The one-child policy: an analysis

Recent research has substantiated with the Chinese government’s assessment that the only way to bring down birth rates was to use government force and finesse instead of letting birth rates decrease naturally.

Although there has been great improvements in living standards, health care and education since the 1950s, China has not modernized enough to reach a level at which people might have been motivated to limit family size spontaneously. Nevertheless, China has attempted and obviously succeeded in beginning to change public attitudes towards marriage and child-bearing, and to bring down population growth. The effective family-planning programme is undoubtedly one of the main causes of the rapid fertility decline in China. (“Major Determinants of China’s Fertility Transition,” Peng 36)

Once the government acknowledged that birth rates were not going to naturally decrease, and that the population would soon rise uncontrollably, politicians began to legitimize governmental control of the population. In a speech at the Third Session of the Fifth National People’s Congress in 1980 the Chairman of the Chinese Communist Party Hau Guofeng presented China’s predicament in the beginning of 1980.

For a long period in the past, chiefly in the 1960’s, we slackened our efforts with regard to family planning. As a result, our population has grown too rapidly and will continue to grow substantially in the coming years. Young people under 30 years of age account for about 65 per cent of the total population, or around 630 million. Some have already reached the age of fertility and the majority of the remainder will do so within the next 10 to 20 years or so. If population growth is not controlled, there will be a dizzy peak, making it virtually impossible for the economy and all our social institutions to cope. Upon careful study, the State Council deems it necessary to launch a crash programme over the coming 20 or 30 years calling on each couple, except those in minority nationality areas with sparse populations, to have a single child, so that the rate of population growth may be brought under control as soon as possible. (Hua 685)[4]

Hua Goufeng’s reason for enacting The One Child Policy was to align individual interests with the interests of the country as a whole. He states, “in the interest of the people themselves, it is essential that the promotion of family planning should continue and that we should continue to encourage, one couple, one child” (Hua 685). The issue is defined as individual costs that are not properly aligned with social costs. This view that individuals cannot make their own decision on the socially optimal number of children continues today.

China’s current view on managing population continued to focus on aligning costs and modernization. In 1995, China’s government released a White Paper on the current population program that showed no lessening of concern over population growth.

The population problem is an important question that touches upon the survival and development of the Chinese nation, the success or failure of China’s modernization drive as well as the coordinated and sustained development between the population on one hand, and the economy, society, resources and environment on the other. (1995 White paper, 386)

By 1995, the Chinese government believed that The One-Child Policy had demonstrated its effectiveness, and chose to continue the program into the future.

Facts have proved and will continue to prove that, while making energetic efforts to develop the economy, the comprehensive promotion of family planning was the correct policy decision, taken in China since the latter half of the 20^th century, which bring benefits to the present and constitutes a meritorious service for the future. (1995 White paper, 386)

At the start of the program, it was “suggested that the total population should be controlled below 1.2 billion and the population growth rate should reach zero by the year 2000” (Peng 53). Besides decreasing population growth rates, the government promised “to quadruple China’s per Capita GNP by the year 2000” (Peng 52). China reached its latter goal if not the former. The fact that they linked these goals together, however, shows that they clearly thought that their current problem stemmed from uncontrolled population growth.

The policy failed to meet the government’s expectations for population control because of the preference for sons in the rural countryside. Sons were valued more than daughters because sons were the main source of the parent’s income in their old age. The One-Child Policy caused unrest when a new family had a female child as their firstborn and were not being allowed by the government to have any more children. The policy was amended to allow “rural couples in some areas with only a girl to have a second birth after an interval of several years” (Peng 54). The combination of a preference for sons and The One-Child Policy has caused female infanticide to be a problem as well as sex selection of births. This has caused the male/female ratio to be increasingly skewed towards males. Although this skewed ratio will be a large problem in the future, it does not have any bearing on my current analysis because of the short time frame I am analyzing.

