>>Geography: Population size and reproduction
Population size and reproduction
1. World population: very fast growth!
Geographers and demographers widely use population census data in their work. All since the beginning of the 19th century. there were more than 2 thousand such censuses in the world, which today in most developed countries are carried out every five or ten years. .
According to the estimates of statisticians and demographers, over the entire history of mankind, more than 100 billion people were born on Earth. But throughout almost this entire story population growth was slow, and acceleration occurred only in the period of modern and especially modern times. Thus, over the last millennium, the first doubling of the population took 600 years, the second 250, the third about 100, and the fourth a little over 40 years. This means that the world's population has never increased as quickly as in the middle and second half of the twentieth century! In 1950 it reached 2.5 billion, in 1980 4.4 billion, and in 2006 6.5 billion people. .
Example. If at the beginning of the twentieth century. The absolute annual growth of the Earth's population was 10 - 15 million, and in the middle of the century 40-50 million, then in the 80-90s of the twentieth century. it reached 80-85 million people, which exceeds the number of inhabitants in any European state except Russia.
1 Ethnology ( ethnography, from Greek. ethpos tribe, people) - the science of the origin of peoples (ethnicities), their characteristic features and the relationships between them, which are determined by ethnic processes.
2 Demography(from the Greek detos people and ggapho I write) the science of the patterns of population reproduction, studying its numbers, natural growth, age and sex composition, etc.
However, in different regions of the world the population is growing unevenly today: in some slowly, in others faster, and in others very quickly. This is explained by the different nature of its reproduction. (Exercise 1.)
2. The concept of population reproduction.
The scientific theory of population considers the population involved in labor, like g the main productive force of society, the basis of all social production. Constantly interacting with nature (geographical environment), the population plays an active role in its transformation. At the same time, the population, and each of you feels this, acts as the main consumer of all created material wealth. That's why number population is one of the important factors in the development of each country, and indeed of all humanity.
In turn, population growth depends on the nature of its reproduction.
Reproduction (natural movement) of the population is understood as the totality of the processes of fertility, mortality and natural increase, which ensure the continuous renewal and change of human generations.
Fertility, mortality, and natural population growth are basically biological processes. But nevertheless, the socio-economic conditions of people’s lives, as well as the relationships between them in society and in the family, have a decisive influence on them. .
The mortality rate depends primarily on the material living conditions of people: nutrition, sanitary and hygienic working and living conditions, and development health. The birth rate also depends on the socio-economic structure of society and the living conditions of people. But this dependence is much more complex and contradictory, causing a lot of controversy in science. As a rule, with the growth of wealth and culture, the increasing involvement of women in production and social activities, the lengthening of children’s education and the general increase in the “price of a child,” the birth rate decreases. But rising incomes can also serve as an incentive to increase it.
Wars, primarily world wars, have a very large negative impact on the reproduction of the population, which lead to huge human losses both as a result of direct military action and as a result of the spread of hunger and disease, and the severance of family ties.
In the most simplified, generalized form, we can talk about two types of population reproduction.
3. The first type of population reproduction: demographic crisis.
The first type of population reproduction is characterized by low rates of fertility, mortality and, accordingly, natural growth. It has become widespread primarily in economically developed countries, where the proportion of elderly and old people is constantly growing; this in itself reduces the birth rate and increases the death rate of the population.
However, in addition to the demographic factor, reasons of a socio-economic nature also play an important role, causing increased mortality from diseases, unsettled life, military conflicts, increased crime, industrial injuries, various types of natural and man-made disasters, accidents, as well as from deterioration in the quality of the environment. environment. But even among the countries of the first type of reproduction, three subgroups can be distinguished. Firstly, these are countries with an average annual natural population growth of approximately 0.5% (or 5 people per 1,000 inhabitants, or 5%0). In such countries, examples of which are the USA, Canada, and Australia, quite significant population growth is achieved.
To do this, it is necessary that approximately half of all families have two children, and half three. Over time, two children “replace” their parents, and the third not only covers losses from illnesses, accidents, etc., but also compensates for the lack of offspring among childless people. but also provides sufficient overall growth.
Secondly, these are countries with zero or close to zero natural growth. Such growth no longer ensures expanded reproduction of the population, which usually stabilizes at the achieved level.
Example. All countries of the second subgroup are located in Europe. These are Belgium, Denmark, Portugal, Poland. Sweden. The population in these countries is no longer growing.
Thirdly, these are countries with negative natural increase, i.e. those where mortality exceeds birth rate.
As a result, the number of their inhabitants not only does not grow, but even decreases. Demographers call this phenomenon depopulation 1(or demographic crisis). It is most typical for Europe.
Example. At the beginning of the 21st century. In Europe, there were already 15 countries with negative natural population growth. Among the CIS countries, these include Russia, Ukraine, and Belarus, where the socio-economic crisis that occurred in the 90s affected the indicators of natural population growth. XX century (see table 12 in the “Appendices”).
1 D e p o p u l i c i a(from French depopulation) a decrease in the population of a country or region as a result of narrowed reproduction, leading to its absolute decline.
The transition from a large family, typical of old Russia, to a small family took place in our country during the existence of the Soviet Union. But in the 90s. XX century First of all, with the emergence of a deep socio-economic crisis, a real “collapse” in the indicators of natural population growth began. The birth rate in Russia (10.4 people per 1000 inhabitants) and at the beginning of the 21st century. remains very low.
Until relatively recently, the type of population reproduction that has developed in economically developed countries was often called rational. However, in the first half of the 90s of the twentieth century. its indicator dropped to 2% 0, and at the beginning of the 21st century. actually became zero. At the same time, many European countries have already entered the demographic crisis, which negatively affects or may affect in the future their entire development.
4. The second type of population reproduction: demographic explosion.
For in second type of reproduction The population is characterized by high and very high birth rates and natural growth rates and relatively low death rates. It is typical primarily for developing countries.
After gaining independence, these countries were able to make wider use of the achievements of modern medicine, sanitation and hygiene, primarily to combat epidemic diseases. This led to a fairly sharp reduction in mortality. The birth rate for the most part remained at a high level.
Of course, this is largely due to the persistence of the thousand-year-old traditions of early marriages and large families. . The average family size is still 6 people; As a rule, this is a three-generation family (parents, their children and grandchildren). In addition, it remains the main means of maintaining a living wage and children continue to serve as the main support for parents in old age. And child mortality in these countries remains significant. Factors such as the predominance of the rural population, insufficient level of education, and weak involvement of women in production continue to have an impact.
At the beginning of the 21st century. the average annual rate of natural growth in developing countries was 1.6%, i.e., it was 16 times higher than in economically developed countries!
But even against this background, the least developed countries, where 800 million people live, or more than 1/10 of the total population of the planet, especially stand out. They are distinguished by the highest rates of fertility and natural increase (2.4%); that is why it is among them that one should look for “world record holders.”
“Record holders” for average annual population growth can be found among the countries of Tropical Africa and South-West Asia. . (Task 2.)
This phenomenon of rapid population growth in countries of the second type of reproduction in the mid-twentieth century. received a figurative name in the literature population explosion. Today, these countries (together with China) account for more than 4/5 of the planet's total population and more than 95% of its annual growth. This means that of the 130 million children born each year, 124 million are born in developing countries. In particular, the population of Asia increases annually by about 40 million people, Africa by almost 30 million, and Latin America by more than 9 million.
If in 1900, of the 15 largest countries in the world by population, seven were in Europe, five in Asia and three in America, then in 2005 only two European countries remained on this list (Germany and Russia), but there were eight Asian countries (China, India , Indonesia, Pakistan, Bangladesh, Japan, Vietnam, Philippines), as well as three American (USA, Brazil, Mexico), two African (Nigeria, Egypt) (see Table 14 in the “Appendices”).
Along with this, one cannot help but pay attention to the fact that in some more “advanced” developing countries a rather noticeable decline in the rate of natural population growth has already begun. Examples of this kind of measures include Brazil, India, Turkey, Morocco, and Tunisia. And China, Argentina, Chile, Sri Lanka, Thailand have actually already moved into the group of countries of the first type of reproduction.
Nevertheless, developing countries have and will continue to have a decisive impact on the size and reproduction of the population, primarily determining the demographic situation throughout the world.(Creative task 3.)
5. Demographic policy, management of population reproduction.
Nowadays, most countries of the world strive to manage the reproduction of the population by conducting state demographic policy.
Demographic policy is a system of administrative, economic, propaganda and other measures through which the state influences the natural movement of the population (primarily the birth rate) in the direction it desires. It is clear that the direction of demographic policy depends primarily on the demographic situation in a particular country.
In countries of the first type of population reproduction, demographic policies aimed at increasing fertility and natural population growth prevail. It is carried out mainly with the help of various stimulating economic measures such as one-time loans to newlyweds, benefits at the birth of each child, monthly benefits for children, paid vacations, etc. Examples of countries pursuing an active demographic policy are France, Japan, and Russia.
Most countries of the second type of reproduction in recent decades have begun to implement demographic policies aimed at reducing the birth rate and natural population growth. Perhaps the greatest efforts in this regard are made by the two largest countries in the world, China and India.
Example 1. The Constitution of the People's Republic of China states that spouses must carry out planned childbearing. A committee for planned childbirth has been created; permission from local authorities must be obtained to give birth to a child. A later age for marriage has been set. During the period of study at the institute, marriages, as a rule, are not permitted. The main motto of the PRC's demographic policy is: “One family, one child.” The implementation of this policy has already yielded results.
