The Impact of Divorce Cooling-Off Period on Registered Divorces: Evidence from China

Abstract

The divorce rate in China has continued to rise since the 21st century. In order to reduce impulsive divorces, the divorce cooling-off period (DCOP) system was established in the Civil Code of China. The aim of this research is to examine the impact of the DCOP on registered divorces. Data from the Ministry of Civil Affairs, National Bureau of Statistics, civil affairs departments, and statistical bureaus of 31 provinces, the 2018 to 2023 editions of the China Statistical Yearbook, and the China Civil Affairs Statistical Yearbook were utilized in this work. The event study analysis reveals that the number of registered divorces decreased significantly in China after the DCOP provision’s enforcement. The ordinary least squares model indicates an average annual decrease of about 2.6 million registered divorces when the DCOP provision takes effect, and the DCOP plays a decisive role in this decrease, which suggests that the DCOP system has achieved its original intention. This research not only highlights the important role of the DCOP in reducing registered divorces but also provides insights on how to improve the DCOP system and promote the stability of marriage.

1. Introduction

Marriage is the foundation of family and society; the stability of marriage is crucial for family harmony and social development. As shown in Figure 1, from 2002–2019, the divorce rate in China surged from 0.9‰ to 3.36‰, a 273% leap in less than 20 years. A high divorce rate impairs the stability of marriage, violates Chinese traditional family cultural concepts, and threatens the sustainable development of society. Due to the decreasing young population and the declining proportion of people who are eligible for marriage, the divorce–marriage ratio continues to rise (Yang and Sun 2021). Against this background, the divorce cooling-off period system came into birth with the mission of reducing impulsive or hasty divorces (Ma 2020). Article 1077 of the Civil Code of China stipulates the divorce cooling-off period system. Where either spouse is unwilling to divorce, he or she may, within 30 days from the day on which the marriage registration authority receives the application for divorce registration, withdraw the application for divorce registration from the marriage registration authority. The divorce rate started to decline from the year 2020 when the law was enacted but not yet valid, which showed a pre-effect of the divorce cooling-off period. In the year 2021, the first year the law came into force, the divorce rate declined to a new low point of 2.01‰, equivalent to the divorce rate level of ten years ago. The decline in the divorce rate indicates that the divorce cooling-off period has a positive impact on marital stability preliminary. This research focuses on the system effect of the divorce cooling-off period, especially its influence on registered divorces, so as to reverse the high divorce rate, improve the system itself, and promote the stability of marriage.

Figure 2 shows the number of marriages and registered divorces in China from 2017 to 2023. The number of marriages has been decreasing year by year, which makes the impact of divorce rate on the stability of marriage more profound. The number of registered divorces showed a trend of increasing first, then decreasing, and then increasing again. The first increase indicates the upward trend of the divorce rate before institutional interference, the downward turn may reveal the potential impact of the divorce cooling-off period on registered divorces, and the second increase may denote new influencing factors or the impact of the cooling-off period is diminishing.

The reasons behind the fluctuations in the number of divorce registrations are complex. The role the divorce cooling-off period played in the fluctuations remains to be explored. This research examines the impact of the divorce cooling-off period on registered divorces and analyzes the mechanism of the cooling-off period on the number of divorces. Through data analysis of the entire mainland China and 31 inland provinces over the past 7 years, we found that the number of registered divorces decreased significantly after the cooling-off period provision’s enforcement compared to previous years. This research thus corroborates that the enforcement of the divorce cooling-off period may be a decisive driving factor behind the decrease in registered divorces. Additionally, given the complexity of marriage, maintaining the stability of marriage requires various measures across multiple aspects.

