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WRONGPANEL_KFW_panelResults_Max_Pre2001_demend_enf.R
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#-------------------------------------------------
#-------------------------------------------------
#Panel Models - KFW
#Testing in Cross Section the impact of being treated BEFORE March, 2001
#On the Mean Level of NDVI, measured as the yearly mean NDVI value (LTDR)
#-------------------------------------------------
#-------------------------------------------------
library(devtools)
devtools::install_github("itpir/SAT@master")
library(SAT)
library(stargazer)
library(lmtest)
library(multiwayvcov)
loadLibs()
#-------------------------------------------------
#-------------------------------------------------
#Load in Processed Data - produced from script KFW_dataMerge.r
#-------------------------------------------------
#-------------------------------------------------
shpfile = "processed_data/kfw_analysis_inputs.shp"
dta_Shp = readShapePoly(shpfile)
#-------------------------------------------------
#-------------------------------------------------
#Pre-processing to create pre-trends
#-------------------------------------------------
#-------------------------------------------------
#Calculate NDVI Trends
dta_Shp$pre_trend_NDVI_mean <- timeRangeTrend(dta_Shp,"MeanL_[0-9][0-9][0-9][0-9]",1982,1995,"id")
dta_Shp$pre_trend_NDVI_max <- timeRangeTrend(dta_Shp,"MaxL_[0-9][0-9][0-9][0-9]",1982,1995,"id")
#Calculate Temp and Precip Pre and Post Trends
dta_Shp$pre_trend_temp_mean <- timeRangeTrend(dta_Shp,"MeanT_[0-9][0-9][0-9][0-9]",1982,1995,"id")
dta_Shp$pre_trend_temp_max <- timeRangeTrend(dta_Shp,"MaxT_[0-9][0-9][0-9][0-9]",1982,1995,"id")
dta_Shp$pre_trend_temp_min <- timeRangeTrend(dta_Shp,"MinT_[0-9][0-9][0-9][0-9]",1982,1995,"id")
dta_Shp$pre_trend_precip_mean <- timeRangeTrend(dta_Shp,"MeanP_[0-9][0-9][0-9][0-9]",1982,1995,"id")
dta_Shp$pre_trend_precip_max <- timeRangeTrend(dta_Shp,"MaxP_[0-9][0-9][0-9][0-9]",1982,1995,"id")
dta_Shp$pre_trend_precip_min <- timeRangeTrend(dta_Shp,"MinP_[0-9][0-9][0-9][0-9]",1982,1995,"id")
#-------------------------------------------------
#-------------------------------------------------
#Define the Treatment Variable and Population
#-------------------------------------------------
#-------------------------------------------------
#Make a binary to test treatment..
dta_Shp@data["TrtBin"] <- 0
dta_Shp@data$TrtBin[dta_Shp@data$demend_y <= 2001] <- 1
dta_Shp@data$TrtBin[(dta_Shp@data$demend_m > 4) & (dta_Shp@data$demend_y==2001)] <- 0
#Remove units that did not ever receive any treatment (within-sample test)
dta_Shp@data$NA_check <- 0
dta_Shp@data$NA_check[is.na(dta_Shp@data$demend_y)] <- 1
int_Shp <- dta_Shp[dta_Shp@data$NA_check != 1,]
dta_Shp <- int_Shp
#-------------------------------------------------
#-------------------------------------------------
#Define and run the first-stage of the PSM, calculating propensity scores
#-------------------------------------------------
#-------------------------------------------------
psmModel <- "TrtBin ~ terrai_are + Pop_1990 + MeanT_1995 + pre_trend_temp_mean + pre_trend_temp_min +
pre_trend_temp_max + MeanP_1995 + pre_trend_precip_min +
pre_trend_NDVI_mean + pre_trend_NDVI_max + Slope + Elevation + MaxL_1995 + Riv_Dist + Road_dist +
pre_trend_precip_mean + pre_trend_precip_max"
psmRes <- SAT::SpatialCausalPSM(dta_Shp,mtd="logit",psmModel,drop="support",visual=TRUE)
#-------------------------------------------------
#-------------------------------------------------
#Based on the Propensity Score Matches, pair comprable treatment and control units.
#-------------------------------------------------
#-------------------------------------------------
drop_set<- c(drop_unmatched=TRUE,drop_method="NONE",drop_thresh=0.25)
psm_Pairs <- SAT(dta = psmRes$data, mtd = "fastNN",constraints=c(groups="UF"),psm_eq = psmModel, ids = "id", drop_opts = drop_set, visual="TRUE", TrtBinColName="TrtBin")
#c(groups=c("UF"),distance=NULL)
trttable <- table (psm_Pairs@data$TrtBin)
View(trttable)
#-------------------------------------------------
#-------------------------------------------------
#Convert from a wide-form dataset for the Cross-sectional
#to a long-form dataset for the panel model.
