World Development Sustainability 4 (2024) 100130
Available online 4 March 2024
2772-655X/© 2024 The Author. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by- nc/4.0/).
Economic growth in China and India: The potential role of population
James A. Yunker
Emeritus Professor of Economics, Western Illinois University, Macomb, IL 61455, United States
A R T I C L E I N F O JEL classifications:
O4 (Economic growth and aggregate productivity)
O57 (Comparative studies of countries) Keywords:
Population growth Economic growth Per capita income China
India
A B S T R A C T
From 1980 through 2020, the rate of population growth declined in both China and India, but the decline was far more pronounced in China. During the same period, per capita income increased substantially in both China and India, but the increase was far more pronounced in China. The fact that China and India are similar in many important respects (ancient cultures, large populations, etc.), but implemented substantially different population control policies during the 1980–2020 interval, suggests an equivalence to a quasi-controlled experiment, of the sort that very rarely occurs in the real world. The control would be India, with a relatively conventional pop- ulation control policy, and the experiment would be China, with its relatively drastic population control policy.
This research investigates the possibility of a causal relation between differential population growth and dif- ferential economic growth in China and India. It is shown that the simulation of a basic economic growth model in which population growth is a key exogenous determinant, and which utilizes the same economic relationships and numerical parameter values for both China and India, produces time paths of growth in per capita income that closely resemble the empirical Chinese and Indian time paths. This finding supports the hypothesis that a significant factor in China’s remarkable economic growth over the last four decades has been its equally remarkable population control policy.
Introduction
The extraordinary economic growth of the People’s Republic of China over the recent past has captured the attention of the world.
Several scholarly analyses and overall evaluations of recent Chinese economic growth have appeared in the literature [1–3]. The impressive economic performance of China is all the more important because until very recently, it was the most populous country in the world, containing approximately a quarter of the world population. According to the authoritative World Development Indicators database, India’s population exceeded that of China for the first time in 2022: 1417 million relative to 1412 million. India has also registered considerable economic progress over the last forty years—although its economic progress over this period of time has not been as dramatic as that of China. Between 1980 and 2020, India’s real per capita income increased by a factor of 4.685, whereas that of China increased by a factor of 24.110.
The most obvious similarity between China and India is that they are Asian nations with long histories of civilization dating back millennia, encompassing very large populations living within relatively confined
land areas. There are also similarities in their economic systems, albeit they have converged from very different starting points. Although In- dia’s economic system is basically capitalist (predominantly private ownership of land and capital), there exists a substantial overlay of so- cial democratic interventions and controls that some would describe as
“socialistic.” During the early years of the People’s Republic of China, its economy was decidedly socialist (virtually no private ownership of land and capital), and operated under a highly centralized planning system similar to that of the Soviet Union. In the post-Mao era, the Chinese economy underwent a dramatic transformation from planning to mar- ket. However, there is still a substantial overlay of interventions and controls with roots in the planning era. Thus it can be said of both China and India that their respective economies today are a complex blend of market forces and government controls, and in this sense they are similar to one another.
Leaving aside their ideological differences (China more influenced by Marxism and India less so), and their political differences (China less democratic in the Western sense and India more so), perhaps the most striking difference between China and India are their respective
Data Availability Statement: Data on Chinese and Indian population and per capita income taken from World Development Indicators Database at:https://datacatalog.
worldbank.org/dataset/world-development-indicators Data included in supplementary file ChinaIndia4.xlsxEconomic Growth in China and India: The Potential Role of Population
E-mail address: [email protected].
Contents lists available at ScienceDirect
World Development Sustainability
journal homepage: www.elsevier.com/locate/wds
https://doi.org/10.1016/j.wds.2024.100130
Received 15 October 2022; Received in revised form 23 February 2024; Accepted 3 March 2024
population control policies. In both countries, it is official policy that the rate of population growth should be slowed down. But China’s commitment to lower population growth, in the form of what some describe as its “draconian” one-child policy (later revised to a two-child policy), has been stronger than that of India. A familiar hypothesis holds that only an extremely authoritarian, virtually totalitarian state could impose such a policy on the general public. As a result of its more determined population control effort, between 1980 and 2020 China’s population increased by a factor of 1.438, whereas India’s increased by a factor of 1.974. Befitting its importance, a substantial literature on population growth and control in China and India has accumulated [4–13].
China-India economic comparisons
The important similarities between China and India in terms of size, population, and history have always motivated considerable interest in the relative economic characteristics and performance of the two countries. Early contributions to the comparison literature [14–16]
suggested that during the early part of their modern histories, from approximately 1950 through 1980, the growth performance of the two nations in terms of per capita income was fairly similar. See, for example, Fig. 1 in [17]. India started out somewhat higher than China and maintained a lead throughout the period. Beginning around 1980, however, there occurred a dramatic transition. The per capita income growth rates of both countries began accelerating, fueling speculation that both had finally entered the “takeoff into sustained growth” described by Walt W. Rostow [18]. Accelerated growth has continued up to the present, but the acceleration has been more pronounced for China than for India.
The comparison literature has become quite abundant and diverse, as various researchers endeavor to isolate the more important reasons for the differential in performance. A variety of diverse hypotheses have been put forward, of which the following are examples: elimination of employment guarantees in state-owned enterprises in China [19];
changes in government policy in China that were not duplicated in India [20]; greater reliance on income taxation in China than in India [21];
higher growth rate in the capital-output ratio in China than in India [22]. Additional contributions in the area of China-India comparisons have been provided by a number of authors [23–30].
