The Expansion of Higher Education and the Returns of Distance Education in China

The returns of traditional face-to-face education are widely analyzed, but there is a need for empirical studies on the returns of distance education. Further, comparative studies on returns of both traditional and distance education using high-quality data are rare. Since 1999, continuous and rapid expansions have occurred in the whole Higher Education system in China. Given this background, what are the changes in returns of both traditional face-to-face education and distance education? This study analyzes the returns of both of these formats from 2003 to 2006 using the data from the China General Social Survey Open Database (Chinese General Social Survey [CGSS], 2018), adding educational background as a dummy variable to the Mincerian income equation. The empirical results show that Distance Higher Education can significantly increase the income of learners, the returns of distance education are lower than those of traditional face-to-face education and that from 2003 to 2006, the returns of distance education decrease dramatically.


Résumé de l'article
The returns of traditional face-to-face education are widely analyzed, but there is a need for empirical studies on the returns of distance education. Further, comparative studies on returns of both traditional and distance education using high-quality data are rare. Since 1999, continuous and rapid expansions have occurred in the whole Higher Education system in China. Given this background, what are the changes in returns of both traditional face-to-face education and distance education? This study analyzes the returns of both of these formats from 2003 to 2006 using the data from the China General Social Survey Open Database (Chinese General Social Survey [CGSS], 2018), adding educational background as a dummy variable to the Mincerian income equation. The empirical results show that Distance Higher Education can significantly increase the income of learners, the returns of distance education are lower than those of traditional face-to-face education and that from 2003 to 2006, the returns of distance education decrease dramatically.

Introduction
Human capital theory sees education as an investment in human capital and regards that education can bring economic benefits to learners as well as promote national economic growth (Becker, 1964;Schultz, 1961). Since the 1950s there have been many studies on the private benefits of education, covering almost every nation and area (Daly, Lewis, Lewis, & Heaslip, 2015;Hartog & Gerritsen, 2016;Heckman, Lochner, & Todd, 2003;Psacharopoulos & Patrinos, 2004). These studies test the statement in human capital theory that education improves individual productivity. Further, private returns of education have become a main factor for individuals and families to make decisions in education investment. For example, comparing the returns of education and returns of other forms of capital investment can indicate whether education is a worthy investment (Psacharopoulos & Patrinos, 2004).
There are two prevalent methods for measuring the private returns of education (Psacharopoulos & Patrinos, 2004). One is the Mincerian rate of return, namely marginal returns to education, which reflects the increased benefit caused by one more year of education. The other method for measuring private returns of education is the internal rate of return, which calculates the discount rate that equalizes the real costs of education during the period of study to the real gains made as a consequence of this study period. Due to the availability of the cost data, most empirical studies estimate the Mincerian rate of return to education.
One important reason for establishing distance education institutes and providing distance education programs is to promote the development and investment of human capital. There is enough evidence which shows that distance education can improve professional skills and promote learners' employment in both developed countries and developing countries (Azeiteiro, Bacelar-Nicolaua, Caetano, & Caeiro, 2015;Siaciwena, 2008). Distance education, with face-to-face schooling, has become an important subsystem of the whole educational system. However, the studies of private returns of education still focus on traditional schooling and training. There are few empirical studies into the private returns of distance education. Although there are many studies on the costs of distance education, there are relatively fewer studies on the benefits or returns of distance education (Bramble & Panda, 2008;Moore, 2013;Rumble, 2001Rumble, , 2004. The content of two books illustrates this point, namely Rumble's (2004) book titled Papers and Debates on the Economics and Costs of Distance and Online Learning and Bramble and Panda's (2008) book titled Economics of Distance and Online Learning. Both books contain many chapters on the costs of distance education but only two chapters focus on the benefits.
Although some scholars have discussed the benefits (Bartolic-Zlomislic & Bates, 1999a, 1999bBates, 1995;Cukier, 1997;Hülsmann, 1997), empirical studies are lacking, as most of the existing studies do not measure the real benefits or returns of distance education. They lack in comparing the real income between those who have received distance education and those who have not received distance education. This situation is not in accordance with the current development of distance education, since the differences of real income after receiving distance education will be a good index for learners to decide whether to invest in distance education.
There are three empirical studies on the returns of distance education, all based on distance education in Mainland China. Li, Xia, Zhao, and Zhang (2009), Ni, Xu, Liang, and Zhu (2011), and Zheng, Chen, and Zhang (2009 reached similar conclusions that distance education can bring positive economic benefits. These three studies used the Mincerian income equation to estimate the return of distance education according to a learner's income, educational background, and working experience (Li, Xia, Zhao, & Zhang, 2009;Ni, Xu, Liang, & Zhu, 2011;Zheng, Chen, & Zhang, 2009). All three studies, however, have the same three shortcomings. First, the sample cases are in-school learners in distance education institutes and the data are not the real cross-section data, which thereby does not meet the assumption of the Mincerian income equation. Second, the sample cases are from one or two Distance Higher Education institutes, and there are problems with generalizability. Third, only sample cases from Distance Higher Education are used, which therefore does not provide a credible comparison. Thus, data of higher quality, wider coverage, and more representation needs to be studied.
Since 1999, the whole Higher Education system in China has been in continuous and rapid state of expansion (Li, Morgan, & Ding, 2008). The scale of this expansion is likely to lead to changes in the returns of education (Psacharopoulos & Patrinos, 2004). Thus, with the expansion, what are the changes of returns of Distance Higher Education? This is important information that individuals and institutions should know.
Given these considerations, this study uses representative data, with wide coverage and rigorous sampling methods to analyze the change trend of private returns of Distance Higher Education. Further, this study compares the changing trend of returns of Distance Higher Education and Traditional Higher Education. Hence this study aims to make two main contributions. First, it is the first study (to our knowledge) to use high-quality and representative data to estimate the private returns of Distance Higher Education. That is, it is the first to measure the real income benefits of distance education. As such it will improve academic understanding of distance education as human capital and promote the study of the economics of distance education. Second, under the background of Higher Education expansion, the study will uncover trends in the changing returns of Distance Higher Education. This has the potential to guide investments regarding distance education, and help institutes plan how to develop and implement programs.
The next section of this paper introduces the expansion of Higher Education in China and the development of Distance Higher Education. The third section introduces the research methods and data.
The fourth section presents the empirical results. The fifth section discusses the findings, and the final section offers a brief conclusion.