#-------------------------------------------------
#-------------------------------------------------
varList = c("MeanL_","MaxL_")
psm_Long <- BuildTimeSeries(dta=psm_Pairs,idField="reu_id",varList_pre=varList,1982,2010,colYears=c("demend_y","enforce_st"),interpYears=c("Slope","Road_dist","Riv_Dist","UF","Elevation","terrai_are","Pop_","MeanT_","MeanP_","MaxT_","MaxP_","MinP_","MinT_"))
psm_Long$Year <- as.numeric(psm_Long$Year)
#Create post-2004 dummy
psm_Long$Post2004 <- 0
psm_Long$Post2004[psm_Long$Year >= 2004] <- 1
#Create arc of deforestation dummy
psm_Long$arc<-NA
psm_Long$arc[which(psm_Long$UF=="PA")] <-1
psm_Long$arc[which(psm_Long$UF!="PA")]<-0
#Create dummy for each year post-demarcation
DemYears <- summaryBy(TrtMnt_demend_y~reu_id, data=psm_Long,FUN=c(mean,sum))
psm_Long2 <- psm_Long
psm_Long3 <- merge(psm_Long2, DemYears, by="reu_id")
psm_Long<-psm_Long3
pModelMax_A <- "MaxL_ ~ TrtMnt_demend_y + TrtMnt_enforce_st + factor(reu_id)"
pModelMax_B <- "MaxL_ ~ TrtMnt_demend_y + TrtMnt_enforce_st + Pop_ + MeanT_ + MeanP_ + MaxT_ + MaxP_ + MinT_ + MinP_ + factor(reu_id) "
pModelMax_C <- "MaxL_ ~ TrtMnt_demend_y + TrtMnt_enforce_st + Pop_ + MeanT_ + MeanP_ + MaxT_ + MaxP_ + MinT_ + MinP_ + Year + factor(reu_id)"
pModelMax_C1 <- "MaxL_ ~ TrtMnt_demend_y + Pop_ + MeanT_ + MeanP_+ MaxT_ + MaxP_ + MinT_ + MinP_ + factor(Year) + factor(reu_id)"
pModelMax_C2 <- "MaxL_ ~ TrtMnt_demend_y + TrtMnt_enforce_st + Pop_ + MeanT_ + MeanP_+ MaxT_ + MaxP_ + MinT_ + MinP_ + factor(Year) + factor(reu_id)"
pModelMax_D <- "MaxL_ ~ TrtMnt_demend_y + Pop_+ MeanT_ + MeanP_ + MaxT_ + MaxP_ + MinT_ + MinP_ + factor(Year) + factor(reu_id) + Post2004 + Post2004*TrtMnt_demend_y + Post2004*TrtMnt_demend_y*Road_dist + Post2004*Road_dist"
pModelMax_E <- "MaxL_ ~ TrtMnt_demend_y + Pop_+TrtMnt_enforce_st + MeanT_ + MeanP_ + MaxT_ + MaxP_ + MinT_ + MinP_ + factor(Year) + factor(reu_id) + Post2004 + Post2004*TrtMnt_demend_y + Post2004*TrtMnt_demend_y*Road_dist + Post2004*Road_dist"
pModelMax_A_fit <- Stage2PSM(pModelMax_A ,psm_Long,type="cmreg", table_out=TRUE,opts=c("reu_id","Year"))
pModelMax_B_fit <- Stage2PSM(pModelMax_B ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_C_fit <- Stage2PSM(pModelMax_C ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_C1_fit <- Stage2PSM(pModelMax_C1 ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_C2_fit <- Stage2PSM(pModelMax_C2 ,psm_Long,type="cmreg", table_out=TRUE,opts=c("reu_id","Year"))
pModelMax_D_fit <- Stage2PSM(pModelMax_D ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_E_fit <- Stage2PSM(pModelMax_E ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
#Interaction with number of years post demarcation (to 2010 end year)
pModelMax_F <- "MaxL_ ~ TrtMnt_demend_y + Pop_ + MeanT_ + MeanP_+ MaxT_ + MaxP_ + MinT_ + MinP_ + TrtMnt_demend_y*factor(TrtMnt_demend_y.sum) + factor(Year) + factor(reu_id)"
pModelMax_G <- "MaxL_ ~ TrtMnt_demend_y + TrtMnt_enforce_st + Pop_ + MeanT_ + MeanP_+ MaxT_ + MaxP_ + MinT_ + MinP_ + TrtMnt_demend_y*factor(TrtMnt_demend_y.sum) + factor(Year) + factor(reu_id)"
pModelMax_F_fit <- Stage2PSM(pModelMax_F ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_G_fit <- Stage2PSM(pModelMax_G ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
##texreg to output regression results visualizations
texreg::plotreg(pModelMax_B_fit$cmreg, custom.model.names=c("Panel Results, Max NDVI"),
omit.coef="(Pop_)|(Min)|(Mean)|(Max)|(Year)|(match)|(Intercept)|(factor)",
custom.note="standard deviation")