2. Literature Review

2.1. Influencing Factors of Divorce

Numerous factors influence divorces, such as economic development, urbanization, employment, education, gender, children, regional disparities, population structure, population movement, regional culture, individualization, and other objective (such as violence between the parties or infidelity) and subjective (falling out of love) factors, all of which impact the divorce rate to differing extents (Chen et al. 2020; Kalmijn and Poortman 2006; Dong and Xie 2023; Shi 2020; Tan and Song 2015; Ren and Hao 2021; Killewald 2016; Bauman 2013; Beck and Beck-Gernsheim 2002; Giddens 1992; Inglehart 1990). Xu (2012) found that disparities in concepts, personalities, interests, lifestyle habits, consumption tendencies, child-rearing, and management of kinship relationships between spouses are also significant factors influencing divorce. Gou (2008) examined the factors influencing divorce through the lens of social exchange theory, underscoring that a strong marital relationship within a family necessitates the collective efforts and sacrifices of both spouses. Solely focusing on one party’s unilaterally selfless dedication is insufficient to foster a happy family. Through empirical analysis, Wang and Peng (2003) revealed a significant correlation between the duration of marriage and various factors such as the divorce age, age at marriage, age difference between spouses, and their educational levels. Lemkin (1944) observed that marital breakdown typically exerts a detrimental effect on children, leaving them as orphans of living parents. Xu and Ye (2001) discovered that while the breakdown of marriage negatively affects the well-being, academic performance, behavior, psychological development, and social adaptation of school-age children, these effects are not as severe as some scholars have hypothesized or as sensationalized by the media. Xu et al. (2013) found that childbirth prior to marriage is detrimental to marital stability. Having a larger number of children enhances marital stability, though the marginal effect decreases. Younger children contribute to the stability of marriages. Couples with sons exhibit a reduced risk of divorce. Children in rural areas offer a stronger marital protective shield than in urban areas. Trent and South (1989) discovered that in regions with higher social development, individuals are more inclined to shed the constraints of traditional marital and familial concepts, pursue gender equality and the elevation of women’s socio-economic status, and also assert independence and autonomy through non-traditional familial actions, such as divorce. Becker et al. (1977) employed the divorce utility theory model to analyze divorce. When the utility of divorce surpasses that of remaining married, couples opt for divorce.

2.2. Reflections on Divorce Cooling-Off Period

Prior to the establishment of the divorce cooling-off period, its feasibility and necessity had already been discussed (Guo 2018). During the drafting phase of the Civil Code, Yang and Jiang (2019) conducted a legislative assessment of the divorce cooling-off period provision, highlighting the issues of the monotony of the current cooling-off period and the absence of accompanying measures. Following the implementation of the divorce cooling-off period, Qin (2021) pointed out that this system might restrict the constitutional right to marital freedom to some extent. Zhou (2022) contended that the existing regulations inadequately mitigate risks like domestic violence and abuse, which contribute to marital breakdown. Zhang (2022) analyzed the application principles of the divorce cooling-off period provision. Jiang (2020) and Wang (2020) discussed the feasibility and necessity of applying the divorce cooling-off period to litigious divorce. Xia (2020) provided a doctrinal interpretation of the divorce cooling-off period provision in the Civil Code, categorizing it into three types: mandatory application, differentiated application, and exclusion from application. Wu (2020) pointed out that the controversy surrounding the divorce cooling-off period centers on issues such as the stability and satisfaction of marriage, the private and public nature of marriage, and the power struggle between the older and younger generations in discourse.

2.3. The Impact of Divorce Cooling-Off Period on Registered Divorces

The concept of the divorce cooling-off period is not exclusive to China; similar provisions can be found in countries such as the United Kingdom, France, the United States, Russia, and South Korea. Take South Korea, for example; Lee (2013) found that the cooling-off policy significantly decreases the divorce rate, whereas there is no significant effect on the divorce filing rate. Wie and Kim (2015) estimated that the number of divorces finalized decreased by approximately 9% in South Korea and that this effect is consistent over the long run. Kickham and Ford (2009) found a significant negative effect of marriage initiatives on divorce prevalence. Song and Li (2022) also discovered a significant correlation between the policy constraining divorce time and the level of divorce rates. Dong (2023) analyzed the impact of China’s divorce cooling-off period regulations on the trend of divorce registration numbers. After the implementation of the divorce cooling-off period in 2021, the number of divorce registrations in each quarter decreased by an average of 33%–42% compared to the previous three years.

