Final Project
Determinants of Civil Society Growth in Developing Countries
Dasha Djukic-Min
5/11/2022
Clear memory
rm(list=ls())Load data: Civil Society Data (csdata)
csdata <- read.csv("/Users/dasha/Desktop/CSDATA1.csv")Check for missing values
is.na(csdata)## Country Year CSO_Growth No_IGO_Memberships Rmtnc_Inflow_GDP Democracy
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## [200,] FALSE FALSE FALSERecode missing vaues into NA
csdata$Democracy[csdata$v1==' '] <- NARemove NA values
csdata1 <- na.omit(csdata) Names of variables
names(csdata1)## [1] "Country" "Year" "CSO_Growth"
## [4] "No_IGO_Memberships" "Rmtnc_Inflow_GDP" "Democracy"
## [7] "GDP" "GDP_Per_Capita" "Population"Browse data
View(csdata1)Summary Statistics
summary(csdata1)## Country Year CSO_Growth No_IGO_Memberships
## Length:180 Min. :2005 Min. :0.0000 Min. :33.00
## Class :character 1st Qu.:2005 1st Qu.:0.0000 1st Qu.:52.00
## Mode :character Median :2015 Median :0.0000 Median :61.50
## Mean :2010 Mean :0.4889 Mean :62.83
## 3rd Qu.:2015 3rd Qu.:1.0000 3rd Qu.:73.25
## Max. :2015 Max. :1.0000 Max. :93.00
## Rmtnc_Inflow_GDP Democracy GDP GDP_Per_Capita
## Min. :0.000000 Min. :0.0000 Min. :3.655e+08 Min. : 0.0
## 1st Qu.:0.005864 1st Qu.:0.0000 1st Qu.:6.670e+09 1st Qu.: 793.8
## Median :0.023067 Median :0.0000 Median :1.744e+10 Median : 1851.5
## Mean :0.050192 Mean :0.4778 Mean :1.740e+11 Mean : 2895.7
## 3rd Qu.:0.070587 3rd Qu.:1.0000 3rd Qu.:6.175e+10 3rd Qu.: 4109.0
## Max. :0.392383 Max. :1.0000 Max. :1.050e+13 Max. :17948.9
## Population
## Min. :4.639e+05
## 1st Qu.:3.854e+06
## Median :1.151e+07
## Mean :5.705e+07
## 3rd Qu.:3.391e+07
## Max. :1.368e+09Summary statistics presented in a table
library(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazerstargazer(csdata1, type="text")##
## =====================================================================================================
## Statistic N Mean St. Dev. Min Max
## -----------------------------------------------------------------------------------------------------
## Year 180 2,010.056 5.014 2,005 2,015
## CSO_Growth 180 0.489 0.501 0 1
## No_IGO_Memberships 180 62.833 14.070 33 93
## Rmtnc_Inflow_GDP 180 0.050 0.068 0.000 0.392
## Democracy 180 0.478 0.501 0 1
## GDP 180 174,009,927,388.000 843,807,886,139.000 365,451,707.000 10,499,697,714,628.000
## GDP_Per_Capita 180 2,895.682 2,870.658 0.000 17,948.920
## Population 180 57,050,103.000 190,261,741.000 463,929.800 1,368,090,500.000
## -----------------------------------------------------------------------------------------------------Data Exploration
Create a new data subset that excludes non-numeric variable (country)
Make a scatterplot matrix to plot the relationships between the variables
cs <- csdata1[,c("CSO_Growth", "No_IGO_Memberships", "Rmtnc_Inflow_GDP", "Democracy", "GDP", "GDP_Per_Capita", "Population")]
pairs(cs,col=cs$CSO_Growth,cex=.5)
Quantify the relationships by computing pairwise correlations
correlation <- cor(cs)
stargazer(correlation, type="text")##
## ============================================================================================================
## CSO_Growth No_IGO_Memberships Rmtnc_Inflow_GDP Democracy GDP GDP_Per_Capita Population
## ------------------------------------------------------------------------------------------------------------
## CSO_Growth 1 0.059 0.158 0.021 0.130 -0.194 0.196
## No_IGO_Memberships 0.059 1 -0.224 0.231 0.248 0.279 0.294
## Rmtnc_Inflow_GDP 0.158 -0.224 1 0.132 -0.098 -0.185 -0.098
## Democracy 0.021 0.231 0.132 1 -0.016 0.269 0.017
## GDP 0.130 0.248 -0.098 -0.016 1 0.194 0.747
## GDP_Per_Capita -0.194 0.279 -0.185 0.269 0.194 1 0.016
## Population 0.196 0.294 -0.098 0.017 0.747 0.016 1
## ------------------------------------------------------------------------------------------------------------Calculate frequencies for CSO_Growth
# install.packages('plyr')
library('plyr')
