In September 1980, an Open Letter on Controlling China’s Population Growth marked the beginning of the one-child policy.
In September 1982, business planning was adopted as a basic state policy.
In December 1982, family planning was written into the Constitution;
In October 2015, the fifth Plenary Session of the 18th CENTRAL Committee of the COMMUNIST Party of China proposed to “fully implement the policy of allowing each couple to have two children and actively carry out actions to cope with the aging population”, marking the end of the one-child policy.
In the 35 years, China has experienced the huge economic development, has experienced the urbanization advancement speeding up unceasingly, has experienced the enrichment of social material life, but also experienced a lot of problems, and brought by “one-child” policy and problems also gradually appeared in China’s social development, eventually led to the policy be end at the end of 2015.
So how has China’s population changed in those 35 years? What trends are emerging?
This paper uses Python to explore the changes of China’s population in the past 35 years by means of visualization from the Chinese population data provided by the National Bureau of Statistics.
First article:Mr State blogMr. Hezhou wechat official Account (ID: Zmister2016)
Original link:”One Child” 35 years, Python visualization of China’s population change – Mr. Zhou
Note: The data used in this article come from the official website of the National Bureau of Statistics
“National Data”
Population data in 1981 and before are household registration statistics; The data of 1982, 1990, 2000 and 2010 are calculated from the census data of the same year. Data for the remaining years were derived from annual population sampling surveys. Active servicemen are included in the total population and population by sex, and active servicemen are included in the urban population by urban and rural population. ———— Description of webpage data of national Data
The numbers in the chart are as follows: 10,000 people, the majority in %, birth rate, death rate and natural growth rate in ‰
On the national data website, we can get the population data from the founding of the People’s Republic of China to 2016, but there are some small limitations on the downloaded data, which is very messy and not easy to sort out. We wrote a small tool in Python to aggregate the data download into a CSV file:
It is convenient to read and operate the Pandas library.
First, the growth trend of China’s total population is slowing down
Let’s start with the trend of China’s total population.
We show the total population data at the end of 67 years from 1949 to 2016 in the form of a bar chart:
From Pyecharts import Line year = data[' year '] total = data[' year '] men = data[' male population '] women = data[' female population '] Men_women = data[' male/female population '] city = data[' male/female population '] no_city = data[' male/female population '] line = Add (" total population ", year,total, legend_top='bottom', legend_top=' legend_top ', is_more_utils=True, Mark_point = [' min ', 'Max' {' coord: [data [data [' year '] = = '1980'). The index and data [data [' year '] = = '1980'] [' at the end of the population (ten thousand people)]. Values [0]]}]. mark_point_symbolsize=80 ) lineCopy the code
In the figure, we set three marker points, which are:
- Founded in 1949;
- 1980 was the year the one-child policy was implemented
- As recently as 2016.
During these 67 years, China’s population increased from more than 500 million at the end of 1949 to nearly 1.4 billion at the end of 2016, among which, the population was more than 980 million at the end of 1980, about to break the 1 billion mark.
To sum up, during the 67 years, the total population of Our country was on the rise.
But it is hard to see from the bar chart how the one-child policy has affected the natural growth of population before and after it was implemented. To give a rough idea of the one-child policy’s impact on population growth, we calculate the total population growth in the 32 years before and after the one-child policy was implemented (the number of years is used for comparison purposes).
Total_1949 = data [data [' year '] = = '1949'] [' at the end of the population (ten thousand people)]. Values total_1980 = data [data [' year '] = = '1980'] [' at the end of the population (ten thousand people)]. The values Total_2012 = data[data[' year ']=='2012 '][' year-end population ']. Values from Pyecharts import Bar cate = [' population growth '] after_total = total_1980-total_1949 last_total = total_2012-total_1980 bar_1980 = Bar(" Comparison of Total Population Growth in China in 32 years before and after the One-child policy ", TITle_pos ='center',subtitle=" Total population Growth in 1949-1980 and total population growth in 1980-2012 ") Bar_1980.add (' first 32 years ',cate,after_total, legend_TOP ='bottom',mark_point=["average"],mark_point_symbol='roundRect') Bar_1980.add (' last 32 years ',cate,last_total, legend_TOP ='bottom',mark_point=["average"],mark_point_symbol='roundRect') bar_1980Copy the code
From the comparison of the total population growth of China in 32 years before and after the one-child policy, it can be seen that from the end of 1949 to the end of 1980, the total population of China increased from 541.67 million to 987.05 million, a total increase of 445.38 million. From 1980, when the one-child policy was introduced to 2012, China’s population increased from 987.05 million to 1.354.04 million, an increase of 366.99 million.
In the 32 years after the introduction of the one-child policy, China’s overall population growth was 78.39 million less than before. It can be said that China’s overall population growth trend is significantly slower than before.
