Using census data from 1990 to 2010, this paper describes the trend of the Chinese marriage market focusing on “leftover men” and “leftover women”. In general, it was identified that there was an increasing share of being single for young and more educated population. The share of single population for less educated men after age 35 was higher than other age groups across the years, providing consistent evidence for leftover men. Meanwhile, more educated women showed no clear difference in marriage rate than others in their late 30s. This study further found that increasing comparative supply of men and more educated women due to demographic change and education reform explains the phenomena observed. In addition, women who get married later were found to have husbands showing disadvantages in age and education level, while men displayed no similar pattern, suggesting that Chinese women might lower their selecting standard as their age increases.
Recently, researchers, policy makers and social media in China have discussed widely about the “leftover women” and “leftover men” phenomena. The discussion on leftover men is mainly based on the skewed sex ratio in China. The one-child policy provoked the traditional son preference, leading to soaring sex ratio at birth since 1970s (Li, 2007, [
Under this circumstance, the leftover women phenomenon seems to be puzzling. A government report in 2007 aiming at “upgrading quality of new-births” firstly introduced the terminology of “leftover women”, referring to those highly educated but unmarried women2. It has been identified that more years in education naturally postpone the time for entering the marriage market, as studies using US data show that women with college education put off marriage compared to lower educated groups. However, they show higher final marriage rates between age 30 to 50 (Goldstein et al., 2001, [
Experiencing continuous drop in the fertility rate and aging society, the discussion on leftover women and leftover men has become heated but there still lacks critical evidence to support this assertion. Whether to get married or not? When to get married? Who to marry with? The answers to these questions are still in vague. Using National Bureau of Statistics (NBS) Population Census datasets from 1990 to 2010, we display the trend of Chinese marriage market and empirically test the existence and mechanism of leftover men and leftover women. This paper found that the share of being single for low educated men was higher than counterparts because of excess supply in the market, or to say low educated men are leftover after age 35. Meanwhile, highly educated women postpone their marriage choice for increasing competence, but show no significant difference after age 35, providing no evidence as leftover women. However, women who get married late in life might suffer from decline in quality of marriage considering husband’s age and education.
The remainder of the paper proceeds as follows. Section 2 describes the datasets we employed and the summary statistics showing the trend of Chinese marriage market. Section 3 provides the empirical results discussing the existence and mechanism of leftover women and leftover men. Section 4 deals with the conclusion.
To analyze the trend of Chinese marriage market, we adapted 4 datasets: 1% sample of 1990 and 2000 NBS National Population Census, 0.2% sample of 2005 NBS Population Survey and national aggregate statistics of 2010 NBS National Population Census, which are the most representative data with sufficient size to capture the feature of the changes in the Chinese marriage market. Each dataset includes various information on individual characteristics, supporting our study considering how the trend might differ across gender, hukou, education and age.
This paper further restricted the sample to the population aged 15 and above according to NBS rules in collecting marriage information. Using the marital status reported in the datasets, we categorized the population by whether the individual has or has not marriage experience, implying that we are mainly considering the trend of first marriage market3. To measure the trend, we calculated the share of being single using the following:
singlerate i , e , a , s = single i , e , a , s totalpopulaton i , e , a , s = single i , e , a , s single i , e , a , s + married i , e , a , s + divorced i , e , a , s + widowed i , e , a , s
singlerate i , e , a , s describes the share of being single of the total population residency at district i with education e, age a and sex s ( totalpopulaton i , e , a , s ), referred to as single rate in following sections. Similarly, married i , e , a , s , divorced i , e , a , s , widowed i , e , a , s describes the share of being married, being divorced and being widowed of the total population respectively.
Another result variable we explored was the age at first marriage, which is available in 2000 and 2005 census. Individuals reporting first marriage age younger than 15 were excluded from the sample according to NBS rules.
To explore the matching pattern in Chinese marriage market, this paper further constructed a matched couple data using 1990, 2000 and 2005 censuses. The methodology employed is as follows. Using the “relationship with household head” variable in the data, we matched married individuals with the opposite gendered one reporting as married and the corresponding category in the relationship variable, specifically matching “household head” with “spouse of household head”, “son/daughter” with “son/daughter in law”. For 1990 census, the survey did not differentiate “son/daughter” from “son/daughter in law”, so the sample was further restricted requiring that the age gap between the couple should be less than 10 years in order to exclude possible mismatch between
brothers and sisters4. For the 2000 and 2005 census, we matched couple reporting
the same first marriage year which could further exclude re-married couples.
