Analyzing the Character of Income Inequality in the Relationship Between Happiness and Fertility Rate

1. 1
Analyzing the Character of Income
Inequality in the Relationship
Between Happiness and Fertility
Rate
——the Case of China
Yapeng Li
Ritsumeikan University
Graduate School of Economics
2023.8.24

2. Structure of the report
⚫Demographic Transition in China
⚫Pioneering Research
⚫Research Data
⚫Empirical Analysis
⚫Conclusion
2

3. Demographic
Transition in China
3
Fluctuations of Birth Rate in China
Population Policy in China
Population Simulation

4. Fluctuations of Birth Rate in China
⚫Before the second
population census(1964) :
➢The birth rate remains at
a high level, while the
death rate is decreasing.
The population growth
rate is on the rise.
⚫After the second
population census(1964) :
➢Birth rates begin to
decline. Death rates
decline until the third
population census
(1982), then remain
relatively stable. Overall,
population growth rates
decrease.
4
Source: Compiled by the author from "China Statistical Yearbook" (various editions).
Note: The horizontal axis indicates the years of each population census, with the First National
Population Census commencing in 1953.
37.0 39.3
22.3 21.1
14.0 11.9
8.5
23.0
27.7
15.7 14.4
7.5
4.8 1.4
14.0
11.6
6.6 6.7
6.5
7.1
7.1
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
1953 1964 1982 1990 2000 2010 2020
‰
year
Changes in brith rate,death rate, and population growth rate
at the time of the population census' year
birth rate population growth rate death rate

5. Population Policy in China
⚫From the establishment of China
➢The Communist Party encouraged higher childbirth rates. According to the
first population Census, the population growth rate was notably high,
reaching 23‰.
➢The Party's central leadership felt the need to formulate population policies,
emphasizing the importance of birth control measures.
⚫One-Child Policy (Implemented in 1979):
➢According to the "Constitution of the People's Republic of China," stipulates
that "both husband and wife have the duty to practice family planning."
➢“Late marriage," "late childbirth," "having fewer children," "spacing births",
and "eugenic births.“ are highly recommended.
➢To those who comply with planned birth, e.g., preferential housing allocation
in urban areas, and priority allocation of “self-retained land” in rural areas.
➢Response to non-compliance, e.g., imposing excess birth fees and reducing
spouses' wages.
5

6. Population Policy in China
⚫Two-Child Policy (Implemented in 2013, Revised in 2015):
➢In 2013, the "Decision of the Central Committee of the Communist Party of
China on Some Major Issues Concerning Comprehensively Deepening Reform"
determined to initiate a policy for couples where one spouse is an only child to
have two children.
➢Furthermore, in December 2015, the "Population and Family Planning Law of
the People's Republic of China" recommended that the state encourage a limit
of two children per couple.
➢To implement the policy, the government encourages the establishment of
non-profit institutions such as women's and children's hospitals, kindergartens,
etc., by private entities.
⚫Three-Child Policy (Implemented in 2021):
➢In 2021, to address long-term population dynamics, the "Decision on Promoting
Balanced and Sustainable Long-Term Development by Optimizing Birth
Policies" announced that the birth of a third child would be permitted.
6

7. Population Simulation ⚫Population simulation:
➢According to Chen (2006)
predictions, the proportion of
children aged 0-14 in China's
total population will be 15.8%
in the year 2030. From 2035
onwards, the proportion of
the elderly population in the
total population is projected
to reach 20%.
⚫The Impact:
➢An increase in labor costs.
➢Heavier burden of elderly
care.
➢ Potential decrease in the
economic growth rate.
7
Source: Cited from Chen (2006).

