Breeding Hope during the Crisis: A Study on Unemployed Laborers’ Willingness to Receive Educational Compensation under the Chinese “Zero-COVID” Policy

Abstract

Under the Chinese “Zero-COVID” policy, many laborers were forced out of work. Participating in educational compensation can effectively help unemployed laborers achieve re-employment. This paper selected Jining, Shandong Province, as the research area, analyzed the data using a questionnaire survey and key interviews, and observed factors that affect and change willingness to receive educational compensation (WTEC) and the willingness to accept the training duration (WTTD) of unemployed laborers. The study found that 77.78% of unemployed laborers are willing to receive educational compensation, and the WTTD is 12.05 days. Among them, eight factors affect WTEC, such as the years of education, the duration of unemployment, whether there are dependents in the family, and the family’s size. Nine factors such as gender, age, education, unemployed duration, dependents, and other supportable incomes affect WTTD. Based on this, by taking measures from the government and laborers, WTEC and WTTD can be improved, and then the rate of re-employment can be enhanced, and finally, the employment problem can be ameliorated.

1. Introduction

Since 2020, the COVID-19 epidemic has swept the world, and countries have quickly adopted various measures to try to control the spread of the epidemic [1]. Due to the highly contagious nature of COVID-19, China adopted many measures in epidemic prevention, such as delaying the resumption of work, home isolation, traffic control, etc., in order to restrict residents’ travel. With joint efforts of the Chinese government and all sectors of society, by April 2020, COVID-19 was initially controlled in China [2]. After that, China’s COVID-19 epidemic gradually entered the normalized prevention and control stage in the entire chain of precise prevention and control, with “Zero-COVID” policy as the general policy. The so-called “Zero-COVID” policy refers to early detection, early diagnosis, early isolation, and early treatment during the normalized prevention and control stage, and it resolutely aims to prevent the continuous spread of the epidemic in the community [3].

The “Zero-COVID” policy is the general policy of epidemic prevention and control and exhibits Chinese characteristics, but it also brings about the objective problem of increasing unemployment. Firstly, since the outbreak of the epidemic, the “Zero-COVID” policy played a good role in epidemic prevention and control. As of 24:00 on 28 November 2022, there were 33,967 confirmed cases (including 106 severe cases) in mainland China, accounting for about three-millionths of the country’s total population and a total of 5233 deaths [4], which is less than one-millionth of the total population of the country. Second, while effectively controlling the spread of the epidemic, the total number of unemployed urban residents in China also increased sharply. The number of registered unemployed persons in China’s cities and towns increased from 9.45 million in 2019 (before the outbreak) to 11.6 million in 2020, although the number declined in 2021 compared to the previous year (to 10.4 million) [5]. In the end, a large number of non-agricultural laborers in China lost their jobs. As the process of urbanization continued to accelerate, some laborers came to work in cities. They make a living mainly in the city’s construction, service, or manufacturing industries. Unemployed laborers under the “Zero-COVID” policy are also included in the group of migrant workers working in cities. In 2021, the total number of migrant workers in the country was 292.51 million, of which 171.72 million were migrant workers [6]. They had flexible employment and were most vulnerable to unemployment due to the impact of the epidemic. Under the impact of the epidemic, job opportunities were reduced or lost, and the risk of families returning to poverty increased [7], making them unable to return to work; moreover, they were stranded in their hometowns and lost their jobs and income.

Unemployed laborers actively responded to the country’s prevention and control measures against the epidemic, resulting in unemployment and substantial losses. Therefore, educational compensation for unemployed laborers is necessary. In this paper, educational compensation refers to providing knowledge-based educational compensation measures for unemployed laborers to make up for the damage caused by their unfavorable situation [8]. Firstly, unemployed laborers face basic living pressures. In 2021, the per capita disposable income of rural residents was CNY 18,931, and the per capita consumption expenditure was CNY 15,916 [9]. Unemployed laborers face economic and psychological pressure. Due to unemployment, normal economic income levels cannot support family expenses, which triggers strong personal self-blame and guilt [10]. Second, unemployed laborers lack relevant knowledge and skills for re-employment. After being unemployed, due to multiple obstacles such as their low level of education and limited access to information, unemployed laborers cannot obtain employment opportunities again within a short period of time. Therefore, what they need should be educational compensation to promote sustainable survival and development [11]. Third, educational compensation can significantly improve the re-employment ability of unemployed laborers. The key to educational compensation is to achieve sustainable development in terms of employment and adaptability via developmental vocational training: that is, to give them a dynamic adaptation mechanism [12]. Based on this, from the perspective of educational compensation, providing unemployed laborers with opportunities to receive re-employment education and helping them acquire professional skills can effectively promote the re-employment of unemployed laborers.

