1. Introduction
China’s economy is going through a structural shift, where consumption has become an essential engine for economic growth. The urbanisation of the permanent resident population, where China’s largest domestic demand potential and development momentum lies, supports implementing the strategy of boosting domestic demand and stabilising economic growth [
1]. In recent times the government has committed to urbanising the mobile population. Local governments have taken the initiative in facilitating the settlement of the mobile population through a series of policies such as gradually cancelling restrictions on household registration. However, the signs of ‘counter-urbanisation’ are also salient as a considerable part of the mobile population has shown less willingness to settle in cities and frequent secondary migration. According to macro data from 2017, China’s domestic mobile population was 244 million, accounting for 17.55% of the total population [
2], of which 48.7% expressed a clear desire to settle [
3]. Under such circumstances, although China’s permanent urban residents’ rate rose to 60.60% by the end of 2019 [
4], the rate is still below the 80% average of developed countries. Hence, for local governments, it is worth pondering over how to raise migrants’ settlement intention, realise the effective long-term agglomeration of migrants in the destination cities and thereby unleash the economic growth potential of population aggregation.
A closer look at the reasons leading to low settlement intention of migrants reveals a phenomenon that has been ignored by previous studies. The low settlement intention of migrants in China is mainly due to the fact that the migrants show an obvious chance behaviour in the choice of destination city [
5,
6]. It reflects an important economic problem: for those who have moved to the destination city, the fundamental reason for their low willingness to settle is that the expected settlement utility based on the actual living experience is lower than the one based on the collected information before the migrants move to the destination city. Incomplete information of the destination city causes the expected bias [
7]. As a result, the source composition of migrants pouring into the destination city includes a large number of migrants who do not match the characteristics of destination city, so that the overall situation shows a low settlement intention. Therefore, to improve migrants’ settlement intention, it is key to use an index with high information transparency of the characteristics of destination city as the market signal, eliminate the misjudgment of migrants’ expected settlement utility caused by information asymmetry, and reduce the mismatch between migrants and destination city to achieve accurate migration.
The existing studies have classified the factors affecting settlement decision into two categories: social characteristics [
8,
9], and economic incentives [
10,
11]. Unfortunately, there is a serious lack of information in the existing indexes of the two categories, to the point that migrants cannot accurately judge the matching degree between them and the destination city when making settlement decision. Therefore, after immigrating to take a chance, the actual settlement utility perception is likely to be lower than the psychological expected utility. For instance, information like social characteristics of the destination is difficult to be confirmed by individual short-term observation, and before they come to the destination city, migrants can only make a judgment relying on the personal feelings of former migrants [
12]. Since the number of individuals who can provide information is limited, such information is not scientific enough to represent the social characteristics of the destination city. In addition, the market signal which usually represents the economic incentives of the destination is the income inequality index. However, this index measures only regional disparities in income levels, but does not indicate which factors migrant possesses are most likely to produce high economic returns in the market of the destination [
13]. Migrants cannot identify the settlement income growth mode of the destination city by the single index of income inequality, so that they cannot form an accurate settlement income growth expectation based on their human capital characteristics, and it is easy to create a psychological gap between expectations and actual feelings. Therefore, it is necessary to build an information transmission mechanism, using concise indexes as the corresponding market signals to provide migrant more detailed information about the economic incentive modes and social characteristics of the destination city, so that migrants can make a reasonable match between their own characteristics and the characteristics of the destination city, forming a relatively accurate expected settlement utility, and reducing fluctuations in psychological expectation. This is also the aim of our paper.