Another challenge the policy faced was international criticism claiming that The One-Child Policy violated human rights. “At the 1984 United Nations International Conference on Population in Mexico City, the Declaration on Population and Development asserted that couples and individuals have ‘a basic human right to decide freely, responsibly, and without coercion the number and spacing of their children’” (United Nations, qtd. in Preston, 623). In addition to the international community, individual countries such as the US “State Department has invoked sanctions against China and against the United Nations Fund for Population Activities for supporting Chinese programs” (Preston 627). This international pressure as well as the negative perception of many in the international community is one of the main drawbacks of the policy.

Another social cost of The One-Child policy is the use of governmental force and abortions to limit population growth. For example, “[n]early one-third of the women and one-fifth of the men from North Anhui said they or their spouses had their last abortion because the family planning cadre had made them have the abortion” (Directorate Immigration and Refugee Board 5). The number of coerced abortions varies from province to province, however, with the above example erring on the high side. The government chose and continues to choose the policy because they feel that these costs are worth the benefits to the population as a whole.

In 1979, Liu Chen defended The One-Child Policy by stating that a large population is not desirable because, “[t]he history of the modernization of production shows that advances in scientific technology reduce the size of labor force required to produce a given output” (560). Thus, China would be able to maintain a level GDP with a smaller population. He also believed that China arrived at a point of decreasing marginal productivity. “When the productive capacity of the economy cannot absorb large increases in the working age population, underutilization of labor is the result. Under such circumstances, not only are major gains in labor productivity impossible, but declines may even set in” (Liu 560). Therefore, the only way out of the crisis was to limit the number of people being absorbed into the labor force.

In addition to labor productivity, in order to spur per capita GDP growth, China must increase its capital per worker. Liu states that “capital accumulation has been greatly limited by a large population and a rapid population growth” (561). In order to increase the capital to labor ratio, it is necessary for individual workers to save more money; however, Liu argued that China’s population was too close to the poverty line to accumulate capital. “It is impossible to achieve a high rate of capital accumulation with low quality of productive forces, because the national income left over after satisfying the basic needs of the population is minimal” (Liu 561). Therefore, since China was such a poor country, an increase in the investment rate would only come from decreasing the number of dependents a family had to support.

China and other third world countries struggle to accumulate capital because the high population growth rate inhibited saving. One solution for developing countries is to limit their population growth. “If...the growth rate were reduced to below 1 percent, the amount of national income that could be transferred from consumption to accumulation because of fewer births would be considerable. The effect of population control on increase in capital accumulation, therefore, should definitely not be regarded as unimportant” (Liu 561). Since China was significantly impoverished, and the only way to accumulate capital was to curtail population growth, The One-Child Policy was essential to sustaining and raising GDP per capita growth.

The latest data from the World Bank shows that China’s GDP per capita has grown dramatically during the last two decades. The average GDP per capita (in international PPP) growth rate from the start of The One-Child Policy in 1979 to 1999 was 12 percent. “The average income of Chinese people in 1997 was 14 times that of 1979, a 212 per cent increase in real terms, according to the State Statistics Bureau (SSB) of China” (Peng 3).

This increase in wealth was accompanied by increases in transportation, education, and health. From 1978 to 1997 “[h]ighways extended to 1.226 million kilometers, up 37.7 per cent; air routes soared to 1.425 million kilometers, a 9.7 fold increase during the same period” (Peng 3). China had more than 6.3 million college students in 1997 while the figure was only 1.2 million 20 years ago” (Peng 5). China’s infant mortality rate falls from 39 per 1,000 live births in 1979 to 30.24 per 1,000 in 1999. Therefore, the average citizen is better off in 1999, nineteen years after The One-Child Policy was instated. The remainder of this paper will focus how important it was that the government established this policy.

Literature Review

The relationship between population and economic variables has been well documented by Robert Malthus, Julian Simon, and Gary Becker; however, there is no consensus on the effects population has on economic variables. Malthus and neo-Malthusians believe that population growth is negatively correlated with economic growth, while Julian Simon argues that the correlation is positive. Gary Becker takes the middle ground and argues that in poor agricultural societies Malthusian dynamics are the most significant relationship between population and economic growth. He also argues that in more densely populated countries, with large urban areas, population is positively correlated with economic growth.

