, or an average annual increase of about 4.6%; in the remote area of Qinghai, the minimum wage of 260 yuan had no increase in the 4 years; and there was no minimum wage established in Tibet. Tibet has not even established a minimum wage. In 2004, the year the Minimum Wage Regulations were promulgated, the minimum wages in most provinces were raised considerably. In Jiangsu, the minimum wage rose from 500 yuan in 2003 to 680 yuan, an increase of 180 yuan, or 12.36%, the largest increase; in Beijing, it rose from 465 yuan to 520 yuan, an increase of 55 yuan, or 11.8%, in just 1 year; in Shanghai, the minimum wage was raised from 553 yuan to 600 yuan, an increase of 47 yuan, or 8.5%; and Tibet, too, set a minimum wage of 479 yuan in that year. Tibet also set the minimum wage at 479 yuan per month that year. In the 6 years following the enactment of the Minimum Wage Regulations, minimum wage standards in the provinces have increased rapidly, for example, in three coastal cities, Shanghai, Guangdong, and Shenzhen, where the minimum wage has risen by more than 1,000 yuan; Beijing has also seen rapid growth, with an average annual increase of more than 10% since 2004, and Shanghai has had a large increase, with an average annual increase of about 10.3%. In 2010, it had reached 1,120 yuan per month, the highest among all provinces.

In general, since the promulgation of the Minimum Wage Regulations in 2004, minimum wage rates in more economically developed provinces such as Shanghai, Beijing, and Guangdong have increased more rapidly, and by 2010 the minimum wage rates in these areas had reached or were close to 1,000 yuan. Some provinces with relatively less developed economies, such as Gansu and Ningxia, have seen relatively slower increases in minimum wage rates, but the overall trend is also upward. The increase in the minimum wage has helped to raise the income level of laborers and enhance their spending power, thereby boosting domestic demand in the economy, but it has also had an impact on the cost of labor for enterprises and on the job market

3.2 Analysis of China’s minimum wage from 2011 to 2024

As can be seen from Table 2, the minimum wage in almost all provinces in China has increased to about 1,000 yuan in 2011. The period from 2011 to 2015 saw the fastest increase in the minimum wage standards of each province. Among them, Gansu recorded the largest increase from 760 yuan in 2011 to 1,470 yuan in 2015, representing an increase of approximately 93.4%; Xinjiang increased by 68.8% from 960 yuan to 1,620 yuan in the same period; Some economically developed areas also experienced significant growth. For example, Shanghai increased from 1,280 yuan to 2,020 yuan, with an increase of about 57.8%. Beijing increased by about 48.3% from 1,160 yuan to 1,720 yuan; Guangdong: from 1,300 yuan to 1,895 yuan, representing an increase of approximately 45.8%. From 2016 to 2020, the minimum wage standards of all provinces were in a period of steady growth. The increase in the minimum wage has slowed down, but it still keeps an increase of about 10–30%. For example, Qinghai has the largest increase of 33.9% from 1,270 yuan in 2011 to 1,700 yuan in 2020. Xizang increased by 26.9% from 1,300 yuan to 1,650 yuan; Beijing increased from 1,720 yuan to 2,200 yuan, an increase of about 27.9%. Shanghai increased from 2,020 yuan to 2,480 yuan, an increase of about 22.8%; and Xinjiang increased by about 20.4% from 1,620 yuan to 1,950 yuan.

Since 2011, the minimum wage system has developed rapidly and has been taken seriously by the state. The increase in the minimum wage will help workers change their quality of life, narrow the income gap between urban and rural areas, contribute to social stability, and reduce social contradictions caused by the gap between the rich and the poor.