2. The overall proportion of male and female population has increased
The gender imbalance in China’s population has always been a favorite topic of the news media. Here’s a quick look at the gender imbalance in China:
Gender_line = Line(" Trends of Male and female population in China from 1949 to 2016 ", TITle_pos =' Center ') Gender_line. add(" male population ",year,men,legend_top='bottom',is_more_utils=True,is_fill=True) Gender_line. add(" female population ",year,women,legend_top='bottom',is_fill=True) gender_line.add(' Male/female population difference ',year,men_women, mark_line=["average"], legend_top='bottom', Mark_point = [{' coord: [data [data [' year '] = = '1980'). The index and data [data [' year '] = = '1980'] [' difference between male and female population] values [0]]}]) gender_lineCopy the code
From the line chart of male and female population of China from 1949 to 2016, we can find that since the founding of the People’s Republic of China, the male population has always exceeded the female population by a certain amount.
Pandas is used to calculate the male and female population difference in Pandas for each year. To see the male and female population difference in Pandas for each year, we present the male and female population difference in Pandas.
Gender_differ_line = Line(" Gender difference in China ", TITle_pos ='center',subtitle="1949-2016 ") Gender_differ_line.add ( Year,men_women, mark_line=["average"], legend_top='bottom', is_fill=True, line_opacity=0.2, area_opacity=0.5, Mark_point = [' min ', 'Max' {' coord: [data [data [' year '] = = '1980'). The index and data [data [' year '] = = '1980'] [' difference between male and female population] values [0]]}]) gender_differ_lineCopy the code
It can be found that from 1949 to 2016, the difference between male and female population in China was the smallest in 1965, which was only over 17 million, and the highest in 2000, which was over 40 million, with an average value of 28 million, which was around the year when the one-child policy was implemented.
Before the implementation of the one-child policy, the male-to-female ratio showed a trend of periodic fluctuations, but the population difference was basically below the average of the population difference over the past 60 years:
- From 1949 to 1959, the gender gap increased slowly;
- From 1960 to 1969, the gender gap quickly bottomed out and stayed there for several years;
- From 1970 to 1980, the gender gap edged up again;
Whether this change was affected by the social and political activities at that time is not to be further investigated for the time being. If you are interested, you can conduct your own research with other data.
After the implementation of the one-child policy, the gap between male and female population in China went up and down, and then reached 41.31 million (2000), the largest in history, and finally dropped step by step, but still remained at a high level.
At the same time, the proportion of female population in China’s total population has never exceeded 50% either in the early days of the founding of the People’s Republic of China or in the recent years (48.04% at the end of 1949 was the lowest, and 49.17% in 1996 was the highest). The following figure shows the change trend of gender population difference and the proportion of male and female population in the three basic years:
When the news media talk about the issue of male and female population, we should at least know that “XXXX million men will become single every year” is behind the continuous growth of China’s population base, and a fundamental factor of the proportion of male and female population.
3. The natural growth rate of population continues to decline
One of the aims of the one-child policy is to reduce the population burden by reducing the number of newborns and the birth rate, and thus the natural growth rate of the population. Then, after the implementation of the “one child” policy, China’s birth rate, death rate and natural growth rate of what changes.
From Pyecharts import Line,Bar year = borth_data[' year '] borth_rate = borth_data[' birth '] death_rate = borth_data[' death '] Natural_rate = borth_data[' natural rate '] borth_rate_line = Title_pos ='center',subtitle=' 1949-2016, unit: ‰') borth_rate_line.add(" birth rate ",year,borth_rate,legend_top='bottom') Borth_rate_line. add(" death rate ",year,death_rate,legend_top='bottom') Borth_rate_line. add(" natural growth ",year,natural_rate,legend_top='bottom',) borth_rate_lineCopy the code
According to the line chart of the trend of birth rate, death rate and natural growth rate of China’s population, we can find that the death rate of China’s population is relatively stable in normal years, while the birth rate and natural growth rate experienced small fluctuations and then decreased year by year after 1981.
It can be seen that the reduction of birth rate and natural growth rate brought by the one-child policy is quite obvious. Under the condition that material life and medical conditions are getting better and better, the mortality rate of the population will not change significantly, so the change of the birth population will directly affect the change of the total population. The change of birth rate directly affects the change of natural population growth rate.
Fourth, the aging of the population
With the decrease of the natural population growth rate, the population age structure will also change correspondingly. Based on this, we collected the population age structure and dependency ratio data to increase the direction of visual exploration.
Due to historical reasons, the “National data” only provides data on the population’s age structure and dependency ratio from 1982 to 2016, so we only look at the change of China’s population’s age structure from the one-child policy.
In most cases, a simple line chart, bar chart comparison, or pie chart structure analysis will make it clear that there is something hidden in the data. So here we still use the line graph to plot the change of the proportion of young people (0-14 years old) and old people (65 years old and above).