texreg::plotreg(pModelMax_C_fit$cmreg,custom.model.names=c("Panel Results, Max NDVI"),
omit.coef="(Pop_)|(Min)|(Mean)|(Max)|(Year)|(Intercept)|(factor)",
custom.note="standard deviation")
## stargazer output with variable labels
stargazer(pModelMax_A_fit$cmreg,pModelMax_B_fit$cmreg,pModelMax_C_fit$cmreg,pModelMax_D_fit$cmreg,pModelMax_E_fit$cmreg,type="html",align=TRUE,keep=c("TrtMnt_demend_y","TrtMnt_enforce_st","MeanT_","MeanP_","Pop_","MaxT_","MaxP_","MinT_","MinP_","Year","Post2004","TrtMnt_demend_y:Post2004","Post2004:Road_dist","TrtMnt_demend_y:Road_dist","TrtMnt_demend_y:Post2004:Road_dist"),
covariate.labels=c("Treatment (Demarcation)","Treatment (Enforcement Support)","Mean Temperature","Mean Precipitation","Population","Max Temperature","Max Precipitation","Min Temperature","Min Precipitation","Year","Post2004","Post2004*TreatmentDemarcation","Post2004*Road Distance","TreatmentDemarcation*Road Distance","Post2004*TreatmentDemarcation*Road Distance"),
omit.stat=c("f","ser"),
title="Regression Results",
dep.var.labels=c("Max NDVI")
)
stargazer(pModelMax_A_fit$cmreg,pModelMax_B_fit$cmreg,pModelMax_C_fit$cmreg,pModelMax_C1_fit$cmreg,pModelMax_D_fit$cmreg,pModelMax_E_fit$cmreg,
type="html",align=TRUE,
keep=c("TrtMnt_demend_y","TrtMnt_enforce_st","MeanT_","MeanP_","Pop_","MaxT_","MaxP_","MinT_","MinP_",
"Year","Post2004","TrtMnt_demend_y:Post2004","Post2004:Road_dist","TrtMnt_demend_y:Road_dist",
"TrtMnt_demend_y:Post2004:Road_dist"),
# covariate.labels=c("Treatment (Demarcation)","Treatment (Enforcement Support)","Mean Temp",
# "Mean Precip","Population","Max Temperature","Max Precipitation","Min Temperature","Min Precipitation","Year","Post2004","Post2004*TreatmentDemarcation","Post2004*Road Distance","TreatmentDemarcation*Road Distance","Post2004*TreatmentDemarcation*Road Distance"),
omit.stat=c("f","ser"),
title="Regression Results",
dep.var.labels=c("Max NDVI")
)
stargazer(pModelMax_C1_fit$cmreg,pModelMax_F_fit$cmreg,pModelMax_G_fit$cmreg,
type="html", align=TRUE,
keep=c("TrtMnt","Pop","Mean","Max","Min","Year","factor"),
title="Regression Results",
dep.var.labels=c("Max NDVI"))
#Used for JEEM submission
stargazer(pModelMax_A_fit$cmreg,pModelMax_B_fit$cmreg,pModelMax_C_fit$cmreg,
pModelMax_C2_fit$cmreg,pModelMax_D_fit$cmreg,pModelMax_E_fit$cmreg,
type="html", align=TRUE,
keep=c("TrtMnt","Pop","Mean","Max","Min","Year","Post2004","Road_dist"),
covariate.labels=c("Treatment (Demarcation)","Treatment (Demarcation + Enforcement Support)","Population","Mean Temp",
"Mean Precip","Max Temp","Max Precip","Min Temp","Min Precip","Year"),
omit.stat=c("f","ser"),
add.lines=list(c("Observations","1914","1914","1914","1914","1914","1914"),
c("Community Fixed Effects?","Yes","Yes","Yes","Yes","Yes","Yes"),
c("Year Fixed Effects?","No","No","No","Yes","Yes","Yes")),
title="Regression Results",
dep.var.labels=c("Max NDVI"))
stargazer(pModelMax_A_fit$cmreg,pModelMax_B_fit$cmreg,pModelMax_C_fit$cmreg,
pModelMax_C1_fit$cmreg,pModelMax_C2_fit$cmreg,
type="html", align=TRUE,
keep=c("TrtMnt","Pop","Mean","Max","Min","Year"),
covariate.labels=c("Treatment (Demarcation)","Treatment (Demarcation + Enforcement Support)","Population","Mean Temp",
"Mean Precip","Max Temp","Max Precip","Min Temp","Min Precip","Year"),
omit.stat=c("f","ser"),
add.lines=list(c("Observations","2146","2146","2146","2146","2146"),
c("Community Fixed Effects?","Yes","Yes","Yes","Yes","Yes","Yes"),
c("Year Fixed Effects?","No","No","No","Yes","Yes")),
title="Regression Results",
dep.var.labels=c("Max NDVI"))