3.3 Comprehensive analysis of China’s minimum wage from 2000 to 2024

As the proximities can be seen in Figure 1, from 2000 to 2024, the minimum wage as a whole has shown a trend of continuous growth. The increase in minimum wage rates is particularly significant between 2004 and 2015. The average minimum wage was about 250 yuan in 2000, rose significantly to about 600 yuan in 2004, a large increase, continued to increase to about 1,200 yuan in 2010, increased further to about 1,600 yuan in 2015, reached about 1,900 yuan in 2020, and is projected to reach approximately 2,100 yuan.

Figure 1

Changes in average minimum wage in China from 2000 to 2024. Source: Compiled from data in Tables 1 and 2.

The graph from Figure 2 demonstrates the changes in the standard deviation of the minimum wage standard of each province and city in China between 2000 and 2024. The standard deviation reflects the degree of dispersion of the minimum wage standard in each province and city, and the larger the standard deviation, the greater the difference of the minimum wage standard in each region.

Figure 2

Changes in the standard deviation of the average minimum wage in China from 2000 to 2024. Data source: Obtained from data collated from Tables 1 and 2.

From 2000 to 2024, the standard deviation of the minimum wage standard shows an overall upward trend. Especially after 2010, the standard deviation fluctuates more significantly, reflecting the increase in the differences in minimum wage standards across regions. From 2000 to 2010, the standard deviation gradually increased from 60 to around 100, indicating that the differences in minimum wage standards across regions began to appear, but the increase was relatively slow; from 2011 to 2012, the standard deviation increased significantly, jumping from around 100 to around 150, reflecting that the differences in minimum wage standards across regions increased during this period, and that the standard deviation of minimum wage standards across regions increased from around 100 to around 150, reflecting the increase in the difference in the adjustment rate of minimum wage standards across regions during this period; from 2013 to 2020, the standard deviation fluctuates between 150 and 200, during which the difference in minimum wage standards across regions continues to widen; and from 2021 to 2024, it is expected that the standard deviation will be around 200, reflecting the fact that the difference in minimum wage standards across regions will still remain at a relatively high level in the future.

3.4 Analysis of the situation of China’s minimum wage standard and the average wage of employees

As illustrated by the aforementioned data, Hebei leads in terms of the average annual growth rate of minimum wages at 9.21%, whereas Xinjiang has the lowest rate at just 7.13%. For the average monthly wage of employees, Inner Mongolia boasts the highest growth rate at 12.78%, and Tianjin records the lowest with a rate of 11.29% (Table 3).

The most substantial gap between the minimum wage and the average monthly wage of employees is observed in Xinjiang, standing at 4.81%. This suggests that the actual income growth for employees in Xinjiang significantly outpaced the increase in the minimum wage. Conversely, Zhejiang exhibits the smallest disparity of 1.02%, indicating a closer alignment between the growth of the minimum wage and the average monthly wage of employees.

Overall, the average annual growth rate of the minimum wage across most regions falls within the range of 7–10%, highlighting a substantial increase in minimum wages over the past 23 years. Meanwhile, the growth rate of average monthly wages for employees ranges from 11 to 13%, reflecting a notable rise in real income that surpasses the growth rate of the minimum wage.

In economically advanced areas such as Beijing and Shenzhen, the growth rate of average monthly wages for employees exceeds 12%, demonstrating more pronounced income growth in these regions (Figure 3).

Figure 3

Ratio of the monthly minimum wage to the average monthly wage of employees by region in China in 2000 and 2023. Source: Official website of the National Bureau of Statistics of the People’s Republic of China.