count(csdata1, "Democracy") ## Democracy freq
## 1 0 94
## 2 1 86Calculate frequencies for Democracy
# install.packages('plyr')
library('plyr')
count(csdata1, "CSO_Growth") ## CSO_Growth freq
## 1 0 92
## 2 1 88Logistic regression
# Fit a logistic regression model to predict "Increase in Number of CSOs" using Remittances, Democracy, GDP/Capita and Population
glm.fit=glm(CSO_Growth~No_IGO_Memberships+Rmtnc_Inflow_GDP+Democracy+GDP+GDP_Per_Capita+Population, data=csdata1,family=binomial)
summary(glm.fit)##
## Call:
## glm(formula = CSO_Growth ~ No_IGO_Memberships + Rmtnc_Inflow_GDP +
## Democracy + GDP + GDP_Per_Capita + Population, family = binomial,
## data = csdata1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8355 -1.0718 -0.4493 1.1344 1.8750
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.647e-01 8.308e-01 -0.559 0.5759
## No_IGO_Memberships 3.661e-03 1.358e-02 0.270 0.7875
## Rmtnc_Inflow_GDP 5.287e+00 2.626e+00 2.013 0.0441 *
## Democracy 1.723e-01 3.487e-01 0.494 0.6211
## GDP 5.348e-13 1.274e-12 0.420 0.6746
## GDP_Per_Capita -1.853e-04 8.375e-05 -2.213 0.0269 *
## Population 9.268e-09 6.403e-09 1.447 0.1478
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 249.44 on 179 degrees of freedom
## Residual deviance: 222.45 on 173 degrees of freedom
## AIC: 236.45
##
## Number of Fisher Scoring iterations: 6Predict function
# I use the predict function to predict the probability of an increase in the number of CSOs, given values of the predictors.
glm.probs=predict(glm.fit,type="response")
glm.probs[1:5]## 2 3 4 5 6
## 0.5087720 0.5654667 0.4258202 0.4134387 0.3692769# I coded "Increase in CSOs" as 1, so glm.probs are the probabilities of No_CSO_Increased. going up. Here I create actual labels instead of probabilities.
glm.pred=ifelse(glm.probs>0.5,"Increase","No Increase")
# Confusion matrix
attach(csdata1)
table(glm.pred,CSO_Growth) # predictions vs. truth## CSO_Growth
## glm.pred 0 1
## Increase 28 51
## No Increase 64 37Check accuracy / Compute the fraction of CSO_Growth for which the prediction was correct
mean(glm.pred==CSO_Growth)## [1] 0Split the data into training and test sets
# Create a vector corresponding to the observations from 2005
train = Year<2015
# Fit the logistic regression using only the train data
glm.fit=glm(CSO_Growth ~ No_IGO_Memberships + Rmtnc_Inflow_GDP + Democracy + GDP + GDP_Per_Capita + Population, data=csdata1,family=binomial, subset=train)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred# Test the model by fitting the test data into the fitted model
glm.probs=predict(glm.fit,newdata=csdata1[!train,],type="response")
# Compute the predictions for 2015 and compare them to the actual
# increase in number of CSOs over that time period
glm.pred=ifelse(glm.probs >0.5,"1","0")
CSO_Growth.2015=csdata1$CSO_Growth[!train]
table(glm.pred,CSO_Growth.2015)## CSO_Growth.2015
## glm.pred 0 1
## 0 24 8
## 1 40 19Test error rate
mean(glm.pred==CSO_Growth.2015)## [1] 0.4725275Training error rate
mean(glm.pred != CSO_Growth.2015)## [1] 0.5274725Check accuracy rate
19/(40+19)## [1] 0.3220339