Scholars have carried out substantial research on unemployed laborers and their willingness to receive educational compensation (WTEC) and willingness to accept the training duration (WTTD) under major public health events. First, research has been carried out from the perspective of WTEC. Chinese scholars’ research on educational compensation is generally well carried out and involves a relatively wide range of topics. The theme mainly focuses on the scope, object, content, mechanism of educational compensation, etc. Educational compensation involving laborers is mostly concentrated on land-lost laborers against the background of urbanization [13]. There are few studies on the WTEC of unemployed laborers under major public health events. Second, research has been carried out from the perspective of the degree of educational compensation. Discussions on the degree of educational compensation mainly include vocational educational compensation for land-lost laborers [14], ecological compensation [15], etc. There is a lack of research on educational compensation in the context of this study. Finally, scholars have carrired out research from the perspective of influencing factors in educational compensation. WTEC is governed by many factors [16]. The age, physical condition, years of education, laborer’s training experience, and understanding of policies have a significant impact on WTEC [17].

It is very necessary to study the WTEC of unemployed laborers against the background of the “Zero-COVID” policy, in addition to adhering to the “Zero-COVID” policy to better balance the relationship between epidemic prevention and control and economic and social development. This can achieve maximum prevention and control effects at a minimum cost and minimize the impact of the epidemic on economic and social development. However, the negative effects it brings to society must be examined. Under this premise, methods for solving these negative effects are challenges that government departments should face. This paper will combine questionnaires and key interviews to collect data on unemployed laborers’ WTEC, analyze data using econometric models, and find out unemployed laborers’ WTEC and influencing factors. By conducting this study, the government realizes the actual WTEC of unemployed laborers and then improves the quality of educational compensation in a targeted manner. It is of great significance to innovate the form of educational compensation and protect the life of unemployed laborers against the background of the “Zero-COVID” policy.

Based on the survey and interview data obtained in the analysis of the literature and survey, we propose the following hypotheses:

Hypotheses 1 (H1).

The WTEC of unemployed laborers is affected by gender.

Hypotheses 2 (H2).

The WTEC of unemployed laborers is affected by age.

Hypotheses 3 (H3).

The WTEC of unemployed laborers is affected by education.

Hypotheses 4 (H4).

The WTEC of unemployed laborers is affected by educational compensation experience.

Hypotheses 5 (H5).

The WTEC of unemployed laborers is affected by unemployment duration.

Hypotheses 6 (H6).

The WTEC of unemployed laborers is affected by dependents.

Hypotheses 7 (H7).

The WTEC of unemployed laborers is affected by family size.

Hypotheses 8 (H8).

The WTEC of unemployed laborers is affected by communism.

Hypotheses 9 (H9).

The WTEC of unemployed laborers is affected by previous annual income.

Hypotheses 10 (H10).

The WTEC of unemployed laborers is affected by the day after compensation.

Hypotheses 11 (H11).

The WTEC of unemployed laborers is affected by family support.

Hypotheses 12 (H12).

The WTEC of unemployed laborers is affected by self-confidence.

2. Methodology

2.1. Research Regions

This paper takes Jining City, Shandong Province, as the research area (Figure 1). First, the data collected in the region are representative. From August to November 2022, Jining City continued to have new local cases every month. As of November 2022, there were 12 high-risk areas in Jining City. For high-risk areas, the Jining Municipal Government adopted the corresponding control measures, which comprise data science sampling. Second, the “Zero-COVID” policy adopted by the region fully complies with the guidelines of the Chinese government. These include the prevention and control measures for the home health monitoring of residents in high-risk areas and the continuous collection of monitoring information. During the stay-at-home monitoring period, the movement of quarantined individuals was restricted [18]. Third, the geographic and cultural characteristics of the region are typical. Jining City is located in the central part of Shandong Province on the North China Plain, and its economic operation is relatively stable. It is a transportation hub city. The population of Jining City is generally stable, with a permanent population of 8.337 million, including 3.235 million in rural areas [19]. Therefore, it is representative to choose this area for research.