8. Pioneering
Research
2023/8/18 8
Research on Fertility
Research Objectives
Research on Chinese Fertility
Originality

9. Research on Fertility
⚫Psychological factors:
➢Tanaka (2009) meticulously compiled Japanese literature concerning the
trend of low birth rates in Japan. Through the examination of official
surveys conducted regularly by NIPSSR on marriage and childbirth, it is
observed that due to psychological and physical constraints related to
child-rearing, the Japanese tend to have fewer children than they desire.
➢Camussi et al. (2023) analyzed the connection between gender equality and
the challenge of low birth rates in Italy. Their study indicates that in Italy,
there's insufficient support for women to balance work and family
responsibilities, resulting in a heavy burden on women for managing family
duties. Addressing gender equality can play a crucial role as an intervention
to bridge the gap in low birth rates, emphasizing the importance of enabling
women to achieve their desired childbirth intentions.
9

10. Research on Fertility
⚫Economic Factors:
➢Berrington, A. & Pattaro, S. (2014) analyzed the relationship between
educational attainment, the desired number of children, and the realization
of desired family size using data from The National Child Development
Study (NCDS). Through results from a multinomial logistic regression
model, they revealed that individuals with higher educational levels desire
more children, but they tend to delay marriage, resulting in a family size
consistently smaller than their desired number of children.
➢Japaridze (2019) uses a utility model for childbirth, and household
consumption levels are compared. Lower-income households tend to
imitate higher-income ones, affecting the decision on the number of
children based on utility from both childbirth and consumption. Using ACS
2010 data, the study found that areas with large income inequality tend to
have lower birth rates.
10

11. Research on Fertility
⚫Economic and psychological factors:
➢Gauthier, A. H., & Philipov, D. (2008) used descriptive statistics to explore
the link between low birth rates in the EU and factors like the Human
Development Index and Gender Equality Index. Analyzing data from the
European Demographic Data Sheet 2008, they found higher economic
development and gender equality are associated with higher birth rates in
the EU.
➢Vignoli et al. (2020) utilized data from the European Social Survey in 2004
and 2010 to investigate the impact of precarious employment on
individuals' childbirth intentions and whether this impact is mediated by
subjective well-being. Using logistic models, the results indicated that
precarious employment has a negative impact on individuals' childbirth
intentions. Furthermore, irrespective of individuals' employment status,
those reporting higher levels of happiness were more likely to become
parents a few years later.
11

12. Research on Chinese Fertility
⚫Wang・ Wang (2016) :
➢Using data from the Chinese General Social Survey 2010 and similar sources,
the study employs descriptive statistics to analyze the gap between the
desired and actual number of children among Chinese individuals. Chinese
people desire around 1.86 children, but actually have about 1.68 children on
average. Policy factors contribute to the gap, and economic and health
considerations also limit actual child numbers.
⚫Song・Hu (2022):
➢The Renmin University of China's Population and Development Research
Center conducted a 2021 national survey titled "Research on Birth
Mechanism and Birth Support in the Context of Low Birth Rates and
Parenting Ages." Using this data, the study used Poisson models to analyze
factors influencing birth intentions. Over 57% of sampled couples desired
two children, while about 30% wished for one child. Based on these findings,
they suggest that the Chinese government could use stable birth intentions
and economic support to enhance birth rates.
12

13. Research Objectives
⚫Background:
➢For years, China has controlled population growth through stringent
policy restrictions. However, there is now a need to shift the population
policy.
➢As observed from birth rates, the actual number of children people
have is low. However, birth intentions remain relatively stable. If these
intentions could be realized, it is believed that it could mitigate aging to
a certain extent.
⚫Objective:
➢This study aims to elucidate which factors, considering China's current
population policy, influence the gap between desired and actual
number of children, and analyze obstacles encountered in birth
behavior, in order to consider appropriate policies.
13

14. Originality
⚫Originality:
➢Building upon
previous research, it
is necessary to
consider both
economic and
psychological factors
simultaneously when
addressing the gap.
➢Taking into account
economic factors
such as economic
uncertainty and
income inequality, as
well as leveraging
psychological factors
like happiness.
14
Gap
Source: Adapted from Vignoli et al. (2020)
Gender
equality
Happiness
Psychological
factors
Income
Inequality
Unemployme
nt Rate
Economic
uncertainty
Economic
factors
Employment
Status