To build such an information transmission mechanism, market signal index should meet two features. First, this concise index can reasonably represent the urban characteristics of the destination city; that is, it is obtained by scientific measurement of sufficient sample size; next, this index contains information about the specific methods of economic incentives and the difficulty of social integration at the same time; that is, it presents higher information transparency. Roemer’s equality of opportunity theory provides a useful reference for setting up the aforementioned market signals. According to the egalitarianism principle of personal responsibility [
14], Roemer [
15] divides income determinants into two categories: factors beyond individual control (called ‘circumstances’) and factors within individual control (called ‘effort’). Therefore, the composition of income inequality can be classified into inequality of opportunity (IO) and inequality of efforts (IE). IO stems from ‘circumstances’ beyond the control of individuals, such as clan, parents’ educational background, and family social status, whereas IE sources from controllable differences in individuals’ ‘efforts’, such as working hours and behaviours to learn knowledge and skills [
16]. This categorises the determinants of income inequality in destination cities, which helps unfold the economic incentives and social characteristics of destination cities, thereby creating a relatively sufficient information condition for migrants to know the destination city and to judge the possibility of their income growth and social integration in the destination city.
As a market signal, IO and IE can directly convey the information of the economic incentive mode. According to Roemer’s theory, the possibility of migrants achieving income growth in a destination city depends on their circumstance and effort factors. In regions where IO prevails, income growth depends more on individuals’ family background and social class rather than on individual efforts. The higher IO in destination cities, the more chances for individuals with advantages in circumstances endowments to form higher expected incomes via circumstances factors [
17], but those with inadequate circumstances endowments have difficulty in increasing their incomes. In addition, IO and IE can indirectly convey information about the social characteristics of the destination city. Excessive IO makes income growth more dependent on innate circumstances factors so that people in the city would question the justice of income growth [
18], which is unconducive to attracting migrants. If IE is high, however, individuals incline to pay more effort to earn a higher expected income [
15], and it is also very likely that efforts will be the determinant of income growth. So, IE is more as a signal in favor of settlement. As far as the difficulty of social integration is concerned, IE and IO can reflect the social conditions of the region [
18,
19]. Excessive IO segments social networks into various small-scale incomparable circles, but excessive IE motivates people to communicate in the markets and frequently interact with each other, possibly resulting in breaking social barriers [
18,
19,
20,
21]. Therefore, the indicators of IO and IE can be used as market signals for destination cities, with which migrants can judge whether their own characteristics are in tune with those of the city and form a relatively accurate expected settlement utility before migrating to the destination city.
The contribution of this study is as follows. In order to solve the problem of high mismatching degree between migrants and settled cities under the condition of insufficient information transmission of destination city, the paper creatively constructs an information transmission mechanism that connects the IO and IE indexes and the possibility of settling down in the destination city, providing sufficient information for migrants to choose the destination accurately. This mechanism adopts IO and IE with higher information transparency as the indexes to convey the specific characteristics information of the destination city, which can help migrants accurately judge the possibility of settling down in the destination city, so as to avoid the hit-and-miss migration behaviour. For the destination city, this mechanism can pre-screen the composition of the source of the migrants pouring into the city, to reduce the possibility of mismatched migrants entering by mistake, so as to improve the settlement intention of the actual migrants in the city. This study also fills the knowledge gap on how to solve the mismatch between migrants and destination city due to the lack of information about the destination.
Based on Roemer’s equality of opportunity theory, and according to the international general measurement methods [
15,
16,
17], we calculated the IO and IE indexes of major representative cities in China. By referring to the China Labour-force Dynamic Survey (CLDS) database, we tested the information transmission of the two market indicators for the characteristics of the destination cities, including the feasibility of the impact mechanism on the settlement intention of migrants. The reasons for choosing migrants moving among cities in China as the sample are as follows. First, China has prioritised urbanisation, but cities have different abilities in drawing migrants to settle in. Local governments are concerned with how to use market means to reduce the mismatch between migrants and the destination cities at its source to secure effective population agglomeration. Our research provides a new course for solution. Second, since the reform and opening-up, migrants have been essential contributors to China’s economic growth. For many years, migrants have longed to settle in cities where they work and share the development dividends of reforms. In the context of urbanisation in which cities begin to accept migrants, the settlement behaviours and life qualities of migrants have always been a matter of great concern for the government. Our mechanism is conductive to elevating settlement intention and happiness. Migrants can effectively screen destination cities with IO and IE indexes. We utilise market signals of IO and IE to solve the long-standing problem of incomplete information on the characteristics of destination cities, avoid the mismatches between migrants and destination cities due to information asymmetry and enable migrants to effectively identify and rationally choose their destinations.