Example 2. India was the first developing country to adopt a national family planning program as an official government policy back in 1951. The age of marriage was significantly raised, mass voluntary sterilization of the population was carried out, and a family of four was promoted under the motto: “We are two, we are two.” As a result of these measures, the birth rate and natural increase have decreased slightly, but nevertheless, almost 1/5 of all newborns in the world are children born in India.
However, many difficulties arise in the implementation of demographic policy, not only financial and economic, but also moral and ethical. In the 90s of the twentieth century. The issue of a woman’s right to terminate a pregnancy, which was sharply opposed by the Catholic Church, caused especially great debate. . Many Muslim Arab countries, especially in South-West Asia, generally reject any measures for “family planning” for reasons of religious morality. The majority of the least developed countries of Tropical Africa do not pursue any demographic policy.
6. Theory of demographic transition.
An important scientific basis for demographic policy is the theory demographic transition, which explains the sequence of changes in demographic processes. The scheme of such a transition itself includes four successive stages.
For first stage, which covered almost the entire history of mankind, was characterized by very high birth and death rates and, accordingly, very low natural increase; Nowadays it almost never occurs.
Second phase characterized by a sharp reduction in mortality (thanks primarily to the successes of medicine) while maintaining the traditional high birth rate. This “fork” between the first and second indicators became the initial cause of the demographic explosion.
The third stage is characterized by the persistence of low mortality rates (and sometimes even a slight increase in them associated with the “aging” of the population). The birth rate also decreases, but usually still slightly exceeds the death rate, ensuring moderate expanded reproduction and population growth.
When going to fourth stage Birth and death rates are the same. This means a transition to population stabilization. (Task 4.)
7. Quality of population as a new complex concept.
Recently, in science and practice, indicators characterizing not only the quantity, but also the quality of the population are becoming increasingly important. This is a complex, comprehensive concept that takes into account economic (employment, per capita income, caloric intake), social (level of health care, safety of citizens, development of democratic institutions), cultural (level of literacy, provision of cultural institutions, printed materials), environmental (state of the environment) and other living conditions of people.
Recently, the UN and other international organizations, when determining the quality of a country's population, have paid the main attention to the state of its health, which, in turn, largely depends on the level of healthcare and general standard of living. In the second half of the twentieth century. Notable progress has been made in this regard, including in developing countries. However, many problems still remain unresolved.
Example. The world average infant mortality rate is 55 children per 1000 live births. In economically developed countries it is only 8 children, while in developing countries it is 60, and in the least developed countries it is 100. Moreover, in Africa and Asia there are also countries where this figure reaches 150-160 (Liberia, Niger, Sierra Leone, Afghanistan ).
Another important generalizing criterion for the health status of a nation is the indicator average life expectancy 1 . At the beginning of the 21st century. it is on average 66 years for the whole world (64 years for men and 68 years for women). The corresponding figures for economically developed countries are 72 and 80, for developing countries 62 and 66, and for least developed countries 51 and 53.
Example 1. The world's highest average life expectancy in Japan is 82 years (men 79, women 86). Sweden, Iceland, Spain, and Canada have almost the same indicators (see Table 15 of the Appendix).
Example 2. The lowest life expectancy in the world is in the African countries of Zambia and Sierra Leone (32-34 years). Similar indicators are slightly higher for some other countries of Tropical Africa (see Table 15 of the Appendix).
1 Average life expectancy - the expected life expectancy of the population, which is determined using calculations based on probability theory. Depends both on biological and hereditary characteristics, as well as on nutrition, work, and living conditions. Measured in number of years.
Average life expectancy in Russia in the 90s. under the influence of the socio-economic crisis, it decreased, amounting to about 65.3 years in 2005 (59 years for men and 72 years for women). By the way, there is no such huge gap between the indicators of both sexes in any other country in the world.
Another important indicator of the quality of the population is the literacy level. In economically developed countries, illiteracy has been virtually completely or almost completely eliminated. But in developing countries, despite recent progress, the educational level in general is still quite low, especially among rural residents.
Example. In Niger, Mali and Burkina Faso, more than 80% of all residents are illiterate, in Somalia more than 70%, in Senegal, Liberia, Ethiopia, Pakistan, and Bangladesh more than 50%.
According to the UN, in 1990 about 960 million people could neither read nor write. Since then, as the population explosion continues, the total number of illiterate people has fallen by 150 million. The absolute number of illiterate people is especially high in South and East Asia and sub-Saharan Africa. In South Asia, illiterates make up about half of the total population.
Population and its importance
A population is a complex collection of people living within defined territories and operating under existing historical conditions. It influences the territorial organization of the economy, the production specialization of the regional economy and the location of branches of the economic complex.
The population is characterized by a system of interrelated indicators, such as the number and density of the population, its composition by gender and age, nationality, language, marital status, education, membership in social groups, etc. The population of any country performs two important functions: on the one hand, it is a producer of material goods, a creator of a social national product, on the other hand, a consumer of material values. The quantitative and qualitative composition of labor resources, traditional occupations and skills of the population largely determine the territorial organization of the economy, the production specialization of the regional economy and the location of branches of the economic complex.
Also, the population size in a country or a particular region has a significant impact on the economic potential and the development of the productive forces of society. However, there is no direct relationship between these concepts. Thus, states with a high level of economic development and a smaller population produce tens of times more gross national product than states that are larger in population but inferior in technical equipment, labor productivity, and the level of qualifications of the workforce.
Population of Russia and trends in its changes
Population size and trends in its change are the result of natural and mechanical population movement (migration).
Natural population movement is a set of processes of fertility, mortality, natural increase or natural decline. The natural movement of the population is ensured by the reproduction regime - the continuous renewal and change of human generations. The main indicators of population reproduction are: the birth rate (the number of births per year to the average population per year), the mortality rate (the number of deaths per year to the average annual population), the natural increase rate (the ratio of natural population growth to the average population for a certain period or the difference between birth and death rates).
Since the beginning of the 90s, the demographic development of the Russian Federation has entered a period of acute crisis, which has affected all major demographic processes: mortality, fertility and migration. The current demographic situation has developed against the backdrop of long-term unfavorable trends in demographic development over a period of more than thirty years, starting from the 60s. At the same time, the evolutionary trends of the constant deterioration of demographic processes were sharply strengthened by the negative impact on the population of the socio-economic crisis in the country, the decline in living standards a significant part of the population, the continued aging of the Russian population, immigration processes, the increased loss of the working age population, unfavorable environmental conditions in many regions of the Russian Federation, etc.
According to the Federal State Statistics Service (Rosstat), the resident population of the Russian Federation as of October 1, 2009 amounted to 141,904.0 thousand people. According to the 2002 Population Census, the population of the Russian Federation was 145,166.7 thousand people. There is a natural population decline, characteristic of 75 constituent entities of the Russian Federation. The population is growing very slowly.
Table 1. Vital statistics
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Vital Statistics |
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Number of births, people |
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Number of deaths, people |
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Natural population growth, people |
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Crude birth rate, per 1000 population |
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Crude mortality rate, per 1000 population |
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General rate of natural increase, per 1000 population |
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Life expectancy at birth, years: |
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men and women |
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Negative natural growth rates are observed in all regions of the European part of Russia. At the same time, positive dynamics remain in the national formations of the North Caucasus, Volga region, Eastern Siberia and the Far East. This is due to the preservation of the historical traditions of large families in these republics, as well as the high proportion of the population living in rural areas, where the birth rate remains high.
Currently, thanks to government policy, mortality has begun to decline significantly. An increase in the number of births was observed in 67 subjects of the Russian Federation, a decrease in the number of deaths - in 75 subjects. In the whole country, the excess of the number of deaths over the number of births was 1.2 times (in January-May 2008 - 1.3 times), in 4 constituent entities of the Russian Federation (Tula, Pskov, Tambov and Leningrad regions) it was 2.0 -2.2 times.
The birth rate of boys exceeds the birth rate of girls and is 104 - 107 people. for 100 girls per year. However, by the age of 30, the ratio of the male to female population levels off. This is due to the higher mortality rate of males (as a result of a large number of accidents, participation in hostilities within the state and beyond). From the age of 40, the female population begins to predominate over the male population (as a consequence of increased mortality among men associated with occupational injuries and drug and alcohol abuse). The greatest excess of women over men occurs in the age group over 70 years, which is largely due to losses during the Second World War. In general, the share of men in the gender and age structure of the population does not exceed 47%, which is slightly lower compared to developed countries of the world. The decline in the proportion of men is also explained by a reduction in life expectancy.
There is also an unfavorable trend in changes in the age structure of the population. The share of the working age population in the total number of deaths reaches 30%. The deformed age structure indicates both a reduction in labor potential now and in the future, and an increase in the unique burden on the employed population, since the maintenance of persons over retirement age falls on the working population. [cm. 1, p. 67-68]
Mechanical movement of the population - migration processes or the movement of people across the borders of certain territories with a change of place of residence forever or for a more or less long time. Migration contributes to the territorial redistribution of population and labor resources and affects the level of socio-economic development of regions.
Indicators of the migration process in the Russian Federation are presented in Table 2.