In 2000, the difference between the monthly minimum wage and the average monthly wage of employees in Jilin, Heilongjiang, Inner Mongolia, and other regions was high, at about 0.6, indicating that the minimum wage standard in these regions was relatively high, close to half or more of the average monthly wage of employees. The ratio is lower in developed regions such as Shanghai and Beijing, especially in Shanghai, where it is only 0.27, indicating that the average monthly wages of employees in these regions are much higher than the minimum wage. By 2023, there is a significant drop in the ratio in most regions, for example, from 0.35 to 0.13 in Beijing. This suggests that despite the increase in the minimum wage, the average monthly wage of employees is increasing at a faster rate. In some regions, such as Fujian and Jiangxi, despite the decline in the ratio, the magnitude of change was relatively small, remaining between 0.2 and 0.25. In most regions, the ratio in 2023 is significantly lower than that in 2000, implying that the average wage of employees grows more rapidly as the economy develops, widening the gap with the minimum wage. The difference between the ratios of developed and less developed regions has narrowed. For example, the ratios for Shanghai and Beijing remain lower in 2023, reflecting higher average wages for employees in these regions. However, the gap with other regions is no longer as pronounced as it was in 2000. Less developed regions such as Jilin and Heilongjiang, which have higher ratios, also show a clear downward trend. In some central regions, such as Henan and Hubei, the ratio was relatively stable at both points in time, reflecting the relatively stable economic development and wage growth in these regions.

3.5 Analysis of China’s minimum wage standard and the per capita consumption level of urban residents

In the year 2000, regions such as Jilin, Heilongjiang, and Inner Mongolia exhibited a high difference between the monthly minimum wage and the average monthly wage of employees, approximately 0.6. This indicated that the minimum wage standards in these areas were relatively elevated, often reaching close to half or more of the average monthly wage. Conversely, developed regions like Shanghai and Beijing showed lower ratios, particularly Shanghai with a ratio of only 0.27, highlighting much higher average monthly wages for employees compared to the minimum wage (Table 4).

By 2023, most regions experienced a significant decline in this ratio. For instance, Beijing’s ratio dropped from 0.35 to 0.13, suggesting that although the minimum wage increased, the average monthly wage of employees grew at a faster pace. In some provinces, such as Fujian and Jiangxi, despite a decrease in the ratio, the change was relatively modest, staying within the range of 0.2–0.25.

Overall, the ratio in 2023 is notably lower than in 2000 for most regions, indicating that the average wage of employees has grown more rapidly as economies have developed, thereby widening the gap with the minimum wage. The disparity between developed and less developed regions has narrowed. While ratios in Shanghai and Beijing remain low in 2023, reflecting higher average employee wages in these regions, the gap with other regions is not as pronounced as it was two decades ago.

Less developed regions, such as Jilin and Heilongjiang, which initially had higher ratios, also demonstrated a clear downward trend. Meanwhile, central regions like Henan and Hubei maintained relatively stable ratios over the two time points, indicative of steady economic development and wage growth in these areas.

This analysis underscores the dynamic changes in wage structures across different regions of China, reflecting broader trends in economic development and income distribution.

3.6 Analysis of minimum wage and GDP per capita in China

GDP is an important economic indicator, which measures the economic activity and economic health of a country or region over a specific period of time. Thus, it provides valuable information for various aspects such as policy making, employment, investment, international trade, and quality of life. GDP per capita is an indicator obtained by dividing the total GDP of a country or region by the total population of that country or region. It is commonly used to measure the average economic productivity and standard of living of a country or region. GDP per capita provides a better perspective on the quality of life of the population because it takes into account demographic factors.

According to Table 5, the highest average annual growth rate of per capita GDP is observed in Guizhou at 13.83%, followed closely by Shaanxi at 13.45% and Chongqing at 13.27%. In contrast, Shenzhen and Shanghai exhibit relatively lower growth rates of 7.76 and 7.44%, respectively. Despite their robust economic foundations, these two regions show a more moderate pace in the increase of per capita GDP.

The average annual growth rate of the minimum wage standard aligns closely with the per capita GDP growth rate in provinces like Zhejiang, Anhui, and Fujian. However, in Shanxi and Inner Mongolia, the minimum wage standard’s growth rate lags significantly behind the per capita GDP growth, indicating that the minimum wage has increased more slowly relative to the rapid economic expansion in these regions.