Some attention has been paid to population, but this factor is nor- mally not accorded central importance. To this author’s knowledge, only one contribution to this substantial literature has focused specif- ically on relative population growth as a key factor in explaining China’s extraordinary economic performance over the recent past [31]. Strictly speaking, however, this study is not a China-India comparison, since the comparison group of eleven Asia-Pacific countries does not include India. The author proposes a modification in the conventional econo- metric equation, based on neoclassical growth theory, for estimating the growth rate of output per capita. According to his hypothesis, there is a structural break in the parameter determining the effect of the labor force participation rate on growth in output per capita, corresponding to the timing of Rostow’s takeoff into sustained growth. The implication is that China’s exceptionally determined population control policies, starting in the late 1970s and early 1980s, may have moved the takeoff threshold forward by more than a decade. The author concludes that the possible effect of stringent population control on economic growth in China “may be twice as large as what is estimated by the traditional econometric method.” Although the methodology utilized in the present research is substantially different from that described, both studies lend support to the hypothesis that exceptionally determined population control was a highly significant contributor to China’s extraordinary recent economic growth.
Methodology
Since the intention of the present research is to focus tightly on the role of population growth, in and of itself, on growth in per capita in- come, the analytical model utilized in this research is simpler than those utilized by some of the above researchers. For example, it omits such factors as trade openness, sectoral breakdown, urbanization, bureau- cratic quality, market orientation, investment ratios, educational attainment, life expectancy, and so on. It also covers the relatively recent time interval of 1980 to 2020, because that is the period during which there was the greatest difference in population policy and population growth between China and India. The purpose of this research is to contribute further to the comparison literature by focusing tightly on the potential role of differential population growth between China and India from 1980 to 2020. Specifically, the question asked by the research is:
To what extent has lower population growth in China than in India been an important reason for substantially higher per capita income growth in China than in India?
In one sense, there is a notable advantage in the narrow focus of the present research. Only two variables are required for China and India:
population and per capita income from 1980 to 2020. Highly reliable information on these variables is available in authoritative databases.
The existing information on various other growth-related variables (e.g., trade openness, market orientation, educational attainment, etc.) is more nebulous and problematic. The more variables that are difficult to measure accurately which are entered into a given analysis, the greater the possibility of misleading indications. That said, it would certainly be of interest to learn if the basic results forthcoming from the present research would be robust against the incorporation of additional vari- ables that are widely assumed to have a significant impact on economic growth, such as trade openness, market orientation, and so on. This points to a possible direction for future research on the population- Fig. 1. Total population and per capita income China and India, 1980–2020.
growth nexus, as it applies to India and China, as well as to various other nations.
The basic data for the present research comprises per capita income and population for China and India from 1980 to 2020, taken from the comprehensive World Bank databank World Development Indicators (WDI). “Population” refers to total population, while “per capita in- come” is represented by GDP per capita in constant 2010 U.S. dollars.
Table 1 and Fig. 1 display the empirical information. Table 1 contains the numerical data while Fig. 1 plots yearly data on population and per capita income for China and India. The upper panel of Fig. 1 clearly shows the more dramatic slowing effect on population growth in China than in India. The lower panel of Fig. 1 clearly shows the more dramatic progress in terms of per capita income growth in China than in India. (A striking feature of Fig. 1’s lower panel is that it clearly manifests the dramatically adverse economic effects of the onset of the coronavirus pandemic in 2020. Growth in Chinese per capita income suddenly slowed, while Indian per capita income actually dropped significantly.) This is the empirical information on Chinese and Indian population growth and per capita income growth during the 1980–2020 time frame for which we aspire to propose a hypothetical explanation—or at least a hypothetical partial explanation.
The approach taken herein represents an effort to approximate a controlled experiment, in which the only factor allowed to vary between the two nations is their respective population growths. It is shown that
the simulation of a relatively simple economic growth model in which population growth is a prime exogenous determinant, and which utilizes the same economic relationships and numerical parameter values for both China and India, produces time paths of growth in per capita in- come that closely approximate the actual Chinese and Indian time paths.
This finding suggests that differential population growth between China and India might well have been an important factor contributing to their differential per capita income growth between 1980 and 2020.
However, it must be emphasized strongly that this indication defi- nitely does not imply that differential population growth was the only important factor contributing to differential per capita income growth, nor that it was necessarily the single most important factor. Obviously several other factors, not included in the model, significantly affected the relative economic growth performance of China and India between 1980 and 2020. Nevertheless, the results reported herein do suggest that any purportedly comprehensive analysis of the differences in Chinese- Indian relative per capita income growth during the recent past should not omit relative population growth.
In considering the relative growth performance of China and India over the 1980–2020 interval, our minds are naturally attracted to the question of what are the more important factors in accounting for the difference—aside from differential population growth. Among the more seemingly plausible explanations for China’s faster growth, relative to India, would be China’s possibly more vigorous participation in the world economy. In economically advanced nations such as the United States, the shelves of mass consumer retailers such as Walmart are crowded with products “made in China.” Despite the persuasiveness of this type of anecdotal evidence, such serious research as has been done on the matter suggests that the openness of the Chinese economy is not as significant a factor as might be imagined.