Table 2. Indicators of migration movements in Russia
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Arrived in the Russian Federation from CIS countries and non-CIS countries, people |
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Arrived in the Russian Federation from CIS countries, people |
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Arrived in the Russian Federation from foreign countries, people |
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Departed from the Russian Federation to the CIS countries and non-CIS countries, people |
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Left the Russian Federation for the CIS countries |
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Left the Russian Federation for non-CIS countries |
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Number of families of internally displaced persons and refugees, units |
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Number of internally displaced persons and refugees, people |
According to statistics, over the past 10-12 years, migration processes in Russia are characterized by the following features:
The total number of registered migration movements, both internal and external, decreased by more than two and a half times - from 6.3 million in 1989 to 2.4 million people in 2001;
The share of internal migrations in the total volume of relocations (which also includes migration exchanges with the CIS countries, the Baltic states and non-CIS countries) increased from 65 to almost 90%;
Migrants are dominated by the population of working age, which accounts for 3/4 of the total number;
The number of those who entered Russia for permanent residence exceeds the number of those who left its borders, which ensures mechanical population growth (since the beginning of the 90s it amounted to almost 3.5 million people);
External migration turnover is dominated by migration exchange (the sum of arrivals and departures) between the Russian Federation and the CIS and Baltic countries, which in the period under review has already exceeded 11 million people;
The main vector of interregional migrations in Russia in recent years has been movement from the north and east of the country to the south and west. The country is clearly divided into two zones - inflow (Central, Volga-Vyatka, Central Black Earth, Ural economic regions; Rostov region, Krasnodar and Stavropol territories of the North Caucasus region; southern regions of Siberia) and outflow of population (European North, northern regions of Eastern Siberia , Far East). This spatial pattern of migration, according to experts, will continue in the foreseeable future.
The greatest outflow of population is observed from the Far Eastern region. During the 90s, it exceeded 840 thousand people (11% of all residents). Over the same period, more than 300 thousand people (5%) left the Northern Economic Region, over 180 thousand people (2%) left Eastern Siberia.
The main area of attraction for migrants has been the Central District for many years. Over the last decade, the migration increase here amounted to 1.2 million people (4% of the population living in the region at the beginning of 1991). Population growth due to migrants in the North Caucasus over the same period exceeded 900 thousand people (5.5%), in the Volga region - 800 thousand people (5%), in the Central Black Earth region - 550 thousand people (7%) .
Since the second half of the 90s, Moscow has become the most noticeable center of attraction for migrants from all regions of the country. Only in 1996-2000. The migration increase in the capital exceeded 200 thousand people, which accounted for half of the migration increase throughout the Central Federal District.
Intra- and interregional migration flows are formed under the influence of various factors. The transition to a market and changes in economic relations led, in particular, to the loss of the incentive value of the benefits and wage allowances previously established by the state in the regions of the Far North and equivalent territories, which were used for many years to attract personnel here. The social living conditions of people in these regions have also deteriorated noticeably. The decline in production in the primary industries, which had a predominant development in the north of the country, led to a reduction in jobs and an increase in unemployment. All this taken together led to an increase in migration outflow from the northern territories.
The consequence of the protracted military conflict in Chechnya and worsened interethnic relations in the North Caucasus was the loss of migration attractiveness of this region and a decrease in the influx of migrants from other regions of the country. The migration growth rate here has decreased significantly.
At the same time, the influx of population to the regions of the west and south of Russia can be explained by the fact that, along with economic incentives, non-economic factors such as climate, political stability, ethnic homogeneity, and geographic location are beginning to play an increasingly noticeable role. Therefore, data on migration speaks much more about real interregional differences in the quality of life than statistics on the monetary income of the population.
Russia has the closest external migration ties with the CIS countries. They account for over 4/5 of the migration exchange between the Russian Federation and foreign countries. At the same time, the incoming flow of migrants to Russia predominates. More than 2/3 of the migrants who entered Russia came from Kazakhstan, Ukraine and Uzbekistan. In the geography of migrants leaving Russia, there are three main directions - Ukraine, Kazakhstan and Belarus. They account for 4/5 of all those leaving the Russian Federation for neighboring countries for permanent residence.
Emigration from Russia to non-CIS countries over the past decade has decreased from 88 thousand in 1991 to 75 thousand people in 2001 (reaching a maximum in 1993 - 114 thousand people). Among the states accepting Russian citizens for permanent residence are Germany, Israel and the United States, which account for 9/10 of all emigrants. The share of other countries receiving migrants from Russia, primarily Finland and Canada, is gradually growing. [cm. 2].
The process of settlement of the peninsula and the increase in its population were accompanied by changes in specialization and forms of economic development of the territory.
Tribes of nomadic cattle breeders have long lived in the northern lowland part of Crimea. Somewhat later, in the 7th century. BC, ancient fortified cities appeared, the population of which was engaged in agriculture and trade. In the Middle Ages, agriculture spread to the mountainous and foothill Crimea, and trade relations expanded. At the same time, the populated territories occupied relatively small areas. In connection with the need to protect cities from raids by nomads, defensive walls were erected, which limited the growth of cities in breadth. Particularly revealing in this regard is a comparison of the areas and populations of medieval and modern cities. For example, Mangup-Kale (the capital of the Principality of Feodoro), with a population equal to the modern population of the city of Bakhchisarai, occupied an area that was only 1/8 of the area of the city, Bakhchisarai.
After the annexation of Crimea by Russia (1783), significant changes occurred in socio-economic development compared to the previous period. New types of economic management began to develop, which could not but affect the settlement of the population. New settlements are appearing, both rural (for example, Petrovskaya Sloboda, Zuya, Mazanka, Izyumovka, etc.) and urban (Sevastopol, Simferopol). Old cities are developing rapidly - Kerch, Evpatoria, Feodosia.
During the Crimean War of 1854-1855. and in the post-war period the population decreased significantly. Almost 150 thousand Crimean Tatars and 5 thousand Nogais left Crimea. Of the 687 Crimean settlements, 315 are completely deserted. After the abolition of serfdom, from 1865 to 1897, the population of Crimea increased almost 3 times and reached 545 thousand people. (see Appendix 2). The bulk of the population were state peasants from among Russian and Ukrainian settlers, to whom plots of land were assigned.
Thus, population is a very dynamic socio-economic category of society. After 1897, under the influence of economic, social and political factors, the population of Crimea constantly changed over time. By 1913, the population was 729 thousand people. However, as a result of the October events of 1917, the population in Crimea decreased to 711 thousand people. By the beginning of the Second World War (by 1940), the population had increased to 1 million 127 thousand people. During the Second World War, the loss of human resources in Crimea was extremely large (over 85 thousand people were taken to Germany, 90 thousand people were destroyed). Stalin’s anti-people policies also affected the decrease in numbers during that period. On the eve of the war, initially the Germans were forcibly evicted from Crimea, then the Crimean Tatars in May 1944, and the Greeks, Bulgarians and representatives of other nationalities in June. By 1950, the population of the republic had decreased to 823 thousand people. Since the late 40s - early 50s, Crimea has been intensively populated by immigrants from the western regions of Ukraine and the Central regions (mainly from the Central Black Earth economic region) of Russia and Belarus. As a result, by 1959 the population in Crimea increased to 1 million 202 thousand people. In all subsequent years until 1993, the population increased constantly. The population grew at its fastest rate during the period from 1985 to 1993. The average annual growth rate for this period was 1.5% (for the period from 1980 to 1985, the average annual growth rate of the population was less than one percent). The high rate of population growth in recent years is explained by the massive return of deported Crimean Tatars and representatives of other nationalities to Crimea.
Population dynamics are influenced not only by mechanical, but also by natural movement. The importance of natural movement as a source of population growth in Crimea is constantly decreasing. In the late 80s - early 90s, there was a steady trend of decreasing birth rates, increasing mortality, decreasing average life expectancy and, in general, a decreasing rate of population growth (with the exception of the migration influx of the Crimean Tatar population). A slight increase in the birth rate was observed in 1983-1984, when the birth rate in Crimea as a whole was 16-17‰, then a decrease in the birth rate and, accordingly, natural increase began. In recent years, the birth rate in Crimea fell to 12‰, and the mortality rate increased to 10.9‰, therefore, the natural increase was only 1.1‰ (1991). The demographic situation in cities has especially worsened. Thus, if in rural areas natural growth has decreased by half since 1985, then in cities during this period it has decreased by 17 times
NUMBER AND DYNAMICS OF POPULATION
Demography(from Greek demos- people and grapho- I am writing) is the science of the patterns of population reproduction, studying its numbers, natural growth, age and sex composition, etc.
The scientific theory of population considers the population participating in labor as the main productive force of society, the basis of all social production. Constantly interacting with nature (geographical environment), the population plays an active role in its transformation. At the same time, the population also acts as the main consumer of all created material goods. That is why population size is one of the important factors in the development of each country, and indeed of all humanity.
