Hugging Face's logo

Datasets:
chuxuan
/
REPRO-Bench

Tasks:
Image-Text-to-Text
Languages:
English
ArXiv:
Tags:
reproducibility
social-science
agents
Dataset card Files
xet
Community
1
REPRO-Bench
File size: 7,182 Bytes 
caa8075
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
 
########################################################################
##### El Salvador - Land Reform - Map of Land Reforms Across LatAm #####
########################################################################

rm(list = ls())       # Clear variables

require(foreign)
require(ggplot2)
require(rgdal)
require(rgeos)
require(RColorBrewer) # creates nice color schemes
require(maptools)     # loads sp library too
require(scales)       # customize scales
require(gridExtra)    # mutiple plots
require(plyr)         # join function
require(dplyr) 
require(mapproj)      # projection tools
require(raster)       # raster tools
require(ggvis)        # visualize estimators
require(rdrobust)     # rd estimation tools
require(stringdist)   # approximate string matching
require(gdata)        
require(rdd)          # sorting tests
require(stargazer)    # format tables
require(ggrepel)      # labeling

########################################

# Approximate String Matching Funtion -- (amatch doesn't work that well for some reason)

string_match <- function(string_to_match, options, smethod="osa") {
  if(string_to_match!="") {
    sdists <- stringdist(string_to_match, options, method=smethod)
    ind <- which(sdists == min(sdists))
    if(length(ind) != 1) {
      ind <- ind[1] # Assumes first index is the most common string to match.
    }
    return(options[ind])
  } else {
    return("")
  }
}

as.numeric.factor <- function(x) {as.numeric(levels(x))[x]} # Function to turn factor vars to numeric variables correctly.

########################################

## Load LatAm Shapefile:

# Path:
latam_path <- "./Data/GIS_LatinAmerica/"

# Load Shapefile:
LatAm <- readOGR(latam_path, "LatinAmerica")

########################################

## Add in Change in Land Gini data from Albertus (2015):
LatAm$CHG_LAND_GINI <- 0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Mexico"] <- -30.0  
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="French Guiana"] <- 0.0  
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Guyana"] <- 0.0  
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Suriname"] <- 0.0 
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Venezuela"] <- -5.0  
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Argentina"] <- 2.5
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Bolivia"] <- -20.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Brazil"] <- 2.5
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Chile"] <- -10.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Ecuador"] <- -5.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Paraguay"] <- 5.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Peru"] <- -15.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Uruguay"] <- 0.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Guatemala"] <- 0.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Belize"] <- 0.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Colombia"] <- -5.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Costa Rica"] <- 0.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="El Salvador"] <- -10.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Honduras"] <- -5.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Nicaragua"] <- -25.0
LatAm$CHG_LAND_GINI[LatAm$CNTRY_NAME=="Panama"] <- 5.0

########################################

## Add in land reform to cooperative indicator from Albertus (2015) and DeJanvry (1982):
LatAm$coop_land_reform <- 0
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Mexico"] <- 1  
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="French Guiana"] <- 0.0  
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Guyana"] <- 0.0  
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Suriname"] <- 0.0  
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Venezuela"] <- 1  
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Argentina"] <- 0.0
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Bolivia"] <- 1
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Brazil"] <- 0
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Chile"] <- 1
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Ecuador"] <- 0  
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Paraguay"] <- 0  
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Peru"] <- 1
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Uruguay"] <- 0.0  
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Guatemala"] <- 0.0   
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Belize"] <- 0.0
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Colombia"] <- 1
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Costa Rica"] <- 1
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="El Salvador"] <- 1
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Honduras"] <- 1.0  
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Nicaragua"] <- 1
LatAm$coop_land_reform[LatAm$CNTRY_NAME=="Panama"] <- 1.0  


########################################

## Plots!

# Set aesthetics:
aesthetics <- list(#guides(color=guide_colorbar(reverse=FALSE)),
  #guides(fill=FALSE),
  #guides(shape=FALSE),
  #guides(size=FALSE),
  coord_equal(),
  theme_bw(),
  theme(#legend.title=element_blank(),
    #legend.justification=c(0,0), 
    #legend.position= "right", #c(1,0),
    text=element_text(family="Palatino"),
    panel.border = element_blank(),
    panel.grid.minor=element_blank(),
    panel.grid.major=element_blank(),
    axis.title.x=element_blank(),
    axis.title.y=element_blank(),
    axis.text=element_blank(),
    axis.ticks=element_blank()))

# Fortify for ggplot
LatAm.df <- fortify(LatAm, region="FIPS_CNTRY")
LatAm@data$id <- LatAm@data$FIPS_CNTRY

# Join Data:
LatAm.df <- join(LatAm.df, LatAm@data, by="id")

# Plot:


# Indicator for Land Reform that created Agricultural Coops w/ El Salvador Highlighted:
ES <- LatAm[LatAm$CNTRY_NAME=="El Salvador",]
ES@data <- mutate(ES@data, ES = ifelse(CNTRY_NAME=="El Salvador",1,0), ES2 = ifelse(FIPS_CNTRY=="ES",1,0))
# Fortify for ggplot
ES.df <- fortify(ES, region="FIPS_CNTRY")
ES@data$id <- ES@data$FIPS_CNTRY

# Join Data:
ES.df <- join(ES.df, ES@data, by="id")

LatAm.ggplot.reform <- geom_polygon(aes(x=long,y=lat, group=group, fill=(coop_land_reform)),data=LatAm.df,size=0.25,col="black")

pdf(file="./Output/LatAm_LRCoops.pdf", height=7, width=7, paper = "letter")
print(ggplot(aes(x=long,y=lat, group=group, fill=(coop_land_reform)),data=LatAm.df) + LatAm.ggplot.reform + coord_equal() + aesthetics
      + scale_fill_distiller(name="Experienced a Land Reform\nthat created Agricultural \nCooperatives, 1920-1990", palette = "Blues", trans = "reverse", breaks = pretty_breaks(n = 1), labels=c("No","Yes"),guide = guide_legend(reverse=TRUE)) 
      + labs(x="Longitude",y="Latitude"))
dev.off()

# w/Labels
EScentroid.df <- as.data.frame(coordinates(ES))
names(EScentroid.df) <- c("long", "lat")
EScentroid.df$CNTRY_NAME <- ES@data$CNTRY_NAME
ES.ggplot2 <- geom_polygon(aes(x=long,y=lat, group=group),data=ES.df,col="red",size=0.25, fill=NA,show.legend=FALSE)

pdf(file="./Output/LatAm_LRCoops_wESLabel2.pdf", height=7, width=7, paper = "letter")
print(ggplot()
      + geom_text_repel( data=EScentroid.df, aes(x=long, y=lat, label=CNTRY_NAME), col="red",size=4,nudge_x=-15, nudge_y=-5)
      + LatAm.ggplot.reform + coord_equal() + aesthetics 
      + ES.ggplot2
      + scale_fill_distiller(name="Experienced a Land Reform\nthat created Agricultural\nCooperatives - 1920-1990", palette = "Blues", trans = "reverse", breaks = pretty_breaks(n = 1), labels=c("No","Yes"),guide = guide_legend(reverse=TRUE)) 
      + labs(x="Longitude",y="Latitude"))
dev.off()