Background

Why is the issue important

Main issues

The two issues being incorporated in this final product respectively, are Chinese retirees pension and Washtenaw County Coordinated Funding Program.

Chinese Retirees Pension

The dual-track pension system has been operated over 60 years since 1949. Throughout the dual-track system, there is a huge gap between the amounts of civil servants’ pension and employees working in private sectors. The civil servants receive higher pension than employees in private secots, whereas contributing little to the money pool of pension savings. More than three hundred million employees working in private enterprises are negatively affected by the inequity because they pay more for their pension savings, but receive less pension than civil servants through the redistribution system.

Since the central government has already realized the inequity, the government is seeking for reform to handle the inequity. However, the schema of pensions system reform is still under extensive discussion.

The dataset I use in this topic is from China Health and Retirement Longitudinal Study, whose data is shared in Peking University Open Research Data. Thus, there are no issues of confidentiality that are relevant to the data I use.

Washtenaw County Coordinated Funding Program

Washtenaw County Coordinated Funding Program is a collaboration among the County government, local nonprofits, and nationwide nonprofits. The primary purpose of the Funding is to financially support human service agencies that are addressing county residents’ needs regarding early childhood development, school-aged youth safety and graduation, affordable housing, safety-net health, nutritious food, and the elderly life. Meanwhile, the Funding also allocate grants to help build agencies’ capacity to operate the agency.

The Funding Program began to support agencies in 2010. The total number of agencies received grants were more than 50 in each fiscal cycle. The total amount of grants in each fiscal cycle were more than three million dollars. Some agencies received grants from Coordinated Funding as their primary financial resources.

The capability of the Funding to pay agencies displayed an ascending trend because the seven funders were contributing a larger amount of grant to the funding pool.

The data I used in this topic are self-report data from agencies that received grants in FY 2015-2016. I obtained consent from Washtenaw County Office of Community and Economic Development to use these data for this course.

Particular Predictors

library(png)

library(jpeg)

library(grid)

img <- readPNG("/Users/apple/Desktop/particularpredictor1.png")

grid.raster(img)

library(png)

library(jpeg)

library(grid)

img <- readPNG("/Users/apple/Desktop/particularpredictor2.png")

grid.raster(img)

## First Section

library(haven)
## Warning: package 'haven' was built under R version 3.4.3
data4 <- read_dta("/Users/apple/Desktop/SW 670 Data/Clean NA/data4.dta")

data4$fd002 <- 
  factor(data4$fd002,
         levels = c(1,2,3,4,5,6,7,8),
         labels = c("Government",
                    "Institutions",
                    "NGO",
                    "Firm",
                    "Individual Firm",
                    "Farmer",
                    "Individual Household",
                    "Other"))

knitr::kable(table(data4$fd002))
Var1 Freq
Government 12
Institutions 9
NGO 0
Firm 89
Individual Firm 33
Farmer 2
Individual Household 2
Other 1

Second Section

library(ggplot2)

library(haven)

library(ggthemes)

data4<-read_dta("/Users/apple/Desktop/SW 670 Data/Clean NA/data4.dta")

employer_pension_contribution <- data4$fg012
employee_pension_contribution <- data4$fg013

ggplot(data4,
       aes(x = fg012,
           y = fg013))+
  geom_point(aes(size=employer_pension_contribution), color = "coral")+
  geom_smooth(method = lm, se=TRUE)+
  theme_solarized_2()+
  labs(title="Employee-Employer Pension Contribution",
       x = "Employer pension contribution",
       y = "Employee pension contribution")

The plot indicates that most employees in this sample contribute fewer than ¥500/month for their pension savings. Regarding these employees, their employers are matching fewer than ¥1,000 for employees’ pension saving. We can tell from the trend line that employers in this sample are making greater contribution to their employees’ pension saving than their employees are, which is a generalizable trend in China’s labor market.

library(haven)

library(ggplot2)

library(ggthemes)

data1 <-read_dta("/Users/apple/Desktop/SW 670 Data/Clean NA/data1.dta")

data1$fd002 <- 
  factor(data1$fd002,
         levels = c(1,2,3,4,5,6,7,8),
         labels = c("Government",
                    "Institutions",
                    "NGO",
                    "Firm",
                    "Individual Firm",
                    "Farmer",
                    "Individual Household",
                    "Other"))

Employer_Types<-data1$fd002

ggplot(data1,
       aes(x = Employer_Types,
           y = fg012,
           fill = Employer_Types))+
  geom_boxplot()+
  theme_solarized()+
  ylim(0,max(data1$fg012))+
  labs(title="Employers Monthly Contribution by Employer Types",
       x = "Employer types",
       y = "Employer's monthly pension contribution")+
  coord_flip()

The plot implies that different types of employers vary in their monthly contribution to their employees’ pension savings. As for the maximum amount of contribution, the government, institutions, and firms are contributing at the same level. However, the government’s 50% line is higher than its counterparts and the range of goverment contribution is narrower than its counterparts, which suggests that government employees are receiving monthly pension contribution from the government more evenly than firm and institution’s employees. The relatively evenly-distributed pension is an important incentive that college graduates want to work for the government in China.

library(haven)

library(ggplot2)

library(ggthemes)

data3<-read_dta("/Users/apple/Desktop/SW 670 Data/Clean NA/data3.dta")

data3$fd002 <- 
  factor(data3$fd002,
         levels = c(1,2,3,4,5,6,7,8),
         labels = c("Government",
                    "Institutions",
                    "NGO",
                    "Firm",
                    "Individual Firm",
                    "Farmer",
                    "Individual Household",
                    "Other"))

Employer_Types<-data3$fd002

ggplot(data3,
       aes(x = fd002,
           y = fg010,
           fill = Employer_Types))+
  geom_boxplot()+
  ylim(0,max = 61000)+
  theme_solarized()+
  labs(title= "Income Base for Social Insurance by Employer Types",
       x = "Employer types",
       y = "Income base for social insurance")+
  coord_flip()
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

The plot reflects the labor force price that works for different sectors. No matter how much employees’ wages are, they distribute 8% of their wage into their pension saving account. Employees who work for firms for firms have the highest income base among all employees because they earn more than employees who work for other types of employers. However, even firms employees contribute the greatest to their pension saving, it it retirees that worked for the government earn the greatest, which is economically injust for non-governmental retirees.

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

Third Section

library(png)

library(jpeg)

library(grid)

img <- readPNG("/Users/apple/Desktop/mapassignment.png")

grid.raster(img)

Fourth Section

Tableau Dashboard 1

library(png)

library(jpeg)

library(grid)

img <- readPNG("/Users/apple/Desktop/tableaudashboard1.png")

grid.raster(img)

In FY 14-15, Whites and African-Americans were the majority of participants in all programs. The programs’ participants composition corresponded to the Washtenaw County population composition, in which Whites and African-Americans consist of 87.2% of the County’s population.

In FY 15-16, most programs witnessed more female participants than male participants. One possible explanation of more female participants in human service programs was that women were more vulnerable than man and had fewer resources than man did to handle barriers, therefore leading them to turn to public services.

Tableau Dashboard 2

library(png)

library(jpeg)

library(grid)

img <- readPNG("/Users/apple/Desktop/tableaudashboard2.png")

grid.raster(img)

The three graphs show the amount of grants that human service agencies in Washtenaw County received from Coordinated Funding Program and numbers of participants in three fiscal years. In Fiscal Year 2015-2016, there was a linear trend between the amount of grants and the number of program participants, whereas in FY 2013-2014 and FY 2014-2015, the trend did not present.