Summary: China is gradually becoming a global leader in the world economy. Impressive economic reports and outstanding statistics has been the norm for China over the last two decades. However, the rapid economic growth has also brought challenges on environmental issues to the world largest economy. To understand the dynamics between environment and political-driven economy within China, we wish to analyze on how local political incentives and leaders’ tenure influence environmental regulation. In this report, we are given a set of variables to be collected from the country. Aiming for a deeper understanding of the relationship between local politics leaders and environments, we utilize the given information to resolve the obstacles.
Introduction
As the largest developing country in the world, China has been developing rapidly, with GDP(Gross Domestic Product) at a rate of 10 percent over the past two decades. But this success comes at the cost of deterioration of the environment. China’s environmental problem, especially air pollution, has become a major threat to the health of Chinese residents.
- Air pollution makes up for the majority of environmental challenge China faces. The main source of air pollution in Chinese cities comes from coal-combustion and motor-vehicle emissions. In 2009, the annual average concentrations of sulfur dioxide (SO2), 48 μg/m3, in 113 medium to large Chinese cities.
China has met its challenge, it has to keep up with the economic development while also trying to protect its environmental issue from being worse. The government lists its macro development plan every 5 years, and each plan has a different focus based on the priorities given by the government. However, it was never known how local political incentives and the prefecture leader’s tenure influenced environmental regulation.
In order to understand the influence of a leader’s tenure and local political incentives, we need first overcome the following 4 challenges:
1) How could we measure the “learning by doing” effect with the data we have? Can we control this variable?
2) What methods can we use to evaluate the non-random assumption for which prefectures are designated targets?
3) How do we deal with tenures covering parts of the 2001-2005 period, which is a period with macro plans focus mostly on economic development, and part of the 2006-2010 period, which focus more on environmental issues?
4) How sensitive is the study to incorrect SO2 removal statistics? At what point of uncertainty do the results stop being meaningful?
This report serves to solve obstacles to uncover the actual influence of local political incentives on environmental regulation in china. We will be discussing how to utilize the limited information we were given, how to deal with inaccurate report data, and how to deal with unparalleled data. We will first go over the given information, evaluate the situation we are in, and finally, give suggestions to the challenges we are facing.
Data
Unfortunately, since the government data is sensitive, we are not able to access the actual data itself. However, we were given 3 lists of variables that are usable, the first one being the basic information, including years, province and such. Second list is the measurement variable that was used to describe environments and economic situations, such as SO2 regulation and GDP. The last list is a list of binary variables that might be useful to the question, for example, is this person female or male, is this person an engineer? All the useable variables are listed below:
| Basic Variable | Measurement Variable | Binary Variable |
| YearProvincePrefecturePrefecture leaderYear in officeTenure length(years in the position)Tenure spell(851 spells in total)AgeEducationYear in office x prefecture treatment status | SO2 regulation(2 measures)Proportion of GDP from the industrial sectorProportion of industrial enterprises that are domestically ownedProfitabilityCapital intensityGDPRevenueExpenditureFixed assetsCargo volumeLuminosityTwo other types of air pollution | Ethic minorityFemalePolitical connection with the top provincial leaderServing in hometownHaving attended the Central Party SchoolEconomistEngineerPromotionRetirementPrefecture treatment status |
Sampling:
We assume the sample to be random and unbiased, since the dataset is large. However, the data does have some stuff within itself. For example, the SO2 removable statistics is not hundred percent correct, since local leaders are afraid to report the truth due to the potential penalties of not meeting the goals from the 5 year macro economic plan.
Recommendation
How could we measure the “learning by doing” effect with the data we have? Can we control this variable?
The learning by doing effect concept in our case is when one leader is in charge of the position for 5 years, it might take him 3 years to learn the process, the same effect would apply to someone else with a total of 7 years in the office as well, but this person might use 5 years to learn about the policy and gradually changes his political plans overtime. This is an important issue since it is a problem of unparalleled samples, which means not all samples are equivalent to another. Our goal is to control this variable of different length of learning curve for different leaders in their own career, which could be reached by categorizing leaders into different groups by the length of their service. We here suggest to divide them into groups of 1 to 3 years in the office, 4 to 6 years in the office and 7 to 10 years in the office, and we would be able to compare individuals within its own group. By constraining their lengths of service, we could control the variable of effect from “learning by doing ”.
What methods can we use to evaluate the non-random assumption for which prefectures are designated targets?
The non-random assumption for which prefectures are designated targets comes from the government’s idea on which cities needed to focus on environments more and which cities should focus more on economic developments. Choosing SO2 levels as a standard, we would be able to evaluate the non-random assumption if we were to randomly choose a group of prefecture from all the prefecture and compare the SO2 level to see if there is an difference in SO2 before and after the new policy has been announced. The analysis can be done multiple times. We can then compare the government chosen cities and the cities that have been randomly chosen by us. If there is no significant difference in SO2 statistics on average, then the non-random assumption can be ignored, and vice versa.
How do we deal with tenures covering parts of the 2001-2005 period, which is a period with macro plans focused mostly on economic development, and part of the 2006-2010 period, which focus more on environmental issues?
This is yet another problem of unparalleled sample, we simply just suggest spreading the leaders into different groups. The first group would be leaders that only worked in the 2001-2005 period, the second group would be leaders that only worked in the 2006-2010 period, and the last group would be the leaders that have been in position in between the two periods. The last group will only be established if it is a big portion of the pool, if not, the last group can just be dropped from the sample.
How sensitive is the study to incorrect SO2 removal statistics? At what point of uncertainty do the results stop being meaningful?
We would solve this problem by just horizontally comparing two sets of data. However, in this case, a sample mean is not enough to explain the difference. We suggest using either ratio or association between two sets of data to see how they are related and different from each other. We would then set satellite data as a control for the reported SO2 data, we would then slowly inflate the satellite data, and repeat the process to find out how far the inflation can be stretched so that the statistics becomes meaningless.
Conclusion
Given that limited research was done in the field, association between political leaders and environmental regulation have always remained uncovered. With information from Victoria and news, we can start to understand and evaluate the effect that was brought by local political leaders on the environment. Actual accessible data would be needed to further uncover the truth behind the association between local leaders and environmental regulation.We see the potential to extend the problem into a wider horizon and deeper understanding. If you have questions or want to discuss more, please feel free to contact us on the problem.
LeoQuidity 