2.2. Questionnaire Design

We designed the questionnaire in three steps. The first step involves designing the first draft of the questionnaire. After referring to a large number of relevant studies, we formulated a survey plan and determined the survey indicators in combination with the actual situation in Jining City. Then, the survey questions were designed around the indicators and formed the first draft questionnaire. The second step comprised the stage of soliciting opinions and the pre-investigation. Before finalizing the draft, we communicated with the party secretary of villages in the survey area with respect to the individual, family, and psychological characteristics of unemployed individuals. In addition, we also solicited opinions and suggestions from scholars engaged in the study of unemployed individuals and conducted a pre-investigation in the rural areas of Jining City. Then, according to the feedback results of the pre-investigation, the questionnaire was optimized. The third step is to finalize the draft. Because the main target group of the research study is laborers, the questionnaire’s questions were worded in a simple and easy-to-understand manner. As the most important question in the questionnaire survey, WTEC is set in the last part of the questionnaire (

Table 1. Questionnaire design.

Variable			Question	Options	Assessment
Independent variable	Personal characteristics		What is your gender?	Male = 1, female = 0	closed
			What is your age?	number	open-ended
			How many years of education have you had?	number	open-ended
			How long have you been unemployed?	number	open-ended
	Family characteristics		Are there any dependents in your family?	yes = 1, no = 0	closed
			Besides the job you have ever had, do you have any other supportable income in the household?	yes = 1, no = 0	closed
			How big is your family?	number	open-ended
			Are there any communists in your family?	yes = 1, no = 0	closed
	Job characteristics		How much did you earn per year before losing your job?	number	open-ended
			What was the technical sophistication of your last job?	1 = very simple-very complicated = 5	closed
			Assuming through educational compensation, how long do you expect to find a new job?	number	open-ended
			Assuming through educational compensation, how much do you expect to earn?	number	open-ended
	Surrounding environment characteristics		Have you ever experienced education compensation?	yes = 1, no = 0	closed
			After losing job, do family members encourage you to look for work, believing that you can find it quickly?	yes = 1, no = 0	closed
			Do you believe that you can quickly find your next job and get out of unemployment?	yes = 1, no = 0	closed
	Cognitive characteristics		How well do you understand the “Zero-COVID” policy?	1 = ignorant-understand = 3	closed
			How well do you understand the educational compensation of unemployed peasants?	1 = ignorant-understand = 3	closed
			The main source of information you learn about educational compensation for unemployed peasants is?	1. Propaganda and guidance from the village where I live; 2. Through the Internet, TV and other media; 3. Heard it from other people; 4. Never heard of it	closed
Dependent variable	WTEC	WTEC	Would you rather receive educational compensation? (WTEC)	yes = 1, no = 0	closed
	WTTD		How many days would you like to training? (WTTD)	number	Open-ended
			If not, why?	1. I can find a new job by myself, and I don’t need to participate in educational compensation; 2. There are other sources of stable income in my family, and I am not in a hurry to find a new job; 3. I think participating in educational compensation is a waste of time, unable to gain from educational compensation; 4. I think education is a matter for the next generation, and I have missed the best age for education	closed

). In the specific research process, researchers ensured that the respondents could complete the survey independently and without external interference. The time for completing each questionnaire was controlled within 20 min.

The independent variables of the questionnaire are divided into 4 categories. First, the first and second parts of the questionnaire are related to personal and family characteristics, respectively. In these two parts, we focus on verifying the relationship between personal and family characteristics and WTEC. Second, the third part investigates the job’s characteristics. This part includes the income before unemployment and the technical aspect of the job, as well as the time required to find a new job and the expected annual income after educational compensation. We hope to understand the relationship between job characteristics and WTEC. Thirdly, the fourth part of the questionnaire is about the surrounding environment’s characteristics. In this part, two theories are mainly introduced, namely the “Pygmalion effect” in psychology and the “resilience” theory in organizational behavior. We hope that with the assistance of these two theories, we can clarify the relationship between them and the surrounding environment for WTEC. Then, the fifth part of the questionnaire provides the types of educational compensation in three different fields for the respondents to choose from. Finally, the seventh part of the questionnaire is the cognition part of the unemployed individuals’ responses to the “Zero-COVID” policy and educational compensation. The purpose is to grasp the attitude and cognition level of unemployed laborers towards the “Zero-COVID” policy and educational compensation and put forward suggestions and new solutions for existing unemployed laborers’ educational compensation policy.

There are 2 dependent variables in the questionnaire. The last question of the questionnaire is about unemployed laborers’ WTEC and WTTD: If the respondents choose not to willing to accept educational compensation, then we ask what the reason is for not accepting it.

2.3. Data Collection

Before collecting data, we calculated the minimum sample size. According to the sample size calculation formula [20], we set the confidence interval to 90% and the degree of accuracy to 3%. The number of standard deviations inclusive of all possible values in the range is 4. By looking at the table, we observe that t = 1.645 at the 90% confidence interval. The calculated minimum sample size is 128, which is basically consistent with the research of Adam et al. [21].