For example, in [32] the authors summarize their findings as follows:
“Our main finding is that the effect of FDI on Chinese economic growth is much smaller than one would expect from a naïve aggregation of existing estimates. Publication bias and a profusion of estimates based on less preferred study and sample characteristics have served to inflate observed estimates. Once these effects are accounted for, the estimated effect of FDI on Chinese economic growth is reduced to statistical insignificance. This suggests that the cause(s) of the Chinese ‘economic miracle’ likely lie elsewhere.” For another example, in [33] the authors state: “[Our] analysis indicates that the domestic capital input is still the primary element that promotes China’s economic growth; by contrast, the effect of foreign trade and foreign investment is faint.” To the extent that the “opening of China” is an apparently less important factor in the generation of extraordinary economic growth, this enhances the likeli- hood that extraordinary population control policy was a more important factor, possibly a decisive factor.
Another aspect of China’s recent economic history that has attracted much attention is the shift from planned socialism to market socialism.
In the view of some observers, the authorities have gone overboard in the cause of marketization, and the result has been a reasonable approximation to laissez faire capitalism as experienced at the time of the Industrial Revolution in Western Europe. While the social conse- quences of laissez faire capitalism can be highly adverse, the system is demonstrably capable of supporting rapid economic growth. Thus it might be reasonably hypothesized that China’s recent high-growth era has been more the consequence of extreme marketization than of stringent population control. While this hypothesis is certainly deserving of notice, it would likely be difficult to quantify “marketiza- tion” sufficiently well to enable statistical investigation. Population, on the other hand, is a variable on which accurate data is currently available.
A simple economic growth model
Our objective is to develop a simple economic model in which pop- ulation growth is a key underlying exogenous factor, that uses the same Table 1
Total population and per capita income China and India, 1980–2020.
Year China India
Population Per Cap Inc Population Per Cap Inc
1980 981,235,000 347.19 698,952,844 422.90
1981 993,885,000 360.29 715,384,993 438.01
1982 1008,630,000 387.04 732,239,504 442.80
1983 1023,310,000 422.57 749,428,958 464.18
1984 1036,825,000 480.42 766,833,410 470.97
1985 1051,040,000 537.58 784,360,008 484.64
1986 1066,790,000 577.04 801,975,244 496.64
1987 1084,035,000 634.06 819,682,102 505.18
1988 1101,630,000 693.96 837,468,930 542.05
1989 1118,650,000 712.14 855,334,678 562.30
1990 1135,185,000 729.28 873,277,798 581.22
1991 1150,780,000 786.04 891,273,209 575.50
1992 1164,970,000 886.91 909,307,016 595.01
1993 1178,440,000 998.50 927,403,860 611.12
1994 1191,835,000 1115.99 945,601,831 639.27 1995 1204,855,000 1224.85 963,922,588 674.62 1996 1217,550,000 1332.35 982,365,243 711.93 1997 1230,075,000 1440.60 1000,900,030 727.04 1998 1241,935,000 1538.79 1019,483,581 757.93 1999 1252,735,000 1642.40 1038,058,156 810.22 2000 1262,645,000 1767.86 1056,575,549 826.59 2001 1271,850,000 1901.36 1075,000,085 851.62 2002 1280,400,000 2061.17 1093,317,189 869.20 2003 1288,400,000 2253.99 1111,523,144 922.17 2004 1296,075,000 2467.25 1129,623,456 979.28 2005 1303,720,000 2732.27 1147,609,927 1040.31 2006 1311,020,000 3062.69 1165,486,291 1106.93 2007 1317,885,000 3480.31 1183,209,472 1173.88 2008 1324,655,000 3796.68 1200,669,765 1192.51 2009 1331,260,000 4132.91 1217,726,215 1268.25 2010 1337,705,000 4550.45 1234,281,170 1357.56 2011 1344,130,000 4961.23 1250,288,729 1410.43 2012 1350,695,000 5325.36 1265,782,790 1469.18 2013 1357,380,000 5710.67 1280,846,129 1544.62 2014 1364,270,000 6103.75 1295,604,184 1640.18 2015 1371,220,000 6500.42 1310,152,403 1751.66 2016 1378,665,000 6908.11 1324,509,589 1875.73 2017 1386,395,000 7346.84 1338,658,835 1986.63 2018 1392,730,000 7807.06 1352,617,328 2086.45 2019 1397,715,000 8242.05 1366,417,754 2151.73 2020 1411,000,000 8370.77 1380,004,385 1981.44 Source: World Development Indicators: Population, total (SP.POP.TOTL);.
Per Cap Inc: GDP per capita, constant 2010 US$ (NY.GDP.PCAP.KD).
equations and the same parameter values for both China and India, and which produces a good fit to per capita income growth in both countries.
If such a model can be found, this enhances the plausibility of the proposition that the differential in population growth has been an important contributor to the differential in per capita income growth.
A necessary prerequisite is that the model should embody conven- tional economic reasoning and assumptions. For example, the produc- tion function must display diminishing marginal product of the factors of production. Some elements of the model eventually settled upon are not as fundamentally axiomatic as diminishing marginal product, but all elements meet reasonable standards of intuitive plausibility based on prevalent beliefs about economic reality. The basic research methodol- ogy might reasonably be described as “trial and error”: it used a com- bination of experimentation with different mathematical forms for the basic equations, together with numerical searches over ranges of parameter values. The numerical searches were facilitated by the development of a computer program which used the slider tool to manipulate parameter values, the effects of whose changes would be instantly incorporated into a time series plot comparing the model es- timate to the empirical per capita income values. Eventually a set of theoretical equations and numerical parameters was determined that satisfied the objective of the research, as shown below in Table 2 and Fig. 2.