In 1798, Robert Malthus observed the dynamics between population and poverty, in, “An essay on the principle of population.” Malthus believed that humans would always live in poverty because any improvements in their living conditions would only last for a short time. Since land is a fixed factor in production, Malthus predicted decreasing returns in productivity of labor. Adding an additional worker to the work force would decrease real wages and thereby increase death rates. Thus, a favorable advance in technology would increase real wages, allowing the population to grow; however, as the number of workers on a given piece of land increased, population would soon outstrip the land’s productivity and the real wage would decline back to a subsistence level. There are two ways to break the Malthusian dynamic. First, continuous technological advances and, second, controlling birth rates as the real wage increases.

Inventions and improving technologies in countries that have a sufficient GDP per capita keep Malthus’ predictions of poverty from becoming reality. This sustained increase in GDP per capita only occurs in first world countries. While it seems that Malthus’ model cannot be applied to first world countries, many economists believe that the link between poverty and population still explains the underdevelopment problems faced by third world countries.

Almost a century and a half after Malthus wrote his essay, a new group of economists used the Malthusian model to analysis the differences in growth rates between various third world countries. These neo-Malthusians[5] believe that government can affect the growth of per capita GDP by decreasing birth rates. “Coale and Hoover (1958) published their influential book providing the intellectual justification for policies and programs seeking to slow rapid population growth” (Ahlburg, 318). They argued that governments who were concerned with the financial well being of their citizens had justification for altering citizens’ reproductive rights. The neo-Malthusians gained international support, particularly from third world countries, and began “calling for solutions ‘beyond family planning,’ such as government-imposed disincentives on childbearing, paying people to be sterilized, and even making bearing a third child illegal and requiring an abortion to terminate all such pregnancies” (Hodgson and Watkins, qtd. in Ahlburgh, 318). These views were gaining significant momentum during the 1970’s, when China was forming their own family planning strategy.

The Neo-Malthusians were opposed by Julian Simon, an economist who created a new growth model. Simon saw population as beneficial to economic growth. In a posthumous analysis of Simon’s theories, Dennis Ahlburg wrote:

Among economists, for the last 20 years Julian Simon was among the most prominent analysts of the relationship between population and development as well as the most prolific commentator on issues of public policy concerning population growth. In these roles he strove to demonstrate that the dominant conventional wisdom that held demographic expansion as a menace to human welfare was wrong and to convince us that policies deliberately seeking to slow population growth were mistaken. (Ahlburg 317)

Julian Simon thought that human beings were the most important resource for economic growth because larger populations generate more new ideas and greater demand for goods and services. Although many new growth models take into account the scale effect that more people generate more ideas, population growth rates are still thought to have both positive and negative effects.

Gary Becker, in a recent article on the dynamics of population and economic variables, states that “under conditions that tend to prevail in poorer, mainly agricultural, economies with limited human capital and rudimentary technology, higher population usually does tend to lower per capita incomes, mainly along Malthusian lines” (Becker 146). He also believes that population growth can have a positive effect, as “the increased density that comes with higher population and greater urbanization promotes specialization and greater investment in human capital, and also more rapid accumulation of new knowledge” (Becker 146). Thus, according to Becker, a densely populated country will actually enjoy increasing returns to labor, while an agrarian society will experience diminishing returns to labor.

Gary Becker and his coauthors also assert that there is a tradeoff between quality and quantity of children. “Parental heads of dynastic families make three choices: they consume, have children, and invest in human capital of their children” (Becker 279). Although, Becker leaves out capital accumulation from this model[6], he does give us an incite to the way The One-Child Policy would effect a family living in China. The child would expect to get more schooling and have higher human capital versus a counterpart with many brothers or sisters. This might be as simple as the parents having more time to spend with the child or the pressure given to a sole heir who will support the parents in their old age.

Empirical tests of the magnitude of the effects of population growth have been conducted using cross-sectional, international data sets from various decades. One study, “by Allen Kelley and Robert Schmidt (1996) show that the positive and negative effects of population growth probably offset each other in 1960s and 1970s” (Ahlburg 321). They also found that, “direct negative impacts of population growth are particularly countered by positive impacts that increase with the level of economic development” (Ahlburg 321). According to empirical tests, Becker has correctly determined that the positive effects of population growth increase with the level of a country’s development.