We collected the data required for this article by using various channels. First, data were collected from the literature. We logged on to government websites at all levels to consult the literature and statistical data and gained a preliminary understanding of the epidemic situation and permanent population of Jining City. Second, data were collected using a survey questionnaire. The survey objects of this study involved more than 70 villages and hundreds of respondents in 10 townships and sub-districts under the jurisdiction of Jining City. The survey’s samples cover all townships under the jurisdiction of Jining City, and the proportion of survey samples in each township to the total survey is determined according to the proportion of the total population of each township in the total population of Jining City; thus, the overall distribution is even. All respondents were randomly sampled. Third, due to the epidemic, we adopted a combination of online and offline methods for data collection. For offline surveys, we entrusted the local village committees and village party secretaries of different villages to conduct surveys on unemployed individuals in their own villages. For the online survey, we converted the paper version of the questionnaire into an electronic version by using the questionnaire’s website, and we collected the questionnaire by distributing the questionnaire link in WeChat Moments. The survey period was from October to November 2022. A total of 400 questionnaires were distributed; 400 questionnaires were returned, and 378 valid questionnaires were returned. During the research process, the author also conducted key interviews with some township cadres and village cadres by using voice calls, learned about the current epidemic situation in the village and the basic situation of unemployed individuals, and listened to their opinions and suggestions regarding educational compensation for unemployed individuals.

2.4. Variable Definitions

According to the questions designed in the questionnaire, the variables formed are shown in

Table 2. Variable definitions and assignments.

Category	Variable Name	Definition and Assignment	Variable Type
Personal Characteristics	gender	male = 1, female = 0	virtual variable
	age	according to actual age	continuous variable
	education	according to actual years	continuous variable
	unemployed duration	according to actual months	continuous variable
Family Characteristics	dependents	yes = 1, no = 0	virtual variable
	other supportable incomes	yes = 1, no = 0	virtual variable
	family size	according to actual size	continuous variable
	communist	yes = 1, no = 0	virtual variable
Job Characteristics	annual income before	according to actual income	continuous variable
	day after compensation	according to expected days	continuous variable
	annual income after	according to expected income	continuous variable
Surrounding Environment Characteristics	educational compensation experience	yes = 1, no = 0	virtual variable
	family support	yes = 1, no = 0	virtual variable
	self-confident	yes = 1, no = 0	virtual variable

.

2.5. Model Selection

We chose the sample selection model proposed by Heckman to solve the problem of sample selection bias [22]. In the first stage, the Probit model is used to analyze whether unemployed laborers are willing to receive educational compensation and its influencing factors. In this process, we used “${WTEC}_i=1$” to indicate that unemployed individuals were willing to receive educational compensation and “${WTEC}_i=0$” to indicate that unemployed laborers were unwilling to receive educational compensation. The observable ${WTEC}_i$ is determined by the unobserved latent variable ${WTEC}_i^{*}$, and the estimated equation is as follows.

$${WTEC}_i^{*}={\alpha }_0{\alpha }_1{X}_i+\sigma (i=1,2,\dots ,n)$$

(1)

In this formula, ${\alpha }_0$ represents the constant term, ${\alpha }_i(i=1,2,\dots ,n)$ represents the regression coefficient of each variable, $X_i(i=1,2,\dots ,n)$ represents each explanatory variable, and $\sigma$ represents the random disturbance term.

Estimated value ${\alpha }_i$ is obtained according to Formula (1), and then the inverse Mills ratio is calculated for each sample’s individual $i$.

$$\stackrel{-}{\lambda }=\frac{\varphi \left({\alpha }_i{X}_i\right)}{\phi \left({\alpha }_i{X}_i\right)}$$

(2)

In Formula (2), $\varphi \left({\alpha }_i{X}_i\right)$ and $\phi \left({\alpha }_i{X}_i\right)$ represent the density function and cumulative density function of the standard normal distribution of variable ${\alpha }_i{X}_i$.

The second stage is the result’s equation, and the OLS model was used for unemployed laborers who were willing to receive educational compensation in order to explore the factors that affect the scale of the laborers’ training duration.

This stage used the inverse Mills ratio obtained in the first stage as an instrumental variable to correct the sample selection bias in the second stage. The estimated equation obtained after modification is as follows.

$${WTTD}_i=\epsilon 0+{\epsilon }_i{V}_i+{\epsilon }_m{\lambda }_m+\delta (i=1,2,\dots ,n)$$

(3)

In Formula (3), $V_i$ is the explanatory variable, ${WTTD}_i$ is the specific value of the year that the ith laborer is ${WTEC}_i$, ${\epsilon }_i(i=1,2,\dots ,n)$ and ${\epsilon }_m$ are the explanatory variables, and $\delta$ is the random perturbation term. $\lambda$ is the inverse Mills ratio calculated according to the first stage, which is used to correct sample selection bias. If $\lambda$ is significantly non-zero, it indicates that there is indeed a selection bias; thus, the model is valid.

