Although the standards of living of Chinese citizens within coastal provinces have risen signifigantly over the last few decades, populations within inland rural provinces have remained stagnant in terms of education, wages, and even lifespans. This development has created a concerning amount of inequality between the two regions, which could result in social unrest in the future. There are a plethora of problems facing rural Chinese citizens, including a lack of access to credit, inability to receive proper education, and exposure to environmental harms such as air pollution. The inherent nature of the gap between coastal and urban populations can be attributed to the United Nation’s sustainability goal of urban development. I have attempted to investigate the cause of a lack of urbanization within rural China with my research, as well successful measures that have worked on a smaller scale.
Citation: Zheng, Siqi. and Khan, Matthew, China’s Bullet Trains Facilitate Market Integration and Mitigate the Cost of Megacity Growth (March 18, 2013). https://www.pnas.org/content/pnas/110/14/E1248.full.pdf
Although the rapid urbanization of China has resulted in a substantial increase to average standards of living, increased concentrations of people have negatively affected public goods such as air quality, urban transportation, and public areas. While numerous policies have been enacted by local governments to address overpopulation problems, a study by Siqi Zheng and Matthew E. Kahn has revealed that bullet trains have been one of the leading solutions to the negative side effects of urbanization.
To understand the economic consequences of new bullet trains within China, researchers analyzed real estate prices, average incomes, population, healthcare facilities, education level, and highway improvements in order to empirically calculate if and by how much has the standard of living improved within urban areas. The model Zheng and Kahn constructed found that bullet trains drastically improved the standards of living in every analyzed area, especially in megacities such as Shanghai and Beijing.
Although there are several explanations as to why there is a positive correlation between bullet train access and standards of living, all of them are related to the importance of transportation infrastructure. Firstly, transportation networks allow the movement of labor, which increases economic efficiency by connecting workers of higher education with more complex jobs. Researchers also found that suburban areas were more common in regions with bullet trains, which reduced urban congestion as well as air pollution.
Zheng’s and Kahn’s research concluded that there are massive externalities to investment in public transportation and conclude that these facilities are essential to the continuation of human development.
Citation: Démurger, Sylvie and Fournier, Martin and Yang, Weiyong, Diversification and Agrarian Change under Environmental Constraints in Rural China: Evidence from a Poor Township of Beijing Municipality (April 1, 2007). http://dx.doi.org/10.2139/ssrn.988057
Over the last few decades, the People’s Republic of China went through several drastic policy changes to better integrate with the global community, including measures such as liberalizing trade, enacting environmental protection regulations, and encouraging entrepreneurship within its rural communities. In Diversification and agrarian change, understanding environmental constraints in rural China: Evidence from a poor township of Beijing municipality, researchers attempt to evaluate the current effectiveness of these policies, as well the current obstacles limiting their full implementation.
While these policies at first may seem to be directed a achieving a wide variety of initiatives, the main of all the measures implemented in China have been to increase economic diversification among rural farmers. In terms of economic development, farming staple goods like grain only offer subsistence due to their low prices and high required labor input. In order to boost its economic growth, China aimed to unlock the immense potential in human capital within its rural population. There were mainly three methods used by the Chinese government to achieve this: it gave extra subsidies for farmers who grew cash crops (mainly American ginseng), encouraged alternative industries, and reduced the barriers to entrepreneurship.
The Chinese government utilized two main methods to reach these goals: regulation and government funding. Some examples include the National Forest Protection Program, which has forced some villages to invest in tourism. Fortunately, this had the desired effect of improving the long-term incomes of several communities that had previously relied on logging. In situations where less cost-effective methods were not available, the central government attempted to encourage different industries that were still within the agricultural sector. Due to the lower human capital requirement, this method could be used in a wider variety of areas, but also was most costly in the short term because cohesion would be achieved through money rather than regulation.
Although these policies have achieved the desired results in select communities, most villages under effected by regulation and subsidies still utilize traditional staple crops. The researches divided the reasons for these shortcomings into a lack of access to capital and a gap in education. There were many villages within the study where farmers had the knowledge and enthusiasm to start new businesses, but due to a lack of a financial history could not acquire a loan. In other situations, there was adequate funding, but farmers were not properly educated in the long-term benefits of diversification, so they chose to minimize risk and keep their current crop rotation.
Citation: Turvey, Calum G. and Kong, Rong, Farmers’ Willingness to Purchase Weather Insurance in Rural China (May 6, 2010). https://ssrn.com/abstract=1601625
Through analyzing the willingness to pay of Chinese farmers of crop failure insurance, researchers claim G. Turvey and Rong Kong attempt to determine the necessity and viability of a privatized insurance market tailored to rural areas. This was done by surveying farmers across rural China, then employing traditional statistical methods such as linear regression, crosstabulation, pert distribution models, and heteroskedastic correction to draw conclusions.
The two biggest conclusions of Turvey’s and Kong’s research was that there was not only an adequate amount of demand for a rural insurance policy exchange, but that the creation of a rural insurance exchange would provide substantially increase the quality of life among lower income Chinese citizens. However, any plan to implement a program would face two obstacles: high insurance premiums due to a lack of information on weather trends, and excessive regulation from the China Regulatory Commission. Since many farmers already have exceedingly high debt to income ratios, the data collected indicates that they would be unable to purchase insurance unless the market can be subsidized or made more efficient to drive prices down. Rong Kong and G. Turvey offer several solutions to these problems. The researchers point to various index insurance programs in other countries which created a publicly funded and available database containing data that could be used by private companies to accurately assess risk. Studies showed that these systems drastically increased the production of farmers since they would no longer have to make less efficient risk adverse decisions when planning the next crop cycle, and that the resulting earnings more than offset the cost of funding the database.