The most fundamental component of any economic growth model is an aggregate production function relating factors of production to the
output level. The single most important theoretical production function in economics since its introduction in the 1920s has been the Cobb- Douglas production function, named for its formulators Charles Cobb and Paul Douglas. This function has been utilized in a wide array of economic studies in a variety of static and dynamic contexts, and has sometimes been applied to the study of economic growth in China and India, as for example in [34] and [17]. In the context of the present research, we hypothesize that at each point in time t, output Yt is a function of capital Kt and labor Lt through a Cobb-Douglas production function in which the parameters α and β, respectively the output elas- ticity of capital and the output elasticity of labor (alternatively the share of capital and the share of labor in national output for a two-factor economy under linear homogeneity and marginal product pricing), are invariant over time, while the total factor productivity coefficient At
varies over time:
Yt=AtKαtLβt (1)
According to most empirical studies of aggregate production in the economically advanced nations, the estimated value of the α parameter is in the range from 0.2 to 0.3, while the estimated value of the β parameter is in the range from 0.7 to 0.8. These are generally consistent with the observed factor shares of capital and labor in these nations. In the present research, however, use of these parameter values produced poor fits of model per capita income growth to empirical per capita in- come growth. The parameter values eventually settled upon in this research were “middle of the road”: α =0.5 and β =0.5.
These values are not too distant from those utilized in [34]: α =0.4 and β =0.6. As emphasized by these authors, for developing economies Table 2
Per capita income, empirical and model estimate China and India, 1980–2020.
Year China Per Cap Income India Per Cap Income
Empirical Model Empirical Model
1980 347.19 347.19 422.90 422.90
1981 360.29 368.27 438.01 431.51
1982 387.04 387.03 442.80 440.24
1983 422.57 407.26 464.18 449.24
1984 480.42 431.31 470.97 458.73
1985 537.58 455.65 484.64 468.86
1986 577.04 478.54 496.64 479.74
1987 634.06 500.07 505.18 491.38
1988 693.96 522.43 542.05 503.85
1989 712.14 547.57 562.30 517.17
1990 729.28 575.61 581.22 531.41
1991 786.04 608.01 575.50 546.66
1992 886.91 646.81 595.01 562.99
1993 998.50 690.96 611.12 580.42
1994 1115.99 738.94 639.27 598.92
1995 1224.85 792.26 674.62 618.53
1996 1332.35 851.38 711.93 639.28
1997 1440.60 916.29 727.04 661.33
1998 1538.79 990.12 757.93 684.85
1999 1642.40 1076.59 810.22 710.08
2000 1767.86 1177.08 826.59 737.25
2001 1901.36 1292.85 851.62 766.59
2002 2061.17 1426.21 869.20 798.31
2003 2253.99 1579.30 922.17 832.60
2004 2467.25 1753.01 979.28 869.65
2005 2732.27 1946.77 1040.31 909.68
2006 3062.69 2167.40 1106.93 952.94
2007 3480.31 2420.59 1173.88 999.85
2008 3796.68 2705.71 1192.51 1051.15
2009 4132.91 3028.46 1268.25 1107.87
2010 4550.45 3394.10 1357.56 1171.05
2011 4961.23 3805.20 1410.43 1241.67
2012 5325.36 4262.98 1469.18 1320.48
2013 5710.67 4773.05 1544.62 1407.99
2014 6103.75 5338.15 1640.18 1504.44
2015 6500.42 5969.18 1751.66 1610.17
2016 6908.11 6655.34 1875.73 1726.01
2017 7346.84 7409.11 1986.63 1853.19
2018 7807.06 8324.17 2086.45 1992.77
2019 8242.05 9440.68 2151.73 2145.81
2020 8370.77 10,210.34 1981.44 2314.30
Source: Empirical: World Development Indicators; Model Estimate: Computed.
Fig. 2.Per capita income, empirical and model estimate China and India, 1980–2020.
it is not so easy to make a hard and fast identification of the measured shares of capital and labor with their respective output elasticities in a Cobb-Douglas aggregate production function. For one thing, in the developing economies there is normally a high percentage of self-employed workers whose output derives from a combination of their own labor with their personally owned capital. Thus it is problematic what should be attributed to labor and what to capital.
Moreover, within the present model, capital Kt is taken in the
“generalized” sense: this means that it encompasses all elements of production aside from raw labor power. These elements include not only the value of ordinary plant and equipment, but also the value of human capital in terms of educational and training inputs into the labor force, as well as the value of social capital such as roads, harbors, educational and medical structures and equipment, and the like. This generalized defi- nition of capital is sufficiently different from the usual plant and equipment definition that the appropriate values of the α and β pa- rameters are not likely to be near to the values associated with the usual definition of capital for the advanced economies.
In the present model, labor Lt is proportional to population through the time-invariant parameter ρ, representing the labor force participa- tion rate:
Lt=ρPt (2)
Making the labor force participation rate a constant over time might seem unusually unrealistic, because it is a commonplace observation within the literature on the demographic transition that lower fertility enables a reduction in the household’s allocation to child-rearing ac- tivities, and thereby an increase in the labor force participation rate.
However, the quantitative importance of this effect may not be too great, at least for China and India. In [17], the authors propose that the growth rate in per capita income can be decomposed into the growth rates of three factors: income per worker (labor productivity), labor force participation rate, and working-age share of the total population.