3. Results

3.1. Descriptive Analysis

Among the 378 valid questionnaires collected, a total of 294 unemployed laborers were willing to accept educational compensation, accounting for 77.78% of valid questionnaires.

Personal characteristics analysis: Unemployed laborers are aged between 18 and 75 (in China, most laborers have no concept of retirement and usually work until they lose their ability to work). The years of education ranged from 0 to 20 years, and the duration of unemployment ranged from 0 months (that is, just unemployed) to 180 months. Among unemployed laborers that are not willing to receive educational compensation, the average age and years of education are 49.42 years old and 6.86 years, respectively. Additionally, the average duration of unemployment is 11.62 months. The average age of those without WTEC is 7.1 years younger than those with WTEC; they have received 1.21 years less education, and their unemployment duration is 7.47 months shorter. It is preliminarily believed that the willingness of unemployed, elderly laborers to accept educational compensation is higher than that of young unemployed laborers; moreover, the lower the education level, the longer the unemployment time, and the stronger the WTTD.

Family characteristics analysis: The average family size with WTEC is 4.32, which is higher than those without WTEC. A total of 353 unemployed laborers have dependents in their families, of whom 275 are willing to receive educational compensation, accounting for 77.9%. A total of 174 unemployed laborers have other supportable income, of which 138 are willing to accept educational compensation, accounting for 79.31%. A total of 115 unemployed laborers have party members in their families, and 97 are willing to receive educational compensation, accounting for 84.35%.

Job characteristics analysis: Among all respondents, the expected average time to find a job was 27.87 days. The average annual income before unemployment is CNY 43,432.40, and the expected average future income via educational compensation is CNY 55,251.32. Among those who are willing to accept educational compensation, the expected average time to find a job is 28.43 days, and the average income is CNY 39,095.94. They are 2.5 days longer than those that are unwilling to receive educational compensation, and the average income is CNY 19,514.05. Among unemployed laborers who are willing and unwilling to accept educational compensation, the average expected income of future work is CNY 51,877.55 and CNY 67,059.52, respectively. More than 80% of unemployed laborers have previously engaged in simple jobs, and only about 15% of unemployed laborers are engaged in complicated jobs.

Surrounding environment characteristics analysis: Among the valid questionnaires, a total of 355 family members of unemployed laborers have confidence in their re-employment, accounting for 93.92%. A total of 90.74% of unemployed laborers believe that they can be employed again. Among the respondents who are willing to accept educational compensation, 90.14% and 96.26% of family members and respondents are confident in re-employment, respectively, both of which are higher than the proportion of those who are unwilling to accept educational compensation under the same conditions.

Cognitive characteristics analysis: In this study, 14.29% and 1.59% of unemployed laborers are very uninformed about the “Zero-COVID” policy and educational compensation. Among the unemployed laborers with WTEC, 44.90% of them are very familiar with the “Zero-COVID” policy, but the number of people who are very familiar with educational compensation is 0.

WTEC characteristics analysis: Among the 294 unemployed laborers with WTEC, the number of people who chose “vocational skills and technology” is the largest, accounting for 51.02%, followed by the number of people who chose “agricultural practical production technology”, accounting for 30.61%, and the number of people who chose “non-technical” is the lowest at only 18.37%.

3.2. Measurement Results

We performed a multicollinearity test for each sample. The annual income after accounting for variables exhibited multicollinearity, so we deleted it when carrying out measurements. At this point, the average variance inflation factor (VIF) is 1.40. WTEC and WTTD are analyzed according to the Heckman model using Stata 15.0 software (StataCorp LLC, College Station, TX, USA). Measurement results show that $\chi$ is 199.25, the $p$ value is 0.0000, and the overall effect is good. The inverse Mills ratio is 0.001, which is significant at 10% of the statistical level. This shows that there is bias in the sample and that the model’s selection is effective. The measurement results are detailed in

Table 3. Measurement results.

