Looking at the relationship between education and income from the distribution of educational opportunities, because the distribution of educational opportunities affects the distribution of income, government budget departments must consider the role of education when planning the long-term distribution of income. However, this issue is still controversial: some economists believe that education level is closely related to how much salary you may earn; However, some economists believe that genetic characteristics, family background and luck are the main factors that determine how much money a person can earn.

Some people think that people with higher education are more productive and should be paid more. In this sense, the relationship between education and income is an important factor to determine whether the allocation of resources is reasonable. However, in addition to the education level of the labor force, the type of wages also reflects many other factors. It is not only personal characteristics such as personal talent and family background that determine the income difference, but also historical and institutional factors. Therefore, some economists believe that education is a form of human capital investment, and resources allocation should be guided by techniques similar to cost-benefit analysis; Some economists believe that education is like a scanning device, which can identify the most productive labor for employers. These two views are completely different. There is also a view against the theory of human capital, which holds that there is actually a dual labor market, because educated workers are concentrated in the main production sector, while workers with low education and workers who are discriminated against because of race or origin are concentrated in the secondary labor market (income is determined by other factors). In any case, the relationship between education and income is important and controversial. The differences mainly focus on the following questions: What factors determine a person's salary? Does the wage difference reflect the productivity difference of labor? How should we use income differences to guide educational planning?

From an example in life, even if two groups of the same age and gender are employed in the same factory and engaged in the same type of work, the average salary of the group with more education is definitely higher than that of the group with less education. This positive correlation between education and income is one of the most amazing discoveries of modern social science, and it is also a reliable conclusion applicable to the labor markets of various countries.

At present, data on average income and education level can be obtained from at least 40 countries. Without exception, workers with more education enjoy higher wages.

In this respect, whether we collect income data vertically (to show how wages change with time) or horizontally (to show how income changes with the age, education level and other personal characteristics of workers at a certain time), we can find that workers with higher education level enjoy higher wages.

The expenses paid for receiving education are actually an investment, and the salary income after receiving education is actually the income of education investment. The relationship between them constitutes the basis of cost-benefit analysis of education. If horizontal data is used, it can be revealed that the improvement of labor quality is helpful to promote economic growth. If longitudinal data are used, the predictive function of education can be revealed.

Second, the relationship between age and income.

Some countries conduct regular surveys on income and education levels, while others rely on sampling surveys to obtain data. At present, there are many such materials. According to these data, some charts can be drawn. In the graph of age-income relationship, workers with different education levels or years have the following three general characteristics: (1) The average income of workers with higher education and workers without education increases with age. When they are in the middle of their career, their income reaches the highest value, and then the income curve tends to extend horizontally or begin to decline. (2) The higher the education level of workers, the faster their income will increase, and in most cases, the starting salary of their careers will be higher. (3) Workers with higher education level reach the peak of income later than those with lower education level, but their income is higher when they retire.

The above three characteristics show that the total income of workers with higher education level is obviously higher than that of workers with lower education level or no education. Therefore, we should focus on the total income of the labor force in a lifetime, and we should not blindly emphasize the income difference at a certain time stage, otherwise the overall economic benefit of education will be seriously underestimated. Comparing the two groups of workers with different education levels on the graph of age-income, we can see that the income gap between them runs through the whole career, which provides a measure for measuring the excess lifelong income brought by higher education. In the United States, for example, researchers have studied the relationship between education and income, with the initial purpose of understanding whether education is a cost-effective investment form. They calculate the life-long excess income of college graduates and compare it with that of middle school graduates. According to the data collected from more than 30 countries, the relationship between education and income is almost universal. At the same time, people also find that age is an important factor in determining the average income of labor. The following will examine the significance of these relationships to human capital theory, and explain how to evaluate the educational benefits of personal investment or social investment according to the age-income relationship diagram.