Summarizing results for 1980 to 2000 shown in their Table 1, the au- thors state: “The figures suggest that faster growth in output per worker accounts for most of the speed-up in growth in China and India, with modest contributions from rising participation rates and increases in the working-age share of the total population.” In the numerical imple- mentation of the present model, the ρ parameter is set equal to 0.5.
It is of course a major simplifying assumption in this model not only that the labor force participation rate is a constant over time, but that it is the same for both China and India. Obviously the labor force partic- ipation rates in China and India are not the same, nor have they remained constant from 1980 through 2020. But this sacrifice of realism goes to the basic purpose of the research, which is to conduct a controlled experiment allowing only the population growth rate to vary between China and India, and nothing else. But it is worthwhile to add that it was determined by experiment that even large variations in the ρ parameter away from its constant benchmark value of 0.5 were observed to have a negligible effect on the growth path of per capita income.
Utilizing the assumption that ρ is a constant, per capita income yt is as follows:
yt=Yt
/Pt=AtρβKtαPβ−t 1 (3)
The time path of yt is thus a function of the time paths of At, Kt, and Pt. Of the three, the time path of Pt is taken as an exogenously determined given. For China these are the empirical Chinese population numbers, and for India the empirical Indian population numbers. The model then links the time paths of Kt and At to the time path of Pt. To start the process, we need initial values for unobserved Kt and At for both coun- tries. As mentioned above, the model envisions capital in the “general- ized” sense as all elements of production other than physical labor power: Kt incorporates not only the value of physical business capital (plant and equipment) but also the value of education and training in- puts into the labor force, as well as the value of social capital inputs. At
present no measures exist for capital in the generalized sense even for the advanced economies. Thus we are required to estimate these amounts for China and India by a process of reverse inference, starting from the observed per capita incomes in both countries.
The model allows for the possibility that the initial-period capital- labor ratios are different for China and India. Using a superscript for the country initial (C for China and I for India), these two ratios are respectively:
ωC=K1C/ LC1 =K1C/
ρPC1 (4)
ωI=KI1/ LI1=K1I/
ρPI1 (5)
For specified values of ωC and ωI, and the observed values of popu- lation in period 1 in China and India, the initial-period values of capital in the respective countries are:
KC1 =ωCρPC1 (6)
KI1=ωIρPI1 (7)
The initial value of the total factor productivity coefficient for each country is then set to be consistent with the empirical (observed) initial- period per capita income of each country:
AC1 =yC1 /
ρβ(K1C)α( PC1)β−1
(8)
AI1=yI1 /
ρβ(K1I)α( PI1)β−1
(9) This specification of the initial values of the K and A variables en- sures that initial-period per capita income y for each nation from the model simulation equals the empirical initial-period per capita income.
Although intuition suggests that the initial-period capital-labor ratios would have a substantial impact on the model growth rates of per capita income, experimentation indicated that even quite wide variations around the benchmark values of ωC =1 for China and ωI =1 for India have a minor effect on the growth paths of per capita income for the two countries.
Now define “change in population” as:
ΔPt=Pt/Pt−1 (10)
The model postulates the following relationship between “change in capital” and “change in population”:
ΔKt=Kt
/
Kt−1=ξ+ψ(ΔPt)−ϕ (11)
where ξ, ψ and ϕ are all positive parameters. According to this equation, there is a convex downward-sloping effect of ΔPt on ΔKt, with a lower asymptotic limit of ξ. The rationale for this is that a higher rate of population growth has a diminishing effect on saving and capital accumulation, but that this diminishing effect is subject to an asymptotic lower limit of ξ.
The tendency for population growth to have a negative effect on saving is a major part of the Coale-Hoover “dependency” hypothesis [35]: rapid population growth from falling infant and child mortality, possibly in conjunction with rising fertility, swells the ranks of depen- dent young, and this demographic event increases consumption re- quirements at the expense of saving. Several empirical investigations have supported this intuitively plausible hypothesis, especially for the Asian “economic miracle” countries [36–40]. The present model posits a very specific mathematical relationship, and this is only in a general sense consistent with the well-established finding of a negative relation between population growth rates and saving rates.
In general, however, it can be said about the equations of this model that their primary support does not come from the inherent, a priori plausibility of each one taken by itself, but rather from the fact that when they are put together in a model and provided with specific
numerical parameter values, they produce growth paths of per capita income in both China and India that closely approximate the empirical time paths. If the equations were actually seriously faulty approxima- tions of reality, it seems unlikely that the model simulations would produce such close fits to the actual time paths of per capita income in the two nations.
The negative effect of population growth on the saving rate, and thereby on the rate of capital accumulation, is one way in which pop- ulation growth can operate as a drag on growth in output. But this model hypothesizes that there is an indirect second drag, which comes about because at least some of the increase in the total factor productivity coefficient At is dependent on the increase in the generalized capital stock Kt. Specifically, the assumption is that the “change in total factor productivity” is related to the “change in capital” through the following equation:
ΔAt=At/At−1=μτ+ (1− μ)(ΔKt)ν (12) where μ is a weighting parameter between 0 and 1. According to this equation, the total change ΔAt is the sum of two components: the first, represented by τ and weighted at μ, is completely disembodied; while the second, weighted at (1 − μ), is in general a non-linear function of ΔKt
with power parameter ν. The second term represents that part of the increase in total factor productivity that must be embodied in new physical capital.