15. Research Data
15
Microdata
Data Process
Definition of Variables
Distribution of Achieve

16. Microdata
⚫Full Name: Chinese General Social Survey (CGSS)
⚫Providing Institution: National Survey Research Center at Renmin
University of China, NSRC.
⚫Start Date: Since 2003, conducting cross-sectional surveys on
approximately 10,000 households in various provinces, municipalities, and
autonomous regions in China.
⚫Study Period: This research utilizes data from the years 2010, 2012, 2013,
2015, 2017, and 2018, considering the sampling design and the availability
of variables for the study.
Note: The survey design differs between the years 2003-2006 and after 2010.
16

17. Data Process
⚫Sample Limitations：
➢Individual ages are restricted to those between 17 and 45 years old.
➢Due to notably small sample sizes, cases with six or more children are
excluded.
➢Similarly, due to significantly small sample sizes, cases with desired
child numbers of six or more are excluded.
➢For income, cases with responses "not applicable," "unknown," or
"refused to answer" are excluded.
➢Education levels are categorized into five levels。
➢Missing values are removed for other variables.
17

18. Definition of Variables
⚫Achieve:
➢In this study, referring to Morgan, S. & Rackin, H. (2010), the following indicator is
constructed:
achieve=1 if intension>actual children
=2 if intension=actual children
=3 if intension<actual children
⚫Income Inequality:
➢Based on Japaridze (2019), this study constructs an income inequality indicator for
provinces, municipalities, and autonomous regions.
inequality= Τ
𝐼90 𝐼50 , 𝐼90 → top 90% of individuals’ income
𝐼50 → bottom 50% of individuals’ income
⚫Economic Uncertainty:
➢Drawing from Blom, N. & Perelli, B. (2021), this study considers the provincial
unemployment rate where individuals reside as representative of the uncertainty
individuals face in their labor market.
➢Furthermore, within CGSS, individuals' employment status is inquired. In this study,
non-agricultural employment is defined as 1, agricultural employment as 2, and
unemployment as 3.
18

19. Definition of Variables
⚫Happiness:
➢Survey Question: Overall, do you feel happy with your life?
➢Survey Response: Extremely Unhappy→1
Relatively Unhappy→２
Neither Happy nor Unhappy→3
Relatively Happy→4
Extremely Happy→5。
➢Happiness and life satisfaction are commonly used indicators when
measuring subjective well-being, and there is consistently a positive
relationship between the two (Diener, 2000; Lin et al, 2010; Badri et al, 2022).
Since life satisfaction was not included in the CGSS survey, in this study,
happiness is considered to represent individuals' subjective well-being.
19

20. Definition of Variables
⚫Gender Equality:
➢Survey Question: Do you agree that men should prioritize their careers while women
should prioritize family?
➢Survey Responses: Extremely Disagree → 1
Relatively Disagree →２
Neither Agree nor Disagree → 3
Relatively Agree → 4
Extremely Agree → 5。
⚫Regional Division:
➢In this study, referencing the China National Bureau of Statistics, the regions are
divided into 4 categories.
➢Eastern → 1 Central → 2 Western → 3 Northeast → 4
⚫Educational Level Division:
➢Illiterate → 0 Primary → 1 High School → 2 Associate → 3 Bachelor → 4
20

21. Distribution of Achieve
⚫Under:
➢Younger individuals are less
likely to achieve their desired
number of children, but as age
increases, this discrepancy
tends to decrease.
⚫Achieved:
➢As age increases, the
proportion of individuals who
achieve their desired number
of children tends to increase.
⚫Over:
➢With increasing age, the
proportion of individuals
exceeding their desired
number of children also tends
to increase.
21
86.62
69.66
44.99
38.98 37.23
10.65
27.54
49.29
52.35
49.49
2.73 2.79
5.72
8.67
13.28
0
10
20
30
40
50
60
70
80
90
100
17-24 25-30 31-40 41-50 51-60
The percent of different achievement at different age range
under achieved over