We showed the trend of single rates in Chinese marriage market by hukou, gender, age and education level as presented in
Women | Men | |||||||
---|---|---|---|---|---|---|---|---|
Age group | 1990 | 2000 | 2005 | 2010 | 1990 | 2000 | 2005 | 2010 |
Middle school and below | ||||||||
15 - 22 | 88.9% | 89.6% | 90.3% | 84.5% | 96.3% | 97.6% | 98.0% | 94.4% |
23 - 29 | 15.1% | 16.6% | 19.5% | 24.1% | 37.9% | 37.2% | 42.9% | 43.7% |
30 - 34 | 1.2% | 2.4% | 3.4% | 4.7% | 6.1% | 8.5% | 12.2% | 11.6% |
35 - 39 | 0.8% | 0.9% | 1.5% | 1.9% | 2.4% | 3.6% | 4.8% | 5.4% |
40 - 44 | 0.5% | 0.5% | 0.8% | 1.0% | 1.3% | 2.2% | 3.3% | 3.2% |
45 - 49 | 0.2% | 0.4% | 0.5% | 0.7% | 1.0% | 1.4% | 2.2% | 2.4% |
50 - 54 | 0.2% | 0.3% | 0.5% | 0.4% | 0.9% | 1.1% | 1.5% | 2.0% |
55 - 59 | 0.2% | 0.2% | 0.4% | 0.4% | 0.8% | 0.9% | 1.1% | 1.6% |
High school | ||||||||
15 - 22 | 94.8% | 97.0% | 97.4% | 96.2% | 98.4% | 99.2% | 99.5% | 98.6% |
23 - 29 | 18.1% | 26.3% | 32.5% | 37.5% | 37.4% | 46.4% | 51.7% | 54.5% |
30 - 34 | 1.9% | 3.1% | 5.1% | 7.8% | 4.9% | 7.6% | 11.9% | 13.9% |
35 - 39 | 1.1% | 1.0% | 1.9% | 3.2% | 1.1% | 2.4% | 4.2% | 5.7% |
40 - 44 | 0.8% | 0.6% | 0.9% | 1.6% | 0.8% | 1.2% | 2.0% | 2.9% |
45 - 49 | 0.4% | 0.5% | 0.8% | 0.9% | 0.6% | 0.5% | 1.3% | 1.8% |
50 - 54 | 0.4% | 0.6% | 0.5% | 0.6% | 0.6% | 0.5% | 0.6% | 1.2% |
55 - 59 | 0.4% | 0.3% | 0.4% | 0.6% | 0.6% | 0.4% | 0.5% | 0.7% |
College and above | ||||||||
15 - 22 | 98.7% | 98.6% | 98.2% | 98.7% | 99.6% | 99.7% | 99.6% | 99.5% |
23 - 29 | 36.7% | 37.0% | 49.8% | 55.3% | 51.0% | 53.6% | 64.4% | 67.2% |
30 - 34 | 6.4% | 4.2% | 6.5% | 10.7% | 4.5% | 7.2% | 11.9% | 15.1% |
35 - 39 | 2.9% | 1.6% | 2.7% | 4.2% | 1.0% | 1.6% | 3.0% | 4.9% |
40 - 44 | 1.9% | 1.6% | 1.0% | 2.1% | 0.5% | 0.6% | 0.9% | 2.2% |
45 - 49 | 0.7% | 1.5% | 1.2% | 1.3% | 0.4% | 0.3% | 0.6% | 1.1% |
50 - 54 | 0.4% | 1.0% | 1.1% | 1.2% | 0.6% | 0.4% | 0.3% | 0.7% |
55 - 59 | 0.7% | 0.7% | 1.0% | 1.1% | 0.8% | 0.3% | 0.2% | 0.5% |
Data source: Census 1990-2010.