At first glance, this might seem to be quite a non-standard assump- tion, because of the tendency of some contributors to economic growth theory to draw a hard-and-fast distinction between growth in capital and growth in total factor productivity. In other words, there is a tendency to restrict the value of capital that in fact embodies improved technology to the value of capital itself, so that none of it leaks over into an increase in total factor productivity. If strictly applied, this would interpret all in- creases in total factor productivity as the result of disembodied tech- nological progress. But in practice there is a gray area between the respective contributions to production of capital, in and of itself, and of the technology that is embodied in new capital. This gray area is espe- cially large in this model because we are using capital in its generalized sense rather than in its limited sense (primarily plant and equipment).
Model simulation
The model described above contains a total of 11 numerical pa- rameters. The following is a listing of these parameters, together with brief word descriptions of their meaning, and their numerical values for the benchmark case that provides the best fit between empirical per capita income and model per capita income for China and India: output elasticity of capital: α =0.5 output elasticity of labor; β =0.5 labor force participation rate; ρ =0.5 asymptotic lower limit on change in capital; ξ
=0.999 linear coefficient of relationship between population change and capital change; ψ =0.03 power coefficient of relationship between population change and capital change; ϕ = 75 rate of disembodied technical progress affecting total factor productivity; τ =1.03 weight of disembodied technical progress relative to embodied; μ =0.30 power coefficient of term expressing capital-embodied technical progress; ν =7 initial-period capital-labor ratio for China; ωC =1 initial-period capital- labor ratio for India: ωI =1.
The basic result from the inquiry is presented in Table 2 and illus- trated by Fig. 2. Table 2 contains the numerical data on empirical per capita income versus economic model estimate per capita income, for China and India, from 1980 to 2020. The upper panel of Fig. 2 plots the Chinese data; the lower panel of Fig. 2 plots the Indian data. The visual fit of the model per capita income with the empirical per capita income is quite close for both nations, but is especially close for India. However, note once again from Fig. 2 that the model, of its nature, cannot account for such a heavy random shock as represented by the coronavirus pandemic which commenced in early 2020. To numerically assess the
goodness-of-fit between the empirical data and the model estimate data, the R-Squared was computed from an ordinary least squares regression of model estimate per capita income on empirical per capital income.
For China, the R-Squared is 0.9534; and for India, the R-Squared is 0.9733. By customary statistical standards, these represent very close fits between the empirical data and the model estimates.
The suggestion of this research is therefore that differential popula- tion growth between China and India during the 1980–2020 period has indeed been quite an important causal factor in differential per capita income growth between the two nations. Had the slowdown in popu- lation growth in India matched that in China, quite possibly India too would have achieved remarkable economic progress during this period.
In fact, to assess this latter possibility, the model for India was again simulated over the 1980–2020 interval, this time using the same pop- ulation growth rates for India that were observed for China. Table 3 and Fig. 3 display the results from this “what if” experiment. Table 3 presents empirical Indian population and potential Indian population, and empirical Indian per capita income and potential Indian per capita in- come, over the 1980–2020 time span. Fig. 3 is a visual representation of the results: the upper panel for population, and the lower panel for per capita income. The suggestion is that if Indian population growth had slowed down to the same extent that China’s had slowed down, India might also have achieved Rostow-type takeoff earlier and experienced the same rapid acceleration in per capita income growth over the 1980–2020 time frame as did China.
Table 3
Population and per capita income, empirical and potential India, 1980–2020.
Year Population Per Capita Income
Empirical Potential Empirical Potential
1980 698,952,844 698,952,844 422.90 422.90
1981 715,384,993 707,963,686 438.01 448.58
1982 732,239,504 718,466,837 442.80 471.43
1983 749,428,958 728,923,688 464.18 496.07
1984 766,833,410 738,550,686 470.97 525.36
1985 784,360,008 748,676,308 484.64 555.01
1986 801,975,244 759,895,341 496.64 582.89
1987 819,682,102 772,179,291 505.18 609.12
1988 837,468,930 784,712,553 542.05 636.36
1989 855,334,678 796,836,231 562.30 666.98
1990 873,277,798 808,614,434 581.22 701.13
1991 891,273,209 819,723,057 575.50 740.60
1992 909,307,016 829,830,871 595.01 787.86
1993 927,403,860 839,425,815 611.12 841.64
1994 945,601,831 848,967,335 639.27 900.08
1995 963,922,588 858,241,735 674.62 965.03
1996 982,365,243 867,284,631 711.93 1037.03
1997 1000,900,030 876,206,433 727.04 1116.10
1998 1019,483,581 884,654,543 757.93 1206.03
1999 1038,058,156 892,347,594 810.22 1311.36
2000 1056,575,549 899,406,680 826.59 1433.76
2001 1075,000,085 905,963,581 851.62 1574.77
2002 1093,317,189 912,053,913 869.20 1737.22
2003 1111,523,144 917,752,469 922.17 1923.70
2004 1129,623,456 923,219,522 979.28 2135.28
2005 1147,609,927 928,665,204 1040.31 2371.29
2006 1165,486,291 933,865,137 1106.93 2640.03
2007 1183,209,472 938,755,210 1173.88 2948.43
2008 1200,669,765 943,577,613 1192.51 3295.73
2009 1217,726,215 948,282,484 1268.25 3688.86
2010 1234,281,170 952,873,383 1357.56 4134.24
2011 1250,288,729 957,450,036 1410.43 4634.98
2012 1265,782,790 962,126,414 1469.18 5192.58
2013 1280,846,129 966,888,270 1544.62 5813.89
2014 1295,604,184 971,796,151 1640.18 6502.21
2015 1310,152,403 976,746,772 1751.66 7270.85
2016 1324,509,589 982,049,991 1875.73 8106.63
2017 1338,658,835 987,556,221 1986.63 9024.78
2018 1352,617,328 992,068,765 2086.45 10,139.38 2019 1366,417,754 995,619,678 2151.73 11,499.37 2020 1380,004,385 1005,082,842 1981.44 12,436.86 Source: Empirical: World Development Indicators; Potential: Computed.