Category	Variable Name	WTEC		WTTD
		Coef.	Std. Err.	Coef.	Std. Err.
Personal characteristic	gender	0.26	0.20	−20.8 ***	5.18
	age	0.02	0.01	−0.65 ***	0.25
	education	0.07 **	0.03	6.91 ***	0.79
	unemployed duration	0.15 ***	0.03	0.32 ***	0.11
Family characteristic	dependents	−1.24 **	0.51	20.79	10.56
	other supportable incomes	−0.31	0.22	−11.32	5.46
	family size	0.23 **	0.09	−3.56	2.46
	communist	0.40	0.28	20.24 ***	5.63
Job characteristics	annual income before	−0.00 ***	0.00	0.00	0.00
	day after compensation	−0.02 ***	0.00	−0.18 **	−0.07
Surrounding environment characteristics	educational compensation experience	−0.66 ***	0.22	−49.34 ***	6.89
	family support	1.47 ***	0.47	78.5 ***	15.57
	self-confident	−0.52	0.39	−61.54 ***	9.87

Note: p-values in parentheses; ***, ** represent 1%, 5% significance levels, respectively.

.

We used SPSS Statistics 26 to test the reliability and validity of the questionnaire. The reliability test results show that Cronbach’s alpha value is 0.67, which is greater than the good reliability standard of 0.60, indicating that the questionnaire has excellent homogeneity reliability and good internal consistency. In the validity test’s results, the KMO value is 0.646, which is greater than 0.600, and meets the standard of construct validity. The significance of Bartlett’s Test of Sphericity is 0.000, which is far less than 0.005, and construct validity was up to standard.

Among the factors affecting WTEC in the first stage, there are eight significant variables. Among them, education, unemployment duration, family size, and family support are positively correlated with WTEC. Dependents, day after compensation, annual income before, and educational compensation experience are negatively correlated with WTEC.

In the second stage, among the factors affecting WTTD, nine factors were significant. Among them, education, unemployment duration, being a communist, and family support are positively related to WTTD. Gender, age, educational compensation experience, day after compensation, and self-confidence are negatively correlated with WTTD.

Calculation of the duration of educational compensation: A total of 77.78% of the respondents had WTEC. It can be observed in Equation (1) that after removing sample selection bias, the WTTD of unemployed laborers is 12.05 days.

3.3. Result Analysis

3.3.1. Personal Characteristics

Gender analysis: It can be observed from the research results that the gender variable passes the significance level test, indicating that it is a significant factor for WTTD. China clearly stated equality between men and women in its Constitution, making the concept of equal rights between men and women a national will; thus, women pay more attention to their own educational opportunities and rights [23]. Therefore, the gender variable is verified among the variables.

Age analysis: It can be observed from research results that the age variable passes the significance level test, indicating that it is a significant factor for WTTD. The younger the unemployed peasant, the more energetic they will be, and they are more willing to spend their time on educational compensation. Therefore, the age variable is verified among the variables.

Education analysis: From the results of this study, it can be observed that the education variable passed the significance level test, indicating that it has a significant impact on WTEC and WTTD. The reason is that the higher the number of years of education the laborer household receives, the higher the education level, and the more Chinese characters they know. This is inseparable from China’s efforts to popularize compulsory education. Compulsory education is the cornerstone of the entire education system, and the rural population accounts for 50.32% of the country’s population. After a large number of rural populations receive compulsory education, the educational level of the rural population will increase, and the level of education is closely related to the future development of people and society [24].

Educational compensation experience analysis: From the results of this study, it can be observed that this variable passed the significance level test, indicating that the educational compensation experience variable has a significant impact on WTEC and WTTD. Unemployed laborers who have not received educational compensation are full of curiosity about it. Therefore, they are more willing to try to help themselves achieve early re-employment and spend time on educational compensation.

Unemployment duration analysis: It can be observed in the research results that the unemployment duration passes the significance level test, indicating that the unemployment duration variable is a significant factor for WTEC and WTTD. Unemployment means that laborers lose their jobs within a certain period of time, which leads to a decline or even interruption in laborers’ income [25] that, in turn, places great pressure on the psychology of unemployed laborers. This forces them to find a job, so they take a greater interest in educational compensation. Therefore, the unemployment duration variable is verified in the variables.

3.3.2. Family Characteristics

Dependents analysis: From the research results, it can be observed that the dependent variable passes the significance level test, indicating that it is a significant factor for WTEC. First, we examine the effect of dependent variables on WTEC. The smaller the number of dependents in an unemployed laborer’s family, the stronger the willingness for WTEC. Unemployed laborers who do not have the burden of dependents are more willing to take the time to learn about educational compensation.

Family size analysis: It can be observed from the model that the family size variable passed the significance level test, indicating that it is a significant factor for WTEC. A large population means increased family living expenses, and each additional member in the family reduces the per capita consumption by 11.8%, indicating that there is an economy of scale effect in a family [26].