However, the question remains, should the governments of third world countries take steps to control population growth?

Two schools of thought have emerged in response to this question. The first view is that population is negatively associated with GDP per capita. The “Orthodoxy...views demographic trends as ‘determinants of economic trends [and views] rapid population growth as a cause of continued underdevelopment. Lowering fertility becomes a way of facilitating structural change.” (Hodgson 1988, qtd. Blanchet 105). Under the orthodoxy view, “fertility declines must be induced by deliberate state intervention, if not by coercion” (Blanchet 19). China’s government follows the orthodoxy theory and controls population growth in the hope of increasing wealth.

The second theory postulates that population variables are not significantly correlated with economic variables.

Revisionism holds that population growth is a neutral phenomenon with respect to economic development, or is even beneficial to it because of constant or increasing returns to scale, or, in a more modern and more interesting formulation, because of endogenous technical progress attributable to, and more than compensating for, population growth. (Blanchet 105)

Revisionism dictates that The One-Child Policy will either have no effect on economic variables or that the government chose the wrong policy to increase economic growth.

New growth models have been developed recently that support the revisionism theory. In 1991, when Robert Barro published his findings on the relationship between GDP per capita and human capital, he stated that in the studies “Rommer[, 1990] human capital is the key input to the research sector, which generates the new products or ideas that underlie technological progress” (Barro 408).[7] Therefore, a larger population is able to generate more ideas than a smaller population, thereby economic growth occurs faster. Population growth leads to positive GDP per capita growth as more humans create more ideas. Robert Barro and Xavier Sala-i-Martin test this theory more extensively in their 1995 book, “Economic Growth.” They use a cross sectional regression to find the determinants of per capita GDP growth. Using data from 97 countries, they determine that the endogenous growth models that use the scale effect are wrong. There does not appear to be any correlation between working age population and per capita growth in all kinds of countries. They also test if the portion of the population that is under the age of 15 is correlated to GDP per capita growth. The variable is insignificant, although it is positive. Since the regression included many first world countries and third world countries, the variable might be significant only at low GDP per capita levels. China was not among the samples surveyed.

Like Barro and Sala-i-Martin, I will test the effects of population and the effect of the portion the population that is under 15 on GDP per capita. I will use a time series multiple regression equation with the growth in birth rates as my measure of population growth rates[8]. If the coefficient of births were positive, then the scale effects predicted by Julian Simon and the revisionism hypothesis would be supported; but, if the coefficient is negative, then the neo-Malthusian and orthodoxy positions are supported. If there seems to be no impact of births on GDP per capita, then the revisionism theory is supported.

Methodology

The methodology I used to analyze The One-Child Policy is a time series OLS regression. The dates used were from 1966-1999 (34 observations) and had around 29 to 25 degrees of freedom. The Durbin-Watson statistic was used to determine if the independent variables errors were correlated throughout time. If the test proved to be inconclusive, then I did not correct the equation. The relationship between the variables appears to be linear and a change in the independent variable will have an immediate effect on the dependent variable.

The standard equation for a regression is:

Equation 1.1 Y_t = b₀ + b₁ z_t + b₂ w_t + u_t

Y_t,GDP per capita, is the dependent variable, while z_t and w_t are independent variables: capital per worker, net primary school enrollment, crude birth rate, etc. When measuring per capita growth, it is useful to analyze the instantaneous rate of change in variables, thus measuring the magnitude that a percent change in the independent variable has on the dependent variable. This is accomplished by taking the log of each variable. Thus the equation becomes:

Equation 1.2 log(Y_t) = log(b₀) + b₁ log(z_t) + b₂ log(w_t) + u_t

The constant is the intercept and the coefficients are now percentages. However, this formula was not used since the independent variables suffered from being autoregressive. To eliminate this autoregressivity, I computed the growth rate of each variable. After converting the variables into growth rates using (Y₁ – Y₀ )/ Y₀, the new equation is:

Equation 1.3 (Y_t – Y_t-1)/Y_t= b₁ (z_t – z_t-1)/z_t-1 + b₂ (w_t – w_t-1)/w_t-1 + u_t

The constant term disappears as the new variables discontinue measuring the level of each variable, but instead, measure the growth rate in each variable. This greatly decreased the auto-correlation.