Summary and evaluation
The economic condition and performance of the two Asian giants, China and India, is a matter of great concern to the entire world. From 1980 through 2020, both nations registered high growth in basic living standards as measured by per capita income. However, while the per capita income growth of India might reasonably be described as
“impressive,” the per capita income growth of China must be described as “extraordinary.” During this period of time, population control efforts in both China and India—in combination with the expected de- mographic transition to lower fertility as living standards rise—were successful in bringing about significant reductions in their respective population growth rates. But the thoroughness and vigor of the Chinese effort, as manifested in the notorious one child policy, was much greater, and brought about a considerably larger reduction in population growth.
Since labor is a factor of production, and labor is proportional to population, it is a truism that output is based on population: thus a larger population generates higher output. But it is also true that individual living standards are based on output divided by population, or per capita output. This in conjunction with the axiomatic economic principle of
diminishing marginal returns to all factors of production including labor, indicates that—holding other things equal—an increase in pop- ulation tends to lower living standards. This tendency might be coun- teracted by increases in the capital factor of production, and/or by improvements in the technology of production that increase total factor productivity. Nevertheless, the tendency remains.
According to the theory of optimum population, at any point in time and within any particular nation with its own natural resource endow- ment, there is a dome-shaped relationship between population and per capita income. While under-population is a theoretical possibility, it would seem that few nations in the contemporary world are actually under-populated—least of all China and India, with their populations numbering in the hundreds of millions. Bearing these kinds of consid- erations in mind, it is hardly a farfetched hypothesis that the signifi- cantly lower population growth rate in China over the last 40 years, relative to India, has been a major factor in its higher performance in terms of per capita income growth.
The present research lends support to this hypothesis. Simulations of a simple model of economic growth, which uses the same theoretical equations and numerical parameter values for both China and India, generates time paths of per capita income growth that closely track the actual time paths in the two countries. There are three main theoretical components of the economic model: (1) a Cobb-Douglas production function in capital and labor; (2) a function relating capital growth negatively to population growth; (3) a function giving growth in total factor productivity as a weighted function of fully disembodied tech- nical progress and capital-embodied technical progress. A trial-and- error procedure involving computer simulation was implemented to establish those specific mathematical forms and numerical parameter values that, when applied to both China and India, produced good fits to the empirical time paths of per capita income in the two countries.
According to the Occam’s Razor principle, as a general rule simple ex- planations for observed phenomena are to be preferred to more complicated explanations, so long as the simple explanations are suffi- ciently accurate and sufficiently in accord with other accepted evidence and expectations.
Although the volume of literature on Chinese economic perfor- mance, especially during its rapid-growth era over the last four decades, has become extremely voluminous, and a considerable proportion of this literature compares Chinese economic performance to that of India, population has not been a central consideration in this literature. This seems somewhat anomalous in view of the strong and dramatic evidence manifested in Table 1 and Fig. 1 herein. It would seem that evidence of this magnitude deserves close examination, and this research has endeavored to provide such examination. If there is indeed, as this research suggests, a direct causative relationship between population growth and per capita income growth in China and India during the 1980–2020 time frame, this relationship possesses great significance as far as the economic future of global human civilization is concerned. But in order to properly assess the significance of this evidence, we must consider the inherent limitations of the research.
As is the case with all economic models, the model developed in this research is a simplified representation of reality that necessarily in- corporates a number of assumptions that are obviously somewhat un- realistic. For example, the model postulates that the labor participation rate is the same in China and India, and time-invariant as well. But it is a standard assumption in demographic economics that a lower rate of population growth enables a higher labor force participation rate. The Cobb-Douglas production function in itself embodies various restrictive assumptions: e.g., that the elasticity of substitution between capital and labor is one. Moreover, the model takes no explicit account of such obviously important factors as foreign trade and investment, economic inequality, educational attainment, socio-political institutions, and environmental conditions.
It is also worthwhile to point out that per capita income, in and of itself, is not a perfect measure of economic welfare. Other factors apart Fig. 3. Population and per capita income empirical and potential
India, 1980–2020.
from material living standards are important to the average quality of life in a nation: the degree of inequality, the condition of the environ- ment, the nature of working conditions, personal health and life ex- pectancy, average family size, national power and prestige, and so on and so forth. Indeed, much attention has been paid in the recent liter- ature to the substantial social costs incurred by China owing to its extremely rapid growth. At a minimum, it must be acknowledged that the relationship between material living standards and personal welfare is subject to diminishing marginal returns, so that the actual difference between China and India in terms of individual utility is not as large as the difference in per capita income.