22. Distribution of Achieve
➢Comparing before and after the two-
child policy, the proportion of cases
where the desired number of children
is not achieved has slightly decreased.
➢The gap shows relatively minor
fluctuations before and after the two-
child policy implementation.
22
Number of Actual Children Freq. Percent Freq. Percent
0 3,992 23.65 2,334 29.36
1 7,943 47.05 3,113 39.16
2 4,209 24.93 2,136 26.87
3 632 3.74 311 3.91
4 88 0.52 49 0.62
5 19 0.11 7 0.09
Total 16,883 100 7,950 100
one-child policy two-child policy
Desired Child Number Freq. Percent Freq. Percent
0 286 1.69 290 3.65
1 4,331 25.65 1,890 23.77
2 11,180 66.22 5,133 64.57
3 843 4.99 488 6.14
4 200 1.18 119 1.5
5 43 0.25 30 0.38
Total 16,883 100 7,950 100
one-child policy two-child policy
Achieve Freq. Percent Freq. Percent
1 9,258 54.84 4,232 53.23
2 6,857 40.61 3,330 41.89
3 768 4.55 388 4.88
Total 16,883 100 7,950 100
one-child policy two-child policy

23. Empirical Analysis
23
Model
Regression Results

24. Model
24
⚫ Ordinal Logistic Regression Analysis：
➢ 𝑃 𝑎𝑐ℎ𝑖𝑒𝑣𝑒𝑚𝑒𝑛𝑡𝑖 = 1 𝑥𝑖 = 𝐹(𝑐𝑢𝑡1 − 𝑥𝑖
′
𝛽)
𝑃 𝑎𝑐ℎ𝑖𝑒𝑣𝑒𝑚𝑒𝑛𝑡𝑖 = 2 𝑥𝑖 = 𝐹 𝑐𝑢𝑡2 − 𝑥𝑖
′
𝛽 − 𝐹(𝑐𝑢𝑡1 − 𝑥𝑖
′
𝛽)
𝑃 𝑎𝑐ℎ𝑖𝑒𝑣𝑒𝑚𝑒𝑛𝑡𝑖 = 3 𝑥𝑖 = 1 − 𝐹(𝑐𝑢𝑡2 − 𝑥𝑖
′
𝛽)
➢ 𝐹𝑐1 = 𝐹 𝑐𝑢𝑡1 − 𝑥𝑖
′
𝛽 =
𝑒𝑥𝑝(𝑐𝑢𝑡1 −𝑥𝑖
′
𝛽)
1+𝑒𝑥𝑝(𝑐𝑢𝑡1 −𝑥𝑖
′
𝛽)
𝐹𝑐2 = 𝐹 𝑐𝑢𝑡2 − 𝑥𝑖
′
𝛽 =
𝑒𝑥𝑝(𝑐𝑢𝑡2 −𝑥𝑖
′
𝛽)
1+𝑒𝑥𝑝(𝑐𝑢𝑡2 −𝑥𝑖
′𝛽)
⚫ 𝑎𝑐ℎ𝑖𝑒𝑣𝑒𝑚𝑒𝑛𝑡: Categorization of Achieve 𝑖: Surveyed Individuals
⚫ 𝑥 :Vector of Explanatory Variables 𝛽 :Vector of Coefficients

25. Regression Results→One-child policy
25
The income inequality coefficient is
positive and significant at the 5% level.
Agriculturists more likely to achieve
desired children than unemployed.
Ordered logistic regression Number of obs = 16,883
LR chi2(23) = 3727.44
Prob > chi2 = 0.0000
Log likelihood = -12250.41 Pseudo R2 = 0.1320
achieve Coefficient Std.err. z P>|z|
income inequality 0.0597 0.0152 3.9300 0.0000 0.0300 0.0895
work
1 0.0259 0.0576 0.4500 0.6520 -0.0869 0.1388
2 0.2328 0.0632 3.6900 0.0000 0.1090 0.3566
unemployment rate -0.0440 0.0307 -1.4300 0.1520 -0.1043 0.0162
expenditure ratios on education -4.0706 0.8586 -4.7400 0.0000 -5.7534 -2.3879
expenditure ratios on health -5.8142 1.7773 -3.2700 0.0010 -9.2976 -2.3308
happiness -0.0227 0.0208 -1.0900 0.2770 -0.0635 0.0182
gender equality
5 0.0616 0.0826 0.7500 0.4560 -0.1003 0.2234
4 -0.0162 0.0624 -0.2600 0.7950 -0.1384 0.1061
2 -0.0024 0.0578 -0.0400 0.9670 -0.1157 0.1109
1 0.0700 0.0669 1.0500 0.2960 -0.0612 0.2011
[95% conf. interval]
→regression outcome connect with next silde
The expenditure ratios on education and
health coefficient are both negative and
significant at the 5% level.