The remaining content is organised as follows:
Section 2 elaborates the mechanism of IO and IE on migrants’ settlement intention and proposes hypotheses to be tested.
Section 3 describes where the data came from, constructs empirical models, and introduces variables and research methods.
Section 4 conducts empirical analysis to test the hypotheses.
Section 5 discusses the research results.
Section 6 concludes and provides policy recommendations.
3. Research Methods
3.1. The IO and IE Indexes of Major Representative Cities in China
This paper is to examine whether there is a correlation between IO and IE indexes and settlement intention. Before conducting metrological regression, it is necessary to measure the IO and IE indexes of representative cities in China with the current standardised measurement methods. Given that the existing research has formed a standard algorithm for the measurement of IO and IE indexes, and the measurement method itself is not the focus of this paper, the paper selects the same variables and the same methods as the existing research to calculate IO and IE Gini coefficients of representative cities in China, to avoid measurement deviations caused by different variables and measurement methods, so as to obtain a general measurement result. This is to ensure that when IO and IE indexes are used to verify the information transmission mechanism, the conclusions obtained are more universal. This paper adopts the same parameterised program and the same variables as Almås et al. [
14] and Bourguignon et al. [
16] to calculate IO and IE Gini coefficients of China’s representative cities in 2015. The specific variables and measurement methods will not be detailed here. The data used are micro data from the 2016 China Labor-force Dynamics Survey (CLDS) [
50], which come from the comprehensive database of the Centre for Social Survey of Sun Yat-sen University. The survey sample is composed of a total of 21,086 individuals, encompassing cross-sectional data on individual laborers aged between 18 and 65 years. The survey data cover 157 cities in 29 provinces, accounting for 95% of the country’s population.
The survey sample of each city is selected by probability sampling, which is proportional to the size of the workforce in the city, and the sampling results are representative of the city. After eliminating the missing value of the samples, we further eliminated cities with sample size lower than 45, to ensure that the final sample is representative of the city. Finally, a total of 14,986 samples from 146 cities were used to measure IO and IE Gini indexes, with a mean of 102, the minimum sample size of 47 (Baoshan, a small city in Yunnan province) and the maximum sample size of 351 (Guangzhou, the provincial capital of Guangdong province). The difference in sample size between cities stems from the difference in the labor base of cities of different sizes. Although the sample size of some cities is relatively low, they are still representative of cities. We plot the IO and IE indexes of major representative cities with ArcGIS 10.1.
The IO Gini coefficient of representative cities in China is shown in
Figure 2. White represents the unstudied areas, while colored areas show the IO Gini coefficient value of the representative cities. It can be seen from the color distribution that, the IO value is relatively low in cities in western China, especially in urban agglomerations in southwest China, the IO in the Yangtze River Delta urban agglomeration is slightly higher than that in the Pearl River Delta urban agglomeration and is relatively high in the Beijing-Tianjin-Hebei region and the three northeastern provinces. The lowest IO value is found in Meishan, Sichuan Province (0.006), while the highest is in Anshan, Liaoning Province (0.725).
Figure 3 shows the IE Gini coefficient of representative cities in China. It can be seen that the IE value is relatively high overall in China, with lots of red and blue cities, but there are obvious differences in different cities. The IE value is relatively low in urban agglomerations in northeast China and northwest China and is generally high in southwest China and eastern and central regions. The value is highest in some of the emerging inland cities, marked prominently in red on the map, which are also the main areas of net population outflow in the past but have seen a wave of return in recent years. The IE value in urban agglomerations in the Yangtze River Delta and the Pearl River Delta is at the middle level. The lowest IE value is found in Anshan, Liaoning province (0.147), and the highest in Zhaotong, Yunnan province (0.745).
