Group09 proposal
Contents
Abstract
Objective
Data Source
This data from the ' China Health and Nutrition Survey '. Click here to see the data.
About Data Set
The survey took place over a 7-day period using a multistage, random cluster process to draw a sample of about 7,200 households with over 30,000 individuals in 15 provinces and municipal cities that vary substantially in geography, economic development, public resources, and health indicators. In addition, detailed community data were collected in surveys of food markets, health facilities, family planning officials, and other social services and community leaders.
Variables
(Dependent variable) Health
(Independent variable) Income inequality, Income Variables, Individual controls, Occupation and Sector
The table below shows the description of main variables that we will be using for our analysis:
Variable | Description |
---|---|
Health | |
Blood pressure | Binary variable. Defined as 0 if the blood pressure of object within normal range, else 1. A normal blood pressure is defined as at or below 120/80 mmHg. |
WHR | The waist-hip ratio. Binary variable. Defined as 1 if the ratio above limit, else 0. A normal WHR is defined as at or below 0.80 for women and 0.90 for men. |
MAMC | Mid-arm muscle circumference. Binary variable. Defined as 1 if this figure is abnormal and 0 otherwise. individuals with 20.88 or more for women and 22.77 or more for men are coded normal. |
Overweightness | Binary variable. Defined as 1 if respondent is overweight, else 0. Non-overweight population in China is defined as below a BMI of 25 kg/m2. |
Income inequality | |
Gini | The Gini-coefficient in the county level, sensitive to changes at middle income levels. |
Theil L | The mean logarithm deviation of the Generalized Entropy (Theil) indices, which is sensitive to changes at the bottom income levels. |
Theil T | The Theil index and is sensitive to changes in upper income levels. |
Theil V | The half the squared coefficient of variation of Theil index. |
Income variables | |
Individual income | The sum of each individual's income source, by adding up all individual income and revenue, minus individual expenditures. Household subsidies and other income that cannot be allocated to individuals in the household are not considered as a part of individual income. |
County mean income(ind.) | Captures the degree of economic development in a county-level unit, calculated by averaging individual income in a county/city for all observations in the CHNS. “Ind” refer to individual. |
Household income | The sum of all individual incomes in a household. |
County mean income(hh.) | Calculated by averaging household income in a county/city for all observation in the CHNS. “hh” refer to household. |
Individual controls | |
Age | The age of respondent. |
Gender | Binary variable. Defined male as 0 and female as 1. |
Married | Binary variable. Married as 1, unmarried as 0. |
Majority | If the nationality of object is Han, then defined as “1”, else 0. |
Years of education | Calculated from the beginning of primary school, 6 years of primary school graduation, 9 years of junior high school graduation, 12 years of high school graduation, and 16 years of university graduation. |
urban | Binary variable. If respondent holds urban household registration then defined as 1, else 0. |
Occupation | |
Services class | Includes “senior professional/technical”, “administrator/executive/manager” and “army officer/police officer”. |
Non-manual worker | Includes “junior professional technical” and “office staff”. |
Skilled worker/supervisor | Includes “skilled worker” and “ordinary soldier, policeman”, “driver” and “athlete, actor, musician”. |
Semi-/non-skilled worker | Includes “non-skilled worker” and “service worker”. |
Farmer | As originally defined by CHNS data. |
Others | The rest of original occupation covered by CHNS data. |
Sector | |
State | Includes “government”, “state service/institute” and “state-owned enterprise”. |
Collective | Includes “small collective enterprise” and “large collective enterprise”. |
Family farming | As original variable “family contract farming” of CHNS data. |
Individual enterprise | As variable “private, individual enterprise”, which originally defined by CHNS |
Private three-cap Enterpr. | The same as “three- capital enterprise” in CHNS data. |
Others | Includes “unknown” data in CHNS. |
Visualization
Exploratory Data Analysis
EDA is a common approach to summarize main characteristics of data, often with visual methods. In the first part of our dashboard, we use chats to tell the reason why we want to test the relationship between economic development and health risks in China, and to tell information about the data used in this project beyond the formal modeling or hypothesis testing task.
Methodology
Three different approaches will be utilized to predict the global CO2 emissions and temperature in the next 10 years:
- Holt exponential smoothing: By applying this approach, consequently each relevant variables’ (e.g. gas fuel, liquid fuel and solid fuel) future value will be obtained. And we can use them to predict the future CO2 emission by employing the linear regression model.
- SARIMA: Seasonal Autoregressive Integrated Moving Average (SARIMA) model, an extension of ARIMA that explicitly supports univariate time series data with a seasonal component will be applied to conduct the prediction. We can use it gain the annual CO2 emission in the future with a lower and upper bound.
- Auto-Regression: The Auto-Regression model describes the relationship between current values and the historical values. And it uses the historical time data as the variable to predict its future value. The factors that influence the CO2 emission, such as solid fuel and gas fuel, can be predicted by Auto-Regression model. As a result, the future global CO2 emission will be predicted by employing the linear regression model.
After completing all prediction methods mentioned above, we intend to compare their result respectively with the actual CO2 emission in recent years as an evaluation and determine which of them is the best fit one.
Critics of Existing Works
Critics | Detail |
---|---|
Lack of visualization | This essay is mainly focus on modelling and statistical analysis. Only present the results with several line graphs, with kinds of graph increased and interactive function added. |
Appropriate selection of variables | Appropriate selection of variables
Different measures are available to evaluate income equality and health. This essay selects physical health factors to avoid any bias raised by difference between education, gender, etc. Also the author assessed income inequality in multiple aspects, Gini index, household income and individual income. These variables are good to use. |
Eliminate the income gap among regions in China | In China context, evident economic development gap exists among different provinces, for example, Xinjiang province verses Jiangsu province. This is one aspect that the paper can be improved. Use geometrical analysis to present different results impacted by geography issue. |