Communist analysis: From the results of this study, it can be observed that the communist variable passed the significance level test, indicating that it has a significant impact on WTTD. Unemployed laborers who live with Communist Party members are more likely to receive guidance and propaganda on China’s policies and are more willing to try and support the policies promulgated by the state, so they will spend more time on educational compensation.

3.3.3. Job Characteristics

Annual income before analysis: Variables for annual income before unemployment passed the significance level test, indicating that it has a direct impact on WTEC. The lower the annual income, the stronger the WTEC. Before unemployment, a low income may barely be able to support normal operations. Once a laborer loses their job, the pressure on family consumption becomes greater. Therefore, low-income unemployed laborers must find new jobs as soon as possible; otherwise, the normal functioning of the family will be threatened more. They are, therefore, more willing to receive educational compensation. Annual income is obtained verified among the variables.

Day-after-compensation analysis: It can be observed in the model that the day-after compensation variable passed the significance level test, indicating that it is a significant factor for both WTEC and WTTD. Via educational compensation, unemployed laborers will have access to different aspects of information. Supplementing the gaps in some previous aspects. In addition, they will also master a new technology because of educational compensation. After that, they will be more competitive in the labor market. Finally, they can find a new job within a shorter period of time.

3.3.4. Surrounding Environment Characteristics

Family support analysis: From the results of this study, it can be observed that the family support variable passed the significance level test, indicating that its impact on WTEC and WTTD is significant. This is a manifestation of the “Pygmalion effect” in psychology, which is a type of social psychological effect and is fully used in school education. This effect means that the teacher’s expectations for a child will subtly affect a student’s daily and final academic performance. This effect shows that the external expectations of individual ability and performance will lead to better and higher performance levels for the individual [27]. Here, family members continue to encourage the unemployed so that they are more willing to spend time on educational compensation. Therefore, the family support variable is validated among the variables.

Self-confidence analysis: From the research results, it can be observed that self-confidence passed the significance level test, indicating that it is a significant factor for WTTD. This mirrors the “resilience” theory in organizational behavior. This theory refers to the positive adaptive ability of individuals in the face of adversity [28]. Unemployed laborers understand that they cannot help themselves, so they can only rely on the outside world. Currently, receiving educational compensation is a life-saving pathway that unemployed laborers can follow. Therefore, unemployed laborers who lack confidence in getting out of trouble are more willing to receive educational compensation.

4. Discussion

For the Chinese “Zero-COVID” policy, it is necessary to take measures to increase the participation willingness of unemployed laborers. First, helping unemployed laborers participate in educational compensation is a practice of the principle of education fairness. In the process of modernization, it is necessary to ensure the fairness of education. Educational fairness is an important part of social fairness. Achieving educational equality can guarantee the right of citizens to receive a good education, and promoting educational equity is also of great significance for ensuring social equity [29]. Education does not distinguish between ages. Via educational compensation, laborers can learn new livelihood techniques and help themselves find new ways of earning a living in the labor market. Secondly, a strong learning atmosphere can be created at the social level to promote unemployed laborers’ WTEC. Via educational compensation, unemployed laborers may become proficient in a new craft or deeply cultivate the original craft and become experts in a certain field. Promoting human development always comprises the pursuit of the intrinsic value of education [30]: for example, organizing policy quizzes with prizes, distributing rewards to increase laborers’ enthusiasm, or organizing more professional and larger-scale tours for publicity work by relevant government departments at a higher level. Finally, helping unemployed laborers receive educational compensation is an effective way to maintain the achievements of poverty alleviation. After losing their income, living conditions are inevitably more difficult than before, which does not meet the country’s expectations for laborers’ lives. Therefore, it is necessary to promote the agglomeration of more talents, intelligence, technology, capital, management, and other elements in the countryside [31]. Additionally, a new type of industrial–laborer–urban–rural relationship comprising “mutual promotion between industry and agriculture, complementarity between urban and rural areas, comprehensive integration, and common prosperity” should be formed, continuously improving the professional skills of the working-age population. Based on the above, it is indeed necessary for China to take measures to improve WTEC and WTTD, and there is great potential for improvement.