The low IO and IE in eastern cities may result from the reform of income marketisation, which has been marketised for 40 years, and a market-oriented income distribution pattern has formed. Therefore, the income created by the circumstances has been restricted, whereas the income created by efforts has been fully released after embracing competitive markets for years. The high IE in western cities may be due to the economic benefits brought by industrialisation. Western cities are in the initial stage of marketisation, serving as the undertaker of industries transferred from the eastern cities. This has greatly stimulated the enthusiasm of workers there, so the IE is high. Nonetheless, the market size of the central and western cities is not as good as the eastern cities, so the competition among market entities for limited profit opportunities is more intense. That is why the income inequality caused by the circumstances is high.
3.2. Empirical Test of the Information Transmission Mechanism
In order to test whether IO and IE indexes in the information transmission mechanism proposed in this paper are really the effective indexes by which migrants judge the possibility of settlement, this paper is going to use CLDS data in 2016 to conduct empirical tests on the three hypotheses proposed in the second part. We adopt IO and IE as explanatory variables and migrants’ settlement intention as the explained variables. Given that the explained variable, namely, settlement intention, is an ordered discrete structure with five levels of finite sequence, this study chooses an ordered probit model for regression. Because both the IO and IE are mainly affected by the regional income distribution system, they would not change sharply in the short-term. Therefore, it is reasonable to use cross-sectional data to verify the influence mechanism of IO and IE on settlement intention. Referring to the research design formulated by Marrero and Rodríguez [
30], the empirical model is constructed as
where the subscripts
,
and
denote individual dimensions and time dimension respectively, and
denotes settlement intention, which is represented by five levels. To avoid estimation errors caused by reverse causality, explanatory variables
is used to denote the inequality index of the previous year, including both the IO and IE indexes. Existing studies related to the settlement have proved that the individual characteristics of migrants and the social security status of destination cities will affect their settlement intention [
51]. Therefore, this study adds
and
as control variables.
refers to other stochastic disturbances. The regression results of model (1) can test our hypothesis 1.
According to H2 and H3, IO and IE may affect settlement intention by promoting perceptions of fairness and social network. To verify these hypotheses, the following mediation effect model is set up echoing with model (1):
On the premise that the coefficients of IO and IE in model (1) are significant, a test is made on models (2) and (3); denotes perceptions of promotion fairness or social network variables; if the coefficients of and are significant, the mediation effect is verified as effective. The regression results of model (2) and model (3) can test hypothesis 2 and hypothesis 3. Specifically, variables are expounded as follows:
(1) Personal settlement intention (): As a subjective decision, is often measured by a natural sequence in the economic sphere. At present, the frequently used method of measuring in labour economics is questionnaires based on large samples, in which is denoted by ordinal numbers in an ordinal scale ranging from 1 to 5, which corresponds to the degree of the settlement intention ‘1’ = no settlement intention and ‘5’ = strong settlement intention. Comparing with IO and IE indexes, it is evident that is positively correlated with the inequality indexes.
(2) Control variables (correspond to
and
in models): Individual control variables (
) include age (
),
, gender (
), political status (
), educational background (
), and state-owned enterprise (
). According to the life cycle theory, there is a nonlinear relation between age and income, and migrants’ settlement intention will change with age [
52].
is calculated as age ∗ age/100.
is coded as 1 = male and 0 = female. When marketisation is at a low level, admission to the Communist Party of China (CPC) helps to obtain higher income [
53].
is coded as 1 = members of the CPC and 0 = others. As China’s wage system becomes increasingly marketised, the longer they receive education (
), the greater the probability of earning high incomes, which would exalt the probability of settlement [
54]. State-owned enterprises provide employees with relatively complete institutional benefits to facilitate employees’ settlement [
55], so it is coded as 1 =
and 0 = others. Regional control variables (
) include resident basic medical insurance (
), new rural cooperative medical scheme (
), regional pension system (
and social endowment insurance for residents (
), as all of them are revenue and medical guarantees provided by government finance for individuals [
56]. The variable is coded as 1 = those who possess social security and 0 = others.