2.4. Summary

There are many factors that influence divorce, and the introduction of the divorce cooling-off period is an effort by the state to legally reduce the divorce rate. Whether this measure will achieve its intended outcomes remains to be tested. The current studies predominantly concentrate on the introspection of the institution, with little attention devoted to empirically analyzing the operational outcomes of the divorce cooling-off period. The aim of this study is to assess the number of divorce registrations before and after the implementation of the divorce cooling-off period through the event study and ordinary least squares methods, to evaluate the practical effects of the system, identify any statistical correlations, and offer insights for system improvement, all in the pursuit of enhancing the stability of marriage.

3. Data and Methodology

3.1. Data

The data for this study were sourced from the 2018 to 2023 editions of China Statistical Yearbook and China Civil Affairs Statistical Yearbook, national and 31 inland provinces’ statistical bulletins on national economic and social development, as well as the websites of the Ministry of Civil Affairs, National Bureau of Statistics, civil affairs departments, and statistical bureaus of 31 provinces of China. This study collected data on the number of registered divorces, litigious divorces, marriage registrations, divorce cooling-off period, GDP, CPI, population, urbanization rate of permanent residents, and per capita disposable income (PCDI) of residents across 31 inland provinces of China and the whole nation from 2017 to 2023.

3.2. Research Methodology

3.2.1. Event Study

The event study method is an empirical research approach that has been initially applied in the financial field to investigate whether stock prices fluctuate and whether there are abnormal returns when an event occurs. In recent years, event study has been extensively applied in the realm of social sciences, particularly for assessing the dynamic effects of policies or laws, which is highly consistent with the topic explored in this article. The event study approach involves analyzing data both before and after the occurrence of an event to evaluate its impact, clearly illustrating how a specific event or policy influences the data. Thus, the following two hypotheses were formulated:

H1. If the divorce cooling-off period system does not exist, the number of registered divorces will maintain a stable development trend.

H2. Changes in the trend of registered divorces are the result of the DCOP’s intervention.

To test Hypothesis 1, we developed a model based on the number of registered divorces in China from 2002 to 2019 to predict counterfactual trends without the intervention of the divorce cooling-off period, in order to determine whether the development trend of registered divorces is stable.

For Hypothesis 2, we employed the event study approach, drawing on national statistical data from 2017 to 2023, to examine the impact of the implementation of the divorce cooling-off period on the number of divorce registrations. We first established the intervention time point, as the Civil Code of China was enacted on 28 May 2020 and took effect on 1 January 2021. We took 2020 as the intervention time point (t = 0); 2021 was the first observation period (t = 1), 2022 the second (t = 2), and 2023 the third (t = 3). The previous years, 2019, 2018, and 2017, were respectively the pre-intervention time periods (t = −1, t = −2, and t = −3). In the event study, the last period before the intervention is often identified as the control group. So we took 2020 (t = 0) as the control group, then compared the number of registered divorces at different time periods with that of the control group (t = 0), while controlling for other variables, and tested whether the differences are statistically significant.

3.2.2. Ordinary Least Squares (OLS)

The ordinary least squares (OLS) regression model is a prevalent tool in linear regression analysis, estimating the linear relationship between a dependent variable and one or more independent variables. It is particularly suitable for analyzing variables with continuous numerical values, such as the number of registered divorces. We employed the OLS model to estimate the correlation between the divorce cooling-off period and the number of registered divorces. The coefficient assigned to each independent variable is a crucial parameter. A positive coefficient suggests an increase in the dependent variable with respect to the independent variable, while a negative coefficient indicates an inverse correlationship. We established an equation using the OLS regression model. Two sets of data were used for analysis, that is, the data of the whole nation and the data of 31 provinces from 2017 to 2023.