Women | Men | |||||||
---|---|---|---|---|---|---|---|---|
Age group | 1990 | 2000 | 2005 | 2010 | 1990 | 2000 | 2005 | 2010 |
Middle school and below | ||||||||
15 - 22 | 79.7% | 87.4% | 86.2% | 79.9% | 89.9% | 96.3% | 95.9% | 92.3% |
23 - 29 | 8.6% | 11.7% | 15.4% | 19.9% | 24.5% | 30.0% | 36.2% | 36.3% |
30 - 34 | 0.3% | 0.9% | 1.7% | 3.9% | 9.5% | 7.9% | 10.8% | 12.8% |
35 - 39 | 0.1% | 0.3% | 0.5% | 1.2% | 7.6% | 5.3% | 5.8% | 7.7% |
40 - 44 | 0.2% | 0.1% | 0.3% | 0.5% | 6.8% | 5.6% | 4.4% | 5.2% |
45 - 49 | 0.2% | 0.1% | 0.2% | 0.3% | 6.9% | 5.4% | 4.7% | 4.2% |
50 - 54 | 0.2% | 0.1% | 0.2% | 0.2% | 6.2% | 5.5% | 4.7% | 4.6% |
55 - 59 | 0.2% | 0.1% | 0.2% | 0.2% | 4.9% | 5.8% | 4.7% | 4.6% |
High school | ||||||||
15 - 22 | 93.0% | 96.6% | 97.4% | 96.4% | 95.2% | 99.0% | 99.3% | 98.4% |
23 - 29 | 13.5% | 29.8% | 34.8% | 37.0% | 16.2% | 39.9% | 47.2% | 48.2% |
30 - 34 | 0.6% | 2.3% | 4.0% | 6.0% | 2.6% | 4.9% | 8.4% | 10.0% |
35 - 39 | 0.3% | 0.4% | 1.2% | 1.9% | 2.1% | 1.4% | 2.6% | 4.0% |
40 - 44 | 0.4% | 0.1% | 0.2% | 0.7% | 1.8% | 1.1% | 1.2% | 1.9% |
45 - 49 | 0.1% | 0.1% | 0.3% | 0.3% | 2.1% | 1.2% | 0.8% | 1.0% |
50 - 54 | 0.0% | 0.1% | 0.1% | 0.2% | 2.8% | 1.3% | 1.1% | 0.8% |
55 - 59 | 0.0% | 0.2% | 0.0% | 0.3% | 3.6% | 1.4% | 1.0% | 0.8% |
College and above | ||||||||
15 - 22 | 97.6% | 97.4% | 97.5% | 97.9% | 97.1% | 99.3% | 99.0% | 99.1% |
23 - 29 | 50.9% | 52.5% | 57.0% | 53.7% | 41.4% | 60.1% | 66.8% | 64.3% |
30 - 34 | 1.7% | 5.6% | 11.2% | 5.9% | 1.6% | 8.4% | 13.4% | 9.5% |
35 - 39 | 2.7% | 2.2% | 1.8% | 1.7% | 2.7% | 3.2% | 1.8% | 2.5% |
40 - 44 | 0.0% | 1.0% | 1.0% | 0.9% | 2.0% | 1.5% | 2.2% | 1.0% |
45 - 49 | 0.0% | 1.2% | 0.0% | 0.5% | 1.4% | 0.5% | 1.3% | 0.5% |
50 - 54 | 0.0% | 0.0% | 0.0% | 0.5% | 4.8% | 1.8% | 0.0% | 0.3% |
55 - 59 | 0.0% | 0.0% | 0.0% | 0.5% | 4.6% | 3.0% | 4.0% | 0.3% |
Data source: Census 1990-2010.
first age group boundary was set at 22, the age expected for would be couples to have finished college/university, while further age grouping was designed to cut at every 5 years to investigate how marriage decision shifts through age (Wu et al., 2014, [
From
For the youngest subjects in the sample aged 15 to 22, single rates were high, around 90% for all categories except the rural women with middle school education or below. It is clear that if males or females spend more years in education, naturally they postpone their entering into the marriage market. Better developed parts of China have lower percentage of early marriage. As we are primarily discussing the difference of marriage choice across gender and education levels, we further restricted the sample to age 23 and above, which allows the population to have the chance to finish higher education.