(3) Mediating variables (correspond to
in model (2) and model (3)): perceptions of promotion fairness (
) can be measured by the question ‘How satisfied are you with the fairness of promotion opportunities?’ in the questionnaire. This subjective feeling is divided into five levels, with ‘5’ = highly satisfied and ‘1’ = highly dissatisfied. Assistance from friends (
AFF) is an integral part of the social network. The variable is mainly measured by the number of local friends who deliver assistance and reflects how harmonious the social interaction in destination cities is and the effectiveness of individuals’ social networks [
57].
(4) Explanatory variables: (correspond to
in models). Explanatory variables include
and
. These two variables can be measured by Gini coefficient results for 146 representative Chinese cities. Detailed measurement results have been shown in
Figure 2 and
Figure 3 in
Section 3.1.
3.3. Descriptive Statistical Analysis
Stata 13.1 is employed to perform descriptive statistical analysis on selected variables (
Table 1). It can be seen that the overall IO in China is relatively low, with an average of 0.174. While the overall IE is relatively high, with an average of 0.425. The IO and IE indexes of the destination cities show a large degree of dispersion in numerical distribution, indicating that there are obvious differences in different cities. The average value of settlement intention (PSI) is 3.561, indicating that there are more individuals willing to settle. The average PPF is neutral, slightly higher than the average value of 3, which conforms to relevant statistical laws. The standard deviation of AFF is high, but its average is comparatively low, indicating that most people have a limited number of supportive friends, which is also in line with reality. The mean value of age variable is 44.825, indicating that the majority of Chinese migrants are middle and young people. The mean value of the gender variable is 0.465, indicating that the ratio of men and women in the migrant group is basically equal. The average EDU is 9.724, and its standard deviation is 3.973. This indicates that many people in China are not well-educated, so their income level is relatively low. The mean value of party member variable (POL) is only 0.079, and SOEs employee variable is only 0.114, indicating that most of the migrants are non-party workers outside the institutional system, and only a small number of migrants can enjoy the benefits of the system. In terms of social security status, the average BMI, NRCMS, and SEI are low, illustrating that the penetration rate of social security is still low. The average NRCMS is high, implying that most people in the sample are from rural areas.
5. Discussion
The regression results in
Table 2 show that IO and IE can significantly promote migrants’ settlement intention. Contrary to conventional thoughts, IO has not negatively affected migrants’ settlement decisions in terms of income growth expectations. Instead, the message of income growth pattern conveyed by IO is an economic incentive approach to attract migrants to settle down. This means that countless migrants hope to achieve long-term family income growth through risk-free returns. High IO transmits a market sign to migrants that destination cities can securely protect property rights so that they can establish the circumstances endowment for their offspring with considerable market benefits. As a result, migrants form a stable expectation for the income growth of their entire family in such destination cities. High IE, however, signals the scarcity of human capital in, hence addressing their concerns about the volatility of income growth due to risks. Both types of information can effectively arouse migrants’ settlement intention. That aside, everyone has a unique preference for risk-free returns, so the coefficient of IO is slightly higher than that of IE.
The indirect mechanism of the influence of IO and IE on settlement intention is primarily realised by influencing PPF and social networks. The test results of the mediation effect in
Table 3 and
Table 4 demonstrate that IE elevates settlement intention by promoting PPF and social network, whereas IO curbs settlement intention by hindering PPF and social network. This coincides with traditional intuition. PPF and social network are both inevitable social factors when migrants settle. As a social feature related to income growth, PPF crystallises the expectations of migrants on their prospects, whether they are positive or negative [
69]. Reviewing the general opinions and research results of existing academic literature, there is a positive correlation between social equity and life satisfaction [
70]. Fair and reasonable promotion can significantly increase employees’ social satisfaction, whereas unfair promotion behaviour may greatly lower their evaluation [
71]. The social network is also a necessary condition for migrants to form effective social communication. The number of supportive friends reflects how well they integrate into society and develop themselves in the destination cities. This can help migrants transform their identities and gear into the local society. High IE indicates a fair social atmosphere, where migrants can establish an effective social network through hard work. High IO reminds migrants that it is difficult to integrate into a destination society.