Table 1. Global population since 1000
Table 2. World population growth 1950-2001.
| Year | Total, million people |
Annual growth, million people |
Year | Total, million people |
Annual growth, million people |
| 1950 | 2527 | 37 | 1981 | 4533 | 80 |
| 1955 | 2779 | 53 | 1982 | 4614 | 81 |
| 1960 | 3060 | 41 | 1983 | 4695 | 80 |
| 1965 | 3345 | 70 | 1984 | 4775 | 81 |
| 1966 | 3414 | 69 | 1985 | 4856 | 83 |
| 1967 | 3484 | 71 | 1986 | 4941 | 86 |
| 1968 | 3355 | 74 | 1987 | 5029 | 87 |
| 1969 | 3629 | 75 | 1988 | 5117 | 86 |
| 1970 | 3724 | 78 | 1989 | 5205 | 87 |
| 1971 | 3782 | 77 | 1990 | 5295 | 88 |
| 1972 | 3859 | 77 | 1991 | 5381 | 83 |
| 1973 | 3962 | 76 | 1992 | 5469 | 81 |
| 1974 | 4012 | 74 | 1993 | 5556 | 80 |
| 1975 | 4086 | 72 | 1994 | 5644 | 80 |
| 1976 | 4159 | 73 | 1995 | 5734 | 78 |
| 1977 | 4131 | 72 | 1996 | 5811 | 77 |
| 1978 | 4301 | 75 | 1997 | 5881 | 71 |
| 1979 | 4380 | 76 | 1998 | 5952 | 71 |
| 1980 | 4457 | 76 | 1999 | 6020 | 68 |
| 2000 | 6091 | 71 |
In 1987, the world population reached 5 million people, and already in 1999, on October 12, it exceeded 6 million people.
Table 3. World population by country groups.
Table 4. Share of individual groups of countries in the world population, world GDP and world exports of goods and services in 2000, in%
| World population | World GDP* | World export | |
| Industrialized countries | 15,4 | 57,1 | 75,7 |
| G7 countries | 11,5 | 45,4 | 47,7 |
| EU | 6,2 | 20 | 36 |
| Developing countries | 77,9 | 37 | 20 |
| Africa | 12,3 | 3,2 | 2,1 |
| Asia | 57,1 | 25,5 | 13,4 |
| Latin America | 8,5 | 8,3 | 4,5 |
| Countries with economies in transition | 6,7 | 5,9 | 4,3 |
| CIS | 4,8 | 3,6 | 2,2 |
| CEE | 1,9 | 2,3 | 2,1 |
| For reference: | 6100 million people | $44550 billion | $7650 billion |
| *By currency purchasing power parity | |||
Table 5. Population of the largest countries in the world (millions of people).
| Countries | Number of inhabitants in 1990, million people |
Countries | Number of inhabitants in 2000, million people |
| China | 1120 | China | 1284 |
| India | 830 | India | 1010 |
| Soviet Union | 289 | USA | 281 |
| USA | 250 | Indonesia | 212 |
| Indonesia | 180 | Brazil | 170 |
| Brazil | 150 | Pakistan | 238,4 |
| Japan | 124 | Russia | 230,3 |
| Pakistan | 112 | Bangladesh | 196,1 |
| Bangladesh | 112 | Japan | 138,5 |
| Nigeria | 90 | Nigeria | 121,6 |
| Mexico | 86 | Mexico | 121,6 |
| Germany | 80 | Germany | 121,6 |
| Vietnam | 68 | Vietnam | 121,6 |
| Philippines | 60 | Philippines | 121,6 |
| Türkiye | 59 | Iran | 121,6 |
| Italy | 58 | Egypt | 121,6 |
| Thailand | 58 | Türkiye | 121,6 |
| Great Britain | 57 | Ethiopia | 121,6 |
| France | 56 | Thailand | 121,6 |
| Ukraine | 52 | France | 121,6 |
| Commentary on Table 21. At the beginning of the 21st century, the population of Russia decreased to 144.1 million people. (data as of 10/01/2001), as a result of which it missed Pakistan ahead. | |||
Table 6. World population forecast for 2025
| The whole world, regions |
Population size, million people |
The whole world, regions |
Population size, million people |
| The whole world | 7825 | Africa | 1300 |
| Economically developed countries |
1215 | North America | 365 |
| Developing | 6610 | Latin America | 695 |
| CIS | 290 | Australia | 40 |
| Foreign Europe | 505 | ||
| Foreign Asia | 4630 |
Table 7. Forecast of the number of inhabitants in the twenty largest countries by population in the world for 2025.
| Countries | Population size, million people |
Countries | Population size, million people |
| China | 1490 | Japan | 120 |
| India | 1330 | Ethiopia | 115 |
| USA | 325 | Vietnam | 110 |
| Indonesia | 275 | Philippines | 110 |
| Pakistan | 265 | Congo | 105 |
| Brazil | 220 | Iran | 95 |
| Nigeria | 185 | Egypt | 95 |
| Bangladesh | 180 | Türkiye | 88 |
| Russia | 138 | Germany | 80 |
| Mexico | 130 | Thailand | 73 |
GROWTH RATE
Population growth rate shows by what percentage the population has increased in the current year compared to some earlier period (most often with the previous year, called the base year).
Doubling time- the time during which the population doubles.
Table 8. Growth rate (in %) and doubling time (in years) of the population.
| Period | World | Africa | Latin America |
North America |
Asia | Europe | Oceania | Former USSR |
| 1965-1970 | 2,06 | 2,64 | 2,6 | 1,13 | 2,44 | 0,66 | 1,97 | 1,00 |
| 1980-1995 | 1,74 | 2,99 | 2,06 | 0,82 | 1,87 | 0,25 | 1,48 | 0,78 |
| 2020-2025 | 0,99 | 1,90 | 1,12 | 0,34 | 0,89 | 0,05 | 0,76 | 0,47 |
| Time Doublings |
71 | 27 | 38 | 63 | 50 | 253 | 63 | 99 |
Minimum doubling time: Brunei (11), Qatar (13), UAE (13).
Maximum doubling time: Bulgaria, Ireland, Hungary (1000 each),
Belgium, Poland, Falkland Islands, Puerto Rico (693 each).
As can be seen from the table, in different regions of the world the population today is growing unevenly: in some more slowly, in others faster, and in others very quickly. This is explained by the different nature of its reproduction.
POPULATION REPRODUCTION
Reproduction (natural movement) of the population- a set of processes of fertility, mortality and natural increase, which ensures the continuous renewal and change of human generations. Or: population reproduction is the process of generational change as a result of natural (increase) movement.
Key Demographics
Absolute indicators:
- natural increase- the difference between the number of births and deaths;
- mechanical gain- the difference between the number of immigrants and emigrants.
Relative:
- birth rate- the ratio of the total number of births in a country per year to the total population of the country, measured in thousands (i.e., the number of births for every thousand inhabitants;
- mortality rate- the ratio of the total number of deaths in the country for the year to the population of the country, measured in thousands (i.e., the number of deaths per thousand inhabitants);
- rate of natural increase- the difference between the birth rate and death rate.
These ratios are measured in ppm (‰), but can be measured in percentage (%), i.e. In this case, calculations are carried out per 100 inhabitants.
"Formula" of reproduction- type of recording of relative demographic indicators: birth rate - death rate = natural increase rate.
Table 9. Demographic indicators of reproduction at the beginning of the 90s (in ‰).
Fertility, mortality, natural population growth are basically biological processes. But, nevertheless, the socio-economic conditions of people’s lives, as well as the relationships between them in society and in the family, have a decisive influence on them.
The mortality rate depends, first of all, on the material living conditions of people: nutrition, sanitary and hygienic working and living conditions, and the development of healthcare.
The birth rate also depends on the socio-economic structure of society and the living conditions of people. But this dependence is much more complex and controversial, causing a lot of controversy in science. Most scientists associate the decline in the birth rate with the growth of cities and the spread of an urban lifestyle, which leads to an increasing involvement of women in production and social activities, an increase in the length of education for children and a general increase in the “price of a child.” Developed pension provision also leads to a decrease in the birth rate, because the role of the child as a “walking pension” is reduced to nothing. On the contrary, the rural lifestyle contributes to high birth rates, because in rural areas, a child already from 9-10 years old has extra labor. In poor countries where the social sphere is poorly developed, the child is the main breadwinner for elderly parents. High birth rates are also typical for Muslim countries, where the tradition of large families is supported by religion.
Wars, especially world wars, have a very large negative impact on the reproduction of the population, which lead to enormous human losses, both as a result of direct military action and as a result of the spread of hunger and disease, and the severance of family ties.
An increase in mortality is caused by an increase in such unfavorable phenomena as crime, industrial injuries, natural and man-made disasters, accidents, and deterioration of environmental quality.
TYPES OF POPULATION REPRODUCTION
In the most simplified form, we can talk about two types of population reproduction.
The first type of population reproduction. Demographic crisis. The first type of population reproduction (synonyms: demographic “winter”, modern or rational type of reproduction) is characterized by low rates of fertility, mortality and, accordingly, natural increase. It has become widespread primarily in economically developed countries, where the proportion of elderly and old people is constantly growing; this in itself reduces the birth rate and increases the death rate.
The decline in the birth rate in industrialized countries is usually associated with the spread of an urban lifestyle, in which children turn out to be a “burden” for parents. Industrial production and the service sector require highly qualified personnel. The consequence of this is the need for long-term study, lasting until the age of 21-23. The decision to have a second or third child is strongly influenced by a woman’s high involvement in the labor process, her desire to make a career and be financially independent.
But even among the countries of the first type of population reproduction, three subgroups can be distinguished.
Firstly, these are countries with an average annual natural population growth of 0.5-1% (or 5-10 people per 1000 inhabitants, or 5-10‰). In such countries, examples of which are the USA, Canada, and Australia, quite significant population growth is achieved.