Three. Factors causing income difference

The relationship between age and income shown in Figures 2 and 3 shows that age and education determine a person's income. The average income of workers increases with age, reaching a peak between 40 and 55 years old. Then, although personal income may continue to rise until retirement, the average income level begins to decline. The average income of all workers is around 60-65 years old, which drops rapidly with retirement. Figure 2-3

The labor force with different education or education level will also affect its average income. The average salary of college graduates is higher than that of high school graduates, and the average salary of high school graduates is higher than that of primary school graduates, middle school graduates and illiterate people without any school education. If age and education are the main reasons for the difference in labor income, then Figures 2 and 3 can prove that education is a very effective investment for individuals. Although age and education are important factors that determine labor income, they are obviously not the only two factors that affect relative income. Because there is discrimination in many enterprises in many countries and people will interfere in the income pattern, race and gender also play a role in determining personal income. In order to exclude the influence of these variables, it is necessary to compare the income of men and women, the income of workers in different regions and the income of different races respectively. From the research work carried out in many countries, age and education are still important factors, but gender and race are also one of the important factors that determine income.

Statistics from Britain and the United States show that the average income of women is usually lower than that of men, and the average income of blacks is usually lower than that of whites. However, if the two variables of race and gender remain unchanged, the income of workers with higher education level is higher than that of workers with lower education level. For example, Woodhull's research found that in the United States, women with primary education earned $65,438 +0.404, women with secondary education earned $2,673, women with college education earned $465,438 +0.65, and women with higher education for more than five years earned $665,438+0.10.4. In Britain, the relationship between education and income is also obvious. The income gap between male university graduates and non-university employees is 2.3, and the income gap between female university graduates and non-university employees is 2.6. In the United States, the higher the education level of women, the closer their income is to the male labor force with the same education. The average income of women with primary education is only one-third of that of men with the same education, while that of women with high school education is 40% of that of men with the same education. The average income of women with five-year higher education is very close to that of men with the same education. Although women's average income is lower than that of male labor force because of short working hours, and most of them are concentrated in low-income industries, it also shows that education can improve their overall income, thus improving economic benefits.

A study on the income of white labor and non-white labor in the United States and Britain found that although race is an important factor in determining wages (the reason may be racial discrimination, differences in personal characteristics, or because most people of color are engaged in low-income occupations), education does cause income differences. People of color with more education do earn more than people of color with less education. Recently, a study conducted in Britain shows that what kind of school you enter is more important to people of color than how many years of education you receive. However, for the white labor force, the factor that has a substantial impact on income is the level of education.

In addition to race, gender, working hours and occupation, there are many other factors that also affect the labor wage model. For example, educated workers earn more, probably because they have more talent, better family background and more education than others. Some researchers believe that the calculation of the ratio of education input to income shows that education itself has no important influence on income. On the contrary, they attribute the excess income of educated workers to talent, motivation, social class, well-paid occupations and even luck. The problem is that this extreme view ignores the fact that people have been trying to compare the influence of other variables on average income by separating education from other variables for many years, but they still find that education plays a key role. For example, some researchers separate the role of school education from factors such as school education, heredity, health status and luck. They use their brothers and sisters as samples in income comparison, so as to achieve the standardization of income, because this kind of design research can keep family background and other environmental factors unchanged. The income of the subjects was analyzed according to the following variables: age, education, school time, standard test scores (as an indicator of quality), occupation, residence, family size, other income and medical expenses (as an indicator of luck). The results show that no matter how old they are, people with more education earn significantly more than those with less education.

Since people studied the influence of education on income, great progress has been made in analytical techniques and data collection methods. Multiple regression analysis and income function are also used as elements of income analysis. We have every reason to believe that age and education are still the most powerful determinants of income, even if we consider various variables in a wide range of fields. In other words, gender, race, occupation, talent, luck, etc. It may be an important factor affecting income, but adding all these factors together is not enough to explain the reason of income difference.

Four. Human capital: training and competence

Although many studies have proved that besides age and education, there are many other factors that also play a decisive role in the income of labor, people pay special attention to the influence of training and ability in these factors. When people explain the influence of work experience on income, they find that personal experience represents the accumulated investment in vocational training and career change. The "vocational training" mentioned here refers not only to formal pre-job training, but also to informal on-the-job training. In fact, if we want to explain the relationship among income, education and age from the perspective of human capital, we must consider the influence of training and ability.

Human capital includes not only investment in formal school education and after-school education (such as training and internship), but also pre-school investment, that is, those environmental factors called "socio-economic background" in taxonomy, or parents' investment in their children. The time (quantity and quality) that parents spend on their children can be regarded as an investment in their quality (human capital).