The equation of OLS regression is established as follows:

(1)$Divorces={\beta }_0+{\beta }_1\cdot {DCOP}_i+{\beta }_2\cdot X_i+{\epsilon }_1$

Within this equation, Divorces is the OLS dependent variable, which represents the number of divorce registrations during that year. ${DCOP}_i$ is the primary independent variable, which denotes whether DCOP has been implemented; value 1 indicates the implementation of DCOP and value 0 in the absence of such. Due to the particularity of the year 2020, we employed the assumption of linear interpolation, setting the value for DCOP to 0.5 in 2020; this assumption holds statistical validity. $X_i$ is the set of variables that may play a certain role, including factors such as GDP, CPI, urbanization rate, population size, and per capita disposable income (PCDI). ${\beta }_0$ is a constant and ${\epsilon }_1$ is the error term. If the regression coefficient ${\beta }_1$ is negative with statistical significance, it indicates that the DCOP system reduces the number of divorce registrations.

4. Results

4.1. Event Study Results

Figure 3 shows the result of the counterfactual trend forecasting model, which indicates a stable development trend of registered divorces without the intervention of DCOP. Additionally, referring to the regression statistics analysis, the R-square value is 0.9969, which suggests that the actual values are closely aligned with the counterfactual trend predictions, thereby reinforcing Hypothesis 1.

As shown in Figure 4 and Table 1, the number of divorce registrations experienced a decline in 2020, contrary to the upward trend from 2017 to 2019, which may indicate the pre-effect of the DCOP system or the influence of the epidemic situation. In the three observation time periods, the actual number of divorce registrations is significantly lower than the model’s prediction, which clearly illustrates that the implementation of the DCOP provision can effectively reduce the number of divorce registrations, thus conforming to Hypothesis 2. Besides, Figure 4 also shows a slight increase in the number of divorce registrations in 2023, which may underscore the necessity to improve the divorce cooling-off period system.

4.2. Ordinary Least Squares (OLS) Results

4.2.1. Descriptive Statistics

Table 2 provides descriptive statistics for all variables of the whole nation from 2017 to 2023. Among the seven-year data of the whole nation, the average number of divorce registrations is 3.16 million; the minimum number recorded is 2.1 million in 2022, contrasting with the maximum number of 4.04 million in 2019. The number of divorce registrations showed an upward trend before the introduction of the divorce cooling-off period policy, but the growth in divorce registrations was restrained and showed a significant decline from 2021 to 2023. Specific data, including GDP, CPI growth rate, permanent resident population, urbanization rate, and per capita disposable income (PCDI), are also presented in Table 2.

Table 3 provides descriptive statistics for all the variables of 31 inland provinces of China from 2017 to 2023. Among the seven-year data of 31 provinces, the average number of divorce registrations is 101,021; the minimum number recorded is 3297 in Xizang in 2017, contrasting with the maximum number of 312,724 in Henan in 2019. There are significant disparities in economic scale among different provinces, with the highest GDP being nearly 100 times that of the lowest over the past seven years. Specific data of other variables, including CPI growth rate, permanent resident population, urbanization rate, and per capita disposable income (PCDI), are also presented in Table 3.

4.2.2. Regression Results

Table 4 presents the detailed OLS regression results of Equation (1) derived from the data of the whole nation from 2017 to 2023. The coefficient of DCOP is −260.69, statistically significant at the 95% confidence level, which indicates that the number of divorce registrations has decreased by 2.6 million following the implementation of the divorce cooling-off period. Given the recent implementation of the divorce cooling-off period system and the resultant limited data at the national level, the regression results may lack robust statistical validity. Consequently, we employed another regression analysis using data from 31 inland provinces.