Since the independent variables have an immediate impact on the dependent variable, one must add lagged variables to calculate the future impact of the independent variables. For example, if a couple forgoes having a child in time t, then the effects of their decision carry into the next year and the following year. The independent variable, Z_t lagged one year is Z_t-1, which inserted into the equation looks like:

Equation 1.4 (Y_t – Y_t-1)/Y_t-1
=b₁ (Z_t – Z_t-1)/Zt_-1 + b₂ (Z_t-1 – Z_t-2)/Z_t-2 + u_t

Therefore, the total effect on the growth rate of Y_t will be the sum of b₁ and b₂ or the long run multiplier.

The independent variables were chosen from the standard Cobb-Douglas, Y = F(K,L) equation, or were China specific, such as the changing structure of the economy and baby booms. The equation was still suffering from serial correlation, and changing the variables to growth rates did little to help lower the Durbin-Watson statistic. Some equations still required AR(1) to correct the serial correlation problem[9].

Data Discussion

The data used in this study was obtained from the World Bank’s The 2001 World Development Indicators CD-Rom. Some years within the sample were missing values, in which case the midpoint of the previous and following year were used to calculate the value. In a single case, the variable of net primary enrollment rates was missing more than one consecutive year; the missing values were estimated using a best-fit line. China reacquired Hong Kong and Macao during this time, but they, and Taiwan, are excluded from the data set. The variables that were used in the regression analysis are presented here with a description and general growth rate during the period of 1970-1999.

GDP per capita

The dependent variable used in my equation was the GDP per capita. The World Bank defines GDP per capita as GDP divided by the midyear population. GDP per capital is measured in constant 1995 US dollars, making the conversion from Yuan to US dollars unnecessary.

The Chinese economy has seen its per capita income increase exponentially since 1970. At the beginning of 1970, the average Chinese citizen made 120 USD per year; by 1980, this average had quickly grown to 167 USD. Then at an astounding 8.2% growth rate, the average GDP per capita grew from 167 USD in 1980 to 768 USD in 1999. The mean of the entire 34 growth rate observations was 6.83% and had a standard deviation of 3.99%.

The GDP per capita measures the economic well being of the Chinese citizens. If The One-Child Policy affected the Chinese economy, a change in the growth rate of crude births would cause a change the growth rate of GDP per capita. The following variables are the independent variables.

Birth rates

The World Bank estimates crude birth rates as the number of live births occurring during the year per 1,000 people, estimated at the midyear. This variable steadily decreases, starting in 1970, and has an average negative growth rate of 2.37% with a standard deviation of 6.38%. The whole series is negative except for 1980-1982 and 1985-1987, which have a positive average growth rate of 6% and 9%, respectively. This variable will be a proxy variable for measuring the impact of The One-Child Policy and will be lagged to measure the effect over time.

The coefficient of birth rates will determine whether or not population has an impact on GDP per capita. If the birth rate coefficient is positive or insignificant, then the revisionism theory would be supported. If the coefficient is negative, then the Neo-Malthusian theory and the Orthodoxy position is supported.

Capital per worker

The capital per worker ratio was computed by dividing the gross fixed capital formation by the total number of workers in the labor force. The gross fixed capital included: land improvements, plant machinery, equipment purchases, roads, railways, schools, offices, hospitals, residential dwellings, and commercial buildings. The ratio was calculated in 1995 constant USD. The total labor force was computed using the International Labor Organization definition of labor.

The capital per worker grows from 54 USD in 1970 to 92 USD 1980 and 462 USD in 1999. The average growth rate during this time was 8.75% and had a standard deviation of 9.44%. This variable is usually correlated with growth and empirically returns around 0.30 percent to GDP per capita growth.

Primary school enrollment

The World Bank estimates net primary school enrollment as the number of children of official school age who are enrolled to the corresponding official school age. Robert Barro and Sala-i-Martin often use this variable in cross sectional analysis as a proxy for human capital. Although educational attainment would have been a better proxy, China has not recorded the variable from 1970-1999.