26. Regression Results→One-child policy
26
female 0.2820 0.0353 7.9900 0.0000 0.2128 0.3512
age 0.0926 0.0025 36.3600 0.0000 0.0876 0.0976
huji(urban=1) -0.3505 0.0435 -8.0500 0.0000 -0.4359 -0.2651
edulevel
1 -0.2100 0.0942 -2.2300 0.0260 -0.3946 -0.0254
2 -0.6085 0.1035 -5.8800 0.0000 -0.8114 -0.4055
3 -0.9425 0.1129 -8.3500 0.0000 -1.1638 -0.7212
4 -1.1984 0.1149 -10.4300 0.0000 -1.4235 -0.9733
area
2 0.1370 0.0624 2.1900 0.0280 0.0146 0.2594
3 -0.0352 0.0685 -0.5100 0.6070 -0.1694 0.0990
4 0.1088 0.0680 1.6000 0.1090 -0.0244 0.2420
income -0.0205 0.0066 -3.1200 0.0020 -0.0334 -0.0076
GDP per capita -0.3169 0.0608 -5.2100 0.0000 -0.4361 -0.1977
/cut1 -7.4381 1.8752 -11.1134 -3.7628
/cut2 -4.2065 1.8743 -7.8801 -0.5328

27. Regression Results→Two-child policy
27
The income inequality coefficient is
positive and significant at the 5% level.
Ordered logistic regression Number of obs = 7,950
LR chi2(23) = 1375.66 LR chi2(23) = 1375.66
Prob > chi2 = 0.0000 Prob > chi2 = 0.0000
Log likelihood = -6049.93 Pseudo R2 = 0.1021
achieve Coefficient Std.err. z P>|z|
income inequality 0.0751 0.0164 4.5800 0.0000 0.0430 0.1073
work
1 -0.1097 0.0743 -1.4800 0.1400 -0.2553 0.0358
2 -0.0518 0.0970 -0.5300 0.5940 -0.2419 0.1384
unemployment rate 0.0254 0.0394 0.6500 0.5190 -0.0518 0.1027
expenditure ratios on education -1.6175 2.0864 -0.7800 0.4380 -5.7067 2.4717
expenditure ratios on health 0.6490 5.0657 0.1300 0.8980 -9.2796 10.5776
happiness -0.0009 0.0309 -0.0300 0.9770 -0.0614 0.0597
gender equality
5 0.3266 0.0999 3.2700 0.0010 0.1308 0.5224
4 0.1104 0.0875 1.2600 0.2070 -0.0612 0.2819
2 0.1672 0.0876 1.9100 0.0560 -0.0045 0.3388
1 0.2757 0.1100 2.5100 0.0120 0.0602 0.4912
→regression outcome connect with next silde
[95% conf. interval]
Both individuals with traditional views and
those without are more likely to achieve
fertility intention.