Table 5 presents the detailed OLS regression results of Equation (1) derived from the data of 31 inland provinces of China from 2017 to 2023. The coefficient of DCOP is −43,530, statistically significant at the 99% confidence level, which indicates that the number of divorce registrations has decreased by 43,530 following the implementation of the divorce cooling-off period, enhancing the previous regression results of the whole nation. The coefficient of GDP is negative too, indicating the same negative effect with DCOP on the number of divorce registrations. The coefficients of CPI, population, and urbanization rate are positive, which denotes that in regions with higher CPI, larger populations, and higher urbanization rates, there are more registered divorces.

Table 6 presents the OLS regression statistics of 31 provinces. High F-statistic indicates that the overall linear relationship of the model is statistically significant. The multiple correlation coefficient (Multiple R) is close to a strong correlation, indicating a strong linear relationship between the independent variable and the dependent variable. The value of R square is 0.80980, which denotes a high level of goodness-of-fit. The value of the adjusted R square is slightly lower, which considers the number of independent variables, still showing good explanatory ability. In general, the model is statistically significant, accounting for the majority of the dependent variable’s variability.

Table 7 is the comparison of DCOP coefficients between the whole nation and 31 provinces; the regression coefficients of DCOP for each province are likewise derived from Equation (1). The sum of DCOP coefficients of 31 provinces is 2.6291 million, which is closely aligned with the coefficient of the whole nation, further illustrating the stability of the regression model.

4.3. Robustness Test

4.3.1. Robustness Test of Event Study

The data of registered divorces in Jiangsu province from 2017 to 2023 were used to perform the robustness test of the event study. The year 2020, which was the last time period before the implementation of the DCOP system, was still chosen as the control group (t = 0). We used the same model as before to test the effect of DCOP, and the result is shown in Figure 5.

We found that the Jiangsu province witnessed a consistent rise in divorce registrations from 2017 to 2019 prior to the implementation of DCOP and a slight decrease in 2020 due to the pre-effect or the epidemic situation, which is also reflected in the national data statistical analysis. Following the implementation of DCOP, a substantial decline in divorce registrations was observed in the Jiangsu province. Using the event study methodology, it is determined that the substantial discrepancy between the actual data and model predictions is attributable to DCOP. This discrepancy confirms the effectiveness of DCOP in reducing divorce registrations, thus underscoring the model’s reliability.

4.3.2. Robustness Test of the Ordinary Least Squares (OLS) Regression Model

The robustness of the OLS regression model results was checked using data from five provinces in East China, namely Jiangsu, Zhejiang, Shanghai, Anhui, and Fujian, and the results are shown in Table 8 and Table 9. The coefficient of DCOP is −49,957.8, presenting a deviation of 4791.8 compared to that of 31 provinces, with an error range of 10.61% and a high value of R square, which indicates the robustness and strong explanatory ability of the model.

Another robustness check was carried out utilizing 5 random subsets of the overall data. Specifically, we randomly deleted 10% of the total sample data and utilized the retained 90% of data to calculate the regression coefficients and statistics, thereby verifying the model’s robustness. This process was repeated five times, and the results are presented in Table 10.

As shown in Table 10, the five DCOP coefficients of the robustness test, statistically significant at the 99% confidence level, are very close to that of the 31 provinces, with a maximum deviation of 3880.2 and an error range of 8.9%. Moreover, the results of all 5 robustness tests unanimously demonstrate that the model exhibits a high R-square value, thereby conforming to its robustness and explanatory ability.

5. Discussion

The research results of both the event study and the ordinary least squares method confirm that the divorce cooling-off period can decrease the number of divorce registrations, suggesting that the system has achieved its intended outcome. The results are largely in line with the findings of Lee (2013), Wie and Kim (2015), Kickham and Ford (2009), Song and Li (2022), and Dong (2023). However, Dong’s analysis was limited to the institutional effects in 2021 due to his earlier research time. We assessed the three-year institutional effects of the divorce cooling-off period since its implementation and found that the system continues to play a crucial role in reducing registered divorces. However, divorce results from a complex interplay of various factors. Given the relatively short implementation time of the divorce cooling-off period, it is currently challenging to precisely anticipate its long-term effects. Currently, there are still voices opposing the divorce cooling-off period, and occasionally, negative social incidents attributed to this period arise. Furthermore, a slight uptick in divorce registrations in 2023 suggests that the efficacy of the divorce cooling-off period may be diminishing.