From Pyecharts import Line,Bar year = age_data[' year '] age_total = age_data['0-14 years old population '] age_men = Age_data [' population aged 15-64 (10,000)'] age_women = age_data[' population aged 65 and over (10,000)'] age_line = Title_pos ='center',subtitle=' center' unit: Add ("0-14 years old ",year,age_total,is_more_utils=True,legend_top='bottom') Age_line. add(" age 65 + ",year,age_women, legend_TOP ='bottom') age_line.add(" age 65 + ",year,age_women,legend_top='bottom') age_lineCopy the code
It can be seen from the “Trend chart of China’s Population age structure” that the elderly population aged 65 and above is increasing year by year, while the youth population aged 0-14 shows an overall downward trend with local fluctuations.
The change in the age structure of the population can be seen more intuitively by looking at the pie chart of the age structure:
Child_1982 = age_data[age_data[' year ']=='1982 ']['0-14 ']. Values mature_1982 = Age_data [age_data [' year '] = = '1982'] [' population aged 15-64 (ten thousand) ']. Values old_1982 = Age_data [age_data[' year ']=='1982 ']. Values pie_1982 = Pie(' age structure of Chinese population in 1982 ',title_pos='center') Pie_1982. Add (" ", [' 0 to 14 ', '15-64 - year - old', '65 and older], [child_1982 mature_1982, old_1982], is_label_show = True, legend_pos="bottom", radius=[20, 40], Rosetype ='area') child_2016 = age_data[age_data[' year ']=='2016 ']['0-14 year old population ']. Values mature_2016 = Age_data [age_data [' year '] = = '2016'] [' population aged 15-64 (ten thousand) ']. Values old_2016 = Age_data [age_data[' year ']=='2016 ']. Values pie_2016 = Pie('2016 China population age structure ',title_pos='center') Pie_2016. Add (" ", [' 0 to 14 ', '15-64 - year - old', '65 and older], [child_2016 mature_2016, old_2016], is_label_show = True, legend_pos="bottom", radius=[20, 40], rosetype='area' )Copy the code
It is obvious that from 1982 to 2016, the proportion of the population aged 0-14 decreased by more than half. The reason is, on the one hand, that teenagers entered the demographic group aged 15-64 (the proportion of the population aged 16-64 increased, which translated into a large labor dividend), on the other hand, It is also the one-child policy that has led to fewer babies being born each year. At the same time, the proportion of the population aged 65 and over has more than doubled.
Due to the gradual decrease of the population aged 0-14 and the gradual increase of the population aged 65 and above, China’s dependency ratio has also undergone considerable changes in the past 35 years.
The dependency ratio, also known as the dependency coefficient, refers to the ratio of the non-working age population to the working age population in the population. The larger the dependency ratio is, the more the number of dependents per worker is, which means the more serious the burden of dependents is.
The child dependency ratio reflects the number of children per 100 working-age people.
The old-age dependency ratio is also called the old-age dependency coefficient. The ratio of the number of elderly people in a population to the number of working-age people. It’s usually expressed as a percentage. Used to show how many elderly people there are for every 100 people of working age. The dependency ratio of the aged population is one of the indicators reflecting the social consequences of the aging population from an economic perspective
From Pyecharts import Line,Bar year = age_data[' year '] age_rate_total = age_data[' total support (%)'] age_rate_men = Age_data [' Child dependency ratio (%)'] age_rate_women = age_data[' elderly dependency ratio (%)'] age_rate_line = Line(" Trend of Population dependency ratio structure in China ") Age_rate_line. add(" year ", age_rate_total) age_rate_line.add(" year ", age_rate_men) age_rate_line.add(" year ", age_rate_men) Age_rate_line. add(" elderly dependency ratio ",year,age_rate_women) age_rate_lineCopy the code
In the first 20 years of the one-child policy, the total dependency ratio of the population declined with the decrease of the child dependency ratio, benefiting from the decrease of the newborn population. Although the dependency ratio of the elderly was gradually rising, its effect would not be realized until after 2009. Since 2010, the child dependency ratio has remained stable to some extent, while a large number of labor force population has entered the ranks of the elderly, leading to a rapid increase in the elderly dependency ratio, so that since 2010, China’s total dependency ratio has gradually started to rise again.
Although now “one-child” policy has been abolished, but on the one hand, after the abolition of the “one-child” may lead to neonatal increased, which makes children’s dependency ratio continues to improve, on the other hand, the demographic dividend continuously drains, aging population continues to increase, elderly dependency ratio will continue to rise, want to in a short period of time the population total raising proportion increase is unlikely.
Five, the small mood
The one-child policy has become a thing of the past, but its impact will not disappear soon. The change of population is related to the development of China’s social politics, economy, industry and other aspects. It remains to be seen whether the impact of the abolition of the one-child policy will soon be eliminated, and what future population policies will be introduced along with the development of Chinese society.