Aging and economic growth: is there a role for a two-child policy in China?

Figures & data

Figure 1. Framework of the conceptual model. Source: The author’s design and calculation results.

Table 1. Main variables: descriptions and sources.

Variable	Description	Unit	Source
resources-city	The number of resources in the city where the agents are located. Expressed as hypothetical values, where the larger the value is, the richer the city’s resources are (e.g., land).	Rank value	Designed by the author
max-resources-city	The maximum number of resources in the city where the agents are located. An assumed value is used to represent the maximum resource limit for a particular period of time.	Rank value	Designed by the author
age	Agent age. Set demographic characteristics and structure by using different age groupings.	Year	Brunow and Hirte (2009), Cuaresma, Lábaj, and Pružinský (2014), Zhang, Zhang, and Zhang (2015), Lee and Shin (2019)
incomes	Agent incomes. Set different values to reflect the income of each subject.	Rank value	Islam (1996), Chen and Sun (2014), Su and Yao (2017)
life-expectancy	Agent life expectancy. Set the life expectancy values for each subject, including the maximum and minimum life expectancy values.	Year	Sunde (2011), He and Li (2018)
consumption	Amounts consumed by agents. Set different values to reflect the amount of resources the entity needs to survive.	Rank value	Designed by the author
working-skills	Agent’s work-related abilities and skills. Set different values to reflect the difference in human capital.	Rank value	Yoon (2006), Siddiqui and Rehman (2016), Haini (2019)
number-people	The number of people.	Tens of millions	Designed by the author
max-working-skills	Maximum value of agent’s work-related abilities and skills.	Rank value	Yoon (2006), Siddiqui and Rehman (2016), Haini (2019)
consumption-max	Maximum value of agent consumption.	Rank value	Designed by the author
life-expectancy-min	Minimum value of agent’s life expectancy.	Year	Designed by the author
life-expectancy-max	Maximum value of agent’s life expectancy.	Year	Designed by the author
percent-abundant-land	Percentage of abundant land.	%	Binswanger and Mcintire (1987), Mo (2018)
resource-growth-technology	Technology used to enhance resource growth. Use different data values to indicate advancement in a technology.	Rank value	Sasmal (2012), Loupias and Wigniolle (2019)
amount-resource-grown	The amount of resources grown. Set different values to reflect changes in resources over a specific period of time.	Rank value	Designed by the author
Wealth	Humans use their work-related skills to develop resources and earn income to satisfy their own consumption needs. If there is a surplus, it can accumulate as wealth (GDP).	Rank value	Zallé (2019)

Table 2. Main variables: overview of baseline data.

Variable	Baseline value	Unit	Description
max-resources	50	Rank value	Controlled variable
number-people	138.3	Tens of millions	138.3^a
max-working-skills	5 [1, 20]	Rank value	Controlled variable
consumption-max	15 [1, 25]	Rank value	Controlled variable
life-expectancy-min	30 [1, 100]	Year	Controlled variable; baseline value is 30
life-expectancy-max	76.5 [1, 100]	Year	Controlled variable; baseline value is 76.5^b
percent-abundant-land	2.9 [1, 100]	%	2.9%^c
resource-growth-technology	2.11 [1, 10]	%	2.11%^d
amount-resource-grown	4 [1, 10]	Rank value	Controlled variable

^a Statistical data from the China National Bureau of Statistics for 2016.

^b In 2016, China’s life expectancy increased to 76.5 years. Source: Xinhua News Agency new media special line (Guangzhou), accessed September 29, 2017.

^c Percent of preferential farmland in mainland China. Source: 2016 China Land and Resources Bulletin (Li, 2017).

^d In 2016, China’s research and development investment intensity reached 2.11%. Source: Statistics Bulletin of national science and technology expenditure in 2016.

Table 3. Parameters of the different simulation scenarios.

Variable	Unit	Simulation	Scenario
Before the two-child policy		SimBT1
number-people	Tens of millions	138.3
life-expectancy-max	Year	76.5
max-working-skills	Rank value	5
consumption-max	Rank value	15
percent-abundant-land	%	2.9
resource-growth-technology	Rank value	2.11
amount-resource-grown	Rank value	4
After the two-child policy		SimAT1	SimAT2
number-people	Tens of millions	143.3^a	143.3
life-expectancy-max	Year	80	80
max-working-skills	Rank value	5	6
consumption-max	Rank value	15	15
percent-abundant-land	%	2.9	8
resource-growth-technology	Rank value	2.11	3.22
amount-resource-grown	Rank value	4	8

^a China’s annual population will increase dramatically after the policy changes, with a peak increase of about 50 million births (Zhai et al., 2014).

Source: The author’s design and calculation results.

Table 4. Statistical analysis of the description of three simulation scenarios.

Simulation scenario	GDP (Wealth) Unit: Rank value
	Range	Minimum	Maximum	Mean	Std. Deviation
SimBT1	1336.00	2371.00	3707.00	2964.70	328.35
SimAT1	1562.00	2481.00	4043.00	3074.00	439.01
SimAT2	3559.00	2677.00	6236.00	4510.90	1210.13
Difference analysis
SimBT1 SimAT1	T = –3.93		Sig. (two-tailed) p = 0.00
SimAT1 SimAT2	T=–11.41		Sig. (two-tailed) p = 0.00

Source: The author’s design and calculation results.

Figure 2. Simulation results before and after implementation of the two-child policy. Source: The author’s design and calculation results.

Figure 3. Simulation results of the two-child policy and human capital policy. Source: The author’s design and calculation results.

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