Unfortunately, not all of the data values are available for every year, so the final data series is estimated using a best-fit line of the 11 data points that had values. The estimated equation was 0.5164t + 84.676, and predicts the values of all the data points that were unknown. At time t = 0, the primary enrollment rate is equal to 84%, which is plausible, considering that China had a 9 year compulsorily education system. My estimation accurately reflects the actual growth in primary enrollment rates since China spent an almost constant amount of GDP on education during this time. The average growth rate of enrollment was 0.567% and had a standard deviation of 1%.

Services value added % of GDP

To control for the changing structure of the economy, services as % of GDP were added to the equation. The World Bank lists services as value added in: wholesale trade, retail trade, transportation, government, financial, professional services, personal services, education, and health care.

The change in services as a percent of GDP varied greatly between 1970 and 1999. The mean was 0.87% and the standard deviation was 5.23%. During the period from 1980-1999 the average growth rate was 2.28%. Therefore, a majority of the growth came after The One-Child Policy was implemented.

Adult to child ratio[10]

This variable is the ratio between adults 15-65 years old to children 15 years or younger. If the ratio were to equal 1, then every working adult would be supporting one child. Since China experienced a population boom during the 1960’s and reduced its birth rate shortly afterward, the child to adult ratio in 1999 is very high: 2.64 adults for every child. The ratio increased steadily and leveled off during 1996-1999 and will probably decrease substantially in the future. The average growth rate of the ratio was 2.15% and the standard deviation was 0.30%. This variable will be used to test Gary Becker’s quality versus quantity dynamic of the population. The ratio will also test Liu Chen’s prediction that a lowering the number of children per family will increase GDP per capita growth because parents will have more money to invest in physical and human capital.

Results

Birth rates as well as population growth rates do not seem to be statistically correlated to GDP per capita. After controlling for various variables, the final equation used to calculate the effects of The One-Child Policy on China’s economy is:

GDP per capita = Capital per worker + Primary Enrollment + Services as % of GDP + Birth Rate + Birth Rate (-1 year) + ...+ Birth rate (-n years)

+ Adult to child ratio

All variables are percentage growth rates. Therefore, a 1 unit increase in the independent variable causes GDP per capita growth to increase/decrease by the percent of the coefficient. I calculated four different regression analyses to control for four different effects of the birth rate. These four equations are reported at the end of this section, while all other regression equations are reported in Appendix C.

Capital per worker was consistently significant at the 1% level and contributed 0.35 - 0.5 percent to the GDP per capita growth rate. Therefore, if capital doubled, a one-unit increase, GDP per capita growth would increase by 35 to 50 percent all else equal. The coefficient was positively correlated with GDP per capita and within range of other cross section empirical tests of China, therefore, the equation specified seems to be accurate.

Primary enrollment rates were significant at the 5% level until the child/adult ratio was added to the equation. The coefficient, at 1.3 to 1.5 percent for the primary enrollment rates, was large in comparison to other independent variables. This means that if enrollment rates had doubled, which is not possible as China started at 82% enrollment in 1970, GDP per capita would have grown to 130 or 150 percent, all else equal.

Services as a % of GDP was significant at the 5% level in all equations except those that contained the adult/child ratio. The coefficient was around 0.30 percent and positively correlated with the GDP per capita growth rate. A one unit increase in the services as a % of GDP would increase GDP per capita growth by around 30% all else equal.

The birth variable was lagged in three different ways to find whether or not birth rates were significant through time. I initially thought that the variable would be significant for the first four years, but the variables were not individually significant or jointly significant. Hence, births are not correlated with GDP growth within the first four years.

I also theorized that the lag on the variable might not show up until seven to ten years into the future. This lag tested whether or not children born seven to ten years prior to the current year have a direct impact on current GDP per capita growth. If children cost more in time and money when they are in grade school, then this should be negatively correlated to GDP per capita growth. The coefficients were all negative and all but one was insignificant; however, the variables together proved to be jointly significant. The magnitude of the variables together was 0.14 percent. Since the birth rate variables are negatively correlated with the GDP per capita, then a permanent 1 unit increase in births would decrease GDP per capita growth by 14 percent. When controlling for the age structure of the population, the variables were jointly insignificant, thus casting doubt on whether or not births are significant during the initial seven to ten years.