(a) Parents' investment in the quality and income of their children

The time invested in preschool children is mainly reflected in the mother's care for the children. Therefore, it will reduce the income of mothers in the labor market. The income that mothers give up to take care of their children is the amount that mothers invest in preschool children. Therefore, if women's time with their children can increase their human capital reserves, make them get better grades in school education, or improve their educational opportunities in some way, their future income may increase accordingly. Of course, when women leave the labor market, they will reduce their income by taking care of their children. When analyzing the relationship between women's work experience and income, the researchers put forward the following rules: (1) Women's desire to participate in the labor market is weakened due to child-rearing tasks, which leads to a decrease in investment in education and training. Compared with the male workforce, they also receive less education and training. (2) During the period of raising children, long-term separation from the labor market will lead to a decline in the skills already acquired. (3) Women who are re-employed after their children go to school have a strong desire to recover their investment. (4) Married women's investment profile is negative (net loss) due to the interruption of their work experience during raising children, while unmarried women's investment profile is close to that of male labor force due to their continuous work experience. (5) The income profile of male workers is sharp, concave and oblique, which is almost the same for women without children, while the income profile of women who become mothers has two peaks, and the overall growth is less.

In addition, one of the effects of education on women's income is that if women have a higher education level, they may return to their original salary level after re-employment. At the same time, due to the growth of work experience, they may enjoy a salary increase again. However, women with low education may not be able to recover their lost income and continue their interrupted work experience after work breaks. Figure 4 shows the average weekly income of British women, and the classification standard is the age when they finish their studies. The average income of those female employees who go to school over 19 years old begins to decline between 30 and 40 years old, and then rises again until they are 60 years old. Those female workers who left school to work at the age of 15 reached the peak of their lifetime income around the age of 20, and the average income has been declining since then until retirement.

Figure 4 shows that for women, the economic benefits of their previous education are realized by investing in vocational training and work experience. Training, work experience and school education are all forms of human capital investment based on learning. Figure 4

Learning is no different from education, vocational training or other forms of investment in form, and it is also one of the investment ways of human capital. The income profile based on learning theory does not conflict with the income profile based on human capital, because the former is only a special case of the latter. If education is recognized as the investment form of human capital, then the proportion of this investment income can be calculated. Only by comparing the present value of the extra income brought by education and appropriately correcting the influence of other factors on income according to the A coefficient can people calculate the proportion of this investment income. For this kind of investment income analysis, the relationship between education and income has dual significance. On the one hand, forgone income is the main cost of education investment, on the other hand, it is this extra income of educated labor that constitutes the main interest of education. The cost of education investment should be calculated not on the basis of monetary expenditure, but on the basis of opportunity cost. Opportunity cost refers to the value brought by putting these resources into education and giving them up for other purposes. In other words, whether for individuals or society, when calculating the cost, we should not only look at the things bought and sold, but also base ourselves on all the resources actually used in the education process. Among all kinds of resources, the most important thing is students' time. Although it has no monetary value, it has economic value and opportunity cost.

(2) Ability and income

Cost-benefit analysis and research on educational investment income often calculate the influence of "ability correction" factors (or a coefficient) other than age and education background on the difference. Correcting the income difference with a coefficient reflects the influence of other factors besides ability, such as social class and family background. Coefficient is a correction coefficient, indicating the degree of income difference caused by education itself. If a coefficient is equal to 0.5 or 50%, it means that half of the income difference of workers with different education levels in the age-income relationship diagram should be attributed to educational factors, and the other half should be attributed to other factors (such as ability and social class).

In the field of educational economics, there are two ways to use a coefficient: one way is to use the income difference of labor as an index to measure the quality of labor. That is to say, the income of the labor sample is tabulated according to age, education level, intelligence test score, academic ranking and parents' socio-economic status, so as to show how much the actual income difference of the labor sample is caused by education and how much is caused by other factors (for example, 60% of the income difference of the educated labor force is caused by education and the other 40% is caused by other factors). Another method is reference multiple regression analysis. That is to say, when considering the ability factor alone, the value of a coefficient can be around 0.8. If the ability and socio-economic background are considered at the same time, the value of a coefficient should be around 0.65. According to these studies, people now generally set the value of a coefficient as 0.6 or 0.67.

