One-Child Policy and Economic Development

One Child Policy and Economic Development
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Table of Contents Introduction. 3 Literature Review.. 4 Data. 6 Empirical Methodology. 7 Results. 9 Robustness Check. 10 Discussion. 11 Conclusion. 13 References. 15 Appendix. 17
One Child Policy and Economic Development
Introduction
The One Child Policy (OCP), implemented in China in the late 1970s, has been a topic of significant debate among economists, policymakers, and demographers. The policy's primary goal was to control population growth in the world's most populous country, but it has also had profound impacts on the social and economic landscape of China. (Feng et al., 2016) However, the long-term implications of such a policy on the overall economic development are difficult to ascertain due to the multitude of factors involved. Some studies have shown that the policy may have had a positive impact on economic growth due to the reduction in the number of dependents in the population (Feng, Cai, & Hu, 2013), which led to increased savings and investment. On the other hand, other studies argue that the policy has led to a shortage of labor, which has driven up wages and led to higher inflation, ultimately slowing economic growth.
This study focuses on analyzing the causal impact of the One Child Policy on China's GDP per capita. The study examines the relationship between the policy and various factors that could affect economic growth, including the total fertility rate, childbearing age, years of schooling, male-to-female birth ratio, and infant mortality rate. Through regression analysis, the study finds that the One Child Policy has a statistically significant negative impact on GDP per capita, even when controlling for other factors. These findings contribute to the ongoing debate about the effectiveness and consequences of the One Child Policy and raise important questions about the long-term implications of such policies on economic development.
The researcher employs an empirical approach using a linear regression model and relying on data from the World Bank's World Development Indicators (WDI). The main result reveals a strong association between the One Child Policy and GDP per capita, with several of the explanatory variables displaying significant effects on economic growth. Specifically, years of schooling and male-to-female birth ratio are found to have a substantial impact, while the total fertility rate and childbearing age show weaker relationships with GDP per capita.
Nevertheless, the researcher is aware of several restrictions that apply to the current investigation. First, there is a possibility that the findings are affected by omitted variable bias. This is because the constructed model did not consider all elements that could affect GDP per capita. Second, the causal effect of the One Child Policy may be obscured by the influence of other policies or external variables that have also had an impact on China's economic progress over time. The use of more robust identification procedures, such as instrumental variable approaches or difference-in-differences analyses, could be implemented in potential future research to investigate these concerns in greater depth.
The current study provides valuable insights into the causal impact of China's One Child Policy on its GDP per capita. While the researcher finds evidence supporting the influence of specific factors related to the policy on economic growth, there is a need for more comprehensive research to fully understand the implications of the One Child Policy on China's long-term economic development.
Literature Review
The literature on the impact of China's One-Child Policy on economic growth is extensive, with various studies examining different aspects of this relationship. This literature review aims to provide a comprehensive overview of the key studies and findings in this domain, contextualizing the present research within the broader academic discourse. Becker, Murphy, and Tamura (1990) conducted one of the pioneering studies on the subject, focusing on the impact of the policy on China's economic development. The researchers posited that the reduction in fertility rates resulting from the policy led to an increase in the savings rate, which, in turn, promoted economic growth. Their findings suggested that the One-Child Policy contributed to an increase in the proportion of the working-age population, resulting in a higher output per capita. This seminal study laid the foundation for subsequent research on the policy's impact on China's economic growth.
Banister (2004) built upon this foundation by examining the policy's impact on labor force participation and demographic dividends. Banister argued that the One-Child Policy led to a temporary demographic dividend in China, as the working-age population increased relative to the dependent population. This demographic shift, in conjunction with increased labor force participation, was found to contribute to China's rapid economic growth in the 1980s and 1990s. Li, Zhang, and Zhu (2008) further explored the policy's consequences by analyzing its impact on human capital accumulation. Their study found that the One-Child Policy led to an increase in educational attainment, which, in turn, resulted in a higher-skilled labor force. This research suggested that the policy's influence on human capital formation played a significant role in driving economic growth during the period under investigation.
Lastly, Ebenstein (2010) investigated the policy's impact on gender imbalances and their subsequent consequences for China's labor market and economic growth. Ebenstein argued that the skewed sex ratio resulting from the One-Child Policy led to a shortage of marriageable women, which could have long-term implications for China's economic growth and social stability.
Notably, the literature on the impact of the One-Child Policy on economic growth underscores the policy's role in shaping China's demographic transition, labor force participation, human capital formation, and gender imbalances. This study seeks to contribute to this body of literature by providing an empirical analysis of the causal relationship between the One-Child Policy and economic growth in China using a linear regression model.
Data
This study relies on data collected between 1979 and 2015 from the World Bank's World Development Indicators (WDI), the Barro-Lee Database, and the United Nations' World Population Prospects (WPP). Table 1 presents the summary statistics of the key variables used in the analysis.
Variable
Min
1st Qu.
Median
Mean
3rd Qu.