28. Regression Results→Two-child policy
28
female 0.2820 0.0353 7.9900 0.0000 0.2128 0.3512
age 0.0806 0.0037 21.8600 0.0000 0.0734 0.0878
huji(urban=1) 0.2709 0.0505 5.3700 0.0000 0.1719 0.3698
edulevel
1 0.1846 0.1281 1.4400 0.1500 -0.0665 0.4357
2 -0.2327 0.1361 -1.7100 0.0870 -0.4995 0.0341
3 -0.5387 0.1453 -3.7100 0.0000 -0.8235 -0.2539
4 -0.7327 0.1436 -5.1000 0.0000 -1.0143 -0.4512
area
2 -0.0521 0.1188 -0.4400 0.6610 -0.2849 0.1807
3 -0.2112 0.1256 -1.6800 0.0930 -0.4574 0.0349
4 -0.0144 0.1471 -0.1000 0.9220 -0.3028 0.2739
income -0.0112 0.0078 -1.4300 0.1510 -0.0266 0.0041
GDP per capita 0.0681 0.1513 0.4500 0.6530 -0.2284 0.3647
/cut1 4.8823 4.7724 -4.4714 14.2360
/cut2 8.0204 4.7732 -1.3349 17.3757

29. Conclusion
29
Summary
Future Research Perspectives
Policy Considerations

30. Summary
➢Income Inequality: Despite policy changes, higher income inequality increases the
likelihood of achieving fertility intention. However, as income inequality widens, there
is a possibility that the desired number of children may decrease.
➢Employment Status: Individuals engaged in agricultural work during the one-child
policy period are more likely to achieve fertility intention compared to the
unemployed. Although the unemployment rate is not statistically significant, the
expansion of economic uncertainty tends to hinder individuals from achieving fertility
intention.
➢Psychological Factors: The significance of happiness is not observed. During the
two-child policy period, regarding gender equality, both individuals with traditional
views and those without are more likely to achieve fertility intention. A deeper
investigation is necessary to understand the underlying factors.
➢Government Expenditure: During the one-child policy period, higher expenditure
ratios on education and health are associated with a lower likelihood of achieving
fertility intention. It is possible that higher expenditure ratios on education and health
lead to greater intention and allocation of support for other children.
30

31. Summary
➢Hukou: During the period of the one-child policy, individuals with urban
hukou were less likely to achieve their fertility intention. However, during
the two-child policy period, individuals with urban hukou are more likely to
achieve their fertility intention. It is possible that the fertility constraint in
urban areas was stricter than in rural areas during the one-child policy
period.
➢Gender: Regardless of policy changes, women are more likely than men to
achieve their desired number of children. This could suggest that women
tend to have smaller desired numbers of children compared to men.
➢Economic Development: During the one-child policy period, as individual
income and GDP per capita increase, individuals are less likely to achieve
their fertility intention. In a wealthier environment, there is a possibility that
individuals' desired number of children may increase.
31

32. Policy Considerations
➢Regarding economic development, during the one-child policy period, higher
individual income and regional economic growth may be associated with a
greater fertility intention. Reversing policy restrictions and further economic
development could enhance individuals' desired number of children and
potentially lead to population growth.
➢In terms of economic uncertainty, during the one-child policy period,
unemployed individuals may have a harder time realizing their desired
number of children. Further strengthening of unemployment protection is
necessary, and the provision of employment support and training could
potentially help individuals find new jobs, creating the economic capacity to
achieve the desired number of children.
➢Regarding expenditures on education and health, they reflect societal well-
being, and as societal well-being increases, there is a possibility of an
increase in the desired number of children. Further increases in societal well-
being can enhance individuals' desired number of children, potentially leading
to population growth.
➢During the two-child policy period, individuals with rural hukou might not
have realized their desired number of children. I believe that policy-related
and economic support for births in rural areas is necessary. 32

33. Future Research Perspectives
➢In this study, it was hypothesized that economic factors and psychological factors do
not influence each other, but there is a possibility that economic factors and
psychological factors are related. Discovering the correlation between these two
could potentially provide a more accurate reflection of the obstacles encountered in
birth-related behaviors.
➢The specific conditions of employment were divided too broadly in this study.
To conduct a more detailed analysis based on specific employment statuses,
individuals with different employment situations can be accurately reflected
in terms of the obstacles they encounter in birth-related behaviors.
➢Referring to previous research, the utility of using the ideal number of children as an
explanatory variable will be discussed in future discussions.
33