Literature Review: The Expansion of Higher Education and the Development of Distance Higher Education in China
At the beginning of the 1950s in Mainland China, distance education developed from correspondence education. In need of more high-quality human resources after the reform and opening up in 1978, Deng Xiaoping, who was the core of the "second generation" of Chinese leadership, put forward the idea that "planning for the development of modern educational instruments such as television and broadcast is an important way of rapidly developing China's education" (MOE, China, 1999). This initiated the establishment of Radio and Television Universities and the rapid growth of Distance Higher Education (MOE, China, 1999). From 1983, Radio and Television Universities started to enroll students from junior college education (Ding, 2001). By 1985, the number of junior college students attending Radio and Television Universities reached 674,000, accounting for 40% of Higher Education students in China (Ding, 2001

Method and Data
This study mainly uses the Mincerian income equation to estimate the returns of distance and traditional education. The Mincerian income equation was proposed by an economist, Jacob Mincer, one of the founders of human capital theory (Mincer, 1974) as: Figure 1. In this, Y is individual's income, LnY is the natural logarithms of individual's income, α is a constant term, S is the years of schooling of individual, EX is the years of working experience of individual worker in the labour market, and ξ is the error term.
Calculating the partial derivative of the equation above gives: Figure 2. In this, β is individual's increasing rate caused by one more year's education. In economic studies, β stands for the percentage increase of an individual worker's income by taking one more year of education, which is named Mincerian rate of return.
Some scholars add the educational degree instead of educational years as a dummy variable in the Mincerian income equation (Carnoy, 1995). With this formulation, the coefficient difference of the educational degree is used to estimate the increasing rate of the individual's income (Carnoy, 1995).
This method can only get the approximate value of return of education, which is not exactly equal to the value calculated by the Mincerian income equation ii .
This study uses sample cases' income, educational background, and working years to estimate returns, by adding the dummy variables of educational degree to the standard Mincerian income equation. includes only in-school Distance Higher Education learners. Compared with these studies, this study has an advantage that the data used includes adults of in-school learners and individuals in the labor market with various educational levels. In order to make the return of distance education more precise, this study analyzes only those individuals in the labor market.
Since the late 1970s, distance education in China has mainly focused on junior college education and undergraduate education, and rarely focused on post-graduate education (Li & Fu, 2015). Thus, Distance Higher Education is restricted to distance junior college and undergraduate education in this study. In order to estimate the return of Distance Higher Education, the laborers with the highest degree of senior secondary education should be the reference group. Therefore, the sample cases selected for this study were individual laborers with the educational level of senior secondary education, junior college education, and undergraduate education respectively.
Junior college education can be divided into full-time and part-time, similar to undergraduate education. In the SPSS data files provided by CGSS, "part-time" is defined as "The learners receive education from adult university, correspondence university, Radio and Television University. Most learners are still in the labor market and they take the courses in the evenings and weekends and faceto-face is not the main teaching method." Obviously, the "part-time" can be deemed as distance education.  The sample cases can be categorized into five kinds, including senior secondary education, distance college, face-to-face/traditional college, distance undergraduate, and face-to-face/traditional undergraduate. The sample cases with no information of annual income and working experience are excluded. Table 1 shows the distribution of the educational background of the sample cases.
The study deems those with senior secondary education as the reference group, and the other four educational levels as the dummy variables added to the Mincerian income equation. Table 2 is the simple statistical description of various variables. Table 3 depicts the regression of the Mincerian income equation in each year. Figure 3 shows the tendency of returns of distance education and traditional education of different educational levels from 2003 to 2006. Table 3 The Regression of Mincerian Income Equation