Max
TFR
1.447
1.522
1.569
1.929
2.546
2.747
InfMRateUN
11.65
23.60
37.97
34.51
43.24
58.38
MtoFbirth
1.070
1.076
1.128
1.121
1.160
1.170
Yschooling
5.144
5.832
6.715
6.657
7.519
7.950
GDPpc
1,585
2,375
3,615
4,757
6,582
12,473
GDPpcGrowth
-0.01956
0.03273
0.06534
0.05818
0.07752
0.10733
Table 1: Summary Statistics
The Total Fertility Rate (TFR) ranges from 1.447 to 2.747, with a mean of 1.929. The Under-Five Mortality Rate by the United Nations (InfMRateUN) has a minimum of 11.65 and a maximum of 58.38, with an average of 34.51. The Male-to-Female birth ratio (MtoFbirth) ranges between 1.070 and 1.170, with a mean value of 1.121. The average years of schooling (Yschooling) varies from 5.144 to 7.950, with an average of 6.657. GDP per capita (GDPpc) ranges from 1,585 to 12,473, while the GDP per capita growth rate (GDPpcGrowth) varies between -0.01956 and 0.10733.
The data include variables related to demographic factors such as TFR, Infant Mortality Rate, Male-to-Female birth ratio, average years of schooling, and macroeconomic indicators such as GDP per capita and GDP per capita growth rate. This dataset provides a comprehensive overview of the factors relevant to the analysis of the causal relationship between the One-Child Policy and economic growth in China.
Empirical Methodology
To analyze the causal impact of the One-Child Policy on economic growth in China, the researcher employed a multiple linear regression model. The dependent variable in the model is the GDP per capita (GDPpc), which represents the economic growth in the country. The main independent variable is the Total Fertility Rate (TFR), which is directly influenced by the One-Child Policy. The model also includes a set of control variables, such as average years of schooling (Yschooling), Male-to-Female birth ratio (MtoFbirth), and Infant Mortality Rate by the United Nations (InfMRateUN). These control variables are included to account for other factors that might also affect economic growth in China.
The resulting multiple linear regression model can be represented as follows:
GDPpc = β0 + β1 * TFR + β2 * Yschooling + β3 * MtoFbirth + β4 * InfMRateUN
Where:
GDPpc is the GDP per capita, representing economic growth
TFR is the Total Fertility Rate, which is our treatment variable
Yschooling represents average years of schooling
MtoFbirth is the Male-to-Female birth ratio
InfMRateUN is the Infant Mortality Rate by the United Nations
β0, β1, β2, β3, and β4 are coefficients to be estimated
The choice of these control variables is based on their potential to confound the relationship between the One-Child Policy and economic growth. The total fertility rate (TFR) is included as the main independent variable for the investigation as it was directly affected by the implementation of the OCP in China. The average years of schooling (Yschooling) is included in the model given the key role that education levels play in informing the economic growth of a given country. A highly educated population is considerably more innovative and productive and the inclusion of the variable is based on the acknowledgment that education levels play a key role in shaping the GDP per capita levels.
One of the significant effects of the OCP was a skewed sex ratio, whereby the policy led to a higher proportion of male birth. Thus, it was essential to include the male-to-female birth ratio (MtoFBirth) in the model given that the ratio influences labor force participation, marriage patterns, and social stability. The variable accounts for the potential influence of the sex ratio on the economic development of China. Infant mortality rate (InfMRateUN) is included in the model given that it is one of the most reliable indicators of the social and health conditions of a given country. A lower infant mortality rate may be associated with improved healthcare, nutrition, and other factors that contribute to economic growth. The inclusion of the variable in the model allowed the researcher to control for the effect of health and social condition changes on economic growth as measured by GDP per capita.
The identification strategy used in the model relies on the assumption that TFR can be attributed to the One-Child Policy. This allows us to estimate the causal effect of the policy on economic growth in China. However, there are some potential threats to our identification strategy. One such threat is the presence of omitted variables that might lead to biased estimates. For example, the lack of control for occupation might create omitted variable bias if occupation affects both fertility decisions and economic growth. Another concern is reverse causality, in which economic growth could influence fertility rates, rather than the other way around.
Results
The results from our multiple linear regression model are presented below. The model aims to estimate the causal effect of the One-Child Policy on economic growth in China, as measured by GDP per capita (GDPpc), after controlling for several variables. The coefficient for the Total Fertility Rate (TFR) is -948.58, with a standard error of 678.24. This indicates that a one-unit increase in TFR is associated with a decrease of approximately 948.58 units in GDP per capita, holding all other variables constant. However, the p-value of 0.17375 suggests that this relationship is not statistically significant at the conventional 0.05 significance level.
The coefficients for the control variables included in our model show varying levels of significance. The average years of schooling (Yschooling) and Male-to-Female birth ratio (MtoFbirth) have coefficients of -5724.41 and 95673.23, respectively, and are stat...