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37. Appendix
37
Variable Obs Mean Std. dev. Min Max
achiv
1 16,883 0.5484 0.4977 0 1
2 16,883 0.4061 0.4911 0 1
3 16,883 0.0455 0.2084 0 1
income inequality 16,883 3.5835 1.2383 0.3429 10
work
1 16,883 0.6281 0.4833 0 1
2 16,883 0.1811 0.3851 0 1
3 16,883 0.1908 0.3929 0 1
unemployment rate 16,883 3.4008 0.6744 1.21 4.5
expenditure ratios on education 16,883 0.1724 0.0250 0.0989 0.2222
expenditure ratios on health 16,883 0.0702 0.0128 0.0439 0.1056
happiness 16,883 3.8297 0.8056 1 5
gender equality
5 16,883 0.0716 0.2578 0 1
4 16,883 0.2418 0.4282 0 1
3 16,883 0.1088 0.3114 0 1
2 16,883 0.4171 0.4931 0 1
1 16,883 0.1606 0.3672 0 1
Descriptive statistics of the one-child period
Variable Obs Mean Std. dev. Min Max
gender
male 16,883 0.4867 0.4998 0 1
female 16,883 0.5133 0.4998 0 1
age 16,883 34.0793 7.5468 17 45
huji(urban=1)
rural 16,883 0.5545 0.4970 0 1
urban 16,883 0.4455 0.4970 0 1
edulevel
0 16,883 0.0327 0.1778 0 1
1 16,883 0.4919 0.4999 0 1
2 16,883 0.2085 0.4062 0 1
3 16,883 0.1223 0.3276 0 1
4 16,883 0.1446 0.3518 0 1
area
1 16,883 0.3669 0.4820 0 1
2 16,883 0.2342 0.4235 0 1
3 16,883 0.2655 0.4416 0 1
4 16,883 0.1334 0.3401 0 1
income 16,883 3.9520 3.4358 -4.8126 11.4541
GDP per capita 16,883 29.0581 0.4635 27.9108 30.0004

38. Appendix
38
Descriptive statistics of the two-child period
Variable Obs Mean Std. dev. Min Max
achiv
1 7,950 0.5323 0.4990 0 1
2 7,950 0.4189 0.4934 0 1
3 7,950 0.0488 0.2155 0 1
income inequality 7,950 4.4088 1.8796 2.0000 10
work
1 7,950 0.6618 0.4731 0 1
2 7,950 0.1004 0.3005 0 1
3 7,950 0.2379 0.4258 0 1
unemployment rate 7,950 3.0854 0.7330 1.4 4.2
expenditure ratios on education 7,950 0.1603 0.0266 0.1099 0.2041
expenditure ratios on health 7,950 0.0811 0.0143 0.0546 0.1044
happiness 7,950 3.9005 0.7795 1 5
gender equality
5 7,950 0.1526 0.3596 0 1
4 7,950 0.3419 0.4744 0 1
3 7,950 0.0994 0.2992 0 1
2 7,950 0.3153 0.4647 0 1
1 7,950 0.0908 0.2874 0 1
Variable Obs Mean Std. dev. Min Max
gender
male 7,950 0.4669 0.4989 0 1
female 7,950 0.5331 0.4989 0 1
age 7,950 33.4226 7.6251 18 45
huji(urban=1)
rural 7,950 0.5499 0.4975 0 1
urban 7,950 0.4501 0.4975 0 1
edulevel
0 7,950 0.0350 0.1837 0 1
1 7,950 0.3925 0.4883 0 1
2 7,950 0.2081 0.4059 0 1
3 7,950 0.1367 0.3436 0 1
4 7,950 0.2278 0.4194 0 1
area
1 7,950 0.4328 0.4955 0 1
2 7,950 0.2057 0.4042 0 1
3 7,950 0.2410 0.4277 0 1
4 7,950 0.1205 0.3256 0 1
income 7,950 3.8798 4.1223 -4.8700 11.3572
GDP per capita 7,950 29.5091 0.4456 28.6751 30.2755