For urban residents of all education levels aged 23 to 29, the single rates increase throughout the years. In urban population also, the gap between college group and above and high school group increased from 10.7% in 2000 to 17.9% in 2010 for women and from 7.2% to 12.8% for men. Meanwhile, the gap for rural population slightly decreased. One possible reason is that college and university education is one of the few channels to change hukou in China, so people with college education might just enter the urban marriage market instead. We show the education composition of the sample by year and hukou in
with high school education and above as high education group, the rest as low education group5.
Gender difference in single rate of individuals with same education is important when discussing leftover women or leftover men in the marriage market. For urban population aged 23 to 29, we observed that the gender gap in college education and above group decreased from 16.6% in 2000 to 11.9% in 2010. This means that there are comparatively more single young women than men
throughout the years. However, men in general still had higher single rate than women for all education groups in each census year. For high education group aged 35 and above, the single rate of men increased slightly compared to women, not supporting leftover women. Meanwhile, the gender gap for low education group was increasing through census years. Especially for rural population, it increased from 7% in 2000 to 8.9% in 2010 for age 30 to 34, and from 4.8% to 6.5% for age 35 to 39, supporting the view of having leftover men.
We further explored the first marriage age of Chinese population by gender, education and hukou in
In this section we empirically examined the trend of Chinese marriage market using the census data of 1990, 2000, and 2005. We employed the Linear Possibility Model (LPM) to determine marriage trend as follow:
Pr ( single = 1 | X i ) p = β 0 + β 1 type i + β 2 group i ∗ T i + γ 1 Y i + γ 2 Z p + ε i , p
The dependent variable is the marriage status of individual i, assigned as 1 for being single and 0 as with marriage experience; groupi describes the individual’s type categorized by gender and education, including dummy variables for low educated women, low educated men, high educated women and high educated men6; as we were mainly discussing the trend in marriage market, Ti implies
the census year the individual i is surveyed, meaning dummy variables for 1990, 2000, 2005; Yi describes individual i’s personal characteristics, including age; Zp is the province fixed effect and εi,p is the random component. As we were mainly interested in the phenomena of leftover women and leftover men, we chose the low educated women as the control group for a clearer comparison. From
Column 1 in
Dependent variable: Being single = 1 | (1) | (2) | (3) | (4) |
---|---|---|---|---|
Age group | 23 - 29 | 30 - 34 | 35 - 39 | 40 - 44 |
Low edu. women *2000 | 0.066*** | 0.012*** | 0.002*** | 0.000 |
(0.001) | (0.001) | (0.000) | (0.000) | |
Low edu. women *2005 | 0.081*** | 0.021*** | 0.007*** | 0.002*** |
(0.003) | (0.001) | (0.001) | (0.001) | |
Low edu. men | 0.203*** | 0.039*** | 0.011*** | 0.007*** |
(0.001) | (0.001) | (0.001) | (0.000) | |
Low edu. men *2000 | 0.068*** | 0.028*** | 0.011*** | 0.005*** |
(0.002) | (0.001) | (0.001) | (0.001) | |
Low edu. men *2005 | 0.087*** | 0.061*** | 0.024*** | 0.013*** |
(0.003) | (0.002) | (0.001) | (0.001) | |
High edu. women | 0.162*** | 0.047*** | 0.019*** | 0.012*** |
(0.003) | (0.004) | (0.002) | (0.002) | |
High edu. women *2000 | 0.032*** | −0.024*** | −0.011*** | −0.001 |
(0.004) | (0.004) | (0.002) | (0.003) | |
High edu. women *2005 | 0.097*** | −0.009** | −0.004 | −0.008*** |
(0.005) | (0.004) | (0.003) | (0.003) | |
High edu. men | 0.300*** | 0.029*** | 0.001 | −0.001 |
(0.002) | (0.002) | (0.001) | (0.001) | |
High edu. men *2000 | 0.066*** | 0.025*** | 0.007*** | 0.002*** |
(0.003) | (0.002) | (0.001) | (0.001) | |
High edu. men *2005 | 0.112*** | 0.062*** | 0.018*** | 0.004*** |
(0.004) | (0.003) | (0.002) | (0.001) | |
Age | −0.102*** | −0.013*** | −0.002*** | −0.001*** |
(0.000) | (0.000) | (0.000) | (0.000) | |
Constant | 2.845*** | 0.445*** | 0.100*** | 0.052*** |
(0.006) | (0.007) | (0.005) | (0.005) | |
Observations | 819,055 | 589,339 | 585,633 | 472,810 |
Adjusted-R2 | 0.271 | 0.041 | 0.013 | 0.008 |
Note: 1) Data source: Census 1990-2005; 2) All regressions include province fixed effects; 3) Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1.