6. Conclusions

This research explores the impact of the divorce cooling-off period system on the stability of marriage in China. The event study and ordinary least squares regression analyses were conducted in this research to examine the correlations between the divorce cooling-off period and registered divorces. The research findings robustly substantiate that the divorce cooling-off period system effectively reduces the number of divorce registrations, achieving an average annual decrease of 2.6 million divorces, which might not be very accurate due to the limited implementation time. As the implementation time increases, future research will uncover a more accurate quantitative relationship, demonstrating that the divorce cooling-off period policy has fulfilled its intended purpose. Meanwhile, more issues regarding divorce are worth further exploration, such as couple breakups outside of marriage. The study of Santos and Sánchez (2022), which explored breakups other than divorce or separation, is a meaningful research direction. The divorce cooling-off period offers adequate time for couples to reflect and compose through the implementation of a divorce time constraint mechanism, effectively decreasing the instances of impulsive or hasty divorces, enhancing the stability of marriage, and safeguarding the stable and sustainable development of the family and society.

The divorce cooling-off period has encountered various theoretical and practical issues in practice, adversely affecting its subsequent effectiveness. This is further supported by the recent changes in divorce registration data. In order to maintain the sustainable effectiveness of the divorce cooling-off period, it is essential to consistently improve the regulations and provide corresponding institutional supporting measures, such as offering marital and family counseling services like emotional support, psychological guidance, and relationship restoration to the involved parties. Additionally, the divorce cooling-off period is merely a temporary strategy implemented by the state to enhance the stability of marriage. To genuinely address the issue of high divorce rates, joint efforts are needed from the state, society, families, and individuals.

Author Contributions

Conceptualization, W.Z.; Methodology, W.Z. and B.N.; Formal Analysis, W.Z. and Z.C.; Data Curation, W.Z. and Z.C.; Writing—original draft, W.Z., B.N. and Z.C.; Writing—review and editing, W.Z. and B.N.; Visualization, W.Z. and Z.C.; Supervision W.Z.; Funding Acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

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Figure 1. Trends of divorce rate in China, 2002–2022.

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Figure 2. Number of marriages and registered divorces in China, 2017–2023.

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Figure 3. Counterfactual trend forecasting model. Note: Although the divorce cooling-off period system has not yet come into effect in 2020, it has been enacted and may have pre-effects. To ensure the accuracy of the model, we did not include the divorce data of 2020 in the model.

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Figure 4. Estimation results of the impact of the divorce cooling-off period on the number of registered divorces in China. Note: t = −1 corresponds to the year 2019, the year before the law was enacted; t = 1 corresponds to the year 2021, the first year of the enforcement of the law.

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Figure 5. Estimation results of the impact of the divorce cooling-off period on the number of registered divorces in Jiangsu province.

Table 1

Lower and upper limit of 95% confidence interval.

Registered Divorces	95% CI(Lower)	95% CI(Upper)
−114.3	−192.80	−35.80
−163.6	−242.10	−85.10
−159.5	−238.00	−81.00
0	−78.50	78.50
31.1	−47.40	109.60
7.6	−70.90	86.10
−3.2	−81.70	75.30

Table 2

Descriptive statistics, the whole nation, 2017–2023.

Variables	Max	Min	Mean
Registered divorces	404.7	210	316.1857143
Divorce cooling-off period	1	0	0.5
GDP	17.88	12.31	15.52285714
CPI	0.029	0.002	0.017328571
Population	14.12	14	14.08428571
Urbanization rate	0.662	0.5852	0.625928571
PCDI	39,218	25,974	32,621.85714

Table 3

Descriptive statistics, 31 provinces, 2017–2023.