From a regional economic development perspective, the developed eastern coastal areas, including Beijing, Shanghai, Jiangsu, and Zhejiang, boast higher levels of per capita GDP but experience relatively stable and moderate growth rates. Conversely, less developed regions in central and western China, such as Guizhou, Shaanxi, and Chongqing, start from a lower GDP per capita base but achieve higher growth rates. This pattern reflects the dynamic economic transformation occurring across different parts of the country, with less developed regions catching up at a faster pace.

Overall, the average annual growth rate of per capita GDP (represented by the orange line) generally exceeds that of the minimum wage (depicted by the blue line). Most regions experience an average annual minimum wage growth rate between 7 and 10%, while the per capita GDP growth rate typically ranges from 8 to 14%. This trend suggests that during the period from 2000 to 2003, China’s economy expanded at a relatively rapid pace, with wage growth lagging behind economic expansion (Figure 4).

Figure 4

Annual average growth rates of minimum wage and GDP per capita in China. Data source: Collated from Table 5.

In particular, provinces such as Shanxi, Inner Mongolia, Chongqing, Guizhou, and Shaanxi exhibit significantly higher per capita GDP growth rates compared to their minimum wage standards. This indicates that in these regions, rapid economic growth outpaced wage increases. Conversely, in provinces like Hunan, Guangxi, and Yunnan, the growth rates of the minimum wage standard and per capita GDP are more closely aligned, suggesting that economic growth is more effectively translating into wage gains for workers.

On the other hand, first-tier cities including Beijing, Tianjin, Shanghai, Guangdong, and Shenzhen show relatively lower growth rates for both per capita GDP and minimum wages. Especially in Shenzhen and Shanghai, despite their strong economic foundations, growth has tended to be more moderate. This pattern reflects the limited growth potential characteristic of economically mature stages in these regions.

4.1 National analysis

4.2 Policy analysis

We find that the minimum wage negatively impact employment, while, some research pointed out the opposite view. Researchers found positive employment effects for small MW increases. To explore these opposite views, we introduce new variables considering policy complementarities into the model.

5.1 Cross-sectional heterogeneity and regional economic performance

Descriptive statistics for minimum wages, GDP per capita, and consumption highlighted significant cross-sectional heterogeneity across different regions within China. Developed areas such as Beijing, Shanghai, Jiangsu, and others consistently outperform less developed regions on these indicators. This observation underscores the importance of considering regional differences in economic studies.

The growth rates of minimum wages also varied considerably among provinces. For example, Zhejiang experienced an impressive increase of 9.03 times its minimum wage, while Anhui saw a more modest 5.15-fold rise. These discrepancies suggest that economic policies and their impacts can differ substantially depending on the initial conditions and development level of each region. Therefore, it is critical to account for these variations in any econometric model aimed at understanding the factors influencing economic outcomes.

Given the observed cross-sectional heterogeneity, we opted for a fixed effects model over a random effects model, as indicated by the test results presented in Table 8. Fixed-effect models control for unobserved heterogeneity that may be correlated with the independent variables, thus providing more accurate estimates of the relationships being studied.

5.2 Correlation analysis and multicollinearity considerations

Correlation analysis, as shown in Table 9, provided valuable insights into the interrelationships among the variables included in our regression model. One key finding was the absence of multicollinearity, a situation where two or more predictor variables are highly correlated. Multicollinearity can inflate standard errors, leading to unreliable coefficient estimates and potentially obscuring the significance of important predictors.

The highest correlation coefficient observed was 0.7465 between ln MW and ln GDP, which falls below the commonly accepted threshold for diagnosing multicollinearity (typically ranging from 0.75 to 0.80). This suggests that our model variables are sufficiently distinct, allowing for precise estimation of their individual effects on the dependent variable.

Additionally, we noted a negative correlation between one variable and its first difference. This pattern is consistent with the convergence hypothesis proposed by the Solow–Swan growth model, which posits that poorer economies tend to grow faster than richer ones, eventually converging in terms of income levels if they have similar savings rates and access to technology. The observed negative correlation implies that regions starting from lower levels of a given variable might experience higher growth rates over time, supporting the theoretical expectations of economic convergence.