Dependent variable: Being single = 1 | (1) | (2) | (3) | (4) |
---|---|---|---|---|
Age group | 23 - 29 | 30 - 34 | 35 - 39 | 40 - 44 |
Low edu. women *2000 | 0.061*** | 0.004*** | 0.001*** | 0.001*** |
(0.001) | (0.000) | (0.000) | (0.000) | |
Low edu. women *2005 | 0.067*** | 0.009*** | 0.003*** | 0.002*** |
(0.001) | (0.000) | (0.000) | (0.000) | |
Low edu. men | 0.155*** | 0.092*** | 0.074*** | 0.067*** |
(0.001) | (0.001) | (0.000) | (0.001) | |
Low edu. men *2000 | 0.090*** | −0.018*** | −0.023*** | −0.011*** |
(0.001) | (0.001) | (0.001) | (0.001) | |
Low edu. men *2005 | 0.122*** | 0.008*** | −0.018*** | −0.024*** |
(0.002) | (0.001) | (0.001) | (0.001) | |
High edu. women | 0.106*** | 0.001 | 0.001 | 0.003* |
(0.002) | (0.001) | (0.001) | (0.001) | |
High edu. women *2000 | 0.103*** | 0.019*** | 0.002*** | −0.003** |
(0.003) | (0.001) | (0.001) | (0.001) | |
High edu. women *2005 | 0.140*** | 0.036*** | 0.010*** | −0.001 |
(0.005) | (0.003) | (0.002) | (0.002) | |
High edu. men | 0.127*** | 0.021*** | 0.018*** | 0.016*** |
(0.001) | (0.001) | (0.001) | (0.001) | |
High edu. men *2000 | 0.193*** | 0.023*** | −0.005*** | −0.007*** |
(0.002) | (0.001) | (0.001) | (0.001) | |
High edu. men *2005 | 0.240*** | 0.056*** | 0.005** | −0.005*** |
(0.004) | (0.003) | (0.002) | (0.002) | |
Age | −0.061*** | −0.006*** | −0.002*** | −0.000*** |
(0.000) | (0.000) | (0.000) | (0.000) | |
Constant | 1.667*** | 0.189*** | 0.058*** | 0.021*** |
(0.003) | (0.004) | (0.004) | (0.005) | |
Observations | 2,432,557 | 1,642,536 | 1,624,238 | 1,199,157 |
Adjusted-R2 | 0.170 | 0.044 | 0.035 | 0.034 |
Note: 1) Data source: Census 1990-2005; 2) All regressions include province fixed effects; 3) Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1.
education group before age 30, and the gap broadened through years. For women, the gap across education group was 16.2% in 1990 but 17.8% in 2005, for men it was 9.7% in 1990 but 12.2% in 2005. The expansion corresponds with increasing return to education in labor market (Li, 2012, [
Comparing the results in column 2 and 3, we noted that the coefficients significantly decreased as age increases. For highly educated women, the coefficient dropped from 16.2% for age 23 - 29 to 4.7% for those aged 30 - 34, 1.9% for the age group 35 - 39. It further shrinks as the year passes, showing that the difference in single rate across education groups was narrowing for senior population in recent years. Considering gender difference in the high education group, the single rate was 1.8% higher for women aged 35 to 39 in 1990 but 5.3% higher for men in 2005. There was no evidence supporting the leftover women in latest marriage market.