To do this, it is necessary that approximately half of all families have two children, and half have three. Over time, two children “replace” their parents, and the third not only covers the loss from illnesses, accidents, etc. and “compensates” for the lack of offspring in the childless, but also ensures a sufficient overall increase.
Secondly, these are countries with “zero” or close to it natural growth. Such growth (for example, in Italy, Great Britain, Poland) no longer ensures expanded reproduction of the population, which usually stabilizes at the achieved level.
Table 10 . European countries with negative natural population growth in 2000
|
Countries |
Natural growth, %o |
Countries |
Natural growth, %o |
|
Spain |
Sweden |
||
|
Switzerland |
Romania |
||
|
Greece |
Hungary |
||
|
Austria |
Estonia |
||
|
Italy |
Latvia |
||
|
Czech |
Belarus |
||
|
Slovenia |
Russia |
||
|
Lithuania |
Bulgaria |
||
|
Germany |
Ukraine |
Thirdly, these are countries with negative natural increase, i.e. those where mortality exceeds birth rate. As a result, the number of their inhabitants not only does not grow, but even decreases. Demographers call this phenomenon depopulation(or demographic crisis).
It is most typical for Europe, where already one and a half dozen countries (Belarus, Ukraine, Hungary, Bulgaria, Germany, etc.) have negative natural growth. Recently, Russia has become one of these countries.
The transition from a large family, typical of old Russia, to a small family took place in our country during the existence of the Soviet Union. But in the 90s. First of all, with the emergence of a deep socio-economic crisis, a real “collapse” in natural population growth rates began.
In the 90s As a result of a sharp decrease in the birth rate and an increase in mortality, the population of Russia was supposed to decrease by several million people. And only thanks to the massive influx of migrants from other CIS countries and the Baltic countries, which compensated for this decline by more than 1/3, the population decline was not so great. The birth rate in Russia (less than 9 people per 1000 inhabitants) and in the late 90s. remains one of the lowest in the world.
So, in general, the economically developed countries of the world (their average natural growth rate is 0.4‰) are characterized by the so-called “rational” or “modern” type of population reproduction, mainly corresponding to the urban image and high standard of living of their population. But this does not exclude the fact that a number of European countries are experiencing a demographic crisis, which negatively affects or may affect their development.
The second type of population reproduction. "Population explosion". The second type of population reproduction (synonyms: demographic “winter”) is characterized by high and very high birth rates and natural increase and relatively low death rates. It is typical primarily for developing countries.
Table 11. Developing countries with the highest natural population growth in 1995-2000
|
Countries |
Natural growth,%O |
Countries |
Natural growth, %o |
|
Yemen |
Benin |
||
|
Somalia |
Ghana |
||
|
Niger |
Liberia |
||
|
Mali |
Mauritania |
||
|
DR Congo |
Pakistan |
Assignments: 9 Tests: 1 |
- Long-Term Population Fluctuations in Historical Societies
- Application. Modeling of structural and demographic mechanisms
Long-Term Population Fluctuations in Historical Societies
This article is a translation of the article revised and supplemented by the author: Turchin, P. 2009. Long-term population cycles in human societies. Pages 1-17 in R. S. Ostfeld and W. H. Schlesinger, editors. The Year in Ecology and Conservation Biology, 2009. Ann. N. Y. Acad. Sci. 1162.
Translation Petra Petrova, editor Svetlana Borinskaya.
about the author
Pyotr Valentinovich Turchin- American scientist of Russian origin, specialist in the field of population dynamics and cliodynamics (mathematical modeling and statistical analysis of historical dynamics). He studied at the Faculty of Biology of Moscow State University, received a bachelor's degree in biology from New York University in 1980, and received a Ph.D. in 1985. in zoology from Duke University. Managed several large environmental projects. He made an important contribution to the development of mathematical models of “secular” socio-demographic cycles. He currently serves as a professor in the Department of Ecology and Evolutionary Biology and an adjunct professor in the Department of Mathematics at the University of Connecticut.
Existing methods for predicting population changes are very imperfect: they usually extrapolate from today's trends to obtain a forecast. In the 1960s, when the world's population was growing at a rate exceeding the rate of exponential growth, demographers predicted an imminent disaster as a result of a “population explosion.” Today the forecast for many European countries, including Russia, is no less sad - only now we are allegedly facing extinction. However, a review of historical data shows that the typical pattern observed in human populations does not correspond to either exponential growth or, especially, a constant decline in population size. In reality, phases of growth and decline alternate, and population dynamics usually look like long-term fluctuations with a periodicity of 150-300 years (the so-called “secular cycles”) against a background of gradual growth.
Until now, such fluctuations have been noted by historians in individual countries or regions, and in most cases local explanations have been given for each region or period. However, recent research has shown that such fluctuations are observed in a wide variety of historical societies for which more or less detailed data on population changes are available. Regular significant drops in numbers (up to 30-50% of the population, and in some cases more) with subsequent growth appear as a typical characteristic of human population dynamics, and political instability, wars, epidemics and famine are subject to certain patterns that have been studied by the author.
This article examines the historical and archaeological evidence for periodic population fluctuations for Eurasian societies from the 2nd century BC. to the 19th century AD and a theoretical explanation of these dynamics is proposed that takes into account the presence of feedback. Feedback, operating with a significant time delay, precisely leads to oscillatory movements in the population. The feedback mechanisms described in the article also operate in modern societies, and we need to learn to take them into account in order to build realistic long-term demographic forecasts and predict surges in political instability.
Introduction
Long-term population dynamics are often represented as almost inevitable exponential growth. Over the past 300 years, the world's population has grown from 0.6 billion in 1700 to 1.63 billion in 1900, and reached 6 billion by 2000.
In the 1960s, there was even an impression that the world's population was growing at a rate exceeding exponential growth, leading to predictions of the end of the world, expected, for example, on Friday, November 13, 2026. (Von Foerster et al. 1960, Berryman and Valenti 1994). During the nineties, when the rate of world population growth slowed markedly (largely due to a sharp drop in fertility in densely populated developing countries, primarily China and India), it became clear that previous predictions of disaster (Ehrlich 1968) require revision. At the same time, the decline in population in most European countries (which is especially noticeable in the countries of Eastern Europe, but would be no less pronounced in Western Europe if not for the masking effect of immigration) has led to the fact that the discussion of this problem in the press has acquired a completely different turnover The concern now is that the dwindling number of people working will not be able to support the growing number of retirees. Some of the predictions being made today go to no less extremes than past doomsday predictions. For example, Russian popular publications regularly predict that by 2050 the country's population will halve.
Many of the reports about possible population changes that appear in the press are sensational and even hysterical, but the main question - how the population of different countries, as well as the entire Earth, will change in the future - is indeed very important. The size and structure of the population have a tremendous impact on the well-being of society and individuals, and the entire biosphere as a whole.
However, current methods for predicting population changes are very imperfect. The simplest way to forecast population changes is to extrapolate from today's trends. Such approaches include an exponential model or a growth model that is even faster than exponential, such as in a “doomsday” scenario. Some more sophisticated approaches take into account possible changes in demographic indicators (fertility, mortality and migration), but assume that these processes are determined by external influences, such as climate change, epidemics and natural disasters. It is noteworthy that these most common approaches to population forecasting do not take into account that population density itself can influence changes in demographic rates.
To predict how the population will change, you need to understand what factors influence these changes. It is impossible to predict the pattern of population changes in the presence of several interacting factors without mathematical models. Models in which a variable value depends only on external parameters, that is, there is no feedback, are called zero order models. Zero-order dynamics models are always nonequilibrium (that is, the number does not reach a constant (equilibrium) value, around which small fluctuations occur), and, depending on the parameters, they assume either an infinite increase in the population size or its decrease to zero (Turchin 2003a:37).
More complex models take into account the influence of population density on further changes in population size, that is, they take into account the presence of feedback. Such models include the so-called logistic model proposed by Verhulst (Gilyarov 1990). This model has an exponential part, which describes rapid growth when population density is low, and slower population growth when population density increases. The dynamic processes described by the logistic model are characterized by convergence to an equilibrium position, often called medium capacity(the capacity of the environment may increase with the emergence of technical innovations, but in a number of models it is considered constant for simplicity). Such models are called first order models, since in them the feedback operates without delay, as a result of which the model is described by one equation with one variable (for example, a logistic model). While the logistic model describes population growth well, it (as in any first-order model) does not contain factors that could cause population fluctuations. According to this model, upon reaching a population corresponding to the capacity of the environment, the situation stabilizes, and population fluctuations can only be explained by external factors. exogenous reasons.
First-order feedback effects occur quickly. For example, in territorial mammals, once the population size reaches a value at which all available territories are occupied, all excess individuals become territoryless “homeless” individuals with low survival rates and zero chances of reproductive success. Thus, as soon as the population size reaches the environmental capacity determined by the total number of territories, the population growth rate immediately decreases to zero.