These conclusions were reached by asking farmers the following questions: how much they would personally be willing to pay for insurance, how enthusiastic they would be for various insurance programs, and finally if they would pay an actuarially determined market price for a policy. Then, crosstabulation was used to combine the results of different questions to draw conclusions about if a farmer would realistically participate in a hypothetical market if given the opportunity. After creating their initial model, the researchers added additional covariates to their model such as the weather trends of their home province, their crop rotation, and the value of their capital goods. Using these covariates, Turvey and Kong used regression to develop a linear model of demand vs. insurance policy prices. Additionally, Kong and Turvey employed heteroskedastic correction through computer software to determine how influential each covariate was in determining demand.
Aunan, Kristin, Shuxiao, Wang, Internal Migration and Urbanization in China: Impacts on Population Exposure and to Household Air Pollution (2016). https://www.sciencedirect.com/science/article/pii/S0048969714002472
Although China’s rapid urbanization has transformed the country into a major world power over the course of only a few decades, the damage done to its environment has turned the nation into a posterchild of the harm’s mankind can inflict on the environment. This resulted in tremendous economic damage in the form of health problems for Chinese citizens, and cancers as well as respiratory diseases quickly became a leading cause of death. While the Chinese government has attempted to control the problem by enforcing new environmental regulations and encouraging the use of clean fuel, researchers Kristin Aunan and Shuxiao Wang have discovered that the recent improvements in Chinese air quality may have been an unintended result of migration.
To conduct their study, Aunan and Shuxiao measured the number of citizens in each province, along with the measured air pollution in each region. They combined these two pieces of data to calculate a population weighted exposure value for each province, which estimated the total harm inflicted on people due to air pollution. To improve accuracy, researchers also considered the estimated time spent of each citizen in each province through a multiplier, and seasonally adjusted their air pollution measurements. Another important factor was the source of pollution in each region: although many would assume that pollution would be worse in urban areas, urban infrastructure allowed the use of cleaner fuel sources. The difference between burning clean fuel and dirty fuel such as coal or biomass was so great that urban areas had significantly less pollution despite a much higher rate of energy consumption. Using population weighted exposure and estimates for the amount of immigrants in each province, researchers were able to calculate various pieces of data, such as the relative risk of mortality due to respiratory diseases, the average daily does of pollutants inhaled by citizen, and a comparison of how much pollution a recent migrant produced versus a typical citizen. By creating a model with these new sets of data, researchers then estimated the total health benefit of migration in US dollars of migration by assuming that saving one life from a premature death was worth about $480,000. This was calculated using a statistic known as the Value of Statistical life.
The result of the study was that not only are urban areas safer compared to non-urban areas, but also that the migrants themselves produced less pollution than their native counterparts after moving. Researchers also calculated that an entire 60 percent of the recent country wide reduction in air pollution can be attributed to migration. The cause of this was attributed to the development of more efficient housing, better education, and a widespread switching of fuel sources. The researchers in their conclusion that urbanization is the main cause of migration, and therefore pollution reduction; therefore, there are significant benefits to be gained by actively encouraging urbanization.
Citation: Gaughan, A., Stevens, F., Huang, Z. et al. Spatiotemporal patterns of population in mainland China, 1990 to 2010. Sci Data 3, 160005 (2016). https://doi.org/10.1038/sdata.2016.5
As population continues to grow across developing countries, strain on our finite natural resources will only increase in the future. This is especially important in regions throughout Africa and Asia, where countries in the process of industrialization experience the highest rates of population growth. To properly plan for the next baby boom, scientists are developing a new field of technology that aims to accurately predict where people live, which will be essential to urban planning and effective use of both financial and natural resources. To assess our current progress in predicting populations in areas without adequate data, Researchers Gaughan and Stevens evaluate the effectiveness of Random Forest Models, or population distributions made utilizing both opensource algorithms made possible by Python and opensource data made available on Worldpop.
To predict population distributions, Random Forest takes existing estimated census data in addition to various covariates that are thought to have an influence over human settlement. The algorithm then uses data regression using a complex network of logic trees to produce a more holistic and accurate model than the original. Some of these covariates include nighttime lights, which indicate urbanization, and Distance Built to Edge, or the estimated area covered of settlements. An important precaution made by the researchers was to have a separate analysis for each year of data, as they noted that the relationship between different covariates and actual population changed over time. Due to limitations in available data, the study only focused on four central regions deemed most representative of China.
In order to analyze the accuracy of the modeled data, researchers measured an “Out of Bag” error estimate for each administrative level, which took one third of the observations made by the algorithm and calculated the mean of all R square errors. This analysis revealed that the accuracy of the random forest model was negatively correlated with population size but was still significantly better quality than previous data.
After reading my five articles, it seems that the most effective solutions to urbanize rural China are not targeted at specific issues. Actions such as investing in public transportation or deregulating internal migration were not designed to solve any problems, but clearly made the most difference in bridging the gap in quality of life between rural and urban populations.