Still another possible problem with the present research has to do with the distinction, well known from regression methodology, between association and causation. Specifically, a high degree of association does not necessarily indicate causation. Regression is a statistical technique designed to uncover numerical associations among variables. It is the responsibility of the investigator to judge whether the uncovered asso- ciations represent true causation, or are merely associations caused by underlying variables that affect both the dependent and the independent variables. The present research did not use a formal statistical technique such as regression to estimate the coefficients of the economic model equations. However, the trial-and-error technique actually utilized is in some respects analogous to a statistical technique, in the sense that the coefficients of the relationships were varied until good fits were ach- ieved between the model estimate and empirical time paths of per capita income.
But just as a high R-squared of a regression equation does not necessarily indicate a causative relation between the dependent variable and one or more of the set of independent variables, so too the close correlation between estimated and empirical per capita income in the two nations does not necessarily indicate that the equations of the specified model represent legitimate causation. Whether or not a high R- squared of a given regression equation can be taken as evidence of causation, or is merely a manifestation of association, depends on the underlying plausibility of the equation being estimated. Does economic theory give us sufficient reason to believe that the specified independent variables have a causative effect on the dependent variable of the nature indicated by the statistical finding? The answer to this question is often highly judgmental—and economists are notorious for their propensity toward disagreement, even about very basic matters.
Still another caveat in evaluating this research is that the basic analytical methodology utilized is not standard and conventional methodology. It represents an ad hoc approach to the question of in- terest that will not be found in textbooks on econometric methods or other approaches to statistical inference. Although undoubtedly similar methods to the “controlled experiment” of the present research are to be found here and there in the literature, there is not a well-known, established literature that utilizes precisely the present approach.
While “innovation and originality” are highly prized attributes in prin- ciple, there is nevertheless a fairly strong predisposition in most re- searchers in favor of “tried and true” methodologies. This factor has to be taken into account in assessing the value of the research reported here.
Finally, in further assessing the limitations of the present research, it should be noted that economic growth in terms of increased per capita income is certainly not the ultimate desiderata of socioeconomic suc- cess. High per capita income is not necessarily advantageous, if it is accompanied by extreme inequality in wealth and income, and/or extreme environmental degradation. Indeed, a large amount of envi- ronmental degradation, for example in the form of air pollution, can affect the welfare of people in areas outside of the national borders.
There are substantial literatures on both income inequality and envi- ronmental problems in developing countries such as China and India. On income inequality see [21,41–44], and on environmental problems see [45–49].
On the other hand, these matters are not unrelated to the overall
prosperity level. The Kuznets curve concept, originally developed to examine economic inequality, has more recently been extended to environmental degradation. According to the theory, just as economic inequality first increases but then decreases as overall prosperity rises, so too environmental degradation first increases and then decreases as overall prosperity rises. Thus rising overall prosperity will presumably have a positive effect—eventually—on socioeconomic desiderata beyond mere material welfare.
All of these considerations, as well as others that might be taken into account, reduce the amount of support provided by this research for the hypothesis that differential population growth between China and India has been an important factor in their differential per capita income growth. Nevertheless, considering the matter as a whole, this hypothesis deserves to be taken seriously. It is an empirical fact that recent per capita income growth in China has been much greater than in India.
Since these two nations are very similar in several important respects, the question arises of what has been responsible for the difference.
There are obvious differences between these two nations in political structure and ideology, in economic institutions, in resource endow- ments, and so on. But it is not implausible to conclude that the differ- ences in population policy and population growth are more striking than in any of these other areas. The population differences are visually manifest in the upper panel of Fig. 1 herein. Taking the two panels of Fig. 1 together, they suggest that the greatest acceleration in Chinese per capita income growth occurred exactly during the period of greatest deceleration in population growth. Given existent economic theory (e.
g., regarding optimal population), it seems reasonably plausible that this was not an accidental association, but rather manifests causation.
Whether the residual support for the hypothesis under consideration herein, that is left after taking due account of the various shortcomings of the theoretical model, should be deemed “significant” or “insignifi- cant,” is a subjective judgment. The role of subjective judgment applies with particular force to certain research areas that are inherently more controversial than the norm. Any research directly or indirectly related to population control falls into this category. Although the general desirability of reducing population growth is widely accepted by most knowledgeable people around the world, opinion is sharply divided on the immediate practical importance of this issue. To some people, population control is inherently suspect simply because it can be labeled
“anti-life.” While these people do not necessarily favor the complete elimination of population control programs, they are against “over- emphasis” on such programs.
While not denying the natural appeal of basically “pro-life” attitudes, which are certainly commendable in and of themselves, a question might be raised whether these attitudes truly justify concerns, given existing global realities, over possible over-emphasis on population control. The current human population on Earth is approximately 8 billion persons. The size of the global human population seems therefore to have reached a point where economies of scale in production for most commodities have been fully realized. In addition, the limits of the fixed supplies of various nonrenewable natural resources are now clearly in view. Moreover, various environmental problems, such as global warming, are increasingly recognized as serious threats, and these problems are rendered more intractable by growing population. Under such conditions, perhaps a more sensible expression of “pro-life” sensi- bilities would be greater concern for the quality of life of human in- dividuals, and less concern for the mere number of human individuals.
In other words, it may be more sensible to emphasize the quality of human life over the quantity of human life—considering that the quantity of human life has become so large.
Around the middle of the twentieth century, at about the same time that the extraordinary growth of the world’s human population during the recent modern era became more or less common knowledge, there developed an alarmist “standing room only” literature. To many people it seemed quite likely that Malthus was right after all. Perhaps the in- tensity of the reaction to the facts of population growth had something