From the former results, we found that high educated women postponed marriage but showed no significant difference in single rate than other sub- groups after age 35, while low educated men showed significantly higher single rate than counterparts for all age groups considered. To study the possible reason of leftover women and leftover men, we further explored the trend of comparative supply in marriage market introduced by Becker (1973) [
The demographic structure and education composition of Chinese population have shifted dramatically since 1990s. On one hand, the widely discussed soaring sex ratio at birth of China leads to increasing comparative supply of men in the marriage market (
Before considering the impact of shifting comparative supply in marriage market on individual marriage decisions, we need to explore the matching pattern of Chinese couples. Using matched couple data from 1990, 2000 and 2005 census, we show the matching pattern considering education level for all couples of which wife’s age ranged between 20 and 40 in
Census year | Wife’s education level | Husband’s education level | |
---|---|---|---|
Low | High | ||
Urban | |||
1990 | Low | 86.10% | 9.20% |
High | 1.80% | 3.00% | |
2000 | Low | 69.70% | 13.00% |
High | 4.20% | 13.10% | |
2005 | Low | 61.10% | 12.20% |
High | 5.60% | 21.00% | |
Rural | |||
1990 | Low | 86.42% | 9.18% |
High | 2.52% | 1.87% | |
2000 | Low | 85.51% | 9.84% |
High | 2.66% | 2.00% | |
2005 | Low | 90.04% | 6.81% |
High | 1.78% | 1.37% |
Data source: Census 1990-2005.
while the percentage of rural low educated men married to high educated women dropped through the years. We also explored the matching pattern considering hukou type and found that the percentage of cross-hukou-type marriage is extremely low and decreasing through years, possibly because the hukou type could be changed through marriage. So we considered urban and rural as two separate marriage markets in following discussions. The matching pattern considering age is more straightforward, husbands are at average about 2 years older
than their wives7.
The ladder-type matching pattern considering education could lead to structural matching failure of high educated women and low educated men. The situation could be worse as the comparative supply of these two groups increased through the years as we discussed earlier. We will try to explore whether the change in comparative supply could explain the trend we observed in following discussions.
To measure the comparative supply each individual faces in actual marriage market, we employed the sex ratio of the group with same province, hukou, and the corresponding education and age group. For example, consider a 25 year old urban high educated women, the comparative supply of her group would be the number of urban women both high educated and low educated aged 22 to 25 divided by the number of urban high educated men aged 25 to 28, referred to as female share. Similarly, for the group of 25 years old rural low educated men, the comparative supply would be the number of rural men both high educated and low educated age 25 to 28 divided by the number of rural low educated women age 22 to 25, referred to as male share. The LPM model was adapted to explore the effect of comparative supply on marriage decision:
Pr ( single = 1 | X i ) p = β 0 + β 1 gender i ∗ education i + β 2 gender i ∗ sexratio i , p + β 3 gender i + β 4 education i + β 4 sexratio i , p , t + γ 1 Y i + γ 2 Z p + ε i , p
From the model, the dependent variable is marriage status for individual i, assigned as 1 for being single and 0 as with marriage experience; genderi describes individual’s gender, female assigned as 1 while considering the impact of comparative supply on female’s marriage choice, male assigned as 1 while considering the impact of comparative supply on male’s marriage choice; educationi describes individual’s education level, high educated assigned as 1 while considering the impact of comparative supply on the marriage choice of high educated, low educated assigned as 1 while considering the impact of comparative supply on the marriage choice of low educated; sexratioi,p described the comparative supply individual i’s group facing, equals female share while considering female’s marriage choice, male share for male; Yi describes individual i’s personal characteristics, including age; Zp is the province fixed effect and εi,p is the random component.