A more complex picture is presented by processes in which population dynamics depend on the influence of an external factor, the intensity of which, in turn, depends on the size of the population being studied. We will call this factor endogenous(“external” to the population being studied, but “internal” to the dynamic system that includes the population). In this case we are dealing with second order feedback. A classic example of population dynamics with second-order feedback in animal ecology is the interaction between predator and prey. When the population density of the prey is high enough to cause an increase in the number of the predator, the effect of this on the growth rate of the prey population is not immediately felt, but with a certain delay. The delay is due to the fact that it takes some time for the predator's numbers to reach a sufficient level to begin to influence the prey's numbers. In addition, when there are many predators and the prey population begins to decline, the predators continue to reduce the number of prey. Even though prey becomes scarce and most predators starve, the resulting extinction of predators takes some time. As a result, second-order feedback acts on populations with a noticeable delay and tends to cause periodic fluctuations in numbers.
Models that take into account the presence of feedback are well developed in ecology to describe fluctuations in the size of natural animal populations. Demographers studying the size of human populations began to seriously develop models incorporating density dependence much later than population ecologists. (Lee 1987).
Some demographic cycles have been discussed in the literature, such as periodic fluctuations in the age structure of populations with a period of approximately one generation (about 25 years). Cycles characterized by alternation of generations with high and low fertility, the average duration of which is about 50 years, were also discussed (Easterlin 1980, Wachter and Lee 1989). In population ecology, such fluctuations are often called generation cycles and first-order cycles, respectively. (Turchin 2003a:25).
However, to my knowledge, demographers still do not consider second-order feedback processes that produce fluctuations with a much longer period, while both rise and fall of the population take 2-3 generations or more. Accordingly, second-order models are practically not used when making forecasts of the dynamics of human populations.
If in historical and prehistoric societies population fluctuations were governed by second-order feedback, then what seemed to be inexplicable, externally driven reversals of population trends may in fact be manifestations of feedback operating with a significant time delay. In this case, it will also be necessary to revise forecasts of future demographic changes to include second-order dynamic processes. Next, we will review the historical and archaeological evidence for periodic population fluctuations and attempt to provide a theoretical explanation for such fluctuations.
Historical overview of population dynamics in agricultural societies
Even a cursory glance at population changes over the past few millennia is enough to convince us that global population growth has not been as relentlessly exponential as is commonly imagined (Figure 1). There appear to have been several periods of rapid growth interspersed with periods in which growth slowed down. In Fig. 1 presents a generalized view of human population dynamics. But in different countries and regions, population changes may be inconsistent, and in order to understand the components reflected in the overall dynamics of the human population, it is necessary to study population changes within the borders of certain countries or provinces.
To determine what time O On a large scale, we need to consider the dynamics of human populations; we use data on other species of mammals. It is known from population ecology that second-order cycles are characterized by periods from 6 to 12-15 generations (sometimes longer periods are observed, but for very rare combinations of parameters). In humans, the period during which generational change occurs can vary depending on both biological (for example, nutritional patterns and the distribution of mortality by age) and social (for example, the age at which it is customary to marry) characteristics of the population. However, in most historical populations, generations succeeded over a period falling within the range of 20 to 30 years. Taking into account the minimum and maximum values of the duration of one generation (20 and 30 years, respectively), we can conclude that for humans the periods of second-order cycles should be in the range from 120 to 450 years, most likely between 200 and 300 years. We will further call such cycles lasting several centuries "secular cycles". To identify such cycles, it is necessary to study periods of time lasting many centuries. In this case, you need to know how the population has changed over periods comparable to the duration of a generation, that is, have data for every 20-30 years.
Now let's look at historical population data. Such data can be extracted from periodic population censuses conducted by past states to assess the tax base, as well as from proxy indicators, which will be discussed later.
Western Europe
The first source of data here can be a population atlas (McEvedy and Jones 1978). The time used in this atlas O Its resolution (100 years after 1000 AD and 50 years after 1500 AD) is not sufficient for statistical analysis of these data, but for some areas where long-term population history is fairly well known, such as Western Europe, the resulting overall picture is very bright.
In Fig. Figure 3 shows population curves for only two countries, but for other countries the curves look approximately the same. First, there has been a general increase in the average population size. Secondly, against the backdrop of this thousand-year trend, two secular cycles are observed, the peaks of which occur around 1300 and 1600. The millennial trend reflects gradual social evolution, which significantly accelerates after the end of the agrarian period, but here we will consider primarily pre-industrial societies. Secular fluctuations look like second-order cycles, but more detailed analysis is needed for final conclusions.
China
Is this pattern of secular fluctuations against the backdrop of a millennial trend observed exclusively in Europe, or is it characteristic of agricultural societies in general? To answer this question, let’s look at the opposite edge of Eurasia. Since the unification in 221 BC. Under the Qin dynasty, central authorities conducted detailed censuses to collect taxes. As a result, we have data on China's population dynamics over a period of more than two thousand years, although there are significant gaps in it, corresponding to periods of political fragmentation and civil wars.
The interpretation of the data obtained is hampered by several complicating circumstances. In the later stages of dynastic cycles, when power weakened, corrupt or negligent officials often manipulated or even completely falsified population data (Ho 1959). Conversion rates from the number of taxable households to the number of inhabitants are often unknown and may well have varied from dynasty to dynasty. The territory controlled by the Chinese state was also constantly changing. Finally, it is often quite difficult to determine whether the number of taxable households decreased in troubled times as a result of demographic changes (mortality, emigration) or as a result of the inability of the authorities to control and count the number of subjects.
Therefore, there is some disagreement among experts regarding what O exactly what the numbers at our disposal mean (Ho 1959, Durand 1960, Song et al. 1985). However, these disagreements concern, first of all, the absolute values of the population, while in matters concerning relative changes in population density (which, of course, are of greatest interest to us), there is quite little disagreement. China's population generally increased during periods of political stability and decreased (sometimes sharply) during periods of social upheaval. As a result, population changes largely reflect China's "dynastic cycles" (Ho 1959, Reinhard et al. 1968, Chu and Lee 1994).
Of all the works known to me, the demographic history of China is described in more detail by Zhao and Xie (Zhao and Xie 1988). If you look at the entire two-thousand-year period, the curve of population changes appears clearly non-stationary. In particular, the demographic regime has undergone two dramatic changes (Turchin 2007). Before the 11th century, population peaks reached 50–60 million (Fig. 4a). However, in the 12th century, peak values doubled, reaching 100-120 million (Turchin 2007: Fig. 8.3).
The mechanism underlying these changes in the demographic regime is known. Until the 11th century, China's population was concentrated in the north, and the southern regions were sparsely populated. During the Zhao Dynasty (Song Empire), the south equaled and then surpassed the north (Reinhard et al. 1968: figs. 14 and 115). In addition, new, high-yielding varieties of rice were developed during this period. The next change in the demographic regime occurred in the 18th century, when the population began to grow at a very high speed, reaching 400 million in the 19th century, and more than 1 billion in the 20th century.
To set aside these regime changes, I will consider here primarily the quasi-stationary period from the beginning of the Western Han Dynasty to the end of the Tang Dynasty, from 201 BC. to 960 AD (for subsequent centuries see Turchin 2007: section 8.3.1). During these twelve centuries, China's population peaked at least four times, each time reaching values of 50–60 million people (Fig. 4a). Each of these peaks occurred during the last phase of the great unifying dynasties, the Eastern and Western Han, the Sui and the Tang. Between these peaks, China's population fell below 20 million (although some researchers, for the reasons listed above, consider these estimates to be too low). The quantitative details of Zhao and Xie's reconstructions remain controversial, but the qualitative picture they paint—population fluctuations associated with dynastic cycles and spanning the expected 2nd to 3rd centuries—is beyond doubt.
Northern Vietnam
Another example of similar fluctuations is given by Victor Lieberman in his book “Strange Parallels: Southeast Asia in a Global Context, ca. 800-1830." (Lieberman 2003). The picture of population fluctuations in Northern Vietnam (Fig. 5) is in many ways reminiscent of the picture observed in Western Europe (Fig. 3): there is an upward millennial trend and secular fluctuations against its background.
Indirect indicators of population dynamics based on archaeological data
Population reconstructions such as those shown in Fig. 1, 3-5, have one significant drawback: their reliability is reduced due to a number of subjective circumstances. To obtain such reconstructions, specialists usually have to bring together many extremely heterogeneous sources of information, including both quantitative and qualitative. At the same time, different data are trusted to varying degrees, without always explaining in detail on what grounds. As a result, different specialists obtain different curves. This does not mean that we should immediately reject the sound judgments of highly professional experts. Thus, the curves of the population dynamics of England during the Early Modern period (XVI-XVIII centuries), reconstructed by experts using informal methods, turned out to be very close to the results subsequently obtained through the formal method of genealogical reconstructions (Wrigley et al. 1997). Nevertheless, it would be advisable to use some other, more objective ways of identifying population dynamics in historical (and prehistoric) human societies.
Archaeological evidence provides us with a basis for such alternative methods. People leave many traces that can be measured. Therefore, the main idea of this approach is to pay special attention indirect indicators, which can directly correlate with the population size of bygone times. Typically, this approach allows us to evaluate not absolute indicators, expressed in the number of individuals per square kilometer, but relative indicators of population dynamics - by what percentage the population size changed from one period to another. Such indicators are quite sufficient for the purposes of this review, because here we are interested precisely in relative changes in numbers. In addition, in some cases, absolute scores can also be obtained.