The third test on birth rates lagged the variable to test if birth rates from 13-15 years prior to the current year impact current GDP per capita growth. This age is significant because the babies born 14 to 16 years prior to the current year will be entering the labor force. The variables were all negatively correlated with GDP per capita, but were insignificant both individually and jointly. Therefore, there does not appear to be a long run relationship between births and GDP per capita. This also means that China was not in a Malthusian dynamic of decreasing marginal returns to labor.

The adult to child ratio was used to examine if the relationship between the number of children to each working adult was significant to economic growth. The coefficient was positive and significant at the 1% level in every analysis run. The magnitude of the coefficient was around 1.17 % or, if the ratio doubled, then the GDP per capita would increase by 117 %, all else equal. This supports the argument that lowering the number of children relative to the number of adults in a third world economy causes an increase in GDP per capita growth.

How can birth rates be jointly insignificant, while the adult to child ratio, which is made up of the crude births, proves to be significant? The actual size of the population seems to be unimportant, while the relative number of productive people (adults) to unproductive people (children) does seem to play an important role in growth.

The adult to child ratio could be significant in light of Gary Becker’s theory on quality versus quantity of children or Liu Chen’s theory that China was too poor to spur growth without cutting a significant expense, in this case, children. The adult to child ratio causes primary enrollment rates to be insignificant when the ratio is added to the equation; this result indicates that Gary Becker’s hypothesis that fewer children per family increases human capita might be correct. However, there is no conclusive proof that this ratio signifies increasing human capital.

The ratio could also be a proxy for increased investment that occurs when families have fewer children, and thus, fewer costs. China was a poor enough country that children could be a major expense to the family. In 1980, China’s average per capita income was $167 (1995 USD), which is less than a dollar per day. Foregoing children would be significantly beneficial to growth for a given family at this level of poverty. A decrease in number of children per family might have allowed parents to save more, thereby increasing investment. Countries in a similar stage of poverty might also increase investment by decreasing the number of children per family.

The final four equations are listed with the coefficient term preceding the variable name and the T-stat under the coefficient in parentheses. Variables that were significant at the 5% level are in bold print. The first equation contains the birth rates lagged for 4 years.

GDP per capita = 0.46 Capital per worker + 1.45 Primary enrollment + 0.30 Services

(8.55) (2.37) (2.24)

+ 0.10 Birth rate + -0.14 Birth rate (-1 years) + 0.10 Birth rate (-2 years)

(0.78) (-0.94) (0.74)

+ -0.02 Birth rate (-3 years)

(-0.17)

Adjusted R-squared 0.29

Durbin-Watson stat 1.41

n 34

The next equation has the birth variable lagged for 6-9 years. The coefficients were jointly significant in this equation, but not jointly significant when the adult to child ratio was added.

GDP per capita = 0.50 Capital per worker + 1.32 Primary enrollment + 0.31 Services (10.74) (2.55) (2.68)

+ -0.05 Birth rate (-6 years) + -0.09 Birth rate (-7 years)

(-1.82) (-2.80)

+ -0.01 Birth rate (-8 years) + -0.02 Birth rate (-9 years)

(-0.23) (-0.66)

Adjusted R-squared 0.49

Durbin-Watson stat 1.98

n 34

The third equation shows that China does not seem to be in a Malthusian dynamic because birth rates are jointly insignificant.

GDP per capita = 0.43 Capital per worker + 1.51 Primary enrollment + 0.34 Services

(8.79) (2.50) (2.56)

+ -0.003 Birth rate (-13 years) + -0.02 Birth rate (-14 years)

(-0.10) (-0.61)

+ -0.02 Birth rate (-15 years)

(-0.58)

Adjusted R-squared 0.32

Durbin-Watson stat 1.51

n 34

The last equation shows the correlation between the adult to child ratio and GDP per capita.