Variables	Max	Min	Mean
Registered divorces	312,724	3297	101,021.2203
Divorce cooling-off period	1	0	0.5
GDP	135,673.16	1310.63	33,759.02765
CPI	0.037	−0.003	0.01680553
Population	12,706	337.15	4543.613456
Urbanization rate	0.893	0.309	0.631
PCDI	84,834	10,330	32,703.99286

Table 4

Estimation results of the whole nation.

Variables	Coefficient
Intercept	−8036.334736
Divorce cooling-off period	−260.686707 **
GDP	−25.39876064 **
CPI	−1501.455159
Population	523.189275
Urbanization rate	2267.835376
PCDI	0.003521964

Note: ** indicates statistical significance at 5%.

Table 5

Estimation results of 31 provinces.

Variables	Coefficient
Intercept	−23,853
Divorce cooling-off period	−43,530 ***
GDP	−0.7349 ***
CPI	567,983 **
Population	24.9301 ***
Urbanization rate	92,876.1 ***
PCDI	−0.3055

Note: *** and ** indicate statistical significance at 1% and 5%, respectively.

Table 6

Regression statistics of 31 provinces.

Regression Statistics
F-statistic	149.0192
Multiple R	0.89989
R Square	0.80980
Adjusted R Square	0.80437

Table 7

DCOP coefficients of the whole nation and 31 provinces.

District	DCOP	District	DCOP
Xizang	−2959.14	Shandong	−139,076.92
Jiangsu	−55,444.61	Jiangxi	−39,226.68
Xinjiang	−15,106.86	Fujian	−29,847.31
Qinghai	−3842.07	Anhui	−81,796.34
Gansu	−28,619.55	Zhejiang	−102,147.53
Shaanxi	−42,865.24	Shanghai	−47,664.95
Yunnan	−62,509.5	Jilin	−159,151.96
Guizhou	−130,815.21	Nei Mongol	−46,758.49
Sichuan	−137,675.64	Tianjin	−25,407.33
Chongqing	−56,281.28	Beijing	−15,946.62
Hainan	−13,765.97	Shanxi	−6529.54
Guangxi	−226,661.46	Heilongjiang	−104,531.33
Guangdong	−130,918.45	Ningxia	−3370.46
Hunan	−286,109.63	Liaoning	−81,906.23
Hubei	−134,612.37	Hebei	−139,625.03
Henan	−277,963.95	Sum of 31 provinces	−2,629,137.65

Table 8

Robustness test results using the data of 5 east provinces.

Variables	Coefficient
Intercept	−9400.122611
Divorce cooling-off period	−49,957.83677 **
GDP	−0.50094911
CPI	96,781.89782
Population	30.76013839 **
Urbanization rate	66,369.06913
PCDI	−0.703203448

Note: ** indicates statistical significance at 5%.

Table 9

Regression statistics of 5 east provinces.

Regression Statistics
F-statistic	20.99655
Multiple R	0.904521
R Square	0.818157
Adjusted R Square	0.779191

Table 10

Robustness test results using the data of 5 random subsets.

Robustness Test 1
Intercept	14,771.79
Coefficient (DCOP)	−44,031.9 ***
R-Square	0.8172
Robustness Test 2
Intercept	17,164.46
Coefficient (DCOP)	−43,380.9 ***
R-Square	0.8154
Robustness Test 3
Intercept	14,427.2
Coefficient (DCOP)	−41,886.3 ***
R-Square	0.8125
Robustness Test 4
Intercept	18,986.48
Coefficient (DCOP)	−47,410.2 ***
R-Square	0.8201
Robustness Test 5
Intercept	13,924.56
Coefficient (DCOP)	−43,069.8 ***
R-Square	0.8239

Note: *** indicates statistical significance at 1%.

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