D.V.: Being single = 1 | 1990 | 2000 | 2005 | |||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Age: 23 - 29 | ||||||
Female *high edu. | 0.060*** | 0.002 | 0.031*** | −0.041*** | 0.054*** | 0.104*** |
(0.004) | (0.008) | (0.003) | (0.008) | (0.006) | (0.013) | |
High edu. | 0.100*** | 0.107*** | 0.097*** | 0.084*** | 0.126*** | 0.135*** |
(0.003) | (0.003) | (0.002) | (0.004) | (0.005) | (0.009) | |
Female *female share | 0.119*** | −0.017 | −0.020 | |||
(0.005) | (0.014) | (0.019) | ||||
Female share | −0.108*** | 0.052*** | −0.015 | |||
(0.005) | (0.014) | (0.019) | ||||
Adjusted-R2 | 0.254 | 0.255 | 0.268 | 0.268 | 0.279 | 0.279 |
Age: 30 - 34 | ||||||
Female *high edu. | 0.057*** | 0.040*** | 0.021*** | 0.010** | 0.024*** | −0.012 |
(0.004) | (0.012) | (0.002) | (0.005) | (0.005) | (0.011) | |
High edu. | −0.008*** | −0.005*** | −0.011*** | −0.003 | −0.011*** | 0.010 |
(0.002) | (0.002) | (0.001) | (0.002) | (0.004) | (0.007) | |
Female *female share | 0.066*** | 0.044*** | 0.087*** | |||
(0.006) | (0.008) | (0.024) | ||||
Female share | −0.064*** | −0.043*** | −0.079*** | |||
(0.006) | (0.008) | (0.024) | ||||
Adjusted-R2 | 0.042 | 0.043 | 0.034 | 0.034 | 0.046 | 0.046 |
Age: 35 - 39 | ||||||
Female *high edu. | 0.029*** | 0.045*** | 0.020*** | 0.014*** | 0.025*** | 0.023*** |
(0.003) | (0.011) | (0.001) | (0.002) | (0.003) | (0.007) | |
High edu. | −0.010*** | −0.009*** | −0.014*** | −0.010*** | −0.017*** | −0.007** |
(0.001) | (0.001) | (0.001) | (0.001) | (0.002) | (0.003) | |
Female *female share | 0.026*** | 0.024*** | 0.040*** | |||
(0.004) | (0.002) | (0.011) | ||||
Female share | −0.028*** | −0.023*** | −0.044*** | |||
(0.004) | (0.002) | (0.011) | ||||
Adjusted-R2 | 0.009 | 0.010 | 0.012 | 0.012 | 0.015 | 0.016 |
Note: 1) Data source: Census 1990-2005; 2) All regressions include gender, age and province fixed effects; 3) Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1.
choice of urban high educated women by census year and age group, while
Columns 1, 3 and 5 in
D.V.: Being single = 1 | 1990 | 2000 | 2005 | ||||||
---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | ||||
Age: 23 - 29 | |||||||||
Male *low edu. | 0.226*** | −0.054** | 0.099*** | −0.183*** | 0.095*** | 0.240*** | |||
(0.027) | (0.026) | (0.014) | (0.015) | (0.016) | (0.018) | ||||
Low edu. | −0.397*** | −0.107*** | −0.330*** | −0.042*** | −0.319*** | −0.481*** | |||
(0.022) | (0.022) | (0.012) | (0.013) | (0.013) | (0.015) | ||||
Male *male share | 0.233*** | 0.175*** | −0.086*** | ||||||
(0.003) | (0.005) | (0.013) | |||||||
Male share | −0.394*** | −0.270*** | 0.214*** | ||||||
(0.004) | (0.004) | (0.010) | |||||||
Adjusted-R2 | 0.131 | 0.137 | 0.190 | 0.193 | 0.205 | 0.207 | |||
Age: 30 - 34 | |||||||||
Male *low edu. | 0.081*** | 0.066*** | 0.038*** | −0.034** | 0.080*** | 0.066** | |||
(0.020) | (0.020) | (0.014) | (0.014) | (0.025) | (0.027) | ||||
Low edu. | −0.024 | −0.011 | −0.041*** | 0.029** | −0.103*** | −0.093*** | |||
(0.018) | (0.018) | (0.012) | (0.012) | (0.022) | (0.023) | ||||
Male *male share | 0.028*** | 0.118*** | 0.066*** | ||||||
(0.002) | (0.005) | (0.010) | |||||||
Male share | −0.013*** | −0.077*** | −0.011 | ||||||
(0.001) | (0.004) | (0.008) | |||||||
Adjusted-R2 | 0.042 | 0.042 | 0.037 | 0.038 | 0.050 | 0.050 | |||
Age: 35 - 39 | |||||||||
Male *low edu. | 0.095* | 0.073 | 0.038*** | 0.038*** | 0.055*** | 0.026 | |||
(0.052) | (0.052) | (0.010) | (0.011) | (0.016) | (0.017) | ||||
Low edu. | −0.055 | −0.035 | −0.023*** | −0.024*** | −0.018 | 0.011 | |||
(0.051) | (0.051) | (0.009) | (0.009) | (0.015) | (0.016) | ||||
Male *male share | 0.062*** | 0.022*** | 0.038*** | ||||||
(0.004) | (0.002) | (0.008) | |||||||
Male share | −0.025*** | −0.001 | −0.034*** | ||||||
(0.004) | (0.003) | (0.007) | |||||||
Adjusted-R2 | 0.037 | 0.038 | 0.024 | 0.024 | 0.030 | 0.030 | |||
Note: 1) Data source: Census 1990-2005; 2) All regressions include gender, age and province fixed effects; 3) Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1.