5.3 Regression coefficient significance testing and model fitness

Our investigation into the significance of regression coefficients, summarized in Table 11, uncovered a statistically significant negative relationship between minimum wages and employment at the 1 × 10⁻⁴ level. This result implies that, holding other factors constant, increases in minimum wages are associated with decreases in employment. This finding contributes to the ongoing debate regarding the impact of minimum wage policies on labor markets and has important implications for policymakers.

Furthermore, the adjusted R-squared value of 0.9916 demonstrates that our model explains nearly all variation in employment, suggesting a high degree of fit. Compared to previous studies, this exceptional fitness offers a robust framework for understanding employment rate volatility. The inclusion of consumption, minimum wages, and LS as explanatory variables provide a comprehensive explanation of employment dynamics, surpassing earlier efforts in capturing the complexity of labor market behavior.

5.4 Policy implications

The immediate conclusion of this study is that an increase in the minimum wage will result in a decrease in employment. However, the Government’s public policy seeks to maximize the welfare of society as a whole, and it is not the case that a lower employment rate necessarily indicates a better policy. If workers increase their wage expectations due to a better welfare policy, the search for employment opportunities will be prolonged, and the unemployment rate will increase. However, an increase in the unemployment rate at this point does not imply a decrease in social welfare or a failure of government policy. The minimum wage level affects the low-income group, and the welfare of this group is highly correlated with their consumption. Therefore, in Section 4, we include consumption in the regression model. It is important to note that, based on the correlation analysis and the multicollinearity observed in the linear regression model, we can assert that the minimum wage and the consumption level of workers show a significant positive correlation. This indicates that an increase in the minimum wage improves the level of consumption of workers, thereby increasing their welfare.

In addition, China’s income disparity is at a high level, and earlier studies suggest that China’s Gini coefficient was roughly in the 0.45–0.6 range, which is higher compared to Japan and South Korea, countries culturally similar to China, as well as the United States, which has more economic freedom. Therefore, the Chinese government’s continuous increase in the minimum wage level has also had the effect of reducing income disparity. It is worth noting that the impact of the minimum wage on the employment rate is strongly related to consumption and UBs. This study also examines the average consumption of urban residents concurrently. Other things being equal, higher UBs reduce the willingness of low-income groups to look for work. UB have also been increasing in China; before 2004, they were virtually non-existent, but since 2008, policies on UBs have been gradually revised and improved. There is a strong relationship between the level of UBs and the average nominal consumption of urban residents. This relationship was pointed out as early as 1798 in Malthus’s Principles of Population, which argued that relief for the poor led to higher grain prices, thereby lowering the welfare level for all. Thus, UBs, nominal consumption, and the prices of basic means of subsistence in China all affect the real effects of minimum wage standards.

It is also evident that unemployment reduces the consumption of the low-income population, while a rise in wages increases average consumption. Therefore, if we raise the minimum wage, there will be two effects: one is that unemployment caused by the minimum wage reduces consumption, and the other is that the increase in the minimum wage raises the overall wage level, thereby increasing consumption. As a result, the relationship between the minimum wage and consumption should follow a U-shaped curve. In the early stages, an increase in the minimum wage raises the level of consumption, as the effect of higher wages dominates. In the later stages, increases in the minimum wage decrease the level of consumption because, at this stage, higher wages lead to more unemployment, thus reducing average consumption. Based on our study, China’s minimum wage is still in the first stage.