Empirical Results
Note. The dependent variable is the natural logarithms of an individual's annual income. Standard errors are presented in parentheses. "***": significant at the 1% level. The coefficient information of the variables of constant, working experience, and its square are omitted.
From Table 3  Secondly, the returns of distance education are lower than that of traditional education. It reveals that although both distance and traditional education can increase learners' income significantly, compared to face-to-face education, distance education has a smaller effect on increasing learners' income.
There are many possible explanations why the economic benefit of distance education is lower than that of face-to-face education. For example, distance education provides more flexibility and convenience for the learner, thus potentially attracting students who may be unable (due to work or life commitments) to study full-time at a campus university. With this, the lower economic returns of distance education may not be due to distance education itself but the result of the learners' extracurricular work or life commitments/inability to study full time. Such explanations are beyond the focus of this paper, but it would be helpful for follow-up research to investigate.
Thirdly, the difference of returns of distance education versus traditional education can be determined at different educational levels by dividing the coefficient of traditional education by that of distance education. Both in 2003 and 2006, the difference of undergraduate education is bigger than that of junior college education. This means that the return of distance education is lower than that of traditional education, and the disparity is more apparent at the level of the bachelor's degree.
Lastly, when looking to Figure 3, the returns of distance college, distance undergraduate, and traditional undergraduate education appears to decline each year, suggesting that the returns of these forms of education have a declining tendency. This tendency is consistent with another empirical study (Ding, Yu, & Yu, 2012). This tendency also contrasts the dramatic increase of returns of Higher Education between 1990 and the early 21 st century (Chen, Chen, & Xia, 2003;He, 2009;PKU GSE, 2005). This shows that the expansion of Higher Education is accompanied by declined returns of Higher Education.

Discussion
This paper has found that Distance Higher Education can apparently increase learners' income.  (Li, Yuan, & Liu, 2008;Yang & Li, 2012

Conclusion
There are plenty of studies on returns of traditional education. Although the economic targets of distance education are to improve learners' skills and to promote their employment (Azeiteiro et al., 2015;Siaciwena, 2008) studies on returns of distance education are fewer, due to the difficulties to obtain data, and the ease of the economic target to be neglected. Based on the empirical results above, this paper suggests that the Distance Higher Education system in China should address this potential looming crisis, especially given that the changing population structures and lowering birth rates are likely to convey further decline (Yang & Li, 2012).
In further research, more attention should be paid to the following three areas. First, the returns of different majors of distance education can be estimated to better guide learners in how to choose majors.
Second, the impact of educational quality on the returns of Distance Higher Education can be analyzed since many empirical studies show that quality will increase the returns of traditional face-to-face Higher Education (Hanushek, Ruhose, & Woessmann, 2015;Hanushek & Woessmann, 2012;Jackson, Johnsonand, & Persico, 2016 ii The reader who are interested in the rate of return to education can read Carnoy (1995) to understand the difference between the two different methods. iii Those reader who have interests can read the two review articles on the chaos on the distance higher education in the "People''s Daily" May-2-2013 (Chinese version). Although some of arguments from these two articles are debatable, but the worrying of Chinese distance higher education's quality is a consensus for many people.