with education level, from which we observed that high educated women showed significantly higher possibility of being single. Columns 2, 4 and 6 added female share and the interaction with gender, which show that for women facing increasing comparative supply, the probability of being single significantly increased. For the group aged 23 to 29 in 1990 and age 30 to 34 in 2005, the change in the comparative supply explained the effect of higher education on marriage choice; for age 30 to 34 in 2000, the coefficients on the interaction in terms of gender and high education dropped more than 52% after adding the female share variables. As shown above, this cohort had experienced the education reform which started in 1980s, so the shifting comparative supply might be more effective. For the population aged 35 to 39, adding the female share showed no clear impact on the coefficients of high education, and the R2 of the model is obviously smaller than younger groups, indicating that for senior population, competence in the market has limited explaining power on individual choices.
Similarly for
Briefly, the shifting comparative supply in the marriage market could partially explain the comparatively higher possibility of being single for urban high educated women aged 23 to 35 and rural low educated men of all age groups. The changes of demographic structure and education level of Chinese population during the years was the main cause of the phenomena observed in marriage market, as high educated women postponing marriage and low educated men being leftover. Besides whether to get married or not and when to get married, who to get married with is also an important consideration. Using the matched couple data, we attempted to explore whether there was difference in the age gap between the couple and the spouse’s education level between people who get
married earlier in life and later for urban population9, which may give us some hint on the marriage choice of high educated women.
To summarize, women who get married later in life have higher possibility to match with older spouse and lower possibility to match with high educated men, and the effect tend to be more severe for high educated women, while men showed no such trend. That is to say, although high educated women might not show significant difference in lifetime single rate than other sub-group population, they may have to sacrifice the quality of marriage for postponing their marriage. High educated women who were unable to make a fulfilling match in earlier years in the market, could lower their standard in order to avoid becoming leftover women.
Using the NBS census datasets from 1990 to 2010, we showed the trend of single rates in the Chinese marriage market. Overall, the single rate of population aged 23 to 29 increased throughout the years, as well as the differences across education levels. Empirically, we found that urban high educated women tended to postpone their marriage but showed no significant difference than other sub- groups in single rate after age 35, providing no evidence for leftover women. Meanwhile, low educated men, in both urban and rural areas, showed consistently higher single rates than other population groups, supporting the view of leftover men. Further exploring the issue of marriage decision, we conclude that shifting comparative supply caused by demographic change and education reform could partially or wholly explain the phenomenon observed above.
High single rate for low educated men is not only a loss in welfare of the group, but it could also have negative effect on the stability of society. High marriage rate for low educated women along with the loosening of birth control policies could lead to a cluster in low income population and increasing inequality. The decrease in marriage quality of high educated women who get married later in life is also of interest for people concerning the welfare level of the whole society. Policies designed to encourage marriage should take all perspectives into consideration, while one key element should be eliminating discrimination and protecting free choice of marriage. As the Chinese population is becoming more educated and more skewed in sex ratio and age structure, facing continuously shifts in comparative supply in marriage market, there is the need to obtain more recent data to support future discussion on single rates and marriage choices.
Peng, Q.Q. and Li, L. (2017) An Empirical Study of the Chinese Marriage Market: “Leftover” or Not? Open Journal of Social Sciences, 5, 229-247. https://doi.org/10.4236/jss.2017.58019