ALMPs are crucial to promoting employment. However, when increasing investment in them, we should not only focus on the amount of funds, but also focus on how to transform these valuable resources into real employment opportunities more effectively. As an important part of ALMPs, the quality and relevance of training projects directly determine whether they can help job seekers, especially young people, improve their employment competitiveness. At present, some training projects are disconnected from market demand. The content learned cannot meet the requirements of the actual position of the enterprise, which makes it still difficult for participants to find suitable jobs after completing the training. In order to solve this problem, policymakers should strengthen communication and cooperation with enterprises and gain an in-depth understanding of the skill requirements and talent specifications of different industries and different positions. According to these market feedback, adjust and optimize the curriculum setting, teaching content, and teaching methods of the training project in a timely manner to ensure that the training content closely fits the actual work scenario, and improve the practicality and relevance of the training. At the same time, we will increase the construction of training teachers, select and cultivate a group of teachers with solid theoretical knowledge and rich practical experience, and provide a strong guarantee for improving the quality of training.

Interregional policy coordination is of significant importance in promoting employment, especially in the field of minimum wage policy. Due to the differences in economic development levels, industrial structures and labor market conditions in different regions, the minimum wage standards vary from place to place. If the interregional minimum wage policy lacks coordination, it may lead to unnecessary labor mobility. When the minimum wage in a region is significantly higher than that of the surrounding area, it may attract a large influx of labor, resulting in oversupply and increased employment competition in the region’s labor market, and also put great pressure on local public services and infrastructure. Labor outflow areas may face a shortage of labor, which will affect the normal development of local industries. In order to avoid this situation, regions should be encouraged to strengthen policy communication and cooperation and establish an interregional policy coordination mechanism. When formulating the minimum wage policy, we should fully consider the situation in the surrounding areas and maintain the relative consistency and coordination of the policy. In this way, it can not only reduce the blind flow of labor and realize the rational allocation of labor resources between regions, but also promote balanced economic development between regions and create a more stable and favorable environment for youth employment.

When designing the minimum wage policy, we must fully consider the cooperation of the UB policy, because the two policies are interrelated and affect employment incentives together. The original intention of UBs is to provide basic living security for the unemployed and help them overcome difficulties, but if the design is unreasonable, it may have a negative impact on their employment enthusiasm. For example, if the level of UBs is too high and the conditions for receiving it are too relaxed, it may make some unemployed people dependent, believing that they can maintain a certain standard of living even if they do not work, thus reducing the motivation to find a job. On the contrary, if the UB is too low and cannot meet the basic living needs of the unemployed, it may cause them to fall into difficulties and affect social stability. Therefore, policymakers need to find a balance between the two. When determining the minimum wage standard, it should be comprehensively considered in combination with the level of unemployment benefits to ensure that the minimum wage can reflect the value of labor and at the same time motivate young people to actively employ. In addition, the period and conditions for receiving UBs should be reasonably set, and the unemployed should be encouraged to actively participate in training and job search activities, so as to achieve re-employment as soon as possible. By optimizing the cooperation of these two policies, a policy synergy to promote employment can be formed.

Summarizing the above discussion and the research in this work, we propose the following policy recommendations: (1) An increase in the minimum wage will increase the unemployment rate, especially for the low-income group. Therefore, particularly in regions with low per capita income and a large number of low-income groups, increases in the minimum wage should be carefully considered to avoid large fluctuations in the unemployment rate. (2) Increases in real consumption are important for the welfare of low-income groups. Thus, a policy instrument combining the minimum wage and unemployment benefits is needed to ensure that low-income groups can maintain access to basic goods. If the minimum wage leads to an increase in unemployment, UBs should also be increased. (3) UBs should not be excessively high. High UBs increase the probability that workers will turn down jobs and spend time searching for more favorable opportunities. This reduces output and wastes social resources. Moreover, higher UBs can lead to increased consumer prices, thereby reducing the real utility for low-income groups. (4) An increase in the minimum wage is positively correlated with an increase in average consumption. Therefore, the current increase in the minimum wage does not, on balance, lead to an increase in the unemployment rate significant enough to reduce social welfare substantially. An increase in the minimum wage remains, on balance, a favorable outcome at this time.