In addition, the awareness level of educational compensation should be improved, and the thinking patterns of unemployed laborers should change. First, the limitations of traditional thinking should be broken. Traditional ideas are bound to accompany traditional thinking patterns. Traditional concepts include certain concepts and habits that people have formed for a long time. The transformation of traditional concepts does not comprise abandoning all traditions but involves abandoning all outdated traditional values. Outdated traditional thinking patterns result in outdated traditional concepts, which seriously hinder Chinese people from understanding new and unknown ideas and laws. The unemployed laborers’ distrust of relevant assistance policies is the most necessary traditional concept that needs to be abandoned. Only by having a full understanding of policies and educational compensation for unemployed laborers can we dispel their doubts and “wait and see” attitude towards educational compensation [32]. The more unemployed laborers know about unemployment policies, the stronger their WTEC. Policy understanding and WTEC for unemployed laborers have a higher impact. Unemployed laborers increase their WTTD once they understand the advantages of educational compensation policies. Secondly, only when the transformation of traditional concepts to modern concepts is realized can unemployed laborers accept the new policy, which is in line with current times. The country needs to further optimize the social public opinion environment and cultural environment and promote people’s modernization concept with a good social environment [33]. Promotion can be carried out in both online and offline dimensions [34]. In the online dimension, major platforms can be taken advantage of, and the power of new media can be utilized to carry out targeted publicity. In the offline dimension, the educational compensation policy can be incorporated into the daily publicity work for the purposes of promotion. Unemployed laborers do not need to have a deep understanding of educational compensation when they first receive it; they need to master the knowledge of it in a subtle way.

To summarize, we believe that the practical improvement in laborer’s WTEC should be carried out mainly by the government and laborers: that is, the GF model. However, the realization of the GF model still faces many challenges. First, the propaganda ability of the grassroots government faces challenges. It is difficult for laborers to understand the policy, and grassroots propaganda cadres are limited by their cognitive level and other aspects; their working strength needs to be tested. It is also uncertain whether laborers will accept the relevant propaganda and whether they have enough trust in a township’s grassroots cadres [35]. Solving the “last mile” of policy promotion is the key [36]. Second, policies are step-by-step distorted in the process of transmission at government levels. In the daily management and operation of contemporary governments, “information” plays a vital role. However, when it is transmitted, distortion inevitably occurs because of various reasons. If the government introduces relevant policies to help unemployed laborers understand educational compensation, the interpretation of policy documents proceeds through different departments, and officials process and filter the information, resulting in information asymmetry at different levels [37]. This can ultimately result in policies that deviate significantly from the effects envisioned by policymakers [38]. The educational compensation policy or the ideological transformation of unemployed laborers is also greatly reduced. Finally, achieving a transformation in the minds of unemployed laborers is challenging. In the process of agricultural production, traditional laborers have long-term self-accumulated production experience, and most are engaged in small and scattered agricultural production activities [39]. The production technology that they use is often relatively simple, and they are only satisfied with self-production and self-sale; moreover, they are not good at management [40]. It is easy to be content with the status quo and lack the urgency to accept new knowledge, new technologies, and new skills. Therefore, they have doubts and wait-and-see attitudes toward new technologies that have been promoted and applied without their own practice. As a result, changing the inherent ideas of laborers poses a certain challenge.

5. Conclusions

Combining China’s national conditions and the current situation of the development of the COVID-19 pneumonia epidemic, encouraging unemployed laborers to participate in educational compensation, and improving the employment of unemployed laborers can help the country in improving its ability to deal with similar situations caused by major emergencies. This paper selects Jining City, Shandong Province, as the research area, collects data using questionnaires and key interviews, and finds out the factors that affect the WTEC and WTTD of unemployed laborers. Among them, eight factors, such as education, unemployment duration, family size, and annual income before, have a significant impact on the WTEC of unemployed laborers. Nine factors, including unemployed laborers’ gender, age, education, unemployment duration, and communism, significantly affect WTTD. At the same time, we further derive that the average WTTD is 12.05 days. Based on this, we propose the GF model of unemployed laborers that participate in educational compensation: That is, taking measures at the two levels of the government, laborers can increase WTEC and WTTD, thereby improving the re-employment of unemployed laborers and ultimately improving the protection of unemployed laborers, employment, and people’s livelihood issues.

6. Research Limitation

This study also has many limitations. This study collects data on unemployed laborers in Jining City. Although the research questionnaire for the thesis involves various regions of Jining City, due to the constraints related to funds, manpower, and epidemic policies, there are differences in culture, lifestyle, and behavioral norms in some areas, and the research data may have certain limitations. It is suggested that follow-up research should collect data from other regions on the basis of this study, and comparative studies should be conducted. In the study, the paper found that some control variables, such as education, unemployment duration, and family size, have a significant impact on individual behavior intentions, but this study did not further investigate the mechanisms of these demographic characteristics and other factors. For further exploration, follow-up research can conduct more detailed exploration from other angles.