As a form of human capital investment, education is a basic premise that education can improve labor productivity, or that the high income of educated workers reflects the value of the products they create. The way to improve productivity through education is to improve labor productivity by imparting knowledge and skills, so that they can create higher value in the labor market than those with less education. If income reflects the difference in marginal productivity, then the excess income of educated workers is their contribution to production. If this hypothesis can be established, then the relationship between income and education contains two meanings: first, the income of workers with higher education level is higher than that of workers with lower education level, which means that income can be used as an indicator to measure the contribution of education to economic growth. Secondly, if the relative income can reflect the difference of production capacity, then another important conclusion about the relationship between education and income is that the income difference can be used as an indicator to measure the economic benefits of education when calculating the rate of return on education investment. However, the statement that income reflects production capacity is based on the view that all markets, including the labor market, are competitive.

It is true that as long as the labor market remains competitive, income is an indicator to measure productivity. The problem is that if the labor market is imperfect and its excess income is mainly caused by non-economic factors such as history and management, then income cannot be used to measure production capacity. In this sense, we should not take income as the best index to measure production capacity, and then think that income difference reflects the lack of resources and the economic value brought by it. However, as long as the market mechanism is working, even if the mechanism is not perfect, the shortage can always be reflected in the price, or the higher wages of educated workers prove their higher production capacity.

(3) Is education a means of screening?

In the theory of human capital, some people think that education is only a tool of socialization and does not affect individuals' later income. Some people think that education is a screening method, which helps employers identify individuals who have better talents, attitudes or characteristics and are qualified for vocational training. On this basis, the higher wages of educated workers can only show that education has played a role as a sieve or filter, but it cannot show that education has improved the productivity of workers. This so-called "screening hypothesis" has caused a heated debate. If education only plays the role of discriminating the merits of individual talents, then the social investment in education is undoubtedly a huge waste of resources, because people can achieve the same goal through other more convenient ways.

In fact, employers failed to think of a more convenient way to choose the labor force, and many of their practices did not conform to the screening hypothesis. For example, on the age-income curve, with the passage of time, the curve of people with different education levels is divergent, not convergent. This shows that although employers know everyone's productivity after the workers have been in office for a period of time, they continue to increase wages for workers with higher education. This shows that in their eyes, education is not a tool for screening employees. On the one hand, if education is the best way to identify the manpower needed by enterprises, we have to admit that education is an economic method before people invent a better screening method; On the other hand, if education is not the best way to determine the manpower needed by enterprises, then it is not clear what employers use to hire people. Obviously, these are two contradictory aspects. Around this issue, we are urged to return to the issue of competition in the labor market. People think that education can directly promote economic development in the form of human capital, mainly because they think that competition dominates the labor market.

If competition dominates the labor market, the screening hypothesis is not worthless, because it is based on the view that education not only has the function of imparting knowledge and skills, but also can influence students' attitudes, motivations and values. All these help to determine the productivity and the possibility of being employed. In this sense, ability is an input in the process of education, and production capacity and the screening function of school education are not mutually exclusive. Therefore, it can be considered that education improves labor productivity, and many employers do regard education as a convenient screening method, because they do not require educational institutions to directly impart skills to students, but attach importance to the cultivation of attitude, ability and social communication ability in the process of education. Of course, it does not rule out that some employers only pay attention to the certificates or diplomas issued by educational institutions, and do not pay attention to the knowledge and skills actually taught by schools. The phenomenon of "only diploma theory" does exist and has become a "diploma disease" in some places. Western scholars have a saying that "education is neither a panacea nor a poison". Education can really improve the productivity of workers and increase their income. Although we can't fully understand how education affects productivity, there is a general consensus that on the one hand, education directly affects the productivity of labor by imparting useful knowledge and skills to students; On the other hand, education affects productivity by cultivating students' attitudes, abilities and social skills. Education also plays a role with other forms of human capital investment, such as on-the-job training. It can be said that the income of educated workers is preschool, school education and post-employment training.