Population dynamics of villages in the Western Roman Empire
One of the serious problems that often reduces the value of archaeological data is the rough time O m resolution. For example, reconstruction of the history of the population of the Deh Luran plain in western Iran (Dewar 1991) indicates at least three significant fluctuations in population density (characterized by a tenfold difference between peaks and troughs). However, these data were obtained for temporary s x segments of 200-300 years. This resolution is not sufficient for our purposes.
Fortunately, there are also detailed archaeological studies in which the time periods being studied s These segments are much shorter (and one can hope that the number of such examples will increase in the future). The first such study concerns the population history of the Roman Empire. This problem has long been the subject of heated scientific debate. (Scheidel 2001). Tamara Lewit summarized both published and unpublished data from archaeological excavation reports of villages in the western Roman Empire and calculated the proportion of those that were inhabited during the 1st century BC to the 1st century AD. and subsequent fifty-year segments until the 5th century. It turned out that the population coefficient went through two large fluctuations over these five centuries (Fig. 6a).
Theoretical explanations for secular cycles
Numerous historical and archaeological data, such as the examples discussed above, show that long-term population fluctuations can be observed in many different areas of the Earth and historical periods. It seems that such secular cycles are a general pattern of the macrohistorical process, and not a set of individual cases, each of which is explained by a particular cause.
As we have already shown in our review of the data, secular cycles are characterized by ascending and descending phases lasting several generations. Such oscillations can be described by models with second-order feedback. Can we offer a theoretical explanation for the observed pattern of periodic population fluctuations?
Malthus' theory
In the search for such an explanation, it is appropriate to start with the ideas of Thomas Robert Malthus (Malthus 1798). The basics of his theory are formulated as follows. A growing population pushes people beyond their means of subsistence: food prices rise and real (that is, expressed in terms of goods consumed, such as kilograms of grain) wages fall, causing per capita consumption to decline , especially among its poorest strata. Economic disasters, often accompanied by famines, epidemics and wars, lead to falling birth rates and rising death rates, causing the rate of population growth to decline (or even become negative), which in turn makes livelihoods more accessible again. Factors limiting fertility weaken and population growth resumes, sooner or later leading to a new livelihood crisis. Thus, the contradiction between the natural tendency of populations to grow and the limitations imposed by the availability of food causes population numbers to tend to fluctuate regularly.
Malthus's theory was expanded and developed by David Ricardo in his theories of the fall of profits and rent (Ricardo 1817). In the 20th century, these ideas were developed by such neo-Malthusians as Michael (Moses Efimovich) Postan, Emmanuel Le Roy Ladurie and Wilhelm Abel (Postan 1966, Le Roy Ladurie 1974, Abel 1980).
These ideas face a number of difficulties, both empirical (discussed below) and theoretical. The theoretical difficulties become apparent if Malthus's idea is rephrased in terms of modern population dynamics. Let us assume that scientific and technological progress proceeds more slowly than the population changes in the course of secular cycles (for pre-industrial societies this seems to be a completely reasonable assumption). Then the capacity of the environment will be determined by the amount of land available for agricultural cultivation and the level of development of agricultural technologies (expressed in specific yield per unit area). As the population approaches the carrying capacity of the environment, all available land will be cultivated. Further population growth will immediately (without delay) lead to a decrease in average consumption. Since there is no time delay, there should not be an excess of the medium’s capacity, and the population should balance out at a level corresponding to the medium’s capacity.
In other words, we are dealing here with dynamic processes with first-order feedback, the simplest model of which is the logistic equation, and our assumptions should imply not cyclical fluctuations, but a stable equilibrium. In the theory of Malthus and the neo-Malthusians, there are no dynamic factors interacting with population density that could provide second-order feedback and periodically repeating fluctuations in numbers.
Structural-demographic theory
Although Malthus mentioned wars as one of the consequences of population growth, he did not develop this conclusion in more detail. Neo-Malthusian theories of the 20th century related exclusively to demographic and economic indicators. A significant refinement of Malthus's model was undertaken by the historical sociologist Jack Goldstone (Goldstone 1991) who took into account the indirect influence of population growth on the structures of society.
Goldstone argued that excessive population growth has a variety of effects on social institutions. First, it leads to runaway inflation, falling real wages, rural distress, urban immigration, and increased frequency of food riots and low-wage protests (essentially the Malthusian component).
Secondly, and more importantly, rapid population growth leads to an increase in the number of people aspiring to occupy elite positions in society. Increasing competition within the elite leads to the emergence of patronage networks vying for state resources. As a result, elites find themselves torn apart by increasing competition and fragmentation.
Third, population growth leads to an increase in the army and bureaucracy and higher production costs. The state has no choice but to raise taxes, despite the resistance of both the elites and the people. However, attempts to increase government revenues cannot overcome spiraling government spending. As a result, even if the state manages to raise taxes, it will still face a financial crisis. The gradual strengthening of all these trends sooner or later leads to the bankruptcy of the state and the resulting loss of control over the army; members of the elite initiate regional and national revolts, and disobedience from above and below leads to uprisings and the fall of the central government (Goldstone 1991).
Goldstone was primarily interested in how population growth causes socio-political instability. But it can be shown that instability acts on population dynamics according to the feedback principle (Turchin 2007). The most obvious manifestation of this feedback is that if the state weakens or collapses, the population will suffer increased mortality caused by increased crime and banditry, as well as foreign and internal wars. In addition, troubled times lead to an increase in migration, associated in particular with the flow of refugees from war-torn areas. Migration can also be expressed in emigration from the country (which should be added to mortality when calculating population decline), and in addition can contribute to the spread of epidemics. Increased vagrancy causes the transfer of infectious diseases between areas that in better times would remain isolated. By gathering in cities, vagrants and beggars can cause the population density to exceed the epidemiological threshold (the critical density above which the disease begins to spread widely). Finally, political instability leads to lower birth rates because in turbulent times people marry later and have children less often. People's choices regarding the size of their families can manifest themselves not only in lower fertility rates, but also in increased rates of infanticide.
Components of instability (using the example of US history in the mid-19th century)
Political instability can take many forms, from urban riots that kill a few people to civil wars that kill hundreds of thousands or even millions of people. Such seemingly different-scale events are nevertheless interconnected. Thus, in the United States in the 40s and 50s of the 19th century, the number of incidents such as city riots, clashes between southerners and northerners, and even bloody clashes on religious grounds (persecution against Mormons) began to increase sharply. In 1861, general instability entered a much more serious phase, and civil war broke out between the northern and southern states. More details about the application of structural-demographic theory to the dynamics of instability in the United States are described in Peter Turchin’s interview with Expert magazine.
City riots
From 1840 to 1860, about a thousand people died in urban riots in the United States.
Conflicts between North and South
Armed clashes between supporters and opponents of slavery in Kansas from 1854 to 1858, called “Bleeding Kansas”:
November-December 1855 - Wakarusa War, 1 dead;
May 24-25, 1856 - Potawatomie Massacre, 5 dead;
August 30, 1856 - Battle of Osawatomie, 5 dead;
May 19, 1858 - Swan Swamp Massacre (Marais des Cygnes Massacre), 5 dead.October 16, 1859 - abolitionist John Brown's attempt to seize the government arsenal in the Virginia town of Harpers Ferry (John Brown's raid on Harpers Ferry), 6 dead.
Prelude to Civil War: Religious Conflicts
1838 - Mormon War in Missouri (Missouri Mormon War): massacre at Haun's Mill massacre, Battle of Crooked River, 22 dead.
Thus, structural-demographic theory(so called because it argues that the effects of population growth are filtered by social structures) presents society as a system of interacting parts, including the people, elites and the state (Goldstone 1991, Nefedov 1999, Turchin 2003c).
One of the strengths of Goldstone's analysis (Goldstone 1991) is the use of quantitative historical data and models in tracing the mechanistic connections between various economic, social and political institutions. However, Goldstone views the engine behind change—population growth—as exogenous variable. His model explains the relationship between population growth and state failure. In my book "Historical Dynamics" (Turchin 2007) I argue that when constructing a model in which population dynamics are endogenous process, it is possible to explain not only the relationship between population growth and state failure, but also the inverse relationship between state failure and population growth.
Model of population dynamics and internal conflicts in agrarian empires
Based on Goldstone's theory, it was possible to develop a mathematical theory of state collapse (Turchin 2007: chapter 7; Turchin, Korotayev 2006). The model includes three structural variables: 1) population size; 2) the strength of the state (measured as the amount of resources that the state taxes) and 3) the intensity of internal armed conflicts (that is, forms of political instability such as major outbreaks of banditry, peasant revolts, local uprisings and civil wars). The model is described in detail in the appendix to this article.
Depending on the values of the parameters, the dynamics predicted by the model are characterized either by a stable equilibrium (which leads to damped oscillations) or by stable limit cycles, such as those shown in Fig. 8. The main parameter that determines the duration of the cycle is the internal rate of population growth. For realistic values of the population growth rate, between 1% and 2% per year, we obtain cycles with a period of about 200 years. In other words, the model predicts a typical pattern of second-order feedback oscillations whose average period is close to that observed in historical data, with the length of the cycle from one state failure to another determined by the rate of population growth. Below is an empirical test of the theory's predictions.
Empirical testing of models
The models discussed above and in the Appendix suggest that structural and demographic mechanisms can produce second-order cycles, the duration of which corresponds to those actually observed. But models do more than just that: they allow specific quantitative predictions to be made that are verified by historical data. One of the impressive predictions of this theory is that the level of political instability should fluctuate with the same period as population density, only it should be out of phase, so that the peak of instability follows the peak of population density.