The results of other control variables also tally with economic common sense. The influence coefficient of age on migrants’ settlement intention is negative, whereas that of
is positive. Young migrants have strong ambitions and motivation to push for better lives and hold higher expectations for future income. Elderly migrants already have enough material wealth to afford settlement expenses. Therefore, the settlement intention of both young and elderly migrants is high. Middle-aged migrants have children to raise, so their main purpose is to acquire short-term benefits rather than having a long-term settlement plan. The influence of state-owned enterprises on settlement intention is positive. That is because, in China, state-owned enterprises and government departments render substantial benefits to staff to ease the pressure of living costs. The influence of being party members is not significant, indicating that the income growth channels of Chinese society mainly rely on market transactions rather than on institutional benefits. Education level promotes settlement intention, as well-educated workers, with a higher stock of human capital to cope with the pressure of market competition, are prone to obtain high incomes. The influence of gender is negative; men migrate out of economic motivations, whereas women are more willing to settle out of family considerations, leading to a more significant population distribution effect than men’s migration [
72]. Moreover, the basic medical insurance, units’ endowment insurance and social endowment insurance for urban residents in destination cities can provide individuals with a guarantee for their income and health.
6. Conclusions
In the advancement of urbanisation, the Chinese government has been committed to continuously optimise the market mechanism and rely more on market forces to adjust settlement locations. A critical reason for current migrants’ low settlement intention is that they are not sufficiently informed of the income growth mode and on how to integrate into the society of destination cities. Income inequality indicators, which originally served as a market signal, only convey the overall income level of destination cities. The inadequate information causes many migrants to swarm into cities without knowing whether their own characteristics match with the features of the market and society. Only after spending several years in the destination cities do they realise how difficult it is to gain higher income and integrate. This kind of mismatch between migrants and destination cities due to incomplete information hinders China’s urbanisation and path of sustainable development.
This paper, by referring to Roemer’s equality of opportunity theory, decomposed the income inequality index of destination cities into IO and IE indexes and conducted an empirical test on the signal mechanism of IO and IE affecting settlement intention by extracting data from the CLDS. The results indicate that IO and IE can transmit information about the economic incentive modes and social characteristics of destination cities. By interpreting the indicators of IO and IE in destination cities, migrants can swiftly identify the market and social characteristics. This can mitigate the uncertainty in settlement decisions, and migration behaviours will change from ‘taking a chance’ to targeted selections.
In the current theoretical circles, administrative measures such as the household registration system, equalisation of public services and citizenship treatment have been regarded as universal approaches to manage the urbanisation of migrants. However, the analytical conclusions of market regulation methods from the perspective of income rarely have clear and operable countermeasures. This study supplemented the market adjustment of migrants’ settlement. When government departments release regional income inequality indicators, they should also announce the IE and IO indices. Through these two informative market indicators, hidden information about the income growth pattern and the way to build social relations beneath the surface of income inequality is more transparent. Such improvement in settlement intention driven by information disclosure will also give an impetus to China’s urbanisation at the macro level.
This study also has limitations. There are many ways for IO and IE to affect the settlement intention, but restricted by the availability of data, this study only analysed from the perspective of promoting fairness and mutual assistance in society. In the future when individual tracking data of places of emigration and destination can be obtained, we will carry out further research to measure how much the impact of network information diffusion can change the mismatch between migrants and destination cities. In addition, due to the limited availability of data dimensions, this paper only investigates the influence of IO and IE indexes on transmission capacity of the macro-characteristic information of the destination city. Of course, if individual occupation information is available in the future, we can further test the human capital transfer effect of migrants in the process of career transition between regions and combined with the influence mechanism of information diffusion in destination city of this paper, migrants will be able to judge more precisely how well they are matched with new jobs in their new destinations, which will be an interesting direction for future research.