GDP per capita = 0.36 Capital per worker + 0.52 Primary enrollment + 0.10 Services

(8.57) (1.03) (0.86)

+ 1.17 Adult to Child Ratio

(4.29)

Adjusted R-squared 0.59

Durbin-Watson stat 1.44

n 34

The other variables that were controlled for, but either were not significant or did not add to equation were population, population density, % of labor in industry, industry as a % of GDP, and age dependency. The regression equations are similarly reported in Appendix C.

Conclusion

The One-Child Policy benefited China economically over the last 20 years. Although my analysis concluded that birth rates are not correlated with GDP per capita, the increase in the adult to child ratio is a significant part of the growth relationship that is achieved by The One-Child Policy.

Since birth rates are not statistically significant when correlated with GDP per capita growth, China was not facing a Malthusian dynamic. China could have continued to add people to the labor force without suffering from decreasing marginal productivity of labor. Adding population might not have the negative effects that the neo-Malthusians would have theorized because China’s urbanization increased during this time and China has a unique system of industry within rural areas.

Since birth rates were not significantly correlated to GDP per capita, then neither the neo-Malthusian dynamic nor Julian Simon’s scale effect, were supported by this study. The lack of correlation between the population and GDP per capita growth, however, does support the revisionism position that population, and consequently birth rates, has no impact on economic variables. The conclusion reached by this study is that, since China was not experiencing a Malthusian dynamic, it was unnecessary for the Chinese government to artificially lower population growth.

However, the policy was also enacted to increase investment in both human and physical capital. The One-Child Policy succeeded in increasing the GDP per capita by increasing the adult to child ratio, which was 1.4 adults for every child in 1970, and leveled off at 2.6 adults per child in 1999. This was an 85% increase over 29 years and resulted less children per family. This allowed families to spend more on human and physical capital.

The adult to child ratio increase could not have occurred without The One-Child Policy. After the baby boom in the 1960’s, the population would have spiked in the 1980’s, without The One-Child Policy.[11] A cyclical increase in population was exactly what Hau Guofeng, the Chairman of the Chinese Communist Party at the time, feared would happen if population was not controlled. The baby boom during the 1960’s would have caused another boom in the 1980’s that would have depressed the adult to child ratio and GDP per capita growth. The significance of the adult to child ratio lends support to the orthodoxy position that a government program is needed to encourage economic growth.

Government intervention appears to have necessary because families were privately choosing a socially inefficient number of children. This might have occurred in China because of the cultural preference for sons. While daughters often go to the husband’s house when they marry, sons stay at the home and take care of the parents in their old age. Therefore, it is important for Chinese households to have a son. This problem proved to be important enough that the rural population refused to follow the policy if their first child was a daughter. The One-Child Policy was created to align this private cost and the social cost.

The One-Child Policy, while increasing GDP per capita in the short run, might have an adverse long run effect because each child born now has two parents and four grandparents that have to be supported in their old age. With no brothers, sisters, or a functioning Social Security and Medicare system to support the older generation, China’s age dependency ratio will likely increase significantly, creating a long-term drag on the economy. Therefore, even though The One-Child policy positively effected GDP per capita growth rates for first twenty years, the overall long-term effect could very well prove to be negative.

Appendix A:

Values

Year	GDP per capita (constant 1995 US$)	Birth rate, crude (per 1,000 people)	Labor force, total	Gross fixed capital formation (constant 1995 US$)	Population, total	School enrollment, primary (% net)	Capital per worker (constant 1995 US$)	Services, etc., value added (% of GDP)	Labor force in industry (% of total)

1969	108	34	411,544,928	17,221,607,424	796,025,024	85	42	26	12
1970	120	33	422,250,528	23,111,397,376	818,315,008	85	55	24	13
1971	123	31	436,533,504	25,260,756,992	841,105,024	86	58	24	13
1972	124	30	449,979,648	25,740,711,936	862,030,016	86	57	24	14
1973	130	28	463,900,448	27,465,338,880	881,939,968	87	59	24	15
1974	131	25	476,285,152	30,871,040,000	900,350,016	87	65	23	15
1975	138	23	487,522,144	35,903,021,056	916,395,008	88	74	22	16
1976	134	20	498,847,168	35,041,349,632	930,684,992	88	70	22	17
1977	139	19	508,522,240	36,372,918,272	943,454,976	89	72	23