To test this prediction empirically, we need to compare data on population change and measures of instability. First, we need to identify the phases of population growth and decline. Although the quantitative details of the population dynamics of historical societies are rarely known with significant accuracy, there is usually a consensus among historical demographers as to the point at which the qualitative pattern of population growth changes. Secondly, it is necessary to take into account the manifestations of instability (such as peasant revolts, separatist insurgencies, civil wars, etc.) that occurred during each phase. Instability data is available from a number of synthesis papers (such as Sorokin 1937, Tilly 1993 or Stearns 2001). Finally, we compare the manifestations of instability between the two phases. Structural demographic theory predicts that instability should be higher during phases of population decline. Since the available data is quite rough, we will compare the average data.
This procedure was applied to all seven complete cycles studied by Turchin and Nefedov (Turchin, Nefedov 2008; table 1). Empirical data corresponds very closely to theoretical predictions: in all cases, the greatest instability is observed during phases of decline rather than growth (t-test: P
Table 1. Manifestations of instability by decade during phases of population growth and decline during secular cycles (according to table 10.2 from: Turchin, Nefedov 2008).
Growth phase Decline phase Years Instability* Years Instability* Plantagenets 1151-1315 0,78 1316-1485 2,53 Tudors 1486-1640 0,47 1641-1730 2,44 Capetians 1216-1315 0,80 1316-1450 3,26 Valois 1451-1570 0,75 1571-1660 6,67 Roman Republic 350-130 BC 0,41 130-30 BC 4,40 Early Roman Empire 30 BC - 165 0,61 165-285 3,83 Moscow Rus' 1465-1565 0,60 1565-1615 3,80 Mean (±SD) 0.6 (±0.06) 3.8 (±0.5) * Instability was assessed as the average for all decades in the period under review, and for each decade the instability coefficient took values from 0 to 10 depending on the number of unstable (marked by wars) years.
Using a similar procedure, we can also test the relationship between population fluctuations and the dynamics of political instability during the imperial periods of Chinese history (from the Han dynasty to the Qing dynasty). Population data taken from Zhao and Xie (Zhao and Xie 1988), instability data - from Lee 1931. The test takes into account only those periods when China was united under the rule of one ruling dynasty (Table 2).
Table 2. Manifestations of instability by decade during phases of population growth and decline during secular cycles.
Growth phase Decline phase Conventional name of the secular cycle Years Instability* Years Instability* Western Han 200 BC - 10 1,5 10-40 10,8 Eastern Han 40-180 1,6 180-220 13,4 Sui 550-610 5,1 610-630 10,5 Tan 630-750 1,1 750-770 7,6 Northern Song 960-1120 3,7 1120-1160 10,6 Yuan 1250-1350 6,7 1350-1410 13,5 Min 1410-1620 2,8 1620-1650 13,1 Qing 1650-1850 5,0 1850-1880 10,8 Average 3,4 11,3 * Instability is assessed as the average number of episodes of military activity over decades.
Once again, we see a remarkable agreement between observations and predictions: the level of instability is consistently higher during phases of population decline than during phases of population growth.
Note that the phases of secular cycles in this empirical test were defined as periods of growth and decline, that is, through the positive or negative value of the first derivative of population density. In this case, the value being tested is not a derivative, but an indicator of the level of instability. This means that instability should peak around the middle of the population decline phase. In other words, the peaks of instability are shifted relative to the peaks of abundance, which, of course, are observed where the growth phase ends and the phase of decline begins.
The importance of this phase shift is that it gives us a clue to the possible mechanisms causing these oscillations. If two dynamic variables oscillate with the same period and there is no shift between their peaks, that is, they occur approximately simultaneously, then this situation contradicts the hypothesis that the observed fluctuations are caused by a dynamic interaction between two variables (Turchin 2003b). On the other hand, if the peak of one variable is offset from the peak of the other, this pattern is consistent with the hypothesis that the oscillations are caused by a dynamic interaction between the two variables. A classic example from ecology is the cycles demonstrated by the Lotka-Volterra predator-prey model and other similar models, where peaks in predator abundance follow peaks in prey abundance. (Turchin 2003a: chapter 4).
The structural and demographic models discussed above and in the Appendix show a similar pattern of dynamics. Note, for example, the phase shift between population sizes ( N) and instability ( W) in Fig. 8. Moreover, in this model the instability indicator is positive only during the phase of population decline.
Analysis of several data sets for which more detailed information is available (early modern England, Han and Tang China, and the Roman Empire) allows the use of so-called regression models for testing. Analysis results (Turchin 2005) show that incorporating instability into a model for the rate of change of population density increases prediction accuracy (the proportion of variance explained by the model). Moreover, population density made it possible to statistically significantly predict the rate of change in the instability index. In other words, these results provide further evidence in favor of the existence of the mechanisms postulated by structural-demographic theory.
conclusions
The data presented show that the typical pattern observed in historical human populations corresponds neither to exponential population growth nor to weak fluctuations around some equilibrium value. Instead, we usually see long-term fluctuations (against the background of a gradually increasing level). These “secular cycles” are typically characteristic of agrarian societies in which the state is present, and we observe such cycles wherever we have any detailed quantitative data on population dynamics. Where we do not have such data, we can infer the presence of secular cycles from the empirical observation that the vast majority of agrarian states in history were subject to repeated waves of instability (Turchin, Nefedov 2008).
Secular fluctuations do not represent strict, mathematically clear cycles. On the contrary, they seem to be characterized by a period that varies quite widely around a mean value. This picture is to be expected, because human societies are complex dynamic systems, many of whose parts are cross-connected with each other by nonlinear feedbacks. It is well known that such dynamic systems tend to be mathematically chaotic or, more strictly speaking, sensitively dependent on initial conditions (Ruelle 1989). Moreover, social systems are open, in the sense that they are subject to external influences such as climate change or the sudden emergence of evolutionarily new pathogens. Finally, people are characterized by free will, and their actions and decisions at the micro level of the individual can have macro-level consequences for the entire society.
Sensitive dependence (chaoticism), external influences and the free will of individuals all combine to produce very complex dynamics, the future nature of which is very difficult (and perhaps impossible) to predict with any degree of accuracy. In addition, this brings into play the well-known difficulties of self-fulfilling and self-refuting prophecies—situations in which the prediction made itself influences the events predicted.
Returning to the problem of long-term forecasting of the Earth's population, I note that the most important conclusion that can be drawn from my review is probably the following. The smooth curves obtained by employees of various departments, both governmental and subordinate to the UN, and given in many ecology textbooks, similar to the logistic curve, where the population of the Earth neatly levels out around 10 or 12 billion, are completely unsuitable as serious forecasts. The Earth's population is a dynamic characteristic determined by the ratio of mortality and birth rates. There is no reason to believe that these two quantities will reach an equilibrium level and completely compensate each other.
During the last two crises experienced by the Earth's population in the 14th and 17th centuries, its numbers decreased significantly, in many regions very sharply. In the 14th century, many regions of Eurasia lost between a third and half of their population (McNeill 1976). In the 17th century, fewer regions of Eurasia suffered as severely (although Germany and Central China saw population declines of between a third and a half). On the other hand, the population of North America may have declined tenfold, although this issue remains controversial. Thus, if we build a forecast based on observed historical patterns, the 21st century should also become a period of population decline.
On the other hand, perhaps the most important aspect of recent human history is that social evolution has accelerated dramatically over the past two centuries. This phenomenon is usually called industrialization (or modernization). Demographic capacity of the Earth (Cohen 1995) increased sharply during this period, and it is very difficult to predict how it will change subsequently. Therefore, it is quite possible to imagine that the trend towards increasing the capacity of the environment will continue and will prevail over the fruits of the sharp increase in population that was observed in the 20th century, which may appear with some delay. We don't know which of these two opposing tendencies will prevail, but it is clear that they cannot simply completely cancel each other out. Thus, the establishment of some constant equilibrium level of the Earth's population in the 21st century is, in fact, an extremely unlikely outcome.
Although the future development of human social systems (including its demographic component, which is the focus of this article) is very difficult to predict with any accuracy, this does not mean that this kind of dynamics is not worth studying at all. The empirically observed patterns of population dynamics, reviewed here, lead one to assume the existence of general principles underlying them and to doubt that history is simply a series of random events. If such principles do exist, then understanding them can help governments and societies anticipate the likely consequences of their decisions. There is no reason to believe that the nature of social dynamics discussed in this article is in any sense inevitable. Of particular interest here are the undesirable consequences of prolonged population growth, such as waves of instability.
Political instability in “failed” or failing states is one of the greatest sources of human suffering today. Since the end of the Cold War s V O Wars between states accounted for less than 10% of all armed conflicts. Most armed conflicts these days take place within a single state. These are, for example, civil wars and armed separatist movements (Harbom, Wallensteen 2007).
I see no reason to believe that humanity will always have to experience periods of state collapse and civil wars. However, at present we still know too little about the social mechanisms underlying waves of instability. We do not have good theories that would allow us to understand how to rebuild government systems to avoid civil wars, but we have hope that such a theory will be developed in the near future (Turchin 2008). Research in this area can not only provide science with new empirically testable theories, but also help alleviate the suffering of many people around the world.
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